diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 00000000..ae0f1c25
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,30 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: "[Notebook issue]"
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+Provide the following if applicable:
++ Your Python & SDK version 
++ Python Scripts or the full notebook name
++ Pipeline definition 
++ Environment definition 
++ Example data 
++ Any log files. 
++ Run and Workspace Id 
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. 
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Additional context**
+Add any other context about the problem here.
diff --git a/.github/ISSUE_TEMPLATE/notebook-issue.md b/.github/ISSUE_TEMPLATE/notebook-issue.md
new file mode 100644
index 00000000..c012da21
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/notebook-issue.md
@@ -0,0 +1,43 @@
+---
+name: Notebook issue
+about: Describe your notebook issue
+title: "[Notebook] DESCRIPTIVE TITLE"
+labels: notebook
+assignees: ''
+
+---
+
+### DESCRIPTION: Describe clearly + concisely
+
+
+.
+### REPRODUCIBLE: Steps
+
+
+.
+### EXPECTATION: Clear description 
+
+
+.
+### CONFIG/ENVIRONMENT: 
+```Provide where applicable
+
+## Your Python & SDK version:
+
+## Environment definition:
+
+## Notebook name or Python scripts:
+
+## Run and Workspace Id:
+
+## Pipeline definition:
+
+## Example data:
+
+## Any log files:
+
+
+
+
+
+```
diff --git a/README.md b/README.md
index 37845282..a935eeef 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,8 @@
 
 This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure.  The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.
 
-![Azure ML workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/service/media/overview-what-is-azure-ml/aml.png)
+![Azure ML Workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/service/media/concept-azure-machine-learning-architecture/workflow.png)
+
 
 ## Quick installation
 ```sh
@@ -38,6 +39,7 @@ The [How to use Azure ML](./how-to-use-azureml) folder contains specific example
 - [Machine Learning Pipelines](./how-to-use-azureml/machine-learning-pipelines) - Examples showing how to create and use reusable pipelines for training and batch scoring
 - [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
 - [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
+- [Monitor Models](./how-to-use-azureml/monitor-models) - Examples showing how to enable model monitoring services such as DataDrift
 
 ---
 ## Documentation
@@ -48,10 +50,14 @@ The [How to use Azure ML](./how-to-use-azureml) folder contains specific example
 
 ---
 
+
+## Community Repository 
+Visit this [community repository](https://github.com/microsoft/MLOps/tree/master/examples) to find useful end-to-end sample notebooks. Also, please follow these [contribution guidelines](https://github.com/microsoft/MLOps/blob/master/contributing.md) when contributing to this repository.   
+
 ## Projects using Azure Machine Learning
 
 Visit following repos to see projects contributed by Azure ML users:
-
+ - [AMLSamples](https://github.com/Azure/AMLSamples) Number of end-to-end examples, including face recognition, predictive maintenance, customer churn and sentiment analysis.
  - [Fine tune natural language processing models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
  - [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)
  
diff --git a/configuration.ipynb b/configuration.ipynb
index e7fb4aab..555b0c73 100644
--- a/configuration.ipynb
+++ b/configuration.ipynb
@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.0.48\r\n of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.0.62 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
diff --git a/model-deployment/README.md b/end-to-end-samples/README.md
similarity index 100%
rename from model-deployment/README.md
rename to end-to-end-samples/README.md
diff --git a/how-to-use-azureml/README.md b/how-to-use-azureml/README.md
index cedd4581..ee4829e0 100644
--- a/how-to-use-azureml/README.md
+++ b/how-to-use-azureml/README.md
@@ -8,7 +8,7 @@ As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) not
 * [train-on-local](./training/train-on-local): Learn how to submit a run to local computer and use Azure ML managed run configuration.
 * [train-on-amlcompute](./training/train-on-amlcompute): Use a 1-n node Azure ML managed compute cluster for remote runs on Azure CPU or GPU infrastructure.
 * [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs.
-* [logging-api](./training/logging-api): Learn about the details of logging metrics to run history.
+* [logging-api](./track-and-monitor-experiments/logging-api): Learn about the details of logging metrics to run history.
 * [register-model-create-image-deploy-service](./deployment/register-model-create-image-deploy-service): Learn about the details of model management.
 * [production-deploy-to-aks](./deployment/production-deploy-to-aks) Deploy a model to production at scale on Azure Kubernetes Service.
 * [enable-data-collection-for-models-in-aks](./deployment/enable-data-collection-for-models-in-aks) Learn about data collection APIs for deployed model.
diff --git a/how-to-use-azureml/automated-machine-learning/README.md b/how-to-use-azureml/automated-machine-learning/README.md
index d31a3729..adc4a1d6 100644
--- a/how-to-use-azureml/automated-machine-learning/README.md
+++ b/how-to-use-azureml/automated-machine-learning/README.md
@@ -155,11 +155,11 @@ jupyter notebook
 - [auto-ml-subsampling-local.ipynb](subsampling/auto-ml-subsampling-local.ipynb)
     - How to enable subsampling
 
-- [auto-ml-dataprep.ipynb](dataprep/auto-ml-dataprep.ipynb)
-    - Using DataPrep for reading data
+- [auto-ml-dataset.ipynb](dataprep/auto-ml-dataset.ipynb)
+    - Using Dataset for reading data
 
-- [auto-ml-dataprep-remote-execution.ipynb](dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb)
-    - Using DataPrep for reading data with remote execution
+- [auto-ml-dataset-remote-execution.ipynb](dataprep-remote-execution/auto-ml-dataset-remote-execution.ipynb)
+    - Using Dataset for reading data with remote execution
 
 - [auto-ml-classification-with-whitelisting.ipynb](classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb)
     - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
@@ -175,10 +175,19 @@ jupyter notebook
     - Example of training an automated ML forecasting model on multiple time-series
 
 - [auto-ml-classification-with-onnx.ipynb](classification-with-onnx/auto-ml-classification-with-onnx.ipynb)
-    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
+    - Dataset: scikit learn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html)
     - Simple example of using automated ML for classification with ONNX models
     - Uses local compute for training
 
+- [auto-ml-remote-amlcompute-with-onnx.ipynb](remote-amlcompute-with-onnx/auto-ml-remote-amlcompute-with-onnx.ipynb)
+    - Dataset: scikit learn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html)
+    - Example of using automated ML for classification using remote AmlCompute for training
+    - Train the models with ONNX compatible config on
+    - Parallel execution of iterations
+    - Async tracking of progress
+    - Cancelling individual iterations or entire run
+    - Retrieving the ONNX models and do the inference with them
+
 - [auto-ml-bank-marketing-subscribers-with-deployment.ipynb](bank-marketing-subscribers-with-deployment/auto-ml-bank-marketing-with-deployment.ipynb)
     - Dataset: UCI's [bank marketing dataset](https://www.kaggle.com/janiobachmann/bank-marketing-dataset)
     - Simple example of using automated ML for classification to predict term deposit subscriptions for a bank
@@ -220,7 +229,7 @@ The main code of the file must be indented so that it is under this condition.
 2. Check that you have conda 64-bit installed rather than 32-bit.  You can check this with the command `conda info`.  The `platform` should be `win-64` for Windows or `osx-64` for Mac.
 3. Check that you have conda 4.4.10 or later.  You can check the version with the command `conda -V`.  If you have a previous version installed, you can update it using the command: `conda update conda`.
 4. On Linux, if the error is `gcc: error trying to exec 'cc1plus': execvp: No such file or directory`, install build essentials using the command `sudo apt-get install build-essential`.
-5. Pass a new name as the first parameter to automl_setup so that it creates a new conda environment. You can view existing conda environments using `conda env list` and remove them with `conda env remove -n <environmentname>`. 
+5. Pass a new name as the first parameter to automl_setup so that it creates a new conda environment. You can view existing conda environments using `conda env list` and remove them with `conda env remove -n <environmentname>`.
 
 ## automl_setup_linux.sh fails
 If automl_setup_linux.sh fails on Ubuntu Linux with the error: `unable to execute 'gcc': No such file or directory`
@@ -255,13 +264,13 @@ Some Windows environments see an error loading numpy with the latest Python vers
 Check the tensorflow version in the automated ml conda environment. Supported versions are < 1.13. Uninstall tensorflow from the environment if version is >= 1.13
 You may check the version of tensorflow and uninstall as follows
 1) start a command shell, activate conda environment where automated ml packages are installed
-2) enter `pip freeze` and look for `tensorflow` , if found, the version listed should be < 1.13 
-3) If the listed version is a not a supported version,  `pip uninstall tensorflow` in the command shell and enter y for confirmation. 
+2) enter `pip freeze` and look for `tensorflow` , if found, the version listed should be < 1.13
+3) If the listed version is a not a supported version,  `pip uninstall tensorflow` in the command shell and enter y for confirmation.
 
-## Remote run: DsvmCompute.create fails 
+## Remote run: DsvmCompute.create fails
 There are several reasons why the DsvmCompute.create can fail.  The reason is usually in the error message but you have to look at the end of the error message for the detailed reason.  Some common reasons are:
 1) `Compute name is invalid, it should start with a letter, be between 2 and 16 character, and only include letters (a-zA-Z), numbers (0-9) and \'-\'.`  Note that underscore is not allowed in the name.
-2) `The requested VM size xxxxx is not available in the current region.`  You can select a different region or vm_size. 
+2) `The requested VM size xxxxx is not available in the current region.`  You can select a different region or vm_size.
 
 ## Remote run: Unable to establish SSH connection
 Automated ML uses the SSH protocol to communicate with remote DSVMs.  This defaults to port 22.  Possible causes for this error are:
@@ -287,4 +296,4 @@ To resolve this issue, allocate a DSVM with more memory or reduce the value spec
 
 ## Remote run: Iterations show as "Not Responding" in the RunDetails widget.
 This can be caused by too many concurrent iterations for a remote DSVM.  Each concurrent iteration usually takes 100% of a core when it is running.  Some iterations can use multiple cores.  So, the max_concurrent_iterations setting should always be less than the number of cores of the DSVM.
-To resolve this issue, try reducing the value specified for the max_concurrent_iterations setting.
\ No newline at end of file
+To resolve this issue, try reducing the value specified for the max_concurrent_iterations setting.
diff --git a/how-to-use-azureml/automated-machine-learning/automl_env.yml b/how-to-use-azureml/automated-machine-learning/automl_env.yml
index 07b7c974..8114c9d8 100644
--- a/how-to-use-azureml/automated-machine-learning/automl_env.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env.yml
@@ -13,10 +13,14 @@ dependencies:
 - scikit-learn>=0.19.0,<=0.20.3
 - pandas>=0.22.0,<=0.23.4
 - py-xgboost<=0.80
+- pyarrow>=0.11.0
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-sdk[automl,explain]
+  - azureml-defaults
+  - azureml-train-automl
   - azureml-widgets
+  - azureml-explain-model
+  - azureml-contrib-explain-model 
   - pandas_ml
   
diff --git a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
index b023d8dd..36114400 100644
--- a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
@@ -2,6 +2,7 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
+- pip
 - nomkl
 - python>=3.5.2,<3.6.8
 - nb_conda
@@ -13,10 +14,14 @@ dependencies:
 - scikit-learn>=0.19.0,<=0.20.3
 - pandas>=0.22.0,<0.23.0
 - py-xgboost<=0.80
+- pyarrow>=0.11.0
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-sdk[automl,explain]
+  - azureml-defaults
+  - azureml-train-automl
   - azureml-widgets
+  - azureml-explain-model
+  - azureml-contrib-explain-model
   - pandas_ml
   
diff --git a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.ipynb b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.ipynb
index 7a44c359..d13b3bb2 100644
--- a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.ipynb
@@ -69,22 +69,17 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import json\n",
         "import logging\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
-        "import numpy as np\n",
         "import pandas as pd\n",
         "import os\n",
-        "from sklearn import datasets\n",
-        "import azureml.dataprep as dprep\n",
-        "from sklearn.model_selection import train_test_split\n",
         "\n",
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.run import AutoMLRun"
+        "from azureml.core.dataset import Dataset\n",
+        "from azureml.train.automl import AutoMLConfig"
       ]
     },
     {
@@ -97,8 +92,6 @@
         "\n",
         "# choose a name for experiment\n",
         "experiment_name = 'automl-classification-bmarketing'\n",
-        "# project folder\n",
-        "project_folder = './sample_projects/automl-classification-bankmarketing'\n",
         "\n",
         "experiment=Experiment(ws, experiment_name)\n",
         "\n",
@@ -108,7 +101,6 @@
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -155,11 +147,12 @@
         "    # Create the cluster.\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
         "    \n",
-        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
+        "print('Checking cluster status...')\n",
+        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
         "    \n",
-        "     # For a more detailed view of current AmlCompute status, use get_status()."
+        "# For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
     {
@@ -168,20 +161,7 @@
       "source": [
         "# Data\n",
         "\n",
-        "Here load the data in the get_data() script to be utilized in azure compute. To do this  first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_Run_config."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if not os.path.isdir('data'):\n",
-        "    os.mkdir('data')\n",
-        "    \n",
-        "if not os.path.exists(project_folder):\n",
-        "    os.makedirs(project_folder)"
+        "Create a run configuration for the remote run."
       ]
     },
     {
@@ -200,11 +180,8 @@
         "# Set compute target to AmlCompute\n",
         "conda_run_config.target = compute_target\n",
         "conda_run_config.environment.docker.enabled = True\n",
-        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
-        "dprep_dependency = 'azureml-dataprep==' + pkg_resources.get_distribution(\"azureml-dataprep\").version\n",
-        "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', dprep_dependency], conda_packages=['numpy','py-xgboost<=0.80'])\n",
+        "cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd"
       ]
     },
@@ -214,7 +191,7 @@
       "source": [
         "### Load Data\n",
         "\n",
-        "Here we create the script to be run in azure comput for loading the data, we load the bank marketing dataset into X_train and y_train. Next X_train and y_train is returned for training the model."
+        "Load the bank marketing dataset into X_train and y_train. X_train contains the training features, which are inputs to the model.  y_train contains the training labels, which are the expected output of the model."
       ]
     },
     {
@@ -224,11 +201,10 @@
       "outputs": [],
       "source": [
         "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
-        "dflow = dprep.auto_read_file(data)\n",
-        "dflow.get_profile()\n",
-        "X_train = dflow.drop_columns(columns=['y'])\n",
-        "y_train = dflow.keep_columns(columns=['y'], validate_column_exists=True)\n",
-        "dflow.head()"
+        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
+        "X_train = dataset.drop_columns(columns=['y'])\n",
+        "y_train = dataset.keep_columns(columns=['y'], validate=True)\n",
+        "dataset.take(5).to_pandas_dataframe()"
       ]
     },
     {
@@ -248,7 +224,6 @@
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
         "\n",
         "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
       ]
@@ -271,7 +246,6 @@
         "\n",
         "automl_config = AutoMLConfig(task = 'classification',\n",
         "                             debug_log = 'automl_errors.log',\n",
-        "                             path = project_folder,\n",
         "                             run_configuration=conda_run_config,\n",
         "                             X = X_train,\n",
         "                             y = y_train,\n",
@@ -406,7 +380,7 @@
         "def run(rawdata):\n",
         "    try:\n",
         "        data = json.loads(rawdata)['data']\n",
-        "        data = numpy.array(data)\n",
+        "        data = np.array(data)\n",
         "        result = model.predict(data)\n",
         "    except Exception as e:\n",
         "        result = str(e)\n",
@@ -443,7 +417,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
+        "for p in ['azureml-train-automl', 'azureml-core']:\n",
         "    print('{}\\t{}'.format(p, dependencies[p]))"
       ]
     },
@@ -453,10 +427,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
         "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
-        "                                 pip_packages=['azureml-sdk[automl]'])\n",
+        "                                 pip_packages=['azureml-train-automl'])\n",
         "\n",
         "conda_env_file_name = 'myenv.yml'\n",
         "myenv.save_to_file('.', conda_env_file_name)"
@@ -476,7 +448,7 @@
         "    content = cefr.read()\n",
         "\n",
         "with open(conda_env_file_name, 'w') as cefw:\n",
-        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
+        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
         "\n",
         "# Substitute the actual model id in the script file.\n",
         "\n",
@@ -618,8 +590,6 @@
       "outputs": [],
       "source": [
         "# Load the bank marketing datasets.\n",
-        "from sklearn.datasets import load_diabetes\n",
-        "from sklearn.model_selection import train_test_split\n",
         "from numpy import array"
       ]
     },
@@ -630,11 +600,10 @@
       "outputs": [],
       "source": [
         "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_validate.csv\"\n",
-        "dflow = dprep.auto_read_file(data)\n",
-        "dflow.get_profile()\n",
-        "X_test = dflow.drop_columns(columns=['y'])\n",
-        "y_test = dflow.keep_columns(columns=['y'], validate_column_exists=True)\n",
-        "dflow.head()"
+        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
+        "X_test = dataset.drop_columns(columns=['y'])\n",
+        "y_test = dataset.keep_columns(columns=['y'], validate=True)\n",
+        "dataset.take(5).to_pandas_dataframe()"
       ]
     },
     {
diff --git a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.yml b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.yml
index a46c905b..4c8a39ca 100644
--- a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.yml
+++ b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.yml
@@ -2,6 +2,8 @@ name: auto-ml-classification-bank-marketing
 dependencies:
 - pip:
   - azureml-sdk
+  - azureml-defaults
+  - azureml-explain-model
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
diff --git a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb b/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
index 00a03fa6..ffcf6261 100644
--- a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
@@ -74,14 +74,12 @@
         "from matplotlib import pyplot as plt\n",
         "import pandas as pd\n",
         "import os\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "import azureml.dataprep as dprep\n",
         "\n",
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.run import AutoMLRun"
+        "from azureml.core.dataset import Dataset\n",
+        "from azureml.train.automl import AutoMLConfig"
       ]
     },
     {
@@ -94,8 +92,6 @@
         "\n",
         "# choose a name for experiment\n",
         "experiment_name = 'automl-classification-ccard'\n",
-        "# project folder\n",
-        "project_folder = './sample_projects/automl-classification-creditcard'\n",
         "\n",
         "experiment=Experiment(ws, experiment_name)\n",
         "\n",
@@ -105,7 +101,6 @@
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -152,11 +147,12 @@
         "    # Create the cluster.\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
         "    \n",
-        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "    \n",
-        "     # For a more detailed view of current AmlCompute status, use get_status()."
+        "print('Checking cluster status...')\n",
+        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
+        "\n",
+        "# For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
     {
@@ -165,20 +161,7 @@
       "source": [
         "# Data\n",
         "\n",
-        "Here load the data in the get_data script to be utilized in azure compute. To do this, first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_run_config."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if not os.path.isdir('data'):\n",
-        "    os.mkdir('data')\n",
-        "    \n",
-        "if not os.path.exists(project_folder):\n",
-        "    os.makedirs(project_folder)"
+        "Create a run configuration for the remote run."
       ]
     },
     {
@@ -197,11 +180,8 @@
         "# Set compute target to AmlCompute\n",
         "conda_run_config.target = compute_target\n",
         "conda_run_config.environment.docker.enabled = True\n",
-        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
-        "dprep_dependency = 'azureml-dataprep==' + pkg_resources.get_distribution(\"azureml-dataprep\").version\n",
-        "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', dprep_dependency], conda_packages=['numpy','py-xgboost<=0.80'])\n",
+        "cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd"
       ]
     },
@@ -211,7 +191,7 @@
       "source": [
         "### Load Data\n",
         "\n",
-        "Here create the script to be run in azure compute for loading the data, load the credit card dataset into cards and store the Class column (y) in the y variable and store the remaining data in the x variable. Next split the data  using train_test_split and return X_train and y_train for training the model."
+        "Load the credit card dataset into X and y. X contains the features, which are inputs to the model. y contains the labels, which are the expected output of the model. Next split the data using random_split and return X_train and y_train for training the model."
       ]
     },
     {
@@ -221,10 +201,9 @@
       "outputs": [],
       "source": [
         "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard.csv\"\n",
-        "dflow = dprep.auto_read_file(data)\n",
-        "dflow.get_profile()\n",
-        "X = dflow.drop_columns(columns=['Class'])\n",
-        "y = dflow.keep_columns(columns=['Class'], validate_column_exists=True)\n",
+        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
+        "X = dataset.drop_columns(columns=['Class'])\n",
+        "y = dataset.keep_columns(columns=['Class'], validate=True)\n",
         "X_train, X_test = X.random_split(percentage=0.8, seed=223)\n",
         "y_train, y_test = y.random_split(percentage=0.8, seed=223)"
       ]
@@ -246,7 +225,6 @@
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
         "\n",
         "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
       ]
@@ -275,8 +253,7 @@
         "}\n",
         "\n",
         "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors_20190417.log',\n",
-        "                             path = project_folder,\n",
+        "                             debug_log = 'automl_errors.log',\n",
         "                             run_configuration=conda_run_config,\n",
         "                             X = X_train,\n",
         "                             y = y_train,\n",
@@ -447,7 +424,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
+        "for p in ['azureml-train-automl', 'azureml-core']:\n",
         "    print('{}\\t{}'.format(p, dependencies[p]))"
       ]
     },
@@ -458,7 +435,7 @@
       "outputs": [],
       "source": [
         "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
-        "                                 pip_packages=['azureml-sdk[automl]'])\n",
+        "                                 pip_packages=['azureml-train-automl'])\n",
         "\n",
         "conda_env_file_name = 'myenv.yml'\n",
         "myenv.save_to_file('.', conda_env_file_name)"
@@ -478,7 +455,7 @@
         "    content = cefr.read()\n",
         "\n",
         "with open(conda_env_file_name, 'w') as cefw:\n",
-        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
+        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
         "\n",
         "# Substitute the actual model id in the script file.\n",
         "\n",
diff --git a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml b/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml
index 14c8fe46..f4a3601e 100644
--- a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml
+++ b/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml
@@ -2,6 +2,8 @@ name: auto-ml-classification-credit-card-fraud
 dependencies:
 - pip:
   - azureml-sdk
+  - azureml-defaults
+  - azureml-explain-model
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
diff --git a/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb b/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb
index 2e00e9c3..930fb4f1 100644
--- a/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb
@@ -92,8 +92,6 @@
         "\n",
         "# choose a name for experiment\n",
         "experiment_name = 'automl-classification-deployment'\n",
-        "# project folder\n",
-        "project_folder = './sample_projects/automl-classification-deployment'\n",
         "\n",
         "experiment=Experiment(ws, experiment_name)\n",
         "\n",
@@ -103,7 +101,6 @@
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -126,8 +123,7 @@
         "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|"
       ]
     },
     {
@@ -148,8 +144,7 @@
         "                             iterations = 10,\n",
         "                             verbosity = logging.INFO,\n",
         "                             X = X_train, \n",
-        "                             y = y_train,\n",
-        "                             path = project_folder)"
+        "                             y = y_train)"
       ]
     },
     {
@@ -297,7 +292,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
+        "for p in ['azureml-train-automl', 'azureml-core']:\n",
         "    print('{}\\t{}'.format(p, dependencies[p]))"
       ]
     },
@@ -310,7 +305,7 @@
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
         "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
-        "                                 pip_packages=['azureml-sdk[automl]'])\n",
+        "                                 pip_packages=['azureml-train-automl'])\n",
         "\n",
         "conda_env_file_name = 'myenv.yml'\n",
         "myenv.save_to_file('.', conda_env_file_name)"
@@ -330,7 +325,7 @@
         "    content = cefr.read()\n",
         "\n",
         "with open(conda_env_file_name, 'w') as cefw:\n",
-        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
+        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
         "\n",
         "# Substitute the actual model id in the script file.\n",
         "\n",
diff --git a/how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.ipynb b/how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.ipynb
index 6a0032af..464e4e9d 100644
--- a/how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.ipynb
@@ -29,7 +29,6 @@
         "1. [Data](#Data)\n",
         "1. [Train](#Train)\n",
         "1. [Results](#Results)\n",
-        "1. [Test](#Test)\n",
         "\n"
       ]
     },
@@ -39,7 +38,7 @@
       "source": [
         "## Introduction\n",
         "\n",
-        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
+        "In this example we use the scikit-learn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use AutoML for a simple classification problem.\n",
         "\n",
         "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
@@ -49,7 +48,8 @@
         "1. Create an `Experiment` in an existing `Workspace`.\n",
         "2. Configure AutoML using `AutoMLConfig`.\n",
         "3. Train the model using local compute with ONNX compatible config on.\n",
-        "4. Explore the results and save the ONNX model."
+        "4. Explore the results and save the ONNX model.\n",
+        "5. Inference with the ONNX model."
       ]
     },
     {
@@ -89,9 +89,8 @@
       "source": [
         "ws = Workspace.from_config()\n",
         "\n",
-        "# Choose a name for the experiment and specify the project folder.\n",
+        "# Choose a name for the experiment.\n",
         "experiment_name = 'automl-classification-onnx'\n",
-        "project_folder = './sample_projects/automl-classification-onnx'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -101,7 +100,6 @@
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -127,9 +125,7 @@
         "X_train, X_test, y_train, y_test = train_test_split(iris.data, \n",
         "                                                    iris.target, \n",
         "                                                    test_size=0.2, \n",
-        "                                                    random_state=0)\n",
-        "\n",
-        "\n"
+        "                                                    random_state=0)"
       ]
     },
     {
@@ -156,11 +152,11 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Train with enable ONNX compatible models config on\n",
+        "## Train\n",
         "\n",
         "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
         "\n",
-        "Set the parameter enable_onnx_compatible_models=True, if you also want to generate the ONNX compatible models. Please note, the forecasting task and TensorFlow models are not ONNX compatible yet.\n",
+        "**Note:** Set the parameter enable_onnx_compatible_models=True, if you also want to generate the ONNX compatible models. Please note, the forecasting task and TensorFlow models are not ONNX compatible yet.\n",
         "\n",
         "|Property|Description|\n",
         "|-|-|\n",
@@ -170,8 +166,7 @@
         "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
-        "|**enable_onnx_compatible_models**|Enable the ONNX compatible models in the experiment.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+        "|**enable_onnx_compatible_models**|Enable the ONNX compatible models in the experiment.|"
       ]
     },
     {
@@ -196,8 +191,7 @@
         "                             X = X_train, \n",
         "                             y = y_train,\n",
         "                             preprocess=True,\n",
-        "                             enable_onnx_compatible_models=True,\n",
-        "                             path = project_folder)"
+        "                             enable_onnx_compatible_models=True)"
       ]
     },
     {
diff --git a/how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb b/how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb
index 2723a277..2f841c92 100644
--- a/how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb
@@ -41,7 +41,7 @@
         "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
         "\n",
         "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
-        "This notebooks shows how can automl can be trained on a a selected list of models,see the readme.md for the models.\n",
+        "This notebooks shows how can automl can be trained on a selected list of models, see the readme.md for the models.\n",
         "This trains the model exclusively on tensorflow based models.\n",
         "\n",
         "In this notebook you will learn how to:\n",
@@ -100,9 +100,8 @@
       "source": [
         "ws = Workspace.from_config()\n",
         "\n",
-        "# Choose a name for the experiment and specify the project folder.\n",
+        "# Choose a name for the experiment.\n",
         "experiment_name = 'automl-local-whitelist'\n",
-        "project_folder = './sample_projects/automl-local-whitelist'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -112,7 +111,6 @@
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -158,7 +156,6 @@
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
         "|**whitelist_models**|List of models that AutoML should use.  The possible values are listed [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#configure-your-experiment-settings).|"
       ]
     },
@@ -177,8 +174,7 @@
         "                             X = X_train, \n",
         "                             y = y_train,\n",
         "                             enable_tf=True,\n",
-        "                             whitelist_models=whitelist_models,\n",
-        "                             path = project_folder)"
+        "                             whitelist_models=whitelist_models)"
       ]
     },
     {
diff --git a/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb b/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb
index cc5fd649..34a6bf37 100644
--- a/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb
@@ -113,9 +113,8 @@
       "source": [
         "ws = Workspace.from_config()\n",
         "\n",
-        "# Choose a name for the experiment and specify the project folder.\n",
+        "# Choose a name for the experiment.\n",
         "experiment_name = 'automl-classification'\n",
-        "project_folder = './sample_projects/automl-classification'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -125,7 +124,6 @@
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -258,7 +256,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "widget-rundetails-sample"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.widgets import RunDetails\n",
diff --git a/how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb b/how-to-use-azureml/automated-machine-learning/dataset-remote-execution/auto-ml-dataset-remote-execution.ipynb
similarity index 77%
rename from how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb
rename to how-to-use-azureml/automated-machine-learning/dataset-remote-execution/auto-ml-dataset-remote-execution.ipynb
index aa85a399..2dd27e1f 100644
--- a/how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/dataset-remote-execution/auto-ml-dataset-remote-execution.ipynb
@@ -21,7 +21,7 @@
       "metadata": {},
       "source": [
         "# Automated Machine Learning\n",
-        "_**Prepare Data using `azureml.dataprep` for Remote Execution (AmlCompute)**_\n",
+        "_**Load Data using `TabularDataset` for Remote Execution (AmlCompute)**_\n",
         "\n",
         "## Contents\n",
         "1. [Introduction](#Introduction)\n",
@@ -37,23 +37,20 @@
       "metadata": {},
       "source": [
         "## Introduction\n",
-        "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
+        "In this example we showcase how you can use AzureML Dataset to load data for AutoML.\n",
         "\n",
         "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
         "In this notebook you will learn how to:\n",
-        "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
-        "2. Pass the `Dataflow` to AutoML for a local run.\n",
-        "3. Pass the `Dataflow` to AutoML for a remote run."
+        "1. Create a `TabularDataset` pointing to the training data.\n",
+        "2. Pass the `TabularDataset` to AutoML for a remote run."
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Setup\n",
-        "\n",
-        "Currently, Data Prep only supports __Ubuntu 16__ and __Red Hat Enterprise Linux 7__. We are working on supporting more linux distros."
+        "## Setup"
       ]
     },
     {
@@ -70,15 +67,13 @@
       "outputs": [],
       "source": [
         "import logging\n",
-        "import time\n",
         "\n",
         "import pandas as pd\n",
         "\n",
         "import azureml.core\n",
-        "from azureml.core.compute import DsvmCompute\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "import azureml.dataprep as dprep\n",
+        "from azureml.core.dataset import Dataset\n",
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
@@ -89,11 +84,9 @@
       "outputs": [],
       "source": [
         "ws = Workspace.from_config()\n",
-        " \n",
+        "\n",
         "# choose a name for experiment\n",
-        "experiment_name = 'automl-dataprep-remote-dsvm'\n",
-        "# project folder\n",
-        "project_folder = './sample_projects/automl-dataprep-remote-dsvm'\n",
+        "experiment_name = 'automl-dataset-remote-bai'\n",
         " \n",
         "experiment = Experiment(ws, experiment_name)\n",
         " \n",
@@ -103,7 +96,6 @@
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -123,35 +115,21 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
         "# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
-        "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
-        "# and convert column types manually.\n",
         "example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
-        "dflow = dprep.auto_read_file(example_data).skip(1)  # Remove the header row.\n",
-        "dflow.get_profile()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# As `Primary Type` is our y data, we need to drop the values those are null in this column.\n",
-        "dflow = dflow.drop_nulls('Primary Type')\n",
-        "dflow.head(5)"
+        "dataset = Dataset.Tabular.from_delimited_files(example_data)\n",
+        "dataset.take(5).to_pandas_dataframe()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Review the Data Preparation Result\n",
+        "### Review the data\n",
         "\n",
-        "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets.\n",
+        "You can peek the result of a `TabularDataset` at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only `j` records, which makes it fast even against large datasets.\n",
         "\n",
-        "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
+        "`TabularDataset` objects are immutable and are composed of a list of subsetting transformations (optional)."
       ]
     },
     {
@@ -160,8 +138,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X = dflow.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
-        "y = dflow.keep_columns(columns=['Primary Type'], validate_column_exists=True)"
+        "X = dataset.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
+        "y = dataset.keep_columns(columns=['Primary Type'], validate=True)"
       ]
     },
     {
@@ -205,7 +183,7 @@
         "from azureml.core.compute import ComputeTarget\n",
         "\n",
         "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster\"\n",
+        "amlcompute_cluster_name = \"automlc2\"\n",
         "\n",
         "found = False\n",
         "\n",
@@ -226,11 +204,12 @@
         "    # Create the cluster.\\n\",\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
         "\n",
-        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
+        "print('Checking cluster status...')\n",
+        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
         "\n",
-        "     # For a more detailed view of current AmlCompute status, use get_status()."
+        "# For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
     {
@@ -249,11 +228,8 @@
         "# Set compute target to AmlCompute\n",
         "conda_run_config.target = compute_target\n",
         "conda_run_config.environment.docker.enabled = True\n",
-        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
-        "dprep_dependency = 'azureml-dataprep==' + pkg_resources.get_distribution(\"azureml-dataprep\").version\n",
-        "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', dprep_dependency], conda_packages=['numpy','py-xgboost<=0.80'])\n",
+        "cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd"
       ]
     },
@@ -261,9 +237,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Pass Data with `Dataflow` Objects\n",
+        "### Pass Data with `TabularDataset` Objects\n",
         "\n",
-        "The `Dataflow` objects captured above can also be passed to the `submit` method for a remote run. AutoML will serialize the `Dataflow` object and send it to the remote compute target. The `Dataflow` will not be evaluated locally."
+        "The `TabularDataset` objects captured above can also be passed to the `submit` method for a remote run. AutoML will serialize the `TabularDataset` object and send it to the remote compute target. The `TabularDataset` will not be evaluated locally."
       ]
     },
     {
@@ -274,7 +250,6 @@
       "source": [
         "automl_config = AutoMLConfig(task = 'classification',\n",
         "                             debug_log = 'automl_errors.log',\n",
-        "                             path = project_folder,\n",
         "                             run_configuration=conda_run_config,\n",
         "                             X = X,\n",
         "                             y = y,\n",
@@ -466,8 +441,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "dflow_test = dprep.auto_read_file(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv').skip(1)\n",
-        "dflow_test = dflow_test.drop_nulls('Primary Type')"
+        "dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
+        "\n",
+        "df_test = dataset_test.to_pandas_dataframe()\n",
+        "df_test = df_test[pd.notnull(df_test['Primary Type'])]\n",
+        "\n",
+        "y_test = df_test[['Primary Type']]\n",
+        "X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)"
       ]
     },
     {
@@ -486,10 +466,6 @@
       "source": [
         "from pandas_ml import ConfusionMatrix\n",
         "\n",
-        "y_test = dflow_test.keep_columns(columns=['Primary Type']).to_pandas_dataframe()\n",
-        "X_test = dflow_test.drop_columns(columns=['Primary Type', 'FBI Code']).to_pandas_dataframe()\n",
-        "\n",
-        "\n",
         "ypred = fitted_model.predict(X_test)\n",
         "\n",
         "cm = ConfusionMatrix(y_test['Primary Type'], ypred)\n",
diff --git a/tutorials/regression-part2-automated-ml.yml b/how-to-use-azureml/automated-machine-learning/dataset-remote-execution/auto-ml-dataset-remote-execution.yml
similarity index 69%
rename from tutorials/regression-part2-automated-ml.yml
rename to how-to-use-azureml/automated-machine-learning/dataset-remote-execution/auto-ml-dataset-remote-execution.yml
index f6e23d59..aa6e4e65 100644
--- a/tutorials/regression-part2-automated-ml.yml
+++ b/how-to-use-azureml/automated-machine-learning/dataset-remote-execution/auto-ml-dataset-remote-execution.yml
@@ -1,10 +1,10 @@
-name: regression-part2-automated-ml
+name: auto-ml-dataset-remote-execution
 dependencies:
 - pip:
   - azureml-sdk
+  - azureml-defaults
+  - azureml-explain-model
   - azureml-train-automl
   - azureml-widgets
-  - azureml-explain-model
   - matplotlib
   - pandas_ml
-  - seaborn
diff --git a/how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.ipynb b/how-to-use-azureml/automated-machine-learning/dataset/auto-ml-dataset.ipynb
similarity index 78%
rename from how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.ipynb
rename to how-to-use-azureml/automated-machine-learning/dataset/auto-ml-dataset.ipynb
index b409f6e6..89ac30d8 100644
--- a/how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/dataset/auto-ml-dataset.ipynb
@@ -21,7 +21,7 @@
       "metadata": {},
       "source": [
         "# Automated Machine Learning\n",
-        "_**Prepare Data using `azureml.dataprep` for Local Execution**_\n",
+        "_**Load Data using `TabularDataset` for Local Execution**_\n",
         "\n",
         "## Contents\n",
         "1. [Introduction](#Introduction)\n",
@@ -37,23 +37,20 @@
       "metadata": {},
       "source": [
         "## Introduction\n",
-        "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
+        "In this example we showcase how you can use AzureML Dataset to load data for AutoML.\n",
         "\n",
         "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
         "In this notebook you will learn how to:\n",
-        "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
-        "2. Pass the `Dataflow` to AutoML for a local run.\n",
-        "3. Pass the `Dataflow` to AutoML for a remote run."
+        "1. Create a `TabularDataset` pointing to the training data.\n",
+        "2. Pass the `TabularDataset` to AutoML for a local run."
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Setup\n",
-        "\n",
-        "Currently, Data Prep only supports __Ubuntu 16__ and __Red Hat Enterprise Linux 7__. We are working on supporting more linux distros."
+        "## Setup"
       ]
     },
     {
@@ -76,7 +73,7 @@
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "import azureml.dataprep as dprep\n",
+        "from azureml.core.dataset import Dataset\n",
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
@@ -89,9 +86,7 @@
         "ws = Workspace.from_config()\n",
         " \n",
         "# choose a name for experiment\n",
-        "experiment_name = 'automl-dataprep-local'\n",
-        "# project folder\n",
-        "project_folder = './sample_projects/automl-dataprep-local'\n",
+        "experiment_name = 'automl-dataset-local'\n",
         " \n",
         "experiment = Experiment(ws, experiment_name)\n",
         " \n",
@@ -101,7 +96,6 @@
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -121,35 +115,21 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
         "# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
-        "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
-        "# and convert column types manually.\n",
         "example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
-        "dflow = dprep.auto_read_file(example_data).skip(1)  # Remove the header row.\n",
-        "dflow.get_profile()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# As `Primary Type` is our y data, we need to drop the values those are null in this column.\n",
-        "dflow = dflow.drop_nulls('Primary Type')\n",
-        "dflow.head(5)"
+        "dataset = Dataset.Tabular.from_delimited_files(example_data)\n",
+        "dataset.take(5).to_pandas_dataframe()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Review the Data Preparation Result\n",
+        "### Review the data\n",
         "\n",
-        "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets.\n",
+        "You can peek the result of a `TabularDataset` at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only `j` records, which makes it fast even against large datasets.\n",
         "\n",
-        "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
+        "`TabularDataset` objects are immutable and are composed of a list of subsetting transformations (optional)."
       ]
     },
     {
@@ -158,8 +138,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X = dflow.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
-        "y = dflow.keep_columns(columns=['Primary Type'], validate_column_exists=True)"
+        "X = dataset.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
+        "y = dataset.keep_columns(columns=['Primary Type'], validate=True)"
       ]
     },
     {
@@ -190,9 +170,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Pass Data with `Dataflow` Objects\n",
+        "### Pass Data with `TabularDataset` Objects\n",
         "\n",
-        "The `Dataflow` objects captured above can be passed to the `submit` method for a local run. AutoML will retrieve the results from the `Dataflow` for model training."
+        "The `TabularDataset` objects captured above can be passed to the `submit` method for a local run. AutoML will retrieve the results from the `TabularDataset` for model training."
       ]
     },
     {
@@ -355,8 +335,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "dflow_test = dprep.auto_read_file(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv').skip(1)\n",
-        "dflow_test = dflow_test.drop_nulls('Primary Type')"
+        "dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
+        "\n",
+        "df_test = dataset_test.to_pandas_dataframe()\n",
+        "df_test = df_test[pd.notnull(df_test['Primary Type'])]\n",
+        "\n",
+        "y_test = df_test[['Primary Type']]\n",
+        "X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)"
       ]
     },
     {
@@ -375,9 +360,6 @@
       "source": [
         "from pandas_ml import ConfusionMatrix\n",
         "\n",
-        "y_test = dflow_test.keep_columns(columns=['Primary Type']).to_pandas_dataframe()\n",
-        "X_test = dflow_test.drop_columns(columns=['Primary Type', 'FBI Code']).to_pandas_dataframe()\n",
-        "\n",
         "ypred = fitted_model.predict(X_test)\n",
         "\n",
         "cm = ConfusionMatrix(y_test['Primary Type'], ypred)\n",
diff --git a/how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.yml b/how-to-use-azureml/automated-machine-learning/dataset/auto-ml-dataset.yml
similarity index 82%
rename from how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.yml
rename to how-to-use-azureml/automated-machine-learning/dataset/auto-ml-dataset.yml
index 4f7748d9..87242fe5 100644
--- a/how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.yml
+++ b/how-to-use-azureml/automated-machine-learning/dataset/auto-ml-dataset.yml
@@ -1,4 +1,4 @@
-name: auto-ml-dataprep
+name: auto-ml-dataset
 dependencies:
 - pip:
   - azureml-sdk
diff --git a/how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb b/how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb
index 3e05980f..61ece379 100644
--- a/how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb
@@ -197,12 +197,12 @@
         "display(HTML('<h3>Iterations</h3>'))\n",
         "RunDetails(ml_run).show() \n",
         "\n",
-        "children = list(ml_run.get_children())\n",
+        "all_metrics = ml_run.get_metrics(recursive=True)\n",
         "metricslist = {}\n",
-        "for run in children:\n",
-        "    properties = run.get_properties()\n",
-        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
-        "    metricslist[int(properties['iteration'])] = metrics\n",
+        "for run_id, metrics in all_metrics.items():\n",
+        "    iteration = int(run_id.split('_')[-1])\n",
+        "    float_metrics = {k: v for k, v in metrics.items() if isinstance(v, float)}\n",
+        "    metricslist[iteration] = float_metrics\n",
         "\n",
         "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
         "display(HTML('<h3>Metrics</h3>'))\n",
diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
index dabce312..e15f87cd 100644
--- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
@@ -97,8 +97,6 @@
         "\n",
         "# choose a name for the run history container in the workspace\n",
         "experiment_name = 'automl-bikeshareforecasting'\n",
-        "# project folder\n",
-        "project_folder = './sample_projects/automl-local-bikeshareforecasting'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -108,7 +106,6 @@
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
         "output['Run History Name'] = experiment_name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -225,7 +222,8 @@
         "|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**country_or_region**|The country/region used to generate holiday features. These should be ISO 3166 two-letter country/region codes (i.e. 'US', 'GB').|\n",
-        "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
+        "\n",
+        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the iteration_timeout_minutes parameter value to get results."
       ]
     },
     {
@@ -246,12 +244,12 @@
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
+        "                             blacklist_models = ['ExtremeRandomTrees'],\n",
         "                             iterations=10,\n",
         "                             iteration_timeout_minutes=5,\n",
         "                             X=X_train,\n",
         "                             y=y_train,\n",
-        "                             n_cross_validations=3,                             \n",
-        "                             path=project_folder,\n",
+        "                             n_cross_validations=3,\n",
         "                             verbosity=logging.INFO,\n",
         "                            **automl_settings)"
       ]
@@ -345,7 +343,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()"
+        "# Get the featurization summary as a list of JSON\n",
+        "featurization_summary = fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()\n",
+        "# View the featurization summary as a pandas dataframe\n",
+        "pd.DataFrame.from_records(featurization_summary)"
       ]
     },
     {
@@ -522,7 +523,7 @@
         "print('MAPE: %.2f' % MAPE(df_all[target_column_name], df_all['predicted']))\n",
         "\n",
         "# Plot outputs\n",
-        "%matplotlib notebook\n",
+        "%matplotlib inline\n",
         "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
         "test_test = plt.scatter(y_test, y_test, color='g')\n",
         "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
@@ -564,7 +565,7 @@
         "df_all_APE = df_all.assign(APE=APE(df_all[target_column_name], df_all['predicted']))\n",
         "APEs = [df_all_APE[df_all['horizon_origin'] == h].APE.values for h in range(1, max_horizon + 1)]\n",
         "\n",
-        "%matplotlib notebook\n",
+        "%matplotlib inline\n",
         "plt.boxplot(APEs)\n",
         "plt.yscale('log')\n",
         "plt.xlabel('horizon')\n",
@@ -578,7 +579,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "xiaga@microsoft.com, tosingli@microsoft.com, erwright@microsoft.com"
+        "name": "erwright"
       }
     ],
     "kernelspec": {
diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
index 88f42473..bf7764e5 100644
--- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
@@ -93,8 +93,6 @@
         "\n",
         "# choose a name for the run history container in the workspace\n",
         "experiment_name = 'automl-energydemandforecasting'\n",
-        "# project folder\n",
-        "project_folder = './sample_projects/automl-local-energydemandforecasting'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -104,7 +102,6 @@
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
         "output['Run History Name'] = experiment_name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -213,8 +210,7 @@
         "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
-        "|**n_cross_validations**|Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way.|\n",
-        "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
+        "|**n_cross_validations**|Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way.|"
       ]
     },
     {
@@ -231,12 +227,12 @@
         "automl_config = AutoMLConfig(task='forecasting',\n",
         "                             debug_log='automl_nyc_energy_errors.log',\n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
+        "                             blacklist_models = ['ExtremeRandomTrees'],\n",
         "                             iterations=10,\n",
         "                             iteration_timeout_minutes=5,\n",
         "                             X=X_train,\n",
         "                             y=y_train,\n",
         "                             n_cross_validations=3,\n",
-        "                             path=project_folder,\n",
         "                             verbosity = logging.INFO,\n",
         "                             **time_series_settings)"
       ]
@@ -432,7 +428,7 @@
         "print('MAPE: %.2f' % MAPE(df_all[target_column_name], df_all['predicted']))\n",
         "\n",
         "# Plot outputs\n",
-        "%matplotlib notebook\n",
+        "%matplotlib inline\n",
         "pred, = plt.plot(df_all[time_column_name], df_all['predicted'], color='b')\n",
         "actual, = plt.plot(df_all[time_column_name], df_all[target_column_name], color='g')\n",
         "plt.xticks(fontsize=8)\n",
@@ -462,7 +458,9 @@
       "source": [
         "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, grain and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation.\n",
         "\n",
-        "Now that we configured target lags, that is the previous values of the target variables, and the prediction is no longer horizon-less. We therefore must still specify the `max_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features."
+        "Now that we configured target lags, that is the previous values of the target variables, and the prediction is no longer horizon-less. We therefore must still specify the `max_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features.\n",
+        "\n",
+        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset.  You can choose to remove models from the blacklist_models list but you may need to increase the iteration_timeout_minutes parameter value to get results."
       ]
     },
     {
@@ -481,13 +479,12 @@
         "automl_config_lags = AutoMLConfig(task='forecasting',\n",
         "                                  debug_log='automl_nyc_energy_errors.log',\n",
         "                                  primary_metric='normalized_root_mean_squared_error',\n",
-        "                                  blacklist_models=['ElasticNet'],\n",
+        "                                  blacklist_models=['ElasticNet','ExtremeRandomTrees','GradientBoosting','XGBoostRegressor'],\n",
         "                                  iterations=10,\n",
         "                                  iteration_timeout_minutes=10,\n",
         "                                  X=X_train,\n",
         "                                  y=y_train,\n",
         "                                  n_cross_validations=3,\n",
-        "                                  path=project_folder,\n",
         "                                  verbosity=logging.INFO,\n",
         "                                  **time_series_settings_with_lags)"
       ]
@@ -543,7 +540,7 @@
         "print('MAPE: %.2f' % MAPE(df_lags[target_column_name], df_lags['predicted']))\n",
         "\n",
         "# Plot outputs\n",
-        "%matplotlib notebook\n",
+        "%matplotlib inline\n",
         "pred, = plt.plot(df_lags[time_column_name], df_lags['predicted'], color='b')\n",
         "actual, = plt.plot(df_lags[time_column_name], df_lags[target_column_name], color='g')\n",
         "plt.xticks(fontsize=8)\n",
@@ -555,7 +552,21 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### What features matter for the forecast?"
+        "### What features matter for the forecast?\n",
+        "The following steps will allow you to compute and visualize engineered feature importance based on your test data for forecasting. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Setup the model explanations for AutoML models\n",
+        "The *fitted_model* can generate the following which will be used for getting the engineered and raw feature explanations using *automl_setup_model_explanations*:-\n",
+        "1. Featurized data from train samples/test samples \n",
+        "2. Gather engineered and raw feature name lists\n",
+        "3. Find the classes in your labeled column in classification scenarios\n",
+        "\n",
+        "The *automl_explainer_setup_obj* contains all the structures from above list. "
       ]
     },
     {
@@ -564,14 +575,74 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.train.automl.automlexplainer import explain_model\n",
-        "\n",
-        "# feature names are everything in the transformed data except the target\n",
-        "features = X_trans_lags.columns[:-1]\n",
-        "expl = explain_model(fitted_model_lags, X_train.copy(), X_test.copy(), features=features, best_run=best_run_lags, y_train=y_train)\n",
-        "# unpack the tuple\n",
-        "shap_values, expected_values, feat_overall_imp, feat_names, per_class_summary, per_class_imp = expl\n",
-        "best_run_lags"
+        "from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
+        "automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, X=X_train.copy(), \n",
+        "                                                             X_test=X_test.copy(), y=y_train, \n",
+        "                                                             task='forecasting')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Initialize the Mimic Explainer for feature importance\n",
+        "For explaining the AutoML models, use the *MimicWrapper* from *azureml.explain.model* package. The *MimicWrapper* can be initialized with fields in *automl_explainer_setup_obj*, your workspace and a LightGBM model which acts as a surrogate model to explain the AutoML model (*fitted_model* here). The *MimicWrapper* also takes the *best_run* object where the raw and engineered explanations will be uploaded."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
+        "from azureml.explain.model.mimic_wrapper import MimicWrapper\n",
+        "explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMExplainableModel, \n",
+        "                         init_dataset=automl_explainer_setup_obj.X_transform, run=best_run,\n",
+        "                         features=automl_explainer_setup_obj.engineered_feature_names, \n",
+        "                         feature_maps=[automl_explainer_setup_obj.feature_map])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Use Mimic Explainer for computing and visualizing engineered feature importance\n",
+        "The *explain()* method in *MimicWrapper* can be called with the transformed test samples to get the feature importance for the generated engineered features. You can also use *ExplanationDashboard* to view the dash board visualization of the feature importance values of the generated engineered features by AutoML featurizers."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "engineered_explanations = explainer.explain(['local', 'global'], eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
+        "print(engineered_explanations.get_feature_importance_dict())\n",
+        "from azureml.contrib.explain.model.visualize import ExplanationDashboard\n",
+        "ExplanationDashboard(engineered_explanations, automl_explainer_setup_obj.automl_estimator, automl_explainer_setup_obj.X_test_transform)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Use Mimic Explainer for computing and visualizing raw feature importance\n",
+        "The *explain()* method in *MimicWrapper* can be again called with the transformed test samples and setting *get_raw* to *True* to get the feature importance for the raw features. You can also use *ExplanationDashboard* to view the dash board visualization of the feature importance values of the raw features."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "raw_explanations = explainer.explain(['local', 'global'], get_raw=True, \n",
+        "                                     raw_feature_names=automl_explainer_setup_obj.raw_feature_names,\n",
+        "                                     eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
+        "print(raw_explanations.get_feature_importance_dict())\n",
+        "from azureml.contrib.explain.model.visualize import ExplanationDashboard\n",
+        "ExplanationDashboard(raw_explanations, automl_explainer_setup_obj.automl_pipeline, automl_explainer_setup_obj.X_test_raw)"
       ]
     },
     {
@@ -587,7 +658,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "xiaga, tosingli, erwright"
+        "name": "erwright"
       }
     ],
     "kernelspec": {
diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml
index 5a2fda3d..693b5f4d 100644
--- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml
@@ -8,3 +8,4 @@ dependencies:
   - pandas_ml
   - statsmodels
   - azureml-explain-model
+  - azureml-contrib-explain-model
diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
index e129c731..eec96f7f 100644
--- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
@@ -89,8 +89,6 @@
         "\n",
         "# choose a name for the run history container in the workspace\n",
         "experiment_name = 'automl-ojforecasting'\n",
-        "# project folder\n",
-        "project_folder = './sample_projects/automl-local-ojforecasting'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -100,7 +98,6 @@
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
         "output['Run History Name'] = experiment_name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -244,9 +241,9 @@
         "|**X**|Training matrix of features as a pandas DataFrame, shape = [n_training_samples, n_features]|\n",
         "|**y**|Target values as a numpy.ndarray, shape = [n_training_samples, ]|\n",
         "|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection|\n",
-        "|**enable_ensembling**|Allow AutoML to create ensembles of the best performing models\n",
+        "|**enable_voting_ensemble**|Allow AutoML to create a Voting ensemble of the best performing models\n",
+        "|**enable_stack_ensemble**|Allow AutoML to create a Stack ensemble of the best performing models\n",
         "|**debug_log**|Log file path for writing debugging information\n",
-        "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
         "|**time_column_name**|Name of the datetime column in the input data|\n",
         "|**grain_column_names**|Name(s) of the columns defining individual series in the input data|\n",
         "|**drop_column_names**|Name(s) of columns to drop prior to modeling|\n",
@@ -273,8 +270,8 @@
         "                             X=X_train,\n",
         "                             y=y_train,\n",
         "                             n_cross_validations=3,\n",
-        "                             enable_ensembling=False,\n",
-        "                             path=project_folder,\n",
+        "                             enable_voting_ensemble=False,\n",
+        "                             enable_stack_ensemble=False,\n",
         "                             verbosity=logging.INFO,\n",
         "                             **time_series_settings)"
       ]
@@ -463,7 +460,7 @@
         "# Plot outputs\n",
         "import matplotlib.pyplot as plt\n",
         "\n",
-        "%matplotlib notebook\n",
+        "%matplotlib inline\n",
         "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
         "test_test = plt.scatter(y_test, y_test, color='g')\n",
         "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
@@ -663,10 +660,10 @@
         "conda_env_file_name = 'fcast_env.yml'\n",
         "\n",
         "dependencies = ml_run.get_run_sdk_dependencies(iteration = best_iteration)\n",
-        "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
+        "for p in ['azureml-train-automl', 'azureml-core']:\n",
         "    print('{}\\t{}'.format(p, dependencies[p]))\n",
         "\n",
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
+        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-train-automl'])\n",
         "\n",
         "myenv.save_to_file('.', conda_env_file_name)"
       ]
@@ -688,7 +685,7 @@
         "    content = cefr.read()\n",
         "\n",
         "with open(conda_env_file_name, 'w') as cefw:\n",
-        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
+        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
         "\n",
         "# Substitute the actual model id in the script file.\n",
         "\n",
@@ -829,7 +826,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "erwright, tosingli"
+        "name": "erwright"
       }
     ],
     "kernelspec": {
diff --git a/how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb b/how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb
index 840cdd80..2fee05c3 100644
--- a/how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb
@@ -93,7 +93,6 @@
         "\n",
         "# Choose a name for the experiment.\n",
         "experiment_name = 'automl-local-missing-data'\n",
-        "project_folder = './sample_projects/automl-local-missing-data'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -103,7 +102,6 @@
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -166,8 +164,7 @@
         "|**experiment_exit_score**|*double* value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
         "|**blacklist_models**|*List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i>|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|"
       ]
     },
     {
@@ -186,8 +183,7 @@
         "                             blacklist_models = ['KNN','LinearSVM'],\n",
         "                             verbosity = logging.INFO,\n",
         "                             X = X_train, \n",
-        "                             y = y_train,\n",
-        "                             path = project_folder)"
+        "                             y = y_train)"
       ]
     },
     {
@@ -360,7 +356,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "fitted_model.named_steps['datatransformer'].get_featurization_summary()"
+        "# Get the featurization summary as a list of JSON\n",
+        "featurization_summary = fitted_model.named_steps['datatransformer'].get_featurization_summary()\n",
+        "# View the featurization summary as a pandas dataframe\n",
+        "pd.DataFrame.from_records(featurization_summary)"
       ]
     },
     {
diff --git a/how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.ipynb b/how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.ipynb
index fff6cc0d..58d00ff6 100644
--- a/how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.ipynb
@@ -69,7 +69,8 @@
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig"
+        "from azureml.train.automl import AutoMLConfig\n",
+        "from azureml.core.dataset import Dataset"
       ]
     },
     {
@@ -107,29 +108,42 @@
         "## Data"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Training Data"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
-        "from sklearn import datasets\n",
-        "\n",
-        "iris = datasets.load_iris()\n",
-        "y = iris.target\n",
-        "X = iris.data\n",
-        "\n",
-        "features = iris.feature_names\n",
-        "\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X,\n",
-        "                                                    y,\n",
-        "                                                    test_size=0.1,\n",
-        "                                                    random_state=100,\n",
-        "                                                    stratify=y)\n",
-        "\n",
-        "X_train = pd.DataFrame(X_train, columns=features)\n",
-        "X_test = pd.DataFrame(X_test, columns=features)"
+        "train_data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
+        "train_dataset = Dataset.Tabular.from_delimited_files(train_data)\n",
+        "X_train = train_dataset.drop_columns(columns=['y']).to_pandas_dataframe()\n",
+        "y_train = train_dataset.keep_columns(columns=['y'], validate=True).to_pandas_dataframe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Test Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "test_data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_validate.csv\"\n",
+        "test_dataset = Dataset.Tabular.from_delimited_files(test_data)\n",
+        "X_test = test_dataset.drop_columns(columns=['y']).to_pandas_dataframe()\n",
+        "y_test = test_dataset.keep_columns(columns=['y'], validate=True).to_pandas_dataframe()"
       ]
     },
     {
@@ -148,8 +162,6 @@
         "|**iterations**|Number of iterations. In each iteration Auto ML trains the data with a specific pipeline|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
-        "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**y_valid**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
         "|**model_explainability**|Indicate to explain each trained pipeline or not |\n",
         "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. |"
       ]
@@ -166,10 +178,10 @@
         "                             iteration_timeout_minutes = 200,\n",
         "                             iterations = 10,\n",
         "                             verbosity = logging.INFO,\n",
+        "                             preprocess = True,\n",
         "                             X = X_train, \n",
         "                             y = y_train,\n",
-        "                             X_valid = X_test,\n",
-        "                             y_valid = y_test,\n",
+        "                             n_cross_validations = 5,\n",
         "                             model_explainability=True,\n",
         "                             path=project_folder)"
       ]
@@ -197,7 +209,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "local_run"
+        "best_run, fitted_model = local_run.get_output()"
       ]
     },
     {
@@ -302,19 +314,21 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Beside retrieve the existed model explanation information, explain the model with different train/test data"
+        "### Computing model explanations and visualizing the explanations using azureml-explain-model package\n",
+        "Beside retrieve the existed model explanation information, explain the model with different train/test data. The following steps will allow you to compute and visualize engineered feature importance and raw feature importance based on your test data. "
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
+      "cell_type": "markdown",
       "metadata": {},
-      "outputs": [],
       "source": [
-        "from azureml.train.automl.automlexplainer import explain_model\n",
+        "#### Setup the model explanations for AutoML models\n",
+        "The *fitted_model* can generate the following which will be used for getting the engineered and raw feature explanations using *automl_setup_model_explanations*:-\n",
+        "1. Featurized data from train samples/test samples \n",
+        "2. Gather engineered and raw feature name lists\n",
+        "3. Find the classes in your labeled column in classification scenarios\n",
         "\n",
-        "shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
-        "    explain_model(fitted_model, X_train, X_test, features=features)"
+        "The *automl_explainer_setup_obj* contains all the structures from above list. "
       ]
     },
     {
@@ -323,8 +337,116 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(overall_summary)\n",
-        "print(overall_imp)"
+        "from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
+        "\n",
+        "automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, X=X_train, \n",
+        "                                                             X_test=X_test, y=y_train, \n",
+        "                                                             task='classification')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Initialize the Mimic Explainer for feature importance\n",
+        "For explaining the AutoML models, use the *MimicWrapper* from *azureml.explain.model* package. The *MimicWrapper* can be initialized with fields in *automl_explainer_setup_obj*, your workspace and a LightGBM model which acts as a surrogate model to explain the AutoML model (*fitted_model* here). The *MimicWrapper* also takes the *best_run* object where the raw and engineered explanations will be uploaded."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
+        "from azureml.explain.model.mimic_wrapper import MimicWrapper\n",
+        "explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMExplainableModel, \n",
+        "                         init_dataset=automl_explainer_setup_obj.X_transform, run=best_run,\n",
+        "                         features=automl_explainer_setup_obj.engineered_feature_names, \n",
+        "                         feature_maps=[automl_explainer_setup_obj.feature_map],\n",
+        "                         classes=automl_explainer_setup_obj.classes)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Use Mimic Explainer for computing and visualizing engineered feature importance\n",
+        "The *explain()* method in *MimicWrapper* can be called with the transformed test samples to get the feature importance for the generated engineered features. You can also use *ExplanationDashboard* to view the dash board visualization of the feature importance values of the generated engineered features by AutoML featurizers."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "engineered_explanations = explainer.explain(['local', 'global'], eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
+        "print(engineered_explanations.get_feature_importance_dict())\n",
+        "from azureml.contrib.explain.model.visualize import ExplanationDashboard\n",
+        "ExplanationDashboard(engineered_explanations, automl_explainer_setup_obj.automl_estimator, automl_explainer_setup_obj.X_test_transform)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Use Mimic Explainer for computing and visualizing raw feature importance\n",
+        "The *explain()* method in *MimicWrapper* can be again called with the transformed test samples and setting *get_raw* to *True* to get the feature importance for the raw features. You can also use *ExplanationDashboard* to view the dash board visualization of the feature importance values of the raw features."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "raw_explanations = explainer.explain(['local', 'global'], get_raw=True, \n",
+        "                                     raw_feature_names=automl_explainer_setup_obj.raw_feature_names,\n",
+        "                                     eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
+        "print(raw_explanations.get_feature_importance_dict())\n",
+        "from azureml.contrib.explain.model.visualize import ExplanationDashboard\n",
+        "ExplanationDashboard(raw_explanations, automl_explainer_setup_obj.automl_pipeline, automl_explainer_setup_obj.X_test_raw)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Download engineered feature importance from artifact store\n",
+        "You can use *ExplanationClient* to download the engineered feature explanations from the artifact store of the *best_run*."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.explain.model._internal.explanation_client import ExplanationClient\n",
+        "client = ExplanationClient.from_run(best_run)\n",
+        "engineered_explanations = client.download_model_explanation(raw=False)\n",
+        "print(engineered_explanations.get_feature_importance_dict())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Download raw feature importance from artifact store\n",
+        "You can use *ExplanationClient* to download the raw feature explanations from the artifact store of the *best_run*."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.explain.model._internal.explanation_client import ExplanationClient\n",
+        "client = ExplanationClient.from_run(best_run)\n",
+        "raw_explanations = client.download_model_explanation(raw=True)\n",
+        "print(raw_explanations.get_feature_importance_dict())"
       ]
     }
   ],
diff --git a/how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.yml b/how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.yml
index 1c4e89af..2d0c7623 100644
--- a/how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.yml
+++ b/how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.yml
@@ -7,3 +7,4 @@ dependencies:
   - matplotlib
   - pandas_ml
   - azureml-explain-model
+  - azureml-contrib-explain-model
diff --git a/how-to-use-azureml/automated-machine-learning/regression-concrete-strength/auto-ml-regression-concrete-strength.ipynb b/how-to-use-azureml/automated-machine-learning/regression-concrete-strength/auto-ml-regression-concrete-strength.ipynb
index 88d8bba0..832902ae 100644
--- a/how-to-use-azureml/automated-machine-learning/regression-concrete-strength/auto-ml-regression-concrete-strength.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/regression-concrete-strength/auto-ml-regression-concrete-strength.ipynb
@@ -70,13 +70,12 @@
         "import numpy as np\n",
         "import pandas as pd\n",
         "import os\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "import azureml.dataprep as dprep\n",
         " \n",
         "\n",
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
+        "from azureml.core.dataset import Dataset\n",
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
@@ -88,9 +87,8 @@
       "source": [
         "ws = Workspace.from_config()\n",
         "\n",
-        "# Choose a name for the experiment and specify the project folder.\n",
+        "# Choose a name for the experiment.\n",
         "experiment_name = 'automl-regression-concrete'\n",
-        "project_folder = './sample_projects/automl-regression-concrete'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -100,7 +98,6 @@
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -147,11 +144,12 @@
         "    # Create the cluster.\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
         "    \n",
-        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
+        "print('Checking cluster status...')\n",
+        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
         "    \n",
-        "     # For a more detailed view of current AmlCompute status, use get_status()."
+        "# For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
     {
@@ -160,20 +158,7 @@
       "source": [
         "# Data\n",
         "\n",
-        "Here load the data in the get_data script to be utilized in azure compute. To do this, first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_run_config."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if not os.path.isdir('data'):\n",
-        "    os.mkdir('data')\n",
-        "    \n",
-        "if not os.path.exists(project_folder):\n",
-        "    os.makedirs(project_folder)"
+        "Create a run configuration for the remote run."
       ]
     },
     {
@@ -192,11 +177,8 @@
         "# Set compute target to AmlCompute\n",
         "conda_run_config.target = compute_target\n",
         "conda_run_config.environment.docker.enabled = True\n",
-        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
-        "dprep_dependency = 'azureml-dataprep==' + pkg_resources.get_distribution(\"azureml-dataprep\").version\n",
-        "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', dprep_dependency], conda_packages=['numpy'])\n",
+        "cd = CondaDependencies.create(conda_packages=['numpy', 'py-xgboost<=0.80'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd"
       ]
     },
@@ -206,7 +188,7 @@
       "source": [
         "### Load Data\n",
         "\n",
-        "Here create the script to be run in azure compute for loading the data, load the concrete strength dataset into the X and y  variables. Next, split the data  using train_test_split and return X_train and y_train for training the model. Finally, return X_train and y_train for training the model."
+        "Load the concrete strength dataset into X and y.  X contains the training features, which are inputs to the model.  y contains the training labels, which are the expected output of the model."
       ]
     },
     {
@@ -216,13 +198,12 @@
       "outputs": [],
       "source": [
         "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/compresive_strength_concrete.csv\"\n",
-        "dflow = dprep.auto_read_file(data)\n",
-        "dflow.get_profile()\n",
-        "X = dflow.drop_columns(columns=['CONCRETE'])\n",
-        "y = dflow.keep_columns(columns=['CONCRETE'], validate_column_exists=True)\n",
+        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
+        "X = dataset.drop_columns(columns=['CONCRETE'])\n",
+        "y = dataset.keep_columns(columns=['CONCRETE'], validate=True)\n",
         "X_train, X_test = X.random_split(percentage=0.8, seed=223)\n",
         "y_train, y_test = y.random_split(percentage=0.8, seed=223) \n",
-        "dflow.head()"
+        "dataset.take(5).to_pandas_dataframe()"
       ]
     },
     {
@@ -242,7 +223,6 @@
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
         "\n",
         "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
       ]
@@ -272,7 +252,6 @@
         "\n",
         "automl_config = AutoMLConfig(task = 'regression',\n",
         "                             debug_log = 'automl.log',\n",
-        "                             path = project_folder,\n",
         "                             run_configuration=conda_run_config,\n",
         "                             X = X_train,\n",
         "                             y = y_train,\n",
@@ -484,7 +463,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
+        "for p in ['azureml-train-automl', 'azureml-core']:\n",
         "    print('{}\\t{}'.format(p, dependencies[p]))"
       ]
     },
@@ -494,9 +473,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
+        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost==0.80'], pip_packages=['azureml-train-automl'])\n",
         "\n",
         "conda_env_file_name = 'myenv.yml'\n",
         "myenv.save_to_file('.', conda_env_file_name)"
@@ -516,7 +493,7 @@
         "    content = cefr.read()\n",
         "\n",
         "with open(conda_env_file_name, 'w') as cefw:\n",
-        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
+        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
         "\n",
         "# Substitute the actual model id in the script file.\n",
         "\n",
diff --git a/how-to-use-azureml/automated-machine-learning/regression-concrete-strength/auto-ml-regression-concrete-strength.yml b/how-to-use-azureml/automated-machine-learning/regression-concrete-strength/auto-ml-regression-concrete-strength.yml
index eb39aa20..e29c5b3e 100644
--- a/how-to-use-azureml/automated-machine-learning/regression-concrete-strength/auto-ml-regression-concrete-strength.yml
+++ b/how-to-use-azureml/automated-machine-learning/regression-concrete-strength/auto-ml-regression-concrete-strength.yml
@@ -2,6 +2,8 @@ name: auto-ml-regression-concrete-strength
 dependencies:
 - pip:
   - azureml-sdk
+  - azureml-defaults
+  - azureml-explain-model
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
diff --git a/how-to-use-azureml/automated-machine-learning/regression-hardware-performance/auto-ml-regression-hardware-performance.ipynb b/how-to-use-azureml/automated-machine-learning/regression-hardware-performance/auto-ml-regression-hardware-performance.ipynb
index 0cebc885..13d7581a 100644
--- a/how-to-use-azureml/automated-machine-learning/regression-hardware-performance/auto-ml-regression-hardware-performance.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/regression-hardware-performance/auto-ml-regression-hardware-performance.ipynb
@@ -70,13 +70,12 @@
         "import numpy as np\n",
         "import pandas as pd\n",
         "import os\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "import azureml.dataprep as dprep\n",
         " \n",
         "\n",
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
+        "from azureml.core.dataset import Dataset\n",
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
@@ -88,9 +87,8 @@
       "source": [
         "ws = Workspace.from_config()\n",
         "\n",
-        "# Choose a name for the experiment and specify the project folder.\n",
+        "# Choose a name for the experiment.\n",
         "experiment_name = 'automl-regression-hardware'\n",
-        "project_folder = './sample_projects/automl-remote-regression'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -100,7 +98,6 @@
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -147,11 +144,12 @@
         "    # Create the cluster.\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
         "    \n",
-        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
+        "print('Checking cluster status...')\n",
+        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
         "    \n",
-        "     # For a more detailed view of current AmlCompute status, use get_status()."
+        "# For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
     {
@@ -160,20 +158,7 @@
       "source": [
         "# Data\n",
         "\n",
-        "Here load the data in the get_data script to be utilized in azure compute. To do this, first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_run_config."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if not os.path.isdir('data'):\n",
-        "    os.mkdir('data')\n",
-        "    \n",
-        "if not os.path.exists(project_folder):\n",
-        "    os.makedirs(project_folder)"
+        "Create a run configuration for the remote run."
       ]
     },
     {
@@ -192,11 +177,8 @@
         "# Set compute target to AmlCompute\n",
         "conda_run_config.target = compute_target\n",
         "conda_run_config.environment.docker.enabled = True\n",
-        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
-        "dprep_dependency = 'azureml-dataprep==' + pkg_resources.get_distribution(\"azureml-dataprep\").version\n",
-        "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', dprep_dependency], conda_packages=['numpy'])\n",
+        "cd = CondaDependencies.create(conda_packages=['numpy', 'py-xgboost<=0.80'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd"
       ]
     },
@@ -206,7 +188,7 @@
       "source": [
         "### Load Data\n",
         "\n",
-        "Here create the script to be run in azure compute for loading the data, load the hardware dataset into the X and y variables. Next split the data  using train_test_split and return X_train and y_train for training the model."
+        "Load the hardware performance dataset into X and y. X contains the training features, which are inputs to the model. y contains the training labels, which are the expected output of the model."
       ]
     },
     {
@@ -216,13 +198,12 @@
       "outputs": [],
       "source": [
         "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv\"\n",
-        "dflow = dprep.auto_read_file(data)\n",
-        "dflow.get_profile()\n",
-        "X = dflow.drop_columns(columns=['ERP'])\n",
-        "y = dflow.keep_columns(columns=['ERP'], validate_column_exists=True)\n",
+        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
+        "X = dataset.drop_columns(columns=['ERP'])\n",
+        "y = dataset.keep_columns(columns=['ERP'], validate=True)\n",
         "X_train, X_test = X.random_split(percentage=0.8, seed=223)\n",
-        "y_train, y_test = y.random_split(percentage=0.8, seed=223) \n",
-        "dflow.head()"
+        "y_train, y_test = y.random_split(percentage=0.8, seed=223)\n",
+        "dataset.take(5).to_pandas_dataframe()"
       ]
     },
     {
@@ -243,7 +224,6 @@
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
         "\n",
         "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
       ]
@@ -272,8 +252,7 @@
         "}\n",
         "\n",
         "automl_config = AutoMLConfig(task = 'regression',\n",
-        "                             debug_log = 'automl_errors_20190417.log',\n",
-        "                             path = project_folder,\n",
+        "                             debug_log = 'automl_errors.log',\n",
         "                             run_configuration=conda_run_config,\n",
         "                             X = X_train,\n",
         "                             y = y_train,\n",
@@ -502,7 +481,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
+        "for p in ['azureml-train-automl', 'azureml-core']:\n",
         "    print('{}\\t{}'.format(p, dependencies[p]))"
       ]
     },
@@ -512,7 +491,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
+        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost==0.80'], pip_packages=['azureml-train-automl'])\n",
         "\n",
         "conda_env_file_name = 'myenv.yml'\n",
         "myenv.save_to_file('.', conda_env_file_name)"
@@ -532,7 +511,7 @@
         "    content = cefr.read()\n",
         "\n",
         "with open(conda_env_file_name, 'w') as cefw:\n",
-        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
+        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
         "\n",
         "# Substitute the actual model id in the script file.\n",
         "\n",
diff --git a/how-to-use-azureml/automated-machine-learning/regression-hardware-performance/auto-ml-regression-hardware-performance.yml b/how-to-use-azureml/automated-machine-learning/regression-hardware-performance/auto-ml-regression-hardware-performance.yml
index ddc29fa8..94323586 100644
--- a/how-to-use-azureml/automated-machine-learning/regression-hardware-performance/auto-ml-regression-hardware-performance.yml
+++ b/how-to-use-azureml/automated-machine-learning/regression-hardware-performance/auto-ml-regression-hardware-performance.yml
@@ -2,6 +2,8 @@ name: auto-ml-regression-hardware-performance
 dependencies:
 - pip:
   - azureml-sdk
+  - azureml-defaults
+  - azureml-explain-model
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
diff --git a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb b/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
index 804e8ff7..56b14d9e 100644
--- a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
@@ -84,9 +84,8 @@
       "source": [
         "ws = Workspace.from_config()\n",
         "\n",
-        "# Choose a name for the experiment and specify the project folder.\n",
+        "# Choose a name for the experiment.\n",
         "experiment_name = 'automl-local-regression'\n",
-        "project_folder = './sample_projects/automl-local-regression'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -96,7 +95,6 @@
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -144,8 +142,7 @@
         "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+        "|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|"
       ]
     },
     {
@@ -162,8 +159,7 @@
         "                             debug_log = 'automl.log',\n",
         "                             verbosity = logging.INFO,\n",
         "                             X = X_train, \n",
-        "                             y = y_train,\n",
-        "                             path = project_folder)"
+        "                             y = y_train)"
       ]
     },
     {
diff --git a/how-to-use-azureml/automated-machine-learning/remote-amlcompute-with-onnx/auto-ml-remote-amlcompute-with-onnx.ipynb b/how-to-use-azureml/automated-machine-learning/remote-amlcompute-with-onnx/auto-ml-remote-amlcompute-with-onnx.ipynb
new file mode 100644
index 00000000..32c06d56
--- /dev/null
+++ b/how-to-use-azureml/automated-machine-learning/remote-amlcompute-with-onnx/auto-ml-remote-amlcompute-with-onnx.ipynb
@@ -0,0 +1,548 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/remote-amlcompute/auto-ml-remote-amlcompute.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Automated Machine Learning\n",
+        "_**Remote Execution using AmlCompute**_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "In this example we use the scikit-learn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use AutoML for a simple classification problem.\n",
+        "\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
+        "\n",
+        "In this notebook you would see\n",
+        "1. Create an `Experiment` in an existing `Workspace`.\n",
+        "2. Create or Attach existing AmlCompute to a workspace.\n",
+        "3. Configure AutoML using `AutoMLConfig`.\n",
+        "4. Train the model using AmlCompute with ONNX compatible config on.\n",
+        "5. Explore the results and save the ONNX model.\n",
+        "6. Inference with the ONNX model.\n",
+        "\n",
+        "In addition this notebook showcases the following features\n",
+        "- **Parallel** executions for iterations\n",
+        "- **Asynchronous** tracking of progress\n",
+        "- **Cancellation** of individual iterations or the entire run\n",
+        "- Retrieving models for any iteration or logged metric\n",
+        "- Specifying AutoML settings as `**kwargs`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "\n",
+        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import logging\n",
+        "import os\n",
+        "\n",
+        "import pandas as pd\n",
+        "from sklearn import datasets\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "import azureml.core\n",
+        "from azureml.core.experiment import Experiment\n",
+        "from azureml.core.workspace import Workspace\n",
+        "from azureml.core.dataset import Dataset\n",
+        "from azureml.train.automl import AutoMLConfig"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "\n",
+        "# Choose a name for the run history container in the workspace.\n",
+        "experiment_name = 'automl-remote-amlcompute-with-onnx'\n",
+        "project_folder = './project'\n",
+        "\n",
+        "experiment = Experiment(ws, experiment_name)\n",
+        "\n",
+        "output = {}\n",
+        "output['SDK version'] = azureml.core.VERSION\n",
+        "output['Subscription ID'] = ws.subscription_id\n",
+        "output['Workspace Name'] = ws.name\n",
+        "output['Resource Group'] = ws.resource_group\n",
+        "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
+        "output['Experiment Name'] = experiment.name\n",
+        "pd.set_option('display.max_colwidth', -1)\n",
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create or Attach existing AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
+        "\n",
+        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
+        "\n",
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import AmlCompute\n",
+        "from azureml.core.compute import ComputeTarget\n",
+        "\n",
+        "# Choose a name for your cluster.\n",
+        "amlcompute_cluster_name = \"automlc2\"\n",
+        "\n",
+        "found = False\n",
+        "# Check if this compute target already exists in the workspace.\n",
+        "cts = ws.compute_targets\n",
+        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
+        "    found = True\n",
+        "    print('Found existing compute target.')\n",
+        "    compute_target = cts[amlcompute_cluster_name]\n",
+        "\n",
+        "if not found:\n",
+        "    print('Creating a new compute target...')\n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
+        "                                                                #vm_priority = 'lowpriority', # optional\n",
+        "                                                                max_nodes = 6)\n",
+        "\n",
+        "    # Create the cluster.\\n\",\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
+        "\n",
+        "print('Checking cluster status...')\n",
+        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
+        "\n",
+        "# For a more detailed view of current AmlCompute status, use get_status()."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Data\n",
+        "For remote executions, you need to make the data accessible from the remote compute.\n",
+        "This can be done by uploading the data to DataStore.\n",
+        "In this example, we upload scikit-learn's [load_iris](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "iris = datasets.load_iris()\n",
+        "\n",
+        "if not os.path.isdir('data'):\n",
+        "    os.mkdir('data')\n",
+        "\n",
+        "if not os.path.exists(project_folder):\n",
+        "    os.makedirs(project_folder)\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(iris.data, \n",
+        "                                                    iris.target, \n",
+        "                                                    test_size=0.2, \n",
+        "                                                    random_state=0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Ensure the x_train and x_test are pandas DataFrame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Convert the X_train and X_test to pandas DataFrame and set column names,\n",
+        "# This is needed for initializing the input variable names of ONNX model, \n",
+        "# and the prediction with the ONNX model using the inference helper.\n",
+        "X_train = pd.DataFrame(X_train, columns=['c1', 'c2', 'c3', 'c4'])\n",
+        "X_test = pd.DataFrame(X_test, columns=['c1', 'c2', 'c3', 'c4'])\n",
+        "y_train = pd.DataFrame(y_train, columns=['label'])\n",
+        "\n",
+        "X_train.to_csv(\"data/X_train.csv\", index=False)\n",
+        "y_train.to_csv(\"data/y_train.csv\", index=False)\n",
+        "\n",
+        "ds = ws.get_default_datastore()\n",
+        "ds.upload(src_dir='./data', target_path='irisdata', overwrite=True, show_progress=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "import pkg_resources\n",
+        "\n",
+        "# create a new RunConfig object\n",
+        "conda_run_config = RunConfiguration(framework=\"python\")\n",
+        "\n",
+        "# Set compute target to AmlCompute\n",
+        "conda_run_config.target = compute_target\n",
+        "conda_run_config.environment.docker.enabled = True\n",
+        "\n",
+        "cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
+        "conda_run_config.environment.python.conda_dependencies = cd"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "###  Creating a TabularDataset\n",
+        "\n",
+        "Defined X and y as `TabularDataset`s, which are passed to automated machine learning in the AutoMLConfig."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X = Dataset.Tabular.from_delimited_files(path=ds.path('irisdata/X_train.csv'))\n",
+        "y = Dataset.Tabular.from_delimited_files(path=ds.path('irisdata/y_train.csv'))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train\n",
+        "\n",
+        "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
+        "\n",
+        "**Note:** Set the parameter enable_onnx_compatible_models=True, if you also want to generate the ONNX compatible models. Please note, the forecasting task and TensorFlow models are not ONNX compatible yet.\n",
+        "\n",
+        "**Note:** When using AmlCompute, you can't pass Numpy arrays directly to the fit method.\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+        "|**n_cross_validations**|Number of cross validation splits.|\n",
+        "|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|\n",
+        "|**enable_onnx_compatible_models**|Enable the ONNX compatible models in the experiment.|"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Set the preprocess=True,  currently the InferenceHelper only supports this mode."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_settings = {\n",
+        "    \"iteration_timeout_minutes\": 10,\n",
+        "    \"iterations\": 10,\n",
+        "    \"n_cross_validations\": 5,\n",
+        "    \"primary_metric\": 'AUC_weighted',\n",
+        "    \"preprocess\": True,\n",
+        "    \"max_concurrent_iterations\": 5,\n",
+        "    \"verbosity\": logging.INFO\n",
+        "}\n",
+        "\n",
+        "automl_config = AutoMLConfig(task = 'classification',\n",
+        "                             debug_log = 'automl_errors.log',\n",
+        "                             path = project_folder,\n",
+        "                             run_configuration=conda_run_config,\n",
+        "                             X = X,\n",
+        "                             y = y,\n",
+        "                             enable_onnx_compatible_models=True, # This will generate ONNX compatible models.\n",
+        "                             **automl_settings\n",
+        "                            )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
+        "In this example, we specify `show_output = False` to suppress console output while the run is in progress."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run = experiment.submit(automl_config, show_output = False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Results\n",
+        "\n",
+        "#### Loading executed runs\n",
+        "In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {},
+      "source": [
+        "remote_run = AutoMLRun(experiment = experiment, run_id = 'AutoML_5db13491-c92a-4f1d-b622-8ab8d973a058')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Widget for Monitoring Runs\n",
+        "\n",
+        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+        "\n",
+        "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
+        "\n",
+        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(remote_run).show() "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Wait until the run finishes.\n",
+        "remote_run.wait_for_completion(show_output = True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Cancelling Runs\n",
+        "\n",
+        "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
+        "# remote_run.cancel()\n",
+        "\n",
+        "# Cancel iteration 1 and move onto iteration 2.\n",
+        "# remote_run.cancel_iteration(1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrieve the Best ONNX Model\n",
+        "\n",
+        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*.\n",
+        "\n",
+        "Set the parameter return_onnx_model=True to retrieve the best ONNX model, instead of the Python model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run, onnx_mdl = remote_run.get_output(return_onnx_model=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Save the best ONNX model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.automl.core.onnx_convert import OnnxConverter\n",
+        "onnx_fl_path = \"./best_model.onnx\"\n",
+        "OnnxConverter.save_onnx_model(onnx_mdl, onnx_fl_path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Predict with the ONNX model, using onnxruntime package"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "import json\n",
+        "from azureml.automl.core.onnx_convert import OnnxConvertConstants\n",
+        "from azureml.train.automl import constants\n",
+        "\n",
+        "if sys.version_info < OnnxConvertConstants.OnnxIncompatiblePythonVersion:\n",
+        "    python_version_compatible = True\n",
+        "else:\n",
+        "    python_version_compatible = False\n",
+        "\n",
+        "try:\n",
+        "    import onnxruntime\n",
+        "    from azureml.automl.core.onnx_convert import OnnxInferenceHelper    \n",
+        "    onnxrt_present = True\n",
+        "except ImportError:\n",
+        "    onnxrt_present = False\n",
+        "\n",
+        "def get_onnx_res(run):\n",
+        "    res_path = 'onnx_resource.json'\n",
+        "    run.download_file(name=constants.MODEL_RESOURCE_PATH_ONNX, output_file_path=res_path)\n",
+        "    with open(res_path) as f:\n",
+        "        return json.load(f)\n",
+        "\n",
+        "if onnxrt_present and python_version_compatible:    \n",
+        "    mdl_bytes = onnx_mdl.SerializeToString()\n",
+        "    onnx_res = get_onnx_res(best_run)\n",
+        "\n",
+        "    onnxrt_helper = OnnxInferenceHelper(mdl_bytes, onnx_res)\n",
+        "    pred_onnx, pred_prob_onnx = onnxrt_helper.predict(X_test)\n",
+        "\n",
+        "    print(pred_onnx)\n",
+        "    print(pred_prob_onnx)\n",
+        "else:\n",
+        "    if not python_version_compatible:\n",
+        "        print('Please use Python version 3.6 or 3.7 to run the inference helper.')    \n",
+        "    if not onnxrt_present:\n",
+        "        print('Please install the onnxruntime package to do the prediction with ONNX model.')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "savitam"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/automated-machine-learning/remote-amlcompute-with-onnx/auto-ml-remote-amlcompute-with-onnx.yml b/how-to-use-azureml/automated-machine-learning/remote-amlcompute-with-onnx/auto-ml-remote-amlcompute-with-onnx.yml
new file mode 100644
index 00000000..22bad59a
--- /dev/null
+++ b/how-to-use-azureml/automated-machine-learning/remote-amlcompute-with-onnx/auto-ml-remote-amlcompute-with-onnx.yml
@@ -0,0 +1,11 @@
+name: auto-ml-remote-amlcompute-with-onnx
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-defaults
+  - azureml-explain-model
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml
+  - onnxruntime
diff --git a/how-to-use-azureml/automated-machine-learning/remote-amlcompute/auto-ml-remote-amlcompute.ipynb b/how-to-use-azureml/automated-machine-learning/remote-amlcompute/auto-ml-remote-amlcompute.ipynb
index 71553abf..c3591826 100644
--- a/how-to-use-azureml/automated-machine-learning/remote-amlcompute/auto-ml-remote-amlcompute.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/remote-amlcompute/auto-ml-remote-amlcompute.ipynb
@@ -74,7 +74,6 @@
       "source": [
         "import logging\n",
         "import os\n",
-        "import csv\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
         "import numpy as np\n",
@@ -84,8 +83,8 @@
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "import azureml.dataprep as dprep"
+        "from azureml.core.dataset import Dataset\n",
+        "from azureml.train.automl import AutoMLConfig"
       ]
     },
     {
@@ -137,7 +136,7 @@
         "from azureml.core.compute import ComputeTarget\n",
         "\n",
         "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster\"\n",
+        "amlcompute_cluster_name = \"automlc2\"\n",
         "\n",
         "found = False\n",
         "# Check if this compute target already exists in the workspace.\n",
@@ -156,11 +155,12 @@
         "    # Create the cluster.\\n\",\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
         "\n",
-        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
+        "print('Checking cluster status...')\n",
+        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
         "\n",
-        "     # For a more detailed view of current AmlCompute status, use get_status()."
+        "# For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
     {
@@ -210,11 +210,8 @@
         "# Set compute target to AmlCompute\n",
         "conda_run_config.target = compute_target\n",
         "conda_run_config.environment.docker.enabled = True\n",
-        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
-        "dprep_dependency = 'azureml-dataprep==' + pkg_resources.get_distribution(\"azureml-dataprep\").version\n",
-        "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', dprep_dependency], conda_packages=['numpy','py-xgboost<=0.80'])\n",
+        "cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd"
       ]
     },
@@ -222,9 +219,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "###  Dprep reference\n",
+        "###  Creating TabularDataset\n",
         "\n",
-        "Defined X and y as dprep references, which are passed to automated machine learning in the AutoMLConfig."
+        "Defined X and y as `TabularDataset`s, which are passed to Automated ML in the AutoMLConfig. `from_delimited_files` by default sets the `infer_column_types` to true, which will infer the columns type automatically. If you do wish to manually set the column types, you can set the `set_column_types` argument to manually set the type of each columns."
       ]
     },
     {
@@ -233,8 +230,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X = dprep.auto_read_file(path=ds.path('digitsdata/X_train.csv'))\n",
-        "y = dprep.auto_read_file(path=ds.path('digitsdata/y_train.csv'))"
+        "X = Dataset.Tabular.from_delimited_files(path=ds.path('digitsdata/X_train.csv'))\n",
+        "y = Dataset.Tabular.from_delimited_files(path=ds.path('digitsdata/y_train.csv'))"
       ]
     },
     {
diff --git a/how-to-use-azureml/automated-machine-learning/remote-amlcompute/auto-ml-remote-amlcompute.yml b/how-to-use-azureml/automated-machine-learning/remote-amlcompute/auto-ml-remote-amlcompute.yml
index 41b4f214..6ec4511a 100644
--- a/how-to-use-azureml/automated-machine-learning/remote-amlcompute/auto-ml-remote-amlcompute.yml
+++ b/how-to-use-azureml/automated-machine-learning/remote-amlcompute/auto-ml-remote-amlcompute.yml
@@ -2,6 +2,8 @@ name: auto-ml-remote-amlcompute
 dependencies:
 - pip:
   - azureml-sdk
+  - azureml-defaults
+  - azureml-explain-model
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
diff --git a/how-to-use-azureml/automated-machine-learning/sql-server/README.md b/how-to-use-azureml/automated-machine-learning/sql-server/README.md
index 338262b3..6db22e41 100644
--- a/how-to-use-azureml/automated-machine-learning/sql-server/README.md
+++ b/how-to-use-azureml/automated-machine-learning/sql-server/README.md
@@ -87,7 +87,7 @@ These instruction setup the integration for SQL Server 2017 on Windows.
    sudo /opt/mssql/mlservices/bin/python/python -m pip install --upgrade sklearn
 ```
 7. Start SQL Server. 
-8. Execute the files aml_model.sql, aml_connection.sql, AutoMLGetMetrics.sql, AutoMLPredict.sql and AutoMLTrain.sql in SQL Server Management Studio. 
+8. Execute the files aml_model.sql, aml_connection.sql, AutoMLGetMetrics.sql, AutoMLPredict.sql, AutoMLForecast.sql and AutoMLTrain.sql in SQL Server Management Studio. 
 9. Create an Azure Machine Learning Workspace.  You can use the instructions at: [https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace)
 10. Create a config.json file file using the subscription id, resource group name and workspace name that you use to create the workspace.  The file is described at: [https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#workspace)
 11. Create an Azure service principal.  You can do this with the commands: 
@@ -109,5 +109,5 @@ First you need to load the sample data in the database.
 
 You can then run the queries in the energy-demand folder:
 * TrainEnergyDemand.sql runs AutoML, trains multiple models on data and selects the best model.
-* PredictEnergyDemand.sql predicts based on the most recent training run.
+* ForecastEnergyDemand.sql forecasts based on the most recent training run.
 * GetMetrics.sql returns all the metrics for each model in the most recent training run.
diff --git a/how-to-use-azureml/automated-machine-learning/sql-server/setup/auto-ml-sql-setup.ipynb b/how-to-use-azureml/automated-machine-learning/sql-server/setup/auto-ml-sql-setup.ipynb
index 8bf4e0d3..cb227bcd 100644
--- a/how-to-use-azureml/automated-machine-learning/sql-server/setup/auto-ml-sql-setup.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/sql-server/setup/auto-ml-sql-setup.ipynb
@@ -342,7 +342,6 @@
         "                                 n_cross_validations = n_cross_validations, \r\n",
         "                                 preprocess = preprocess,\r\n",
         "                                 verbosity = logging.INFO, \r\n",
-        "                                 enable_ensembling = False,\r\n",
         "                                 X = X_train,  \r\n",
         "                                 y = y_train, \r\n",
         "                                 path = project_folder,\r\n",
diff --git a/how-to-use-azureml/azure-databricks/README.md b/how-to-use-azureml/azure-databricks/README.md
index 4749c0c6..3a063f0c 100644
--- a/how-to-use-azureml/azure-databricks/README.md
+++ b/how-to-use-azureml/azure-databricks/README.md
@@ -1,33 +1,73 @@
-Azure Databricks is a managed Spark offering on Azure and customers already use it for advanced analytics. It provides a collaborative Notebook based environment with CPU or GPU based compute cluster. 
+Azure Databricks is a managed Spark offering on Azure and customers already use it for advanced analytics. It provides a collaborative Notebook based environment with CPU or GPU based compute cluster.
 
-In this section, you will find sample notebooks on how to use Azure Machine Learning SDK with Azure Databricks. You can train a model using Spark MLlib and then deploy the model to ACI/AKS from within Azure Databricks. You can also use Automated ML capability (**public preview**) of Azure ML SDK with Azure Databricks. 
+In this section, you will find sample notebooks on how to use Azure Machine Learning SDK with Azure Databricks. You can train a model using Spark MLlib and then deploy the model to ACI/AKS from within Azure Databricks. You can also use Automated ML capability (**public preview**) of Azure ML SDK with Azure Databricks.
 
-- Customers who use Azure Databricks for advanced analytics can now use the same cluster to run experiments with or without automated machine learning. 
-- You can keep the data within the same cluster. 
-- You can leverage the local worker nodes with autoscale and auto termination capabilities. 
-- You can use multiple cores of your Azure Databricks cluster to perform simultenous training. 
-- You can further tune the model generated by automated machine learning if you chose to. 
-- Every run (including the best run) is available as a pipeline, which you can tune further if needed. 
+- Customers who use Azure Databricks for advanced analytics can now use the same cluster to run experiments with or without automated machine learning.
+- You can keep the data within the same cluster.
+- You can leverage the local worker nodes with autoscale and auto termination capabilities.
+- You can use multiple cores of your Azure Databricks cluster to perform simultenous training.
+- You can further tune the model generated by automated machine learning if you chose to.
+- Every run (including the best run) is available as a pipeline, which you can tune further if needed.
 - The model trained using Azure Databricks can be registered in Azure ML SDK workspace and then deployed to Azure managed compute (ACI or AKS) using the Azure Machine learning SDK.
 
 Please follow our [Azure doc](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#azure-databricks) to install the sdk in your Azure Databricks cluster before trying any of the sample notebooks.
 
-**Single file** - 
+**Single file** -
 The following archive contains all the sample notebooks. You can the run notebooks after importing [DBC](Databricks_AMLSDK_1-4_6.dbc) in your Databricks workspace instead of downloading individually.
 
-Notebooks 1-4 have to be run sequentially & are related to Income prediction experiment based on this [dataset](https://archive.ics.uci.edu/ml/datasets/adult) and demonstrate how to data prep, train and operationalize a Spark ML model with Azure ML Python SDK from within Azure Databricks. 
+Notebooks 1-4 have to be run sequentially & are related to Income prediction experiment based on this [dataset](https://archive.ics.uci.edu/ml/datasets/adult) and demonstrate how to data prep, train and operationalize a Spark ML model with Azure ML Python SDK from within Azure Databricks.
 
 Notebook 6 is an Automated ML sample notebook for Classification.
 
 Learn more about [how to use Azure Databricks as a development environment](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment#azure-databricks) for Azure Machine Learning service.
 
-**Databricks as a Compute Target from AML Pipelines**
-You can use Azure Databricks as a compute target from [Azure Machine Learning Pipelines](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines). Take a look at this notebook for details: [aml-pipelines-use-databricks-as-compute-target.ipynb](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb). 
+**Databricks as a Compute Target from Azure ML Pipelines**
+You can use Azure Databricks as a compute target from [Azure Machine Learning Pipelines](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines). Take a look at this notebook for details: [aml-pipelines-use-databricks-as-compute-target.ipynb](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb).
+
+# Linked Azure Databricks and Azure Machine Learning Workspaces (Preview)
+Customers can now link Azure Databricks and AzureML Workspaces to better enable cross-Azure ML scenarios by [managing their tracking data in a single place when using the MLflow client](https://mlflow.org/docs/latest/tracking.html#mlflow-tracking) - the Azure ML workspace.
+
+## Linking the Workspaces (Admin operation)
+
+1. The Azure Databricks Azure portal blade now includes a new button to link an Azure ML workspace.
+![New ADB Portal Link button](./img/adb-link-button.png)
+2. Both a new or existing Azure ML Workspace can be linked in the resulting prompt. Follow any instructions to set up the Azure ML Workspace.
+![Link Prompt](./img/link-prompt.png)
+3. After a successful link operation, you should see the Azure Databricks overview reflect the linked status
+![Linked Successfully](./img/adb-successful-link.png)
+
+## Configure MLflow to send data to Azure ML (All roles)
+
+1. Add azureml-mlflow as a library to any notebook or cluster that should send data to Azure ML. You can do this via:
+    1. [DBUtils](https://docs.azuredatabricks.net/user-guide/dev-tools/dbutils.html#dbutils-library)
+        ```
+        dbutils.library.installPyPI("azureml-mlflow")
+        dbutils.library.restartPython()  # Removes Python state
+        ```
+    2. [Cluster Libraries](https://docs.azuredatabricks.net/user-guide/libraries.html#install-a-library-on-a-cluster)
+    ![Cluster Library](./img/cluster-library.png)
+2. [Set the MLflow tracking URI](https://mlflow.org/docs/latest/tracking.html#where-runs-are-recorded) to the following scheme:
+    ```
+    adbazureml://${azuremlRegion}.experiments.azureml.net/history/v1.0/subscriptions/${azuremlSubscriptionId}/resourceGroups/${azuremlResourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/${azuremlWorkspaceName}
+    ```
+    1. You can automatically configure this on your clusters for all subsequent notebook sessions using this helper script instead of manually setting the tracking URI in the notebook:
+        * [AzureML Tracking Cluster Init Script](./linking/README.md)
+3. If configured correctly, you'll now be able to see your MLflow tracking data in both Azure ML (via the REST API and all clients) and Azure Databricks (in the MLflow UI and using the MLflow client)
+
+
+## Known Preview Limitations
+While we roll this experience out to customers for feedback, there are some known limitations we'd love comments on in addition to any other issues seen in your workflow.
+### 1-to-1 Workspace linking
+Currently, an Azure ML Workspace can only be linked to one Azure Databricks Workspace at a time.
+### Data synchronization
+At the moment, data is only generated in the Azure Machine Learning workspace for tracking. Editing tags via the Azure Databricks MLflow UI won't be reflected in the Azure ML UI.
+### Java and R support
+The experience currently is only available from the Python MLflow client.
 
 For more on SDK concepts, please refer to [notebooks](https://github.com/Azure/MachineLearningNotebooks).
 
 **Please let us know your feedback.**
 
- 
 
-![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-databricks/README.png) 
\ No newline at end of file
+
+![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-databricks/README.png)
diff --git a/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb b/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb
index d38240d6..23a79fda 100644
--- a/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb
+++ b/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb
@@ -314,25 +314,18 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Load Training Data Using DataPrep"
+        "## Load Training Data Using Dataset"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Automated ML takes a Dataflow as input.\n",
+        "Automated ML takes a `TabularDataset` as input.\n",
         "\n",
-        "If you are familiar with Pandas and have done your data preparation work in Pandas already, you can use the `read_pandas_dataframe` method in dprep to convert the DataFrame to a Dataflow.\n",
-        "```python\n",
-        "df = pd.read_csv(...)\n",
-        "# apply some transforms\n",
-        "dprep.read_pandas_dataframe(df, temp_folder='/path/accessible/by/both/driver/and/worker')\n",
-        "```\n",
+        "You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it.\n",
         "\n",
-        "If you just need to ingest data without doing any preparation, you can directly use AzureML Data Prep (Data Prep) to do so. The code below demonstrates this scenario. Data Prep also has data preparation capabilities, we have many [sample notebooks](https://github.com/Microsoft/AMLDataPrepDocs) demonstrating the capabilities.\n",
-        "\n",
-        "You will get the datastore you registered previously and pass it to Data Prep for reading. The data comes from the digits dataset: `sklearn.datasets.load_digits()`. `DataPath` points to a specific location within a datastore. "
+        "You will get the datastore you registered previously and pass it to Dataset for reading. The data comes from the digits dataset: `sklearn.datasets.load_digits()`. `DataPath` points to a specific location within a datastore. "
       ]
     },
     {
@@ -341,21 +334,21 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import azureml.dataprep as dprep\n",
+        "from azureml.core.dataset import Dataset\n",
         "from azureml.data.datapath import DataPath\n",
         "\n",
         "datastore = Datastore.get(workspace = ws, datastore_name = datastore_name)\n",
         "\n",
-        "X_train = dprep.read_csv(datastore.path('X.csv'))\n",
-        "y_train = dprep.read_csv(datastore.path('y.csv')).to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
+        "X_train = Dataset.Tabular.from_delimited_files(datastore.path('X.csv'))\n",
+        "y_train = Dataset.Tabular.from_delimited_files(datastore.path('y.csv'))"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Review the Data Preparation Result\n",
-        "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only j records for all the steps in the Dataflow, which makes it fast even against large datasets."
+        "## Review the TabularDataset\n",
+        "You can peek the result of a TabularDataset at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only j records for all the steps in the TabularDataset, which makes it fast even against large datasets."
       ]
     },
     {
@@ -364,7 +357,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X_train.get_profile()"
+        "X_train.take(5).to_pandas_dataframe()"
       ]
     },
     {
@@ -373,7 +366,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "y_train.get_profile()"
+        "y_train.take(5).to_pandas_dataframe()"
       ]
     },
     {
@@ -593,7 +586,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "fitted_model.named_steps['datatransformer'].get_featurization_summary()"
+        "# Get the featurization summary as a list of JSON\n",
+        "featurization_summary = fitted_model.named_steps['datatransformer'].get_featurization_summary()\n",
+        "# View the featurization summary as a pandas dataframe\n",
+        "pd.DataFrame.from_records(featurization_summary)"
       ]
     },
     {
diff --git a/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb b/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb
index 56b23696..f765cccf 100644
--- a/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb
+++ b/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb
@@ -331,25 +331,18 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Load Training Data Using DataPrep"
+        "## Load Training Data Using Dataset"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Automated ML takes a Dataflow as input.\n",
+        "Automated ML takes a `TabularDataset` as input.\n",
         "\n",
-        "If you are familiar with Pandas and have done your data preparation work in Pandas already, you can use the `read_pandas_dataframe` method in dprep to convert the DataFrame to a Dataflow.\n",
-        "```python\n",
-        "df = pd.read_csv(...)\n",
-        "# apply some transforms\n",
-        "dprep.read_pandas_dataframe(df, temp_folder='/path/accessible/by/both/driver/and/worker')\n",
-        "```\n",
+        "You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it.\n",
         "\n",
-        "If you just need to ingest data without doing any preparation, you can directly use AzureML Data Prep (Data Prep) to do so. The code below demonstrates this scenario. Data Prep also has data preparation capabilities, we have many [sample notebooks](https://github.com/Microsoft/AMLDataPrepDocs) demonstrating the capabilities.\n",
-        "\n",
-        "You will get the datastore you registered previously and pass it to Data Prep for reading. The data comes from the digits dataset: `sklearn.datasets.load_digits()`. `DataPath` points to a specific location within a datastore. "
+        "You will get the datastore you registered previously and pass it to Dataset for reading. The data comes from the digits dataset: `sklearn.datasets.load_digits()`. `DataPath` points to a specific location within a datastore. "
       ]
     },
     {
@@ -358,21 +351,21 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import azureml.dataprep as dprep\n",
+        "from azureml.core.dataset import Dataset\n",
         "from azureml.data.datapath import DataPath\n",
         "\n",
         "datastore = Datastore.get(workspace = ws, datastore_name = datastore_name)\n",
         "\n",
-        "X_train = dprep.read_csv(datastore.path('X.csv'))\n",
-        "y_train = dprep.read_csv(datastore.path('y.csv')).to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
+        "X_train = Dataset.Tabular.from_delimited_files(datastore.path('X.csv'))\n",
+        "y_train = Dataset.Tabular.from_delimited_files(datastore.path('y.csv'))"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Review the Data Preparation Result\n",
-        "You can peek the result of a Dataflow at any range using skip(i) and head(j). Doing so evaluates only j records for all the steps in the Dataflow, which makes it fast even against large datasets."
+        "## Review the TabularDataset\n",
+        "You can peek the result of a TabularDataset at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only j records for all the steps in the TabularDataset, which makes it fast even against large datasets."
       ]
     },
     {
@@ -381,7 +374,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X_train.get_profile()"
+        "X_train.take(5).to_pandas_dataframe()"
       ]
     },
     {
@@ -390,7 +383,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "y_train.get_profile()"
+        "y_train.take(5).to_pandas_dataframe()"
       ]
     },
     {
diff --git a/how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb b/how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb
index e5b071da..3e2ea7ac 100644
--- a/how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb
+++ b/how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb
@@ -13,7 +13,7 @@
       "metadata": {},
       "source": [
         "# Using Databricks as a Compute Target from Azure Machine Learning Pipeline\n",
-        "To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines), a [DatabricksStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n",
+        "To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://aka.ms/pl-concept), a [DatabricksStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n",
         "\n",
         "The notebook will show:\n",
         "1. Running an arbitrary Databricks notebook that the customer has in Databricks workspace\n",
@@ -675,7 +675,7 @@
       "metadata": {},
       "source": [
         "# Next: ADLA as a Compute Target\n",
-        "To use ADLA as a compute target from Azure Machine Learning Pipeline, a AdlaStep is used. This [notebook](./aml-pipelines-use-adla-as-compute-target.ipynb) demonstrates the use of AdlaStep in Azure Machine Learning Pipeline."
+        "To use ADLA as a compute target from Azure Machine Learning Pipeline, a AdlaStep is used. This [notebook](https://aka.ms/pl-adla) demonstrates the use of AdlaStep in Azure Machine Learning Pipeline."
       ]
     },
     {
diff --git a/how-to-use-azureml/azure-databricks/img/adb-link-button.png b/how-to-use-azureml/azure-databricks/img/adb-link-button.png
new file mode 100755
index 00000000..03cc1d4e
Binary files /dev/null and b/how-to-use-azureml/azure-databricks/img/adb-link-button.png differ
diff --git a/how-to-use-azureml/azure-databricks/img/adb-successful-link.png b/how-to-use-azureml/azure-databricks/img/adb-successful-link.png
new file mode 100755
index 00000000..f2d62cbf
Binary files /dev/null and b/how-to-use-azureml/azure-databricks/img/adb-successful-link.png differ
diff --git a/how-to-use-azureml/azure-databricks/img/cluster-library.png b/how-to-use-azureml/azure-databricks/img/cluster-library.png
new file mode 100755
index 00000000..b86c5f51
Binary files /dev/null and b/how-to-use-azureml/azure-databricks/img/cluster-library.png differ
diff --git a/how-to-use-azureml/azure-databricks/img/link-prompt.png b/how-to-use-azureml/azure-databricks/img/link-prompt.png
new file mode 100755
index 00000000..3384edc1
Binary files /dev/null and b/how-to-use-azureml/azure-databricks/img/link-prompt.png differ
diff --git a/how-to-use-azureml/azure-databricks/linking/README.md b/how-to-use-azureml/azure-databricks/linking/README.md
new file mode 100644
index 00000000..5bcb788f
--- /dev/null
+++ b/how-to-use-azureml/azure-databricks/linking/README.md
@@ -0,0 +1,56 @@
+# Adding an init script to an Azure Databricks cluster
+
+The [azureml-cluster-init.sh](./azureml-cluster-init.sh) script configures the environment to
+1. Use the configured AzureML Workspace with Workspace.from_config()
+2. Set the default MLflow Tracking Server to be the AzureML managed one
+
+Modify azureml-cluster-init.sh by providing the values for region, subscriptionId, resourceGroupName, and workspaceName of your target Azure ML workspace in the highlighted section at the top of the script.
+
+To create the Azure Databricks cluster-scoped init script
+
+1. Create the base directory you want to store the init script in if it does not exist.
+    ```
+    dbutils.fs.mkdirs("dbfs:/databricks/<directory>/")
+    ```
+
+2. Create the script by copying the contents of azureml-cluster-init.sh
+    ```
+    dbutils.fs.put("/databricks/<directory>/azureml-cluster-init.sh","""
+    <configured_contents_of_azureml-cluster-init.sh>
+    """, True)
+
+3. Check that the script exists.
+    ```
+    display(dbutils.fs.ls("dbfs:/databricks/<directory>/azureml-cluster-init.sh"))
+    ```
+
+1. Configure the cluster to run the script.
+    * Using the cluster configuration page
+        1. On the cluster configuration page, click the Advanced Options toggle.
+        1. At the bottom of the page, click the Init Scripts tab.
+        1. In the Destination drop-down, select a destination type. Example: 'DBFS'
+        1. Specify a path to the init script.
+            ```
+            dbfs:/databricks/<directory>/azureml-cluster-init.sh
+            ```
+        1. Click Add
+
+    * Using the API.
+        ```
+        curl -n -X POST -H 'Content-Type: application/json' -d '{
+        "cluster_id": "<cluster_id>",
+        "num_workers": <num_workers>,
+        "spark_version": "<spark_version>",
+        "node_type_id": "<node_type_id>",
+        "cluster_log_conf": {
+            "dbfs" : {
+            "destination": "dbfs:/cluster-logs"
+            }
+        },
+        "init_scripts": [ {
+            "dbfs": {
+            "destination": "dbfs:/databricks/<directory>/azureml-cluster-init.sh"
+            }
+        } ]
+        }' https://<databricks-instance>/api/2.0/clusters/edit
+        ```
diff --git a/how-to-use-azureml/azure-databricks/linking/azureml-cluster-init.sh b/how-to-use-azureml/azure-databricks/linking/azureml-cluster-init.sh
new file mode 100644
index 00000000..36ecfa52
--- /dev/null
+++ b/how-to-use-azureml/azure-databricks/linking/azureml-cluster-init.sh
@@ -0,0 +1,24 @@
+#!/bin/bash
+# This script configures the environment to
+# 1. Use the configured AzureML Workspace with azureml.core.Workspace.from_config()
+# 2. Set the default MLflow Tracking Server to be the AzureML managed one
+
+############## START CONFIGURATION #################
+# Provide the required *AzureML* workspace information
+region="" # example: westus2
+subscriptionId="" # example: bcb65f42-f234-4bff-91cf-9ef816cd9936
+resourceGroupName="" # example: dev-rg
+workspaceName="" # example: myazuremlws
+
+# Optional config directory
+configLocation="/databricks/config.json"
+############### END CONFIGURATION #################
+
+
+# Drop the workspace configuration on the cluster
+sudo touch $configLocation
+sudo echo {\\"subscription_id\\": \\"${subscriptionId}\\", \\"resource_group\\": \\"${resourceGroupName}\\", \\"workspace_name\\": \\"${workspaceName}\\"} > $configLocation
+
+# Set the MLflow Tracking URI
+trackingUri="adbazureml://${region}.experiments.azureml.net/history/v1.0/subscriptions/${subscriptionId}/resourceGroups/${resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/${workspaceName}"
+sudo echo export MLFLOW_TRACKING_URI=${trackingUri} >> /databricks/spark/conf/spark-env.sh
diff --git a/how-to-use-azureml/data-drift/azure-ml-datadrift.ipynb b/how-to-use-azureml/data-drift/azure-ml-datadrift.ipynb
deleted file mode 100644
index 4f9a3737..00000000
--- a/how-to-use-azureml/data-drift/azure-ml-datadrift.ipynb
+++ /dev/null
@@ -1,709 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Track Data Drift between Training and Inference Data in Production \n",
-        "\n",
-        "With this notebook, you will learn how to enable the DataDrift service to automatically track and determine whether your inference data is drifting from the data your model was initially trained on. The DataDrift service provides metrics and visualizations to help stakeholders identify which specific features cause the concept drift to occur.\n",
-        "\n",
-        "Please email driftfeedback@microsoft.com with any issues. A member from the DataDrift team will respond shortly. \n",
-        "\n",
-        "The DataDrift Public Preview API can be found [here](https://docs.microsoft.com/en-us/python/api/azureml-contrib-datadrift/?view=azure-ml-py). "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/datadrift/azureml-datadrift.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Prerequisites and Setup"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Install the DataDrift package\n",
-        "\n",
-        "Install the azureml-contrib-datadrift, azureml-contrib-opendatasets and lightgbm packages before running this notebook.\n",
-        "```\n",
-        "pip install azureml-contrib-datadrift\n",
-        "pip install azureml-contrib-datasets\n",
-        "pip install lightgbm\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Import Dependencies"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "import os\n",
-        "import time\n",
-        "from datetime import datetime, timedelta\n",
-        "\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import requests\n",
-        "from azureml.contrib.datadrift import DataDriftDetector, AlertConfiguration\n",
-        "from azureml.contrib.opendatasets import NoaaIsdWeather\n",
-        "from azureml.core import Dataset, Workspace, Run\n",
-        "from azureml.core.compute import AksCompute, ComputeTarget\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.image import ContainerImage\n",
-        "from azureml.core.model import Model\n",
-        "from azureml.core.webservice import Webservice, AksWebservice\n",
-        "from azureml.widgets import RunDetails\n",
-        "from sklearn.externals import joblib\n",
-        "from sklearn.model_selection import train_test_split\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Set up Configuraton and Create Azure ML Workspace\n",
-        "\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../configuration.ipynb) first if you haven't already to establish your connection to the AzureML Workspace."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Please type in your initials/alias. The prefix is prepended to the names of resources created by this notebook. \n",
-        "prefix = \"dd\"\n",
-        "\n",
-        "# NOTE: Please do not change the model_name, as it's required by the score.py file\n",
-        "model_name = \"driftmodel\"\n",
-        "image_name = \"{}driftimage\".format(prefix)\n",
-        "service_name = \"{}driftservice\".format(prefix)\n",
-        "\n",
-        "# optionally, set email address to receive an email alert for DataDrift\n",
-        "email_address = \"\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Generate Train/Testing Data\n",
-        "\n",
-        "For this demo, we will use NOAA weather data from [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/). You may replace this step with your own dataset. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "usaf_list = ['725724', '722149', '723090', '722159', '723910', '720279',\n",
-        "             '725513', '725254', '726430', '720381', '723074', '726682',\n",
-        "             '725486', '727883', '723177', '722075', '723086', '724053',\n",
-        "             '725070', '722073', '726060', '725224', '725260', '724520',\n",
-        "             '720305', '724020', '726510', '725126', '722523', '703333',\n",
-        "             '722249', '722728', '725483', '722972', '724975', '742079',\n",
-        "             '727468', '722193', '725624', '722030', '726380', '720309',\n",
-        "             '722071', '720326', '725415', '724504', '725665', '725424',\n",
-        "             '725066']\n",
-        "\n",
-        "columns = ['usaf', 'wban', 'datetime', 'latitude', 'longitude', 'elevation', 'windAngle', 'windSpeed', 'temperature', 'stationName', 'p_k']\n",
-        "\n",
-        "def enrich_weather_noaa_data(noaa_df):\n",
-        "    hours_in_day = 23\n",
-        "    week_in_year = 52\n",
-        "    \n",
-        "\n",
-        "    noaa_df = noaa_df.assign(hour=noaa_df[\"datetime\"].dt.hour,\n",
-        "                          weekofyear=noaa_df[\"datetime\"].dt.week,\n",
-        "                          sine_weekofyear=noaa_df['datetime'].transform(lambda x: np.sin((2*np.pi*x.dt.week-1)/week_in_year)),\n",
-        "                          cosine_weekofyear=noaa_df['datetime'].transform(lambda x: np.cos((2*np.pi*x.dt.week-1)/week_in_year)),\n",
-        "                          sine_hourofday=noaa_df['datetime'].transform(lambda x: np.sin(2*np.pi*x.dt.hour/hours_in_day)),\n",
-        "                          cosine_hourofday=noaa_df['datetime'].transform(lambda x: np.cos(2*np.pi*x.dt.hour/hours_in_day))\n",
-        "                  )\n",
-        "    \n",
-        "    return noaa_df\n",
-        "\n",
-        "\n",
-        "def add_window_col(input_df):\n",
-        "    shift_interval = pd.Timedelta('-7 days') # your X days interval\n",
-        "    df_shifted = input_df.copy()\n",
-        "    df_shifted.loc[:,'datetime'] = df_shifted['datetime'] - shift_interval\n",
-        "    df_shifted.drop(list(input_df.columns.difference(['datetime', 'usaf', 'wban', 'sine_hourofday', 'temperature'])), axis=1, inplace=True)\n",
-        "\n",
-        "    # merge, keeping only observations where -1 lag is present\n",
-        "    df2 = pd.merge(input_df,\n",
-        "                   df_shifted,\n",
-        "                   on=['datetime', 'usaf', 'wban', 'sine_hourofday'],\n",
-        "                   how='inner',  # use 'left' to keep observations without lags\n",
-        "                   suffixes=['', '-7'])\n",
-        "    return df2\n",
-        "\n",
-        "def get_noaa_data(start_time, end_time, cols, station_list):\n",
-        "    isd = NoaaIsdWeather(start_time, end_time, cols=cols)\n",
-        "    # Read into Pandas data frame.\n",
-        "    noaa_df = isd.to_pandas_dataframe()\n",
-        "    noaa_df = noaa_df.rename(columns={\"stationName\": \"station_name\"})\n",
-        "    \n",
-        "    df_filtered = noaa_df[noaa_df[\"usaf\"].isin(station_list)]\n",
-        "    df_filtered.reset_index(drop=True)\n",
-        "    \n",
-        "    # Enrich with time features\n",
-        "    df_enriched = enrich_weather_noaa_data(df_filtered)\n",
-        "    \n",
-        "    return df_enriched\n",
-        "\n",
-        "def get_featurized_noaa_df(start_time, end_time, cols, station_list):\n",
-        "    df_1 = get_noaa_data(start_time - timedelta(days=7), start_time - timedelta(seconds=1), cols, station_list)\n",
-        "    df_2 = get_noaa_data(start_time, end_time, cols, station_list)\n",
-        "    noaa_df = pd.concat([df_1, df_2])\n",
-        "    \n",
-        "    print(\"Adding window feature\")\n",
-        "    df_window = add_window_col(noaa_df)\n",
-        "    \n",
-        "    cat_columns = df_window.dtypes == object\n",
-        "    cat_columns = cat_columns[cat_columns == True]\n",
-        "    \n",
-        "    print(\"Encoding categorical columns\")\n",
-        "    df_encoded = pd.get_dummies(df_window, columns=cat_columns.keys().tolist())\n",
-        "    \n",
-        "    print(\"Dropping unnecessary columns\")\n",
-        "    df_featurized = df_encoded.drop(['windAngle', 'windSpeed', 'datetime', 'elevation'], axis=1).dropna().drop_duplicates()\n",
-        "    \n",
-        "    return df_featurized"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Train model on Jan 1 - 14, 2009 data\n",
-        "df = get_featurized_noaa_df(datetime(2009, 1, 1), datetime(2009, 1, 14, 23, 59, 59), columns, usaf_list)\n",
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "label = \"temperature\"\n",
-        "x_df = df.drop(label, axis=1)\n",
-        "y_df = df[[label]]\n",
-        "x_train, x_test, y_train, y_test = train_test_split(df, y_df, test_size=0.2, random_state=223)\n",
-        "print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)\n",
-        "\n",
-        "training_dir = 'outputs/training'\n",
-        "training_file = \"training.csv\"\n",
-        "\n",
-        "# Generate training dataframe to register as Training Dataset\n",
-        "os.makedirs(training_dir, exist_ok=True)\n",
-        "training_df = pd.merge(x_train.drop(label, axis=1), y_train, left_index=True, right_index=True)\n",
-        "training_df.to_csv(training_dir + \"/\" + training_file)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create/Register Training Dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dataset_name = \"dataset\"\n",
-        "name_suffix = datetime.utcnow().strftime(\"%Y-%m-%d-%H-%M-%S\")\n",
-        "snapshot_name = \"snapshot-{}\".format(name_suffix)\n",
-        "\n",
-        "dstore = ws.get_default_datastore()\n",
-        "dstore.upload(training_dir, \"data/training\", show_progress=True)\n",
-        "dpath = dstore.path(\"data/training/training.csv\")\n",
-        "trainingDataset = Dataset.auto_read_files(dpath, include_path=True)\n",
-        "trainingDataset = trainingDataset.register(workspace=ws, name=dataset_name, description=\"dset\", exist_ok=True)\n",
-        "\n",
-        "trainingDataSnapshot = trainingDataset.create_snapshot(snapshot_name=snapshot_name, compute_target=None, create_data_snapshot=True)\n",
-        "datasets = [(Dataset.Scenario.TRAINING, trainingDataSnapshot)]\n",
-        "print(\"dataset registration done.\\n\")\n",
-        "datasets"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train and Save Model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import lightgbm as lgb\n",
-        "\n",
-        "train = lgb.Dataset(data=x_train, \n",
-        "                    label=y_train)\n",
-        "\n",
-        "test = lgb.Dataset(data=x_test, \n",
-        "                   label=y_test,\n",
-        "                   reference=train)\n",
-        "\n",
-        "params = {'learning_rate'    : 0.1,\n",
-        "          'boosting'         : 'gbdt',\n",
-        "          'metric'           : 'rmse',\n",
-        "          'feature_fraction' : 1,\n",
-        "          'bagging_fraction' : 1,\n",
-        "          'max_depth': 6,\n",
-        "          'num_leaves'       : 31,\n",
-        "          'objective'        : 'regression',\n",
-        "          'bagging_freq'     : 1,\n",
-        "          \"verbose\": -1,\n",
-        "          'min_data_per_leaf': 100}\n",
-        "\n",
-        "model = lgb.train(params, \n",
-        "                  num_boost_round=500,\n",
-        "                  train_set=train,\n",
-        "                  valid_sets=[train, test],\n",
-        "                  verbose_eval=50,\n",
-        "                  early_stopping_rounds=25)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model_file = 'outputs/{}.pkl'.format(model_name)\n",
-        "\n",
-        "os.makedirs('outputs', exist_ok=True)\n",
-        "joblib.dump(model, model_file)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Register Model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model = Model.register(model_path=model_file,\n",
-        "                       model_name=model_name,\n",
-        "                       workspace=ws,\n",
-        "                       datasets=datasets)\n",
-        "\n",
-        "print(model_name, image_name, service_name, model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Deploy Model To AKS"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prepare Environment"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn', 'joblib', 'lightgbm', 'pandas'],\n",
-        "                                 pip_packages=['azureml-monitoring', 'azureml-sdk[automl]'])\n",
-        "\n",
-        "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(myenv.serialize_to_string())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create Image"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Image creation may take up to 15 minutes.\n",
-        "\n",
-        "image_name = image_name + str(model.version)\n",
-        "\n",
-        "if not image_name in ws.images:\n",
-        "    # Use the score.py defined in this directory as the execution script\n",
-        "    # NOTE: The Model Data Collector must be enabled in the execution script for DataDrift to run correctly\n",
-        "    image_config = ContainerImage.image_configuration(execution_script=\"score.py\",\n",
-        "                                                      runtime=\"python\",\n",
-        "                                                      conda_file=\"myenv.yml\",\n",
-        "                                                      description=\"Image with weather dataset model\")\n",
-        "    image = ContainerImage.create(name=image_name,\n",
-        "                                  models=[model],\n",
-        "                                  image_config=image_config,\n",
-        "                                  workspace=ws)\n",
-        "\n",
-        "    image.wait_for_creation(show_output=True)\n",
-        "else:\n",
-        "    image = ws.images[image_name]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create Compute Target"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "aks_name = 'dd-demo-e2e'\n",
-        "prov_config = AksCompute.provisioning_configuration()\n",
-        "\n",
-        "if not aks_name in ws.compute_targets:\n",
-        "    aks_target = ComputeTarget.create(workspace=ws,\n",
-        "                                      name=aks_name,\n",
-        "                                      provisioning_configuration=prov_config)\n",
-        "\n",
-        "    aks_target.wait_for_completion(show_output=True)\n",
-        "    print(aks_target.provisioning_state)\n",
-        "    print(aks_target.provisioning_errors)\n",
-        "else:\n",
-        "    aks_target=ws.compute_targets[aks_name]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deploy Service"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "aks_service_name = service_name\n",
-        "\n",
-        "if not aks_service_name in ws.webservices:\n",
-        "    aks_config = AksWebservice.deploy_configuration(collect_model_data=True, enable_app_insights=True)\n",
-        "    aks_service = Webservice.deploy_from_image(workspace=ws,\n",
-        "                                               name=aks_service_name,\n",
-        "                                               image=image,\n",
-        "                                               deployment_config=aks_config,\n",
-        "                                               deployment_target=aks_target)\n",
-        "    aks_service.wait_for_deployment(show_output=True)\n",
-        "    print(aks_service.state)\n",
-        "else:\n",
-        "    aks_service = ws.webservices[aks_service_name]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Run DataDrift Analysis"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Send Scoring Data to Service"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Download Scoring Data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Score Model on March 15, 2016 data\n",
-        "scoring_df = get_noaa_data(datetime(2016, 3, 15) - timedelta(days=7), datetime(2016, 3, 16),  columns, usaf_list)\n",
-        "# Add the window feature column\n",
-        "scoring_df = add_window_col(scoring_df)\n",
-        "\n",
-        "# Drop features not used by the model\n",
-        "print(\"Dropping unnecessary columns\")\n",
-        "scoring_df = scoring_df.drop(['windAngle', 'windSpeed', 'datetime', 'elevation'], axis=1).dropna()\n",
-        "scoring_df.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# One Hot Encode the scoring dataset to match the training dataset schema\n",
-        "columns_dict = model.datasets[\"training\"][0].get_profile().columns\n",
-        "extra_cols = ('Path', 'Column1')\n",
-        "for k in extra_cols:\n",
-        "    columns_dict.pop(k, None)\n",
-        "training_columns = list(columns_dict.keys())\n",
-        "\n",
-        "categorical_columns = scoring_df.dtypes == object\n",
-        "categorical_columns = categorical_columns[categorical_columns == True]\n",
-        "\n",
-        "test_df = pd.get_dummies(scoring_df[categorical_columns.keys().tolist()])\n",
-        "encoded_df = scoring_df.join(test_df)\n",
-        "\n",
-        "# Populate missing OHE columns with 0 values to match traning dataset schema\n",
-        "difference = list(set(training_columns) - set(encoded_df.columns.tolist()))\n",
-        "for col in difference:\n",
-        "    encoded_df[col] = 0\n",
-        "encoded_df.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Serialize dataframe to list of row dictionaries\n",
-        "encoded_dict = encoded_df.to_dict('records')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Submit Scoring Data to Service"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "\n",
-        "# retreive the API keys. AML generates two keys.\n",
-        "key1, key2 = aks_service.get_keys()\n",
-        "\n",
-        "total_count = len(scoring_df)\n",
-        "i = 0\n",
-        "load = []\n",
-        "for row in encoded_dict:\n",
-        "    load.append(row)\n",
-        "    i = i + 1\n",
-        "    if i % 100 == 0:\n",
-        "        payload = json.dumps({\"data\": load})\n",
-        "        \n",
-        "        # construct raw HTTP request and send to the service\n",
-        "        payload_binary = bytes(payload,encoding = 'utf8')\n",
-        "        headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
-        "        resp = requests.post(aks_service.scoring_uri, payload_binary, headers=headers)\n",
-        "        \n",
-        "        print(\"prediction:\", resp.content, \"Progress: {}/{}\".format(i, total_count))   \n",
-        "\n",
-        "        load = []\n",
-        "        time.sleep(3)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure DataDrift"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "services = [service_name]\n",
-        "start = datetime.now() - timedelta(days=2)\n",
-        "end = datetime(year=2020, month=1, day=22, hour=15, minute=16)\n",
-        "feature_list = ['usaf', 'wban', 'latitude', 'longitude', 'station_name', 'p_k',  'sine_hourofday', 'cosine_hourofday', 'temperature-7']\n",
-        "alert_config = AlertConfiguration([email_address]) if email_address else None\n",
-        "\n",
-        "# there will be an exception indicating using get() method if DataDrift object already exist\n",
-        "try:\n",
-        "    datadrift = DataDriftDetector.create(ws, model.name, model.version, services, frequency=\"Day\", alert_config=alert_config)\n",
-        "except KeyError:\n",
-        "    datadrift = DataDriftDetector.get(ws, model.name, model.version)\n",
-        "    \n",
-        "print(\"Details of DataDrift Object:\\n{}\".format(datadrift))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Run an Adhoc DataDriftDetector Run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "target_date = datetime.today()\n",
-        "run = datadrift.run(target_date, services, feature_list=feature_list, create_compute_target=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "exp = Experiment(ws, datadrift._id)\n",
-        "dd_run = Run(experiment=exp, run_id=run)\n",
-        "RunDetails(dd_run).show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Get Drift Analysis Results"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "children = list(dd_run.get_children())\n",
-        "for child in children:\n",
-        "    child.wait_for_completion()\n",
-        "\n",
-        "drift_metrics = datadrift.get_output(start_time=start, end_time=end)\n",
-        "drift_metrics"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Show all drift figures, one per serivice.\n",
-        "# If setting with_details is False (by default), only drift will be shown; if it's True, all details will be shown.\n",
-        "\n",
-        "drift_figures = datadrift.show(with_details=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Enable DataDrift Schedule"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "datadrift.enable_schedule()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "rafarmah"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/how-to-use-azureml/data-drift/readme.md b/how-to-use-azureml/data-drift/readme.md
deleted file mode 100644
index 9eb416c2..00000000
--- a/how-to-use-azureml/data-drift/readme.md
+++ /dev/null
@@ -1,3 +0,0 @@
-## Using data drift APIs
-
-1. [Detect data drift for a model](azure-ml-datadrift.ipynb): Detect data drift for a deployed model.
\ No newline at end of file
diff --git a/how-to-use-azureml/data-drift/score.py b/how-to-use-azureml/data-drift/score.py
deleted file mode 100644
index cda62f8b..00000000
--- a/how-to-use-azureml/data-drift/score.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import pickle
-import json
-import numpy
-import azureml.train.automl
-from sklearn.externals import joblib
-from sklearn.linear_model import Ridge
-from azureml.core.model import Model
-from azureml.core.run import Run
-from azureml.monitoring import ModelDataCollector
-import time
-import pandas as pd
-
-
-def init():
-    global model, inputs_dc, prediction_dc, feature_names, categorical_features
-
-    print("Model is initialized" + time.strftime("%H:%M:%S"))
-    model_path = Model.get_model_path(model_name="driftmodel")
-    model = joblib.load(model_path)
-
-    feature_names = ["usaf", "wban", "latitude", "longitude", "station_name", "p_k",
-                     "sine_weekofyear", "cosine_weekofyear", "sine_hourofday", "cosine_hourofday",
-                     "temperature-7"]
-
-    categorical_features = ["usaf", "wban", "p_k", "station_name"]
-
-    inputs_dc = ModelDataCollector(model_name="driftmodel",
-                                   identifier="inputs",
-                                   feature_names=feature_names)
-
-    prediction_dc = ModelDataCollector("driftmodel",
-                                       identifier="predictions",
-                                       feature_names=["temperature"])
-
-
-def run(raw_data):
-    global inputs_dc, prediction_dc
-
-    try:
-        data = json.loads(raw_data)["data"]
-        data = pd.DataFrame(data)
-
-        # Remove the categorical features as the model expects OHE values
-        input_data = data.drop(categorical_features, axis=1)
-
-        result = model.predict(input_data)
-
-        # Collect the non-OHE dataframe
-        collected_df = data[feature_names]
-
-        inputs_dc.collect(collected_df.values)
-        prediction_dc.collect(result)
-        return result.tolist()
-    except Exception as e:
-        error = str(e)
-
-        print(error + time.strftime("%H:%M:%S"))
-        return error
diff --git a/how-to-use-azureml/deploy-to-local/dockerSharedDrive.JPG b/how-to-use-azureml/deploy-to-local/dockerSharedDrive.JPG
deleted file mode 100644
index 554b8a71..00000000
Binary files a/how-to-use-azureml/deploy-to-local/dockerSharedDrive.JPG and /dev/null differ
diff --git a/how-to-use-azureml/deployment/accelerated-models/NOTICE.txt b/how-to-use-azureml/deployment/accelerated-models/NOTICE.txt
new file mode 100644
index 00000000..0451fc53
--- /dev/null
+++ b/how-to-use-azureml/deployment/accelerated-models/NOTICE.txt
@@ -0,0 +1,217 @@
+
+NOTICES AND INFORMATION
+Do Not Translate or Localize
+
+This Azure Machine Learning service example notebooks repository includes material from the projects listed below. 
+
+
+1. SSD-Tensorflow  (https://github.com/balancap/ssd-tensorflow)
+
+
+%% SSD-Tensorflow NOTICES AND INFORMATION BEGIN HERE
+=========================================
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
+=========================================
+END OF SSD-Tensorflow NOTICES AND INFORMATION
diff --git a/how-to-use-azureml/deployment/accelerated-models/README.md b/how-to-use-azureml/deployment/accelerated-models/README.md
index b9981ac9..f49b6b30 100644
--- a/how-to-use-azureml/deployment/accelerated-models/README.md
+++ b/how-to-use-azureml/deployment/accelerated-models/README.md
@@ -12,7 +12,7 @@ Easily create and train a model using various deep neural networks (DNNs) as a f
 To learn more about the azureml-accel-model classes, see the section [Model Classes](#model-classes) below or the [Azure ML Accel Models SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel?view=azure-ml-py).
 
 ### Step 1: Create an Azure ML workspace
-Follow [these instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python) to install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step.
+Follow [these instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/setup-create-workspace) to install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step.
 
 ### Step 2: Check your FPGA quota
 Use the Azure CLI to check whether you have quota.
diff --git a/how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.ipynb b/how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.ipynb
index 5191da87..ab65977d 100644
--- a/how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.ipynb
+++ b/how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.ipynb
@@ -1,5 +1,12 @@
 {
   "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -230,11 +237,14 @@
         "\n",
         "# Convert model\n",
         "convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensors, output_tensors_str)\n",
-        "# If it fails, you can run wait_for_completion again with show_output=True.\n",
-        "convert_request.wait_for_completion(show_output=False)\n",
-        "converted_model = convert_request.result\n",
-        "print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
-        "      converted_model.id, converted_model.created_time, '\\n')\n",
+        "if convert_request.wait_for_completion(show_output = False):\n",
+        "    # If the above call succeeded, get the converted model\n",
+        "    converted_model = convert_request.result\n",
+        "    print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
+        "          converted_model.id, converted_model.created_time, '\\n')\n",
+        "else:\n",
+        "    print(\"Model conversion failed. Showing output.\")\n",
+        "    convert_request.wait_for_completion(show_output = True)\n",
         "\n",
         "# Package into AccelContainerImage\n",
         "image_config = AccelContainerImage.image_configuration()\n",
@@ -298,6 +308,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "%%time\n",
         "aks_target.wait_for_completion(show_output = True)\n",
         "print(aks_target.provisioning_state)\n",
         "print(aks_target.provisioning_errors)"
@@ -316,6 +327,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "%%time\n",
         "from azureml.core.webservice import Webservice, AksWebservice\n",
         "\n",
         "# Set the web service configuration (for creating a test service, we don't want autoscale enabled)\n",
@@ -324,7 +336,7 @@
         "                                                num_replicas=1,\n",
         "                                                auth_enabled = False)\n",
         "\n",
-        "aks_service_name ='my-aks-service'\n",
+        "aks_service_name ='my-aks-service-3'\n",
         "\n",
         "aks_service = Webservice.deploy_from_image(workspace = ws,\n",
         "                                           name = aks_service_name,\n",
@@ -342,10 +354,9 @@
         "## 5. Test the service\n",
         "<a id=\"create-client\"></a>\n",
         "### 5.a. Create Client\n",
-        "The image supports gRPC and the TensorFlow Serving \"predict\" API. We have a client that can call into the docker image to get predictions. \n",
-        "\n",
-        "**Note:** If you chose to use auth_enabled=True when creating your AksWebservice.deploy_configuration(), see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).",
+        "The image supports gRPC and the TensorFlow Serving \"predict\" API. We will create a PredictionClient from the Webservice object that can call into the docker image to get predictions. If you do not have the Webservice object, you can also create [PredictionClient](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.predictionclient?view=azure-ml-py) directly.\n",
         "\n",
+        "**Note:** If you chose to use auth_enabled=True when creating your AksWebservice.deploy_configuration(), see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).\n",
         "**WARNING:** If you are running on Azure Notebooks free compute, you will not be able to make outgoing calls to your service. Try locating your client on a different machine to consume it."
       ]
     },
@@ -356,18 +367,10 @@
       "outputs": [],
       "source": [
         "# Using the grpc client in AzureML Accelerated Models SDK\n",
-        "from azureml.accel.client import PredictionClient\n",
-        "\n",
-        "address = aks_service.scoring_uri\n",
-        "ssl_enabled = address.startswith(\"https\")\n",
-        "address = address[address.find('/')+2:].strip('/')\n",
-        "port = 443 if ssl_enabled else 80\n",
+        "from azureml.accel import client_from_service\n",
         "\n",
         "# Initialize AzureML Accelerated Models client\n",
-        "client = PredictionClient(address=address,\n",
-        "                          port=port,\n",
-        "                          use_ssl=ssl_enabled,\n",
-        "                          service_name=aks_service.name)"
+        "client = client_from_service(aks_service)"
       ]
     },
     {
@@ -486,7 +489,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.0"
+      "version": "3.5.6"
     }
   },
   "nbformat": 4,
diff --git a/how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.yml b/how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.yml
new file mode 100644
index 00000000..5d3499d5
--- /dev/null
+++ b/how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.yml
@@ -0,0 +1,8 @@
+name: accelerated-models-object-detection
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-accel-models
+  - tensorflow
+  - opencv-python
+  - matplotlib
diff --git a/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb b/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb
index 8589b1dd..fdac2adf 100644
--- a/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb
+++ b/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb
@@ -1,5 +1,12 @@
 {
   "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -270,12 +277,15 @@
         "from azureml.accel import AccelOnnxConverter\n",
         "\n",
         "convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensors, output_tensors)\n",
-        "# If it fails, you can run wait_for_completion again with show_output=True.\n",
-        "convert_request.wait_for_completion(show_output = False)\n",
-        "# If the above call succeeded, get the converted model\n",
-        "converted_model = convert_request.result\n",
-        "print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
-        "      converted_model.id, converted_model.created_time, '\\n')"
+        "\n",
+        "if convert_request.wait_for_completion(show_output = False):\n",
+        "    # If the above call succeeded, get the converted model\n",
+        "    converted_model = convert_request.result\n",
+        "    print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
+        "          converted_model.id, converted_model.created_time, '\\n')\n",
+        "else:\n",
+        "    print(\"Model conversion failed. Showing output.\")\n",
+        "    convert_request.wait_for_completion(show_output = True)"
       ]
     },
     {
@@ -366,6 +376,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "%%time\n",
         "aks_target.wait_for_completion(show_output = True)\n",
         "print(aks_target.provisioning_state)\n",
         "print(aks_target.provisioning_errors)"
@@ -384,15 +395,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "%%time\n",
         "from azureml.core.webservice import Webservice, AksWebservice\n",
         "\n",
-        "#Set the web service configuration (for creating a test service, we don't want autoscale enabled)\n",
+        "# Set the web service configuration (for creating a test service, we don't want autoscale enabled)\n",
         "# Authentication is enabled by default, but for testing we specify False\n",
         "aks_config = AksWebservice.deploy_configuration(autoscale_enabled=False,\n",
         "                                                num_replicas=1,\n",
         "                                                auth_enabled = False)\n",
         "\n",
-        "aks_service_name ='my-aks-service'\n",
+        "aks_service_name ='my-aks-service-1'\n",
         "\n",
         "aks_service = Webservice.deploy_from_image(workspace = ws,\n",
         "                                           name = aks_service_name,\n",
@@ -415,10 +427,9 @@
       "metadata": {},
       "source": [
         "### 7.a. Create Client\n",
-        "The image supports gRPC and the TensorFlow Serving \"predict\" API. We have a client that can call into the docker image to get predictions.\n",
-        "\n",
-        "**Note:** If you chose to use auth_enabled=True when creating your AksWebservice, see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).",
+        "The image supports gRPC and the TensorFlow Serving \"predict\" API. We will create a PredictionClient from the Webservice object that can call into the docker image to get predictions. If you do not have the Webservice object, you can also create [PredictionClient](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.predictionclient?view=azure-ml-py) directly.\n",
         "\n",
+        "**Note:** If you chose to use auth_enabled=True when creating your AksWebservice, see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).\n",
         "**WARNING:** If you are running on Azure Notebooks free compute, you will not be able to make outgoing calls to your service. Try locating your client on a different machine to consume it."
       ]
     },
@@ -429,18 +440,10 @@
       "outputs": [],
       "source": [
         "# Using the grpc client in AzureML Accelerated Models SDK\n",
-        "from azureml.accel.client import PredictionClient\n",
-        "\n",
-        "address = aks_service.scoring_uri\n",
-        "ssl_enabled = address.startswith(\"https\")\n",
-        "address = address[address.find('/')+2:].strip('/')\n",
-        "port = 443 if ssl_enabled else 80\n",
+        "from azureml.accel import client_from_service\n",
         "\n",
         "# Initialize AzureML Accelerated Models client\n",
-        "client = PredictionClient(address=address,\n",
-        "                          port=port,\n",
-        "                          use_ssl=ssl_enabled,\n",
-        "                          service_name=aks_service.name)"
+        "client = client_from_service(aks_service)"
       ]
     },
     {
@@ -540,7 +543,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.0"
+      "version": "3.7.3"
     }
   },
   "nbformat": 4,
diff --git a/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.yml b/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.yml
new file mode 100644
index 00000000..bda43dfb
--- /dev/null
+++ b/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.yml
@@ -0,0 +1,6 @@
+name: accelerated-models-quickstart
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-accel-models
+  - tensorflow
diff --git a/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb b/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb
index 778e5310..88918c22 100644
--- a/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb
+++ b/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb
@@ -1,5 +1,12 @@
 {
   "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -410,6 +417,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "%%time\n",
         "# Launch the training\n",
         "tf.reset_default_graph()\n",
         "sess = tf.Session(graph=tf.get_default_graph())\n",
@@ -582,11 +590,14 @@
         "\n",
         "# Convert model\n",
         "convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensors, output_tensors)\n",
-        "# If it fails, you can run wait_for_completion again with show_output=True.\n",
-        "convert_request.wait_for_completion(show_output=False)\n",
-        "converted_model = convert_request.result\n",
-        "print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
-        "      converted_model.id, converted_model.created_time, '\\n')\n",
+        "if convert_request.wait_for_completion(show_output = False):\n",
+        "    # If the above call succeeded, get the converted model\n",
+        "    converted_model = convert_request.result\n",
+        "    print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
+        "          converted_model.id, converted_model.created_time, '\\n')\n",
+        "else:\n",
+        "    print(\"Model conversion failed. Showing output.\")\n",
+        "    convert_request.wait_for_completion(show_output = True)\n",
         "\n",
         "# Package into AccelContainerImage\n",
         "image_config = AccelContainerImage.image_configuration()\n",
@@ -655,6 +666,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "%%time\n",
         "aks_target.wait_for_completion(show_output = True)\n",
         "print(aks_target.provisioning_state)\n",
         "print(aks_target.provisioning_errors)"
@@ -673,6 +685,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "%%time\n",
         "from azureml.core.webservice import Webservice, AksWebservice\n",
         "\n",
         "# Set the web service configuration (for creating a test service, we don't want autoscale enabled)\n",
@@ -681,7 +694,7 @@
         "                                                num_replicas=1,\n",
         "                                                auth_enabled = False)\n",
         "\n",
-        "aks_service_name ='my-aks-service'\n",
+        "aks_service_name ='my-aks-service-2'\n",
         "\n",
         "aks_service = Webservice.deploy_from_image(workspace = ws,\n",
         "                                           name = aks_service_name,\n",
@@ -700,10 +713,9 @@
         "\n",
         "<a id=\"create-client\"></a>\n",
         "### 9.a. Create Client\n",
-        "The image supports gRPC and the TensorFlow Serving \"predict\" API. We have a client that can call into the docker image to get predictions. \n",
-        "\n",
-        "**Note:** If you chose to use auth_enabled=True when creating your AksWebservice.deploy_configuration(), see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).",
+        "The image supports gRPC and the TensorFlow Serving \"predict\" API. We will create a PredictionClient from the Webservice object that can call into the docker image to get predictions. If you do not have the Webservice object, you can also create [PredictionClient](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.predictionclient?view=azure-ml-py) directly.\n",
         "\n",
+        "**Note:** If you chose to use auth_enabled=True when creating your AksWebservice.deploy_configuration(), see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).\n",
         "**WARNING:** If you are running on Azure Notebooks free compute, you will not be able to make outgoing calls to your service. Try locating your client on a different machine to consume it."
       ]
     },
@@ -714,18 +726,10 @@
       "outputs": [],
       "source": [
         "# Using the grpc client in AzureML Accelerated Models SDK\n",
-        "from azureml.accel.client import PredictionClient\n",
-        "\n",
-        "address = aks_service.scoring_uri\n",
-        "ssl_enabled = address.startswith(\"https\")\n",
-        "address = address[address.find('/')+2:].strip('/')\n",
-        "port = 443 if ssl_enabled else 80\n",
+        "from azureml.accel import client_from_service\n",
         "\n",
         "# Initialize AzureML Accelerated Models client\n",
-        "client = PredictionClient(address=address,\n",
-        "                          port=port,\n",
-        "                          use_ssl=ssl_enabled,\n",
-        "                          service_name=aks_service.name)"
+        "client = client_from_service(aks_service)"
       ]
     },
     {
@@ -854,7 +858,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.0"
+      "version": "3.5.6"
     }
   },
   "nbformat": 4,
diff --git a/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.yml b/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.yml
new file mode 100644
index 00000000..3a3c7022
--- /dev/null
+++ b/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.yml
@@ -0,0 +1,9 @@
+name: accelerated-models-training
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-accel-models
+  - tensorflow
+  - keras
+  - tqdm 
+  - sklearn
diff --git a/how-to-use-azureml/deploy-to-cloud/README.md b/how-to-use-azureml/deployment/deploy-to-cloud/README.md
similarity index 100%
rename from how-to-use-azureml/deploy-to-cloud/README.md
rename to how-to-use-azureml/deployment/deploy-to-cloud/README.md
diff --git a/how-to-use-azureml/deploy-to-cloud/helloworld.txt b/how-to-use-azureml/deployment/deploy-to-cloud/helloworld.txt
similarity index 100%
rename from how-to-use-azureml/deploy-to-cloud/helloworld.txt
rename to how-to-use-azureml/deployment/deploy-to-cloud/helloworld.txt
diff --git a/how-to-use-azureml/deploy-to-cloud/model-register-and-deploy.ipynb b/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb
similarity index 72%
rename from how-to-use-azureml/deploy-to-cloud/model-register-and-deploy.ipynb
rename to how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb
index bdb88a42..ef681642 100644
--- a/how-to-use-azureml/deploy-to-cloud/model-register-and-deploy.ipynb
+++ b/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb
@@ -13,7 +13,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/deploy-to-cloud/model-register-and-deploy.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.png)"
       ]
     },
     {
@@ -77,7 +77,7 @@
         "from azureml.core import Workspace\n",
         "\n",
         "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
       ]
     },
     {
@@ -108,11 +108,41 @@
       "source": [
         "from azureml.core.model import Model\n",
         "\n",
-        "model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
-        "                       model_name = \"sklearn_regression_model.pkl\",\n",
-        "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                       description = \"Ridge regression model to predict diabetes\",\n",
-        "                       workspace = ws)"
+        "model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
+        "                       model_name=\"sklearn_regression_model.pkl\",\n",
+        "                       tags={'area': \"diabetes\", 'type': \"regression\"},\n",
+        "                       description=\"Ridge regression model to predict diabetes\",\n",
+        "                       workspace=ws)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create Environment"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can now create and/or use an Environment object when deploying a Webservice. The Environment can have been previously registered with your Workspace, or it will be registered with it as a part of the Webservice deployment. Only Environments that were created using azureml-defaults version 1.0.48 or later will work with this new handling however.\n",
+        "\n",
+        "More information can be found in our [using environments notebook](../training/using-environments/using-environments.ipynb)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment\n",
+        "\n",
+        "env = Environment.from_conda_specification(name='deploytocloudenv', file_path='myenv.yml')\n",
+        "\n",
+        "# This is optional at this point\n",
+        "# env.register(workspace=ws)"
       ]
     },
     {
@@ -153,10 +183,7 @@
       "source": [
         "from azureml.core.model import InferenceConfig\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
-        "                                   entry_script=\"score.py\",\n",
-        "                                   conda_file=\"myenv.yml\", \n",
-        "                                   extra_docker_file_steps=\"helloworld.txt\")"
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)"
       ]
     },
     {
@@ -171,13 +198,17 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "azuremlexception-remarks-sample"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.core.webservice import AciWebservice, Webservice\n",
         "from azureml.exceptions import WebserviceException\n",
         "\n",
-        "deployment_config = AciWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1)\n",
+        "deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
         "aci_service_name = 'aciservice1'\n",
         "\n",
         "try:\n",
@@ -215,7 +246,7 @@
         "    [10,9,8,7,6,5,4,3,2,1]\n",
         "]})\n",
         "\n",
-        "test_sample_encoded = bytes(test_sample,encoding = 'utf8')\n",
+        "test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
         "prediction = service.run(input_data=test_sample_encoded)\n",
         "print(prediction)"
       ]
@@ -247,15 +278,38 @@
       "source": [
         "### Model Profiling\n",
         "\n",
-        "you can also take advantage of profiling feature for model\n",
+        "You can also take advantage of the profiling feature to estimate CPU and memory requirements for models.\n",
         "\n",
         "```python\n",
-        "\n",
-        "profile = model.profile(ws, \"profilename\", [model], inference_config, test_sample)\n",
+        "profile = Model.profile(ws, \"profilename\", [model], inference_config, test_sample)\n",
         "profile.wait_for_profiling(True)\n",
         "profiling_results = profile.get_results()\n",
         "print(profiling_results)\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Model Packaging\n",
         "\n",
+        "If you want to build a Docker image that encapsulates your model and its dependencies, you can use the model packaging option. The output image will be pushed to your workspace's ACR.\n",
+        "\n",
+        "You must include an Environment object in your inference configuration to use `Model.package()`.\n",
+        "\n",
+        "```python\n",
+        "package = Model.package(ws, [model], inference_config)\n",
+        "package.wait_for_creation(show_output=True)  # Or show_output=False to hide the Docker build logs.\n",
+        "package.pull()\n",
+        "```\n",
+        "\n",
+        "Instead of a fully-built image, you can also generate a Dockerfile and download all the assets needed to build an image on top of your Environment.\n",
+        "\n",
+        "```python\n",
+        "package = Model.package(ws, [model], inference_config, generate_dockerfile=True)\n",
+        "package.wait_for_creation(show_output=True)\n",
+        "package.save(\"./local_context_dir\")\n",
         "```"
       ]
     }
diff --git a/how-to-use-azureml/deploy-to-cloud/model-register-and-deploy.yml b/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.yml
similarity index 100%
rename from how-to-use-azureml/deploy-to-cloud/model-register-and-deploy.yml
rename to how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.yml
diff --git a/how-to-use-azureml/deploy-to-cloud/myenv.yml b/how-to-use-azureml/deployment/deploy-to-cloud/myenv.yml
similarity index 100%
rename from how-to-use-azureml/deploy-to-cloud/myenv.yml
rename to how-to-use-azureml/deployment/deploy-to-cloud/myenv.yml
diff --git a/how-to-use-azureml/deploy-to-cloud/score.py b/how-to-use-azureml/deployment/deploy-to-cloud/score.py
similarity index 100%
rename from how-to-use-azureml/deploy-to-cloud/score.py
rename to how-to-use-azureml/deployment/deploy-to-cloud/score.py
diff --git a/how-to-use-azureml/deploy-to-cloud/sklearn_regression_model.pkl b/how-to-use-azureml/deployment/deploy-to-cloud/sklearn_regression_model.pkl
similarity index 100%
rename from how-to-use-azureml/deploy-to-cloud/sklearn_regression_model.pkl
rename to how-to-use-azureml/deployment/deploy-to-cloud/sklearn_regression_model.pkl
diff --git a/how-to-use-azureml/deploy-to-local/README.md b/how-to-use-azureml/deployment/deploy-to-local/README.md
similarity index 100%
rename from how-to-use-azureml/deploy-to-local/README.md
rename to how-to-use-azureml/deployment/deploy-to-local/README.md
diff --git a/how-to-use-azureml/deploy-to-local/helloworld.txt b/how-to-use-azureml/deployment/deploy-to-local/helloworld.txt
similarity index 100%
rename from how-to-use-azureml/deploy-to-local/helloworld.txt
rename to how-to-use-azureml/deployment/deploy-to-local/helloworld.txt
diff --git a/how-to-use-azureml/deploy-to-local/myenv.yml b/how-to-use-azureml/deployment/deploy-to-local/myenv.yml
similarity index 100%
rename from how-to-use-azureml/deploy-to-local/myenv.yml
rename to how-to-use-azureml/deployment/deploy-to-local/myenv.yml
diff --git a/how-to-use-azureml/deploy-to-local/register-model-deploy-local-advanced.ipynb b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb
similarity index 89%
rename from how-to-use-azureml/deploy-to-local/register-model-deploy-local-advanced.ipynb
rename to how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb
index 86496502..b0461399 100644
--- a/how-to-use-azureml/deploy-to-local/register-model-deploy-local-advanced.ipynb
+++ b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb
@@ -13,7 +13,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deploy-to-local/register-model-deploy-local-advanced.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.png)"
       ]
     },
     {
@@ -72,7 +72,7 @@
         "from azureml.core import Workspace\n",
         "\n",
         "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
       ]
     },
     {
@@ -103,11 +103,11 @@
       "source": [
         "from azureml.core.model import Model\n",
         "\n",
-        "model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
-        "                       model_name = \"sklearn_regression_model.pkl\",\n",
-        "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                       description = \"Ridge regression model to predict diabetes\",\n",
-        "                       workspace = ws)"
+        "model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
+        "                       model_name=\"sklearn_regression_model.pkl\",\n",
+        "                       tags={'area': \"diabetes\", 'type': \"regression\"},\n",
+        "                       description=\"Ridge regression model to predict diabetes\",\n",
+        "                       workspace=ws)"
       ]
     },
     {
@@ -127,10 +127,10 @@
         "\n",
         "source_directory = \"C:/abc\"\n",
         "\n",
-        "os.makedirs(source_directory, exist_ok = True)\n",
-        "os.makedirs(\"C:/abc/x/y\", exist_ok = True)\n",
-        "os.makedirs(\"C:/abc/env\", exist_ok = True)\n",
-        "os.makedirs(\"C:/abc/dockerstep\", exist_ok = True)"
+        "os.makedirs(source_directory, exist_ok=True)\n",
+        "os.makedirs(\"C:/abc/x/y\", exist_ok=True)\n",
+        "os.makedirs(\"C:/abc/env\", exist_ok=True)\n",
+        "os.makedirs(\"C:/abc/dockerstep\", exist_ok=True)"
       ]
     },
     {
@@ -253,7 +253,7 @@
         "from azureml.core.model import InferenceConfig\n",
         "\n",
         "inference_config = InferenceConfig(source_directory=\"C:/abc\",\n",
-        "                                   runtime= \"python\", \n",
+        "                                   runtime=\"python\", \n",
         "                                   entry_script=\"x/y/score.py\",\n",
         "                                   conda_file=\"env/myenv.yml\", \n",
         "                                   extra_docker_file_steps=\"dockerstep/customDockerStep.txt\")"
@@ -271,15 +271,10 @@
         "\n",
         "NOTE:\n",
         "\n",
-        "we require docker running with linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running\n",
+        "The Docker image runs as a Linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running:\n",
         "\n",
-        "    powershell command to switch to linux engine\n",
-        "    & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine\n",
-        "\n",
-        "and c drive is shared https://docs.docker.com/docker-for-windows/#shared-drives\n",
-        "sometimes you have to reshare c drive as docker \n",
-        "\n",
-        "<img src=\"./dockerSharedDrive.JPG\" align=\"left\"/>"
+        "    # PowerShell command to switch to Linux engine\n",
+        "    & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine"
       ]
     },
     {
@@ -295,7 +290,7 @@
       "source": [
         "from azureml.core.webservice import LocalWebservice\n",
         "\n",
-        "#this is optional, if not provided we choose random port\n",
+        "# This is optional, if not provided Docker will choose a random unused port.\n",
         "deployment_config = LocalWebservice.deploy_configuration(port=6789)\n",
         "\n",
         "local_service = Model.deploy(ws, \"test\", [model], inference_config, deployment_config)\n",
@@ -427,9 +422,8 @@
         "local_service.reload()\n",
         "print(\"--------------------------------------------------------------\")\n",
         "\n",
-        "# after reload now if you call run this will return updated return message\n",
-        "\n",
-        "print(local_service.run(input_data=sample_input))"
+        "# After calling reload(), run() will return the updated message.\n",
+        "local_service.run(input_data=sample_input)"
       ]
     },
     {
@@ -442,9 +436,9 @@
         "\n",
         "```python\n",
         "\n",
-        "local_service.update(models = [SomeOtherModelObject],\n",
-        "             deployment_config = local_config,\n",
-        "             inference_config = inference_config)\n",
+        "local_service.update(models=[SomeOtherModelObject],\n",
+        "                     deployment_config=local_config,\n",
+        "                     inference_config=inference_config)\n",
         "```"
       ]
     },
@@ -468,7 +462,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "raymondl"
+        "name": "keriehm"
       }
     ],
     "kernelspec": {
diff --git a/how-to-use-azureml/deploy-to-local/register-model-deploy-local.ipynb b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb
similarity index 83%
rename from how-to-use-azureml/deploy-to-local/register-model-deploy-local.ipynb
rename to how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb
index 2ef008ef..fc3e541f 100644
--- a/how-to-use-azureml/deploy-to-local/register-model-deploy-local.ipynb
+++ b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb
@@ -13,7 +13,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deploy-to-local/register-model-deploy-local.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.png)"
       ]
     },
     {
@@ -68,7 +68,7 @@
         "from azureml.core import Workspace\n",
         "\n",
         "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
       ]
     },
     {
@@ -99,11 +99,31 @@
       "source": [
         "from azureml.core.model import Model\n",
         "\n",
-        "model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
-        "                       model_name = \"sklearn_regression_model.pkl\",\n",
-        "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                       description = \"Ridge regression model to predict diabetes\",\n",
-        "                       workspace = ws)"
+        "model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
+        "                       model_name=\"sklearn_regression_model.pkl\",\n",
+        "                       tags={'area': \"diabetes\", 'type': \"regression\"},\n",
+        "                       description=\"Ridge regression model to predict diabetes\",\n",
+        "                       workspace=ws)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create Environment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "from azureml.core.environment import Environment\n",
+        "\n",
+        "environment = Environment(\"LocalDeploy\")\n",
+        "environment.python.conda_dependencies = CondaDependencies(\"myenv.yml\")"
       ]
     },
     {
@@ -121,9 +141,8 @@
       "source": [
         "from azureml.core.model import InferenceConfig\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
-        "                                   entry_script=\"score.py\",\n",
-        "                                   conda_file=\"myenv.yml\")"
+        "inference_config = InferenceConfig(entry_script=\"score.py\",\n",
+        "                                   environment=environment)"
       ]
     },
     {
@@ -138,15 +157,10 @@
         "\n",
         "NOTE:\n",
         "\n",
-        "we require docker running with linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running\n",
+        "The Docker image runs as a Linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running:\n",
         "\n",
-        "    powershell command to switch to linux engine\n",
-        "    & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine\n",
-        "\n",
-        "and c drive is shared https://docs.docker.com/docker-for-windows/#shared-drives\n",
-        "sometimes you have to reshare c drive as docker \n",
-        "\n",
-        "<img src=\"./dockerSharedDrive.JPG\" align=\"left\"/>"
+        "    # PowerShell command to switch to Linux engine\n",
+        "    & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine"
       ]
     },
     {
@@ -157,7 +171,7 @@
       "source": [
         "from azureml.core.webservice import LocalWebservice\n",
         "\n",
-        "#this is optional, if not provided we choose random port\n",
+        "# This is optional, if not provided Docker will choose a random unused port.\n",
         "deployment_config = LocalWebservice.deploy_configuration(port=6789)\n",
         "\n",
         "local_service = Model.deploy(ws, \"test\", [model], inference_config, deployment_config)\n",
@@ -221,7 +235,7 @@
         "\n",
         "sample_input = bytes(sample_input, encoding='utf-8')\n",
         "\n",
-        "print(local_service.run(input_data=sample_input))"
+        "local_service.run(input_data=sample_input)"
       ]
     },
     {
@@ -282,9 +296,8 @@
         "local_service.reload()\n",
         "print(\"--------------------------------------------------------------\")\n",
         "\n",
-        "# after reload now if you call run this will return updated return message\n",
-        "\n",
-        "print(local_service.run(input_data=sample_input))"
+        "# After calling reload(), run() will return the updated message.\n",
+        "local_service.run(input_data=sample_input)"
       ]
     },
     {
@@ -296,10 +309,9 @@
         "If you want to change your model(s), Conda dependencies, or deployment configuration, call `update()` to rebuild the Docker image.\n",
         "\n",
         "```python\n",
-        "\n",
-        "local_service.update(models = [SomeOtherModelObject],\n",
-        "             deployment_config = local_config,\n",
-        "             inference_config = inference_config)\n",
+        "local_service.update(models=[SomeOtherModelObject],\n",
+        "                     inference_config=inference_config,\n",
+        "                     deployment_config=local_config)\n",
         "```"
       ]
     },
@@ -323,7 +335,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "raymondl"
+        "name": "keriehm"
       }
     ],
     "kernelspec": {
diff --git a/how-to-use-azureml/deploy-to-local/score.py b/how-to-use-azureml/deployment/deploy-to-local/score.py
similarity index 100%
rename from how-to-use-azureml/deploy-to-local/score.py
rename to how-to-use-azureml/deployment/deploy-to-local/score.py
diff --git a/how-to-use-azureml/deploy-to-local/sklearn_regression_model.pkl b/how-to-use-azureml/deployment/deploy-to-local/sklearn_regression_model.pkl
similarity index 100%
rename from how-to-use-azureml/deploy-to-local/sklearn_regression_model.pkl
rename to how-to-use-azureml/deployment/deploy-to-local/sklearn_regression_model.pkl
diff --git a/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb b/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb
index 79d98eac..7a1831c5 100644
--- a/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb
+++ b/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb
@@ -22,7 +22,7 @@
         "If you want to log custom traces, you will follow the standard deplyment process for AKS and you will:\n",
         "1. Update scoring file.\n",
         "2. Update aks configuration.\n",
-        "3. Build new image and deploy it. "
+        "3. Deploy the model with this new configuration. "
       ]
     },
     {
@@ -178,7 +178,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## 6. Create your new Image"
+        "## 6. Create Inference Configuration"
       ]
     },
     {
@@ -187,22 +187,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.image import ContainerImage\n",
+        "from azureml.core.model import InferenceConfig\n",
         "\n",
-        "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
-        "                                                  runtime = \"python\",\n",
-        "                                                  conda_file = \"myenv.yml\",\n",
-        "                                                  description = \"Image with ridge regression model\",\n",
-        "                                                  tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
-        "                                                 )\n",
-        "\n",
-        "image = ContainerImage.create(name = \"myimage1\",\n",
-        "                              # this is the model object\n",
-        "                              models = [model],\n",
-        "                              image_config = image_config,\n",
-        "                              workspace = ws)\n",
-        "\n",
-        "image.wait_for_creation(show_output = True)"
+        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "                                   entry_script=\"score.py\",\n",
+        "                                   conda_file=\"myenv.yml\")"
       ]
     },
     {
@@ -220,7 +209,7 @@
       "source": [
         "from azureml.core.webservice import AciWebservice\n",
         "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
+        "aci_deployment_config = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
         "                                               memory_gb = 1, \n",
         "                                               tags = {'area': \"diabetes\", 'type': \"regression\"}, \n",
         "                                               description = 'Predict diabetes using regression model',\n",
@@ -236,11 +225,7 @@
         "from azureml.core.webservice import Webservice\n",
         "\n",
         "aci_service_name = 'my-aci-service-4'\n",
-        "print(aci_service_name)\n",
-        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-        "                                           image = image,\n",
-        "                                           name = aci_service_name,\n",
-        "                                           workspace = ws)\n",
+        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aci_deployment_config)\n",
         "aci_service.wait_for_deployment(True)\n",
         "print(aci_service.state)"
       ]
@@ -361,7 +346,7 @@
       "outputs": [],
       "source": [
         "#Set the web service configuration\n",
-        "aks_config = AksWebservice.deploy_configuration(enable_app_insights=True)"
+        "aks_deployment_config = AksWebservice.deploy_configuration(enable_app_insights=True)"
       ]
     },
     {
@@ -379,12 +364,12 @@
       "source": [
         "if aks_target.provisioning_state== \"Succeeded\": \n",
         "    aks_service_name ='aks-w-dc5'\n",
-        "    aks_service = Webservice.deploy_from_image(workspace = ws, \n",
-        "                                               name = aks_service_name,\n",
-        "                                               image = image,\n",
-        "                                               deployment_config = aks_config,\n",
-        "                                               deployment_target = aks_target\n",
-        "                                               )\n",
+        "    aks_service = Model.deploy(ws,\n",
+        "                               aks_service_name, \n",
+        "                               [model], \n",
+        "                               inference_config, \n",
+        "                               aks_deployment_config, \n",
+        "                               deployment_target = aks_target)    \n",
         "    aks_service.wait_for_deployment(show_output = True)\n",
         "    print(aks_service.state)\n",
         "else:\n",
@@ -464,7 +449,6 @@
         "%%time\n",
         "aks_service.delete()\n",
         "aci_service.delete()\n",
-        "image.delete()\n",
         "model.delete()"
       ]
     }
diff --git a/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb b/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb
index 9c147c3c..b7f35149 100644
--- a/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb
@@ -243,7 +243,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Create container image\n",
+        "### Setting up inference configuration\n",
         "First we create a YAML file that specifies which dependencies we would like to see in our container."
       ]
     },
@@ -265,7 +265,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Then we have Azure ML create the container. This step will likely take a few minutes."
+        "Then we create the inference configuration."
       ]
     },
     {
@@ -274,48 +274,19 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.image import ContainerImage\n",
+        "from azureml.core.model import InferenceConfig\n",
         "\n",
-        "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
-        "                                                  runtime = \"python\",\n",
-        "                                                  conda_file = \"myenv.yml\",\n",
-        "                                                  docker_file = \"Dockerfile\",\n",
-        "                                                  description = \"TinyYOLO ONNX Demo\",\n",
-        "                                                  tags = {\"demo\": \"onnx\"}\n",
-        "                                                 )\n",
-        "\n",
-        "\n",
-        "image = ContainerImage.create(name = \"onnxyolo\",\n",
-        "                              models = [model],\n",
-        "                              image_config = image_config,\n",
-        "                              workspace = ws)\n",
-        "\n",
-        "image.wait_for_creation(show_output = True)"
+        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "                                   entry_script=\"score.py\",\n",
+        "                                   conda_file=\"myenv.yml\",\n",
+        "                                   extra_docker_file_steps = \"Dockerfile\")"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "In case you need to debug your code, the next line of code accesses the log file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(image.image_build_log_uri)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We're all set! Let's get our model chugging.\n",
-        "\n",
-        "### Deploy the container image"
+        "### Deploy the model"
       ]
     },
     {
@@ -336,7 +307,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The following cell will likely take a few minutes to run as well."
+        "The following cell will take a few minutes to run as the model gets packaged up and deployed to ACI."
       ]
     },
     {
@@ -348,14 +319,9 @@
         "from azureml.core.webservice import Webservice\n",
         "from random import randint\n",
         "\n",
-        "aci_service_name = 'onnx-tinyyolo'+str(randint(0,100))\n",
+        "aci_service_name = 'my-aci-service-15ad'\n",
         "print(\"Service\", aci_service_name)\n",
-        "\n",
-        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-        "                                           image = image,\n",
-        "                                           name = aci_service_name,\n",
-        "                                           workspace = ws)\n",
-        "\n",
+        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
         "aci_service.wait_for_deployment(True)\n",
         "print(aci_service.state)"
       ]
diff --git a/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb b/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb
index 3f3a0fd9..f57f927c 100644
--- a/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb
@@ -54,7 +54,7 @@
         "\n",
         "### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n",
         "\n",
-        "In the following lines of code, we download [the trained ONNX Emotion FER+ model and corresponding test data](https://github.com/onnx/models/tree/master/emotion_ferplus) and place them in the same folder as this tutorial notebook. For more information about the FER+ dataset, please visit Microsoft Researcher Emad Barsoum's [FER+ source data repository](https://github.com/ebarsoum/FERPlus)."
+        "In the following lines of code, we download [the trained ONNX Emotion FER+ model and corresponding test data](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus) and place them in the same folder as this tutorial notebook. For more information about the FER+ dataset, please visit Microsoft Researcher Emad Barsoum's [FER+ source data repository](https://github.com/ebarsoum/FERPlus)."
       ]
     },
     {
@@ -176,7 +176,7 @@
       "source": [
         "### ONNX FER+ Model Methodology\n",
         "\n",
-        "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the well-known FER+ data set, provided as part of the [trained Emotion Recognition model](https://github.com/onnx/models/tree/master/emotion_ferplus) in the ONNX model zoo.\n",
+        "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the well-known FER+ data set, provided as part of the [trained Emotion Recognition model](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus) in the ONNX model zoo.\n",
         "\n",
         "The original Facial Emotion Recognition (FER) Dataset was released in 2013 by Pierre-Luc Carrier and Aaron Courville as part of a [Kaggle Competition](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data), but some of the labels are not entirely appropriate for the expression. In the FER+ Dataset, each photo was evaluated by at least 10 croud sourced reviewers, creating a more accurate basis for ground truth. \n",
         "\n",
@@ -341,9 +341,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Create the Container Image\n",
-        "\n",
-        "This step will likely take a few minutes."
+        "### Setup inference configuration"
       ]
     },
     {
@@ -352,48 +350,19 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.image import ContainerImage\n",
+        "from azureml.core.model import InferenceConfig\n",
         "\n",
-        "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
-        "                                                  runtime = \"python\",\n",
-        "                                                  conda_file = \"myenv.yml\",\n",
-        "                                                  docker_file = \"Dockerfile\",\n",
-        "                                                  description = \"Emotion ONNX Runtime container\",\n",
-        "                                                  tags = {\"demo\": \"onnx\"})\n",
-        "\n",
-        "\n",
-        "image = ContainerImage.create(name = \"onnximage\",\n",
-        "                              # this is the model object\n",
-        "                              models = [model],\n",
-        "                              image_config = image_config,\n",
-        "                              workspace = ws)\n",
-        "\n",
-        "image.wait_for_creation(show_output = True)"
+        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "                                   entry_script=\"score.py\",\n",
+        "                                   conda_file=\"myenv.yml\",\n",
+        "                                   extra_docker_file_steps = \"Dockerfile\")"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "In case you need to debug your code, the next line of code accesses the log file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(image.image_build_log_uri)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We're all done specifying what we want our virtual machine to do. Let's configure and deploy our container image.\n",
-        "\n",
-        "### Deploy the container image"
+        "### Deploy the model"
       ]
     },
     {
@@ -410,6 +379,13 @@
         "                                               description = 'ONNX for emotion recognition model')"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The following cell will likely take a few minutes to run as well."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -420,23 +396,11 @@
         "\n",
         "aci_service_name = 'onnx-demo-emotion'\n",
         "print(\"Service\", aci_service_name)\n",
-        "\n",
-        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-        "                                           image = image,\n",
-        "                                           name = aci_service_name,\n",
-        "                                           workspace = ws)\n",
-        "\n",
+        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
         "aci_service.wait_for_deployment(True)\n",
         "print(aci_service.state)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The following cell will likely take a few minutes to run as well."
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -470,7 +434,7 @@
         "\n",
         "### Useful Helper Functions\n",
         "\n",
-        "We preprocess and postprocess our data (see score.py file) using the helper functions specified in the [ONNX FER+ Model page in the Model Zoo repository](https://github.com/onnx/models/tree/master/emotion_ferplus)."
+        "We preprocess and postprocess our data (see score.py file) using the helper functions specified in the [ONNX FER+ Model page in the Model Zoo repository](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus)."
       ]
     },
     {
diff --git a/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb b/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb
index 43c22a09..4e3e83cc 100644
--- a/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb
@@ -54,7 +54,7 @@
         "\n",
         "### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n",
         "\n",
-        "In the following lines of code, we download [the trained ONNX MNIST model and corresponding test data](https://github.com/onnx/models/tree/master/mnist) and place them in the same folder as this tutorial notebook. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/)."
+        "In the following lines of code, we download [the trained ONNX MNIST model and corresponding test data](https://github.com/onnx/models/tree/master/vision/classification/mnist) and place them in the same folder as this tutorial notebook. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/)."
       ]
     },
     {
@@ -187,7 +187,7 @@
       "source": [
         "### ONNX MNIST Model Methodology\n",
         "\n",
-        "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the famous MNIST data set, provided as part of the [trained MNIST model](https://github.com/onnx/models/tree/master/mnist) in the ONNX model zoo.\n",
+        "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the famous MNIST data set, provided as part of the [trained MNIST model](https://github.com/onnx/models/tree/master/vision/classification/mnist) in the ONNX model zoo.\n",
         "\n",
         "***Input: Handwritten Images from MNIST Dataset***\n",
         "\n",
@@ -325,8 +325,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Create the Container Image\n",
-        "This step will likely take a few minutes."
+        "### Create Inference Configuration"
       ]
     },
     {
@@ -335,48 +334,19 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.image import ContainerImage\n",
+        "from azureml.core.model import InferenceConfig\n",
         "\n",
-        "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
-        "                                                  runtime = \"python\",\n",
-        "                                                  conda_file = \"myenv.yml\",\n",
-        "                                                  docker_file = \"Dockerfile\",\n",
-        "                                                  description = \"MNIST ONNX Runtime container\",\n",
-        "                                                  tags = {\"demo\": \"onnx\"}) \n",
-        "\n",
-        "\n",
-        "image = ContainerImage.create(name = \"onnximage\",\n",
-        "                              # this is the model object\n",
-        "                              models = [model],\n",
-        "                              image_config = image_config,\n",
-        "                              workspace = ws)\n",
-        "\n",
-        "image.wait_for_creation(show_output = True)"
+        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "                                   entry_script=\"score.py\",\n",
+        "                                   extra_docker_file_steps = \"Dockerfile\",\n",
+        "                                   conda_file=\"myenv.yml\")"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "In case you need to debug your code, the next line of code accesses the log file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(image.image_build_log_uri)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We're all done specifying what we want our virtual machine to do. Let's configure and deploy our container image.\n",
-        "\n",
-        "### Deploy the container image"
+        "### Deploy the model"
       ]
     },
     {
@@ -397,7 +367,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The following cell will likely take a few minutes to run as well."
+        "The following cell will likely take a few minutes to run."
       ]
     },
     {
@@ -410,12 +380,7 @@
         "\n",
         "aci_service_name = 'onnx-demo-mnist'\n",
         "print(\"Service\", aci_service_name)\n",
-        "\n",
-        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-        "                                           image = image,\n",
-        "                                           name = aci_service_name,\n",
-        "                                           workspace = ws)\n",
-        "\n",
+        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
         "aci_service.wait_for_deployment(True)\n",
         "print(aci_service.state)"
       ]
diff --git a/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb b/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb
index 1213c12f..af3f5f1c 100644
--- a/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb
@@ -28,7 +28,7 @@
         "ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n",
         "\n",
         "## ResNet50 Details\n",
-        "ResNet classifies the major object in an input image into a set of 1000 pre-defined classes. For more information about the ResNet50 model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/models/image_classification/resnet). "
+        "ResNet classifies the major object in an input image into a set of 1000 pre-defined classes. For more information about the ResNet50 model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/vision/classification/resnet). "
       ]
     },
     {
@@ -221,7 +221,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Create container image"
+        "### Create inference configuration"
       ]
     },
     {
@@ -249,7 +249,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Then we have Azure ML create the container. This step will likely take a few minutes."
+        "Create the inference configuration object"
       ]
     },
     {
@@ -258,48 +258,19 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.image import ContainerImage\n",
+        "from azureml.core.model import InferenceConfig\n",
         "\n",
-        "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
-        "                                                  runtime = \"python\",\n",
-        "                                                  conda_file = \"myenv.yml\",\n",
-        "                                                  docker_file = \"Dockerfile\",\n",
-        "                                                  description = \"ONNX ResNet50 Demo\",\n",
-        "                                                  tags = {\"demo\": \"onnx\"}\n",
-        "                                                 )\n",
-        "\n",
-        "\n",
-        "image = ContainerImage.create(name = \"onnxresnet50v2\",\n",
-        "                              models = [model],\n",
-        "                              image_config = image_config,\n",
-        "                              workspace = ws)\n",
-        "\n",
-        "image.wait_for_creation(show_output = True)"
+        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "                                   entry_script=\"score.py\",\n",
+        "                                   conda_file=\"myenv.yml\",\n",
+        "                                   extra_docker_file_steps = \"Dockerfile\")"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "In case you need to debug your code, the next line of code accesses the log file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(image.image_build_log_uri)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We're all set! Let's get our model chugging.\n",
-        "\n",
-        "### Deploy the container image"
+        "### Deploy the model"
       ]
     },
     {
@@ -334,12 +305,7 @@
         "\n",
         "aci_service_name = 'onnx-demo-resnet50'+str(randint(0,100))\n",
         "print(\"Service\", aci_service_name)\n",
-        "\n",
-        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-        "                                           image = image,\n",
-        "                                           name = aci_service_name,\n",
-        "                                           workspace = ws)\n",
-        "\n",
+        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
         "aci_service.wait_for_deployment(True)\n",
         "print(aci_service.state)"
       ]
diff --git a/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb b/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb
index 9a7f2035..a8a18aa2 100644
--- a/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb
@@ -28,7 +28,7 @@
         "ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n",
         "\n",
         "## MNIST Details\n",
-        "The Modified National Institute of Standards and Technology (MNIST) dataset consists of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing numbers from 0 to 9. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/). For more information about the MNIST model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/mnist). "
+        "The Modified National Institute of Standards and Technology (MNIST) dataset consists of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing numbers from 0 to 9. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/). For more information about the MNIST model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/vision/classification/mnist). "
       ]
     },
     {
@@ -401,7 +401,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Create container image\n",
+        "### Create inference configuration\n",
         "First we create a YAML file that specifies which dependencies we would like to see in our container."
       ]
     },
@@ -423,7 +423,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Then we have Azure ML create the container. This step will likely take a few minutes."
+        "Then we setup the inference configuration "
       ]
     },
     {
@@ -432,48 +432,19 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.image import ContainerImage\n",
+        "from azureml.core.model import InferenceConfig\n",
         "\n",
-        "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
-        "                                                  runtime = \"python\",\n",
-        "                                                  conda_file = \"myenv.yml\",\n",
-        "                                                  docker_file = \"Dockerfile\",\n",
-        "                                                  description = \"MNIST ONNX Demo\",\n",
-        "                                                  tags = {\"demo\": \"onnx\"}\n",
-        "                                                 )\n",
-        "\n",
-        "\n",
-        "image = ContainerImage.create(name = \"onnxmnistdemo\",\n",
-        "                              models = [model],\n",
-        "                              image_config = image_config,\n",
-        "                              workspace = ws)\n",
-        "\n",
-        "image.wait_for_creation(show_output = True)"
+        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "                                   entry_script=\"score.py\",\n",
+        "                                   conda_file=\"myenv.yml\",\n",
+        "                                   extra_docker_file_steps = \"Dockerfile\")"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "In case you need to debug your code, the next line of code accesses the log file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(image.image_build_log_uri)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We're all set! Let's get our model chugging.\n",
-        "\n",
-        "### Deploy the container image"
+        "### Deploy the model"
       ]
     },
     {
@@ -504,16 +475,12 @@
       "outputs": [],
       "source": [
         "from azureml.core.webservice import Webservice\n",
+        "from azureml.core.model import Model\n",
         "from random import randint\n",
         "\n",
         "aci_service_name = 'onnx-demo-mnist'+str(randint(0,100))\n",
         "print(\"Service\", aci_service_name)\n",
-        "\n",
-        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-        "                                           image = image,\n",
-        "                                           name = aci_service_name,\n",
-        "                                           workspace = ws)\n",
-        "\n",
+        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
         "aci_service.wait_for_deployment(True)\n",
         "print(aci_service.state)"
       ]
diff --git a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb
index 292eb621..0d934276 100644
--- a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb
+++ b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb
@@ -34,7 +34,6 @@
         "from azureml.core import Workspace\n",
         "from azureml.core.compute import AksCompute, ComputeTarget\n",
         "from azureml.core.webservice import Webservice, AksWebservice\n",
-        "from azureml.core.image import Image\n",
         "from azureml.core.model import Model"
       ]
     },
@@ -97,8 +96,51 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Create an image\n",
-        "Create an image using the registered model the script that will load and run the model."
+        "# Create the Environment\n",
+        "Create an environment that the model will be deployed with"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment\n",
+        "from azureml.core.conda_dependencies import CondaDependencies \n",
+        "\n",
+        "conda_deps = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-defaults'])\n",
+        "myenv = Environment(name='myenv')\n",
+        "myenv.python.conda_dependencies = conda_deps"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Use a custom Docker image\n",
+        "\n",
+        "You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n",
+        "\n",
+        "Only supported with `python` runtime.\n",
+        "```python\n",
+        "# use an image available in public Container Registry without authentication\n",
+        "myenv.docker.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n",
+        "\n",
+        "# or, use an image available in a private Container Registry\n",
+        "myenv.docker.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n",
+        "myenv.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n",
+        "myenv.docker.base_image_registry.username = \"username\"\n",
+        "myenv.docker.base_image_registry.password = \"password\"\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Write the Entry Script\n",
+        "Write the script that will be used to predict on your model"
       ]
     },
     {
@@ -136,67 +178,23 @@
         "        return error"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
-        "\n",
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
-        "\n",
-        "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(myenv.serialize_to_string())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.image import ContainerImage\n",
-        "\n",
-        "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
-        "                                                  runtime = \"python\",\n",
-        "                                                  conda_file = \"myenv.yml\",\n",
-        "                                                  description = \"Image with ridge regression model\",\n",
-        "                                                  tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
-        "                                                 )\n",
-        "\n",
-        "image = ContainerImage.create(name = \"myimage1\",\n",
-        "                              # this is the model object\n",
-        "                              models = [model],\n",
-        "                              image_config = image_config,\n",
-        "                              workspace = ws)\n",
-        "\n",
-        "image.wait_for_creation(show_output = True)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Use a custom Docker image\n",
+        "# Create the InferenceConfig\n",
+        "Create the inference config that will be used when deploying the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import InferenceConfig\n",
         "\n",
-        "You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n",
-        "\n",
-        "Only Supported for `ContainerImage`(from azureml.core.image) with `python` runtime.\n",
-        "```python\n",
-        "# use an image available in public Container Registry without authentication\n",
-        "image_config.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n",
-        "\n",
-        "# or, use an image available in a private Container Registry\n",
-        "image_config.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n",
-        "image_config.base_image_registry.address = \"myregistry.azurecr.io\"\n",
-        "image_config.base_image_registry.username = \"username\"\n",
-        "image_config.base_image_registry.password = \"password\"\n",
-        "\n",
-        "# or, use an image built during training.\n",
-        "image_config.base_image = run.properties[\"AzureML.DerivedImageName\"]\n",
-        "```\n",
-        "You can get the address of training image from the properties of a Run object. Only new runs submitted with azureml-sdk>=1.0.22 to AMLCompute targets will have the 'AzureML.DerivedImageName' property. Instructions on how to get a Run can be found in [manage-runs](../../training/manage-runs/manage-runs.ipynb). \n"
+        "inf_config = InferenceConfig(entry_script='score.py', environment=myenv)"
       ]
     },
     {
@@ -237,23 +235,21 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "'''\n",
-        "from azureml.core.compute import ComputeTarget, AksCompute\n",
+        "# from azureml.core.compute import ComputeTarget, AksCompute\n",
         "\n",
-        "# Create the compute configuration and set virtual network information\n",
-        "config = AksCompute.provisioning_configuration(location=\"eastus2\")\n",
-        "config.vnet_resourcegroup_name = \"mygroup\"\n",
-        "config.vnet_name = \"mynetwork\"\n",
-        "config.subnet_name = \"default\"\n",
-        "config.service_cidr = \"10.0.0.0/16\"\n",
-        "config.dns_service_ip = \"10.0.0.10\"\n",
-        "config.docker_bridge_cidr = \"172.17.0.1/16\"\n",
+        "# # Create the compute configuration and set virtual network information\n",
+        "# config = AksCompute.provisioning_configuration(location=\"eastus2\")\n",
+        "# config.vnet_resourcegroup_name = \"mygroup\"\n",
+        "# config.vnet_name = \"mynetwork\"\n",
+        "# config.subnet_name = \"default\"\n",
+        "# config.service_cidr = \"10.0.0.0/16\"\n",
+        "# config.dns_service_ip = \"10.0.0.10\"\n",
+        "# config.docker_bridge_cidr = \"172.17.0.1/16\"\n",
         "\n",
-        "# Create the compute target\n",
-        "aks_target = ComputeTarget.create(workspace = ws,\n",
-        "                                  name = \"myaks\",\n",
-        "                                  provisioning_configuration = config)\n",
-        "'''"
+        "# # Create the compute target\n",
+        "# aks_target = ComputeTarget.create(workspace = ws,\n",
+        "#                                   name = \"myaks\",\n",
+        "#                                   provisioning_configuration = config)"
       ]
     },
     {
@@ -300,17 +296,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "'''\n",
-        "# Use the default configuration (can also provide parameters to customize)\n",
-        "resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n",
+        "# # Use the default configuration (can also provide parameters to customize)\n",
+        "# resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n",
         "\n",
-        "create_name='my-existing-aks' \n",
-        "# Create the cluster\n",
-        "attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
-        "aks_target = ComputeTarget.attach(workspace=ws, name=create_name, attach_configuration=attach_config)\n",
-        "# Wait for the operation to complete\n",
-        "aks_target.wait_for_completion(True)\n",
-        "'''"
+        "# create_name='my-existing-aks' \n",
+        "# # Create the cluster\n",
+        "# attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
+        "# aks_target = ComputeTarget.attach(workspace=ws, name=create_name, attach_configuration=attach_config)\n",
+        "# # Wait for the operation to complete\n",
+        "# aks_target.wait_for_completion(True)"
       ]
     },
     {
@@ -326,8 +320,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#Set the web service configuration (using default here)\n",
-        "aks_config = AksWebservice.deploy_configuration()"
+        "# Set the web service configuration (using default here)\n",
+        "aks_config = AksWebservice.deploy_configuration()\n",
+        "\n",
+        "# # Enable token auth and disable (key) auth on the webservice\n",
+        "# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)\n"
       ]
     },
     {
@@ -339,11 +336,13 @@
         "%%time\n",
         "aks_service_name ='aks-service-1'\n",
         "\n",
-        "aks_service = Webservice.deploy_from_image(workspace = ws, \n",
-        "                                           name = aks_service_name,\n",
-        "                                           image = image,\n",
-        "                                           deployment_config = aks_config,\n",
-        "                                           deployment_target = aks_target)\n",
+        "aks_service = Model.deploy(workspace=ws,\n",
+        "                           name=aks_service_name,\n",
+        "                           models=[model],\n",
+        "                           inference_config=inf_config,\n",
+        "                           deployment_config=aks_config,\n",
+        "                           deployment_target=aks_target)\n",
+        "\n",
         "aks_service.wait_for_deployment(show_output = True)\n",
         "print(aks_service.state)"
       ]
@@ -390,11 +389,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# retreive the API keys. AML generates two keys.\n",
-        "'''\n",
-        "key1, Key2 = aks_service.get_keys()\n",
-        "print(key1)\n",
-        "'''"
+        "# # if (key) auth is enabled, retrieve the API keys. AML generates two keys.\n",
+        "# key1, Key2 = aks_service.get_keys()\n",
+        "# print(key1)\n",
+        "\n",
+        "# # if token auth is enabled, retrieve the token.\n",
+        "# access_token, refresh_after = aks_service.get_token()"
       ]
     },
     {
@@ -404,27 +404,28 @@
       "outputs": [],
       "source": [
         "# construct raw HTTP request and send to the service\n",
-        "'''\n",
-        "%%time\n",
+        "# %%time\n",
         "\n",
-        "import requests\n",
+        "# import requests\n",
         "\n",
-        "import json\n",
+        "# import json\n",
         "\n",
-        "test_sample = json.dumps({'data': [\n",
-        "    [1,2,3,4,5,6,7,8,9,10], \n",
-        "    [10,9,8,7,6,5,4,3,2,1]\n",
-        "]})\n",
-        "test_sample = bytes(test_sample,encoding = 'utf8')\n",
+        "# test_sample = json.dumps({'data': [\n",
+        "#     [1,2,3,4,5,6,7,8,9,10], \n",
+        "#     [10,9,8,7,6,5,4,3,2,1]\n",
+        "# ]})\n",
+        "# test_sample = bytes(test_sample,encoding = 'utf8')\n",
         "\n",
-        "# Don't forget to add key to the HTTP header.\n",
-        "headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
+        "# # If (key) auth is enabled, don't forget to add key to the HTTP header.\n",
+        "# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
         "\n",
-        "resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)\n",
+        "# # If token auth is enabled, don't forget to add token to the HTTP header.\n",
+        "# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + access_token}\n",
+        "\n",
+        "# resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)\n",
         "\n",
         "\n",
-        "print(\"prediction:\", resp.text)\n",
-        "'''"
+        "# print(\"prediction:\", resp.text)"
       ]
     },
     {
@@ -443,7 +444,6 @@
       "source": [
         "%%time\n",
         "aks_service.delete()\n",
-        "image.delete()\n",
         "model.delete()"
       ]
     }
diff --git a/how-to-use-azureml/explain-model/README.md b/how-to-use-azureml/explain-model/README.md
index 4fc9147a..4973e51f 100644
--- a/how-to-use-azureml/explain-model/README.md
+++ b/how-to-use-azureml/explain-model/README.md
@@ -1,8 +1,11 @@
 ## Using explain model APIs
 
+<a name="samples"></a>
+# Explain Model SDK Sample Notebooks
+
 Follow these sample notebooks to learn:
 
-1. [Explain tabular data locally](explain-tabular-data-local): Basic example of explaining model trained on tabular data.
-4. [Explain on remote AMLCompute](explain-on-amlcompute): Explain a model on a remote AMLCompute target.
-5. [Explain tabular data with Run History](explain-tabular-data-run-history): Explain a model with Run History.
-7. [Explain raw features](explain-tabular-data-raw-features): Explain the raw features of a trained model.
+1. [Explain tabular data locally](tabular-data): Basic examples of explaining model trained on tabular data.
+2. [Explain on remote AMLCompute](azure-integration/remote-explanation): Explain a model on a remote AMLCompute target.
+3. [Explain tabular data with Run History](azure-integration/run-history): Explain a model with Run History.
+4. [Operationalize model explanation](azure-integration/scoring-time): Operationalize model explanation as a web service.
diff --git a/how-to-use-azureml/explain-model/explain-on-amlcompute/regression-sklearn-on-amlcompute.ipynb b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb
similarity index 73%
rename from how-to-use-azureml/explain-model/explain-on-amlcompute/regression-sklearn-on-amlcompute.ipynb
rename to how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb
index c0273170..fbec37b1 100644
--- a/how-to-use-azureml/explain-model/explain-on-amlcompute/regression-sklearn-on-amlcompute.ipynb
+++ b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb
@@ -13,33 +13,68 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-on-amlcompute/regression-sklearn-on-amlcompute.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Train using Azure Machine Learning Compute\n",
+        "# Train and explain models remotely via Azure Machine Learning Compute\n",
         "\n",
-        "* Initialize a Workspace\n",
-        "* Create an Experiment\n",
-        "* Introduction to AmlCompute\n",
-        "* Submit an AmlCompute run in a few different ways\n",
-        "    - Provision as a run based compute target \n",
-        "    - Provision as a persistent compute target (Basic)\n",
-        "    - Provision as a persistent compute target (Advanced)\n",
-        "* Additional operations to perform on AmlCompute\n",
-        "* Download model explanation data from the Run History Portal\n",
-        "* Print the explanation data"
+        "\n",
+        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to train and explain a regression model remotely on an Azure Machine Leanrning Compute Target (AMLCompute).**_\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "    1. Initialize a Workspace\n",
+        "    1. Create an Experiment\n",
+        "    1. Introduction to AmlCompute\n",
+        "    1. Submit an AmlCompute run in a few different ways\n",
+        "        1. Option 1: Provision as a run based compute target \n",
+        "        1. Option 2: Provision as a persistent compute target (Basic)\n",
+        "        1. Option 3: Provision as a persistent compute target (Advanced)\n",
+        "1. Additional operations to perform on AmlCompute\n",
+        "1. [Download model explanations from Azure Machine Learning Run History](#Download)\n",
+        "1. [Visualize explanations](#Visualize)\n",
+        "1. [Next steps](#Next)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Prerequisites\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't."
+        "## Introduction\n",
+        "\n",
+        "This notebook showcases how to train and explain a regression model remotely via Azure Machine Learning Compute (AMLCompute), and download the calculated explanations locally for visualization.\n",
+        "It demonstrates the API calls that you need to make to submit a run for training and explaining a model to AMLCompute, download the compute explanations remotely, and visualizing the global and local explanations via a visualization dashboard that provides an interactive way of discovering patterns in model predictions and downloaded explanations.\n",
+        "\n",
+        "We will showcase one of the tabular data explainers: TabularExplainer (SHAP).\n",
+        "\n",
+        "Problem: Boston Housing Price Prediction with scikit-learn (train a model and run an explainer remotely via AMLCompute, and download and visualize the remotely-calculated explanations.)\n",
+        "\n",
+        "| ![explanations-run-history](./img/explanations-run-history.PNG) |\n",
+        "|:--:|\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't.\n",
+        "\n",
+        "\n",
+        "If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
+        "If you are using Jupyter Labs run the following command:\n",
+        "```\n",
+        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
+        "```\n"
       ]
     },
     {
@@ -116,7 +151,7 @@
         "**Note**: As with other Azure services, there are limits on certain resources (for eg. AmlCompute quota) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n",
         "\n",
         "\n",
-        "The training script `run_explainer.py` is already created for you. Let's have a look."
+        "The training script `train_explain.py` is already created for you. Let's have a look."
       ]
     },
     {
@@ -162,14 +197,14 @@
         "\n",
         "project_folder = './explainer-remote-run-on-amlcompute'\n",
         "os.makedirs(project_folder, exist_ok=True)\n",
-        "shutil.copy('run_explainer.py', project_folder)"
+        "shutil.copy('train_explain.py', project_folder)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Provision as a run based compute target\n",
+        "### Option 1: Provision as a run based compute target\n",
         "\n",
         "You can provision AmlCompute as a compute target at run-time. In this case, the compute is auto-created for your run, scales up to max_nodes that you specify, and then **deleted automatically** after the run completes."
       ]
@@ -205,7 +240,7 @@
         "\n",
         "azureml_pip_packages = [\n",
         "    'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
-        "    'azureml-explain-model'\n",
+        "    'azureml-explain-model', 'sklearn-pandas', 'azureml-dataprep'\n",
         "]\n",
         "\n",
         "# specify CondaDependencies obj\n",
@@ -216,7 +251,7 @@
         "from azureml.core.script_run_config import ScriptRunConfig\n",
         "\n",
         "script_run_config = ScriptRunConfig(source_directory=project_folder,\n",
-        "                                    script='run_explainer.py',\n",
+        "                                    script='train_explain.py',\n",
         "                                    run_config=run_config)\n",
         "\n",
         "run = experiment.submit(script_run_config)\n",
@@ -247,7 +282,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Provision as a persistent compute target (Basic)\n",
+        "### Option 2: Provision as a persistent compute target (Basic)\n",
         "\n",
         "You can provision a persistent AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
         "\n",
@@ -306,7 +341,7 @@
         "\n",
         "azureml_pip_packages = [\n",
         "    'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
-        "    'azureml-explain-model'\n",
+        "    'azureml-explain-model', 'azureml-dataprep'\n",
         "]\n",
         "\n",
         "# specify CondaDependencies obj\n",
@@ -317,7 +352,7 @@
         "from azureml.core import ScriptRunConfig\n",
         "\n",
         "src = ScriptRunConfig(source_directory=project_folder, \n",
-        "                      script='run_explainer.py', \n",
+        "                      script='train_explain.py', \n",
         "                      run_config=run_config) \n",
         "run = experiment.submit(config=src)\n",
         "run"
@@ -347,7 +382,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Provision as a persistent compute target (Advanced)\n",
+        "### Option 3: Provision as a persistent compute target (Advanced)\n",
         "\n",
         "You can also specify additional properties or change defaults while provisioning AmlCompute using a more advanced configuration. This is useful when you want a dedicated cluster of 4 nodes (for example you can set the min_nodes and max_nodes to 4), or want the compute to be within an existing VNet in your subscription.\n",
         "\n",
@@ -417,9 +452,11 @@
         "\n",
         "azureml_pip_packages = [\n",
         "    'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
-        "    'azureml-explain-model'\n",
+        "    'azureml-explain-model', 'azureml-dataprep'\n",
         "]\n",
         "\n",
+        "\n",
+        "\n",
         "# specify CondaDependencies obj\n",
         "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'],\n",
         "                                                                            pip_packages=azureml_pip_packages)\n",
@@ -428,7 +465,7 @@
         "from azureml.core import ScriptRunConfig\n",
         "\n",
         "src = ScriptRunConfig(source_directory=project_folder, \n",
-        "                      script='run_explainer.py', \n",
+        "                      script='train_explain.py', \n",
         "                      run_config=run_config) \n",
         "run = experiment.submit(config=src)\n",
         "run"
@@ -515,7 +552,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Download Model Explanation Data"
+        "## Download \n",
+        "1. Download model explanation data."
       ]
     },
     {
@@ -528,9 +566,9 @@
         "\n",
         "# Get model explanation data\n",
         "client = ExplanationClient.from_run(run)\n",
-        "explanation = client.download_model_explanation()\n",
-        "local_importance_values = explanation.local_importance_values\n",
-        "expected_values = explanation.expected_values\n"
+        "global_explanation = client.download_model_explanation()\n",
+        "local_importance_values = global_explanation.local_importance_values\n",
+        "expected_values = global_explanation.expected_values\n"
       ]
     },
     {
@@ -541,9 +579,9 @@
       "source": [
         "# Or you can use the saved run.id to retrive the feature importance values\n",
         "client = ExplanationClient.from_run_id(ws, experiment_name, run.id)\n",
-        "explanation = client.download_model_explanation()\n",
-        "local_importance_values = explanation.local_importance_values\n",
-        "expected_values = explanation.expected_values"
+        "global_explanation = client.download_model_explanation()\n",
+        "local_importance_values = global_explanation.local_importance_values\n",
+        "expected_values = global_explanation.expected_values"
       ]
     },
     {
@@ -553,9 +591,9 @@
       "outputs": [],
       "source": [
         "# Get the top k (e.g., 4) most important features with their importance values\n",
-        "explanation = client.download_model_explanation(top_k=4)\n",
-        "global_importance_values = explanation.get_ranked_global_values()\n",
-        "global_importance_names = explanation.get_ranked_global_names()"
+        "global_explanation_topk = client.download_model_explanation(top_k=4)\n",
+        "global_importance_values = global_explanation_topk.get_ranked_global_values()\n",
+        "global_importance_names = global_explanation_topk.get_ranked_global_names()"
       ]
     },
     {
@@ -572,9 +610,101 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Success!\n",
-        "Great, you are ready to move on to the remaining notebooks."
+        "2. Download model file."
       ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# retrieve model for visualization and deployment\n",
+        "from azureml.core.model import Model\n",
+        "from sklearn.externals import joblib\n",
+        "original_model = Model(ws, 'model_explain_model_on_amlcomp')\n",
+        "model_path = original_model.download(exist_ok=True)\n",
+        "original_model = joblib.load(model_path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "3. Download test dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# retrieve x_test for visualization\n",
+        "from sklearn.externals import joblib\n",
+        "x_test_path = './x_test_boston_housing.pkl'\n",
+        "run.download_file('x_test_boston_housing.pkl', output_file_path=x_test_path)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "x_test = joblib.load('x_test_boston_housing.pkl')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualize\n",
+        "Load the visualization dashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ExplanationDashboard(global_explanation, original_model, x_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next\n",
+        "Learn about other use cases of the explain package on a:\n",
+        "1. [Training time: regression problem](../../tabular-data/explain-binary-classification-local.ipynb)       \n",
+        "1. [Training time: binary classification problem](../../tabular-data/explain-binary-classification-local.ipynb)\n",
+        "1. [Training time: multiclass classification problem](../../tabular-data/explain-multiclass-classification-local.ipynb)\n",
+        "1. Explain models with engineered features:\n",
+        "    1. [Simple feature transformations](../../tabular-data/simple-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Advanced feature transformations](../../tabular-data/advanced-feature-transformations-explain-local.ipynb)\n",
+        "1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n",
+        "1. Inferencing time: deploy a classification model and explainer:\n",
+        "    1. [Deploy a locally-trained model and explainer](../scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
+        "    1. [Deploy a remotely-trained model and explainer](../scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
     }
   ],
   "metadata": {
diff --git a/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml
new file mode 100644
index 00000000..53d58768
--- /dev/null
+++ b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml
@@ -0,0 +1,8 @@
+name: explain-model-on-amlcompute
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-explain-model
+  - azureml-contrib-explain-model
+  - sklearn-pandas
+  - azureml-dataprep
diff --git a/how-to-use-azureml/explain-model/azure-integration/remote-explanation/img/AzureMachineLearningCycle.png b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/img/AzureMachineLearningCycle.png
new file mode 100644
index 00000000..52de479f
Binary files /dev/null and b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/img/AzureMachineLearningCycle.png differ
diff --git a/how-to-use-azureml/explain-model/azure-integration/remote-explanation/img/explanations-run-history.png b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/img/explanations-run-history.png
new file mode 100644
index 00000000..a58ef3b6
Binary files /dev/null and b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/img/explanations-run-history.png differ
diff --git a/how-to-use-azureml/explain-model/explain-on-amlcompute/run_explainer.py b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/train_explain.py
similarity index 75%
rename from how-to-use-azureml/explain-model/explain-on-amlcompute/run_explainer.py
rename to how-to-use-azureml/explain-model/azure-integration/remote-explanation/train_explain.py
index 72072080..c38839cc 100644
--- a/how-to-use-azureml/explain-model/explain-on-amlcompute/run_explainer.py
+++ b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/train_explain.py
@@ -11,7 +11,8 @@ from sklearn.externals import joblib
 import os
 import numpy as np
 
-os.makedirs('./outputs', exist_ok=True)
+OUTPUT_DIR = './outputs/'
+os.makedirs(OUTPUT_DIR, exist_ok=True)
 
 boston_data = datasets.load_boston()
 
@@ -22,6 +23,12 @@ X_train, X_test, y_train, y_test = train_test_split(boston_data.data,
                                                     boston_data.target,
                                                     test_size=0.2,
                                                     random_state=0)
+# write x_test out as a pickle file for later visualization
+x_test_pkl = 'x_test.pkl'
+with open(x_test_pkl, 'wb') as file:
+    joblib.dump(value=X_test, filename=os.path.join(OUTPUT_DIR, x_test_pkl))
+run.upload_file('x_test_boston_housing.pkl', os.path.join(OUTPUT_DIR, x_test_pkl))
+
 
 alpha = 0.5
 # Use Ridge algorithm to create a regression model
@@ -34,9 +41,14 @@ run.log('alpha', alpha)
 model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
 # save model in the outputs folder so it automatically get uploaded
 with open(model_file_name, 'wb') as file:
-    joblib.dump(value=reg, filename=os.path.join('./outputs/',
+    joblib.dump(value=reg, filename=os.path.join(OUTPUT_DIR,
                                                  model_file_name))
 
+# register the model
+run.upload_file('original_model.pkl', os.path.join('./outputs/', model_file_name))
+original_model = run.register_model(model_name='model_explain_model_on_amlcomp',
+                                    model_path='original_model.pkl')
+
 # Explain predictions on your local machine
 tabular_explainer = TabularExplainer(model, X_train, features=boston_data.feature_names)
 
diff --git a/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb b/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb
new file mode 100644
index 00000000..d8fbce8a
--- /dev/null
+++ b/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb
@@ -0,0 +1,631 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Save and retrieve explanations via Azure Machine Learning Run History\n",
+        "\n",
+        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to save and retrieve classification model explanations to/from Azure Machine Learning Run History.**_\n",
+        "\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Run model explainer locally at training time](#Explain)\n",
+        "    1. Apply feature transformations\n",
+        "    1. Train a binary classification model\n",
+        "    1. Explain the model on raw features\n",
+        "        1. Generate global explanations\n",
+        "        1. Generate local explanations\n",
+        "1. [Upload model explanations to Azure Machine Learning Run History](#Upload)\n",
+        "1. [Download model explanations from Azure Machine Learning Run History](#Download)\n",
+        "1. [Visualize explanations](#Visualize)\n",
+        "1. [Next steps](#Next)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "This notebook showcases how to explain a classification model predictions locally at training time, upload explanations to the Azure Machine Learning's run history, and download previously-uploaded explanations from the Run History.\n",
+        "It demonstrates the API calls that you need to make to upload/download the global and local explanations and a visualization dashboard that provides an interactive way of discovering patterns in data and downloaded explanations.\n",
+        "\n",
+        "We will showcase three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
+        "\n",
+        "\n",
+        "\n",
+        "Problem: IBM employee attrition classification with scikit-learn (run model explainer locally and upload explanation to the Azure Machine Learning Run History)\n",
+        "\n",
+        "1. Train a SVM classification model using Scikit-learn\n",
+        "2. Run 'explain_model' with AML Run History, which leverages run history service to store and manage the explanation data\n",
+        "---\n",
+        "\n",
+        "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
+        "If you are using Jupyter Labs run the following command:\n",
+        "```\n",
+        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
+        "```\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Explain\n",
+        "\n",
+        "### Run model explainer locally at training time"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+        "from sklearn.svm import SVC\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# Explainers:\n",
+        "# 1. SHAP Tabular Explainer\n",
+        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
+        "\n",
+        "# OR\n",
+        "\n",
+        "# 2. Mimic Explainer\n",
+        "from azureml.explain.model.mimic.mimic_explainer import MimicExplainer\n",
+        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
+        "from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
+        "from azureml.explain.model.mimic.models.linear_model import LinearExplainableModel\n",
+        "from azureml.explain.model.mimic.models.linear_model import SGDExplainableModel\n",
+        "from azureml.explain.model.mimic.models.tree_model import DecisionTreeExplainableModel\n",
+        "\n",
+        "# OR\n",
+        "\n",
+        "# 3. PFI Explainer\n",
+        "from azureml.explain.model.permutation.permutation_importance import PFIExplainer "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load the IBM employee attrition data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# get the IBM employee attrition dataset\n",
+        "outdirname = 'dataset.6.21.19'\n",
+        "try:\n",
+        "    from urllib import urlretrieve\n",
+        "except ImportError:\n",
+        "    from urllib.request import urlretrieve\n",
+        "import zipfile\n",
+        "zipfilename = outdirname + '.zip'\n",
+        "urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)\n",
+        "with zipfile.ZipFile(zipfilename, 'r') as unzip:\n",
+        "    unzip.extractall('.')\n",
+        "attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')\n",
+        "\n",
+        "# Dropping Employee count as all values are 1 and hence attrition is independent of this feature\n",
+        "attritionData = attritionData.drop(['EmployeeCount'], axis=1)\n",
+        "# Dropping Employee Number since it is merely an identifier\n",
+        "attritionData = attritionData.drop(['EmployeeNumber'], axis=1)\n",
+        "\n",
+        "attritionData = attritionData.drop(['Over18'], axis=1)\n",
+        "\n",
+        "# Since all values are 80\n",
+        "attritionData = attritionData.drop(['StandardHours'], axis=1)\n",
+        "\n",
+        "# Converting target variables from string to numerical values\n",
+        "target_map = {'Yes': 1, 'No': 0}\n",
+        "attritionData[\"Attrition_numerical\"] = attritionData[\"Attrition\"].apply(lambda x: target_map[x])\n",
+        "target = attritionData[\"Attrition_numerical\"]\n",
+        "\n",
+        "attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Split data into train and test\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train, x_test, y_train, y_test = train_test_split(attritionXData, \n",
+        "                                                    target, \n",
+        "                                                    test_size = 0.2,\n",
+        "                                                    random_state=0,\n",
+        "                                                    stratify=target)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Creating dummy columns for each categorical feature\n",
+        "categorical = []\n",
+        "for col, value in attritionXData.iteritems():\n",
+        "    if value.dtype == 'object':\n",
+        "        categorical.append(col)\n",
+        "        \n",
+        "# Store the numerical columns in a list numerical\n",
+        "numerical = attritionXData.columns.difference(categorical)        "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Transform raw features"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.compose import ColumnTransformer\n",
+        "\n",
+        "# We create the preprocessing pipelines for both numeric and categorical data.\n",
+        "numeric_transformer = Pipeline(steps=[\n",
+        "    ('imputer', SimpleImputer(strategy='median')),\n",
+        "    ('scaler', StandardScaler())])\n",
+        "\n",
+        "categorical_transformer = Pipeline(steps=[\n",
+        "    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
+        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
+        "\n",
+        "transformations = ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        ('num', numeric_transformer, numerical),\n",
+        "        ('cat', categorical_transformer, categorical)])\n",
+        "\n",
+        "# Append classifier to preprocessing pipeline.\n",
+        "# Now we have a full prediction pipeline.\n",
+        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
+        "                      ('classifier', SVC(kernel='linear', C = 1.0, probability=True))])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "'''\n",
+        "# Uncomment below if sklearn-pandas is not installed\n",
+        "#!pip install sklearn-pandas\n",
+        "from sklearn_pandas import DataFrameMapper\n",
+        "\n",
+        "# Impute, standardize the numeric features and one-hot encode the categorical features.    \n",
+        "\n",
+        "\n",
+        "numeric_transformations = [([f], Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])) for f in numerical]\n",
+        "\n",
+        "categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]\n",
+        "\n",
+        "transformations = numeric_transformations + categorical_transformations\n",
+        "\n",
+        "# Append classifier to preprocessing pipeline.\n",
+        "# Now we have a full prediction pipeline.\n",
+        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
+        "                      ('classifier', SVC(kernel='linear', C = 1.0, probability=True))]) \n",
+        "\n",
+        "\n",
+        "\n",
+        "'''"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Train a SVM classification model, which you want to explain"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model = clf.fit(x_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Explain predictions on your local machine"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 1. Using SHAP TabularExplainer\n",
+        "# clf.steps[-1][1] returns the trained classification model\n",
+        "explainer = TabularExplainer(clf.steps[-1][1], \n",
+        "                             initialization_examples=x_train, \n",
+        "                             features=attritionXData.columns, \n",
+        "                             classes=[\"Not leaving\", \"leaving\"], \n",
+        "                             transformations=transformations)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# 2. Using MimicExplainer\n",
+        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
+        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
+        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
+        "# explainer = MimicExplainer(clf.steps[-1][1], \n",
+        "#                            x_train, \n",
+        "#                            LGBMExplainableModel, \n",
+        "#                            augment_data=True, \n",
+        "#                            max_num_of_augmentations=10, \n",
+        "#                            features=attritionXData.columns, \n",
+        "#                            classes=[\"Not leaving\", \"leaving\"], \n",
+        "#                            transformations=transformations)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# 3. Using PFIExplainer\n",
+        "\n",
+        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
+        "# Note that if a metric function is provided a higher value must be better.\n",
+        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
+        "# Default metrics: \n",
+        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
+        "# Mean absolute error for regression\n",
+        "\n",
+        "# explainer = PFIExplainer(clf.steps[-1][1], \n",
+        "#                          features=x_train.columns, \n",
+        "#                          transformations=transformations,\n",
+        "#                          classes=[\"Not leaving\", \"leaving\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate global explanations\n",
+        "Explain overall model predictions (global explanation)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
+        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
+        "global_explanation = explainer.explain_global(x_test)\n",
+        "\n",
+        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
+        "# global_explanation = explainer.explain_global(x_test, true_labels=y_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Sorted SHAP values\n",
+        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
+        "# Corresponding feature names\n",
+        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
+        "# Feature ranks (based on original order of features)\n",
+        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
+        "\n",
+        "# Note: PFIExplainer does not support per class explanations\n",
+        "# Per class feature names\n",
+        "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
+        "# Per class feature importance values\n",
+        "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Print out a dictionary that holds the sorted feature importance names and values\n",
+        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Explain overall model predictions as a collection of local (instance-level) explanations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# feature shap values for all features and all data points in the training data\n",
+        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate local explanations\n",
+        "Explain local data points (individual instances)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Note: PFIExplainer does not support local explanations\n",
+        "# You can pass a specific data point or a group of data points to the explain_local function\n",
+        "\n",
+        "# E.g., Explain the first data point in the test set\n",
+        "instance_num = 1\n",
+        "local_explanation = explainer.explain_local(x_test[:instance_num])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get the prediction for the first member of the test set and explain why model made that prediction\n",
+        "prediction_value = clf.predict(x_test)[instance_num]\n",
+        "\n",
+        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
+        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
+        "\n",
+        "print('local importance values: {}'.format(sorted_local_importance_values))\n",
+        "print('local importance names: {}'.format(sorted_local_importance_names))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Upload\n",
+        "Upload explanations to Azure Machine Learning Run History"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "from azureml.core import Workspace, Experiment, Run\n",
+        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
+        "from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
+        "# Check core SDK version number\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "experiment_name = 'explain_model'\n",
+        "experiment = Experiment(ws, experiment_name)\n",
+        "run = experiment.start_logging()\n",
+        "client = ExplanationClient.from_run(run)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Uploading model explanation data for storage or visualization in webUX\n",
+        "# The explanation can then be downloaded on any compute\n",
+        "# Multiple explanations can be uploaded\n",
+        "client.upload_model_explanation(global_explanation, comment='global explanation: all features')\n",
+        "# Or you can only upload the explanation object with the top k feature info\n",
+        "#client.upload_model_explanation(global_explanation, top_k=2, comment='global explanation: Only top 2 features')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Uploading model explanation data for storage or visualization in webUX\n",
+        "# The explanation can then be downloaded on any compute\n",
+        "# Multiple explanations can be uploaded\n",
+        "client.upload_model_explanation(local_explanation, comment='local explanation for test point 1: all features')\n",
+        "\n",
+        "# Alterntively, you can only upload the local explanation object with the top k feature info\n",
+        "#client.upload_model_explanation(local_explanation, top_k=2, comment='local explanation: top 2 features')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Download\n",
+        "Download explanations from Azure Machine Learning Run History"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# List uploaded explanations\n",
+        "client.list_model_explanations()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "for explanation in client.list_model_explanations():\n",
+        "    \n",
+        "    if explanation['comment'] == 'local explanation for test point 1: all features':\n",
+        "        downloaded_local_explanation = client.download_model_explanation(explanation_id=explanation['id'])\n",
+        "        # You can pass a k value to only download the top k feature importance values\n",
+        "        downloaded_local_explanation_top2 = client.download_model_explanation(top_k=2, explanation_id=explanation['id'])\n",
+        "    \n",
+        "    \n",
+        "    elif explanation['comment'] == 'global explanation: all features':\n",
+        "        downloaded_global_explanation = client.download_model_explanation(explanation_id=explanation['id'])\n",
+        "        # You can pass a k value to only download the top k feature importance values\n",
+        "        downloaded_global_explanation_top2 = client.download_model_explanation(top_k=2, explanation_id=explanation['id'])\n",
+        "    "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualize\n",
+        "Load the visualization dashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ExplanationDashboard(downloaded_global_explanation, model, x_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next\n",
+        "Learn about other use cases of the explain package on a:\n",
+        "1. [Training time: regression problem](../../tabular-data/explain-binary-classification-local.ipynb)       \n",
+        "1. [Training time: binary classification problem](../../tabular-data/explain-binary-classification-local.ipynb)\n",
+        "1. [Training time: multiclass classification problem](../../tabular-data/explain-multiclass-classification-local.ipynb)\n",
+        "1. Explain models with engineered features:\n",
+        "    1. [Simple feature transformations](../../tabular-data/simple-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Advanced feature transformations](../../tabular-data/advanced-feature-transformations-explain-local.ipynb)\n",
+        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n",
+        "1. Inferencing time: deploy a classification model and explainer:\n",
+        "    1. [Deploy a locally-trained model and explainer](../scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
+        "    1. [Deploy a remotely-trained model and explainer](../scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "mesameki"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-regression.yml b/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml
similarity index 68%
rename from how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-regression.yml
rename to how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml
index 69f81e2a..bc244c09 100644
--- a/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-regression.yml
+++ b/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml
@@ -1,4 +1,4 @@
-name: explain-run-history-sklearn-regression
+name: save-retrieve-explanations-run-history
 dependencies:
 - pip:
   - azureml-sdk
diff --git a/how-to-use-azureml/explain-model/azure-integration/scoring-time/img/azure-machine-learning-cycle.png b/how-to-use-azureml/explain-model/azure-integration/scoring-time/img/azure-machine-learning-cycle.png
new file mode 100644
index 00000000..52de479f
Binary files /dev/null and b/how-to-use-azureml/explain-model/azure-integration/scoring-time/img/azure-machine-learning-cycle.png differ
diff --git a/how-to-use-azureml/explain-model/azure-integration/scoring-time/score.py b/how-to-use-azureml/explain-model/azure-integration/scoring-time/score.py
new file mode 100644
index 00000000..82b0bd0f
--- /dev/null
+++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/score.py
@@ -0,0 +1,33 @@
+import json
+import numpy as np
+import pandas as pd
+import os
+import pickle
+from sklearn.externals import joblib
+from sklearn.linear_model import LogisticRegression
+from azureml.core.model import Model
+
+
+def init():
+
+    global original_model
+    global scoring_explainer
+
+    # Retrieve the path to the model file using the model name
+    # Assume original model is named original_prediction_model
+    original_model_path = Model.get_model_path('original_model')
+    scoring_explainer_path = Model.get_model_path('IBM_attrition_explainer')
+
+    original_model = joblib.load(original_model_path)
+    scoring_explainer = joblib.load(scoring_explainer_path)
+
+
+def run(raw_data):
+    # Get predictions and explanations for each data point
+    data = pd.read_json(raw_data)
+    # Make prediction
+    predictions = original_model.predict(data)
+    # Retrieve model explanations
+    local_importance_values = scoring_explainer.explain(data)
+    # You can return any data type as long as it is JSON-serializable
+    return {'predictions': predictions.tolist(), 'local_importance_values': local_importance_values}
diff --git a/how-to-use-azureml/explain-model/azure-integration/scoring-time/score_local_explain.py b/how-to-use-azureml/explain-model/azure-integration/scoring-time/score_local_explain.py
new file mode 100644
index 00000000..c102f909
--- /dev/null
+++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/score_local_explain.py
@@ -0,0 +1,33 @@
+import json
+import numpy as np
+import pandas as pd
+import os
+import pickle
+from sklearn.externals import joblib
+from sklearn.linear_model import LogisticRegression
+from azureml.core.model import Model
+
+
+def init():
+
+    global original_model
+    global scoring_explainer
+
+    # Retrieve the path to the model file using the model name
+    # Assume original model is named original_prediction_model
+    original_model_path = Model.get_model_path('local_deploy_model')
+    scoring_explainer_path = Model.get_model_path('IBM_attrition_explainer')
+
+    original_model = joblib.load(original_model_path)
+    scoring_explainer = joblib.load(scoring_explainer_path)
+
+
+def run(raw_data):
+    # Get predictions and explanations for each data point
+    data = pd.read_json(raw_data)
+    # Make prediction
+    predictions = original_model.predict(data)
+    # Retrieve model explanations
+    local_importance_values = scoring_explainer.explain(data)
+    # You can return any data type as long as it is JSON-serializable
+    return {'predictions': predictions.tolist(), 'local_importance_values': local_importance_values}
diff --git a/how-to-use-azureml/explain-model/azure-integration/scoring-time/score_remote_explain.py b/how-to-use-azureml/explain-model/azure-integration/scoring-time/score_remote_explain.py
new file mode 100644
index 00000000..7ffc21b3
--- /dev/null
+++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/score_remote_explain.py
@@ -0,0 +1,33 @@
+import json
+import numpy as np
+import pandas as pd
+import os
+import pickle
+from sklearn.externals import joblib
+from sklearn.linear_model import LogisticRegression
+from azureml.core.model import Model
+
+
+def init():
+
+    global original_model
+    global scoring_explainer
+
+    # Retrieve the path to the model file using the model name
+    # Assume original model is named original_prediction_model
+    original_model_path = Model.get_model_path('amlcompute_deploy_model')
+    scoring_explainer_path = Model.get_model_path('IBM_attrition_explainer')
+
+    original_model = joblib.load(original_model_path)
+    scoring_explainer = joblib.load(scoring_explainer_path)
+
+
+def run(raw_data):
+    # Get predictions and explanations for each data point
+    data = pd.read_json(raw_data)
+    # Make prediction
+    predictions = original_model.predict(data)
+    # Retrieve model explanations
+    local_importance_values = scoring_explainer.explain(data)
+    # You can return any data type as long as it is JSON-serializable
+    return {'predictions': predictions.tolist(), 'local_importance_values': local_importance_values}
diff --git a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb
new file mode 100644
index 00000000..37f41b7f
--- /dev/null
+++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb
@@ -0,0 +1,514 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Train and explain models locally and deploy model and scoring explainer\n",
+        "\n",
+        "\n",
+        "_**This notebook illustrates how to use the Azure Machine Learning Interpretability SDK to deploy a locally-trained model and its corresponding scoring explainer to Azure Container Instances (ACI) as a web service.**_\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "Problem: IBM employee attrition classification with scikit-learn (train and explain a model locally and use Azure Container Instances (ACI) for deploying your model and its corresponding scoring explainer as a web service.)\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Run model explainer locally at training time](#Explain)\n",
+        "    1. Apply feature transformations\n",
+        "    1. Train a binary classification model\n",
+        "    1. Explain the model on raw features\n",
+        "        1. Generate global explanations\n",
+        "        1. Generate local explanations\n",
+        "1. [Visualize explanations](#Visualize)\n",
+        "1. [Deploy model and scoring explainer](#Deploy)\n",
+        "1. [Next steps](#Next)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "\n",
+        "This notebook showcases how to train and explain a classification model locally, and deploy the trained model and its corresponding explainer to Azure Container Instances (ACI).\n",
+        "It demonstrates the API calls that you need to make to submit a run for training and explaining a model to AMLCompute, download the compute explanations remotely, and visualizing the global and local explanations via a visualization dashboard that provides an interactive way of discovering patterns in model predictions and downloaded explanations. It also demonstrates how to use Azure Machine Learning MLOps capabilities to deploy your model and its corresponding explainer.\n",
+        "\n",
+        "We will showcase one of the tabular data explainers: TabularExplainer (SHAP) and follow these steps:\n",
+        "1.\tDevelop a machine learning script in Python which involves the training script and the explanation script.\n",
+        "2.\tRun the script locally.\n",
+        "3.\tUse the interpretability toolkit\u00e2\u20ac\u2122s visualization dashboard to visualize predictions and their explanation. If the metrics and explanations don't indicate a desired outcome, loop back to step 1 and iterate on your scripts.\n",
+        "5.\tAfter a satisfactory run is found, create a scoring explainer and register the persisted model and its corresponding explainer in the model registry.\n",
+        "6.\tDevelop a scoring script.\n",
+        "7.\tCreate an image and register it in the image registry.\n",
+        "8.\tDeploy the image as a web service in Azure.\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "Make sure you go through the [configuration notebook](../../../../configuration.ipynb) first if you haven't."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize a Workspace\n",
+        "\n",
+        "Initialize a workspace object from persisted configuration"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "create workspace"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Explain\n",
+        "Create An Experiment: **Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "experiment_name = 'explain_model_at_scoring_time'\n",
+        "experiment = Experiment(workspace=ws, name=experiment_name)\n",
+        "run = experiment.start_logging()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# get IBM attrition data\n",
+        "import os\n",
+        "import pandas as pd\n",
+        "\n",
+        "outdirname = 'dataset.6.21.19'\n",
+        "try:\n",
+        "    from urllib import urlretrieve\n",
+        "except ImportError:\n",
+        "    from urllib.request import urlretrieve\n",
+        "import zipfile\n",
+        "zipfilename = outdirname + '.zip'\n",
+        "urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)\n",
+        "with zipfile.ZipFile(zipfilename, 'r') as unzip:\n",
+        "    unzip.extractall('.')\n",
+        "attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.externals import joblib\n",
+        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn_pandas import DataFrameMapper\n",
+        "\n",
+        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
+        "\n",
+        "os.makedirs('./outputs', exist_ok=True)\n",
+        "\n",
+        "# Dropping Employee count as all values are 1 and hence attrition is independent of this feature\n",
+        "attritionData = attritionData.drop(['EmployeeCount'], axis=1)\n",
+        "# Dropping Employee Number since it is merely an identifier\n",
+        "attritionData = attritionData.drop(['EmployeeNumber'], axis=1)\n",
+        "attritionData = attritionData.drop(['Over18'], axis=1)\n",
+        "# Since all values are 80\n",
+        "attritionData = attritionData.drop(['StandardHours'], axis=1)\n",
+        "\n",
+        "# Converting target variables from string to numerical values\n",
+        "target_map = {'Yes': 1, 'No': 0}\n",
+        "attritionData[\"Attrition_numerical\"] = attritionData[\"Attrition\"].apply(lambda x: target_map[x])\n",
+        "target = attritionData[\"Attrition_numerical\"]\n",
+        "\n",
+        "attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)\n",
+        "\n",
+        "# Creating dummy columns for each categorical feature\n",
+        "categorical = []\n",
+        "for col, value in attritionXData.iteritems():\n",
+        "    if value.dtype == 'object':\n",
+        "        categorical.append(col)\n",
+        "\n",
+        "# Store the numerical columns in a list numerical\n",
+        "numerical = attritionXData.columns.difference(categorical)\n",
+        "\n",
+        "numeric_transformations = [([f], Pipeline(steps=[\n",
+        "    ('imputer', SimpleImputer(strategy='median')),\n",
+        "    ('scaler', StandardScaler())])) for f in numerical]\n",
+        "\n",
+        "categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]\n",
+        "\n",
+        "transformations = numeric_transformations + categorical_transformations\n",
+        "\n",
+        "# Append classifier to preprocessing pipeline.\n",
+        "# Now we have a full prediction pipeline.\n",
+        "clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),\n",
+        "                      ('classifier', RandomForestClassifier())])\n",
+        "\n",
+        "# Split data into train and test\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train, x_test, y_train, y_test = train_test_split(attritionXData,\n",
+        "                                                    target,\n",
+        "                                                    test_size = 0.2,\n",
+        "                                                    random_state=0,\n",
+        "                                                    stratify=target)\n",
+        "\n",
+        "# preprocess the data and fit the classification model\n",
+        "clf.fit(x_train, y_train)\n",
+        "model = clf.steps[-1][1]\n",
+        "\n",
+        "model_file_name = 'log_reg.pkl'\n",
+        "\n",
+        "# save model in the outputs folder so it automatically get uploaded\n",
+        "with open(model_file_name, 'wb') as file:\n",
+        "    joblib.dump(value=clf, filename=os.path.join('./outputs/',\n",
+        "                                                 model_file_name))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Explain predictions on your local machine\n",
+        "tabular_explainer = TabularExplainer(model, \n",
+        "                                     initialization_examples=x_train, \n",
+        "                                     features=attritionXData.columns, \n",
+        "                                     classes=[\"Not leaving\", \"leaving\"], \n",
+        "                                     transformations=transformations)\n",
+        "\n",
+        "# Explain overall model predictions (global explanation)\n",
+        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
+        "# x_train can be passed as well, but with more examples explanations it will\n",
+        "# take longer although they may be more accurate\n",
+        "global_explanation = tabular_explainer.explain_global(x_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer, save\n",
+        "# ScoringExplainer\n",
+        "scoring_explainer = TreeScoringExplainer(tabular_explainer)\n",
+        "# Pickle scoring explainer locally\n",
+        "save(scoring_explainer, exist_ok=True)\n",
+        "\n",
+        "# Register original model\n",
+        "run.upload_file('original_model.pkl', os.path.join('./outputs/', model_file_name))\n",
+        "original_model = run.register_model(model_name='local_deploy_model', \n",
+        "                                    model_path='original_model.pkl')\n",
+        "\n",
+        "# Register scoring explainer\n",
+        "run.upload_file('IBM_attrition_explainer.pkl', 'scoring_explainer.pkl')\n",
+        "scoring_explainer_model = run.register_model(model_name='IBM_attrition_explainer', model_path='IBM_attrition_explainer.pkl')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualize\n",
+        "Visualize the explanations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ExplanationDashboard(global_explanation, clf, x_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Deploy \n",
+        "\n",
+        "Deploy Model and ScoringExplainer"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice\n",
+        "\n",
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
+        "                                               memory_gb=1, \n",
+        "                                               tags={\"data\": \"IBM_Attrition\",  \n",
+        "                                                     \"method\" : \"local_explanation\"}, \n",
+        "                                               description='Get local explanations for IBM Employee Attrition data')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.conda_dependencies import CondaDependencies \n",
+        "\n",
+        "# WARNING: to install this, g++ needs to be available on the Docker image and is not by default (look at the next cell)\n",
+        "azureml_pip_packages = [\n",
+        "    'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
+        "    'azureml-explain-model'\n",
+        "]\n",
+        " \n",
+        "\n",
+        "# specify CondaDependencies obj\n",
+        "myenv = CondaDependencies.create(conda_packages=['scikit-learn', 'pandas'],\n",
+        "                                 pip_packages=['sklearn-pandas', 'pyyaml'] + azureml_pip_packages,\n",
+        "                                 pin_sdk_version=False)\n",
+        "\n",
+        "with open(\"myenv.yml\",\"w\") as f:\n",
+        "    f.write(myenv.serialize_to_string())\n",
+        "\n",
+        "with open(\"myenv.yml\",\"r\") as f:\n",
+        "    print(f.read())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile dockerfile\n",
+        "RUN apt-get update && apt-get install -y g++ "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import Model\n",
+        "# retrieve scoring explainer for deployment\n",
+        "scoring_explainer_model = Model(ws, 'IBM_attrition_explainer')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import Webservice\n",
+        "from azureml.core.image import ContainerImage\n",
+        "\n",
+        "# Use the custom scoring, docker, and conda files we created above\n",
+        "image_config = ContainerImage.image_configuration(execution_script=\"score_local_explain.py\",\n",
+        "                                                  docker_file=\"dockerfile\", \n",
+        "                                                  runtime=\"python\", \n",
+        "                                                  conda_file=\"myenv.yml\")\n",
+        "\n",
+        "# Use configs and models generated above\n",
+        "service = Webservice.deploy_from_model(workspace=ws,\n",
+        "                                       name='model-scoring',\n",
+        "                                       deployment_config=aciconfig,\n",
+        "                                       models=[scoring_explainer_model, original_model],\n",
+        "                                       image_config=image_config)\n",
+        "\n",
+        "service.wait_for_deployment(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import requests\n",
+        "import json\n",
+        "\n",
+        "\n",
+        "# Create data to test service with\n",
+        "sample_data = '{\"Age\":{\"899\":49},\"BusinessTravel\":{\"899\":\"Travel_Rarely\"},\"DailyRate\":{\"899\":1098},\"Department\":{\"899\":\"Research & Development\"},\"DistanceFromHome\":{\"899\":4},\"Education\":{\"899\":2},\"EducationField\":{\"899\":\"Medical\"},\"EnvironmentSatisfaction\":{\"899\":1},\"Gender\":{\"899\":\"Male\"},\"HourlyRate\":{\"899\":85},\"JobInvolvement\":{\"899\":2},\"JobLevel\":{\"899\":5},\"JobRole\":{\"899\":\"Manager\"},\"JobSatisfaction\":{\"899\":3},\"MaritalStatus\":{\"899\":\"Married\"},\"MonthlyIncome\":{\"899\":18711},\"MonthlyRate\":{\"899\":12124},\"NumCompaniesWorked\":{\"899\":2},\"OverTime\":{\"899\":\"No\"},\"PercentSalaryHike\":{\"899\":13},\"PerformanceRating\":{\"899\":3},\"RelationshipSatisfaction\":{\"899\":3},\"StockOptionLevel\":{\"899\":1},\"TotalWorkingYears\":{\"899\":23},\"TrainingTimesLastYear\":{\"899\":2},\"WorkLifeBalance\":{\"899\":4},\"YearsAtCompany\":{\"899\":1},\"YearsInCurrentRole\":{\"899\":0},\"YearsSinceLastPromotion\":{\"899\":0},\"YearsWithCurrManager\":{\"899\":0}}'\n",
+        "\n",
+        "\n",
+        "\n",
+        "headers = {'Content-Type':'application/json'}\n",
+        "\n",
+        "# send request to service\n",
+        "resp = requests.post(service.scoring_uri, sample_data, headers=headers)\n",
+        "\n",
+        "print(\"POST to url\", service.scoring_uri)\n",
+        "# can covert back to Python objects from json string if desired\n",
+        "print(\"prediction:\", resp.text)\n",
+        "result = json.loads(resp.text)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#plot the feature importance for the prediction\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt; plt.rcdefaults()\n",
+        "\n",
+        "labels = json.loads(sample_data)\n",
+        "labels = labels.keys()\n",
+        "objects = labels\n",
+        "y_pos = np.arange(len(objects))\n",
+        "performance = result[\"local_importance_values\"][0][0]\n",
+        "\n",
+        "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
+        "plt.xticks(y_pos, objects)\n",
+        "locs, labels = plt.xticks()\n",
+        "plt.setp(labels, rotation=90)\n",
+        "plt.ylabel('Feature impact - leaving vs not leaving')\n",
+        "plt.title('Local feature importance for prediction')\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "service.delete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next\n",
+        "Learn about other use cases of the explain package on a:\n",
+        "1. [Training time: regression problem](../../tabular-data/explain-binary-classification-local.ipynb)       \n",
+        "1. [Training time: binary classification problem](../../tabular-data/explain-binary-classification-local.ipynb)\n",
+        "1. [Training time: multiclass classification problem](../../tabular-data/explain-multiclass-classification-local.ipynb)\n",
+        "1. Explain models with engineered features:\n",
+        "    1. [Simple feature transformations](../../tabular-data/simple-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Advanced feature transformations](../../tabular-data/advanced-feature-transformations-explain-local.ipynb)\n",
+        "1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n",
+        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n",
+        "1. [Inferencing time: deploy a remotely-trained model and explainer](./train-explain-model-on-amlcompute-and-deploy.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "mesameki"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/explain-model/explain-tabular-data-raw-features/explain-sklearn-raw-features.yml b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml
similarity index 72%
rename from how-to-use-azureml/explain-model/explain-tabular-data-raw-features/explain-sklearn-raw-features.yml
rename to how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml
index 7d213fab..8338f5fe 100644
--- a/how-to-use-azureml/explain-model/explain-tabular-data-raw-features/explain-sklearn-raw-features.yml
+++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml
@@ -1,4 +1,4 @@
-name: explain-sklearn-raw-features
+name: train-explain-model-locally-and-deploy
 dependencies:
 - pip:
   - azureml-sdk
diff --git a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb
new file mode 100644
index 00000000..33e5d191
--- /dev/null
+++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb
@@ -0,0 +1,548 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Train and explain models remotely via Azure Machine Learning Compute and deploy model and scoring explainer\n",
+        "\n",
+        "\n",
+        "_**This notebook illustrates how to use the Azure Machine Learning Interpretability SDK to train and explain a classification model remotely on an Azure Machine Leanrning Compute Target (AMLCompute), and use Azure Container Instances (ACI) for deploying your model and its corresponding scoring explainer as a web service.**_\n",
+        "\n",
+        "Problem: IBM employee attrition classification with scikit-learn (train a model and run an explainer remotely via AMLCompute, and deploy model and its corresponding explainer.)\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Run model explainer locally at training time](#Explain)\n",
+        "    1. Apply feature transformations\n",
+        "    1. Train a binary classification model\n",
+        "    1. Explain the model on raw features\n",
+        "        1. Generate global explanations\n",
+        "        1. Generate local explanations\n",
+        "1. [Visualize results](#Visualize)\n",
+        "1. [Deploy model and scoring explainer](#Deploy)\n",
+        "1. [Next steps](#Next)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "This notebook showcases how to train and explain a classification model remotely via Azure Machine Learning Compute (AMLCompute), download the calculated explanations locally for visualization and inspection, and deploy the final model and its corresponding explainer to Azure Container Instances (ACI).\n",
+        "It demonstrates the API calls that you need to make to submit a run for training and explaining a model to AMLCompute, download the compute explanations remotely, and visualizing the global and local explanations via a visualization dashboard that provides an interactive way of discovering patterns in model predictions and downloaded explanations, and using Azure Machine Learning MLOps capabilities to deploy your model and its corresponding explainer.\n",
+        "\n",
+        "We will showcase one of the tabular data explainers: TabularExplainer (SHAP) and follow these steps:\n",
+        "1.\tDevelop a machine learning script in Python which involves the training script and the explanation script.\n",
+        "2.\tCreate and configure a compute target.\n",
+        "3.\tSubmit the scripts to the configured compute target to run in that environment. During training, the scripts can read from or write to datastore. And the records of execution (e.g., model, metrics, prediction explanations) are saved as runs in the workspace and grouped under experiments.\n",
+        "4.\tQuery the experiment for logged metrics and explanations from the current and past runs. Use the interpretability toolkit\u00e2\u20ac\u2122s visualization dashboard to visualize predictions and their explanation. If the metrics and explanations don't indicate a desired outcome, loop back to step 1 and iterate on your scripts.\n",
+        "5.\tAfter a satisfactory run is found, create a scoring explainer and register the persisted model and its corresponding explainer in the model registry.\n",
+        "6.\tDevelop a scoring script.\n",
+        "7.\tCreate an image and register it in the image registry.\n",
+        "8.\tDeploy the image as a web service in Azure.\n",
+        "\n",
+        "| ![azure-machine-learning-cycle](./img/azure-machine-learning-cycle.PNG) |\n",
+        "|:--:|"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "Make sure you go through the [configuration notebook](../../../../configuration.ipynb) first if you haven't."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize a Workspace\n",
+        "\n",
+        "Initialize a workspace object from persisted configuration"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "create workspace"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Explain\n",
+        "\n",
+        "Create An Experiment: **Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "experiment_name = 'explainer-remote-run-on-amlcompute'\n",
+        "experiment = Experiment(workspace=ws, name=experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction to AmlCompute\n",
+        "\n",
+        "Azure Machine Learning Compute is managed compute infrastructure that allows the user to easily create single to multi-node compute of the appropriate VM Family. It is created **within your workspace region** and is a resource that can be used by other users in your workspace. It autoscales by default to the max_nodes, when a job is submitted, and executes in a containerized environment packaging the dependencies as specified by the user. \n",
+        "\n",
+        "Since it is managed compute, job scheduling and cluster management are handled internally by Azure Machine Learning service. \n",
+        "\n",
+        "For more information on Azure Machine Learning Compute, please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)\n",
+        "\n",
+        "If you are an existing BatchAI customer who is migrating to Azure Machine Learning, please read [this article](https://aka.ms/batchai-retirement)\n",
+        "\n",
+        "**Note**: As with other Azure services, there are limits on certain resources (for eg. AmlCompute quota) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n",
+        "\n",
+        "\n",
+        "The training script `run_explainer.py` is already created for you. Let's have a look."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Submit an AmlCompute run in a few different ways\n",
+        "\n",
+        "First lets check which VM families are available in your region. Azure is a regional service and some specialized SKUs (especially GPUs) are only available in certain regions. Since AmlCompute is created in the region of your workspace, we will use the supported_vms () function to see if the VM family we want to use ('STANDARD_D2_V2') is supported.\n",
+        "\n",
+        "You can also pass a different region to check availability and then re-create your workspace in that region through the [configuration notebook](../../../configuration.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "\n",
+        "AmlCompute.supported_vmsizes(workspace=ws)\n",
+        "# AmlCompute.supported_vmsizes(workspace=ws, location='southcentralus')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create project directory\n",
+        "\n",
+        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import shutil\n",
+        "\n",
+        "project_folder = './explainer-remote-run-on-amlcompute'\n",
+        "os.makedirs(project_folder, exist_ok=True)\n",
+        "shutil.copy('train_explain.py', project_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Provision as a run based compute target\n",
+        "\n",
+        "You can provision AmlCompute as a compute target at run-time. In this case, the compute is auto-created for your run, scales up to max_nodes that you specify, and then **deleted automatically** after the run completes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n",
+        "\n",
+        "# create a new runconfig object\n",
+        "run_config = RunConfiguration()\n",
+        "\n",
+        "# signal that you want to use AmlCompute to execute script.\n",
+        "run_config.target = \"amlcompute\"\n",
+        "\n",
+        "# AmlCompute will be created in the same region as workspace\n",
+        "# Set vm size for AmlCompute\n",
+        "run_config.amlcompute.vm_size = 'STANDARD_D2_V2'\n",
+        "\n",
+        "# enable Docker \n",
+        "run_config.environment.docker.enabled = True\n",
+        "\n",
+        "# set Docker base image to the default CPU-based image\n",
+        "run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n",
+        "\n",
+        "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
+        "run_config.environment.python.user_managed_dependencies = False\n",
+        "\n",
+        "# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
+        "run_config.auto_prepare_environment = True\n",
+        "\n",
+        "azureml_pip_packages = [\n",
+        "    'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
+        "    'azureml-explain-model', 'azureml-dataprep'\n",
+        "]\n",
+        " \n",
+        "\n",
+        "\n",
+        "# specify CondaDependencies obj\n",
+        "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'],\n",
+        "                                pip_packages=['sklearn_pandas', 'pyyaml'] + azureml_pip_packages,\n",
+        "                                pin_sdk_version=False)\n",
+        "# Now submit a run on AmlCompute\n",
+        "from azureml.core.script_run_config import ScriptRunConfig\n",
+        "\n",
+        "script_run_config = ScriptRunConfig(source_directory=project_folder,\n",
+        "                                    script='train_explain.py',\n",
+        "                                    run_config=run_config)\n",
+        "\n",
+        "run = experiment.submit(script_run_config)\n",
+        "\n",
+        "# Show run details\n",
+        "run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "# Shows output of the run on stdout.\n",
+        "run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Delete () is used to deprovision and delete the AmlCompute target. Useful if you want to re-use the compute name \n",
+        "# 'cpucluster' in this case but use a different VM family for instance.\n",
+        "\n",
+        "# cpu_cluster.delete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Download Model Explanation, Model, and Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# retrieve model for visualization and deployment\n",
+        "from azureml.core.model import Model\n",
+        "from sklearn.externals import joblib\n",
+        "original_model = Model(ws, 'amlcompute_deploy_model')\n",
+        "model_path = original_model.download(exist_ok=True)\n",
+        "original_svm_model = joblib.load(model_path)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# retrieve global explanation for visualization\n",
+        "from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
+        "\n",
+        "# get model explanation data\n",
+        "client = ExplanationClient.from_run(run)\n",
+        "global_explanation = client.download_model_explanation()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# retrieve x_test for visualization\n",
+        "from sklearn.externals import joblib\n",
+        "x_test_path = './x_test.pkl'\n",
+        "run.download_file('x_test_ibm.pkl', output_file_path=x_test_path)\n",
+        "x_test = joblib.load(x_test_path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualize\n",
+        "Visualize the explanations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ExplanationDashboard(global_explanation, original_svm_model, x_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Deploy\n",
+        "Deploy Model and ScoringExplainer"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice\n",
+        "\n",
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
+        "                                               memory_gb=1, \n",
+        "                                               tags={\"data\": \"IBM_Attrition\",  \n",
+        "                                                     \"method\" : \"local_explanation\"}, \n",
+        "                                               description='Get local explanations for IBM Employee Attrition data')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.conda_dependencies import CondaDependencies \n",
+        "\n",
+        "# WARNING: to install this, g++ needs to be available on the Docker image and is not by default (look at the next cell)\n",
+        "azureml_pip_packages = [\n",
+        "    'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
+        "    'azureml-explain-model'\n",
+        "]\n",
+        " \n",
+        "\n",
+        "# specify CondaDependencies obj\n",
+        "myenv = CondaDependencies.create(conda_packages=['scikit-learn', 'pandas'],\n",
+        "                                 pip_packages=['sklearn-pandas', 'pyyaml'] + azureml_pip_packages,\n",
+        "                                 pin_sdk_version=False)\n",
+        "\n",
+        "with open(\"myenv.yml\",\"w\") as f:\n",
+        "    f.write(myenv.serialize_to_string())\n",
+        "\n",
+        "with open(\"myenv.yml\",\"r\") as f:\n",
+        "    print(f.read())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile dockerfile\n",
+        "RUN apt-get update && apt-get install -y g++ "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# retrieve scoring explainer for deployment\n",
+        "scoring_explainer_model = Model(ws, 'IBM_attrition_explainer')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import Webservice\n",
+        "from azureml.core.image import ContainerImage\n",
+        "\n",
+        "# Use the custom scoring, docker, and conda files we created above\n",
+        "image_config = ContainerImage.image_configuration(execution_script=\"score_remote_explain.py\",\n",
+        "                                                  docker_file=\"dockerfile\", \n",
+        "                                                  runtime=\"python\", \n",
+        "                                                  conda_file=\"myenv.yml\")\n",
+        "\n",
+        "# Use configs and models generated above\n",
+        "service = Webservice.deploy_from_model(workspace=ws,\n",
+        "                                       name='model-scoring-service',\n",
+        "                                       deployment_config=aciconfig,\n",
+        "                                       models=[scoring_explainer_model, original_model],\n",
+        "                                       image_config=image_config)\n",
+        "\n",
+        "service.wait_for_deployment(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import requests\n",
+        "\n",
+        "# create data to test service with\n",
+        "examples = x_test[:4]\n",
+        "input_data = examples.to_json()\n",
+        "\n",
+        "headers = {'Content-Type':'application/json'}\n",
+        "\n",
+        "# send request to service\n",
+        "resp = requests.post(service.scoring_uri, input_data, headers=headers)\n",
+        "\n",
+        "print(\"POST to url\", service.scoring_uri)\n",
+        "# can covert back to Python objects from json string if desired\n",
+        "print(\"prediction:\", resp.text)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "service.delete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next\n",
+        "Learn about other use cases of the explain package on a:\n",
+        "1. [Training time: regression problem](../../tabular-data/explain-binary-classification-local.ipynb)       \n",
+        "1. [Training time: binary classification problem](../../tabular-data/explain-binary-classification-local.ipynb)\n",
+        "1. [Training time: multiclass classification problem](../../tabular-data/explain-multiclass-classification-local.ipynb)\n",
+        "1. Explain models with engineered features:\n",
+        "    1. [Simple feature transformations](../../tabular-data/simple-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Advanced feature transformations](../../tabular-data/advanced-feature-transformations-explain-local.ipynb)\n",
+        "1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n",
+        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n",
+        "1. [Inferencing time: deploy a locally-trained model and explainer](./train-explain-model-locally-and-deploy.ipynb)\n",
+        "   "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "mesameki"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml
new file mode 100644
index 00000000..5657cbe3
--- /dev/null
+++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml
@@ -0,0 +1,8 @@
+name: train-explain-model-on-amlcompute-and-deploy
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-explain-model
+  - azureml-contrib-explain-model
+  - sklearn-pandas
+  - azureml-dataprep
diff --git a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train_explain.py b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train_explain.py
new file mode 100644
index 00000000..b8fb1bd8
--- /dev/null
+++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train_explain.py
@@ -0,0 +1,129 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import os
+import pandas as pd
+import zipfile
+from sklearn.model_selection import train_test_split
+from sklearn.externals import joblib
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.impute import SimpleImputer
+from sklearn.pipeline import Pipeline
+from sklearn.linear_model import LogisticRegression
+from sklearn_pandas import DataFrameMapper
+
+from azureml.core.run import Run
+from azureml.explain.model.tabular_explainer import TabularExplainer
+from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient
+from azureml.explain.model.scoring.scoring_explainer import LinearScoringExplainer, save
+
+OUTPUT_DIR = './outputs/'
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# get the IBM employee attrition dataset
+outdirname = 'dataset.6.21.19'
+try:
+    from urllib import urlretrieve
+except ImportError:
+    from urllib.request import urlretrieve
+zipfilename = outdirname + '.zip'
+urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)
+with zipfile.ZipFile(zipfilename, 'r') as unzip:
+    unzip.extractall('.')
+attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')
+
+# dropping Employee count as all values are 1 and hence attrition is independent of this feature
+attritionData = attritionData.drop(['EmployeeCount'], axis=1)
+# dropping Employee Number since it is merely an identifier
+attritionData = attritionData.drop(['EmployeeNumber'], axis=1)
+attritionData = attritionData.drop(['Over18'], axis=1)
+# since all values are 80
+attritionData = attritionData.drop(['StandardHours'], axis=1)
+
+# converting target variables from string to numerical values
+target_map = {'Yes': 1, 'No': 0}
+attritionData["Attrition_numerical"] = attritionData["Attrition"].apply(lambda x: target_map[x])
+target = attritionData["Attrition_numerical"]
+
+attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)
+
+# creating dummy columns for each categorical feature
+categorical = []
+for col, value in attritionXData.iteritems():
+    if value.dtype == 'object':
+        categorical.append(col)
+
+# store the numerical columns
+numerical = attritionXData.columns.difference(categorical)
+
+numeric_transformations = [([f], Pipeline(steps=[
+    ('imputer', SimpleImputer(strategy='median')),
+    ('scaler', StandardScaler())])) for f in numerical]
+
+categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]
+
+transformations = numeric_transformations + categorical_transformations
+
+# append classifier to preprocessing pipeline
+clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),
+                      ('classifier', LogisticRegression(solver='lbfgs'))])
+
+# get the run this was submitted from to interact with run history
+run = Run.get_context()
+
+# create an explanation client to store the explanation (contrib API)
+client = ExplanationClient.from_run(run)
+
+# Split data into train and test
+x_train, x_test, y_train, y_test = train_test_split(attritionXData,
+                                                    target,
+                                                    test_size=0.2,
+                                                    random_state=0,
+                                                    stratify=target)
+
+# write x_test out as a pickle file for later visualization
+x_test_pkl = 'x_test.pkl'
+with open(x_test_pkl, 'wb') as file:
+    joblib.dump(value=x_test, filename=os.path.join(OUTPUT_DIR, x_test_pkl))
+run.upload_file('x_test_ibm.pkl', os.path.join(OUTPUT_DIR, x_test_pkl))
+
+# preprocess the data and fit the classification model
+clf.fit(x_train, y_train)
+model = clf.steps[-1][1]
+
+# save model for use outside the script
+model_file_name = 'log_reg.pkl'
+with open(model_file_name, 'wb') as file:
+    joblib.dump(value=clf, filename=os.path.join(OUTPUT_DIR, model_file_name))
+
+# register the model with the model management service for later use
+run.upload_file('original_model.pkl', os.path.join(OUTPUT_DIR, model_file_name))
+original_model = run.register_model(model_name='amlcompute_deploy_model',
+                                    model_path='original_model.pkl')
+
+# create an explainer to validate or debug the model
+tabular_explainer = TabularExplainer(model,
+                                     initialization_examples=x_train,
+                                     features=attritionXData.columns,
+                                     classes=["Not leaving", "leaving"],
+                                     transformations=transformations)
+
+# explain overall model predictions (global explanation)
+# passing in test dataset for evaluation examples - note it must be a representative sample of the original data
+# more data (e.g. x_train) will likely lead to higher accuracy, but at a time cost
+global_explanation = tabular_explainer.explain_global(x_test)
+
+# uploading model explanation data for storage or visualization
+comment = 'Global explanation on classification model trained on IBM employee attrition dataset'
+client.upload_model_explanation(global_explanation, comment=comment)
+
+# also create a lightweight explainer for scoring time
+scoring_explainer = LinearScoringExplainer(tabular_explainer)
+# pickle scoring explainer locally
+save(scoring_explainer, directory=OUTPUT_DIR, exist_ok=True)
+
+# register scoring explainer
+run.upload_file('IBM_attrition_explainer.pkl', os.path.join(OUTPUT_DIR, 'scoring_explainer.pkl'))
+scoring_explainer_model = run.register_model(model_name='IBM_attrition_explainer',
+                                             model_path='IBM_attrition_explainer.pkl')
diff --git a/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-binary-classification.ipynb b/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-binary-classification.ipynb
deleted file mode 100644
index 2c57d6a6..00000000
--- a/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-binary-classification.ipynb
+++ /dev/null
@@ -1,279 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Breast cancer diagnosis classification with scikit-learn (run model explainer locally)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-binary-classification.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Explain a model with the AML explain-model package\n",
-        "\n",
-        "1. Train a SVM classification model using Scikit-learn\n",
-        "2. Run 'explain_model' with full data in local mode, which doesn't contact any Azure services\n",
-        "3. Run 'explain_model' with summarized data in local mode, which doesn't contact any Azure services\n",
-        "4. Visualize the global and local explanations with the visualization dashboard."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.datasets import load_breast_cancer\n",
-        "from sklearn import svm\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 1. Run model explainer locally with full data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load the breast cancer diagnosis data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "breast_cancer_data = load_breast_cancer()\n",
-        "classes = breast_cancer_data.target_names.tolist()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train a SVM classification model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tabular_explainer = TabularExplainer(model, x_train, features=breast_cancer_data.feature_names, classes=classes)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = tabular_explainer.explain_global(x_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Sorted SHAP values\n",
-        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
-        "# Corresponding feature names\n",
-        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
-        "# feature ranks (based on original order of features)\n",
-        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
-        "# per class feature names\n",
-        "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
-        "# per class feature importance values\n",
-        "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dict(zip(global_explanation.get_ranked_global_names(), global_explanation.get_ranked_global_values()))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions as a collection of local (instance-level) explanations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# feature shap values for all features and all data points in the training data\n",
-        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# explain the first member of the test set\n",
-        "instance_num = 0\n",
-        "local_explanation = tabular_explainer.explain_local(x_test[instance_num,:])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# get the prediction for the first member of the test set and explain why model made that prediction\n",
-        "prediction_value = clf.predict(x_test)[instance_num]\n",
-        "\n",
-        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
-        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
-        "\n",
-        "\n",
-        "dict(zip(sorted_local_importance_names, sorted_local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 2. Load visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note you will need to have extensions enabled prior to jupyter kernel starting\n",
-        "!jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
-        "!jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
-        "# Or, in Jupyter Labs, uncomment below\n",
-        "# jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "# jupyter labextension install microsoft-mli-widget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "mesameki"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-multiclass-classification.ipynb b/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-multiclass-classification.ipynb
deleted file mode 100644
index 0f8dd7ce..00000000
--- a/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-multiclass-classification.ipynb
+++ /dev/null
@@ -1,280 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Iris flower classification with scikit-learn (run model explainer locally)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-multiclass-classification.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Explain a model with the AML explain-model package\n",
-        "\n",
-        "1. Train a SVM classification model using Scikit-learn\n",
-        "2. Run 'explain_model' with full data in local mode, which doesn't contact any Azure services\n",
-        "3. Run 'explain_model' with summarized data in local mode, which doesn't contact any Azure services\n",
-        "4. Visualize the global and local explanations with the visualization dashboard."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.datasets import load_iris\n",
-        "from sklearn import svm\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 1. Run model explainer locally with full data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load the breast cancer diagnosis data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "iris = load_iris()\n",
-        "X = iris['data']\n",
-        "y = iris['target']\n",
-        "classes = iris['target_names']\n",
-        "feature_names = iris['feature_names']"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train a SVM classification model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tabular_explainer = TabularExplainer(model, x_train, features = feature_names, classes=classes)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "global_explanation = tabular_explainer.explain_global(x_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Sorted SHAP values\n",
-        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
-        "# Corresponding feature names\n",
-        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
-        "# feature ranks (based on original order of features)\n",
-        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
-        "# per class feature names\n",
-        "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
-        "# per class feature importance values\n",
-        "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dict(zip(global_explanation.get_ranked_global_names(), global_explanation.get_ranked_global_values()))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions as a collection of local (instance-level) explanations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# feature shap values for all features and all data points in the training data\n",
-        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# explain the first member of the test set\n",
-        "instance_num = 0\n",
-        "local_explanation = tabular_explainer.explain_local(x_test[instance_num,:])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# get the prediction for the first member of the test set and explain why model made that prediction\n",
-        "prediction_value = clf.predict(x_test)[instance_num]\n",
-        "\n",
-        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
-        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
-        "\n",
-        "\n",
-        "dict(zip(sorted_local_importance_names, sorted_local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note you will need to have extensions enabled prior to jupyter kernel starting\n",
-        "!jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
-        "!jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
-        "# Or, in Jupyter Labs, uncomment below\n",
-        "# jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "# jupyter labextension install microsoft-mli-widget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "mesameki"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-multiclass-classification.yml b/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-multiclass-classification.yml
deleted file mode 100644
index 51cc039c..00000000
--- a/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-multiclass-classification.yml
+++ /dev/null
@@ -1,6 +0,0 @@
-name: explain-local-sklearn-multiclass-classification
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-explain-model
-  - azureml-contrib-explain-model
diff --git a/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-regression.ipynb b/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-regression.ipynb
deleted file mode 100644
index 926144fe..00000000
--- a/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-regression.ipynb
+++ /dev/null
@@ -1,272 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Boston Housing Price Prediction with scikit-learn (run model explainer locally)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-regression.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Explain a model with the AML explain-model package\n",
-        "\n",
-        "1. Train a GradientBoosting regression model using Scikit-learn\n",
-        "2. Run 'explain_model' with full dataset in local mode, which doesn't contact any Azure services.\n",
-        "3. Run 'explain_model' with summarized dataset in local mode, which doesn't contact any Azure services.\n",
-        "4. Visualize the global and local explanations with the visualization dashboard."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn import datasets\n",
-        "from sklearn.ensemble import GradientBoostingRegressor\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 1. Run model explainer locally with full data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load the Boston house price data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "boston_data = datasets.load_boston()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(boston_data.data, boston_data.target, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train a GradientBoosting Regression model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "reg = GradientBoostingRegressor(n_estimators=100, max_depth=4,\n",
-        "                                learning_rate=0.1, loss='huber',\n",
-        "                                random_state=1)\n",
-        "model = reg.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tabular_explainer = TabularExplainer(model, x_train, features = boston_data.feature_names)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = tabular_explainer.explain_global(x_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Sorted SHAP values \n",
-        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
-        "# Corresponding feature names\n",
-        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
-        "# feature ranks (based on original order of features)\n",
-        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dict(zip(global_explanation.get_ranked_global_names(), global_explanation.get_ranked_global_values()))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions as a collection of local (instance-level) explanations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# feature shap values for all features and all data points in the training data\n",
-        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_explanation = tabular_explainer.explain_local(x_test[0,:])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# sorted local feature importance information; reflects the original feature order\n",
-        "sorted_local_importance_names = local_explanation.get_ranked_local_names()\n",
-        "sorted_local_importance_values = local_explanation.get_ranked_local_values()\n",
-        "\n",
-        "print('sorted local importance names: {}'.format(sorted_local_importance_names))\n",
-        "print('sorted local importance values: {}'.format(sorted_local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note you will need to have extensions enabled prior to jupyter kernel starting\n",
-        "!jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
-        "!jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
-        "# Or, in Jupyter Labs, uncomment below\n",
-        "# jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "# jupyter labextension install microsoft-mli-widget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "mesameki"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/how-to-use-azureml/explain-model/explain-tabular-data-raw-features/explain-sklearn-raw-features.ipynb b/how-to-use-azureml/explain-model/explain-tabular-data-raw-features/explain-sklearn-raw-features.ipynb
deleted file mode 100644
index f55dbb9f..00000000
--- a/how-to-use-azureml/explain-model/explain-tabular-data-raw-features/explain-sklearn-raw-features.ipynb
+++ /dev/null
@@ -1,337 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Summary\n",
-        "From raw data that is a mixture of categoricals and numeric, featurize the categoricals using one hot encoding. Use tabular explainer to get explain object and then get raw feature importances"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-raw-features/explain-sklearn-raw-features.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Explain a model with the AML explain-model package on raw features\n",
-        "\n",
-        "1. Train a Logistic Regression model using Scikit-learn\n",
-        "2. Run 'explain_model' with full dataset in local mode, which doesn't contact any Azure services.\n",
-        "3. Run 'explain_model' with summarized dataset in local mode, which doesn't contact any Azure services.\n",
-        "4. Visualize the global and local explanations with the visualization dashboard."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.pipeline import Pipeline\n",
-        "from sklearn.impute import SimpleImputer\n",
-        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
-        "from sklearn.linear_model import LogisticRegression\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
-        "import pandas as pd\n",
-        "import numpy as np"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "titanic_url = ('https://raw.githubusercontent.com/amueller/'\n",
-        "               'scipy-2017-sklearn/091d371/notebooks/datasets/titanic3.csv')\n",
-        "data = pd.read_csv(titanic_url)\n",
-        "# fill missing values\n",
-        "data = data.fillna(method=\"ffill\")\n",
-        "data = data.fillna(method=\"bfill\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 1. Run model explainer locally with full data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Similar to example [here](https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py), use a subset of columns"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.model_selection import train_test_split\n",
-        "\n",
-        "numeric_features = ['age', 'fare']\n",
-        "categorical_features = ['embarked', 'sex', 'pclass']\n",
-        "\n",
-        "y = data['survived'].values\n",
-        "X = data[categorical_features + numeric_features]\n",
-        "\n",
-        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "sklearn imports"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.pipeline import Pipeline\n",
-        "from sklearn.impute import SimpleImputer\n",
-        "from sklearn.preprocessing import StandardScaler, OneHotEncoder"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.compose import ColumnTransformer\n",
-        "\n",
-        "transformations = ColumnTransformer([\n",
-        "    (\"age_fare\", Pipeline(steps=[\n",
-        "        ('imputer', SimpleImputer(strategy='median')),\n",
-        "        ('scaler', StandardScaler())\n",
-        "    ]), [\"age\", \"fare\"]),\n",
-        "    (\"embarked\", Pipeline(steps=[\n",
-        "        (\"imputer\", SimpleImputer(strategy='constant', fill_value='missing')), \n",
-        "        (\"encoder\", OneHotEncoder(sparse=False))]), [\"embarked\"]),\n",
-        "    (\"sex_pclass\", OneHotEncoder(sparse=False), [\"sex\", \"pclass\"])    \n",
-        "])\n",
-        "\n",
-        "\n",
-        "# Append classifier to preprocessing pipeline.\n",
-        "# Now we have a full prediction pipeline.\n",
-        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
-        "                      ('classifier', LogisticRegression(solver='lbfgs'))])\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "'''\n",
-        "# Uncomment below if sklearn-pandas is not installed\n",
-        "#!pip install sklearn-pandas\n",
-        "from sklearn_pandas import DataFrameMapper\n",
-        "\n",
-        "# Impute, standardize the numeric features and one-hot encode the categorical features.    \n",
-        "\n",
-        "transformations = [\n",
-        "    ([\"age\", \"fare\"], Pipeline(steps=[\n",
-        "        ('imputer', SimpleImputer(strategy='median')),\n",
-        "        ('scaler', StandardScaler())\n",
-        "    ])),\n",
-        "    ([\"embarked\"], Pipeline(steps=[\n",
-        "        (\"imputer\", SimpleImputer(strategy='constant', fill_value='missing')), \n",
-        "        (\"encoder\", OneHotEncoder(sparse=False))])),\n",
-        "    ([\"sex\", \"pclass\"], OneHotEncoder(sparse=False))    \n",
-        "]\n",
-        "\n",
-        "\n",
-        "# Append classifier to preprocessing pipeline.\n",
-        "# Now we have a full prediction pipeline.\n",
-        "clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),\n",
-        "                      ('classifier', LogisticRegression(solver='lbfgs'))])\n",
-        "'''"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train a Logistic Regression model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tabular_explainer = TabularExplainer(clf.steps[-1][1], initialization_examples=x_train, features=x_train.columns, transformations=transformations)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = tabular_explainer.explain_global(x_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "sorted_global_importance_values = global_explanation.get_ranked_global_values()\n",
-        "sorted_global_importance_names = global_explanation.get_ranked_global_names()\n",
-        "dict(zip(sorted_global_importance_names, sorted_global_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions as a collection of local (instance-level) explanations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# explain the first member of the test set\n",
-        "local_explanation = tabular_explainer.explain_local(x_test[:1])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# get the prediction for the first member of the test set and explain why model made that prediction\n",
-        "prediction_value = clf.predict(x_test)[0]\n",
-        "\n",
-        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
-        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
-        "\n",
-        "# Sorted local SHAP values\n",
-        "print('ranked local importance values: {}'.format(sorted_local_importance_values))\n",
-        "# Corresponding feature names\n",
-        "print('ranked local importance names: {}'.format(sorted_local_importance_names))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 2. Load visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note you will need to have extensions enabled prior to jupyter kernel starting\n",
-        "!jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
-        "!jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
-        "# Or, in Jupyter Labs, uncomment below\n",
-        "# jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "# jupyter labextension install microsoft-mli-widget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "mesameki"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-classification.ipynb b/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-classification.ipynb
deleted file mode 100644
index 0ac1032a..00000000
--- a/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-classification.ipynb
+++ /dev/null
@@ -1,262 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Breast cancer diagnosis classification with scikit-learn (save model explanations via AML Run History)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-classification.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Explain a model with the AML explain-model package\n",
-        "\n",
-        "1. Train a SVM classification model using Scikit-learn\n",
-        "2. Run 'explain_model' with AML Run History, which leverages run history service to store and manage the explanation data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.datasets import load_breast_cancer\n",
-        "from sklearn import svm\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 1. Run model explainer locally with full data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load the breast cancer diagnosis data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "breast_cancer_data = load_breast_cancer()\n",
-        "classes = breast_cancer_data.target_names.tolist()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train a SVM classification model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tabular_explainer = TabularExplainer(model, x_train, features=breast_cancer_data.feature_names, classes=classes)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = tabular_explainer.explain_global(x_test)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 2. Save Model Explanation With AML Run History"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "from azureml.core import Workspace, Experiment, Run\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
-        "from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
-        "# Check core SDK version number\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "experiment_name = 'explain_model'\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "run = experiment.start_logging()\n",
-        "client = ExplanationClient.from_run(run)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Uploading model explanation data for storage or visualization in webUX\n",
-        "# The explanation can then be downloaded on any compute\n",
-        "client.upload_model_explanation(global_explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get model explanation data\n",
-        "explanation = client.download_model_explanation()\n",
-        "local_importance_values = explanation.local_importance_values\n",
-        "expected_values = explanation.expected_values"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get the top k (e.g., 4) most important features with their importance values\n",
-        "explanation = client.download_model_explanation(top_k=4)\n",
-        "global_importance_values = explanation.get_ranked_global_values()\n",
-        "global_importance_names = explanation.get_ranked_global_names()\n",
-        "per_class_names = explanation.get_ranked_per_class_names()[0]\n",
-        "per_class_values = explanation.get_ranked_per_class_values()[0]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print('per class feature importance values: {}'.format(per_class_values))\n",
-        "print('per class feature importance names: {}'.format(per_class_names))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dict(zip(per_class_names, per_class_values))"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "mesameki"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-regression.ipynb b/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-regression.ipynb
deleted file mode 100644
index 1fbdb4ea..00000000
--- a/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-regression.ipynb
+++ /dev/null
@@ -1,276 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Boston Housing Price Prediction with scikit-learn (save model explanations via AML Run History)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-regression.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Explain a model with the AML explain-model package\n",
-        "\n",
-        "1. Train a GradientBoosting regression model using Scikit-learn\n",
-        "2. Run 'explain_model' with AML Run History, which leverages run history service to store and manage the explanation data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Save Model Explanation With AML Run History"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Import Iris dataset\n",
-        "from sklearn import datasets\n",
-        "from sklearn.ensemble import GradientBoostingRegressor\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core import Workspace, Experiment, Run\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
-        "from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
-        "# Check core SDK version number\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "experiment_name = 'explain_model'\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "run = experiment.start_logging()\n",
-        "client = ExplanationClient.from_run(run)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load the Boston house price data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "boston_data = datasets.load_boston()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(boston_data.data, boston_data.target, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train a GradientBoosting Regression model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "clf = GradientBoostingRegressor(n_estimators=100, max_depth=4,\n",
-        "                                learning_rate=0.1, loss='huber',\n",
-        "                                random_state=1)\n",
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tabular_explainer = TabularExplainer(model, x_train, features=boston_data.feature_names)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = tabular_explainer.explain_global(x_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Uploading model explanation data for storage or visualization in webUX\n",
-        "# The explanation can then be downloaded on any compute\n",
-        "client.upload_model_explanation(global_explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get model explanation data\n",
-        "explanation = client.download_model_explanation()\n",
-        "local_importance_values = explanation.local_importance_values\n",
-        "expected_values = explanation.expected_values"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Print the values\n",
-        "print('expected values: {}'.format(expected_values))\n",
-        "print('local importance values: {}'.format(local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get the top k (e.g., 4) most important features with their importance values\n",
-        "explanation = client.download_model_explanation(top_k=4)\n",
-        "global_importance_values = explanation.get_ranked_global_values()\n",
-        "global_importance_names = explanation.get_ranked_global_names()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print('global importance values: {}'.format(global_importance_values))\n",
-        "print('global importance names: {}'.format(global_importance_names))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain individual instance predictions (local explanation) ##### needs to get updated with the new build"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_explanation = tabular_explainer.explain_local(x_test[0,:])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# local feature importance information\n",
-        "local_importance_values = local_explanation.local_importance_values\n",
-        "print('local importance values: {}'.format(local_importance_values))"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "mesameki"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/how-to-use-azureml/explain-model/tabular-data/advanced-feature-transformations-explain-local.ipynb b/how-to-use-azureml/explain-model/tabular-data/advanced-feature-transformations-explain-local.ipynb
new file mode 100644
index 00000000..32364bdb
--- /dev/null
+++ b/how-to-use-azureml/explain-model/tabular-data/advanced-feature-transformations-explain-local.ipynb
@@ -0,0 +1,509 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/advanced-feature-transformations-explain-local.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Explain binary classification model predictions with raw feature transformations\n",
+        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to explain and visualize a binary classification model that uses advanced many to one or many to many feature transformations.**_\n",
+        "\n",
+        "\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Run model explainer locally at training time](#Explain)\n",
+        "    1. Apply feature transformations\n",
+        "    1. Train a binary classification model\n",
+        "    1. Explain the model on raw features\n",
+        "        1. Generate global explanations\n",
+        "        1. Generate local explanations\n",
+        "1. [Visualize results](#Visualize)\n",
+        "1. [Next steps](#Next)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "This notebook illustrates creating explanations for a binary classification model, Titanic passenger data classification, that uses many to one and many to many feature transformations from raw data to engineered features. For the many to one transformation, we sum 2 features `age` and `fare`. For many to many transformations two features are computed: one that is product of `age` and `fare` and another that is square of this product. Our tabular data explainer is then used to get the explanation object with the flag `allow_all_transformations` passed. The object is then used to get raw feature importances.\n",
+        "\n",
+        "\n",
+        "We will showcase raw feature transformations with three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
+        "\n",
+        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.PNG) |\n",
+        "|:--:|\n",
+        "| *Interpretability Toolkit Architecture* |\n",
+        "\n",
+        "Problem: Titanic passenger data classification with scikit-learn (run model explainer locally)\n",
+        "\n",
+        "1. Transform raw features to engineered features\n",
+        "2. Train a Logistic Regression model using Scikit-learn\n",
+        "3. Run 'explain_model' globally and locally with full dataset in local mode, which doesn't contact any Azure services.\n",
+        "4. Visualize the global and local explanations with the visualization dashboard.\n",
+        "---\n",
+        "\n",
+        "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
+        "If you are using Jupyter Labs run the following command:\n",
+        "```\n",
+        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
+        "```\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Explain\n",
+        "\n",
+        "### Run model explainer locally at training time"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# Explainers:\n",
+        "# 1. SHAP Tabular Explainer\n",
+        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
+        "\n",
+        "# OR\n",
+        "\n",
+        "# 2. Mimic Explainer\n",
+        "from azureml.explain.model.mimic.mimic_explainer import MimicExplainer\n",
+        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
+        "from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
+        "from azureml.explain.model.mimic.models.linear_model import LinearExplainableModel\n",
+        "from azureml.explain.model.mimic.models.linear_model import SGDExplainableModel\n",
+        "from azureml.explain.model.mimic.models.tree_model import DecisionTreeExplainableModel\n",
+        "\n",
+        "# OR\n",
+        "\n",
+        "# 3. PFI Explainer\n",
+        "from azureml.explain.model.permutation.permutation_importance import PFIExplainer "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load the Titanic passenger data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "titanic_url = ('https://raw.githubusercontent.com/amueller/'\n",
+        "               'scipy-2017-sklearn/091d371/notebooks/datasets/titanic3.csv')\n",
+        "data = pd.read_csv(titanic_url)\n",
+        "# fill missing values\n",
+        "data = data.fillna(method=\"ffill\")\n",
+        "data = data.fillna(method=\"bfill\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Similar to example [here](https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py), use a subset of columns"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "numeric_features = ['age', 'fare']\n",
+        "categorical_features = ['embarked', 'sex', 'pclass']\n",
+        "\n",
+        "y = data['survived'].values\n",
+        "X = data[categorical_features + numeric_features]\n",
+        "\n",
+        "# Split data into train and test\n",
+        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Transform raw features"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# We add many to one and many to many transformations for illustration purposes.\n",
+        "# The support for raw feature explanations with many to one and many to many transformations are only supported \n",
+        "# When allow_all_transformations is set to True on explainer creation\n",
+        "from sklearn.preprocessing import FunctionTransformer\n",
+        "many_to_one_transformer = FunctionTransformer(lambda x: x.sum(axis=1).reshape(-1, 1))\n",
+        "many_to_many_transformer = FunctionTransformer(lambda x: np.hstack(\n",
+        "    (np.prod(x, axis=1).reshape(-1, 1), (np.prod(x, axis=1)**2).reshape(-1, 1))\n",
+        "))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.compose import ColumnTransformer\n",
+        "\n",
+        "transformations = ColumnTransformer([\n",
+        "    (\"age_fare_1\", Pipeline(steps=[\n",
+        "        ('imputer', SimpleImputer(strategy='median')),\n",
+        "        ('scaler', StandardScaler())\n",
+        "    ]), [\"age\", \"fare\"]),\n",
+        "    (\"age_fare_2\", many_to_one_transformer, [\"age\", \"fare\"]),\n",
+        "    (\"age_fare_3\", many_to_many_transformer, [\"age\", \"fare\"]),\n",
+        "    (\"embarked\", Pipeline(steps=[\n",
+        "        (\"imputer\", SimpleImputer(strategy='constant', fill_value='missing')), \n",
+        "        (\"encoder\", OneHotEncoder(sparse=False))]), [\"embarked\"]),\n",
+        "    (\"sex_pclass\", OneHotEncoder(sparse=False), [\"sex\", \"pclass\"])    \n",
+        "])\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "'''\n",
+        "# Uncomment below if sklearn-pandas is not installed\n",
+        "#!pip install sklearn-pandas\n",
+        "from sklearn_pandas import DataFrameMapper\n",
+        "\n",
+        "# Impute, standardize the numeric features and one-hot encode the categorical features.    \n",
+        "\n",
+        "transformations = [\n",
+        "    ([\"age\", \"fare\"], Pipeline(steps=[\n",
+        "        ('imputer', SimpleImputer(strategy='median')),\n",
+        "        ('scaler', StandardScaler())\n",
+        "    ])),\n",
+        "    ([\"age\", \"fare\"], many_to_one_transformer),\n",
+        "    ([\"age\", \"fare\"], many_to_many_transformer),\n",
+        "    ([\"embarked\"], Pipeline(steps=[\n",
+        "        (\"imputer\", SimpleImputer(strategy='constant', fill_value='missing')), \n",
+        "        (\"encoder\", OneHotEncoder(sparse=False))])),\n",
+        "    ([\"sex\", \"pclass\"], OneHotEncoder(sparse=False))    \n",
+        "]\n",
+        "\n",
+        "\n",
+        "# Append classifier to preprocessing pipeline.\n",
+        "# Now we have a full prediction pipeline.\n",
+        "clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),\n",
+        "                      ('classifier', LogisticRegression(solver='lbfgs'))])\n",
+        "'''"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Train a Logistic Regression model, which you want to explain"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Append classifier to preprocessing pipeline.\n",
+        "# Now we have a full prediction pipeline.\n",
+        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
+        "                      ('classifier', LogisticRegression(solver='lbfgs'))])\n",
+        "model = clf.fit(x_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Explain predictions on your local machine"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 1. Using SHAP TabularExplainer\n",
+        "# When the last parameter allow_all_transformations is passed, we handle many to one and many to many transformations to \n",
+        "# generate approximations to raw feature importances. When this flag is passed, for transformations not recognized as one to \n",
+        "# many, we distribute feature importances evenly to raw features generating them.\n",
+        "# clf.steps[-1][1] returns the trained classification model\n",
+        "explainer = TabularExplainer(clf.steps[-1][1], \n",
+        "                             initialization_examples=x_train, \n",
+        "                             features=x_train.columns, \n",
+        "                             transformations=transformations, \n",
+        "                             allow_all_transformations=True)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# 2. Using MimicExplainer\n",
+        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
+        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
+        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
+        "# explainer = MimicExplainer(clf.steps[-1][1], \n",
+        "#                            x_train, \n",
+        "#                            LGBMExplainableModel, \n",
+        "#                            augment_data=True, \n",
+        "#                            max_num_of_augmentations=10, \n",
+        "#                            features=x_train.columns, \n",
+        "#                            transformations=transformations, \n",
+        "#                            allow_all_transformations=True)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# 3. Using PFIExplainer\n",
+        "\n",
+        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
+        "# Note that if a metric function is provided a higher value must be better.\n",
+        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
+        "# Default metrics: \n",
+        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
+        "# Mean absolute error for regression\n",
+        "\n",
+        "\n",
+        "# explainer = PFIExplainer(clf.steps[-1][1], \n",
+        "#                          features=x_train.columns, \n",
+        "#                          transformations=transformations)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate global explanations\n",
+        "Explain overall model predictions (global explanation)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
+        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
+        "\n",
+        "global_explanation = explainer.explain_global(x_test)\n",
+        "\n",
+        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
+        "# global_explanation = explainer.explain_global(x_test, true_labels=y_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Sorted SHAP values\n",
+        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
+        "# Corresponding feature names\n",
+        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
+        "# Feature ranks (based on original order of features)\n",
+        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
+        "# Per class feature names\n",
+        "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
+        "# Per class feature importance values\n",
+        "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Print out a dictionary that holds the sorted feature importance names and values\n",
+        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Explain overall model predictions as a collection of local (instance-level) explanations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# feature shap values for all features and all data points in the training data\n",
+        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate local explanations\n",
+        "Explain local data points (individual instances)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Note: PFIExplainer does not support local explanations\n",
+        "# You can pass a specific data point or a group of data points to the explain_local function\n",
+        "\n",
+        "# E.g., Explain the first data point in the test set\n",
+        "instance_num = 1\n",
+        "local_explanation = explainer.explain_local(x_test[:instance_num])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get the prediction for the first member of the test set and explain why model made that prediction\n",
+        "prediction_value = clf.predict(x_test)[instance_num]\n",
+        "\n",
+        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
+        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
+        "\n",
+        "print('local importance values: {}'.format(sorted_local_importance_values))\n",
+        "print('local importance names: {}'.format(sorted_local_importance_names))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualize\n",
+        "Load the visualization dashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ExplanationDashboard(global_explanation, model, x_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next\n",
+        "Learn about other use cases of the explain package on a:\n",
+        "       \n",
+        "1. [Training time: regression problem](./explain-regression-local.ipynb)\n",
+        "1. [Training time: binary classification problem](./explain-binary-classification-local.ipynb)\n",
+        "1. [Training time: multiclass classification problem](./explain-multiclass-classification-local.ipynb)\n",
+        "1. [Explain models with simple feature transformations](./simple-feature-transformations-explain-local.ipynb)\n",
+        "1. [Save model explanations via Azure Machine Learning Run History](../azure-integration/run-history/save-retrieve-explanations-run-history.ipynb)\n",
+        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb)\n",
+        "1. Inferencing time: deploy a classification model and explainer:\n",
+        "    1. [Deploy a locally-trained model and explainer](../azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
+        "    1. [Deploy a remotely-trained model and explainer](../azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "mesameki"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/explain-model/tabular-data/advanced-feature-transformations-explain-local.yml b/how-to-use-azureml/explain-model/tabular-data/advanced-feature-transformations-explain-local.yml
new file mode 100644
index 00000000..f3ff11fb
--- /dev/null
+++ b/how-to-use-azureml/explain-model/tabular-data/advanced-feature-transformations-explain-local.yml
@@ -0,0 +1,7 @@
+name: advanced-feature-transformations-explain-local
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-explain-model
+  - azureml-contrib-explain-model
+  - sklearn-pandas
diff --git a/how-to-use-azureml/explain-model/tabular-data/explain-binary-classification-local.ipynb b/how-to-use-azureml/explain-model/tabular-data/explain-binary-classification-local.ipynb
new file mode 100644
index 00000000..ef7003fa
--- /dev/null
+++ b/how-to-use-azureml/explain-model/tabular-data/explain-binary-classification-local.ipynb
@@ -0,0 +1,390 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/explain-binary-classification-local.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Explain binary classification model predictions\n",
+        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to explain and visualize a binary classification model predictions.**_\n",
+        "\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Run model explainer locally at training time](#Explain)\n",
+        "    1. Train a binary classification model\n",
+        "    1. Explain the model\n",
+        "        1. Generate global explanations\n",
+        "        1. Generate local explanations\n",
+        "1. [Visualize results](#Visualize)\n",
+        "1. [Next steps](#Next)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "This notebook illustrates how to explain a binary classification model predictions locally at training time without contacting any Azure services.\n",
+        "It demonstrates the API calls that you need to make to get the global and local explanations and a visualization dashboard that provides an interactive way of discovering patterns in data and explanations.\n",
+        "\n",
+        "We will showcase three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
+        "\n",
+        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.PNG) |\n",
+        "|:--:|\n",
+        "| *Interpretability Toolkit Architecture* |\n",
+        "\n",
+        "Problem: Breast cancer diagnosis classification with scikit-learn (run model explainer locally)\n",
+        "\n",
+        "1. Train a SVM classification model using Scikit-learn\n",
+        "2. Run 'explain_model' globally and locally with full dataset in local mode, which doesn't contact any Azure services.\n",
+        "3. Visualize the global and local explanations with the visualization dashboard.\n",
+        "---\n",
+        "\n",
+        "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
+        "If you are using Jupyter Labs run the following command:\n",
+        "```\n",
+        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
+        "```\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Explain\n",
+        "\n",
+        "### Run model explainer locally at training time"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.datasets import load_breast_cancer\n",
+        "from sklearn import svm\n",
+        "\n",
+        "# Explainers:\n",
+        "# 1. SHAP Tabular Explainer\n",
+        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
+        "\n",
+        "# OR\n",
+        "\n",
+        "# 2. Mimic Explainer\n",
+        "from azureml.explain.model.mimic.mimic_explainer import MimicExplainer\n",
+        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
+        "from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
+        "from azureml.explain.model.mimic.models.linear_model import LinearExplainableModel\n",
+        "from azureml.explain.model.mimic.models.linear_model import SGDExplainableModel\n",
+        "from azureml.explain.model.mimic.models.tree_model import DecisionTreeExplainableModel\n",
+        "\n",
+        "# OR\n",
+        "\n",
+        "# 3. PFI Explainer\n",
+        "from azureml.explain.model.permutation.permutation_importance import PFIExplainer "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load the breast cancer diagnosis data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "breast_cancer_data = load_breast_cancer()\n",
+        "classes = breast_cancer_data.target_names.tolist()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Split data into train and test\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state=0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Train a SVM classification model, which you want to explain"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
+        "model = clf.fit(x_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Explain predictions on your local machine"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 1. Using SHAP TabularExplainer\n",
+        "explainer = TabularExplainer(model, \n",
+        "                             x_train, \n",
+        "                             features=breast_cancer_data.feature_names, \n",
+        "                             classes=classes)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# 2. Using MimicExplainer\n",
+        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
+        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
+        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
+        "# explainer = MimicExplainer(model, \n",
+        "#                            x_train, \n",
+        "#                            LGBMExplainableModel, \n",
+        "#                            augment_data=True, \n",
+        "#                            max_num_of_augmentations=10, \n",
+        "#                            features=breast_cancer_data.feature_names, \n",
+        "#                            classes=classes)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# 3. Using PFIExplainer\n",
+        "\n",
+        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
+        "# Note that if a metric function is provided a higher value must be better.\n",
+        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
+        "# Default metrics: \n",
+        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
+        "# Mean absolute error for regression\n",
+        "\n",
+        "# explainer = PFIExplainer(model, \n",
+        "#                          features=breast_cancer_data.feature_names, \n",
+        "#                          classes=classes)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate global explanations\n",
+        "Explain overall model predictions (global explanation)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
+        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
+        "global_explanation = explainer.explain_global(x_test)\n",
+        "\n",
+        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
+        "# global_explanation = explainer.explain_global(x_test, true_labels=y_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Sorted SHAP values\n",
+        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
+        "# Corresponding feature names\n",
+        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
+        "# Feature ranks (based on original order of features)\n",
+        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
+        "\n",
+        "# Note: PFIExplainer does not support per class explanations\n",
+        "# Per class feature names\n",
+        "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
+        "# Per class feature importance values\n",
+        "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Print out a dictionary that holds the sorted feature importance names and values\n",
+        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Explain overall model predictions as a collection of local (instance-level) explanations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# feature shap values for all features and all data points in the training data\n",
+        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate local explanations\n",
+        "Explain local data points (individual instances)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Note: PFIExplainer does not support local explanations\n",
+        "# You can pass a specific data point or a group of data points to the explain_local function\n",
+        "\n",
+        "# E.g., Explain the first data point in the test set\n",
+        "instance_num = 0\n",
+        "local_explanation = explainer.explain_local(x_test[instance_num,:])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get the prediction for the first member of the test set and explain why model made that prediction\n",
+        "prediction_value = clf.predict(x_test)[instance_num]\n",
+        "\n",
+        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
+        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
+        "\n",
+        "print('local importance values: {}'.format(sorted_local_importance_values))\n",
+        "print('local importance names: {}'.format(sorted_local_importance_names))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualize\n",
+        "Load the visualization dashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ExplanationDashboard(global_explanation, model, x_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next\n",
+        "Learn about other use cases of the explain package on a:\n",
+        "       \n",
+        "1. [Training time: regression problem](./explain-regression-local.ipynb)\n",
+        "1. [Training time: multiclass classification problem](./explain-multiclass-classification-local.ipynb)\n",
+        "1. Explain models with engineered features:\n",
+        "    1. [Simple feature transformations](./simple-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Advanced feature transformations](./advanced-feature-transformations-explain-local.ipynb)\n",
+        "1. [Save model explanations via Azure Machine Learning Run History](../azure-integration/run-history/save-retrieve-explanations-run-history.ipynb)\n",
+        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb)\n",
+        "1. Inferencing time: deploy a classification model and explainer:\n",
+        "    1. [Deploy a locally-trained model and explainer](../azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
+        "    1. [Deploy a remotely-trained model and explainer](../azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "mesameki"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/explain-model/explain-on-amlcompute/regression-sklearn-on-amlcompute.yml b/how-to-use-azureml/explain-model/tabular-data/explain-binary-classification-local.yml
similarity index 69%
rename from how-to-use-azureml/explain-model/explain-on-amlcompute/regression-sklearn-on-amlcompute.yml
rename to how-to-use-azureml/explain-model/tabular-data/explain-binary-classification-local.yml
index 49144ae8..08042837 100644
--- a/how-to-use-azureml/explain-model/explain-on-amlcompute/regression-sklearn-on-amlcompute.yml
+++ b/how-to-use-azureml/explain-model/tabular-data/explain-binary-classification-local.yml
@@ -1,4 +1,4 @@
-name: regression-sklearn-on-amlcompute
+name: explain-binary-classification-local
 dependencies:
 - pip:
   - azureml-sdk
diff --git a/how-to-use-azureml/explain-model/tabular-data/explain-multiclass-classification-local.ipynb b/how-to-use-azureml/explain-model/tabular-data/explain-multiclass-classification-local.ipynb
new file mode 100644
index 00000000..a5d7e7f9
--- /dev/null
+++ b/how-to-use-azureml/explain-model/tabular-data/explain-multiclass-classification-local.ipynb
@@ -0,0 +1,388 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/explain-multiclass-classification-local.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Explain multiclass classification model's predictions\n",
+        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to explain and visualize a multiclass classification model predictions.**_\n",
+        "\n",
+        "\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Run model explainer locally at training time](#Explain)\n",
+        "    1. Train a multiclass classification model\n",
+        "    1. Explain the model\n",
+        "        1. Generate global explanations\n",
+        "        1. Generate local explanations\n",
+        "1. [Visualize results](#Visualize)\n",
+        "1. [Next steps](#Next)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "This notebook illustrates how to explain a multiclass classification model predictions locally at training time without contacting any Azure services.\n",
+        "It demonstrates the API calls that you need to make to get the global and local explanations and a visualization dashboard that provides an interactive way of discovering patterns in data and explanations.\n",
+        "\n",
+        "We will showcase three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
+        "\n",
+        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.PNG) |\n",
+        "|:--:|\n",
+        "| *Interpretability Toolkit Architecture* |\n",
+        "\n",
+        "Problem: Iris flower classification with scikit-learn (run model explainer locally)\n",
+        "\n",
+        "1. Train a SVM classification model using Scikit-learn\n",
+        "2. Run 'explain_model' globally and locally with full dataset in local mode, which doesn't contact any Azure services.\n",
+        "3. Visualize the global and local explanations with the visualization dashboard.\n",
+        "---\n",
+        "\n",
+        "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
+        "If you are using Jupyter Labs run the following command:\n",
+        "```\n",
+        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
+        "```\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Explain\n",
+        "\n",
+        "### Run model explainer locally at training time"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.datasets import load_iris\n",
+        "from sklearn import svm\n",
+        "\n",
+        "# Explainers:\n",
+        "# 1. SHAP Tabular Explainer\n",
+        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
+        "\n",
+        "# OR\n",
+        "\n",
+        "# 2. Mimic Explainer\n",
+        "from azureml.explain.model.mimic.mimic_explainer import MimicExplainer\n",
+        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
+        "from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
+        "from azureml.explain.model.mimic.models.linear_model import LinearExplainableModel\n",
+        "from azureml.explain.model.mimic.models.linear_model import SGDExplainableModel\n",
+        "from azureml.explain.model.mimic.models.tree_model import DecisionTreeExplainableModel\n",
+        "\n",
+        "# OR\n",
+        "\n",
+        "# 3. PFI Explainer\n",
+        "from azureml.explain.model.permutation.permutation_importance import PFIExplainer "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load the Iris flower dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "iris = load_iris()\n",
+        "X = iris['data']\n",
+        "y = iris['target']\n",
+        "classes = iris['target_names']\n",
+        "feature_names = iris['feature_names']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Split data into train and test\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Train a SVM classification model, which you want to explain"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
+        "model = clf.fit(x_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Explain predictions on your local machine"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 1. Using SHAP TabularExplainer\n",
+        "explainer = TabularExplainer(model, \n",
+        "                             x_train, \n",
+        "                             features=feature_names, \n",
+        "                             classes=classes)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# 2. Using MimicExplainer\n",
+        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
+        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
+        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
+        "# explainer = MimicExplainer(model, \n",
+        "#                            x_train, \n",
+        "#                            LGBMExplainableModel, \n",
+        "#                            augment_data=True, \n",
+        "#                            max_num_of_augmentations=10, \n",
+        "#                            features=feature_names, \n",
+        "#                            classes=classes)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# 3. Using PFIExplainer\n",
+        "\n",
+        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
+        "# Note that if a metric function is provided a higher value must be better.\n",
+        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
+        "# Default metrics: \n",
+        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
+        "# Mean absolute error for regression\n",
+        "\n",
+        "# explainer = PFIExplainer(model, \n",
+        "#                          features=feature_names, \n",
+        "#                          classes=classes)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate global explanations\n",
+        "Explain overall model predictions (global explanation)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
+        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
+        "global_explanation = explainer.explain_global(x_test)\n",
+        "\n",
+        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
+        "# global_explanation = explainer.explain_global(x_test, true_labels=y_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Sorted SHAP values\n",
+        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
+        "# Corresponding feature names\n",
+        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
+        "# Feature ranks (based on original order of features)\n",
+        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
+        "\n",
+        "# Note: PFIExplainer does not support per class explanations\n",
+        "# Per class feature names\n",
+        "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
+        "# Per class feature importance values\n",
+        "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Print out a dictionary that holds the sorted feature importance names and values\n",
+        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Explain overall model predictions as a collection of local (instance-level) explanations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# feature shap values for all features and all data points in the training data\n",
+        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate local explanations\n",
+        "Explain local data points (individual instances)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Note: PFIExplainer does not support local explanations\n",
+        "# You can pass a specific data point or a group of data points to the explain_local function\n",
+        "\n",
+        "# E.g., Explain the first data point in the test set\n",
+        "instance_num = 0\n",
+        "local_explanation = explainer.explain_local(x_test[instance_num,:])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get the prediction for the first member of the test set and explain why model made that prediction\n",
+        "prediction_value = clf.predict(x_test)[instance_num]\n",
+        "\n",
+        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
+        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
+        "\n",
+        "print('local importance values: {}'.format(sorted_local_importance_values))\n",
+        "print('local importance names: {}'.format(sorted_local_importance_names))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualize\n",
+        "Load the visualization dashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ExplanationDashboard(global_explanation, model, x_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next\n",
+        "Learn about other use cases of the explain package on a:\n",
+        "\n",
+        "1. [Training time: regression problem](./explain-regression-local.ipynb)       \n",
+        "1. [Training time: binary classification problem](./explain-binary-classification-local.ipynb)\n",
+        "1. Explain models with engineered features:\n",
+        "    1. [Simple feature transformations](./simple-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Advanced feature transformations](./advanced-feature-transformations-explain-local.ipynb)\n",
+        "1. [Save model explanations via Azure Machine Learning Run History](../azure-integration/run-history/save-retrieve-explanations-run-history.ipynb)\n",
+        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb)\n",
+        "1. Inferencing time: deploy a classification model and explainer:\n",
+        "    1. [Deploy a locally-trained model and explainer](../azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
+        "    1. [Deploy a remotely-trained model and explainer](../azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)\n",
+        "\u00e2\u20ac\u2039\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "mesameki"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-classification.yml b/how-to-use-azureml/explain-model/tabular-data/explain-multiclass-classification-local.yml
similarity index 66%
rename from how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-classification.yml
rename to how-to-use-azureml/explain-model/tabular-data/explain-multiclass-classification-local.yml
index 067971f5..98f22a4d 100644
--- a/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-classification.yml
+++ b/how-to-use-azureml/explain-model/tabular-data/explain-multiclass-classification-local.yml
@@ -1,4 +1,4 @@
-name: explain-run-history-sklearn-classification
+name: explain-multiclass-classification-local
 dependencies:
 - pip:
   - azureml-sdk
diff --git a/how-to-use-azureml/explain-model/tabular-data/explain-regression-local.ipynb b/how-to-use-azureml/explain-model/tabular-data/explain-regression-local.ipynb
new file mode 100644
index 00000000..655c21fe
--- /dev/null
+++ b/how-to-use-azureml/explain-model/tabular-data/explain-regression-local.ipynb
@@ -0,0 +1,383 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/explain-regression-local.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Explain regression model predictions\n",
+        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to explain and visualize a regression model predictions.**_\n",
+        "\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Run model explainer locally at training time](#Explain)\n",
+        "    1. Train a regressor model\n",
+        "    1. Explain the model\n",
+        "        1. Generate global explanations\n",
+        "        1. Generate local explanations\n",
+        "1. [Visualize results](#Visualize)\n",
+        "1. [Next steps](#Next)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "This notebook illustrates how to explain regression model predictions locally at training time without contacting any Azure services.\n",
+        "It demonstrates the API calls that you need to make to get the global and local explanations and a visualization dashboard that provides an interactive way of discovering patterns in data and explanations.\n",
+        "\n",
+        "We will showcase three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
+        "\n",
+        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.PNG) |\n",
+        "|:--:|\n",
+        "| *Interpretability Toolkit Architecture* |\n",
+        "\n",
+        "Problem: Boston Housing Price Prediction with scikit-learn (run model explainer locally)\n",
+        "\n",
+        "1. Train a GradientBoosting regression model using Scikit-learn\n",
+        "2. Run 'explain_model' globally and locally with full dataset in local mode, which doesn't contact any Azure services.\n",
+        "3. Visualize the global and local explanations with the visualization dashboard.\n",
+        "---\n",
+        "\n",
+        "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
+        "If you are using Jupyter Labs run the following command:\n",
+        "```\n",
+        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
+        "```\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Explain\n",
+        "\n",
+        "### Run model explainer locally at training time"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn import datasets\n",
+        "from sklearn.ensemble import GradientBoostingRegressor\n",
+        "\n",
+        "# Explainers:\n",
+        "# 1. SHAP Tabular Explainer\n",
+        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
+        "\n",
+        "# OR\n",
+        "\n",
+        "# 2. Mimic Explainer\n",
+        "from azureml.explain.model.mimic.mimic_explainer import MimicExplainer\n",
+        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
+        "from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
+        "from azureml.explain.model.mimic.models.linear_model import LinearExplainableModel\n",
+        "from azureml.explain.model.mimic.models.linear_model import SGDExplainableModel\n",
+        "from azureml.explain.model.mimic.models.tree_model import DecisionTreeExplainableModel\n",
+        "\n",
+        "# OR\n",
+        "\n",
+        "# 3. PFI Explainer\n",
+        "from azureml.explain.model.permutation.permutation_importance import PFIExplainer "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load the Boston house price data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "boston_data = datasets.load_boston()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Split data into train and test\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train, x_test, y_train, y_test = train_test_split(boston_data.data, boston_data.target, test_size=0.2, random_state=0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Train a GradientBoosting regression model, which you want to explain"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "reg = GradientBoostingRegressor(n_estimators=100, max_depth=4,\n",
+        "                                learning_rate=0.1, loss='huber',\n",
+        "                                random_state=1)\n",
+        "model = reg.fit(x_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Explain predictions on your local machine"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 1. Using SHAP TabularExplainer\n",
+        "explainer = TabularExplainer(model, \n",
+        "                             x_train, \n",
+        "                             features = boston_data.feature_names)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# 2. Using MimicExplainer\n",
+        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
+        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
+        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
+        "# explainer = MimicExplainer(model, \n",
+        "#                            x_train, \n",
+        "#                            LGBMExplainableModel, \n",
+        "#                            augment_data=True, \n",
+        "#                            max_num_of_augmentations=10, \n",
+        "#                            features=boston_data.feature_names)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# 3. Using PFIExplainer\n",
+        "\n",
+        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
+        "# Note that if a metric function is provided a higher value must be better.\n",
+        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
+        "# Default metrics: \n",
+        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
+        "# Mean absolute error for regression\n",
+        "\n",
+        "# explainer = PFIExplainer(model, \n",
+        "#                          features=boston_data.feature_names)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate global explanations\n",
+        "Explain overall model predictions (global explanation)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
+        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
+        "global_explanation = explainer.explain_global(x_test)\n",
+        "\n",
+        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
+        "# global_explanation = explainer.explain_global(x_test, true_labels=y_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Sorted SHAP values \n",
+        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
+        "# Corresponding feature names\n",
+        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
+        "# Feature ranks (based on original order of features)\n",
+        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Print out a dictionary that holds the sorted feature importance names and values\n",
+        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Explain overall model predictions as a collection of local (instance-level) explanations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Note: PFIExplainer does not support local explanations\n",
+        "# feature shap values for all features and all data points in the training data\n",
+        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate local explanations\n",
+        "Explain local data points (individual instances)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Note: PFIExplainer does not support local explanations\n",
+        "# You can pass a specific data point or a group of data points to the explain_local function\n",
+        "\n",
+        "# E.g., Explain the first data point in the test set\n",
+        "local_explanation = explainer.explain_local(x_test[0,:])\n",
+        "\n",
+        "# E.g., Explain the first five data points in the test set\n",
+        "# local_explanation_group = explainer.explain_local(x_test[0:4,:])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Sorted local feature importance information; reflects the original feature order\n",
+        "sorted_local_importance_names = local_explanation.get_ranked_local_names()\n",
+        "sorted_local_importance_values = local_explanation.get_ranked_local_values()\n",
+        "\n",
+        "print('sorted local importance names: {}'.format(sorted_local_importance_names))\n",
+        "print('sorted local importance values: {}'.format(sorted_local_importance_values))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualize\n",
+        "Load the visualization dashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ExplanationDashboard(global_explanation, model, x_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next\n",
+        "Learn about other use cases of the explain package on a:\n",
+        "       \n",
+        "1. [Training time: binary classification problem](./explain-binary-classification-local.ipynb)\n",
+        "1. [Training time: multiclass classification problem](./explain-multiclass-classification-local.ipynb)\n",
+        "1. Explain models with engineered features:\n",
+        "    1. [Simple feature transformations](./simple-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Advanced feature transformations](./advanced-feature-transformations-explain-local.ipynb)\n",
+        "1. [Save model explanations via Azure Machine Learning Run History](../azure-integration/run-history/save-retrieve-explanations-run-history.ipynb)\n",
+        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb)\n",
+        "1. Inferencing time: deploy a classification model and explainer:\n",
+        "    1. [Deploy a locally-trained model and explainer](../azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
+        "    1. [Deploy a remotely-trained model and explainer](../azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "mesameki"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-regression.yml b/how-to-use-azureml/explain-model/tabular-data/explain-regression-local.yml
similarity index 71%
rename from how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-regression.yml
rename to how-to-use-azureml/explain-model/tabular-data/explain-regression-local.yml
index 0d91a84a..6002b9ab 100644
--- a/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-regression.yml
+++ b/how-to-use-azureml/explain-model/tabular-data/explain-regression-local.yml
@@ -1,4 +1,4 @@
-name: explain-local-sklearn-regression
+name: explain-regression-local
 dependencies:
 - pip:
   - azureml-sdk
diff --git a/how-to-use-azureml/explain-model/tabular-data/img/interpretability-architecture.png b/how-to-use-azureml/explain-model/tabular-data/img/interpretability-architecture.png
new file mode 100644
index 00000000..a1eff1cc
Binary files /dev/null and b/how-to-use-azureml/explain-model/tabular-data/img/interpretability-architecture.png differ
diff --git a/how-to-use-azureml/explain-model/tabular-data/simple-feature-transformations-explain-local.ipynb b/how-to-use-azureml/explain-model/tabular-data/simple-feature-transformations-explain-local.ipynb
new file mode 100644
index 00000000..6e4b280f
--- /dev/null
+++ b/how-to-use-azureml/explain-model/tabular-data/simple-feature-transformations-explain-local.ipynb
@@ -0,0 +1,517 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/simple-feature-transformations-explain-local.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Explain binary classification model predictions with raw feature transformations\n",
+        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to explain and visualize a binary classification model that uses one to one and one to many feature transformations.**_\n",
+        "\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Run model explainer locally at training time](#Explain)\n",
+        "    1. Apply feature transformations\n",
+        "    1. Train a binary classification model\n",
+        "    1. Explain the model on raw features\n",
+        "        1. Generate global explanations\n",
+        "        1. Generate local explanations\n",
+        "1. [Visualize results](#Visualize)\n",
+        "1. [Next steps](#Next%20steps)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "This notebook illustrates creating explanations for a binary classification model, IBM employee attrition classification, that uses one to one and one to many feature transformations from raw data to engineered features. The one to many feature transformations include one hot encoding on categorical features. The one to one feature transformations apply standard scaling on numeric features. Our tabular data explainer is then used to get raw feature importances.\n",
+        "\n",
+        "\n",
+        "We will showcase raw feature transformations with three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
+        "\n",
+        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.PNG) |\n",
+        "|:--:|\n",
+        "| *Interpretability Toolkit Architecture* |\n",
+        "\n",
+        "Problem: IBM employee attrition classification with scikit-learn (run model explainer locally)\n",
+        "\n",
+        "1. Transform raw features to engineered features\n",
+        "2. Train a SVC classification model using Scikit-learn\n",
+        "3. Run 'explain_model' globally and locally with full dataset in local mode, which doesn't contact any Azure services.\n",
+        "4. Visualize the global and local explanations with the visualization dashboard.\n",
+        "---\n",
+        "\n",
+        "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
+        "If you are using Jupyter Labs run the following command:\n",
+        "```\n",
+        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
+        "```\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Explain\n",
+        "\n",
+        "### Run model explainer locally at training time"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+        "from sklearn.svm import SVC\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# Explainers:\n",
+        "# 1. SHAP Tabular Explainer\n",
+        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
+        "\n",
+        "# OR\n",
+        "\n",
+        "# 2. Mimic Explainer\n",
+        "from azureml.explain.model.mimic.mimic_explainer import MimicExplainer\n",
+        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
+        "from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
+        "from azureml.explain.model.mimic.models.linear_model import LinearExplainableModel\n",
+        "from azureml.explain.model.mimic.models.linear_model import SGDExplainableModel\n",
+        "from azureml.explain.model.mimic.models.tree_model import DecisionTreeExplainableModel\n",
+        "\n",
+        "# OR\n",
+        "\n",
+        "# 3. PFI Explainer\n",
+        "from azureml.explain.model.permutation.permutation_importance import PFIExplainer "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load the IBM employee attrition data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# get the IBM employee attrition dataset\n",
+        "outdirname = 'dataset.6.21.19'\n",
+        "try:\n",
+        "    from urllib import urlretrieve\n",
+        "except ImportError:\n",
+        "    from urllib.request import urlretrieve\n",
+        "import zipfile\n",
+        "zipfilename = outdirname + '.zip'\n",
+        "urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)\n",
+        "with zipfile.ZipFile(zipfilename, 'r') as unzip:\n",
+        "    unzip.extractall('.')\n",
+        "attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')\n",
+        "\n",
+        "# Dropping Employee count as all values are 1 and hence attrition is independent of this feature\n",
+        "attritionData = attritionData.drop(['EmployeeCount'], axis=1)\n",
+        "# Dropping Employee Number since it is merely an identifier\n",
+        "attritionData = attritionData.drop(['EmployeeNumber'], axis=1)\n",
+        "\n",
+        "attritionData = attritionData.drop(['Over18'], axis=1)\n",
+        "\n",
+        "# Since all values are 80\n",
+        "attritionData = attritionData.drop(['StandardHours'], axis=1)\n",
+        "\n",
+        "# Converting target variables from string to numerical values\n",
+        "target_map = {'Yes': 1, 'No': 0}\n",
+        "attritionData[\"Attrition_numerical\"] = attritionData[\"Attrition\"].apply(lambda x: target_map[x])\n",
+        "target = attritionData[\"Attrition_numerical\"]\n",
+        "\n",
+        "attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Split data into train and test\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train, x_test, y_train, y_test = train_test_split(attritionXData, \n",
+        "                                                    target, \n",
+        "                                                    test_size = 0.2,\n",
+        "                                                    random_state=0,\n",
+        "                                                    stratify=target)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Creating dummy columns for each categorical feature\n",
+        "categorical = []\n",
+        "for col, value in attritionXData.iteritems():\n",
+        "    if value.dtype == 'object':\n",
+        "        categorical.append(col)\n",
+        "        \n",
+        "# Store the numerical columns in a list numerical\n",
+        "numerical = attritionXData.columns.difference(categorical)        "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Transform raw features"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.compose import ColumnTransformer\n",
+        "\n",
+        "# We create the preprocessing pipelines for both numeric and categorical data.\n",
+        "numeric_transformer = Pipeline(steps=[\n",
+        "    ('imputer', SimpleImputer(strategy='median')),\n",
+        "    ('scaler', StandardScaler())])\n",
+        "\n",
+        "categorical_transformer = Pipeline(steps=[\n",
+        "    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
+        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
+        "\n",
+        "transformations = ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        ('num', numeric_transformer, numerical),\n",
+        "        ('cat', categorical_transformer, categorical)])\n",
+        "\n",
+        "# Append classifier to preprocessing pipeline.\n",
+        "# Now we have a full prediction pipeline.\n",
+        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
+        "                      ('classifier', SVC(kernel='linear', C = 1.0, probability=True))])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "'''\n",
+        "# Uncomment below if sklearn-pandas is not installed\n",
+        "#!pip install sklearn-pandas\n",
+        "from sklearn_pandas import DataFrameMapper\n",
+        "\n",
+        "# Impute, standardize the numeric features and one-hot encode the categorical features.    \n",
+        "\n",
+        "\n",
+        "numeric_transformations = [([f], Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])) for f in numerical]\n",
+        "\n",
+        "categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]\n",
+        "\n",
+        "transformations = numeric_transformations + categorical_transformations\n",
+        "\n",
+        "# Append classifier to preprocessing pipeline.\n",
+        "# Now we have a full prediction pipeline.\n",
+        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
+        "                      ('classifier', SVC(kernel='linear', C = 1.0, probability=True))]) \n",
+        "\n",
+        "\n",
+        "\n",
+        "'''"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Train a SVM classification model, which you want to explain"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model = clf.fit(x_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Explain predictions on your local machine"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 1. Using SHAP TabularExplainer\n",
+        "# clf.steps[-1][1] returns the trained classification model\n",
+        "explainer = TabularExplainer(clf.steps[-1][1], \n",
+        "                             initialization_examples=x_train, \n",
+        "                             features=attritionXData.columns, \n",
+        "                             classes=[\"Not leaving\", \"leaving\"], \n",
+        "                             transformations=transformations)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# 2. Using MimicExplainer\n",
+        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
+        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
+        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
+        "# explainer = MimicExplainer(clf.steps[-1][1], \n",
+        "#                            x_train, \n",
+        "#                            LGBMExplainableModel, \n",
+        "#                            augment_data=True, \n",
+        "#                            max_num_of_augmentations=10, \n",
+        "#                            features=attritionXData.columns, \n",
+        "#                            classes=[\"Not leaving\", \"leaving\"], \n",
+        "#                            transformations=transformations)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# 3. Using PFIExplainer\n",
+        "\n",
+        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
+        "# Note that if a metric function is provided a higher value must be better.\n",
+        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
+        "# Default metrics: \n",
+        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
+        "# Mean absolute error for regression\n",
+        "\n",
+        "# explainer = PFIExplainer(clf.steps[-1][1], \n",
+        "#                          features=x_train.columns, \n",
+        "#                          transformations=transformations,\n",
+        "#                          classes=[\"Not leaving\", \"leaving\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate global explanations\n",
+        "Explain overall model predictions (global explanation)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
+        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
+        "global_explanation = explainer.explain_global(x_test)\n",
+        "\n",
+        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
+        "# global_explanation = explainer.explain_global(x_test, true_labels=y_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Sorted SHAP values\n",
+        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
+        "# Corresponding feature names\n",
+        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
+        "# Feature ranks (based on original order of features)\n",
+        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
+        "\n",
+        "# Note: PFIExplainer does not support per class explanations\n",
+        "# Per class feature names\n",
+        "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
+        "# Per class feature importance values\n",
+        "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Print out a dictionary that holds the sorted feature importance names and values\n",
+        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Explain overall model predictions as a collection of local (instance-level) explanations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# feature shap values for all features and all data points in the training data\n",
+        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate local explanations\n",
+        "Explain local data points (individual instances)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Note: PFIExplainer does not support local explanations\n",
+        "# You can pass a specific data point or a group of data points to the explain_local function\n",
+        "\n",
+        "# E.g., Explain the first data point in the test set\n",
+        "instance_num = 1\n",
+        "local_explanation = explainer.explain_local(x_test[:instance_num])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get the prediction for the first member of the test set and explain why model made that prediction\n",
+        "prediction_value = clf.predict(x_test)[instance_num]\n",
+        "\n",
+        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
+        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
+        "\n",
+        "print('local importance values: {}'.format(sorted_local_importance_values))\n",
+        "print('local importance names: {}'.format(sorted_local_importance_names))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualize\n",
+        "Load the visualization dashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ExplanationDashboard(global_explanation, model, x_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next\n",
+        "Learn about other use cases of the explain package on a:\n",
+        "       \n",
+        "1. [Training time: regression problem](./explain-regression-local.ipynb)\n",
+        "1. [Training time: binary classification problem](./explain-binary-classification-local.ipynb)\n",
+        "1. [Training time: multiclass classification problem](./explain-multiclass-classification-local.ipynb)\n",
+        "1. [Explain models with advanced feature transformations](./advanced-feature-transformations-explain-local.ipynb)\n",
+        "1. [Save model explanations via Azure Machine Learning Run History](../azure-integration/run-history/save-retrieve-explanations-run-history.ipynb)\n",
+        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb)\n",
+        "1. Inferencing time: deploy a classification model and explainer:\n",
+        "    1. [Deploy a locally-trained model and explainer](../azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
+        "    1. [Deploy a remotely-trained model and explainer](../azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "mesameki"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-binary-classification.yml b/how-to-use-azureml/explain-model/tabular-data/simple-feature-transformations-explain-local.yml
similarity index 58%
rename from how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-binary-classification.yml
rename to how-to-use-azureml/explain-model/tabular-data/simple-feature-transformations-explain-local.yml
index 744335c8..969e1f52 100644
--- a/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-binary-classification.yml
+++ b/how-to-use-azureml/explain-model/tabular-data/simple-feature-transformations-explain-local.yml
@@ -1,6 +1,7 @@
-name: explain-local-sklearn-binary-classification
+name: simple-feature-transformations-explain-local
 dependencies:
 - pip:
   - azureml-sdk
   - azureml-explain-model
   - azureml-contrib-explain-model
+  - sklearn-pandas
diff --git a/how-to-use-azureml/machine-learning-pipelines/README.md b/how-to-use-azureml/machine-learning-pipelines/README.md
index aec5dce2..094b21a1 100644
--- a/how-to-use-azureml/machine-learning-pipelines/README.md
+++ b/how-to-use-azureml/machine-learning-pipelines/README.md
@@ -36,13 +36,12 @@ Azure Machine Learning Pipelines optimize for simplicity, speed, and efficiency.
 
 In this directory, there are two types of notebooks: 
 
-* The first type of notebooks will introduce you to core Azure Machine Learning Pipelines features. These notebooks below belong in this category, and are designed to go in sequence; they're all located in the "intro-to-pipelines" folder:
-Take a look at [intro-to-pipelines](./intro-to-pipelines/) for the list of notebooks that introduce Azure Machine Learning concepts for you.
+* The first type of notebooks will introduce you to core Azure Machine Learning Pipelines features. Notebooks in this category are designed to go in sequence; they're all located in the [intro-to-pipelines](./intro-to-pipelines/) folder.
 
 * The second type of notebooks illustrate more sophisticated scenarios, and are independent of each other. These notebooks include:
 
 1. [pipeline-batch-scoring.ipynb](https://aka.ms/pl-batch-score): This notebook demonstrates how to run a batch scoring job using Azure Machine Learning pipelines.
-2. [pipeline-style-transfer.ipynb](https://aka.ms/pl-style-trans): This notebook demonstrates a multi-step pipeline that uses GPU compute.
+2. [pipeline-style-transfer.ipynb](https://aka.ms/pl-style-trans): This notebook demonstrates a multi-step pipeline that uses GPU compute. This sample also showcases how to use conda dependencies using runconfig when using Pipelines.
 3. [nyc-taxi-data-regression-model-building.ipynb](https://aka.ms/pl-nyctaxi-tutorial): This notebook is an AzureML Pipelines version of the previously published two part sample. 
 
  ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/README.png)
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/README.md b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/README.md
index 274c579b..6437e363 100644
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/README.md
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/README.md
@@ -12,7 +12,10 @@ These notebooks below are designed to go in sequence.
 7. [aml-pipelines-how-to-use-estimatorstep.ipynb](https://aka.ms/pl-estimator): This notebook shows how to use the EstimatorStep.
 8. [aml-pipelines-parameter-tuning-with-hyperdrive.ipynb](https://aka.ms/pl-hyperdrive): HyperDriveStep in Pipelines shows how you can do hyper parameter tuning using Pipelines.
 9. [aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb](https://aka.ms/pl-azbatch): AzureBatchStep can be used to run your custom code in AzureBatch cluster.
-10. [aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb](https://aka.ms/pl-schedule): Once you publish a Pipeline, you can schedule it to trigger based on an interval or on data change in a defined datastore. 
-
+10. [aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb](https://aka.ms/pl-schedule): Once you publish a Pipeline, you can schedule it to trigger based on an interval or on data change in a defined datastore.
+11. [aml-pipelines-with-automated-machine-learning-step.ipynb](https://aka.ms/pl-automl): AutoMLStep in Pipelines shows how you can do automated machine learning using Pipelines.
+12. [aml-pipelines-setup-versioned-pipeline-endpoints.ipynb](https://aka.ms/pl-ver-endpoint): This notebook shows how you can setup PipelineEndpoint and submit a Pipeline using the PipelineEndpoint.
+13. [aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb](https://aka.ms/pl-datapath): This notebook showcases how to use DataPath and PipelineParameter in AML Pipeline.
+14. [aml-pipelines-how-to-use-pipeline-drafts.ipynb](http://aka.ms/pl-pl-draft): This notebook shows how to use Pipeline Drafts. Pipeline Drafts are mutable pipelines which can be used to submit runs and create Published Pipelines.
 
  ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/README.png)
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb
index 437b4d90..b22fb9fa 100644
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb
@@ -22,9 +22,19 @@
         "# Azure Machine Learning Pipeline with DataTranferStep\n",
         "This notebook is used to demonstrate the use of DataTranferStep in Azure Machine Learning Pipeline.\n",
         "\n",
-        "In certain cases, you will need to transfer data from one data location to another. For example, your data may be in Files storage and you may want to move it to Blob storage. Or, if your data is in an ADLS account and you want to make it available in the Blob storage. The built-in **DataTransferStep** class helps you transfer data in these situations.\n",
+        "In certain cases, you will need to transfer data from one data location to another. For example, your data may be in Azure SQL Database and you may want to move it to Azure Data Lake storage. Or, your data is in an ADLS account and you want to make it available in the Blob storage. The built-in **DataTransferStep** class helps you transfer data in these situations.\n",
         "\n",
-        "The below example shows how to move data between an ADLS account, Blob storage, SQL Server, PostgreSQL server. "
+        "The below examples show how to move data between an ADLS account, Blob storage, SQL Server, PostgreSQL server. \n",
+        "\n",
+        "## Data transfer currently supports following storage types:\n",
+        "\n",
+        "| Data store | Supported as a source | Supported as a sink |\n",
+        "| --- | --- | --- |\n",
+        "| Azure Blob Storage | Yes | Yes |\n",
+        "| Azure Data Lake Storage Gen 1 | Yes | Yes |\n",
+        "| Azure Data Lake Storage Gen 2 | Yes | Yes |\n",
+        "| Azure SQL Database | Yes | Yes |\n",
+        "| Azure Database for PostgreSQL | Yes | No |"
       ]
     },
     {
@@ -62,8 +72,7 @@
         "\n",
         "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure the config file is present at .\\config.json\n",
         "\n",
-        "If you don't have a config.json file, please go through the configuration Notebook located here:\n",
-        "https://github.com/Azure/MachineLearningNotebooks. \n",
+        "If you don't have a config.json file, please go through the [configuration Notebook](https://aka.ms/pl-config) first.\n",
         "\n",
         "This sets you up with a working config file that has information on your workspace, subscription id, etc. "
       ]
@@ -86,15 +95,58 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Register Datastores\n",
-        "\n",
-        "In the code cell below, you will need to fill in the appropriate values for the workspace name, datastore name, subscription id, resource group, store name, tenant id, client id, and client secret that are associated with your ADLS datastore. \n",
+        "## Register Datastores and create DataReferences\n",
         "\n",
         "For background on registering your data store, consult this article:\n",
         "\n",
         "https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-service-to-service-authenticate-using-active-directory\n",
         "\n",
-        "### register datastores for Azure Data Lake and Azure Blob storage"
+        "> Please make sure to update the following code examples with appropriate values."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Azure Blob Storage"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from msrest.exceptions import HttpOperationError\n",
+        "\n",
+        "blob_datastore_name='MyBlobDatastore'\n",
+        "account_name=os.getenv(\"BLOB_ACCOUNTNAME_62\", \"<my-account-name>\") # Storage account name\n",
+        "container_name=os.getenv(\"BLOB_CONTAINER_62\", \"<my-container-name>\") # Name of Azure blob container\n",
+        "account_key=os.getenv(\"BLOB_ACCOUNT_KEY_62\", \"<my-account-key>\") # Storage account key\n",
+        "\n",
+        "try:\n",
+        "    blob_datastore = Datastore.get(ws, blob_datastore_name)\n",
+        "    print(\"found blob datastore with name: %s\" % blob_datastore_name)\n",
+        "except HttpOperationError:\n",
+        "    blob_datastore = Datastore.register_azure_blob_container(\n",
+        "        workspace=ws,\n",
+        "        datastore_name=blob_datastore_name,\n",
+        "        account_name=account_name, # Storage account name\n",
+        "        container_name=container_name, # Name of Azure blob container\n",
+        "        account_key=account_key) # Storage account key\n",
+        "    print(\"registered blob datastore with name: %s\" % blob_datastore_name)\n",
+        "\n",
+        "blob_data_ref = DataReference(\n",
+        "    datastore=blob_datastore,\n",
+        "    data_reference_name=\"blob_test_data\",\n",
+        "    path_on_datastore=\"testdata\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Azure Data Lake Storage Gen1"
       ]
     },
     {
@@ -128,34 +180,57 @@
         "        client_secret=client_secret) # the secret of service principal\n",
         "    print(\"registered datastore with name: %s\" % datastore_name)\n",
         "\n",
-        "\n",
-        "\n",
-        "blob_datastore_name='MyBlobDatastore'\n",
-        "account_name=os.getenv(\"BLOB_ACCOUNTNAME_62\", \"<my-account-name>\") # Storage account name\n",
-        "container_name=os.getenv(\"BLOB_CONTAINER_62\", \"<my-container-name>\") # Name of Azure blob container\n",
-        "account_key=os.getenv(\"BLOB_ACCOUNT_KEY_62\", \"<my-account-key>\") # Storage account key\n",
-        "\n",
-        "try:\n",
-        "    blob_datastore = Datastore.get(ws, blob_datastore_name)\n",
-        "    print(\"found blob datastore with name: %s\" % blob_datastore_name)\n",
-        "except HttpOperationError:\n",
-        "    blob_datastore = Datastore.register_azure_blob_container(\n",
-        "        workspace=ws,\n",
-        "        datastore_name=blob_datastore_name,\n",
-        "        account_name=account_name, # Storage account name\n",
-        "        container_name=container_name, # Name of Azure blob container\n",
-        "        account_key=account_key) # Storage account key\"\n",
-        "    print(\"registered blob datastore with name: %s\" % blob_datastore_name)\n",
-        "\n",
-        "# CLI:\n",
-        "# az ml datastore attach-blob -n <datastore-name> -a <account-name> -c <container-name> -k <account-key> [-t <sas-token>]"
+        "adls_data_ref = DataReference(\n",
+        "    datastore=adls_datastore,\n",
+        "    data_reference_name=\"adls_test_data\",\n",
+        "    path_on_datastore=\"testdata\")"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### register datastores for Azure SQL Server and Azure database for PostgreSQL"
+        "### Azure Data Lake Storage Gen2"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "\n",
+        "adlsgen2_datastore_name = 'myadlsgen2datastore'\n",
+        "account_name=os.getenv(\"ADLSGEN2_ACCOUNTNAME_62\", \"<my-account-name>\") # ADLS Gen2 account name\n",
+        "tenant_id=os.getenv(\"ADLSGEN2_TENANT_62\", \"<my-tenant-id>\") # tenant id of service principal\n",
+        "client_id=os.getenv(\"ADLSGEN2_CLIENTID_62\", \"<my-client-id>\") # client id of service principal\n",
+        "client_secret=os.getenv(\"ADLSGEN2_CLIENT_SECRET_62\", \"<my-client-secret>\") # the secret of service principal\n",
+        "\n",
+        "try:\n",
+        "    adlsgen2_datastore = Datastore.get(ws, adlsgen2_datastore_name)\n",
+        "    print(\"found ADLS Gen2 datastore with name: %s\" % adlsgen2_datastore_name)\n",
+        "except:\n",
+        "    adlsgen2_datastore = Datastore.register_azure_data_lake_gen2(\n",
+        "        workspace=ws,\n",
+        "        datastore_name=adlsgen2_datastore_name,\n",
+        "        filesystem='test', # Name of ADLS Gen2 filesystem\n",
+        "        account_name=account_name, # ADLS Gen2 account name\n",
+        "        tenant_id=tenant_id, # tenant id of service principal\n",
+        "        client_id=client_id, # client id of service principal\n",
+        "        client_secret=client_secret) # the secret of service principal\n",
+        "    print(\"registered datastore with name: %s\" % adlsgen2_datastore_name)\n",
+        "\n",
+        "adlsgen2_data_ref = DataReference(\n",
+        "    datastore=adlsgen2_datastore,\n",
+        "    data_reference_name='adlsgen2_test_data',\n",
+        "    path_on_datastore='testdata')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Azure SQL Database"
       ]
     },
     {
@@ -186,7 +261,28 @@
         "        tenant_id=tenant_id)\n",
         "    print(\"registered sql databse datastore with name: %s\" % sql_datastore_name)\n",
         "\n",
-        "    \n",
+        "from azureml.data.sql_data_reference import SqlDataReference\n",
+        "\n",
+        "sql_query_data_ref = SqlDataReference(\n",
+        "    datastore=sql_datastore,\n",
+        "    data_reference_name=\"sql_query_data_ref\",\n",
+        "    sql_query=\"select top 1 * from TestData\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Azure Database for PostgreSQL"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "\n",
         "psql_datastore_name=\"MyPostgreSqlDatastore\"\n",
         "server_name=os.getenv(\"PSQL_SERVERNAME_62\", \"<my-server-name>\") # Name of PostgreSQL server \n",
         "database_name=os.getenv(\"PSQL_DATBASENAME_62\", \"<my-database-name>\") # Name of PostgreSQL database\n",
@@ -205,73 +301,13 @@
         "        user_id=user_id,\n",
         "        user_password=user_password)\n",
         "    print(\"registered PostgreSQL databse datastore with name: %s\" % psql_datastore_name)\n",
-        "    "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create DataReferences\n",
-        "### create DataReferences for Azure Data Lake and Azure Blob storage"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "adls_datastore = Datastore(workspace=ws, name=\"MyAdlsDatastore\")\n",
         "\n",
-        "# adls\n",
-        "adls_data_ref = DataReference(\n",
-        "    datastore=adls_datastore,\n",
-        "    data_reference_name=\"adls_test_data\",\n",
-        "    path_on_datastore=\"testdata\")\n",
-        "\n",
-        "blob_datastore = Datastore(workspace=ws, name=\"MyBlobDatastore\")\n",
-        "\n",
-        "# blob data\n",
-        "blob_data_ref = DataReference(\n",
-        "    datastore=blob_datastore,\n",
-        "    data_reference_name=\"blob_test_data\",\n",
-        "    path_on_datastore=\"testdata\")\n",
-        "\n",
-        "print(\"obtained adls, blob data references\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### create DataReferences for Azure SQL Server and Azure database for PostgreSQL"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
         "from azureml.data.sql_data_reference import SqlDataReference\n",
         "\n",
-        "sql_datastore = Datastore(workspace=ws, name=\"MySqlDatastore\")\n",
-        "\n",
-        "sql_query_data_ref = SqlDataReference(\n",
-        "    datastore=sql_datastore,\n",
-        "    data_reference_name=\"sql_query_data_ref\",\n",
-        "    sql_query=\"select top 1 * from TestData\")\n",
-        "\n",
-        "\n",
-        "psql_datastore = Datastore(workspace=ws, name=\"MyPostgreSqlDatastore\")\n",
-        "\n",
         "psql_query_data_ref = SqlDataReference(\n",
         "    datastore=psql_datastore,\n",
         "    data_reference_name=\"psql_query_data_ref\",\n",
-        "    sql_query=\"SELECT * FROM testtable\")\n",
-        "\n",
-        "print(\"obtained Sql server, PostgreSQL data references\")"
+        "    sql_query=\"SELECT * FROM testtable\")"
       ]
     },
     {
@@ -304,11 +340,7 @@
         "            \n",
         "data_factory_compute = get_or_create_data_factory(ws, data_factory_name)\n",
         "\n",
-        "print(\"setup data factory account complete\")\n",
-        "\n",
-        "# CLI:\n",
-        "# Create: az ml computetarget setup datafactory -n <name>\n",
-        "# BYOC: az ml computetarget attach datafactory -n <name> -i <resource-id>"
+        "print(\"setup data factory account complete\")"
       ]
     },
     {
@@ -357,6 +389,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "\n",
+        "transfer_adlsgen2_to_blob = DataTransferStep(\n",
+        "    name='transfer_adlsgen2_to_blob',\n",
+        "    source_data_reference=adlsgen2_data_ref,\n",
+        "    destination_data_reference=blob_data_ref,\n",
+        "    compute_target=data_factory_compute)\n",
+        "\n",
         "transfer_sql_to_blob = DataTransferStep(\n",
         "    name=\"transfer_sql_to_blob\",\n",
         "    source_data_reference=sql_query_data_ref,\n",
@@ -405,7 +444,7 @@
         "pipeline_02 = Pipeline(\n",
         "    description=\"data_transfer_02\",\n",
         "    workspace=ws,\n",
-        "    steps=[transfer_sql_to_blob,transfer_psql_to_blob])\n",
+        "    steps=[transfer_sql_to_blob,transfer_psql_to_blob, transfer_adlsgen2_to_blob])\n",
         "\n",
         "pipeline_run_02 = Experiment(ws, \"Data_Transfer_example_02\").submit(pipeline_02)\n",
         "pipeline_run_02.wait_for_completion()"
@@ -439,11 +478,12 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {},
       "source": [
         "# Next: Databricks as a Compute Target\n",
-        "To use Databricks as a compute target from Azure Machine Learning Pipeline, a DatabricksStep is used. This [notebook](./aml-pipelines-use-databricks-as-compute-target.ipynb) demonstrates the use of a DatabricksStep in an Azure Machine Learning Pipeline."
+        "To use Databricks as a compute target from Azure Machine Learning Pipeline, a DatabricksStep is used. This [notebook](https://aka.ms/pl-databricks) demonstrates the use of a DatabricksStep in an Azure Machine Learning Pipeline."
       ]
     }
   ],
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb
index c11afc66..490113df 100644
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb
@@ -33,7 +33,7 @@
         "\n",
         "Azure's Machine Learning pipelines give you a way to combine multiple steps like these into one configurable workflow, so that multiple agents/users can share and/or reuse this workflow. Machine learning pipelines thus provide a consistent, reproducible mechanism for building, evaluating, deploying, and running ML systems.\n",
         "\n",
-        "To get more information about Azure machine learning pipelines, please read our [Azure Machine Learning Pipelines](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines) overview, or the [readme article](../README.md).\n",
+        "To get more information about Azure machine learning pipelines, please read our [Azure Machine Learning Pipelines](https://aka.ms/pl-concept) overview, or the [readme article](https://aka.ms/pl-readme).\n",
         "\n",
         "In this notebook, we provide a gentle introduction to Azure machine learning pipelines. We build a pipeline that runs jobs unattended on different compute clusters; in this notebook, you'll see how to use the basic Azure ML SDK APIs for constructing this pipeline.\n",
         " "
@@ -44,7 +44,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites and Azure Machine Learning Basics\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n"
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n"
       ]
     },
     {
@@ -127,7 +127,7 @@
       "metadata": {},
       "source": [
         "### Required data and script files for the the tutorial\n",
-        "Sample files required to finish this tutorial are already copied to the corresponding source_directory locations. Even though the .py provided in the samples don't have much \"ML work,\" as a data scientist, you will work on this extensively as part of your work. To complete this tutorial, the contents of these files are not very important. The one-line files are for demostration purpose only."
+        "Sample files required to finish this tutorial are already copied to the corresponding source_directory locations. Even though the .py provided in the samples does not have much \"ML work\" as a data scientist, you will work on this extensively as part of your work. To complete this tutorial, the contents of these files are not very important. The one-line files are for demostration purpose only."
       ]
     },
     {
@@ -460,7 +460,7 @@
       "source": [
         "# Submit syntax\n",
         "# submit(experiment_name, \n",
-        "#        pipeline_params=None, \n",
+        "#        pipeline_parameters=None, \n",
         "#        continue_on_step_failure=False, \n",
         "#        regenerate_outputs=False)\n",
         "\n",
@@ -597,7 +597,7 @@
       "metadata": {},
       "source": [
         "# Next: Pipelines with data dependency\n",
-        "The next [notebook](./aml-pipelines-with-data-dependency-steps.ipynb) demostrates how to construct a pipeline with data dependency."
+        "The next [notebook](https://aka.ms/pl-data-dep) demostrates how to construct a pipeline with data dependency."
       ]
     }
   ],
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb
index 07cf623c..16f46f2e 100644
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb
@@ -74,7 +74,7 @@
       "source": [
         "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json\n",
         "\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, If you don't have a config.json file, please go through the configuration Notebook located [here](https://github.com/Azure/MachineLearningNotebooks).  \n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, If you don't have a config.json file, please go through the [configuration Notebook](https://aka.ms/pl-config) located [here](https://github.com/Azure/MachineLearningNotebooks).  \n",
         "\n",
         "This sets you up with a working config file that has information on your workspace, subscription id, etc. "
       ]
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-estimatorstep.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-estimatorstep.ipynb
index b2de5ff4..c5011aaa 100644
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-estimatorstep.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-estimatorstep.ipynb
@@ -27,7 +27,7 @@
         "\n",
         "## Prerequisite:\n",
         "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to:\n",
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](https://aka.ms/pl-config) to:\n",
         "    * install the AML SDK\n",
         "    * create a workspace and its configuration file (`config.json`)"
       ]
@@ -150,7 +150,9 @@
         "> Estimator object initialization involves specifying a list of DataReference objects in its 'inputs' parameter.\n",
         "    In Pipelines, a step can take another step's output or DataReferences as input. So when creating an EstimatorStep,\n",
         "    the parameters 'inputs' and 'outputs' need to be set explicitly and that will override 'inputs' parameter\n",
-        "    specified in the Estimator object."
+        "    specified in the Estimator object.\n",
+        "   \n",
+        "> The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
       ]
     },
     {
@@ -170,7 +172,9 @@
         "    data_reference_name=\"input_data\",\n",
         "    path_on_datastore=\"20newsgroups/20news.pkl\")\n",
         "\n",
-        "output = PipelineData(\"output\", datastore=def_blob_store)"
+        "output = PipelineData(\"output\", datastore=def_blob_store)\n",
+        "\n",
+        "source_directory = 'estimator_train'"
       ]
     },
     {
@@ -181,7 +185,7 @@
       "source": [
         "from azureml.train.estimator import Estimator\n",
         "\n",
-        "est = Estimator(source_directory='.', \n",
+        "est = Estimator(source_directory=source_directory, \n",
         "                compute_target=cpu_cluster, \n",
         "                entry_script='dummy_train.py', \n",
         "                conda_packages=['scikit-learn'])"
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb
new file mode 100644
index 00000000..21cabfe2
--- /dev/null
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb
@@ -0,0 +1,266 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# How to Use Pipeline Drafts\n",
+        "In this notebook, we will show you how you can use Pipeline Drafts. Pipeline Drafts are mutable pipelines which can be used to submit runs and create Published Pipelines."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites and AML Basics\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc.\n",
+        "\n",
+        "### Initialization Steps"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "from azureml.core import Workspace\n",
+        "from azureml.core import Run, Experiment, Datastore\n",
+        "from azureml.widgets import RunDetails\n",
+        "\n",
+        "# Check core SDK version number\n",
+        "print(\"SDK version:\", azureml.core.VERSION)\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Compute Target\n",
+        "Retrieve an already attached Azure Machine Learning Compute to use in the Pipeline."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
+        "aml_compute_target = \"cpu-cluster\"\n",
+        "try:\n",
+        "    aml_compute = AmlCompute(ws, aml_compute_target)\n",
+        "    print(\"Found existing compute target: {}\".format(aml_compute_target))\n",
+        "except:\n",
+        "    print(\"Creating new compute target: {}\".format(aml_compute_target))\n",
+        "    \n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n",
+        "                                                                min_nodes = 1, \n",
+        "                                                                max_nodes = 4)    \n",
+        "    aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n",
+        "    aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Build a Pipeline\n",
+        "Build a simple pipeline to use to create a PipelineDraft."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import Pipeline\n",
+        "from azureml.pipeline.steps import PythonScriptStep\n",
+        "\n",
+        "source_directory = \"publish_run_train\"\n",
+        "\n",
+        "train_step = PythonScriptStep(\n",
+        "    name=\"Training_Step\",\n",
+        "    script_name=\"train.py\", \n",
+        "    compute_target=aml_compute_target, \n",
+        "    source_directory=source_directory)\n",
+        "print(\"train step created\")\n",
+        "\n",
+        "pipeline = Pipeline(workspace=ws, steps=[train_step])\n",
+        "print (\"Pipeline is built\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a Pipeline Draft\n",
+        "Create a PipelineDraft by specifying a name, description, experiment_name and Pipeline. You can also specify tags, properties and pipeline_parameter values.\n",
+        "\n",
+        "In this example we use the previously created Pipeline object to create the Pipeline Draft. You can also create a Pipeline Draft from an existing Pipeline Run, Published Pipeline, or other Pipeline Draft."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import PipelineDraft\n",
+        "\n",
+        "pipeline_draft = PipelineDraft.create(ws, name=\"TestPipelineDraft\",\n",
+        "                                      description=\"draft description\",\n",
+        "                                      experiment_name=\"helloworld\",\n",
+        "                                      pipeline=pipeline,\n",
+        "                                      continue_on_step_failure=True,\n",
+        "                                      tags={'dev': 'true'},\n",
+        "                                      properties={'train': 'value'})\n",
+        "\n",
+        "created_pipeline_draft_id = pipeline_draft.id"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### List Pipeline Drafts in a Workspace\n",
+        "Use the PipelineDraft.list() function to list all PipelineDrafts in a Workspace. You can use the optional tags parameter to filter on specified tag values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_drafts = PipelineDraft.list(ws, tags={'dev': 'true'})\n",
+        "\n",
+        "for pipeline_draft in pipeline_drafts:\n",
+        "    print(pipeline_draft)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Get a Pipeline Draft by Id"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_draft = PipelineDraft.get(ws, id=created_pipeline_draft_id)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Update a Pipeline Draft\n",
+        "The update() function of a pipeline draft can be used to update the name, description, experiment name, pipeline parameter assignments, continue on step failure setting and Pipeline associated with the PipelineDraft. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "new_train_step = PythonScriptStep(\n",
+        "    name=\"New_Training_Step\",\n",
+        "    script_name=\"train.py\", \n",
+        "    compute_target=aml_compute_target, \n",
+        "    source_directory=source_directory)\n",
+        "\n",
+        "new_pipeline = Pipeline(workspace=ws, steps=[new_train_step])\n",
+        "\n",
+        "pipeline_draft.update(name=\"UpdatedPipelineDraft\", description=\"has updated train step\", pipeline=new_pipeline)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit a Pipeline Run from a Pipeline Draft\n",
+        "Use the pipeline_draft.submit() function to submit a PipelineRun. After the run is submitted, the PipelineDraft can still be edited and used to submit new runs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_run = pipeline_draft.submit_run()\n",
+        "pipeline_run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a Published Pipeline from a Pipeline Draft\n",
+        "Use the pipeline_draft.publish() function to create a Published Pipeline from the Pipeline Draft. After creating a Published Pipeline, the Pipeline Draft can still be edited and used to create other Published Pipelines."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "published_pipeline = pipeline_draft.publish()\n",
+        "published_pipeline"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "elihop"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.yml b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.yml
new file mode 100644
index 00000000..198569b0
--- /dev/null
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.yml
@@ -0,0 +1,5 @@
+name: aml-pipelines-how-to-use-pipeline-drafts
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb
index 940f816e..2b5139e0 100644
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb
@@ -30,7 +30,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites and Azure Machine Learning Basics\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n",
         "\n",
         "## Azure Machine Learning and Pipeline SDK-specific imports"
       ]
@@ -88,7 +88,11 @@
       "metadata": {},
       "source": [
         "## Create an Azure ML experiment\n",
-        "Let's create an experiment named \"tf-mnist\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure.\n"
+        "Let's create an experiment named \"tf-mnist\" and a folder to hold the training scripts. \n",
+        "\n",
+        "> The best practice is to use separate folders for scripts and its dependent files for each step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step. \n",
+        "\n",
+        "> The script runs will be recorded under the experiment in Azure."
       ]
     },
     {
@@ -317,7 +321,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "hyperdriveconfig-remarks-sample"
+        ]
+      },
       "outputs": [],
       "source": [
         "hd_config = HyperDriveConfig(estimator=est, \n",
@@ -325,7 +333,7 @@
         "                             policy=early_termination_policy,\n",
         "                             primary_metric_name='validation_acc', \n",
         "                             primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, \n",
-        "                             max_total_runs=10,\n",
+        "                             max_total_runs=4,\n",
         "                             max_concurrent_runs=4)"
       ]
     },
@@ -433,8 +441,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# PUBLISHONLY\n",
-        "# pipeline_run.wait_for_completion()"
+        "pipeline_run.wait_for_completion()"
       ]
     },
     {
@@ -451,9 +458,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# PUBLISHONLY\n",
-        "# metrics_output = pipeline_run.get_pipeline_output(metrics_output_name)\n",
-        "# num_file_downloaded = metrics_output.download('.', show_progress=True)"
+        "metrics_output = pipeline_run.get_pipeline_output(metrics_output_name)\n",
+        "num_file_downloaded = metrics_output.download('.', show_progress=True)"
       ]
     },
     {
@@ -462,15 +468,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# PUBLISHONLY\n",
-        "# import pandas as pd\n",
-        "# import json\n",
-        "# with open(metrics_output._path_on_datastore) as f:  \n",
-        "#    metrics_output_result = f.read()\n",
+        "import pandas as pd\n",
+        "import json\n",
+        "with open(metrics_output._path_on_datastore) as f:  \n",
+        "   metrics_output_result = f.read()\n",
         "    \n",
-        "# deserialized_metrics_output = json.loads(metrics_output_result)\n",
-        "# df = pd.DataFrame(deserialized_metrics_output)\n",
-        "# df"
+        "deserialized_metrics_output = json.loads(metrics_output_result)\n",
+        "df = pd.DataFrame(deserialized_metrics_output)\n",
+        "df"
       ]
     },
     {
@@ -487,10 +492,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# PUBLISHONLY\n",
-        "# hd_step_run = HyperDriveStepRun(step_run=pipeline_run.find_step_run(hd_step_name)[0])\n",
-        "# best_run = hd_step_run.get_best_run_by_primary_metric()\n",
-        "# best_run"
+        "hd_step_run = HyperDriveStepRun(step_run=pipeline_run.find_step_run(hd_step_name)[0])\n",
+        "best_run = hd_step_run.get_best_run_by_primary_metric()\n",
+        "best_run"
       ]
     },
     {
@@ -506,8 +510,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# PUBLISHONLY\n",
-        "# print(best_run.get_file_names())"
+        "print(best_run.get_file_names())"
       ]
     },
     {
@@ -523,8 +526,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# PUBLISHONLY\n",
-        "# model = best_run.register_model(model_name='tf-dnn-mnist', model_path='outputs/model')"
+        "model = best_run.register_model(model_name='tf-dnn-mnist', model_path='outputs/model')"
       ]
     },
     {
@@ -588,15 +590,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# PUBLISHONLY\n",
-        "# from azureml.core.runconfig import CondaDependencies\n",
+        "from azureml.core.runconfig import CondaDependencies\n",
         "\n",
-        "# cd = CondaDependencies.create()\n",
-        "# cd.add_conda_package('numpy')\n",
-        "# cd.add_tensorflow_conda_package()\n",
-        "# cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",
+        "cd = CondaDependencies.create()\n",
+        "cd.add_conda_package('numpy')\n",
+        "cd.add_tensorflow_conda_package()\n",
+        "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",
         "\n",
-        "# print(cd.serialize_to_string())"
+        "print(cd.serialize_to_string())"
       ]
     },
     {
@@ -613,13 +614,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# PUBLISHONLY\n",
-        "# from azureml.core.webservice import AciWebservice\n",
+        "from azureml.core.webservice import AciWebservice\n",
         "\n",
-        "# aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
-        "#                                                memory_gb=1, \n",
-        "#                                                tags={'name':'mnist', 'framework': 'TensorFlow DNN'},\n",
-        "#                                                description='Tensorflow DNN on MNIST')"
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
+        "                                               memory_gb=1, \n",
+        "                                               tags={'name':'mnist', 'framework': 'TensorFlow DNN'},\n",
+        "                                               description='Tensorflow DNN on MNIST')"
       ]
     },
     {
@@ -644,12 +644,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# PUBLISHONLY\n",
-        "# from azureml.core.image import ContainerImage\n",
+        "from azureml.core.image import ContainerImage\n",
         "\n",
-        "# imgconfig = ContainerImage.image_configuration(execution_script=\"score.py\", \n",
-        "#                                                runtime=\"python\", \n",
-        "#                                                conda_file=\"myenv.yml\")"
+        "imgconfig = ContainerImage.image_configuration(execution_script=\"score.py\", \n",
+        "                                               runtime=\"python\", \n",
+        "                                               conda_file=\"myenv.yml\")"
       ]
     },
     {
@@ -658,17 +657,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# PUBLISHONLY\n",
-        "# %%time\n",
-        "# from azureml.core.webservice import Webservice\n",
+        "%%time\n",
+        "from azureml.core.webservice import Webservice\n",
         "\n",
-        "# service = Webservice.deploy_from_model(workspace=ws,\n",
-        "#                                        name='tf-mnist-svc',\n",
-        "#                                        deployment_config=aciconfig,\n",
-        "#                                        models=[model],\n",
-        "#                                        image_config=imgconfig)\n",
+        "service = Webservice.deploy_from_model(workspace=ws,\n",
+        "                                       name='tf-mnist-svc',\n",
+        "                                       deployment_config=aciconfig,\n",
+        "                                       models=[model],\n",
+        "                                       image_config=imgconfig)\n",
         "\n",
-        "# service.wait_for_deployment(show_output=True)"
+        "service.wait_for_deployment(show_output=True)"
       ]
     },
     {
@@ -684,8 +682,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# PUBLISHONLY\n",
-        "# print(service.get_logs())"
+        "print(service.get_logs())"
       ]
     },
     {
@@ -701,8 +698,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# PUBLISHONLY\n",
-        "# print(service.scoring_uri)"
+        "print(service.scoring_uri)"
       ]
     },
     {
@@ -721,37 +717,36 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# PUBLISHONLY\n",
-        "# import json\n",
+        "import json\n",
         "\n",
-        "# # find 30 random samples from test set\n",
-        "# n = 30\n",
-        "# sample_indices = np.random.permutation(X_test.shape[0])[0:n]\n",
+        "# find 30 random samples from test set\n",
+        "n = 30\n",
+        "sample_indices = np.random.permutation(X_test.shape[0])[0:n]\n",
         "\n",
-        "# test_samples = json.dumps({\"data\": X_test[sample_indices].tolist()})\n",
-        "# test_samples = bytes(test_samples, encoding='utf8')\n",
+        "test_samples = json.dumps({\"data\": X_test[sample_indices].tolist()})\n",
+        "test_samples = bytes(test_samples, encoding='utf8')\n",
         "\n",
-        "# # predict using the deployed model\n",
-        "# result = service.run(input_data=test_samples)\n",
+        "# predict using the deployed model\n",
+        "result = service.run(input_data=test_samples)\n",
         "\n",
-        "# # compare actual value vs. the predicted values:\n",
-        "# i = 0\n",
-        "# plt.figure(figsize = (20, 1))\n",
+        "# compare actual value vs. the predicted values:\n",
+        "i = 0\n",
+        "plt.figure(figsize = (20, 1))\n",
         "\n",
-        "# for s in sample_indices:\n",
-        "#     plt.subplot(1, n, i + 1)\n",
-        "#     plt.axhline('')\n",
-        "#     plt.axvline('')\n",
+        "for s in sample_indices:\n",
+        "    plt.subplot(1, n, i + 1)\n",
+        "    plt.axhline('')\n",
+        "    plt.axvline('')\n",
         "    \n",
-        "#     # use different color for misclassified sample\n",
-        "#     font_color = 'red' if y_test[s] != result[i] else 'black'\n",
-        "#     clr_map = plt.cm.gray if y_test[s] != result[i] else plt.cm.Greys\n",
+        "    # use different color for misclassified sample\n",
+        "    font_color = 'red' if y_test[s] != result[i] else 'black'\n",
+        "    clr_map = plt.cm.gray if y_test[s] != result[i] else plt.cm.Greys\n",
         "    \n",
-        "#     plt.text(x=10, y=-10, s=y_hat[s], fontsize=18, color=font_color)\n",
-        "#     plt.imshow(X_test[s].reshape(28, 28), cmap=clr_map)\n",
+        "    plt.text(x=10, y=-10, s=y_hat[s], fontsize=18, color=font_color)\n",
+        "    plt.imshow(X_test[s].reshape(28, 28), cmap=clr_map)\n",
         "    \n",
-        "#     i = i + 1\n",
-        "# plt.show()"
+        "    i = i + 1\n",
+        "plt.show()"
       ]
     },
     {
@@ -767,21 +762,20 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# PUBLISHONLY\n",
-        "# import requests\n",
+        "import requests\n",
         "\n",
-        "# # send a random row from the test set to score\n",
-        "# random_index = np.random.randint(0, len(X_test)-1)\n",
-        "# input_data = \"{\\\"data\\\": [\" + str(list(X_test[random_index])) + \"]}\"\n",
+        "# send a random row from the test set to score\n",
+        "random_index = np.random.randint(0, len(X_test)-1)\n",
+        "input_data = \"{\\\"data\\\": [\" + str(list(X_test[random_index])) + \"]}\"\n",
         "\n",
-        "# headers = {'Content-Type':'application/json'}\n",
+        "headers = {'Content-Type':'application/json'}\n",
         "\n",
-        "# resp = requests.post(service.scoring_uri, input_data, headers=headers)\n",
+        "resp = requests.post(service.scoring_uri, input_data, headers=headers)\n",
         "\n",
-        "# print(\"POST to url\", service.scoring_uri)\n",
-        "# print(\"input data:\", input_data)\n",
-        "# print(\"label:\", y_test[random_index])\n",
-        "# print(\"prediction:\", resp.text)"
+        "print(\"POST to url\", service.scoring_uri)\n",
+        "print(\"input data:\", input_data)\n",
+        "print(\"label:\", y_test[random_index])\n",
+        "print(\"prediction:\", resp.text)"
       ]
     },
     {
@@ -800,18 +794,17 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# PUBLISHONLY\n",
-        "# models = ws.models\n",
-        "# for name, model in models.items():\n",
-        "#     print(\"Model: {}, ID: {}\".format(name, model.id))\n",
+        "models = ws.models\n",
+        "for name, model in models.items():\n",
+        "    print(\"Model: {}, ID: {}\".format(name, model.id))\n",
         "    \n",
-        "# images = ws.images\n",
-        "# for name, image in images.items():\n",
-        "#     print(\"Image: {}, location: {}\".format(name, image.image_location))\n",
+        "images = ws.images\n",
+        "for name, image in images.items():\n",
+        "    print(\"Image: {}, location: {}\".format(name, image.image_location))\n",
         "    \n",
-        "# webservices = ws.webservices\n",
-        "# for name, webservice in webservices.items():\n",
-        "#     print(\"Webservice: {}, scoring URI: {}\".format(name, webservice.scoring_uri))"
+        "webservices = ws.webservices\n",
+        "for name, webservice in webservices.items():\n",
+        "    print(\"Webservice: {}, scoring URI: {}\".format(name, webservice.scoring_uri))"
       ]
     },
     {
@@ -828,15 +821,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# PUBLISHONLY\n",
-        "# service.delete()"
+        "service.delete()"
       ]
     }
   ],
   "metadata": {
     "authors": [
       {
-        "name": "sonnyp"
+        "name": "sanpil"
       }
     ],
     "kernelspec": {
diff --git a/how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.yml b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.yml
similarity index 56%
rename from how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.yml
rename to how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.yml
index c9e18056..95c1bc95 100644
--- a/how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.yml
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.yml
@@ -1,8 +1,8 @@
-name: auto-ml-dataprep-remote-execution
+name: aml-pipelines-parameter-tuning-with-hyperdrive
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
   - azureml-widgets
   - matplotlib
+  - numpy
   - pandas_ml
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb
index 42230356..04cccd09 100644
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb
@@ -28,7 +28,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites and Azure Machine Learning Basics\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n",
         "\n",
         "### Initialization Steps"
       ]
@@ -57,10 +57,8 @@
         "ws = Workspace.from_config()\n",
         "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n",
         "\n",
-        "# Default datastore (Azure file storage)\n",
-        "def_file_store = ws.get_default_datastore() \n",
-        "print(\"Default datastore's name: {}\".format(def_file_store.name))\n",
-        "\n",
+        "# Default datastore (Azure blob storage)\n",
+        "# def_blob_store = ws.get_default_datastore()\n",
         "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
         "print(\"Blobstore's name: {}\".format(def_blob_store.name))"
       ]
@@ -147,7 +145,9 @@
         "#### Define a Step that consumes a datasource and produces intermediate data.\n",
         "In this step, we define a step that consumes a datasource and produces intermediate data.\n",
         "\n",
-        "**Open `train.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** "
+        "**Open `train.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** \n",
+        "\n",
+        "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
       ]
     },
     {
@@ -158,13 +158,16 @@
       "source": [
         "# trainStep consumes the datasource (Datareference) in the previous step\n",
         "# and produces processed_data1\n",
+        "\n",
+        "source_directory = \"publish_run_train\"\n",
+        "\n",
         "trainStep = PythonScriptStep(\n",
         "    script_name=\"train.py\", \n",
         "        arguments=[\"--input_data\", blob_input_data, \"--output_train\", processed_data1],\n",
         "    inputs=[blob_input_data],\n",
         "    outputs=[processed_data1],\n",
         "    compute_target=aml_compute, \n",
-        "    source_directory='.'\n",
+        "    source_directory=source_directory\n",
         ")\n",
         "print(\"trainStep created\")"
       ]
@@ -188,6 +191,7 @@
         "# extractStep to use the intermediate data produced by step4\n",
         "# This step also produces an output processed_data2\n",
         "processed_data2 = PipelineData(\"processed_data2\", datastore=def_blob_store)\n",
+        "source_directory = \"publish_run_extract\"\n",
         "\n",
         "extractStep = PythonScriptStep(\n",
         "    script_name=\"extract.py\",\n",
@@ -195,7 +199,7 @@
         "    inputs=[processed_data1],\n",
         "    outputs=[processed_data2],\n",
         "    compute_target=aml_compute, \n",
-        "    source_directory='.')\n",
+        "    source_directory=source_directory)\n",
         "print(\"extractStep created\")"
       ]
     },
@@ -247,8 +251,7 @@
       "source": [
         "# Now define step6 that takes two inputs (both intermediate data), and produce an output\n",
         "processed_data3 = PipelineData(\"processed_data3\", datastore=def_blob_store)\n",
-        "\n",
-        "\n",
+        "source_directory = \"publish_run_compare\"\n",
         "\n",
         "compareStep = PythonScriptStep(\n",
         "    script_name=\"compare.py\",\n",
@@ -256,7 +259,7 @@
         "    inputs=[processed_data1, processed_data2],\n",
         "    outputs=[processed_data3],    \n",
         "    compute_target=aml_compute, \n",
-        "    source_directory='.')\n",
+        "    source_directory=source_directory)\n",
         "print(\"compareStep created\")"
       ]
     },
@@ -390,7 +393,7 @@
       "metadata": {},
       "source": [
         "# Next: Data Transfer\n",
-        "The next [notebook](./aml-pipelines-data-transfer.ipynb) will showcase data transfer steps between different types of data stores."
+        "The next [notebook](https://aka.ms/pl-data-trans) will showcase data transfer steps between different types of data stores."
       ]
     }
   ],
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb
index 5b9976c7..7a410a75 100644
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb
@@ -28,7 +28,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites and AML Basics\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc.\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc.\n",
         "\n",
         "### Initialization Steps"
       ]
@@ -103,7 +103,7 @@
       "metadata": {},
       "source": [
         "### Define a pipeline step\n",
-        "Define a single step pipeline for demonstration purpose."
+        "Define a single step pipeline for demonstration purpose. The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
       ]
     },
     {
@@ -114,11 +114,13 @@
       "source": [
         "from azureml.pipeline.steps import PythonScriptStep\n",
         "\n",
+        "source_directory = \"publish_run_train\"\n",
+        "\n",
         "trainStep = PythonScriptStep(\n",
         "    name=\"Training_Step\",\n",
         "    script_name=\"train.py\", \n",
         "    compute_target=aml_compute_target, \n",
-        "    source_directory='.'\n",
+        "    source_directory=source_directory\n",
         ")\n",
         "print(\"TrainStep created\")"
       ]
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.yml b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.yml
new file mode 100644
index 00000000..f35bb648
--- /dev/null
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.yml
@@ -0,0 +1,5 @@
+name: aml-pipelines-setup-schedule-for-a-published-pipeline
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb
index 5afa9070..4b956e86 100644
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb
@@ -21,10 +21,10 @@
       "source": [
         "\n",
         "# How to Setup a PipelineEndpoint and Submit a Pipeline Using the PipelineEndpoint.\n",
-        "In this notebook, we will see how to setup a PipelineEndpoint and run specific pipeline version.\n",
+        "In this notebook, we will see how to setup a PipelineEndpoint and run a specific pipeline version.\n",
         "\n",
-        "PipelineEndpoint can be used to update a published pipeline while maintaining same endpoint.\n",
-        "PipelineEndpoint, provides a way to keep track of [PublishedPipelines](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.publishedpipeline) using versions. PipelineEndpoint uses endpoint with version information to trigger underlying published pipeline. Pipeline endpoints are uniquely named within a workspace.  \n"
+        "PipelineEndpoint can be used to update a published pipeline while maintaining the same endpoint.\n",
+        "PipelineEndpoint provides a way to keep track of [PublishedPipelines](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.publishedpipeline) using versions. PipelineEndpoint uses endpoint with version information to trigger an underlying published pipeline. Pipeline endpoints are uniquely named within a workspace.  \n"
       ]
     },
     {
@@ -32,7 +32,7 @@
       "metadata": {},
       "source": [
         "### Prerequisites and AML Basics\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://github.com/Azure/MachineLearningNotebooks) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc.\n"
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc.\n"
       ]
     },
     {
@@ -76,7 +76,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "####  Initialization, Steps to create a Pipeline"
+        "####  Initialization, Steps to create a Pipeline\n",
+        "\n",
+        "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
       ]
     },
     {
@@ -105,7 +107,7 @@
         "    aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
         "\n",
         "# source_directory\n",
-        "source_directory = '.'\n",
+        "source_directory = 'publish_run_train'\n",
         "# define a single step pipeline for demonstration purpose.\n",
         "trainStep = PythonScriptStep(\n",
         "    name=\"Training_Step\",\n",
@@ -313,7 +315,25 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Set Published Pipeline to default version"
+        "#### Add Published Pipeline to PipelineEndpoint, \n",
+        "Adds a published pipeline (if its not present) using add() and if you want to add and set to default use add_default()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_endpoint_by_name.add(published_pipeline)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Add Published pipeline to PipelineEndpoint and set it to default version\n",
+        "Adding published pipeline to PipelineEndpoint if not present and set it to default"
       ]
     },
     {
@@ -389,40 +409,6 @@
         "pipeline_endpoint_by_name.set_name(name=\"NewName\")"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Add Published Pipeline to PipelineEndpoint, \n",
-        "Adding published pipeline, if its not present in PipelineEndpoint."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "pipeline_endpoint_by_name.add(published_pipeline)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Add Published pipeline to PipelineEndpoint and set it to default version\n",
-        "Adding published pipeline to PipelineEndpoint if not present and set it to default"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "pipeline_endpoint_by_name.add_default(published_pipeline)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -447,7 +433,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "pipeline_endpoint_by_name = PipelineEndpoint.get(workspace=ws, name=\"PipelineEndpointTest\")\n",
+        "pipeline_endpoint_by_name = PipelineEndpoint.get(workspace=ws, name=\"NewName\")\n",
         "\n",
         "# endpoint with id \n",
         "rest_endpoint_id =  pipeline_endpoint_by_name.endpoint\n",
@@ -529,11 +515,11 @@
       "outputs": [],
       "source": [
         "# submit pipeline with specific version\n",
-        "run_id = pipeline_endpoint_by_name.submit(\"TestPipelineEndpoint\", pipeline_version=\"0\")\n",
+        "run_id = pipeline_endpoint_by_name.submit(\"NewName\", pipeline_version=\"0\")\n",
         "print(run_id)\n",
         "\n",
         "# submit pipeline with default version\n",
-        "run_id = pipeline_endpoint_by_name.submit(\"TestPipelineEndpoint\")\n",
+        "run_id = pipeline_endpoint_by_name.submit(\"NewName\")\n",
         "print(run_id)"
       ]
     }
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.yml b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.yml
new file mode 100644
index 00000000..aae504eb
--- /dev/null
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.yml
@@ -0,0 +1,6 @@
+name: aml-pipelines-setup-versioned-pipeline-endpoints
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
+  - requests
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb
new file mode 100644
index 00000000..903362cf
--- /dev/null
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb
@@ -0,0 +1,479 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Showcasing DataPath and PipelineParameter\n",
+        "\n",
+        "This notebook demonstrateas the use of [**DataPath**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.datapath.datapath?view=azure-ml-py) and [**PipelineParameters**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py) in AML Pipeline. You will learn how strings and [**DataPath**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.datapath.datapath?view=azure-ml-py) can be parameterized and submitted to AML Pipelines via [**PipelineParameters**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py).\n",
+        "To see more about how parameters work between steps, please refer [aml-pipelines-with-data-dependency-steps](https://aka.ms/pl-data-dep).\n",
+        "\n",
+        "* [How to create a Pipeline with a DataPath PipelineParameter](#index1)\n",
+        "* [How to submit a Pipeline with a DataPath PipelineParameter](#index2)\n",
+        "* [How to submit a Pipeline and change the DataPath PipelineParameter value from the sdk](#index3)\n",
+        "* [How to submit a Pipeline and change the DataPath PipelineParameter value using a REST call](#index4)\n",
+        "* [How to create a datastore trigger schedule and use the data_path_parameter_name to get the path of the changed blob in the Pipeline](#index5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Azure Machine Learning and Pipeline SDK-specific imports"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "from azureml.core import Workspace, Experiment\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.data.datapath import DataPath, DataPathComputeBinding\n",
+        "from azureml.widgets import RunDetails\n",
+        "\n",
+        "from azureml.pipeline.core import PipelineParameter\n",
+        "from azureml.pipeline.core import Pipeline, PipelineRun\n",
+        "from azureml.pipeline.steps import PythonScriptStep\n",
+        "\n",
+        "# Check core SDK version number\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize Workspace\n",
+        "\n",
+        "Initialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure the config file is present at .\\config.json\n",
+        "\n",
+        "If you don't have a config.json file, please go through the configuration Notebook first.\n",
+        "\n",
+        "This sets you up with a working config file that has information on your workspace, subscription id, etc."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create an Azure ML experiment\n",
+        "\n",
+        "Let's create an experiment named \"automl-classification\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Choose a name for the run history container in the workspace.\n",
+        "experiment_name = 'showcasing-datapath'\n",
+        "source_directory  = '.'\n",
+        "\n",
+        "experiment = Experiment(ws, experiment_name)\n",
+        "experiment"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create or Attach an AmlCompute cluster\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Choose a name for your cluster.\n",
+        "amlcompute_cluster_name = \"cpu-cluster\"\n",
+        "\n",
+        "found = False\n",
+        "# Check if this compute target already exists in the workspace.\n",
+        "cts = ws.compute_targets\n",
+        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
+        "    found = True\n",
+        "    print('Found existing compute target.')\n",
+        "    compute_target = cts[amlcompute_cluster_name]\n",
+        "    \n",
+        "if not found:\n",
+        "    print('Creating a new compute target...')\n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
+        "                                                                #vm_priority = 'lowpriority', # optional\n",
+        "                                                                max_nodes = 4)\n",
+        "\n",
+        "    # Create the cluster.\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
+        "    \n",
+        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "    compute_target.wait_for_completion(show_output = True, timeout_in_minutes = 10)\n",
+        "    \n",
+        "     # For a more detailed view of current AmlCompute status, use get_status()."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Data and arguments setup \n",
+        "\n",
+        "We will setup a trining script to run and its arguments to be used. The sample training script below will print the two arguments to show what has been passed to pipeline."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile train_with_datapath.py\n",
+        "import argparse\n",
+        "import os\n",
+        "\n",
+        "parser = argparse.ArgumentParser(\"train\")\n",
+        "parser.add_argument(\"--arg1\", type=str, help=\"sample string argument\")\n",
+        "parser.add_argument(\"--arg2\", type=str, help=\"sample datapath argument\")\n",
+        "args = parser.parse_args()\n",
+        "\n",
+        "print(\"Sample string argument  : %s\" % args.arg1)\n",
+        "print(\"Sample datapath argument: %s\" % args.arg2)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Let's setup string and DataPath arguments using PipelineParameter. \n",
+        "\n",
+        "Note that Pipeline accepts a tuple of the form ([**PipelineParameters**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py) , [**DataPathComputeBinding**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.datapath.datapathcomputebinding?view=azure-ml-py)) as an input. DataPath defines the location of input data. DataPathComputeBinding defines how the data is consumed during step execution. The DataPath can be modified at pipeline submission time with a DataPath parameter, while the compute binding does not change. For static data inputs, we use [**DataReference**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.data_reference.datareference?view=azure-ml-py) which defines both the data location and compute binding."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def_blob_store = ws.get_default_datastore()\n",
+        "print(\"Default datastore's name: {}\".format(def_blob_store.name))\n",
+        "\n",
+        "data_path = DataPath(datastore=def_blob_store, path_on_datastore='sample_datapath1')\n",
+        "datapath1_pipeline_param = PipelineParameter(name=\"input_datapath\", default_value=data_path)\n",
+        "datapath_input = (datapath1_pipeline_param, DataPathComputeBinding(mode='mount'))\n",
+        "\n",
+        "string_pipeline_param = PipelineParameter(name=\"input_string\", default_value='sample_string1')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='index1'></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create a Pipeline with a DataPath PipelineParameter\n",
+        "\n",
+        "Note that the ```datapath_input``` is specified on both arguments and inputs to create a step."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "train_step = PythonScriptStep(\n",
+        "    name='train_step',\n",
+        "    script_name=\"train_with_datapath.py\",\n",
+        "    arguments=[\"--arg1\", string_pipeline_param, \"--arg2\", datapath_input],\n",
+        "    inputs=[datapath_input],\n",
+        "    compute_target=compute_target, \n",
+        "    source_directory=source_directory)\n",
+        "print(\"train_step created\")\n",
+        "\n",
+        "pipeline = Pipeline(workspace=ws, steps=[train_step])\n",
+        "print(\"pipeline with the train_step created\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='index2'></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Submit a Pipeline with a DataPath PipelineParameter\n",
+        "\n",
+        "Pipelines can be submitted with default values of PipelineParameters by not specifying any parameters."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_run = experiment.submit(pipeline)\n",
+        "print(\"Pipeline is submitted for execution\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "RunDetails(pipeline_run).show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_run.wait_for_completion()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='index3'></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Submit a Pipeline and change the DataPath PipelineParameter value from the sdk\n",
+        "\n",
+        "Or Pipelines can be submitted with values other than default ones by using pipeline_parameters. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_run_with_params = experiment.submit(pipeline, \\\n",
+        "        pipeline_parameters={'input_datapath': DataPath(datastore=def_blob_store, path_on_datastore='sample_datapath2'),\n",
+        "                         'input_string': 'sample_string2'}) "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "RunDetails(pipeline_run_with_params).show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_run_with_params.wait_for_completion()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='index4'></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Submit a Pipeline and change the DataPath PipelineParameter value using a REST call\n",
+        "\n",
+        "Let's published the pipeline to use the rest endpoint of the published pipeline."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "published_pipeline = pipeline.publish(name=\"DataPath_Pipeline\", description=\"Pipeline to test Datapath\", continue_on_step_failure=True)\n",
+        "published_pipeline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
+        "import requests\n",
+        "\n",
+        "auth = InteractiveLoginAuthentication()\n",
+        "aad_token = auth.get_authentication_header()\n",
+        "\n",
+        "rest_endpoint = published_pipeline.endpoint\n",
+        "\n",
+        "print(\"You can perform HTTP POST on URL {} to trigger this pipeline\".format(rest_endpoint))\n",
+        "\n",
+        "# specify the param when running the pipeline\n",
+        "response = requests.post(rest_endpoint, \n",
+        "                         headers=aad_token, \n",
+        "                         json={\"ExperimentName\": \"MyRestPipeline\",\n",
+        "                               \"RunSource\": \"SDK\",\n",
+        "                               \"DataPathAssignments\": {\n",
+        "                                   \"input_datapath\": { \n",
+        "                                       \"DataStoreName\": def_blob_store.name,\n",
+        "                                       \"RelativePath\": 'sample_datapath3'\n",
+        "                                   }\n",
+        "                               },\n",
+        "                               \"ParameterAssignments\": {\"input_string\": \"sample_string3\"}\n",
+        "                              }\n",
+        "                        )\n",
+        "\n",
+        "run_id = response.json()[\"Id\"]\n",
+        "print('Submitted pipeline run: ', run_id)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "published_pipeline_run_via_rest = PipelineRun(ws.experiments[\"MyRestPipeline\"], run_id)\n",
+        "RunDetails(published_pipeline_run_via_rest).show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "published_pipeline_run_via_rest.wait_for_completion()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='index5'></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create a Datastore trigger schedule and use data path parameter\n",
+        "\n",
+        "When the Pipeline is scheduled with DataPath parameter, it will be triggered by the modified or added data in the DataPath. ```path_on_datastore``` should be a folder and the value of the DataPath will be replaced by the path of the modified data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import Schedule\n",
+        "\n",
+        "schedule = Schedule.create(workspace=ws, \n",
+        "                           name=\"Datastore_trigger_schedule\",\n",
+        "                           pipeline_id=published_pipeline.id, \n",
+        "                           experiment_name='Scheduled_Pipeline',\n",
+        "                           datastore=def_blob_store,\n",
+        "                           wait_for_provisioning=True,\n",
+        "                           description=\"Datastore trigger schedule demo\",\n",
+        "                           path_on_datastore=\"sample_datapath_for_folder\",\n",
+        "                           data_path_parameter_name=\"input_datapath\") #Same name as used above to create PipelineParameter\n",
+        "\n",
+        "print(\"Created schedule with id: {}\".format(schedule.id))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "schedule.disable()\n",
+        "schedule"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "sanpil"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.yml b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.yml
new file mode 100644
index 00000000..0463f025
--- /dev/null
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.yml
@@ -0,0 +1,5 @@
+name: aml-pipelines-showcasing-datapath-and-pipelineparameter
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb
index 18dcf054..dbf3caa1 100644
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb
@@ -290,7 +290,9 @@
         "- **priority:** the priority value to use for the current job *(optional)*\n",
         "- **runtime_version:** the runtime version of the Data Lake Analytics engine *(optional)*\n",
         "- **source_directory:** folder that contains the script, assemblies etc. *(optional)*\n",
-        "- **hash_paths:** list of paths to hash to detect a change (script file is always hashed) *(optional)*"
+        "- **hash_paths:** list of paths to hash to detect a change (script file is always hashed) *(optional)*\n",
+        "\n",
+        "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
       ]
     },
     {
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb
index 7330c80f..afbd1082 100644
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb
@@ -20,7 +20,7 @@
       "metadata": {},
       "source": [
         "# Using Databricks as a Compute Target from Azure Machine Learning Pipeline\n",
-        "To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines), a [DatabricksStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n",
+        "To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://aka.ms/pl-concept), a [DatabricksStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n",
         "\n",
         "The notebook will show:\n",
         "1. Running an arbitrary Databricks notebook that the customer has in Databricks workspace\n",
@@ -175,7 +175,7 @@
       "metadata": {},
       "source": [
         "## Data Connections with Inputs and Outputs\n",
-        "The DatabricksStep supports Azure Blob and ADLS for inputs and outputs. You also will need to define a [Secrets](https://docs.azuredatabricks.net/user-guide/secrets/index.html) scope to enable authentication to external data sources such as Blob and ADLS from Databricks.\n",
+        "The DatabricksStep supports DBFS, Azure Blob and ADLS for inputs and outputs. You also will need to define a [Secrets](https://docs.azuredatabricks.net/user-guide/secrets/index.html) scope to enable authentication to external data sources such as Blob and ADLS from Databricks.\n",
         "\n",
         "- Databricks documentation on [Azure Blob](https://docs.azuredatabricks.net/spark/latest/data-sources/azure/azure-storage.html)\n",
         "- Databricks documentation on [ADLS](https://docs.databricks.com/spark/latest/data-sources/azure/azure-datalake.html)\n",
@@ -290,7 +290,7 @@
       "metadata": {},
       "source": [
         "## Use Databricks from Azure Machine Learning Pipeline\n",
-        "To use Databricks as a compute target from Azure Machine Learning Pipeline, a DatabricksStep is used. Let's define a datasource (via DataReference) and intermediate data (via PipelineData) to be used in DatabricksStep."
+        "To use Databricks as a compute target from Azure Machine Learning Pipeline, a DatabricksStep is used. Let's define a datasource (via DataReference), intermediate data (via PipelineData) and a pipeline parameter (via PipelineParameter) to be used in DatabricksStep."
       ]
     },
     {
@@ -299,10 +299,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.pipeline.core import PipelineParameter\n",
+        "\n",
         "# Use the default blob storage\n",
         "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
         "print('Datastore {} will be used'.format(def_blob_store.name))\n",
         "\n",
+        "pipeline_param = PipelineParameter(name=\"my_pipeline_param\", default_value=\"pipeline_param1\")\n",
+        "\n",
         "# We are uploading a sample file in the local directory to be used as a datasource\n",
         "def_blob_store.upload_files(files=[\"./testdata.txt\"], target_path=\"dbtest\", overwrite=False)\n",
         "\n",
@@ -406,7 +410,9 @@
         "### 1. Running the demo notebook already added to the Databricks workspace\n",
         "Create a notebook in the Azure Databricks workspace, and provide the path to that notebook as the value associated with the environment variable \"DATABRICKS_NOTEBOOK_PATH\". This will then set the variable\u00c2\u00a0notebook_path\u00c2\u00a0when you run the code cell below:\n",
         "\n",
-        "your notebook's path in Azure Databricks UI by hovering over to notebook's title. A typical path of notebook looks like this `/Users/example@databricks.com/example`. See [Databricks Workspace](https://docs.azuredatabricks.net/user-guide/workspace.html) to learn about the folder structure."
+        "your notebook's path in Azure Databricks UI by hovering over to notebook's title. A typical path of notebook looks like this `/Users/example@databricks.com/example`. See [Databricks Workspace](https://docs.azuredatabricks.net/user-guide/workspace.html) to learn about the folder structure.\n",
+        "\n",
+        "Note: DataPath `PipelineParameter` should be provided in list of inputs. Such parameters can be accessed by the datapath `name`."
       ]
     },
     {
@@ -423,7 +429,8 @@
         "    outputs=[step_1_output],\n",
         "    num_workers=1,\n",
         "    notebook_path=notebook_path,\n",
-        "    notebook_params={'myparam': 'testparam'},\n",
+        "    notebook_params={'myparam': 'testparam', \n",
+        "                     'myparam2': pipeline_param},\n",
         "    run_name='DB_Notebook_demo',\n",
         "    compute_target=databricks_compute,\n",
         "    allow_reuse=True\n",
@@ -434,7 +441,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Build and submit the Experiment"
+        "#### Build and submit the Experiment\n",
+        "\n",
+        "Note: Default value of `pipeline_param` will be used if different value is not specified in pipeline parameters during submission"
       ]
     },
     {
@@ -479,7 +488,9 @@
         "dbfs cp ./train-db-dbfs.py dbfs:/train-db-dbfs.py\n",
         "```\n",
         "\n",
-        "The code in the below cell assumes that you have completed the previous step of uploading the script `train-db-dbfs.py` to the root folder in DBFS."
+        "The code in the below cell assumes that you have completed the previous step of uploading the script `train-db-dbfs.py` to the root folder in DBFS.\n",
+        "\n",
+        "Note: `pipeline_param` will add two values in the python_script_params, a name followed by value. the name will be in this format `--MY_PIPELINE_PARAM`. For example, in the given case, python_script_params will be `[\"arg1\", \"--MY_PIPELINE_PARAM\", \"pipeline_param1\", \"arg2\"]`"
       ]
     },
     {
@@ -495,7 +506,7 @@
         "    inputs=[step_1_input],\n",
         "    num_workers=1,\n",
         "    python_script_path=python_script_path,\n",
-        "    python_script_params={'--input_data'},\n",
+        "    python_script_params={'arg1', pipeline_param, 'arg2},\n",
         "    run_name='DB_Python_demo',\n",
         "    compute_target=databricks_compute,\n",
         "    allow_reuse=True\n",
@@ -545,7 +556,9 @@
         "### 3. Running a Python script in Databricks that currenlty is in local computer\n",
         "To run a Python script that is currently in your local computer, follow the instructions below. \n",
         "\n",
-        "The commented out code below code assumes that you have `train-db-local.py` in the `scripts` subdirectory under the current working directory.\n",
+        "The commented out code below code assumes that you have `train-db-local.py` in the `source_directory` subdirectory under the current working directory. \n",
+        "\n",
+        "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step.\n",
         "\n",
         "In this case, the Python script will be uploaded first to DBFS, and then the script will be run in Databricks."
       ]
@@ -557,7 +570,7 @@
       "outputs": [],
       "source": [
         "python_script_name = \"train-db-local.py\"\n",
-        "source_directory = \".\"\n",
+        "source_directory = \"./databricks_train\"\n",
         "\n",
         "dbPythonInLocalMachineStep = DatabricksStep(\n",
         "    name=\"DBPythonInLocalMachine\",\n",
@@ -618,7 +631,9 @@
         "\n",
         "```\n",
         "dbfs cp ./train-db-dbfs.jar dbfs:/train-db-dbfs.jar\n",
-        "```"
+        "```\n",
+        "\n",
+        "Note: `pipeline_param` will add two values in the python_script_params, a name followed by value. the name will be in this format `--MY_PIPELINE_PARAM`. For example, in the given case, python_script_params will be `[\"arg1\", \"--MY_PIPELINE_PARAM\", \"pipeline_param1\", \"arg2\"]`"
       ]
     },
     {
@@ -635,7 +650,7 @@
         "    inputs=[step_1_input],\n",
         "    num_workers=1,\n",
         "    main_class_name=main_jar_class_name,\n",
-        "    jar_params={'arg1', 'arg2'},\n",
+        "    jar_params={'arg1', pipeline_param, 'arg2'},\n",
         "    run_name='DB_JAR_demo',\n",
         "    jar_libraries=[JarLibrary(jar_library_dbfs_path)],\n",
         "    compute_target=databricks_compute,\n",
@@ -684,7 +699,7 @@
       "metadata": {},
       "source": [
         "# Next: ADLA as a Compute Target\n",
-        "To use ADLA as a compute target from Azure Machine Learning Pipeline, a AdlaStep is used. This [notebook](./aml-pipelines-use-adla-as-compute-target.ipynb) demonstrates the use of AdlaStep in Azure Machine Learning Pipeline."
+        "To use ADLA as a compute target from Azure Machine Learning Pipeline, a AdlaStep is used. This [notebook](https://aka.ms/pl-adla) demonstrates the use of AdlaStep in Azure Machine Learning Pipeline."
       ]
     }
   ],
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb
index b43f6afa..aa943c48 100644
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb
@@ -28,24 +28,19 @@
       "metadata": {},
       "source": [
         "## Introduction\n",
-        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
+        "In this example we showcase how you can use AzureML Dataset to load data for AutoML via AML Pipeline. \n",
         "\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you have executed the [configuration](https://aka.ms/pl-config) before running this notebook.\n",
         "\n",
-        "In this notebook you would see\n",
+        "In this notebook you will learn how to:\n",
         "1. Create an `Experiment` in an existing `Workspace`.\n",
         "2. Create or Attach existing AmlCompute to a workspace.\n",
-        "3. Configure AutoML using `AutoMLConfig`.\n",
-        "4. Use AutoMLStep\n",
-        "5. Train the model using AmlCompute\n",
-        "6. Explore the results.\n",
-        "7. Test the best fitted model.\n",
-        "\n",
-        "In addition this notebook showcases the following features\n",
-        "- **Parallel** executions for iterations\n",
-        "- **Asynchronous** tracking of progress\n",
-        "- Retrieving models for any iteration or logged metric\n",
-        "- Specifying AutoML settings as `**kwargs`"
+        "3. Define data loading in a `TabularDataset`.\n",
+        "4. Configure AutoML using `AutoMLConfig`.\n",
+        "5. Use AutoMLStep\n",
+        "6. Train the model using AmlCompute\n",
+        "7. Explore the results.\n",
+        "8. Test the best fitted model."
       ]
     },
     {
@@ -69,6 +64,7 @@
         "import numpy as np\n",
         "import pandas as pd\n",
         "from sklearn import datasets\n",
+        "import pkg_resources\n",
         "\n",
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
@@ -76,6 +72,7 @@
         "from azureml.train.automl import AutoMLConfig\n",
         "from azureml.core.compute import AmlCompute\n",
         "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.dataset import Dataset\n",
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
@@ -108,7 +105,9 @@
       "metadata": {},
       "source": [
         "## Create an Azure ML experiment\n",
-        "Let's create an experiment named \"automl-classification\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure.\n"
+        "Let's create an experiment named \"automl-classification\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure.\n",
+        "\n",
+        "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
       ]
     },
     {
@@ -129,7 +128,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Create or Attach existing AmlCompute\n",
+        "### Create or Attach an AmlCompute cluster\n",
         "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
       ]
     },
@@ -166,45 +165,6 @@
         "     # For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prepare and Point to Data\n",
-        "For remote executions, you need to make the data accessible from the remote compute.\n",
-        "This can be done by uploading the data to DataStore.\n",
-        "In this example, we upload scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "data_train = datasets.load_digits()\n",
-        "\n",
-        "if not os.path.isdir('data'):\n",
-        "    os.mkdir('data')\n",
-        "    \n",
-        "if not os.path.exists(project_folder):\n",
-        "    os.makedirs(project_folder)\n",
-        "    \n",
-        "pd.DataFrame(data_train.data).to_csv(\"data/X_train.tsv\", index=False, header=False, quoting=csv.QUOTE_ALL, sep=\"\\t\")\n",
-        "pd.DataFrame(data_train.target).to_csv(\"data/y_train.tsv\", index=False, header=False, sep=\"\\t\")\n",
-        "\n",
-        "ds = ws.get_default_datastore()\n",
-        "ds.upload(src_dir='./data', target_path='bai_data', overwrite=True, show_progress=True)\n",
-        "\n",
-        "from azureml.data.data_reference import DataReference      \n",
-        "input_data = DataReference(datastore=ds, \n",
-        "                           data_reference_name=\"input_data_reference\",\n",
-        "                           path_on_datastore='bai_data',\n",
-        "                           mode='download',\n",
-        "                           path_on_compute='/tmp/azureml_runs',\n",
-        "                           overwrite=False)"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -214,54 +174,72 @@
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
         "\n",
-        "# Set compute target to AmlCompute\n",
-        "#conda_run_config.target = compute_target\n",
-        "\n",
         "conda_run_config.environment.docker.enabled = True\n",
         "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
         "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], \n",
-        "                              conda_packages=['numpy', 'py-xgboost'], \n",
-        "                              pin_sdk_version=False)\n",
+        "                              conda_packages=['numpy', 'py-xgboost<=0.80'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd\n",
         "\n",
         "print('run config is ready')"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Data"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
-        "%%writefile $project_folder/get_data.py\n",
-        "\n",
-        "import pandas as pd\n",
-        "\n",
-        "def get_data():\n",
-        "    X_train = pd.read_csv(\"/tmp/azureml_runs/bai_data/X_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
-        "    y_train = pd.read_csv(\"/tmp/azureml_runs/bai_data/y_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
-        "\n",
-        "    return { \"X\" : X_train.values, \"y\" : y_train.values.flatten() }\n"
+        "# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
+        "example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
+        "dataset = Dataset.Tabular.from_delimited_files(example_data)\n",
+        "dataset.to_pandas_dataframe().describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "dataset.take(5).to_pandas_dataframe()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Set up AutoMLConfig for Training\n",
+        "### Review the Dataset Result\n",
         "\n",
-        "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
+        "You can peek the result of a TabularDataset at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only `j` records for all the steps in the TabularDataset, which makes it fast even against large datasets.\n",
         "\n",
-        "**Note:** When using AmlCompute, you can't pass Numpy arrays directly to the fit method.\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
-        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
-        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|"
+        "`TabularDataset` objects are composed of a list of transformation steps (optional)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X = dataset.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
+        "y = dataset.keep_columns(columns=['Primary Type'], validate=True)\n",
+        "print('X and y are ready!')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train\n",
+        "This creates a general AutoML settings object."
       ]
     },
     {
@@ -271,20 +249,19 @@
       "outputs": [],
       "source": [
         "automl_settings = {\n",
-        "    \"iteration_timeout_minutes\": 5,\n",
-        "    \"iterations\": 20,\n",
-        "    \"n_cross_validations\": 5,\n",
-        "    \"primary_metric\": 'AUC_weighted',\n",
-        "    \"preprocess\": False,\n",
-        "    \"max_concurrent_iterations\": 3,\n",
-        "    \"verbosity\": logging.INFO\n",
+        "    \"iteration_timeout_minutes\" : 5,\n",
+        "    \"iterations\" : 2,\n",
+        "    \"primary_metric\" : 'AUC_weighted',\n",
+        "    \"preprocess\" : True,\n",
+        "    \"verbosity\" : logging.INFO\n",
         "}\n",
         "automl_config = AutoMLConfig(task = 'classification',\n",
         "                             debug_log = 'automl_errors.log',\n",
         "                             path = project_folder,\n",
         "                             compute_target=compute_target,\n",
         "                             run_configuration=conda_run_config,\n",
-        "                             data_script = project_folder + \"/get_data.py\",\n",
+        "                             X = X,\n",
+        "                             y = y,\n",
         "                             **automl_settings\n",
         "                            )"
       ]
@@ -293,15 +270,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
-        "In this example, we specify `show_output = False` to suppress console output while the run is in progress."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Define AutoMLStep"
+        "You can define outputs for the AutoMLStep using TrainingOutput."
       ]
     },
     {
@@ -312,6 +281,7 @@
       "source": [
         "from azureml.pipeline.core import PipelineData, TrainingOutput\n",
         "\n",
+        "ds = ws.get_default_datastore()\n",
         "metrics_output_name = 'metrics_output'\n",
         "best_model_output_name = 'best_model_output'\n",
         "\n",
@@ -325,6 +295,13 @@
         "                           training_output=TrainingOutput(type='Model'))"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create an AutoMLStep."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -333,9 +310,7 @@
       "source": [
         "automl_step = AutoMLStep(\n",
         "    name='automl_module',\n",
-        "    experiment=experiment,\n",
         "    automl_config=automl_config,\n",
-        "    inputs=[input_data],\n",
         "    outputs=[metirics_data, model_data],\n",
         "    allow_reuse=True)"
       ]
@@ -409,7 +384,7 @@
       "source": [
         "import json\n",
         "with open(metrics_output._path_on_datastore) as f:  \n",
-        "    metrics_output_result = f.read()\n",
+        "   metrics_output_result = f.read()\n",
         "    \n",
         "deserialized_metrics_output = json.loads(metrics_output_result)\n",
         "df = pd.DataFrame(deserialized_metrics_output)\n",
@@ -439,11 +414,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        " import pickle\n",
+        "import pickle\n",
         "\n",
-        " with open(best_model_output._path_on_datastore, \"rb\" ) as f:\n",
-        "     best_model = pickle.load(f)\n",
-        " best_model"
+        "with open(best_model_output._path_on_datastore, \"rb\" ) as f:\n",
+        "    best_model = pickle.load(f)\n",
+        "best_model"
       ]
     },
     {
@@ -451,7 +426,8 @@
       "metadata": {},
       "source": [
         "### Test the Model\n",
-        "#### Load Test Data"
+        "#### Load Test Data\n",
+        "For the test data, it should have the same preparation step as the train data. Otherwise it might get failed at the preprocessing step."
       ]
     },
     {
@@ -460,17 +436,21 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "digits = datasets.load_digits()\n",
-        "X_test = digits.data[:10, :]\n",
-        "y_test = digits.target[:10]\n",
-        "images = digits.images[:10]"
+        "dataset = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
+        "df_test = dataset_test.to_pandas_dataframe()\n",
+        "df_test = df_test[pd.notnull(df['Primary Type'])]\n",
+        "\n",
+        "y_test = df_test[['Primary Type']]\n",
+        "X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Testing Best Model"
+        "#### Testing Our Best Fitted Model\n",
+        "\n",
+        "We will use confusion matrix to see how our model works."
       ]
     },
     {
@@ -479,17 +459,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Randomly select digits and test.\n",
-        "for index in np.random.choice(len(y_test), 3, replace = False):\n",
-        "   print(index)\n",
-        "   predicted = best_model.predict(X_test[index:index + 1])[0]\n",
-        "   label = y_test[index]\n",
-        "   title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
-        "   fig = plt.figure(1, figsize=(3,3))\n",
-        "   ax1 = fig.add_axes((0,0,.8,.8))\n",
-        "   ax1.set_title(title)\n",
-        "   plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
-        "   plt.show()"
+        "from pandas_ml import ConfusionMatrix\n",
+        "\n",
+        "ypred = best_model.predict(X_test)\n",
+        "\n",
+        "cm = ConfusionMatrix(y_test['Primary Type'], ypred)\n",
+        "\n",
+        "print(cm)\n",
+        "\n",
+        "cm.plot()"
       ]
     }
   ],
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb
index 831fdf6a..3a32e59a 100644
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb
@@ -20,7 +20,7 @@
       "metadata": {},
       "source": [
         "# Azure Machine Learning Pipelines with Data Dependency\n",
-        "In this notebook, we will see how we can build a pipeline with implicit data dependancy."
+        "In this notebook, we will see how we can build a pipeline with implicit data dependency."
       ]
     },
     {
@@ -28,7 +28,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites and Azure Machine Learning Basics\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n",
         "\n",
         "### Azure Machine Learning and Pipeline SDK-specific Imports"
       ]
@@ -76,14 +76,20 @@
         "ws = Workspace.from_config()\n",
         "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n",
         "\n",
-        "# Default datastore (Azure file storage)\n",
-        "def_file_store = ws.get_default_datastore() \n",
-        "print(\"Default datastore's name: {}\".format(def_file_store.name))\n",
-        "\n",
+        "# Default datastore (Azure blob storage)\n",
+        "# def_blob_store = ws.get_default_datastore()\n",
         "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
         "print(\"Blobstore's name: {}\".format(def_blob_store.name))"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Source Directory\n",
+        "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -91,7 +97,7 @@
       "outputs": [],
       "source": [
         "# source directory\n",
-        "source_directory = '.'\n",
+        "source_directory = 'data_dependency_run_train'\n",
         "    \n",
         "print('Sample scripts will be created in {} directory.'.format(source_directory))"
       ]
@@ -340,6 +346,7 @@
         "# step5 to use the intermediate data produced by step4\n",
         "# This step also produces an output processed_data2\n",
         "processed_data2 = PipelineData(\"processed_data2\", datastore=def_blob_store)\n",
+        "source_directory = \"data_dependency_run_extract\"\n",
         "\n",
         "extractStep = PythonScriptStep(\n",
         "    script_name=\"extract.py\",\n",
@@ -386,6 +393,7 @@
       "source": [
         "# Now define the compare step which takes two inputs and produces an output\n",
         "processed_data3 = PipelineData(\"processed_data3\", datastore=def_blob_store)\n",
+        "source_directory = \"data_dependency_run_compare\"\n",
         "\n",
         "compareStep = PythonScriptStep(\n",
         "    script_name=\"compare.py\",\n",
@@ -509,7 +517,7 @@
       "metadata": {},
       "source": [
         "# Next: Publishing the Pipeline and calling it from the REST endpoint\n",
-        "See this [notebook](./aml-pipelines-publish-and-run-using-rest-endpoint.ipynb) to understand how the pipeline is published and you can call the REST endpoint to run the pipeline."
+        "See this [notebook](https://aka.ms/pl-pub-rep) to understand how the pipeline is published and you can call the REST endpoint to run the pipeline."
       ]
     }
   ],
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/data_dependency_run_compare/compare.py b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/data_dependency_run_compare/compare.py
new file mode 100644
index 00000000..1784bc7b
--- /dev/null
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/data_dependency_run_compare/compare.py
@@ -0,0 +1,24 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import argparse
+import os
+
+print("In compare.py")
+print("As a data scientist, this is where I use my compare code.")
+parser = argparse.ArgumentParser("compare")
+parser.add_argument("--compare_data1", type=str, help="compare_data1 data")
+parser.add_argument("--compare_data2", type=str, help="compare_data2 data")
+parser.add_argument("--output_compare", type=str, help="output_compare directory")
+parser.add_argument("--pipeline_param", type=int, help="pipeline parameter")
+
+args = parser.parse_args()
+
+print("Argument 1: %s" % args.compare_data1)
+print("Argument 2: %s" % args.compare_data2)
+print("Argument 3: %s" % args.output_compare)
+print("Argument 4: %s" % args.pipeline_param)
+
+if not (args.output_compare is None):
+    os.makedirs(args.output_compare, exist_ok=True)
+    print("%s created" % args.output_compare)
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/data_dependency_run_extract/extract.py b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/data_dependency_run_extract/extract.py
new file mode 100644
index 00000000..0134a090
--- /dev/null
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/data_dependency_run_extract/extract.py
@@ -0,0 +1,21 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import argparse
+import os
+
+print("In extract.py")
+print("As a data scientist, this is where I use my extract code.")
+
+parser = argparse.ArgumentParser("extract")
+parser.add_argument("--input_extract", type=str, help="input_extract data")
+parser.add_argument("--output_extract", type=str, help="output_extract directory")
+
+args = parser.parse_args()
+
+print("Argument 1: %s" % args.input_extract)
+print("Argument 2: %s" % args.output_extract)
+
+if not (args.output_extract is None):
+    os.makedirs(args.output_extract, exist_ok=True)
+    print("%s created" % args.output_extract)
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/data_dependency_run_train/train.py b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/data_dependency_run_train/train.py
new file mode 100644
index 00000000..961f5ebf
--- /dev/null
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/data_dependency_run_train/train.py
@@ -0,0 +1,22 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import argparse
+import os
+
+print("In train.py")
+print("As a data scientist, this is where I use my training code.")
+
+parser = argparse.ArgumentParser("train")
+
+parser.add_argument("--input_data", type=str, help="input data")
+parser.add_argument("--output_train", type=str, help="output_train directory")
+
+args = parser.parse_args()
+
+print("Argument 1: %s" % args.input_data)
+print("Argument 2: %s" % args.output_train)
+
+if not (args.output_train is None):
+    os.makedirs(args.output_train, exist_ok=True)
+    print("%s created" % args.output_train)
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/databricks_train/train-db-local.py b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/databricks_train/train-db-local.py
new file mode 100644
index 00000000..99b511af
--- /dev/null
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/databricks_train/train-db-local.py
@@ -0,0 +1,5 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+print("In train.py")
+print("As a data scientist, this is where I use my training code.")
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/estimator_train/dummy_train.py b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/estimator_train/dummy_train.py
new file mode 100644
index 00000000..0ad3b5ff
--- /dev/null
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/estimator_train/dummy_train.py
@@ -0,0 +1,30 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+import argparse
+import os
+
+print("*********************************************************")
+print("Hello Azure ML!")
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--datadir', type=str, help="data directory")
+parser.add_argument('--output', type=str, help="output")
+args = parser.parse_args()
+
+print("Argument 1: %s" % args.datadir)
+print("Argument 2: %s" % args.output)
+
+if not (args.output is None):
+    os.makedirs(args.output, exist_ok=True)
+    print("%s created" % args.output)
+
+try:
+    from azureml.core import Run
+    run = Run.get_context()
+    print("Log Fibonacci numbers.")
+    run.log_list('Fibonacci numbers', [0, 1, 1, 2, 3, 5, 8, 13, 21, 34])
+    run.complete()
+except:
+    print("Warning: you need to install Azure ML SDK in order to log metrics.")
+
+print("*********************************************************")
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/publish_run_compare/compare.py b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/publish_run_compare/compare.py
new file mode 100644
index 00000000..1784bc7b
--- /dev/null
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/publish_run_compare/compare.py
@@ -0,0 +1,24 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import argparse
+import os
+
+print("In compare.py")
+print("As a data scientist, this is where I use my compare code.")
+parser = argparse.ArgumentParser("compare")
+parser.add_argument("--compare_data1", type=str, help="compare_data1 data")
+parser.add_argument("--compare_data2", type=str, help="compare_data2 data")
+parser.add_argument("--output_compare", type=str, help="output_compare directory")
+parser.add_argument("--pipeline_param", type=int, help="pipeline parameter")
+
+args = parser.parse_args()
+
+print("Argument 1: %s" % args.compare_data1)
+print("Argument 2: %s" % args.compare_data2)
+print("Argument 3: %s" % args.output_compare)
+print("Argument 4: %s" % args.pipeline_param)
+
+if not (args.output_compare is None):
+    os.makedirs(args.output_compare, exist_ok=True)
+    print("%s created" % args.output_compare)
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/publish_run_extract/extract.py b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/publish_run_extract/extract.py
new file mode 100644
index 00000000..0134a090
--- /dev/null
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/publish_run_extract/extract.py
@@ -0,0 +1,21 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import argparse
+import os
+
+print("In extract.py")
+print("As a data scientist, this is where I use my extract code.")
+
+parser = argparse.ArgumentParser("extract")
+parser.add_argument("--input_extract", type=str, help="input_extract data")
+parser.add_argument("--output_extract", type=str, help="output_extract directory")
+
+args = parser.parse_args()
+
+print("Argument 1: %s" % args.input_extract)
+print("Argument 2: %s" % args.output_extract)
+
+if not (args.output_extract is None):
+    os.makedirs(args.output_extract, exist_ok=True)
+    print("%s created" % args.output_extract)
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/publish_run_train/train.py b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/publish_run_train/train.py
new file mode 100644
index 00000000..961f5ebf
--- /dev/null
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/publish_run_train/train.py
@@ -0,0 +1,22 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import argparse
+import os
+
+print("In train.py")
+print("As a data scientist, this is where I use my training code.")
+
+parser = argparse.ArgumentParser("train")
+
+parser.add_argument("--input_data", type=str, help="input data")
+parser.add_argument("--output_train", type=str, help="output_train directory")
+
+args = parser.parse_args()
+
+print("Argument 1: %s" % args.input_data)
+print("Argument 2: %s" % args.output_train)
+
+if not (args.output_train is None):
+    os.makedirs(args.output_train, exist_ok=True)
+    print("%s created" % args.output_train)
diff --git a/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb b/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb
index 7b4fedf5..371b8411 100644
--- a/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb
@@ -1,5 +1,12 @@
 {
   "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -187,7 +194,19 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.core.compute import AmlCompute\n",
+        "from azureml.core.compute import ComputeTarget\n",
+        "\n",
         "aml_compute = ws.get_default_compute_target(\"CPU\")\n",
+        "\n",
+        "if aml_compute is None:\n",
+        "    amlcompute_cluster_name = \"cpu-cluster\"\n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n",
+        "                                                                max_nodes = 4)\n",
+        "\n",
+        "    aml_compute = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
+        "    aml_compute.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
+        "\n",
         "aml_compute"
       ]
     },
@@ -229,7 +248,7 @@
         "# Specify CondaDependencies obj, add necessary packages\n",
         "aml_run_config.environment.python.conda_dependencies = CondaDependencies.create(\n",
         "    conda_packages=['pandas','scikit-learn'], \n",
-        "    pip_packages=['azureml-sdk', 'azureml-dataprep', 'azureml-train-automl==1.0.33'], \n",
+        "    pip_packages=['azureml-sdk', 'azureml-dataprep', 'azureml-train-automl'], \n",
         "    pin_sdk_version=False)\n",
         "\n",
         "print (\"Run configuration created.\")"
@@ -735,6 +754,8 @@
       "outputs": [],
       "source": [
         "%%writefile $train_model_folder/get_data.py\n",
+        "import os\n",
+        "import pandas as pd\n",
         "\n",
         "def get_data():\n",
         "    print(\"In get_data\")\n",
diff --git a/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb b/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb
index bf2a4dae..a10f9297 100644
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb
@@ -387,11 +387,15 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "pipelineparameterssample"
+        ]
+      },
       "outputs": [],
       "source": [
         "pipeline = Pipeline(workspace=ws, steps=[batch_score_step])\n",
-        "pipeline_run = Experiment(ws, 'batch_scoring').submit(pipeline, pipeline_params={\"param_batch_size\": 20})"
+        "pipeline_run = Experiment(ws, 'batch_scoring').submit(pipeline, pipeline_parameters={\"param_batch_size\": 20})"
       ]
     },
     {
diff --git a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.ipynb b/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.ipynb
index b0deef24..6ddaba93 100644
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.ipynb
@@ -384,7 +384,7 @@
       "source": [
         "pipeline = Pipeline(workspace=ws, steps=[stitch_video_step])\n",
         "# submit the pipeline and provide values for the PipelineParameters used in the pipeline\n",
-        "pipeline_run = Experiment(ws, 'style_transfer').submit(pipeline, pipeline_params={\"style\": \"mosaic\", \"nodecount\": 3})"
+        "pipeline_run = Experiment(ws, 'style_transfer').submit(pipeline, pipeline_parameters={\"style\": \"mosaic\", \"nodecount\": 3})"
       ]
     },
     {
diff --git a/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azure-ml.ipynb b/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azure-ml.ipynb
deleted file mode 100644
index 4cb487ae..00000000
--- a/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azure-ml.ipynb
+++ /dev/null
@@ -1,260 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License.\n",
-        "\n",
-        "## Authentication in Azure Machine Learning\n",
-        "\n",
-        "This notebook shows you how to authenticate to your Azure ML Workspace using\n",
-        "\n",
-        " 1. Interactive Login Authentication\n",
-        " 2. Azure CLI Authentication\n",
-        " 3. Service Principal Authentication\n",
-        " \n",
-        "The interactive authentication is suitable for local experimentation on your own computer. Azure CLI authentication is suitable if you are already using Azure CLI for managing Azure resources, and want to sign in only once. The Service Principal authentication is suitable for automated workflows, for example as part of Azure Devops build."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.png)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Interactive Authentication\n",
-        "\n",
-        "Interactive authentication is the default mode when using Azure ML SDK.\n",
-        "\n",
-        "When you connect to your workspace using workspace.from_config, you will get an interactive login dialog."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Also, if you explicitly specify the subscription ID, resource group and resource group, you will get the dialog."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace(subscription_id=\"my-subscription-id\",\n",
-        "               resource_group=\"my-ml-rg\",\n",
-        "               workspace_name=\"my-ml-workspace\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Note the user you're authenticated as must have access to the subscription and resource group. If you receive an error\n",
-        "\n",
-        "```\n",
-        "AuthenticationException: You don't have access to xxxxxx-xxxx-xxx-xxx-xxxxxxxxxx subscription. All the subscriptions that you have access to = ...\n",
-        "```\n",
-        "\n",
-        "check that the you used correct login and entered the correct subscription ID."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In some cases, you may see a version of the error message containing text: ```All the subscriptions that you have access to = []```\n",
-        "\n",
-        "In such a case, you may have to specify the tenant ID of the Azure Active Directory you're using. An example would be accessing a subscription as a guest to a tenant that is not your default. You specify the tenant by explicitly instantiating _InteractiveLoginAuthentication_ with tenant ID as argument ([see instructions how to obtain tenant Id](#get-tenant-id))."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
-        "\n",
-        "interactive_auth = InteractiveLoginAuthentication(tenant_id=\"my-tenant-id\")\n",
-        "\n",
-        "ws = Workspace(subscription_id=\"my-subscription-id\",\n",
-        "               resource_group=\"my-ml-rg\",\n",
-        "               workspace_name=\"my-ml-workspace\",\n",
-        "               auth=interactive_auth)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Azure CLI Authentication\n",
-        "\n",
-        "If you have installed azure-cli package, and used ```az login``` command to log in to your Azure Subscription, you can use _AzureCliAuthentication_ class.\n",
-        "\n",
-        "Note that interactive authentication described above won't use existing Azure CLI auth tokens. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.authentication import AzureCliAuthentication\n",
-        "\n",
-        "cli_auth = AzureCliAuthentication()\n",
-        "\n",
-        "ws = Workspace(subscription_id=\"my-subscription-id\",\n",
-        "               resource_group=\"my-ml-rg\",\n",
-        "               workspace_name=\"my-ml-workspace\",\n",
-        "               auth=cli_auth)\n",
-        "\n",
-        "print(\"Found workspace {} at location {}\".format(ws.name, ws.location))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Service Principal Authentication\n",
-        "\n",
-        "When setting up a machine learning workflow as an automated process, we recommend using Service Principal Authentication. This approach decouples the authentication from any specific user login, and allows managed access control.\n",
-        "\n",
-        "Note that you must have administrator privileges over the Azure subscription to complete these steps.\n",
-        "\n",
-        "The first step is to create a service principal. First, go to [Azure Portal](https://portal.azure.com), select **Azure Active Directory** and **App Registrations**. Then select **+New application registration**, give your service principal a name, for example _my-svc-principal_. You can leave application type as is, and specify a dummy value for Sign-on URL, such as _https://invalid_.\n",
-        "\n",
-        "Then click **Create**.\n",
-        "\n",
-        "![service principal creation]<img src=\"images/svc-pr-1.PNG\">"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The next step is to obtain the _Application ID_ (also called username) and create _password_ for the service principal.\n",
-        "\n",
-        "From the page for your newly created service principal, copy the _Application ID_. Then select **Settings** and **Keys**, write a description for your key, and select duration. Then click **Save**, and copy the _password_ to a secure location.\n",
-        "\n",
-        "![application id and password](images/svc-pr-2.PNG)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id =\"get-tenant-id\"></a>\n",
-        "\n",
-        "Also, you need to obtain the tenant ID of your Azure subscription. Go back to **Azure Active Directory**, select **Properties** and copy _Directory ID_.\n",
-        "\n",
-        "![tenant id](images/svc-pr-3.PNG)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Finally, you need to give the service principal permissions to access your workspace. Navigate to **Resource Groups**, to the resource group for your Machine Learning Workspace. \n",
-        "\n",
-        "Then select **Access Control (IAM)** and **Add a role assignment**. For _Role_, specify which level of access you need to grant, for example _Contributor_. Start entering your service principal name and once it is found, select it, and click **Save**.\n",
-        "\n",
-        "![add role](images/svc-pr-4.PNG)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now you are ready to use the service principal authentication. For example, to connect to your Workspace, see code below and enter your own values for tenant ID, application ID, subscription ID, resource group and workspace.\n",
-        "\n",
-        "**We strongly recommended that you do not insert the secret password to code**. Instead, you can use environment variables to pass it to your code, for example through Azure Key Vault, or through secret build variables in Azure DevOps. For local testing, you can for example use following PowerShell command to set the environment variable.\n",
-        "\n",
-        "```\n",
-        "$env:AZUREML_PASSWORD = \"my-password\"\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "from azureml.core.authentication import ServicePrincipalAuthentication\n",
-        "\n",
-        "svc_pr_password = os.environ.get(\"AZUREML_PASSWORD\")\n",
-        "\n",
-        "svc_pr = ServicePrincipalAuthentication(\n",
-        "    tenant_id=\"my-tenant-id\",\n",
-        "    service_principal_id=\"my-application-id\",\n",
-        "    service_principal_password=svc_pr_password)\n",
-        "\n",
-        "\n",
-        "ws = Workspace(\n",
-        "    subscription_id=\"my-subscription-id\",\n",
-        "    resource_group=\"my-ml-rg\",\n",
-        "    workspace_name=\"my-ml-workspace\",\n",
-        "    auth=svc_pr\n",
-        "    )\n",
-        "\n",
-        "print(\"Found workspace {} at location {}\".format(ws.name, ws.location))"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "roastala"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.5"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb b/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb
index 047068f3..5b4618b9 100644
--- a/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb
+++ b/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb
@@ -26,9 +26,10 @@
         "\n",
         " 1. Interactive Login Authentication\n",
         " 2. Azure CLI Authentication\n",
-        " 3. Service Principal Authentication\n",
+        " 3. Managed Service Identity (MSI) Authentication\n",
+        " 4. Service Principal Authentication\n",
         " \n",
-        "The interactive authentication is suitable for local experimentation on your own computer. Azure CLI authentication is suitable if you are already using Azure CLI for managing Azure resources, and want to sign in only once. The Service Principal authentication is suitable for automated workflows, for example as part of Azure Devops build."
+        "The interactive authentication is suitable for local experimentation on your own computer. Azure CLI authentication is suitable if you are already using Azure CLI for managing Azure resources, and want to sign in only once. The MSI and Service Principal authentication are suitable for automated workflows, for example as part of Azure Devops build."
       ]
     },
     {
@@ -145,6 +146,43 @@
         "print(\"Found workspace {} at location {}\".format(ws.name, ws.location))"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### MSI Authentication\n",
+        "\n",
+        "__Note__: _MSI authentication is supported only when using SDK from Azure Virtual Machine. The code below will fail on local computer._\n",
+        "\n",
+        "When using Azure ML SDK on Azure Virtual Machine (VM), you can use Managed Service Identity (MSI) based authentication. This mode allows the VM connect to the Workspace without storing credentials in the Python code.\n",
+        "\n",
+        "As a pre-requisite, enable System-assigned Managed Identity for your VM as described in [this document](https://docs.microsoft.com/en-us/azure/active-directory/managed-identities-azure-resources/qs-configure-portal-windows-vm).\n",
+        "\n",
+        "Then, assign the VM access to your Workspace. For example from Azure Portal, navigate to your workspace, select __Access Control (IAM)__, __Add Role Assignment__,  specify __Virtual Machine__ for __Assign Access To__ dropdown, and select your VM's identity.\n",
+        "\n",
+        "![msi assignment](images/msiaccess.PNG)\n",
+        "\n",
+        "After completing these steps, you can use authenticate using MsiAuthentication instance."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.authentication import MsiAuthentication\n",
+        "\n",
+        "msi_auth = MsiAuthentication()\n",
+        "\n",
+        "ws = Workspace(subscription_id=\"my-subscription-id\",\n",
+        "               resource_group=\"my-ml-rg\",\n",
+        "               workspace_name=\"my-ml-workspace\",\n",
+        "               auth=msi_auth)\n",
+        "\n",
+        "print(\"Found workspace {} at location {}\".format(ws.name, ws.location))"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -238,6 +276,135 @@
         "See [Register an application with the Microsoft identity platform](https://docs.microsoft.com/en-us/azure/active-directory/develop/quickstart-register-app) quickstart for more details about application registrations. "
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Using Secrets in Remote Runs\n",
+        "\n",
+        "Sometimes, you may have to pass a secret to a remote run, for example username and password to authenticate against external data source.\n",
+        "\n",
+        "Azure ML SDK enables this use case through Key Vault associated with your workspace. The workflow for adding a secret is following.\n",
+        "\n",
+        "On local computer:\n",
+        "\n",
+        " 1. Read in a local secret, for example from environment variable or user input. To keep them secret, do not insert secret values into code as hard-coded strings.\n",
+        " 2. Obtain a reference to the keyvault\n",
+        " 3. Add the secret name-value pair in the key vault.\n",
+        " \n",
+        "The secret is then available for remote runs as shown further below.\n",
+        "\n",
+        "__Note__: The _azureml.core.keyvault.Keyvault_ is different from _azure.keyvault_ library. It is intended as simplified wrapper for setting, getting and listing user secrets in Workspace Key Vault."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os, uuid\n",
+        "\n",
+        "local_secret = os.environ.get(\"LOCAL_SECRET\", default = str(uuid.uuid4())) # Use random UUID as a substitute for real secret.\n",
+        "keyvault = ws.get_default_keyvault()\n",
+        "keyvault.set_secret(name=\"secret-name\", value = local_secret)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The _set_secret_ method adds a new secret if one doesn't exist, or updates an existing one with new value.\n",
+        "\n",
+        "You can list secret names you've added. This method doesn't return the values of the secrets."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "keyvault.list_secrets()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can retrieve the value of the secret, and validate that it matches the original value. \n",
+        "\n",
+        "__Note__: This method returns the secret value. Take care not to write the the secret value to output."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "retrieved_secret = keyvault.get_secret(name=\"secret-name\")\n",
+        "local_secret==retrieved_secret"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In submitted runs on local and remote compute, you can use the get_secret method of Run instance to get the secret value from Key Vault. \n",
+        "\n",
+        "The method gives you a simple shortcut: the Run instance is aware of its Workspace and Keyvault, so it can directly obtain the secret without you having to instantiate the Workspace and Keyvault within remote run.\n",
+        "\n",
+        "__Note__: This method returns the secret value. Take care not to write the secret to output.\n",
+        "\n",
+        "For example, let's create a simple script _get_secret.py_ that gets the secret we set earlier. In an actual appication, you would use the secret, for example to access a database or other password-protected resource."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile get_secret.py\n",
+        "\n",
+        "from azureml.core import Run\n",
+        "\n",
+        "run = Run.get_context()\n",
+        "secret_value = run.get_secret(name=\"secret-name\")\n",
+        "print(\"Got secret value {} , but don't write it out!\".format(len(secret_value) * \"*\"))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Then, submit the script as a regular script run, and find the obfuscated secret value in run output. You can use the same approach to other kinds of runs, such as Estimator ones."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment, Run\n",
+        "from azureml.core.script_run_config import ScriptRunConfig\n",
+        "\n",
+        "exp = Experiment(workspace = ws, name=\"try-secret\")\n",
+        "src = ScriptRunConfig(source_directory=\".\", script=\"get_secret.py\")\n",
+        "\n",
+        "run = exp.submit(src)\n",
+        "run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Furthermore, you can set and get multiple secrets using set_secrets and get_secrets methods."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -267,7 +434,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.4"
+      "version": "3.6.9"
     }
   },
   "nbformat": 4,
diff --git a/how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/chainer_mnist.py b/how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/chainer_mnist.py
new file mode 100644
index 00000000..df2d6a6e
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/chainer_mnist.py
@@ -0,0 +1,139 @@
+
+import argparse
+import os
+
+import numpy as np
+
+import chainer
+from chainer import backend
+from chainer import backends
+from chainer.backends import cuda
+from chainer import Function, gradient_check, report, training, utils, Variable
+from chainer import datasets, iterators, optimizers, serializers
+from chainer import Link, Chain, ChainList
+import chainer.functions as F
+import chainer.links as L
+from chainer.training import extensions
+from chainer.dataset import concat_examples
+from chainer.backends.cuda import to_cpu
+
+from azureml.core.run import Run
+run = Run.get_context()
+
+
+class MyNetwork(Chain):
+
+    def __init__(self, n_mid_units=100, n_out=10):
+        super(MyNetwork, self).__init__()
+        with self.init_scope():
+            self.l1 = L.Linear(None, n_mid_units)
+            self.l2 = L.Linear(n_mid_units, n_mid_units)
+            self.l3 = L.Linear(n_mid_units, n_out)
+
+    def forward(self, x):
+        h = F.relu(self.l1(x))
+        h = F.relu(self.l2(h))
+        return self.l3(h)
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Chainer example: MNIST')
+    parser.add_argument('--batchsize', '-b', type=int, default=100,
+                        help='Number of images in each mini-batch')
+    parser.add_argument('--epochs', '-e', type=int, default=20,
+                        help='Number of sweeps over the dataset to train')
+    parser.add_argument('--output_dir', '-o', default='./outputs',
+                        help='Directory to output the result')
+    parser.add_argument('--gpu_id', '-g', default=0,
+                        help='ID of the GPU to be used. Set to -1 if you use CPU')
+    args = parser.parse_args()
+
+    # Download the MNIST data if you haven't downloaded it yet
+    train, test = datasets.mnist.get_mnist(withlabel=True, ndim=1)
+
+    gpu_id = args.gpu_id
+    batchsize = args.batchsize
+    epochs = args.epochs
+    run.log('Batch size', np.int(batchsize))
+    run.log('Epochs', np.int(epochs))
+
+    train_iter = iterators.SerialIterator(train, batchsize)
+    test_iter = iterators.SerialIterator(test, batchsize,
+                                         repeat=False, shuffle=False)
+
+    model = MyNetwork()
+
+    if gpu_id >= 0:
+        # Make a specified GPU current
+        chainer.backends.cuda.get_device_from_id(0).use()
+        model.to_gpu()  # Copy the model to the GPU
+
+    # Choose an optimizer algorithm
+    optimizer = optimizers.MomentumSGD(lr=0.01, momentum=0.9)
+
+    # Give the optimizer a reference to the model so that it
+    # can locate the model's parameters.
+    optimizer.setup(model)
+
+    while train_iter.epoch < epochs:
+        # ---------- One iteration of the training loop ----------
+        train_batch = train_iter.next()
+        image_train, target_train = concat_examples(train_batch, gpu_id)
+
+        # Calculate the prediction of the network
+        prediction_train = model(image_train)
+
+        # Calculate the loss with softmax_cross_entropy
+        loss = F.softmax_cross_entropy(prediction_train, target_train)
+
+        # Calculate the gradients in the network
+        model.cleargrads()
+        loss.backward()
+
+        # Update all the trainable parameters
+        optimizer.update()
+        # --------------------- until here ---------------------
+
+        # Check the validation accuracy of prediction after every epoch
+        if train_iter.is_new_epoch:  # If this iteration is the final iteration of the current epoch
+
+            # Display the training loss
+            print('epoch:{:02d} train_loss:{:.04f} '.format(
+                train_iter.epoch, float(to_cpu(loss.array))), end='')
+
+            test_losses = []
+            test_accuracies = []
+            while True:
+                test_batch = test_iter.next()
+                image_test, target_test = concat_examples(test_batch, gpu_id)
+
+                # Forward the test data
+                prediction_test = model(image_test)
+
+                # Calculate the loss
+                loss_test = F.softmax_cross_entropy(prediction_test, target_test)
+                test_losses.append(to_cpu(loss_test.array))
+
+                # Calculate the accuracy
+                accuracy = F.accuracy(prediction_test, target_test)
+                accuracy.to_cpu()
+                test_accuracies.append(accuracy.array)
+
+                if test_iter.is_new_epoch:
+                    test_iter.epoch = 0
+                    test_iter.current_position = 0
+                    test_iter.is_new_epoch = False
+                    test_iter._pushed_position = None
+                    break
+
+            val_accuracy = np.mean(test_accuracies)
+            print('val_loss:{:.04f} val_accuracy:{:.04f}'.format(
+                np.mean(test_losses), val_accuracy))
+
+            run.log("Accuracy", np.float(val_accuracy))
+
+    serializers.save_npz(os.path.join(args.output_dir, 'model.npz'), model)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/chainer_score.py b/how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/chainer_score.py
new file mode 100644
index 00000000..f6ec3a6c
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/chainer_score.py
@@ -0,0 +1,45 @@
+import numpy as np
+import os
+import json
+
+from chainer import serializers, using_config, Variable, datasets
+import chainer.functions as F
+import chainer.links as L
+from chainer import Chain
+
+from azureml.core.model import Model
+
+
+class MyNetwork(Chain):
+
+    def __init__(self, n_mid_units=100, n_out=10):
+        super(MyNetwork, self).__init__()
+        with self.init_scope():
+            self.l1 = L.Linear(None, n_mid_units)
+            self.l2 = L.Linear(n_mid_units, n_mid_units)
+            self.l3 = L.Linear(n_mid_units, n_out)
+
+    def forward(self, x):
+        h = F.relu(self.l1(x))
+        h = F.relu(self.l2(h))
+        return self.l3(h)
+
+
+def init():
+    global model
+
+    model_root = Model.get_model_path('chainer-dnn-mnist')
+
+    # Load our saved artifacts
+    model = MyNetwork()
+    serializers.load_npz(model_root, model)
+
+
+def run(input_data):
+    i = np.array(json.loads(input_data)['data'])
+
+    _, test = datasets.get_mnist()
+    x = Variable(np.asarray([test[i][0]]))
+    y = model(x)
+
+    return np.ndarray.tolist(y.data.argmax(axis=1))
diff --git a/how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb b/how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb
new file mode 100644
index 00000000..db24ffee
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb
@@ -0,0 +1,725 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Train and hyperparameter tune with Chainer\n",
+        "\n",
+        "In this tutorial, we demonstrate how to use the Azure ML Python SDK to train a Convolutional Neural Network (CNN) on a single-node GPU with Chainer to perform handwritten digit recognition on the popular MNIST dataset. We will also demonstrate how to perform hyperparameter tuning of the model using Azure ML's HyperDrive service."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!jupyter nbextension install --py --user azureml.widgets\n",
+        "!jupyter nbextension enable --py --user azureml.widgets"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Diagnostics\n",
+        "Opt-in diagnostics for better experience, quality, and security of future releases."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "Diagnostics"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.telemetry import set_diagnostics_collection\n",
+        "\n",
+        "set_diagnostics_collection(send_diagnostics=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize workspace\n",
+        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.workspace import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create or Attach existing AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource.\n",
+        "\n",
+        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n",
+        "\n",
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "cluster_name = \"gpu-cluster\"\n",
+        "\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+        "    print('Found existing compute target.')\n",
+        "except ComputeTargetException:\n",
+        "    print('Creating a new compute target...')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
+        "                                                           min_nodes=2,\n",
+        "                                                           max_nodes=4)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+        "\n",
+        "    compute_target.wait_for_completion(show_output=True)\n",
+        "\n",
+        "# use get_status() to get a detailed status for the current cluster. \n",
+        "print(compute_target.get_status().serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train model on the remote compute\n",
+        "Now that you have your data and training script prepared, you are ready to train on your remote compute cluster. You can take advantage of Azure compute to leverage GPUs to cut down your training time. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a project directory\n",
+        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "project_folder = './chainer-mnist'\n",
+        "os.makedirs(project_folder, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prepare training script\n",
+        "Now you will need to create your training script. In this tutorial, the training script is already provided for you at `chainer_mnist.py`. In practice, you should be able to take any custom training script as is and run it with Azure ML without having to modify your code.\n",
+        "\n",
+        "However, if you would like to use Azure ML's [tracking and metrics](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#metrics) capabilities, you will have to add a small amount of Azure ML code inside your training script. \n",
+        "\n",
+        "In `chainer_mnist.py`, we will log some metrics to our Azure ML run. To do so, we will access the Azure ML `Run` object within the script:\n",
+        "```Python\n",
+        "from azureml.core.run import Run\n",
+        "run = Run.get_context()\n",
+        "```\n",
+        "Further within `chainer_mnist.py`, we log the batchsize and epochs parameters, and the highest accuracy the model achieves:\n",
+        "```Python\n",
+        "run.log('Batch size', np.int(args.batchsize))\n",
+        "run.log('Epochs', np.int(args.epochs))\n",
+        "\n",
+        "run.log('Accuracy', np.float(val_accuracy))\n",
+        "```\n",
+        "These run metrics will become particularly important when we begin hyperparameter tuning our model in the \"Tune model hyperparameters\" section."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Once your script is ready, copy the training script `chainer_mnist.py` into your project directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import shutil\n",
+        "\n",
+        "shutil.copy('chainer_mnist.py', project_folder)\n",
+        "shutil.copy('chainer_score.py', project_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create an experiment\n",
+        "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this Chainer tutorial. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "experiment_name = 'chainer-mnist'\n",
+        "experiment = Experiment(ws, name=experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a Chainer estimator\n",
+        "The Azure ML SDK's Chainer estimator enables you to easily submit Chainer training jobs for both single-node and distributed runs. The following code will define a single-node Chainer job."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "dnn-chainer-remarks-sample"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.train.dnn import Chainer\n",
+        "\n",
+        "script_params = {\n",
+        "    '--epochs': 10,\n",
+        "    '--batchsize': 128,\n",
+        "    '--output_dir': './outputs'\n",
+        "}\n",
+        "\n",
+        "estimator = Chainer(source_directory=project_folder, \n",
+        "                    script_params=script_params,\n",
+        "                    compute_target=compute_target,\n",
+        "                    pip_packages=['numpy', 'pytest'],\n",
+        "                    entry_script='chainer_mnist.py',\n",
+        "                    use_gpu=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The `script_params` parameter is a dictionary containing the command-line arguments to your training script `entry_script`. To leverage the Azure VM's GPU for training, we set `use_gpu=True`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit job\n",
+        "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run = experiment.submit(estimator)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Monitor your run\n",
+        "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# to get more details of your run\n",
+        "print(run.get_details())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Tune model hyperparameters\n",
+        "Now that we've seen how to do a simple Chainer training run using the SDK, let's see if we can further improve the accuracy of our model. We can optimize our model's hyperparameters using Azure Machine Learning's hyperparameter tuning capabilities."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Start a hyperparameter sweep\n",
+        "First, we will define the hyperparameter space to sweep over. Let's tune the batch size and epochs parameters. In this example we will use random sampling to try different configuration sets of hyperparameters to maximize our primary metric, accuracy.\n",
+        "\n",
+        "Then, we specify the early termination policy to use to early terminate poorly performing runs. Here we use the `BanditPolicy`, which will terminate any run that doesn't fall within the slack factor of our primary evaluation metric. In this tutorial, we will apply this policy every epoch (since we report our `Accuracy` metric every epoch and `evaluation_interval=1`). Notice we will delay the first policy evaluation until after the first `3` epochs (`delay_evaluation=3`).\n",
+        "Refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-tune-hyperparameters#specify-an-early-termination-policy) for more information on the BanditPolicy and other policies available."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.hyperdrive.runconfig import HyperDriveConfig\n",
+        "from azureml.train.hyperdrive.sampling import RandomParameterSampling\n",
+        "from azureml.train.hyperdrive.policy import BanditPolicy\n",
+        "from azureml.train.hyperdrive.run import PrimaryMetricGoal\n",
+        "from azureml.train.hyperdrive.parameter_expressions import choice\n",
+        "    \n",
+        "\n",
+        "param_sampling = RandomParameterSampling( {\n",
+        "    \"--batchsize\": choice(128, 256),\n",
+        "    \"--epochs\": choice(5, 10, 20, 40)\n",
+        "    }\n",
+        ")\n",
+        "\n",
+        "hyperdrive_config = HyperDriveConfig(estimator=estimator,\n",
+        "                                     hyperparameter_sampling=param_sampling, \n",
+        "                                     primary_metric_name='Accuracy',\n",
+        "                                     policy=BanditPolicy(evaluation_interval=1, slack_factor=0.1, delay_evaluation=3),\n",
+        "                                     primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n",
+        "                                     max_total_runs=8,\n",
+        "                                     max_concurrent_runs=4)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, lauch the hyperparameter tuning job."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# start the HyperDrive run\n",
+        "hyperdrive_run = experiment.submit(hyperdrive_config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Monitor HyperDrive runs\n",
+        "You can monitor the progress of the runs with the following Jupyter widget. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "RunDetails(hyperdrive_run).show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "hyperdrive_run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Find and register best model\n",
+        "When all jobs finish, we can find out the one that has the highest accuracy."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run = hyperdrive_run.get_best_run_by_primary_metric()\n",
+        "print(best_run.get_details()['runDefinition']['arguments'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, let's list the model files uploaded during the run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(best_run.get_file_names())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can then register the folder (and all files in it) as a model named `chainer-dnn-mnist` under the workspace for deployment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model = best_run.register_model(model_name='chainer-dnn-mnist', model_path='outputs/model.npz')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Deploy the model in ACI\n",
+        "Now, we are ready to deploy the model as a web service running in Azure Container Instance, [ACI](https://azure.microsoft.com/en-us/services/container-instances/). Azure Machine Learning accomplishes this by constructing a Docker image with the scoring logic and model baked in.\n",
+        "\n",
+        "### Create scoring script\n",
+        "First, we will create a scoring script that will be invoked by the web service call.\n",
+        "+ Now that the scoring script must have two required functions, `init()` and `run(input_data)`.\n",
+        "    + In `init()`, you typically load the model into a global object. This function is executed only once when the Docker contianer is started.\n",
+        "    + In `run(input_data)`, the model is used to predict a value based on the input data. The input and output to `run` uses NPZ as the serialization and de-serialization format because it is the preferred format for Chainer, but you are not limited to it.\n",
+        "    \n",
+        "Refer to the scoring script `chainer_score.py` for this tutorial. Our web service will use this file to predict. When writing your own scoring script, don't forget to test it locally first before you go and deploy the web service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "shutil.copy('chainer_score.py', project_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create myenv.yml\n",
+        "We also need to create an environment file so that Azure Machine Learning can install the necessary packages in the Docker image which are required by your scoring script. In this case, we need to specify conda packages `numpy` and `chainer`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import CondaDependencies\n",
+        "\n",
+        "cd = CondaDependencies.create()\n",
+        "cd.add_conda_package('numpy')\n",
+        "cd.add_conda_package('chainer')\n",
+        "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",
+        "\n",
+        "print(cd.serialize_to_string())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Deploy to ACI\n",
+        "We are almost ready to deploy. Create the inference configuration and deployment configuration and deploy to ACI. This cell will run for about 7-8 minutes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice\n",
+        "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.webservice import Webservice\n",
+        "from azureml.core.model import Model\n",
+        "\n",
+        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "                                   entry_script=\"chainer_score.py\",\n",
+        "                                   conda_file=\"myenv.yml\")\n",
+        "\n",
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1,\n",
+        "                                               auth_enabled=True, # this flag generates API keys to secure access\n",
+        "                                               memory_gb=1,\n",
+        "                                               tags={'name': 'mnist', 'framework': 'Chainer'},\n",
+        "                                               description='Chainer DNN with MNIST')\n",
+        "\n",
+        "service = Model.deploy(workspace=ws, \n",
+        "                           name='chainer-mnist-1', \n",
+        "                           models=[model], \n",
+        "                           inference_config=inference_config, \n",
+        "                           deployment_config=aciconfig)\n",
+        "service.wait_for_deployment(True)\n",
+        "print(service.state)\n",
+        "print(service.scoring_uri)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**Tip: If something goes wrong with the deployment, the first thing to look at is the logs from the service by running the following command:** `print(service.get_logs())`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This is the scoring web service endpoint: `print(service.scoring_uri)`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Test the deployed model\n",
+        "Let's test the deployed model. Pick a random sample from the test set, and send it to the web service hosted in ACI for a prediction. Note, here we are using the an HTTP request to invoke the service.\n",
+        "\n",
+        "We can retrieve the API keys used for accessing the HTTP endpoint and construct a raw HTTP request to send to the service. Don't forget to add key to the HTTP header."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# retreive the API keys. two keys were generated.\n",
+        "key1, Key2 = service.get_keys()\n",
+        "print(key1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "import matplotlib.pyplot as plt\n",
+        "import urllib\n",
+        "import gzip\n",
+        "import numpy as np\n",
+        "import struct\n",
+        "import requests\n",
+        "\n",
+        "\n",
+        "# load compressed MNIST gz files and return numpy arrays\n",
+        "def load_data(filename, label=False):\n",
+        "    with gzip.open(filename) as gz:\n",
+        "        struct.unpack('I', gz.read(4))\n",
+        "        n_items = struct.unpack('>I', gz.read(4))\n",
+        "        if not label:\n",
+        "            n_rows = struct.unpack('>I', gz.read(4))[0]\n",
+        "            n_cols = struct.unpack('>I', gz.read(4))[0]\n",
+        "            res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8)\n",
+        "            res = res.reshape(n_items[0], n_rows * n_cols)\n",
+        "        else:\n",
+        "            res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8)\n",
+        "            res = res.reshape(n_items[0], 1)\n",
+        "    return res\n",
+        "\n",
+        "os.makedirs('./data/mnist', exist_ok=True)\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename = './data/mnist/test-images.gz')\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename = './data/mnist/test-labels.gz')\n",
+        "\n",
+        "X_test = load_data('./data/mnist/test-images.gz', False)\n",
+        "y_test = load_data('./data/mnist/test-labels.gz', True).reshape(-1)\n",
+        "\n",
+        "\n",
+        "# send a random row from the test set to score\n",
+        "random_index = np.random.randint(0, len(X_test)-1)\n",
+        "input_data = \"{\\\"data\\\": [\" + str(random_index) + \"]}\"\n",
+        "\n",
+        "headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
+        "\n",
+        "# send sample to service for scoring\n",
+        "resp = requests.post(service.scoring_uri, input_data, headers=headers)\n",
+        "\n",
+        "print(\"label:\", y_test[random_index])\n",
+        "print(\"prediction:\", resp.text[1])\n",
+        "\n",
+        "plt.imshow(X_test[random_index].reshape((28,28)), cmap='gray')\n",
+        "plt.axis('off')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Let's look at the workspace after the web service was deployed. You should see\n",
+        "\n",
+        " + a registered model named 'chainer-dnn-mnist' and with the id 'chainer-dnn-mnist:1'\n",
+        " + a webservice called 'chainer-mnist-svc' with some scoring URL"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "models = ws.models\n",
+        "for name, model in models.items():\n",
+        "    print(\"Model: {}, ID: {}\".format(name, model.id))\n",
+        "       \n",
+        "webservices = ws.webservices\n",
+        "for name, webservice in webservices.items():\n",
+        "    print(\"Webservice: {}, scoring URI: {}\".format(name, webservice.scoring_uri))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Clean up"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can delete the ACI deployment with a simple delete API call."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "service.delete()"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "dipeck"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    },
+    "msauthor": "dipeck"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.yml b/how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.yml
new file mode 100644
index 00000000..6024bba0
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.yml
@@ -0,0 +1,12 @@
+name: train-hyperparameter-tune-deploy-with-chainer
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
+  - numpy
+  - matplotlib
+  - json
+  - urllib
+  - gzip
+  - struct
+  - requests
diff --git a/how-to-use-azureml/ml-frameworks/chainer/training/distributed-chainer/distributed-chainer.ipynb b/how-to-use-azureml/ml-frameworks/chainer/training/distributed-chainer/distributed-chainer.ipynb
new file mode 100644
index 00000000..012cc9d2
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/chainer/training/distributed-chainer/distributed-chainer.ipynb
@@ -0,0 +1,320 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/chainer/training/distributed-chainer/distributed-chainer.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Distributed Chainer\n",
+        "In this tutorial, you will run a Chainer training example on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using ChainerMN distributed training across a GPU cluster."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Diagnostics\n",
+        "Opt-in diagnostics for better experience, quality, and security of future releases."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "Diagnostics"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.telemetry import set_diagnostics_collection\n",
+        "\n",
+        "set_diagnostics_collection(send_diagnostics=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize workspace\n",
+        "\n",
+        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.workspace import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create or attach existing AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource. Specifically, the below code creates an `STANDARD_NC6` GPU cluster that autoscales from `0` to `4` nodes.\n",
+        "\n",
+        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n",
+        "\n",
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "cluster_name = \"gpu-cluster\"\n",
+        "\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+        "    print('Found existing compute target.')\n",
+        "except ComputeTargetException:\n",
+        "    print('Creating a new compute target...')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6',\n",
+        "                                                           max_nodes=4)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+        "\n",
+        "    compute_target.wait_for_completion(show_output=True)\n",
+        "\n",
+        "# use get_status() to get a detailed status for the current AmlCompute. \n",
+        "print(compute_target.get_status().serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The above code creates GPU compute. If you instead want to create CPU compute, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train model on the remote compute\n",
+        "Now that we have the AmlCompute ready to go, let's run our distributed training job."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a project directory\n",
+        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "project_folder = './chainer-distr'\n",
+        "os.makedirs(project_folder, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prepare training script\n",
+        "Now you will need to create your training script. In this tutorial, the script for distributed training of MNIST is already provided for you at `train_mnist.py`. In practice, you should be able to take any custom Chainer training script as is and run it with Azure ML without having to modify your code."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Once your script is ready, copy the training script `train_mnist.py` into the project directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import shutil\n",
+        "\n",
+        "shutil.copy('train_mnist.py', project_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create an experiment\n",
+        "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed Chainer tutorial. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "experiment_name = 'chainer-distr'\n",
+        "experiment = Experiment(ws, name=experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a Chainer estimator\n",
+        "The Azure ML SDK's Chainer estimator enables you to easily submit Chainer training jobs for both single-node and distributed runs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.dnn import Chainer, Mpi\n",
+        "\n",
+        "estimator = Chainer(source_directory=project_folder,\n",
+        "                    compute_target=compute_target,\n",
+        "                    entry_script='train_mnist.py',\n",
+        "                    node_count=2,\n",
+        "                    distributed_training=Mpi(),\n",
+        "                    use_gpu=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to execute a distributed run using MPI, you must provide the argument `distributed_backend=Mpi()`. To specify `i` workers per node, you must provide the argument `distributed_backend=Mpi(process_count_per_node=i)`.Using this estimator with these settings, Chainer and its dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `Chainer` constructor's `pip_packages` or `conda_packages` parameters."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit job\n",
+        "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run = experiment.submit(estimator)\n",
+        "print(run)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Monitor your run\n",
+        "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes. You can see that the widget automatically plots and visualizes the loss metric that we logged to the Azure ML run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.wait_for_completion(show_output=True)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "ninhu"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/ml-frameworks/chainer/training/distributed-chainer/distributed-chainer.yml b/how-to-use-azureml/ml-frameworks/chainer/training/distributed-chainer/distributed-chainer.yml
new file mode 100644
index 00000000..0c2ef761
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/chainer/training/distributed-chainer/distributed-chainer.yml
@@ -0,0 +1,5 @@
+name: distributed-chainer
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
diff --git a/how-to-use-azureml/ml-frameworks/chainer/training/distributed-chainer/train_mnist.py b/how-to-use-azureml/ml-frameworks/chainer/training/distributed-chainer/train_mnist.py
new file mode 100644
index 00000000..29c77f2d
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/chainer/training/distributed-chainer/train_mnist.py
@@ -0,0 +1,125 @@
+# Official ChainerMN example taken from
+# https://github.com/chainer/chainer/blob/master/examples/chainermn/mnist/train_mnist.py
+
+from __future__ import print_function
+
+import argparse
+
+import chainer
+import chainer.functions as F
+import chainer.links as L
+from chainer import training
+from chainer.training import extensions
+
+import chainermn
+
+
+class MLP(chainer.Chain):
+
+    def __init__(self, n_units, n_out):
+        super(MLP, self).__init__(
+            # the size of the inputs to each layer will be inferred
+            l1=L.Linear(784, n_units),  # n_in -> n_units
+            l2=L.Linear(n_units, n_units),  # n_units -> n_units
+            l3=L.Linear(n_units, n_out),  # n_units -> n_out
+        )
+
+    def __call__(self, x):
+        h1 = F.relu(self.l1(x))
+        h2 = F.relu(self.l2(h1))
+        return self.l3(h2)
+
+
+def main():
+    parser = argparse.ArgumentParser(description='ChainerMN example: MNIST')
+    parser.add_argument('--batchsize', '-b', type=int, default=100,
+                        help='Number of images in each mini-batch')
+    parser.add_argument('--communicator', type=str,
+                        default='non_cuda_aware', help='Type of communicator')
+    parser.add_argument('--epoch', '-e', type=int, default=20,
+                        help='Number of sweeps over the dataset to train')
+    parser.add_argument('--gpu', '-g', default=True,
+                        help='Use GPU')
+    parser.add_argument('--out', '-o', default='result',
+                        help='Directory to output the result')
+    parser.add_argument('--resume', '-r', default='',
+                        help='Resume the training from snapshot')
+    parser.add_argument('--unit', '-u', type=int, default=1000,
+                        help='Number of units')
+    args = parser.parse_args()
+
+    # Prepare ChainerMN communicator.
+
+    if args.gpu:
+        if args.communicator == 'naive':
+            print("Error: 'naive' communicator does not support GPU.\n")
+            exit(-1)
+        comm = chainermn.create_communicator(args.communicator)
+        device = comm.intra_rank
+    else:
+        if args.communicator != 'naive':
+            print('Warning: using naive communicator '
+                  'because only naive supports CPU-only execution')
+        comm = chainermn.create_communicator('naive')
+        device = -1
+
+    if comm.rank == 0:
+        print('==========================================')
+        print('Num process (COMM_WORLD): {}'.format(comm.size))
+        if args.gpu:
+            print('Using GPUs')
+        print('Using {} communicator'.format(args.communicator))
+        print('Num unit: {}'.format(args.unit))
+        print('Num Minibatch-size: {}'.format(args.batchsize))
+        print('Num epoch: {}'.format(args.epoch))
+        print('==========================================')
+
+    model = L.Classifier(MLP(args.unit, 10))
+    if device >= 0:
+        chainer.cuda.get_device_from_id(device).use()
+        model.to_gpu()
+
+    # Create a multi node optimizer from a standard Chainer optimizer.
+    optimizer = chainermn.create_multi_node_optimizer(
+        chainer.optimizers.Adam(), comm)
+    optimizer.setup(model)
+
+    # Split and distribute the dataset. Only worker 0 loads the whole dataset.
+    # Datasets of worker 0 are evenly split and distributed to all workers.
+    if comm.rank == 0:
+        train, test = chainer.datasets.get_mnist()
+    else:
+        train, test = None, None
+    train = chainermn.scatter_dataset(train, comm, shuffle=True)
+    test = chainermn.scatter_dataset(test, comm, shuffle=True)
+
+    train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
+    test_iter = chainer.iterators.SerialIterator(test, args.batchsize,
+                                                 repeat=False, shuffle=False)
+
+    updater = training.StandardUpdater(train_iter, optimizer, device=device)
+    trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
+
+    # Create a multi node evaluator from a standard Chainer evaluator.
+    evaluator = extensions.Evaluator(test_iter, model, device=device)
+    evaluator = chainermn.create_multi_node_evaluator(evaluator, comm)
+    trainer.extend(evaluator)
+
+    # Some display and output extensions are necessary only for one worker.
+    # (Otherwise, there would just be repeated outputs.)
+    if comm.rank == 0:
+        trainer.extend(extensions.dump_graph('main/loss'))
+        trainer.extend(extensions.LogReport())
+        trainer.extend(extensions.PrintReport(
+            ['epoch', 'main/loss', 'validation/main/loss',
+             'main/accuracy', 'validation/main/accuracy', 'elapsed_time']))
+        trainer.extend(extensions.ProgressBar())
+
+    if args.resume:
+        chainer.serializers.load_npz(args.resume, trainer)
+
+    trainer.run()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/pytorch_score.py b/how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/pytorch_score.py
new file mode 100644
index 00000000..5df2d8dc
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/pytorch_score.py
@@ -0,0 +1,31 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import torch
+import torch.nn as nn
+from torchvision import transforms
+import json
+
+from azureml.core.model import Model
+
+
+def init():
+    global model
+    model_path = Model.get_model_path('pytorch-birds')
+    model = torch.load(model_path, map_location=lambda storage, loc: storage)
+    model.eval()
+
+
+def run(input_data):
+    input_data = torch.tensor(json.loads(input_data)['data'])
+
+    # get prediction
+    with torch.no_grad():
+        output = model(input_data)
+        classes = ['chicken', 'turkey']
+        softmax = nn.Softmax(dim=1)
+        pred_probs = softmax(output).numpy()[0]
+        index = torch.argmax(output, 1)
+
+    result = {"label": classes[index], "probability": str(pred_probs[index])}
+    return result
diff --git a/how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/pytorch_train.py b/how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/pytorch_train.py
new file mode 100644
index 00000000..733c9a22
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/pytorch_train.py
@@ -0,0 +1,206 @@
+# Copyright (c) 2017, PyTorch contributors
+# Modifications copyright (C) Microsoft Corporation
+# Licensed under the BSD license
+# Adapted from https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
+
+from __future__ import print_function, division
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.optim import lr_scheduler
+from torchvision import datasets, models, transforms
+import numpy as np
+import time
+import os
+import copy
+import argparse
+
+from azureml.core.run import Run
+# get the Azure ML run object
+run = Run.get_context()
+
+
+def load_data(data_dir):
+    """Load the train/val data."""
+
+    # Data augmentation and normalization for training
+    # Just normalization for validation
+    data_transforms = {
+        'train': transforms.Compose([
+            transforms.RandomResizedCrop(224),
+            transforms.RandomHorizontalFlip(),
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+        ]),
+        'val': transforms.Compose([
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+        ]),
+    }
+
+    image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
+                                              data_transforms[x])
+                      for x in ['train', 'val']}
+    dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
+                                                  shuffle=True, num_workers=4)
+                   for x in ['train', 'val']}
+    dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
+    class_names = image_datasets['train'].classes
+
+    return dataloaders, dataset_sizes, class_names
+
+
+def train_model(model, criterion, optimizer, scheduler, num_epochs, data_dir):
+    """Train the model."""
+
+    # load training/validation data
+    dataloaders, dataset_sizes, class_names = load_data(data_dir)
+
+    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+
+    since = time.time()
+
+    best_model_wts = copy.deepcopy(model.state_dict())
+    best_acc = 0.0
+
+    for epoch in range(num_epochs):
+        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
+        print('-' * 10)
+
+        # Each epoch has a training and validation phase
+        for phase in ['train', 'val']:
+            if phase == 'train':
+                scheduler.step()
+                model.train()  # Set model to training mode
+            else:
+                model.eval()   # Set model to evaluate mode
+
+            running_loss = 0.0
+            running_corrects = 0
+
+            # Iterate over data.
+            for inputs, labels in dataloaders[phase]:
+                inputs = inputs.to(device)
+                labels = labels.to(device)
+
+                # zero the parameter gradients
+                optimizer.zero_grad()
+
+                # forward
+                # track history if only in train
+                with torch.set_grad_enabled(phase == 'train'):
+                    outputs = model(inputs)
+                    _, preds = torch.max(outputs, 1)
+                    loss = criterion(outputs, labels)
+
+                    # backward + optimize only if in training phase
+                    if phase == 'train':
+                        loss.backward()
+                        optimizer.step()
+
+                # statistics
+                running_loss += loss.item() * inputs.size(0)
+                running_corrects += torch.sum(preds == labels.data)
+
+            epoch_loss = running_loss / dataset_sizes[phase]
+            epoch_acc = running_corrects.double() / dataset_sizes[phase]
+
+            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
+                phase, epoch_loss, epoch_acc))
+
+            # deep copy the model
+            if phase == 'val' and epoch_acc > best_acc:
+                best_acc = epoch_acc
+                best_model_wts = copy.deepcopy(model.state_dict())
+
+            # log the best val accuracy to AML run
+            run.log('best_val_acc', np.float(best_acc))
+
+        print()
+
+    time_elapsed = time.time() - since
+    print('Training complete in {:.0f}m {:.0f}s'.format(
+        time_elapsed // 60, time_elapsed % 60))
+    print('Best val Acc: {:4f}'.format(best_acc))
+
+    # load best model weights
+    model.load_state_dict(best_model_wts)
+    return model
+
+
+def fine_tune_model(num_epochs, data_dir, learning_rate, momentum):
+    """Load a pretrained model and reset the final fully connected layer."""
+
+    # log the hyperparameter metrics to the AML run
+    run.log('lr', np.float(learning_rate))
+    run.log('momentum', np.float(momentum))
+
+    model_ft = models.resnet18(pretrained=True)
+    num_ftrs = model_ft.fc.in_features
+    model_ft.fc = nn.Linear(num_ftrs, 2)  # only 2 classes to predict
+
+    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+    model_ft = model_ft.to(device)
+
+    criterion = nn.CrossEntropyLoss()
+
+    # Observe that all parameters are being optimized
+    optimizer_ft = optim.SGD(model_ft.parameters(),
+                             lr=learning_rate, momentum=momentum)
+
+    # Decay LR by a factor of 0.1 every 7 epochs
+    exp_lr_scheduler = lr_scheduler.StepLR(
+        optimizer_ft, step_size=7, gamma=0.1)
+
+    model = train_model(model_ft, criterion, optimizer_ft,
+                        exp_lr_scheduler, num_epochs, data_dir)
+
+    return model
+
+
+def download_data():
+    """Download and extract the training data."""
+    import urllib
+    from zipfile import ZipFile
+    # download data
+    data_file = './fowl_data.zip'
+    download_url = 'https://msdocsdatasets.blob.core.windows.net/pytorchfowl/fowl_data.zip'
+    urllib.request.urlretrieve(download_url, filename=data_file)
+
+    # extract files
+    with ZipFile(data_file, 'r') as zip:
+        print('extracting files...')
+        zip.extractall()
+        print('finished extracting')
+        data_dir = zip.namelist()[0]
+
+    # delete zip file
+    os.remove(data_file)
+    return data_dir
+
+
+def main():
+    print("Torch version:", torch.__version__)
+
+    # get command-line arguments
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--num_epochs', type=int, default=25,
+                        help='number of epochs to train')
+    parser.add_argument('--output_dir', type=str, help='output directory')
+    parser.add_argument('--learning_rate', type=float,
+                        default=0.001, help='learning rate')
+    parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
+    args = parser.parse_args()
+
+    data_dir = download_data()
+    print("data directory is: " + data_dir)
+    model = fine_tune_model(args.num_epochs, data_dir,
+                            args.learning_rate, args.momentum)
+    os.makedirs(args.output_dir, exist_ok=True)
+    torch.save(model, os.path.join(args.output_dir, 'model.pt'))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/test_img.jpg b/how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/test_img.jpg
new file mode 100644
index 00000000..f2878b48
Binary files /dev/null and b/how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/test_img.jpg differ
diff --git a/how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb b/how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb
new file mode 100644
index 00000000..821381ac
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb
@@ -0,0 +1,715 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Train, hyperparameter tune, and deploy with PyTorch\n",
+        "\n",
+        "In this tutorial, you will train, hyperparameter tune, and deploy a PyTorch model using the Azure Machine Learning (Azure ML) Python SDK.\n",
+        "\n",
+        "This tutorial will train an image classification model using transfer learning, based on PyTorch's [Transfer Learning tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html). The model is trained to classify chickens and turkeys by first using a pretrained ResNet18 model that has been trained on the [ImageNet](http://image-net.org/index) dataset."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Diagnostics\n",
+        "Opt-in diagnostics for better experience, quality, and security of future releases."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "Diagnostics"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.telemetry import set_diagnostics_collection\n",
+        "\n",
+        "set_diagnostics_collection(send_diagnostics=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize workspace\n",
+        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.workspace import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep='\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create or Attach existing AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource.\n",
+        "\n",
+        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n",
+        "\n",
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "cluster_name = \"gpu-cluster\"\n",
+        "\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+        "    print('Found existing compute target.')\n",
+        "except ComputeTargetException:\n",
+        "    print('Creating a new compute target...')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
+        "                                                           max_nodes=4)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+        "\n",
+        "    compute_target.wait_for_completion(show_output=True)\n",
+        "\n",
+        "# use get_status() to get a detailed status for the current cluster. \n",
+        "print(compute_target.get_status().serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train model on the remote compute\n",
+        "Now that you have your data and training script prepared, you are ready to train on your remote compute cluster. You can take advantage of Azure compute to leverage GPUs to cut down your training time. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a project directory\n",
+        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "project_folder = './pytorch-birds'\n",
+        "os.makedirs(project_folder, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Download training data\n",
+        "The dataset we will use (located on a public blob [here](https://msdocsdatasets.blob.core.windows.net/pytorchfowl/fowl_data.zip) as a zip file) consists of about 120 training images each for turkeys and chickens, with 100 validation images for each class. The images are a subset of the [Open Images v5 Dataset](https://storage.googleapis.com/openimages/web/index.html). We will download and extract the dataset as part of our training script `pytorch_train.py`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prepare training script\n",
+        "Now you will need to create your training script. In this tutorial, the training script is already provided for you at `pytorch_train.py`. In practice, you should be able to take any custom training script as is and run it with Azure ML without having to modify your code.\n",
+        "\n",
+        "However, if you would like to use Azure ML's [tracking and metrics](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#metrics) capabilities, you will have to add a small amount of Azure ML code inside your training script. \n",
+        "\n",
+        "In `pytorch_train.py`, we will log some metrics to our Azure ML run. To do so, we will access the Azure ML `Run` object within the script:\n",
+        "```Python\n",
+        "from azureml.core.run import Run\n",
+        "run = Run.get_context()\n",
+        "```\n",
+        "Further within `pytorch_train.py`, we log the learning rate and momentum parameters, and the best validation accuracy the model achieves:\n",
+        "```Python\n",
+        "run.log('lr', np.float(learning_rate))\n",
+        "run.log('momentum', np.float(momentum))\n",
+        "\n",
+        "run.log('best_val_acc', np.float(best_acc))\n",
+        "```\n",
+        "These run metrics will become particularly important when we begin hyperparameter tuning our model in the \"Tune model hyperparameters\" section."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Once your script is ready, copy the training script `pytorch_train.py` into your project directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import shutil\n",
+        "\n",
+        "shutil.copy('pytorch_train.py', project_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create an experiment\n",
+        "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this transfer learning PyTorch tutorial. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "experiment_name = 'pytorch-birds'\n",
+        "experiment = Experiment(ws, name=experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a PyTorch estimator\n",
+        "The Azure ML SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for both single-node and distributed runs. For more information on the PyTorch estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-pytorch). The following code will define a single-node PyTorch job."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "dnn-pytorch-remarks-sample"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.train.dnn import PyTorch\n",
+        "\n",
+        "script_params = {\n",
+        "    '--num_epochs': 30,\n",
+        "    '--output_dir': './outputs'\n",
+        "}\n",
+        "\n",
+        "estimator = PyTorch(source_directory=project_folder, \n",
+        "                    script_params=script_params,\n",
+        "                    compute_target=compute_target,\n",
+        "                    entry_script='pytorch_train.py',\n",
+        "                    use_gpu=True,\n",
+        "                    pip_packages=['pillow==5.4.1'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The `script_params` parameter is a dictionary containing the command-line arguments to your training script `entry_script`. Please note the following:\n",
+        "- We passed our training data reference `ds_data` to our script's `--data_dir` argument. This will 1) mount our datastore on the remote compute and 2) provide the path to the training data `fowl_data` on our datastore.\n",
+        "- We specified the output directory as `./outputs`. The `outputs` directory is specially treated by Azure ML in that all the content in this directory gets uploaded to your workspace as part of your run history. The files written to this directory are therefore accessible even once your remote run is over. In this tutorial, we will save our trained model to this output directory.\n",
+        "\n",
+        "To leverage the Azure VM's GPU for training, we set `use_gpu=True`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit job\n",
+        "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run = experiment.submit(estimator)\n",
+        "print(run)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# to get more details of your run\n",
+        "print(run.get_details())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Monitor your run\n",
+        "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Alternatively, you can block until the script has completed training before running more code."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Tune model hyperparameters\n",
+        "Now that we've seen how to do a simple PyTorch training run using the SDK, let's see if we can further improve the accuracy of our model. We can optimize our model's hyperparameters using Azure Machine Learning's hyperparameter tuning capabilities."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Start a hyperparameter sweep\n",
+        "First, we will define the hyperparameter space to sweep over. Since our training script uses a learning rate schedule to decay the learning rate every several epochs, let's tune the initial learning rate and the momentum parameters. In this example we will use random sampling to try different configuration sets of hyperparameters to maximize our primary metric, the best validation accuracy (`best_val_acc`).\n",
+        "\n",
+        "Then, we specify the early termination policy to use to early terminate poorly performing runs. Here we use the `BanditPolicy`, which will terminate any run that doesn't fall within the slack factor of our primary evaluation metric. In this tutorial, we will apply this policy every epoch (since we report our `best_val_acc` metric every epoch and `evaluation_interval=1`). Notice we will delay the first policy evaluation until after the first `10` epochs (`delay_evaluation=10`).\n",
+        "Refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-tune-hyperparameters#specify-an-early-termination-policy) for more information on the BanditPolicy and other policies available."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveConfig, uniform, PrimaryMetricGoal\n",
+        "\n",
+        "param_sampling = RandomParameterSampling( {\n",
+        "        'learning_rate': uniform(0.0005, 0.005),\n",
+        "        'momentum': uniform(0.9, 0.99)\n",
+        "    }\n",
+        ")\n",
+        "\n",
+        "early_termination_policy = BanditPolicy(slack_factor=0.15, evaluation_interval=1, delay_evaluation=10)\n",
+        "\n",
+        "hyperdrive_config = HyperDriveConfig(estimator=estimator,\n",
+        "                                     hyperparameter_sampling=param_sampling, \n",
+        "                                     policy=early_termination_policy,\n",
+        "                                     primary_metric_name='best_val_acc',\n",
+        "                                     primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n",
+        "                                     max_total_runs=8,\n",
+        "                                     max_concurrent_runs=4)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, lauch the hyperparameter tuning job."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# start the HyperDrive run\n",
+        "hyperdrive_run = experiment.submit(hyperdrive_config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Monitor HyperDrive runs\n",
+        "You can monitor the progress of the runs with the following Jupyter widget. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "RunDetails(hyperdrive_run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Or block until the HyperDrive sweep has completed:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "hyperdrive_run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Find and register the best model\n",
+        "Once all the runs complete, we can find the run that produced the model with the highest accuracy."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run = hyperdrive_run.get_best_run_by_primary_metric()\n",
+        "best_run_metrics = best_run.get_metrics()\n",
+        "print(best_run)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print('Best Run is:\\n  Validation accuracy: {0:.5f} \\n  Learning rate: {1:.5f} \\n  Momentum: {2:.5f}'.format(\n",
+        "        best_run_metrics['best_val_acc'][-1],\n",
+        "        best_run_metrics['lr'],\n",
+        "        best_run_metrics['momentum'])\n",
+        "     )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, register the model from your best-performing run to your workspace. The `model_path` parameter takes in the relative path on the remote VM to the model file in your `outputs` directory. In the next section, we will deploy this registered model as a web service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model = best_run.register_model(model_name = 'pytorch-birds', model_path = 'outputs/model.pt')\n",
+        "print(model.name, model.id, model.version, sep = '\\t')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Deploy model as web service\n",
+        "Once you have your trained model, you can deploy the model on Azure. In this tutorial, we will deploy the model as a web service in [Azure Container Instances](https://docs.microsoft.com/en-us/azure/container-instances/) (ACI). For more information on deploying models using Azure ML, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-and-where)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create scoring script\n",
+        "\n",
+        "First, we will create a scoring script that will be invoked by the web service call. Note that the scoring script must have two required functions:\n",
+        "* `init()`: In this function, you typically load the model into a `global` object. This function is executed only once when the Docker container is started. \n",
+        "* `run(input_data)`: In this function, the model is used to predict a value based on the input data. The input and output typically use JSON as serialization and deserialization format, but you are not limited to that.\n",
+        "\n",
+        "Refer to the scoring script `pytorch_score.py` for this tutorial. Our web service will use this file to predict whether an image is a chicken or a turkey. When writing your own scoring script, don't forget to test it locally first before you go and deploy the web service."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create environment file\n",
+        "Then, we will need to create an environment file (`myenv.yml`) that specifies all of the scoring script's package dependencies. This file is used to ensure that all of those dependencies are installed in the Docker image by Azure ML. In this case, we need to specify `azureml-core`, `torch` and `torchvision`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.conda_dependencies import CondaDependencies \n",
+        "\n",
+        "myenv = CondaDependencies.create(pip_packages=['azureml-defaults', 'torch', 'torchvision'])\n",
+        "\n",
+        "with open(\"myenv.yml\",\"w\") as f:\n",
+        "    f.write(myenv.serialize_to_string())\n",
+        "    \n",
+        "print(myenv.serialize_to_string())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Deploy to ACI container\n",
+        "We are ready to deploy. Create an inference configuration which gives specifies the inferencing environment and scripts. Create a deployment configuration file to specify the number of CPUs and gigabytes of RAM needed for your ACI container. While it depends on your model, the default of `1` core and `1` gigabyte of RAM is usually sufficient for many models. This cell will run for about 7-8 minutes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice\n",
+        "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.webservice import Webservice\n",
+        "from azureml.core.model import Model\n",
+        "\n",
+        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "                                   entry_script=\"pytorch_score.py\",\n",
+        "                                   conda_file=\"myenv.yml\")\n",
+        "\n",
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
+        "                                               memory_gb=1, \n",
+        "                                               tags={'data': 'birds',  'method':'transfer learning', 'framework':'pytorch'},\n",
+        "                                               description='Classify turkey/chickens using transfer learning with PyTorch')\n",
+        "\n",
+        "service = Model.deploy(workspace=ws, \n",
+        "                           name='aci-birds', \n",
+        "                           models=[model], \n",
+        "                           inference_config=inference_config, \n",
+        "                           deployment_config=aciconfig)\n",
+        "service.wait_for_deployment(True)\n",
+        "print(service.state)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If your deployment fails for any reason and you need to redeploy, make sure to delete the service before you do so: `service.delete()`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**Tip: If something goes wrong with the deployment, the first thing to look at is the logs from the service by running the following command:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "service.get_logs()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Get the web service's HTTP endpoint, which accepts REST client calls. This endpoint can be shared with anyone who wants to test the web service or integrate it into an application."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(service.scoring_uri)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Test the web service\n",
+        "Finally, let's test our deployed web service. We will send the data as a JSON string to the web service hosted in ACI and use the SDK's `run` API to invoke the service. Here we will take an image from our validation data to predict on."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "from PIL import Image\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "%matplotlib inline\n",
+        "plt.imshow(Image.open('test_img.jpg'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "from torchvision import transforms\n",
+        "    \n",
+        "def preprocess(image_file):\n",
+        "    \"\"\"Preprocess the input image.\"\"\"\n",
+        "    data_transforms = transforms.Compose([\n",
+        "        transforms.Resize(256),\n",
+        "        transforms.CenterCrop(224),\n",
+        "        transforms.ToTensor(),\n",
+        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
+        "    ])\n",
+        "\n",
+        "    image = Image.open(image_file)\n",
+        "    image = data_transforms(image).float()\n",
+        "    image = torch.tensor(image)\n",
+        "    image = image.unsqueeze(0)\n",
+        "    return image.numpy()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "input_data = preprocess('test_img.jpg')\n",
+        "result = service.run(input_data=json.dumps({'data': input_data.tolist()}))\n",
+        "print(result)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Clean up\n",
+        "Once you no longer need the web service, you can delete it with a simple API call."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "service.delete()"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "ninhu"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.yml b/how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.yml
new file mode 100644
index 00000000..09f8d5a9
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.yml
@@ -0,0 +1,9 @@
+name: train-hyperparameter-tune-deploy-with-pytorch
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
+  - pillow==5.4.1
+  - matplotlib
+  - https://download.pytorch.org/whl/cpu/torch-1.1.0-cp35-cp35m-win_amd64.whl
+  - https://download.pytorch.org/whl/cpu/torchvision-0.3.0-cp35-cp35m-win_amd64.whl
diff --git a/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb b/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb
new file mode 100644
index 00000000..2aaf0d8c
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb
@@ -0,0 +1,340 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Distributed PyTorch with Horovod\n",
+        "In this tutorial, you will train a PyTorch model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using distributed training via [Horovod](https://github.com/uber/horovod) across a GPU cluster."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`\n",
+        "* Review the [tutorial](../train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) on single-node PyTorch training using Azure Machine Learning"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Diagnostics\n",
+        "Opt-in diagnostics for better experience, quality, and security of future releases."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "Diagnostics"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.telemetry import set_diagnostics_collection\n",
+        "\n",
+        "set_diagnostics_collection(send_diagnostics=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize workspace\n",
+        "\n",
+        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.workspace import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep='\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create or attach existing AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource. Specifically, the below code creates an `STANDARD_NC6` GPU cluster that autoscales from `0` to `4` nodes.\n",
+        "\n",
+        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n",
+        "\n",
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "cluster_name = \"gpu-cluster\"\n",
+        "\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+        "    print('Found existing compute target.')\n",
+        "except ComputeTargetException:\n",
+        "    print('Creating a new compute target...')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6',\n",
+        "                                                           max_nodes=4)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+        "\n",
+        "    compute_target.wait_for_completion(show_output=True)\n",
+        "\n",
+        "# use get_status() to get a detailed status for the current AmlCompute. \n",
+        "print(compute_target.get_status().serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The above code creates GPU compute. If you instead want to create CPU compute, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train model on the remote compute\n",
+        "Now that we have the AmlCompute ready to go, let's run our distributed training job."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a project directory\n",
+        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "project_folder = './pytorch-distr-hvd'\n",
+        "os.makedirs(project_folder, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prepare training script\n",
+        "Now you will need to create your training script. In this tutorial, the script for distributed training of MNIST is already provided for you at `pytorch_horovod_mnist.py`. In practice, you should be able to take any custom PyTorch training script as is and run it with Azure ML without having to modify your code.\n",
+        "\n",
+        "However, if you would like to use Azure ML's [metric logging](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#logging) capabilities, you will have to add a small amount of Azure ML logic inside your training script. In this example, at each logging interval, we will log the loss for that minibatch to our Azure ML run.\n",
+        "\n",
+        "To do so, in `pytorch_horovod_mnist.py`, we will first access the Azure ML `Run` object within the script:\n",
+        "```Python\n",
+        "from azureml.core.run import Run\n",
+        "run = Run.get_context()\n",
+        "```\n",
+        "Later within the script, we log the loss metric to our run:\n",
+        "```Python\n",
+        "run.log('loss', loss.item())\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Once your script is ready, copy the training script `pytorch_horovod_mnist.py` into the project directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import shutil\n",
+        "\n",
+        "shutil.copy('pytorch_horovod_mnist.py', project_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create an experiment\n",
+        "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed PyTorch tutorial. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "experiment_name = 'pytorch-distr-hvd'\n",
+        "experiment = Experiment(ws, name=experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a PyTorch estimator\n",
+        "The Azure ML SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for both single-node and distributed runs. For more information on the PyTorch estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-pytorch)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.dnn import PyTorch, Mpi\n",
+        "\n",
+        "estimator = PyTorch(source_directory=project_folder,\n",
+        "                    compute_target=compute_target,\n",
+        "                    entry_script='pytorch_horovod_mnist.py',\n",
+        "                    node_count=2,\n",
+        "                    distributed_training=Mpi(),\n",
+        "                    use_gpu=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to execute a distributed run using MPI/Horovod, you must provide the argument `distributed_backend=Mpi()`. To specify `i` workers per node, you must provide the argument `distributed_backend=Mpi(process_count_per_node=i)`. Using this estimator with these settings, PyTorch, Horovod and their dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `PyTorch` constructor's `pip_packages` or `conda_packages` parameters."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit job\n",
+        "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run = experiment.submit(estimator)\n",
+        "print(run)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Monitor your run\n",
+        "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes. You can see that the widget automatically plots and visualizes the loss metric that we logged to the Azure ML run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Alternatively, you can block until the script has completed training before running more code."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.wait_for_completion(show_output=True) # this provides a verbose log"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "ninhu"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.yml b/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.yml
new file mode 100644
index 00000000..58bb77d8
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.yml
@@ -0,0 +1,5 @@
+name: distributed-pytorch-with-horovod
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
diff --git a/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-horovod/pytorch_horovod_mnist.py b/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-horovod/pytorch_horovod_mnist.py
new file mode 100644
index 00000000..83562526
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-horovod/pytorch_horovod_mnist.py
@@ -0,0 +1,170 @@
+# Copyright (c) 2017, PyTorch contributors
+# Modifications copyright (C) Microsoft Corporation
+# Licensed under the BSD license
+# Adapted from https://github.com/uber/horovod/blob/master/examples/pytorch_mnist.py
+
+from __future__ import print_function
+import argparse
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import datasets, transforms
+import torch.utils.data.distributed
+import horovod.torch as hvd
+
+from azureml.core.run import Run
+# get the Azure ML run object
+run = Run.get_context()
+
+print("Torch version:", torch.__version__)
+
+# Training settings
+parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
+parser.add_argument('--batch-size', type=int, default=64, metavar='N',
+                    help='input batch size for training (default: 64)')
+parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
+                    help='input batch size for testing (default: 1000)')
+parser.add_argument('--epochs', type=int, default=10, metavar='N',
+                    help='number of epochs to train (default: 10)')
+parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
+                    help='learning rate (default: 0.01)')
+parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
+                    help='SGD momentum (default: 0.5)')
+parser.add_argument('--no-cuda', action='store_true', default=False,
+                    help='disables CUDA training')
+parser.add_argument('--seed', type=int, default=42, metavar='S',
+                    help='random seed (default: 42)')
+parser.add_argument('--log-interval', type=int, default=10, metavar='N',
+                    help='how many batches to wait before logging training status')
+parser.add_argument('--fp16-allreduce', action='store_true', default=False,
+                    help='use fp16 compression during allreduce')
+args = parser.parse_args()
+args.cuda = not args.no_cuda and torch.cuda.is_available()
+
+hvd.init()
+torch.manual_seed(args.seed)
+
+if args.cuda:
+    # Horovod: pin GPU to local rank.
+    torch.cuda.set_device(hvd.local_rank())
+    torch.cuda.manual_seed(args.seed)
+
+
+kwargs = {}
+train_dataset = \
+    datasets.MNIST('data-%d' % hvd.rank(), train=True, download=True,
+                   transform=transforms.Compose([
+                       transforms.ToTensor(),
+                       transforms.Normalize((0.1307,), (0.3081,))
+                   ]))
+train_sampler = torch.utils.data.distributed.DistributedSampler(
+    train_dataset, num_replicas=hvd.size(), rank=hvd.rank())
+train_loader = torch.utils.data.DataLoader(
+    train_dataset, batch_size=args.batch_size, sampler=train_sampler, **kwargs)
+
+test_dataset = \
+    datasets.MNIST('data-%d' % hvd.rank(), train=False, transform=transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.1307,), (0.3081,))
+    ]))
+test_sampler = torch.utils.data.distributed.DistributedSampler(
+    test_dataset, num_replicas=hvd.size(), rank=hvd.rank())
+test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.test_batch_size,
+                                          sampler=test_sampler, **kwargs)
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
+        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
+        self.conv2_drop = nn.Dropout2d()
+        self.fc1 = nn.Linear(320, 50)
+        self.fc2 = nn.Linear(50, 10)
+
+    def forward(self, x):
+        x = F.relu(F.max_pool2d(self.conv1(x), 2))
+        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
+        x = x.view(-1, 320)
+        x = F.relu(self.fc1(x))
+        x = F.dropout(x, training=self.training)
+        x = self.fc2(x)
+        return F.log_softmax(x)
+
+
+model = Net()
+
+if args.cuda:
+    # Move model to GPU.
+    model.cuda()
+
+# Horovod: broadcast parameters.
+hvd.broadcast_parameters(model.state_dict(), root_rank=0)
+
+# Horovod: scale learning rate by the number of GPUs.
+optimizer = optim.SGD(model.parameters(), lr=args.lr * hvd.size(),
+                      momentum=args.momentum)
+
+# Horovod: (optional) compression algorithm.
+compression = hvd.Compression.fp16 if args.fp16_allreduce else hvd.Compression.none
+
+# Horovod: wrap optimizer with DistributedOptimizer.
+optimizer = hvd.DistributedOptimizer(optimizer,
+                                     named_parameters=model.named_parameters(),
+                                     compression=compression)
+
+
+def train(epoch):
+    model.train()
+    train_sampler.set_epoch(epoch)
+    for batch_idx, (data, target) in enumerate(train_loader):
+        if args.cuda:
+            data, target = data.cuda(), target.cuda()
+        optimizer.zero_grad()
+        output = model(data)
+        loss = F.nll_loss(output, target)
+        loss.backward()
+        optimizer.step()
+        if batch_idx % args.log_interval == 0:
+            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(data), len(train_sampler),
+                100. * batch_idx / len(train_loader), loss.item()))
+
+            # log the loss to the Azure ML run
+            run.log('loss', loss.item())
+
+
+def metric_average(val, name):
+    tensor = torch.tensor(val)
+    avg_tensor = hvd.allreduce(tensor, name=name)
+    return avg_tensor.item()
+
+
+def test():
+    model.eval()
+    test_loss = 0.
+    test_accuracy = 0.
+    for data, target in test_loader:
+        if args.cuda:
+            data, target = data.cuda(), target.cuda()
+        output = model(data)
+        # sum up batch loss
+        test_loss += F.nll_loss(output, target, size_average=False).item()
+        # get the index of the max log-probability
+        pred = output.data.max(1, keepdim=True)[1]
+        test_accuracy += pred.eq(target.data.view_as(pred)).cpu().float().sum()
+
+    test_loss /= len(test_sampler)
+    test_accuracy /= len(test_sampler)
+
+    test_loss = metric_average(test_loss, 'avg_loss')
+    test_accuracy = metric_average(test_accuracy, 'avg_accuracy')
+
+    if hvd.rank() == 0:
+        print('\nTest set: Average loss: {:.4f}, Accuracy: {:.2f}%\n'.format(
+            test_loss, 100. * test_accuracy))
+
+
+for epoch in range(1, args.epochs + 1):
+    train(epoch)
+    test()
diff --git a/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-nccl-gloo/distributed-pytorch-with-nccl-gloo.ipynb b/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-nccl-gloo/distributed-pytorch-with-nccl-gloo.ipynb
new file mode 100644
index 00000000..151cce38
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-nccl-gloo/distributed-pytorch-with-nccl-gloo.ipynb
@@ -0,0 +1,382 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Distributed PyTorch \n",
+        "In this tutorial, you will train a PyTorch model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using distributed training via Nccl/Gloo across a GPU cluster. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Diagnostics\n",
+        "Opt-in diagnostics for better experience, quality, and security of future releases."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "Diagnostics"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.telemetry import set_diagnostics_collection\n",
+        "\n",
+        "set_diagnostics_collection(send_diagnostics=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize workspace\n",
+        "\n",
+        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.workspace import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep='\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create or attach existing AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource. Specifically, the below code creates an `STANDARD_NC6` GPU cluster that autoscales from `0` to `4` nodes.\n",
+        "\n",
+        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n",
+        "\n",
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "cluster_name = \"gpu-cluster\"\n",
+        "\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+        "    print('Found existing compute target.')\n",
+        "except ComputeTargetException:\n",
+        "    print('Creating a new compute target...')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6',\n",
+        "                                                           max_nodes=4)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+        "\n",
+        "    compute_target.wait_for_completion(show_output=True)\n",
+        "\n",
+        "# use get_status() to get a detailed status for the current AmlCompute. \n",
+        "print(compute_target.get_status().serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The above code creates GPU compute. If you instead want to create CPU compute, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train model on the remote compute\n",
+        "Now that we have the AmlCompute ready to go, let's run our distributed training job."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a project directory\n",
+        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "project_folder = './pytorch-distr'\n",
+        "os.makedirs(project_folder, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prepare training script\n",
+        "Now you will need to create your training script. In this tutorial, the script for distributed training of MNIST is already provided for you at `pytorch_mnist.py`. In practice, you should be able to take any custom PyTorch training script as is and run it with Azure ML without having to modify your code.\n",
+        "\n",
+        "However, if you would like to use Azure ML's [metric logging](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#logging) capabilities, you will have to add a small amount of Azure ML logic inside your training script. In this example, at each logging interval, we will log the loss for that minibatch to our Azure ML run.\n",
+        "\n",
+        "To do so, in `pytorch_mnist.py`, we will first access the Azure ML `Run` object within the script:\n",
+        "```Python\n",
+        "from azureml.core.run import Run\n",
+        "run = Run.get_context()\n",
+        "```\n",
+        "Later within the script, we log the loss metric to our run:\n",
+        "```Python\n",
+        "run.log('loss', losses.avg)\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Once your script is ready, copy the training script `pytorch_mnist.py` into the project directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import shutil\n",
+        "\n",
+        "shutil.copy('pytorch_mnist.py', project_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create an experiment\n",
+        "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed PyTorch tutorial. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "experiment_name = 'pytorch-distr'\n",
+        "experiment = Experiment(ws, name=experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a PyTorch estimator(Nccl Backend)\n",
+        "The Azure ML SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for both single-node and distributed runs. For more information on the PyTorch estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-pytorch)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.dnn import PyTorch, Nccl\n",
+        "\n",
+        "estimator = PyTorch(source_directory=project_folder,\n",
+        "                    script_params={\"--dist-backend\" : \"nccl\",\n",
+        "                                   \"--dist-url\": \"$AZ_BATCHAI_PYTORCH_INIT_METHOD\",\n",
+        "                                   \"--rank\": \"$AZ_BATCHAI_TASK_INDEX\",\n",
+        "                                   \"--world-size\": 2},\n",
+        "                    compute_target=compute_target,\n",
+        "                    entry_script='pytorch_mnist.py',\n",
+        "                    node_count=2,\n",
+        "                    distributed_training=Nccl(),\n",
+        "                    use_gpu=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In the above code, `script_params` uses Azure ML generated `AZ_BATCHAI_PYTORCH_INIT_METHOD` for shared file-system initialization and `AZ_BATCHAI_TASK_INDEX` as rank of each worker process.\n",
+        "The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to execute a distributed run using Nccl, you must provide the argument `distributed_training=Nccl()`. Using this estimator with these settings, PyTorch and dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `PyTorch` constructor's `pip_packages` or `conda_packages` parameters."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit job\n",
+        "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run = experiment.submit(estimator)\n",
+        "print(run)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Monitor your run\n",
+        "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes. You can see that the widget automatically plots and visualizes the loss metric that we logged to the Azure ML run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Alternatively, you can block until the script has completed training before running more code."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.wait_for_completion(show_output=True) # this provides a verbose log"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a PyTorch estimator(Gloo Backend)\n",
+        "The Azure ML SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for both single-node and distributed runs. For more information on the PyTorch estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-pytorch)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.dnn import PyTorch, Gloo\n",
+        "\n",
+        "estimator = PyTorch(source_directory=project_folder,\n",
+        "                    script_params={\"--dist-backend\" : \"gloo\",\n",
+        "                                   \"--dist-url\": \"$AZ_BATCHAI_PYTORCH_INIT_METHOD\",\n",
+        "                                   \"--rank\": \"$AZ_BATCHAI_TASK_INDEX\",\n",
+        "                                   \"--world-size\": 2},\n",
+        "                    compute_target=compute_target,\n",
+        "                    entry_script='pytorch_mnist.py',\n",
+        "                    node_count=2,\n",
+        "                    distributed_training=Gloo(),\n",
+        "                    use_gpu=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In the above code, `script_params` uses Azure ML generated `AZ_BATCHAI_PYTORCH_INIT_METHOD` for shared file-system initialization and `AZ_BATCHAI_TASK_INDEX` as rank of each worker process.\n",
+        "The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to execute a distributed run using Gloo, you must provide the argument `distributed_training=Gloo()`. Using this estimator with these settings, PyTorch and dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `PyTorch` constructor's `pip_packages` or `conda_packages` parameters.\n",
+        "\n",
+        "Once you create the estimaotr you can follow the submit steps as shown above to submit a PyTorch run with `Gloo` backend. "
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "ninhu"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-nccl-gloo/distributed-pytorch-with-nccl-gloo.yml b/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-nccl-gloo/distributed-pytorch-with-nccl-gloo.yml
new file mode 100644
index 00000000..a960ad7e
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-nccl-gloo/distributed-pytorch-with-nccl-gloo.yml
@@ -0,0 +1,5 @@
+name: distributed-pytorch-with-nccl-gloo
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
diff --git a/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-nccl-gloo/pytorch_mnist.py b/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-nccl-gloo/pytorch_mnist.py
new file mode 100644
index 00000000..e2b982d2
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-nccl-gloo/pytorch_mnist.py
@@ -0,0 +1,209 @@
+# Copyright (c) 2017, PyTorch contributors
+# Modifications copyright (C) Microsoft Corporation
+# Licensed under the BSD license
+# Adapted from https://github.com/Azure/BatchAI/tree/master/recipes/PyTorch/PyTorch-GPU-Distributed-Gloo
+
+from __future__ import print_function
+import argparse
+import os
+import shutil
+import time
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import datasets, transforms
+import torch.nn.parallel
+import torch.backends.cudnn as cudnn
+import torch.distributed as dist
+import torch.utils.data
+import torch.utils.data.distributed
+import torchvision.models as models
+
+from azureml.core.run import Run
+# get the Azure ML run object
+run = Run.get_context()
+
+# Training settings
+parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
+parser.add_argument('--batch-size', type=int, default=64, metavar='N',
+                    help='input batch size for training (default: 64)')
+parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
+                    help='input batch size for testing (default: 1000)')
+parser.add_argument('--epochs', type=int, default=10, metavar='N',
+                    help='number of epochs to train (default: 10)')
+parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
+                    help='learning rate (default: 0.01)')
+parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
+                    help='SGD momentum (default: 0.5)')
+parser.add_argument('--seed', type=int, default=1, metavar='S',
+                    help='random seed (default: 1)')
+parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
+                    help='number of data loading workers (default: 4)')
+parser.add_argument('--log-interval', type=int, default=10, metavar='N',
+                    help='how many batches to wait before logging training status')
+parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
+                    metavar='W', help='weight decay (default: 1e-4)')
+parser.add_argument('--world-size', default=1, type=int,
+                    help='number of distributed processes')
+parser.add_argument('--dist-url', type=str,
+                    help='url used to set up distributed training')
+parser.add_argument('--dist-backend', default='nccl', type=str,
+                    help='distributed backend')
+parser.add_argument('--rank', default=-1, type=int,
+                    help='rank of the worker')
+
+best_prec1 = 0
+args = parser.parse_args()
+
+args.distributed = args.world_size >= 2
+
+if args.distributed:
+    dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
+                            world_size=args.world_size, rank=args.rank)
+
+train_dataset = datasets.MNIST('data', train=True, download=True,
+                               transform=transforms.Compose([
+                                   transforms.ToTensor(),
+                                   transforms.Normalize((0.1307,), (0.3081,))
+                               ]))
+
+if args.distributed:
+    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
+else:
+    train_sampler = None
+
+train_loader = torch.utils.data.DataLoader(
+    train_dataset,
+    batch_size=args.batch_size, shuffle=(train_sampler is None),
+    num_workers=args.workers, pin_memory=True, sampler=train_sampler)
+
+
+test_loader = torch.utils.data.DataLoader(
+    train_dataset,
+    batch_size=args.batch_size, shuffle=False,
+    num_workers=args.workers, pin_memory=True)
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
+        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
+        self.conv2_drop = nn.Dropout2d()
+        self.fc1 = nn.Linear(320, 50)
+        self.fc2 = nn.Linear(50, 10)
+
+    def forward(self, x):
+        x = F.relu(F.max_pool2d(self.conv1(x), 2))
+        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
+        x = x.view(-1, 320)
+        x = F.relu(self.fc1(x))
+        x = F.dropout(x, training=self.training)
+        x = self.fc2(x)
+        return F.log_softmax(x)
+
+
+model = Net()
+
+if not args.distributed:
+    model = torch.nn.DataParallel(model).cuda()
+else:
+    model.cuda()
+    model = torch.nn.parallel.DistributedDataParallel(model)
+
+# define loss function (criterion) and optimizer
+criterion = nn.CrossEntropyLoss().cuda()
+
+optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
+
+
+def train(epoch):
+    batch_time = AverageMeter()
+    data_time = AverageMeter()
+    losses = AverageMeter()
+    top1 = AverageMeter()
+    top5 = AverageMeter()
+
+    # switch to train mode
+    model.train()
+    end = time.time()
+    for i, (input, target) in enumerate(train_loader):
+        # measure data loading time
+        data_time.update(time.time() - end)
+
+        input, target = input.cuda(), target.cuda()
+
+        # compute output
+        try:
+            output = model(input)
+            loss = criterion(output, target)
+
+            # measure accuracy and record loss
+            prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
+            losses.update(loss.item(), input.size(0))
+            top1.update(prec1[0], input.size(0))
+            top5.update(prec5[0], input.size(0))
+
+            # compute gradient and do SGD step
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            # measure elapsed time
+            batch_time.update(time.time() - end)
+            end = time.time()
+
+            if i % 10 == 0:
+                run.log("loss", losses.avg)
+                run.log("prec@1", "{0:.3f}".format(top1.avg))
+                run.log("prec@5", "{0:.3f}".format(top5.avg))
+                print('Epoch: [{0}][{1}/{2}]\t'
+                      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
+                      'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
+                      'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
+                      'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
+                      'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(epoch, i, len(train_loader),
+                                                                      batch_time=batch_time, data_time=data_time,
+                                                                      loss=losses, top1=top1, top5=top5))
+        except:
+            import sys
+            print("Unexpected error:", sys.exc_info()[0])
+
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+
+def accuracy(output, target, topk=(1,)):
+    """Computes the precision@k for the specified values of k"""
+    maxk = max(topk)
+    batch_size = target.size(0)
+
+    _, pred = output.topk(maxk, 1, True, True)
+    pred = pred.t()
+    correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+    res = []
+    for k in topk:
+        correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+        res.append(correct_k.mul_(100.0 / batch_size))
+    return res
+
+
+for epoch in range(1, args.epochs + 1):
+    train(epoch)
diff --git a/how-to-use-azureml/ml-frameworks/scikit-learn/training/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb b/how-to-use-azureml/ml-frameworks/scikit-learn/training/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb
new file mode 100644
index 00000000..7b274376
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/scikit-learn/training/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb
@@ -0,0 +1,568 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/scikit-learn/training/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Train and hyperparameter tune on Iris Dataset with Scikit-learn\n",
+        "In this tutorial, we demonstrate how to use the Azure ML Python SDK to train a support vector machine (SVM) on a single-node CPU with Scikit-learn to perform classification on the popular [Iris dataset](https://archive.ics.uci.edu/ml/datasets/iris). We will also demonstrate how to perform hyperparameter tuning of the model using Azure ML's HyperDrive service."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "* Go through the [Configuration](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML Workspace"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Diagnostics"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Opt-in diagnostics for better experience, quality, and security of future releases."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.telemetry import set_diagnostics_collection\n",
+        "\n",
+        "set_diagnostics_collection(send_diagnostics=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize workspace"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.workspace import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create AmlCompute"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource.\n",
+        "\n",
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_D2_V2` CPU VMs. This process is broken down into 3 steps:\n",
+        "1. create the configuration (this step is local and only takes a second)\n",
+        "2. create the cluster (this step will take about **20 seconds**)\n",
+        "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "cluster_name = \"cpu-cluster\"\n",
+        "\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+        "    print('Found existing compute target')\n",
+        "except ComputeTargetException:\n",
+        "    print('Creating a new compute target...')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2', \n",
+        "                                                           max_nodes=4)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+        "\n",
+        "    # can poll for a minimum number of nodes and for a specific timeout. \n",
+        "    # if no min node count is provided it uses the scale settings for the cluster\n",
+        "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
+        "\n",
+        "# use get_status() to get a detailed status for the current cluster. \n",
+        "print(compute_target.get_status().serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The above code retrieves a CPU compute target. Scikit-learn does not support GPU computing."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train model on the remote compute"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that you have your data and training script prepared, you are ready to train on your remote compute. You can take advantage of Azure compute to leverage a CPU cluster."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a project directory"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "project_folder = './sklearn-iris'\n",
+        "os.makedirs(project_folder, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prepare training script"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now you will need to create your training script. In this tutorial, the training script is already provided for you at `train_iris`.py. In practice, you should be able to take any custom training script as is and run it with Azure ML without having to modify your code.\n",
+        "\n",
+        "However, if you would like to use Azure ML's [tracking and metrics](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#metrics) capabilities, you will have to add a small amount of Azure ML code inside your training script.\n",
+        "\n",
+        "In `train_iris.py`, we will log some metrics to our Azure ML run. To do so, we will access the Azure ML Run object within the script:\n",
+        "\n",
+        "```python\n",
+        "from azureml.core.run import Run\n",
+        "run = Run.get_context()\n",
+        "```\n",
+        "\n",
+        "Further within `train_iris.py`, we log the kernel and penalty parameters, and the highest accuracy the model achieves:\n",
+        "\n",
+        "```python\n",
+        "run.log('Kernel type', np.string(args.kernel))\n",
+        "run.log('Penalty', np.float(args.penalty))\n",
+        "\n",
+        "run.log('Accuracy', np.float(accuracy))\n",
+        "```\n",
+        "\n",
+        "These run metrics will become particularly important when we begin hyperparameter tuning our model in the \"Tune model hyperparameters\" section.\n",
+        "\n",
+        "Once your script is ready, copy the training script `train_iris.py` into your project directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import shutil\n",
+        "\n",
+        "shutil.copy('train_iris.py', project_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create an experiment"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this Scikit-learn tutorial."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "experiment_name = 'train_iris'\n",
+        "experiment = Experiment(ws, name=experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a Scikit-learn estimator"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The Azure ML SDK's Scikit-learn estimator enables you to easily submit Scikit-learn training jobs for single-node runs. The following code will define a single-node Scikit-learn job."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "sklearn-remarks-sample"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.train.sklearn import SKLearn\n",
+        "\n",
+        "script_params = {\n",
+        "    '--kernel': 'linear',\n",
+        "    '--penalty': 1.0,\n",
+        "}\n",
+        "\n",
+        "estimator = SKLearn(source_directory=project_folder, \n",
+        "                    script_params=script_params,\n",
+        "                    compute_target=compute_target,\n",
+        "                    entry_script='train_iris.py',\n",
+        "                    pip_packages=['joblib==0.13.2']\n",
+        "                   )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The `script_params` parameter is a dictionary containing the command-line arguments to your training script `entry_script`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit job"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run = experiment.submit(estimator)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Monitor your run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.cancel()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Tune model hyperparameters"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that we've seen how to do a simple Scikit-learn training run using the SDK, let's see if we can further improve the accuracy of our model. We can optimize our model's hyperparameters using Azure Machine Learning's hyperparameter tuning capabilities."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Start a hyperparameter sweep"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First, we will define the hyperparameter space to sweep over. Let's tune the `kernel` and `penalty` parameters. In this example we will use random sampling to try different configuration sets of hyperparameters to maximize our primary metric, `Accuracy`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.hyperdrive.runconfig import HyperDriveRunConfig\n",
+        "from azureml.train.hyperdrive.sampling import RandomParameterSampling\n",
+        "from azureml.train.hyperdrive.run import PrimaryMetricGoal\n",
+        "from azureml.train.hyperdrive.parameter_expressions import choice\n",
+        "    \n",
+        "\n",
+        "param_sampling = RandomParameterSampling( {\n",
+        "    \"--kernel\": choice('linear', 'rbf', 'poly', 'sigmoid'),\n",
+        "    \"--penalty\": choice(0.5, 1, 1.5)\n",
+        "    }\n",
+        ")\n",
+        "\n",
+        "hyperdrive_run_config = HyperDriveRunConfig(estimator=estimator,\n",
+        "                                            hyperparameter_sampling=param_sampling, \n",
+        "                                            primary_metric_name='Accuracy',\n",
+        "                                            primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n",
+        "                                            max_total_runs=12,\n",
+        "                                            max_concurrent_runs=4)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, lauch the hyperparameter tuning job."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# start the HyperDrive run\n",
+        "hyperdrive_run = experiment.submit(hyperdrive_run_config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Monitor HyperDrive runs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can monitor the progress of the runs with the following Jupyter widget."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "RunDetails(hyperdrive_run).show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "hyperdrive_run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Find and register best model\n",
+        "When all jobs finish, we can find out the one that has the highest accuracy."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run = hyperdrive_run.get_best_run_by_primary_metric()\n",
+        "print(best_run.get_details()['runDefinition']['arguments'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, let's list the model files uploaded during the run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(best_run.get_file_names())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can then register the folder (and all files in it) as a model named `sklearn-iris` under the workspace for deployment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model = best_run.register_model(model_name='sklearn-iris', model_path='outputs/model.joblib')"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "dipeck"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    },
+    "msauthor": "dipeck"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/ml-frameworks/scikit-learn/training/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.yml b/how-to-use-azureml/ml-frameworks/scikit-learn/training/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.yml
new file mode 100644
index 00000000..2691a849
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/scikit-learn/training/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.yml
@@ -0,0 +1,6 @@
+name: train-hyperparameter-tune-deploy-with-sklearn
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
+  - numpy
diff --git a/how-to-use-azureml/ml-frameworks/scikit-learn/training/train-hyperparameter-tune-deploy-with-sklearn/train_iris.py b/how-to-use-azureml/ml-frameworks/scikit-learn/training/train-hyperparameter-tune-deploy-with-sklearn/train_iris.py
new file mode 100644
index 00000000..bc9099d8
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/scikit-learn/training/train-hyperparameter-tune-deploy-with-sklearn/train_iris.py
@@ -0,0 +1,60 @@
+# Modified from https://www.geeksforgeeks.org/multiclass-classification-using-scikit-learn/
+
+import argparse
+import os
+
+# importing necessary libraries
+import numpy as np
+
+from sklearn import datasets
+from sklearn.metrics import confusion_matrix
+from sklearn.model_selection import train_test_split
+
+import joblib
+
+from azureml.core.run import Run
+run = Run.get_context()
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--kernel', type=str, default='linear',
+                        help='Kernel type to be used in the algorithm')
+    parser.add_argument('--penalty', type=float, default=1.0,
+                        help='Penalty parameter of the error term')
+
+    args = parser.parse_args()
+    run.log('Kernel type', np.str(args.kernel))
+    run.log('Penalty', np.float(args.penalty))
+
+    # loading the iris dataset
+    iris = datasets.load_iris()
+
+    # X -> features, y -> label
+    X = iris.data
+    y = iris.target
+
+    # dividing X, y into train and test data
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+
+    # training a linear SVM classifier
+    from sklearn.svm import SVC
+    svm_model_linear = SVC(kernel=args.kernel, C=args.penalty).fit(X_train, y_train)
+    svm_predictions = svm_model_linear.predict(X_test)
+
+    # model accuracy for X_test
+    accuracy = svm_model_linear.score(X_test, y_test)
+    print('Accuracy of SVM classifier on test set: {:.2f}'.format(accuracy))
+    run.log('Accuracy', np.float(accuracy))
+    # creating a confusion matrix
+    cm = confusion_matrix(y_test, svm_predictions)
+    print(cm)
+
+    os.makedirs('outputs', exist_ok=True)
+    # files saved in the "outputs" folder are automatically uploaded into run history
+    joblib.dump(svm_model_linear, 'outputs/model.joblib')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/nn.png b/how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/nn.png
new file mode 100644
index 00000000..8910281e
Binary files /dev/null and b/how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/nn.png differ
diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/tf_mnist.py b/how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/tf_mnist.py
new file mode 100644
index 00000000..f5ab7099
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/tf_mnist.py
@@ -0,0 +1,106 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import numpy as np
+import argparse
+import os
+import tensorflow as tf
+
+from azureml.core import Run
+from utils import load_data
+
+print("TensorFlow version:", tf.VERSION)
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--data-folder', type=str, dest='data_folder', help='data folder mounting point')
+parser.add_argument('--batch-size', type=int, dest='batch_size', default=50, help='mini batch size for training')
+parser.add_argument('--first-layer-neurons', type=int, dest='n_hidden_1', default=100,
+                    help='# of neurons in the first layer')
+parser.add_argument('--second-layer-neurons', type=int, dest='n_hidden_2', default=100,
+                    help='# of neurons in the second layer')
+parser.add_argument('--learning-rate', type=float, dest='learning_rate', default=0.01, help='learning rate')
+args = parser.parse_args()
+
+data_folder = os.path.join(args.data_folder, 'mnist')
+
+print('training dataset is stored here:', data_folder)
+
+X_train = load_data(os.path.join(data_folder, 'train-images.gz'), False) / 255.0
+X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / 255.0
+
+y_train = load_data(os.path.join(data_folder, 'train-labels.gz'), True).reshape(-1)
+y_test = load_data(os.path.join(data_folder, 'test-labels.gz'), True).reshape(-1)
+
+print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep='\n')
+training_set_size = X_train.shape[0]
+
+n_inputs = 28 * 28
+n_h1 = args.n_hidden_1
+n_h2 = args.n_hidden_2
+n_outputs = 10
+learning_rate = args.learning_rate
+n_epochs = 20
+batch_size = args.batch_size
+
+with tf.name_scope('network'):
+    # construct the DNN
+    X = tf.placeholder(tf.float32, shape=(None, n_inputs), name='X')
+    y = tf.placeholder(tf.int64, shape=(None), name='y')
+    h1 = tf.layers.dense(X, n_h1, activation=tf.nn.relu, name='h1')
+    h2 = tf.layers.dense(h1, n_h2, activation=tf.nn.relu, name='h2')
+    output = tf.layers.dense(h2, n_outputs, name='output')
+
+with tf.name_scope('train'):
+    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=output)
+    loss = tf.reduce_mean(cross_entropy, name='loss')
+    optimizer = tf.train.GradientDescentOptimizer(learning_rate)
+    train_op = optimizer.minimize(loss)
+
+with tf.name_scope('eval'):
+    correct = tf.nn.in_top_k(output, y, 1)
+    acc_op = tf.reduce_mean(tf.cast(correct, tf.float32))
+
+init = tf.global_variables_initializer()
+saver = tf.train.Saver()
+
+# start an Azure ML run
+run = Run.get_context()
+
+with tf.Session() as sess:
+    init.run()
+    for epoch in range(n_epochs):
+
+        # randomly shuffle training set
+        indices = np.random.permutation(training_set_size)
+        X_train = X_train[indices]
+        y_train = y_train[indices]
+
+        # batch index
+        b_start = 0
+        b_end = b_start + batch_size
+        for _ in range(training_set_size // batch_size):
+            # get a batch
+            X_batch, y_batch = X_train[b_start: b_end], y_train[b_start: b_end]
+
+            # update batch index for the next batch
+            b_start = b_start + batch_size
+            b_end = min(b_start + batch_size, training_set_size)
+
+            # train
+            sess.run(train_op, feed_dict={X: X_batch, y: y_batch})
+        # evaluate training set
+        acc_train = acc_op.eval(feed_dict={X: X_batch, y: y_batch})
+        # evaluate validation set
+        acc_val = acc_op.eval(feed_dict={X: X_test, y: y_test})
+
+        # log accuracies
+        run.log('training_acc', np.float(acc_train))
+        run.log('validation_acc', np.float(acc_val))
+        print(epoch, '-- Training accuracy:', acc_train, '\b Validation accuracy:', acc_val)
+        y_hat = np.argmax(output.eval(feed_dict={X: X_test}), axis=1)
+
+    run.log('final_acc', np.float(acc_val))
+
+    os.makedirs('./outputs/model', exist_ok=True)
+    # files saved in the "./outputs" folder are automatically uploaded into run history
+    saver.save(sess, './outputs/model/mnist-tf.model')
diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb b/how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb
new file mode 100644
index 00000000..ff9786c0
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb
@@ -0,0 +1,1145 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nbpresent": {
+          "id": "bf74d2e9-2708-49b1-934b-e0ede342f475"
+        }
+      },
+      "source": [
+        "# Training, hyperparameter tune, and deploy with TensorFlow\n",
+        "\n",
+        "## Introduction\n",
+        "This tutorial shows how to train a simple deep neural network using the MNIST dataset and TensorFlow on Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of `28x28` pixels, representing number from 0 to 9. The goal is to create a multi-class classifier to identify the digit each image represents, and deploy it as a web service in Azure.\n",
+        "\n",
+        "For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/).\n",
+        "\n",
+        "## Prerequisite:\n",
+        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to:\n",
+        "    * install the AML SDK\n",
+        "    * create a workspace and its configuration file (`config.json`)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Let's get started. First let's import some Python libraries."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "nbpresent": {
+          "id": "c377ea0c-0cd9-4345-9be2-e20fb29c94c3"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "import numpy as np\n",
+        "import os\n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "nbpresent": {
+          "id": "edaa7f2f-2439-4148-b57a-8c794c0945ec"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "import azureml\n",
+        "from azureml.core import Workspace\n",
+        "\n",
+        "# check core SDK version number\n",
+        "print(\"Azure ML SDK Version: \", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Diagnostics\n",
+        "Opt-in diagnostics for better experience, quality, and security of future releases."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "Diagnostics"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.telemetry import set_diagnostics_collection\n",
+        "\n",
+        "set_diagnostics_collection(send_diagnostics=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize workspace\n",
+        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nbpresent": {
+          "id": "59f52294-4a25-4c92-bab8-3b07f0f44d15"
+        }
+      },
+      "source": [
+        "## Create an Azure ML experiment\n",
+        "Let's create an experiment named \"tf-mnist\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "nbpresent": {
+          "id": "bc70f780-c240-4779-96f3-bc5ef9a37d59"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "script_folder = './tf-mnist'\n",
+        "os.makedirs(script_folder, exist_ok=True)\n",
+        "\n",
+        "exp = Experiment(workspace=ws, name='tf-mnist')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nbpresent": {
+          "id": "defe921f-8097-44c3-8336-8af6700804a7"
+        }
+      },
+      "source": [
+        "## Download MNIST dataset\n",
+        "In order to train on the MNIST dataset we will first need to download it from Yan LeCun's web site directly and save them in a `data` folder locally."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import urllib\n",
+        "\n",
+        "os.makedirs('./data/mnist', exist_ok=True)\n",
+        "\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz', filename = './data/mnist/train-images.gz')\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz', filename = './data/mnist/train-labels.gz')\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename = './data/mnist/test-images.gz')\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename = './data/mnist/test-labels.gz')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nbpresent": {
+          "id": "c3f2f57c-7454-4d3e-b38d-b0946cf066ea"
+        }
+      },
+      "source": [
+        "## Show some sample images\n",
+        "Let's load the downloaded compressed file into numpy arrays using some utility functions included in the `utils.py` library file from the current folder. Then we use `matplotlib` to plot 30 random images from the dataset along with their labels."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "nbpresent": {
+          "id": "396d478b-34aa-4afa-9898-cdce8222a516"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from utils import load_data\n",
+        "\n",
+        "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the neural network converge faster.\n",
+        "X_train = load_data('./data/mnist/train-images.gz', False) / 255.0\n",
+        "y_train = load_data('./data/mnist/train-labels.gz', True).reshape(-1)\n",
+        "\n",
+        "X_test = load_data('./data/mnist/test-images.gz', False) / 255.0\n",
+        "y_test = load_data('./data/mnist/test-labels.gz', True).reshape(-1)\n",
+        "\n",
+        "count = 0\n",
+        "sample_size = 30\n",
+        "plt.figure(figsize = (16, 6))\n",
+        "for i in np.random.permutation(X_train.shape[0])[:sample_size]:\n",
+        "    count = count + 1\n",
+        "    plt.subplot(1, sample_size, count)\n",
+        "    plt.axhline('')\n",
+        "    plt.axvline('')\n",
+        "    plt.text(x = 10, y = -10, s = y_train[i], fontsize = 18)\n",
+        "    plt.imshow(X_train[i].reshape(28, 28), cmap = plt.cm.Greys)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Upload MNIST dataset to default datastore \n",
+        "A [datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data) is a place where data can be stored that is then made accessible to a Run either by means of mounting or copying the data to the compute target. A datastore can either be backed by an Azure Blob Storage or and Azure File Share (ADLS will be supported in the future). For simple data handling, each workspace provides a default datastore that can be used, in case the data is not already in Blob Storage or File Share."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ds = ws.get_default_datastore()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this next step, we will upload the training and test set into the workspace's default datastore, which we will then later be mount on an `AmlCompute` cluster for training."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ds.upload(src_dir='./data/mnist', target_path='mnist', overwrite=True, show_progress=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create or Attach existing AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_NC6` GPU VMs. This process is broken down into 3 steps:\n",
+        "1. create the configuration (this step is local and only takes a second)\n",
+        "2. create the cluster (this step will take about **20 seconds**)\n",
+        "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "cluster_name = \"gpu-cluster\"\n",
+        "\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+        "    print('Found existing compute target')\n",
+        "except ComputeTargetException:\n",
+        "    print('Creating a new compute target...')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
+        "                                                           max_nodes=4)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+        "\n",
+        "    # can poll for a minimum number of nodes and for a specific timeout. \n",
+        "    # if no min node count is provided it uses the scale settings for the cluster\n",
+        "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
+        "\n",
+        "# use get_status() to get a detailed status for the current cluster. \n",
+        "print(compute_target.get_status().serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'gpu-cluster' of type `AmlCompute`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "compute_targets = ws.compute_targets\n",
+        "for name, ct in compute_targets.items():\n",
+        "    print(name, ct.type, ct.provisioning_state)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Copy the training files into the script folder\n",
+        "The TensorFlow training script is already created for you. You can simply copy it into the script folder, together with the utility library used to load compressed data file into numpy array."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import shutil\n",
+        "\n",
+        "# the training logic is in the tf_mnist.py file.\n",
+        "shutil.copy('./tf_mnist.py', script_folder)\n",
+        "\n",
+        "# the utils.py just helps loading data from the downloaded MNIST dataset into numpy arrays.\n",
+        "shutil.copy('./utils.py', script_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nbpresent": {
+          "id": "2039d2d5-aca6-4f25-a12f-df9ae6529cae"
+        }
+      },
+      "source": [
+        "## Construct neural network in TensorFlow\n",
+        "In the training script `tf_mnist.py`, it creates a very simple DNN (deep neural network), with just 2 hidden layers. The input layer has 28 * 28 = 784 neurons, each representing a pixel in an image. The first hidden layer has 300 neurons, and the second hidden layer has 100 neurons. The output layer has 10 neurons, each representing a targeted label from 0 to 9.\n",
+        "\n",
+        "![DNN](nn.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Azure ML concepts  \n",
+        "Please note the following three things in the code below:\n",
+        "1. The script accepts arguments using the argparse package. In this case there is one argument `--data_folder` which specifies the file system folder in which the script can find the MNIST data\n",
+        "```\n",
+        "    parser = argparse.ArgumentParser()\n",
+        "    parser.add_argument('--data_folder')\n",
+        "```\n",
+        "2. The script is accessing the Azure ML `Run` object by executing `run = Run.get_context()`. Further down the script is using the `run` to report the training accuracy and the validation accuracy as training progresses.\n",
+        "```\n",
+        "    run.log('training_acc', np.float(acc_train))\n",
+        "    run.log('validation_acc', np.float(acc_val))\n",
+        "```\n",
+        "3. When running the script on Azure ML, you can write files out to a folder `./outputs` that is relative to the root directory. This folder is specially tracked by Azure ML in the sense that any files written to that folder during script execution on the remote target will be picked up by Run History; these files (known as artifacts) will be available as part of the run history record."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The next cell will print out the training code for you to inspect it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "with open(os.path.join(script_folder, './tf_mnist.py'), 'r') as f:\n",
+        "    print(f.read())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create TensorFlow estimator\n",
+        "Next, we construct an `azureml.train.dnn.TensorFlow` estimator object, use the Batch AI cluster as compute target, and pass the mount-point of the datastore to the training code as a parameter.\n",
+        "\n",
+        "The TensorFlow estimator is providing a simple way of launching a TensorFlow training job on a compute target. It will automatically provide a docker image that has TensorFlow installed -- if additional pip or conda packages are required, their names can be passed in via the `pip_packages` and `conda_packages` arguments and they will be included in the resulting docker.\n",
+        "\n",
+        "The TensorFlow estimator also takes a `framework_version` parameter -- if no version is provided, the estimator will default to the latest version supported by AzureML. Use `TensorFlow.get_supported_versions()` to get a list of all versions supported by your current SDK version or see the [SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn?view=azure-ml-py) for the versions supported in the most current release."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "dnn-tensorflow-remarks-sample"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.train.dnn import TensorFlow\n",
+        "\n",
+        "script_params = {\n",
+        "    '--data-folder': ws.get_default_datastore().as_mount(),\n",
+        "    '--batch-size': 50,\n",
+        "    '--first-layer-neurons': 300,\n",
+        "    '--second-layer-neurons': 100,\n",
+        "    '--learning-rate': 0.01\n",
+        "}\n",
+        "\n",
+        "est = TensorFlow(source_directory=script_folder,\n",
+        "                 script_params=script_params,\n",
+        "                 compute_target=compute_target,\n",
+        "                 entry_script='tf_mnist.py', \n",
+        "                 use_gpu=True, \n",
+        "                 framework_version='1.13')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Submit job to run\n",
+        "Submit the estimator to an Azure ML experiment to kick off the execution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run = exp.submit(est)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Monitor the Run <a class=\"anchor\" id=\"monitor-run\"></a>\n",
+        "As the Run is executed, it will go through the following stages:\n",
+        "1. Preparing: A docker image is created matching the Python environment specified by the TensorFlow estimator and it will be uploaded to the workspace's Azure Container Registry. This step will only happen once for each Python environment -- the container will then be cached for subsequent runs. Creating and uploading the image takes about **5 minutes**. While the job is preparing, logs are streamed to the run history and can be viewed to monitor the progress of the image creation.\n",
+        "\n",
+        "2. Scaling: If the compute needs to be scaled up (i.e. the Batch AI cluster requires more nodes to execute the run than currently available), the cluster will attempt to scale up in order to make the required amount of nodes available. Scaling typically takes about **5 minutes**.\n",
+        "\n",
+        "3. Running: All scripts in the script folder are uploaded to the compute target, data stores are mounted/copied and the `entry_script` is executed. While the job is running, stdout and the `./logs` folder are streamed to the run history and can be viewed to monitor the progress of the run.\n",
+        "\n",
+        "4. Post-Processing: The `./outputs` folder of the run is copied over to the run history\n",
+        "\n",
+        "There are multiple ways to check the progress of a running job. We can use a Jupyter notebook widget. \n",
+        "\n",
+        "**Note: The widget will automatically update ever 10-15 seconds, always showing you the most up-to-date information about the run**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can also periodically check the status of the run object, and navigate to Azure portal to monitor the run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### The Run object <a class=\"anchor\" id=\"run-object\"></a>\n",
+        "The Run object provides the interface to the run history -- both to the job and to the control plane (this notebook), and both while the job is running and after it has completed. It provides a number of interesting features for instance:\n",
+        "* `run.get_details()`: Provides a rich set of properties of the run\n",
+        "* `run.get_metrics()`: Provides a dictionary with all the metrics that were reported for the Run\n",
+        "* `run.get_file_names()`: List all the files that were uploaded to the run history for this Run. This will include the `outputs` and `logs` folder, azureml-logs and other logs, as well as files that were explicitly uploaded to the run using `run.upload_file()`\n",
+        "\n",
+        "Below are some examples -- please run through them and inspect their output. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.get_details()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.get_metrics()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.get_file_names()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot accuracy over epochs\n",
+        "Since we can retrieve the metrics from the run, we can easily make plots using `matplotlib` in the notebook. Then we can add the plotted image to the run using `run.log_image()`, so all information about the run is kept together."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "\n",
+        "os.makedirs('./imgs', exist_ok=True)\n",
+        "metrics = run.get_metrics()\n",
+        "\n",
+        "plt.figure(figsize = (13,5))\n",
+        "plt.plot(metrics['validation_acc'], 'r-', lw=4, alpha=.6)\n",
+        "plt.plot(metrics['training_acc'], 'b--', alpha=0.5)\n",
+        "plt.legend(['Full evaluation set', 'Training set mini-batch'])\n",
+        "plt.xlabel('epochs', fontsize=14)\n",
+        "plt.ylabel('accuracy', fontsize=14)\n",
+        "plt.title('Accuracy over Epochs', fontsize=16)\n",
+        "run.log_image(name='acc_over_epochs.png', plot=plt)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Download the saved model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In the training script, a TensorFlow `saver` object is used to persist the model in a local folder (local to the compute target). The model was saved to the `./outputs` folder on the disk of the Batch AI cluster node where the job is run. Azure ML automatically uploaded anything written in the `./outputs` folder into run history file store. Subsequently, we can use the `Run` object to download the model files the `saver` object saved. They are under the the `outputs/model` folder in the run history file store, and are downloaded into a local folder named `model`. Note the TensorFlow model consists of four files in binary format and they are not human-readable."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# create a model folder in the current directory\n",
+        "os.makedirs('./model', exist_ok=True)\n",
+        "\n",
+        "for f in run.get_file_names():\n",
+        "    if f.startswith('outputs/model'):\n",
+        "        output_file_path = os.path.join('./model', f.split('/')[-1])\n",
+        "        print('Downloading from {} to {} ...'.format(f, output_file_path))\n",
+        "        run.download_file(name=f, output_file_path=output_file_path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Predict on the test set\n",
+        "Now load the saved TensorFlow graph, and list all operations under the `network` scope. This way we can discover the input tensor `network/X:0` and the output tensor `network/output/MatMul:0`, and use them in the scoring script in the next step.\n",
+        "\n",
+        "Note: if your local TensorFlow version is different than the version running in the cluster where the model is trained, you might see a \"compiletime version mismatch\" warning. You can ignore it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf\n",
+        "\n",
+        "tf.reset_default_graph()\n",
+        "\n",
+        "saver = tf.train.import_meta_graph(\"./model/mnist-tf.model.meta\")\n",
+        "graph = tf.get_default_graph()\n",
+        "\n",
+        "for op in graph.get_operations():\n",
+        "    if op.name.startswith('network'):\n",
+        "        print(op.name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Feed test dataset to the persisted model to get predictions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# input tensor. this is an array of 784 elements, each representing the intensity of a pixel in the digit image.\n",
+        "X = tf.get_default_graph().get_tensor_by_name(\"network/X:0\")\n",
+        "# output tensor. this is an array of 10 elements, each representing the probability of predicted value of the digit.\n",
+        "output = tf.get_default_graph().get_tensor_by_name(\"network/output/MatMul:0\")\n",
+        "\n",
+        "with tf.Session() as sess:\n",
+        "    saver.restore(sess, './model/mnist-tf.model')\n",
+        "    k = output.eval(feed_dict={X : X_test})\n",
+        "# get the prediction, which is the index of the element that has the largest probability value.\n",
+        "y_hat = np.argmax(k, axis=1)\n",
+        "\n",
+        "# print the first 30 labels and predictions\n",
+        "print('labels:  \\t', y_test[:30])\n",
+        "print('predictions:\\t', y_hat[:30])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Calculate the overall accuracy by comparing the predicted value against the test set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"Accuracy on the test set:\", np.average(y_hat == y_test))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Intelligent hyperparameter tuning\n",
+        "We have trained the model with one set of hyperparameters, now let's how we can do hyperparameter tuning by launching multiple runs on the cluster. First let's define the parameter space using random sampling."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveConfig, PrimaryMetricGoal\n",
+        "from azureml.train.hyperdrive import choice, loguniform\n",
+        "\n",
+        "ps = RandomParameterSampling(\n",
+        "    {\n",
+        "        '--batch-size': choice(25, 50, 100),\n",
+        "        '--first-layer-neurons': choice(10, 50, 200, 300, 500),\n",
+        "        '--second-layer-neurons': choice(10, 50, 200, 500),\n",
+        "        '--learning-rate': loguniform(-6, -1)\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, we will create a new estimator without the above parameters since they will be passed in later. Note we still need to keep the `data-folder` parameter since that's not a hyperparamter we will sweep."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "est = TensorFlow(source_directory=script_folder,\n",
+        "                 script_params={'--data-folder': ws.get_default_datastore().as_mount()},\n",
+        "                 compute_target=compute_target,\n",
+        "                 entry_script='tf_mnist.py', \n",
+        "                 use_gpu=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we will define an early termnination policy. The `BanditPolicy` basically states to check the job every 2 iterations. If the primary metric (defined later) falls outside of the top 10% range, Azure ML terminate the job. This saves us from continuing to explore hyperparameters that don't show promise of helping reach our target metric."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "policy = BanditPolicy(evaluation_interval=2, slack_factor=0.1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we are ready to configure a run configuration object, and specify the primary metric `validation_acc` that's recorded in your training runs. If you go back to visit the training script, you will notice that this value is being logged after every epoch (a full batch set). We also want to tell the service that we are looking to maximizing this value. We also set the number of samples to 20, and maximal concurrent job to 4, which is the same as the number of nodes in our computer cluster."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "htc = HyperDriveConfig(estimator=est, \n",
+        "                       hyperparameter_sampling=ps, \n",
+        "                       policy=policy, \n",
+        "                       primary_metric_name='validation_acc', \n",
+        "                       primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, \n",
+        "                       max_total_runs=8,\n",
+        "                       max_concurrent_runs=4)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, let's launch the hyperparameter tuning job."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "htr = exp.submit(config=htc)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can use a run history widget to show the progress. Be patient as this might take a while to complete."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "RunDetails(htr).show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "htr.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Find and register best model <a class=\"anchor\" id=\"register-model\"></a>\n",
+        "When all the jobs finish, we can find out the one that has the highest accuracy."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run = htr.get_best_run_by_primary_metric()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now let's list the model files uploaded during the run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(best_run.get_file_names())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can then register the folder (and all files in it) as a model named `tf-dnn-mnist` under the workspace for deployment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model = best_run.register_model(model_name='tf-dnn-mnist', model_path='outputs/model')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Deploy the model in ACI\n",
+        "Now we are ready to deploy the model as a web service running in Azure Container Instance [ACI](https://azure.microsoft.com/en-us/services/container-instances/). Azure Machine Learning accomplishes this by constructing a Docker image with the scoring logic and model baked in.\n",
+        "### Create score.py\n",
+        "First, we will create a scoring script that will be invoked by the web service call. \n",
+        "\n",
+        "* Note that the scoring script must have two required functions, `init()` and `run(input_data)`. \n",
+        "  * In `init()` function, you typically load the model into a global object. This function is executed only once when the Docker container is started. \n",
+        "  * In `run(input_data)` function, the model is used to predict a value based on the input data. The input and output to `run` typically use JSON as serialization and de-serialization format but you are not limited to that."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile score.py\n",
+        "import json\n",
+        "import numpy as np\n",
+        "import os\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "from azureml.core.model import Model\n",
+        "\n",
+        "def init():\n",
+        "    global X, output, sess\n",
+        "    tf.reset_default_graph()\n",
+        "    model_root = Model.get_model_path('tf-dnn-mnist')\n",
+        "    saver = tf.train.import_meta_graph(os.path.join(model_root, 'mnist-tf.model.meta'))\n",
+        "    X = tf.get_default_graph().get_tensor_by_name(\"network/X:0\")\n",
+        "    output = tf.get_default_graph().get_tensor_by_name(\"network/output/MatMul:0\")\n",
+        "    \n",
+        "    sess = tf.Session()\n",
+        "    saver.restore(sess, os.path.join(model_root, 'mnist-tf.model'))\n",
+        "\n",
+        "def run(raw_data):\n",
+        "    data = np.array(json.loads(raw_data)['data'])\n",
+        "    # make prediction\n",
+        "    out = output.eval(session=sess, feed_dict={X: data})\n",
+        "    y_hat = np.argmax(out, axis=1)\n",
+        "    return y_hat.tolist()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create myenv.yml\n",
+        "We also need to create an environment file so that Azure Machine Learning can install the necessary packages in the Docker image which are required by your scoring script. In this case, we need to specify packages `numpy`, `tensorflow`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import CondaDependencies\n",
+        "\n",
+        "cd = CondaDependencies.create()\n",
+        "cd.add_conda_package('numpy')\n",
+        "cd.add_tensorflow_conda_package()\n",
+        "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",
+        "\n",
+        "print(cd.serialize_to_string())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Deploy to ACI\n",
+        "We are almost ready to deploy. Create the inference configuration and deployment configuration and deploy to ACI. This cell will run for about 7-8 minutes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice\n",
+        "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.webservice import Webservice\n",
+        "from azureml.core.model import Model\n",
+        "\n",
+        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "                                   entry_script=\"score.py\",\n",
+        "                                   conda_file=\"myenv.yml\")\n",
+        "\n",
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
+        "                                               memory_gb=1, \n",
+        "                                               tags={'name':'mnist', 'framework': 'TensorFlow DNN'},\n",
+        "                                               description='Tensorflow DNN on MNIST')\n",
+        "\n",
+        "service = Model.deploy(workspace=ws, \n",
+        "                           name='tf-mnist-svc', \n",
+        "                           models=[model], \n",
+        "                           inference_config=inference_config, \n",
+        "                           deployment_config=aciconfig)\n",
+        "\n",
+        "service.wait_for_deployment(True)\n",
+        "print(service.state)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**Tip: If something goes wrong with the deployment, the first thing to look at is the logs from the service by running the following command:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(service.get_logs())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This is the scoring web service endpoint:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(service.scoring_uri)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Test the deployed model\n",
+        "Let's test the deployed model. Pick 30 random samples from the test set, and send it to the web service hosted in ACI. Note here we are using the `run` API in the SDK to invoke the service. You can also make raw HTTP calls using any HTTP tool such as curl.\n",
+        "\n",
+        "After the invocation, we print the returned predictions and plot them along with the input images. Use red font color and inversed image (white on black) to highlight the misclassified samples. Note since the model accuracy is pretty high, you might have to run the below cell a few times before you can see a misclassified sample."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "\n",
+        "# find 30 random samples from test set\n",
+        "n = 30\n",
+        "sample_indices = np.random.permutation(X_test.shape[0])[0:n]\n",
+        "\n",
+        "test_samples = json.dumps({\"data\": X_test[sample_indices].tolist()})\n",
+        "test_samples = bytes(test_samples, encoding='utf8')\n",
+        "\n",
+        "# predict using the deployed model\n",
+        "result = service.run(input_data=test_samples)\n",
+        "\n",
+        "# compare actual value vs. the predicted values:\n",
+        "i = 0\n",
+        "plt.figure(figsize = (20, 1))\n",
+        "\n",
+        "for s in sample_indices:\n",
+        "    plt.subplot(1, n, i + 1)\n",
+        "    plt.axhline('')\n",
+        "    plt.axvline('')\n",
+        "    \n",
+        "    # use different color for misclassified sample\n",
+        "    font_color = 'red' if y_test[s] != result[i] else 'black'\n",
+        "    clr_map = plt.cm.gray if y_test[s] != result[i] else plt.cm.Greys\n",
+        "    \n",
+        "    plt.text(x=10, y=-10, s=y_hat[s], fontsize=18, color=font_color)\n",
+        "    plt.imshow(X_test[s].reshape(28, 28), cmap=clr_map)\n",
+        "    \n",
+        "    i = i + 1\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can also send raw HTTP request to the service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import requests\n",
+        "\n",
+        "# send a random row from the test set to score\n",
+        "random_index = np.random.randint(0, len(X_test)-1)\n",
+        "input_data = \"{\\\"data\\\": [\" + str(list(X_test[random_index])) + \"]}\"\n",
+        "\n",
+        "headers = {'Content-Type':'application/json'}\n",
+        "\n",
+        "resp = requests.post(service.scoring_uri, input_data, headers=headers)\n",
+        "\n",
+        "print(\"POST to url\", service.scoring_uri)\n",
+        "#print(\"input data:\", input_data)\n",
+        "print(\"label:\", y_test[random_index])\n",
+        "print(\"prediction:\", resp.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Let's look at the workspace after the web service was deployed. You should see \n",
+        "* a registered model named 'model' and with the id 'model:1'\n",
+        "* a webservice called 'tf-mnist' with some scoring URL"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "models = ws.models\n",
+        "for name, model in models.items():\n",
+        "    print(\"Model: {}, ID: {}\".format(name, model.id))\n",
+        "    \n",
+        "webservices = ws.webservices\n",
+        "for name, webservice in webservices.items():\n",
+        "    print(\"Webservice: {}, scoring URI: {}\".format(name, webservice.scoring_uri))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Clean up\n",
+        "You can delete the ACI deployment with a simple delete API call."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "service.delete()"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "ninhu"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.yml b/how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.yml
new file mode 100644
index 00000000..4b9dd138
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.yml
@@ -0,0 +1,8 @@
+name: train-hyperparameter-tune-deploy-with-tensorflow
+dependencies:
+- numpy
+- tensorflow
+- matplotlib
+- pip:
+  - azureml-sdk
+  - azureml-widgets
diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/utils.py b/how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/utils.py
new file mode 100644
index 00000000..98170ada
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/utils.py
@@ -0,0 +1,27 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import gzip
+import numpy as np
+import struct
+
+
+# load compressed MNIST gz files and return numpy arrays
+def load_data(filename, label=False):
+    with gzip.open(filename) as gz:
+        struct.unpack('I', gz.read(4))
+        n_items = struct.unpack('>I', gz.read(4))
+        if not label:
+            n_rows = struct.unpack('>I', gz.read(4))[0]
+            n_cols = struct.unpack('>I', gz.read(4))[0]
+            res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8)
+            res = res.reshape(n_items[0], n_rows * n_cols)
+        else:
+            res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8)
+            res = res.reshape(n_items[0], 1)
+    return res
+
+
+# one-hot encode a 1-D array
+def one_hot_encode(array, num_of_classes):
+    return np.eye(num_of_classes)[array.reshape(-1)]
diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb b/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb
new file mode 100644
index 00000000..568b7648
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb
@@ -0,0 +1,409 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/manage-runs/manage-runs.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Distributed Tensorflow with Horovod\n",
+        "In this tutorial, you will train a word2vec model in TensorFlow using distributed training via [Horovod](https://github.com/uber/horovod)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n",
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to:\n",
+        "    * install the AML SDK\n",
+        "    * create a workspace and its configuration file (`config.json`)\n",
+        "* Review the [tutorial](../train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) on single-node TensorFlow training using the SDK"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Diagnostics\n",
+        "Opt-in diagnostics for better experience, quality, and security of future releases."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "Diagnostics"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.telemetry import set_diagnostics_collection\n",
+        "\n",
+        "set_diagnostics_collection(send_diagnostics=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize workspace\n",
+        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.workspace import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep='\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create or Attach existing AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
+        "\n",
+        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
+        "\n",
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "cluster_name = \"gpu-cluster\"\n",
+        "\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+        "    print('Found existing compute target')\n",
+        "except ComputeTargetException:\n",
+        "    print('Creating a new compute target...')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
+        "                                                           max_nodes=4)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+        "\n",
+        "    compute_target.wait_for_completion(show_output=True)\n",
+        "\n",
+        "# use get_status() to get a detailed status for the current cluster. \n",
+        "print(compute_target.get_status().serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Upload data to datastore\n",
+        "To make data accessible for remote training, AML provides a convenient way to do so via a [Datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data). The datastore provides a mechanism for you to upload/download data to Azure Storage, and interact with it from your remote compute targets. \n",
+        "\n",
+        "If your data is already stored in Azure, or you download the data as part of your training script, you will not need to do this step. For this tutorial, although you can download the data in your training script, we will demonstrate how to upload the training data to a datastore and access it during training to illustrate the datastore functionality."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First, download the training data from [here](http://mattmahoney.net/dc/text8.zip) to your local machine:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import urllib\n",
+        "\n",
+        "os.makedirs('./data', exist_ok=True)\n",
+        "download_url = 'http://mattmahoney.net/dc/text8.zip'\n",
+        "urllib.request.urlretrieve(download_url, filename='./data/text8.zip')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Each workspace is associated with a default datastore. In this tutorial, we will upload the training data to this default datastore."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ds = ws.get_default_datastore()\n",
+        "print(ds.datastore_type, ds.account_name, ds.container_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Upload the contents of the data directory to the path `./data` on the default datastore."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ds.upload(src_dir='data', target_path='data', overwrite=True, show_progress=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For convenience, let's get a reference to the path on the datastore with the zip file of training data. We can do so using the `path` method. In the next section, we can then pass this reference to our training script's `--input_data` argument. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "path_on_datastore = 'data/text8.zip'\n",
+        "ds_data = ds.path(path_on_datastore)\n",
+        "print(ds_data)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train model on the remote compute"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a project directory\n",
+        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "project_folder = './tf-distr-hvd'\n",
+        "os.makedirs(project_folder, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copy the training script `tf_horovod_word2vec.py` into this project directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import shutil\n",
+        "\n",
+        "shutil.copy('tf_horovod_word2vec.py', project_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create an experiment\n",
+        "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed TensorFlow tutorial. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "experiment_name = 'tf-distr-hvd'\n",
+        "experiment = Experiment(ws, name=experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a TensorFlow estimator\n",
+        "The AML SDK's TensorFlow estimator enables you to easily submit TensorFlow training jobs for both single-node and distributed runs. For more information on the TensorFlow estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-tensorflow).\n",
+        "\n",
+        "The TensorFlow estimator also takes a `framework_version` parameter -- if no version is provided, the estimator will default to the latest version supported by AzureML. Use `TensorFlow.get_supported_versions()` to get a list of all versions supported by your current SDK version or see the [SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn?view=azure-ml-py) for the versions supported in the most current release."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.dnn import TensorFlow, Mpi\n",
+        "\n",
+        "script_params={\n",
+        "    '--input_data': ds_data\n",
+        "}\n",
+        "\n",
+        "estimator= TensorFlow(source_directory=project_folder,\n",
+        "                      compute_target=compute_target,\n",
+        "                      script_params=script_params,\n",
+        "                      entry_script='tf_horovod_word2vec.py',\n",
+        "                      node_count=2,\n",
+        "                      distributed_training=Mpi(),\n",
+        "                      framework_version='1.13')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to execute a distributed run using MPI/Horovod, you must provide the argument `distributed_backend=Mpi()`. To specify `i` workers per node, you must provide the argument `distributed_backend=Mpi(process_count_per_node=i)`. Using this estimator with these settings, TensorFlow, Horovod and their dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `TensorFlow` constructor's `pip_packages` or `conda_packages` parameters.\n",
+        "\n",
+        "Note that we passed our training data reference `ds_data` to our script's `--input_data` argument. This will 1) mount our datastore on the remote compute and 2) provide the path to the data zip file on our datastore."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit job\n",
+        "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run = experiment.submit(estimator)\n",
+        "print(run)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Monitor your run\n",
+        "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Alternatively, you can block until the script has completed training before running more code."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.wait_for_completion(show_output=True)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "roastala"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.yml b/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.yml
new file mode 100644
index 00000000..15d0a491
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.yml
@@ -0,0 +1,5 @@
+name: distributed-tensorflow-with-horovod
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-horovod/tf_horovod_word2vec.py b/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-horovod/tf_horovod_word2vec.py
new file mode 100644
index 00000000..f29fb278
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-horovod/tf_horovod_word2vec.py
@@ -0,0 +1,259 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+# Modifications copyright (C) 2017 Uber Technologies, Inc.
+# Additional modifications copyright (C) Microsoft Corporation
+# Licensed under the Apache License, Version 2.0
+# Script adapted from: https://github.com/uber/horovod/blob/master/examples/tensorflow_word2vec.py
+# ======================================
+"""Basic word2vec example."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import math
+import os
+import random
+import zipfile
+import argparse
+
+import numpy as np
+from six.moves import urllib
+from six.moves import xrange  # pylint: disable=redefined-builtin
+import tensorflow as tf
+import horovod.tensorflow as hvd
+from azureml.core.run import Run
+
+# Horovod: initialize Horovod.
+hvd.init()
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--input_data', type=str, help='training data')
+
+args = parser.parse_args()
+
+input_data = args.input_data
+print("the input data is at %s" % input_data)
+
+# Step 1: Download the data.
+url = 'http://mattmahoney.net/dc/text8.zip'
+
+
+def maybe_download(filename, expected_bytes):
+    """Download a file if not present, and make sure it's the right size."""
+    if not filename:
+        filename = "text8.zip"
+    if not os.path.exists(filename):
+        print("Downloading the data from http://mattmahoney.net/dc/text8.zip")
+        filename, _ = urllib.request.urlretrieve(url, filename)
+    else:
+        print("Use the data from %s" % input_data)
+    statinfo = os.stat(filename)
+    if statinfo.st_size == expected_bytes:
+        print('Found and verified', filename)
+    else:
+        print(statinfo.st_size)
+        raise Exception(
+            'Failed to verify ' + url + '. Can you get to it with a browser?')
+    return filename
+
+
+filename = maybe_download(input_data, 31344016)
+
+
+# Read the data into a list of strings.
+def read_data(filename):
+    """Extract the first file enclosed in a zip file as a list of words."""
+    with zipfile.ZipFile(filename) as f:
+        data = tf.compat.as_str(f.read(f.namelist()[0])).split()
+    return data
+
+
+vocabulary = read_data(filename)
+print('Data size', len(vocabulary))
+
+# Step 2: Build the dictionary and replace rare words with UNK token.
+vocabulary_size = 50000
+
+
+def build_dataset(words, n_words):
+    """Process raw inputs into a dataset."""
+    count = [['UNK', -1]]
+    count.extend(collections.Counter(words).most_common(n_words - 1))
+    dictionary = dict()
+    for word, _ in count:
+        dictionary[word] = len(dictionary)
+    data = list()
+    unk_count = 0
+    for word in words:
+        if word in dictionary:
+            index = dictionary[word]
+        else:
+            index = 0  # dictionary['UNK']
+            unk_count += 1
+        data.append(index)
+    count[0][1] = unk_count
+    reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
+    return data, count, dictionary, reversed_dictionary
+
+
+data, count, dictionary, reverse_dictionary = build_dataset(vocabulary,
+                                                            vocabulary_size)
+del vocabulary  # Hint to reduce memory.
+print('Most common words (+UNK)', count[:5])
+print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])
+
+
+# Step 3: Function to generate a training batch for the skip-gram model.
+def generate_batch(batch_size, num_skips, skip_window):
+    assert num_skips <= 2 * skip_window
+    # Adjust batch_size to match num_skips
+    batch_size = batch_size // num_skips * num_skips
+    span = 2 * skip_window + 1  # [ skip_window target skip_window ]
+    # Backtrack a little bit to avoid skipping words in the end of a batch
+    data_index = random.randint(0, len(data) - span - 1)
+    batch = np.ndarray(shape=(batch_size), dtype=np.int32)
+    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
+    buffer = collections.deque(maxlen=span)
+    for _ in range(span):
+        buffer.append(data[data_index])
+        data_index = (data_index + 1) % len(data)
+    for i in range(batch_size // num_skips):
+        target = skip_window  # target label at the center of the buffer
+        targets_to_avoid = [skip_window]
+        for j in range(num_skips):
+            while target in targets_to_avoid:
+                target = random.randint(0, span - 1)
+            targets_to_avoid.append(target)
+            batch[i * num_skips + j] = buffer[skip_window]
+            labels[i * num_skips + j, 0] = buffer[target]
+        buffer.append(data[data_index])
+        data_index = (data_index + 1) % len(data)
+    return batch, labels
+
+
+batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)
+for i in range(8):
+    print(batch[i], reverse_dictionary[batch[i]],
+          '->', labels[i, 0], reverse_dictionary[labels[i, 0]])
+
+# Step 4: Build and train a skip-gram model.
+
+max_batch_size = 128
+embedding_size = 128  # Dimension of the embedding vector.
+skip_window = 1       # How many words to consider left and right.
+num_skips = 2         # How many times to reuse an input to generate a label.
+
+# We pick a random validation set to sample nearest neighbors. Here we limit the
+# validation samples to the words that have a low numeric ID, which by
+# construction are also the most frequent.
+valid_size = 16     # Random set of words to evaluate similarity on.
+valid_window = 100  # Only pick dev samples in the head of the distribution.
+valid_examples = np.random.choice(valid_window, valid_size, replace=False)
+num_sampled = 64    # Number of negative examples to sample.
+
+graph = tf.Graph()
+
+with graph.as_default():
+
+    # Input data.
+    train_inputs = tf.placeholder(tf.int32, shape=[None])
+    train_labels = tf.placeholder(tf.int32, shape=[None, 1])
+    valid_dataset = tf.constant(valid_examples, dtype=tf.int32)
+
+    # Look up embeddings for inputs.
+    embeddings = tf.Variable(
+        tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
+    embed = tf.nn.embedding_lookup(embeddings, train_inputs)
+
+    # Construct the variables for the NCE loss
+    nce_weights = tf.Variable(
+        tf.truncated_normal([vocabulary_size, embedding_size],
+                            stddev=1.0 / math.sqrt(embedding_size)))
+    nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
+
+    # Compute the average NCE loss for the batch.
+    # tf.nce_loss automatically draws a new sample of the negative labels each
+    # time we evaluate the loss.
+    loss = tf.reduce_mean(
+        tf.nn.nce_loss(weights=nce_weights,
+                       biases=nce_biases,
+                       labels=train_labels,
+                       inputs=embed,
+                       num_sampled=num_sampled,
+                       num_classes=vocabulary_size))
+
+    # Horovod: adjust learning rate based on number of GPUs.
+    optimizer = tf.train.GradientDescentOptimizer(1.0 * hvd.size())
+
+    # Horovod: add Horovod Distributed Optimizer.
+    optimizer = hvd.DistributedOptimizer(optimizer)
+
+    train_op = optimizer.minimize(loss)
+
+    # Compute the cosine similarity between minibatch examples and all embeddings.
+    norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
+    normalized_embeddings = embeddings / norm
+    valid_embeddings = tf.nn.embedding_lookup(
+        normalized_embeddings, valid_dataset)
+    similarity = tf.matmul(
+        valid_embeddings, normalized_embeddings, transpose_b=True)
+
+    # Add variable initializer.
+    init = tf.global_variables_initializer()
+
+    # Horovod: broadcast initial variable states from rank 0 to all other processes.
+    # This is necessary to ensure consistent initialization of all workers when
+    # training is started with random weights or restored from a checkpoint.
+    bcast = hvd.broadcast_global_variables(0)
+
+# Step 5: Begin training.
+
+# Horovod: adjust number of steps based on number of GPUs.
+num_steps = 4000 // hvd.size() + 1
+
+# Horovod: pin GPU to be used to process local rank (one GPU per process)
+config = tf.ConfigProto()
+config.gpu_options.allow_growth = True
+config.gpu_options.visible_device_list = str(hvd.local_rank())
+
+with tf.Session(graph=graph, config=config) as session:
+    # We must initialize all variables before we use them.
+    init.run()
+    bcast.run()
+    print('Initialized')
+    run = Run.get_context()
+    average_loss = 0
+    for step in xrange(num_steps):
+        # simulate various sentence length by randomization
+        batch_size = random.randint(max_batch_size // 2, max_batch_size)
+        batch_inputs, batch_labels = generate_batch(
+            batch_size, num_skips, skip_window)
+        feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}
+
+        # We perform one update step by evaluating the optimizer op (including it
+        # in the list of returned values for session.run()
+        _, loss_val = session.run([train_op, loss], feed_dict=feed_dict)
+        average_loss += loss_val
+
+        if step % 2000 == 0:
+            if step > 0:
+                average_loss /= 2000
+            # The average loss is an estimate of the loss over the last 2000 batches.
+            print('Average loss at step ', step, ': ', average_loss)
+            run.log("Loss", average_loss)
+            average_loss = 0
+    final_embeddings = normalized_embeddings.eval()
+
+    # Evaluate similarity in the end on worker 0.
+    if hvd.rank() == 0:
+        sim = similarity.eval()
+        for i in xrange(valid_size):
+            valid_word = reverse_dictionary[valid_examples[i]]
+            top_k = 8  # number of nearest neighbors
+            nearest = (-sim[i, :]).argsort()[1:top_k + 1]
+            log_str = 'Nearest to %s:' % valid_word
+            for k in xrange(top_k):
+                close_word = reverse_dictionary[nearest[k]]
+                log_str = '%s %s,' % (log_str, close_word)
+            print(log_str)
diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb b/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb
new file mode 100644
index 00000000..a5e3e143
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb
@@ -0,0 +1,321 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Distributed TensorFlow with parameter server\n",
+        "In this tutorial, you will train a TensorFlow model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using native [distributed TensorFlow](https://www.tensorflow.org/deploy/distributed)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n",
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to:\n",
+        "    * install the AML SDK\n",
+        "    * create a workspace and its configuration file (`config.json`)\n",
+        "* Review the [tutorial](../train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) on single-node TensorFlow training using the SDK"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Diagnostics\n",
+        "Opt-in diagnostics for better experience, quality, and security of future releases."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "Diagnostics"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.telemetry import set_diagnostics_collection\n",
+        "\n",
+        "set_diagnostics_collection(send_diagnostics=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize workspace\n",
+        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.workspace import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create or Attach existing AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
+        "\n",
+        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
+        "\n",
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "cluster_name = \"gpu-cluster\"\n",
+        "\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+        "    print('Found existing compute target.')\n",
+        "except ComputeTargetException:\n",
+        "    print('Creating a new compute target...')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
+        "                                                           max_nodes=4)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+        "\n",
+        "    compute_target.wait_for_completion(show_output=True)\n",
+        "\n",
+        "# use get_status() to get a detailed status for the current cluster. \n",
+        "print(compute_target.get_status().serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train model on the remote compute\n",
+        "Now that we have the cluster ready to go, let's run our distributed training job."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a project directory\n",
+        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "project_folder = './tf-distr-ps'\n",
+        "os.makedirs(project_folder, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copy the training script `tf_mnist_replica.py` into this project directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import shutil\n",
+        "\n",
+        "shutil.copy('tf_mnist_replica.py', project_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create an experiment\n",
+        "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed TensorFlow tutorial. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "experiment_name = 'tf-distr-ps'\n",
+        "experiment = Experiment(ws, name=experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a TensorFlow estimator\n",
+        "The AML SDK's TensorFlow estimator enables you to easily submit TensorFlow training jobs for both single-node and distributed runs. For more information on the TensorFlow estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-tensorflow)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.dnn import TensorFlow, ParameterServer\n",
+        "\n",
+        "script_params={\n",
+        "    '--num_gpus': 1,\n",
+        "    '--train_steps': 500\n",
+        "}\n",
+        "\n",
+        "estimator = TensorFlow(source_directory=project_folder,\n",
+        "                       compute_target=compute_target,\n",
+        "                       script_params=script_params,\n",
+        "                       entry_script='tf_mnist_replica.py',\n",
+        "                       node_count=2,\n",
+        "                       distributed_training=ParameterServer(worker_count=2),\n",
+        "                       use_gpu=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The above code specifies that we will run our training script on `2` nodes, with two workers and one parameter server. In order to execute a native distributed TensorFlow run, you must provide the argument `distributed_backend=ParameterServer()`. Using this estimator with these settings, TensorFlow and its dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `TensorFlow` constructor's `pip_packages` or `conda_packages` parameters."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit job\n",
+        "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run = experiment.submit(estimator)\n",
+        "print(run)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Monitor your run\n",
+        "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Alternatively, you can block until the script has completed training before running more code."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.wait_for_completion(show_output=True) # this provides a verbose log"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "ninhu"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.yml b/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.yml
new file mode 100644
index 00000000..bc5a30eb
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.yml
@@ -0,0 +1,5 @@
+name: distributed-tensorflow-with-parameter-server
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-parameter-server/tf_mnist_replica.py b/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-parameter-server/tf_mnist_replica.py
new file mode 100644
index 00000000..96d40fed
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-parameter-server/tf_mnist_replica.py
@@ -0,0 +1,271 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0
+# Script adapted from:
+# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dist_test/python/mnist_replica.py
+# ==============================================================================
+"""Distributed MNIST training and validation, with model replicas.
+A simple softmax model with one hidden layer is defined. The parameters
+(weights and biases) are located on one parameter server (ps), while the ops
+are executed on two worker nodes by default. The TF sessions also run on the
+worker node.
+Multiple invocations of this script can be done in parallel, with different
+values for --task_index. There should be exactly one invocation with
+--task_index, which will create a master session that carries out variable
+initialization. The other, non-master, sessions will wait for the master
+session to finish the initialization before proceeding to the training stage.
+The coordination between the multiple worker invocations occurs due to
+the definition of the parameters on the same ps devices. The parameter updates
+from one worker is visible to all other workers. As such, the workers can
+perform forward computation and gradient calculation in parallel, which
+should lead to increased training speed for the simple model.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import math
+import sys
+import tempfile
+import time
+import json
+
+import tensorflow as tf
+from tensorflow.examples.tutorials.mnist import input_data
+from azureml.core.run import Run
+
+flags = tf.app.flags
+flags.DEFINE_string("data_dir", "/tmp/mnist-data",
+                    "Directory for storing mnist data")
+flags.DEFINE_boolean("download_only", False,
+                     "Only perform downloading of data; Do not proceed to "
+                     "session preparation, model definition or training")
+flags.DEFINE_integer("num_gpus", 0, "Total number of gpus for each machine."
+                     "If you don't use GPU, please set it to '0'")
+flags.DEFINE_integer("replicas_to_aggregate", None,
+                     "Number of replicas to aggregate before parameter update "
+                     "is applied (For sync_replicas mode only; default: "
+                     "num_workers)")
+flags.DEFINE_integer("hidden_units", 100,
+                     "Number of units in the hidden layer of the NN")
+flags.DEFINE_integer("train_steps", 200,
+                     "Number of (global) training steps to perform")
+flags.DEFINE_integer("batch_size", 100, "Training batch size")
+flags.DEFINE_float("learning_rate", 0.01, "Learning rate")
+flags.DEFINE_boolean(
+    "sync_replicas", False,
+    "Use the sync_replicas (synchronized replicas) mode, "
+    "wherein the parameter updates from workers are aggregated "
+    "before applied to avoid stale gradients")
+flags.DEFINE_boolean(
+    "existing_servers", False, "Whether servers already exists. If True, "
+    "will use the worker hosts via their GRPC URLs (one client process "
+    "per worker host). Otherwise, will create an in-process TensorFlow "
+    "server.")
+
+FLAGS = flags.FLAGS
+
+IMAGE_PIXELS = 28
+
+
+def main(unused_argv):
+    data_root = os.path.join("outputs", "MNIST")
+    mnist = None
+    tf_config = os.environ.get("TF_CONFIG")
+    if not tf_config or tf_config == "":
+        raise ValueError("TF_CONFIG not found.")
+    tf_config_json = json.loads(tf_config)
+    cluster = tf_config_json.get('cluster')
+    job_name = tf_config_json.get('task', {}).get('type')
+    task_index = tf_config_json.get('task', {}).get('index')
+    job_name = "worker" if job_name == "master" else job_name
+    sentinel_path = os.path.join(data_root, "complete.txt")
+    if job_name == "worker" and task_index == 0:
+        mnist = input_data.read_data_sets(data_root, one_hot=True)
+        with open(sentinel_path, 'w+') as f:
+            f.write("download complete")
+    else:
+        while not os.path.exists(sentinel_path):
+            time.sleep(0.01)
+        mnist = input_data.read_data_sets(data_root, one_hot=True)
+
+    if FLAGS.download_only:
+        sys.exit(0)
+
+    print("job name = %s" % job_name)
+    print("task index = %d" % task_index)
+    print("number of GPUs = %d" % FLAGS.num_gpus)
+
+    # Construct the cluster and start the server
+    cluster_spec = tf.train.ClusterSpec(cluster)
+
+    # Get the number of workers.
+    num_workers = len(cluster_spec.task_indices("worker"))
+
+    if not FLAGS.existing_servers:
+        # Not using existing servers. Create an in-process server.
+        server = tf.train.Server(
+            cluster_spec, job_name=job_name, task_index=task_index)
+        if job_name == "ps":
+            server.join()
+
+    is_chief = (task_index == 0)
+    if FLAGS.num_gpus > 0:
+        # Avoid gpu allocation conflict: now allocate task_num -> #gpu
+        # for each worker in the corresponding machine
+        gpu = (task_index % FLAGS.num_gpus)
+        worker_device = "/job:worker/task:%d/gpu:%d" % (task_index, gpu)
+    elif FLAGS.num_gpus == 0:
+        # Just allocate the CPU to worker server
+        cpu = 0
+        worker_device = "/job:worker/task:%d/cpu:%d" % (task_index, cpu)
+    # The device setter will automatically place Variables ops on separate
+    # parameter servers (ps). The non-Variable ops will be placed on the workers.
+    # The ps use CPU and workers use corresponding GPU
+    with tf.device(
+        tf.train.replica_device_setter(
+            worker_device=worker_device,
+            ps_device="/job:ps/cpu:0",
+            cluster=cluster)):
+        global_step = tf.Variable(0, name="global_step", trainable=False)
+
+        # Variables of the hidden layer
+        hid_w = tf.Variable(
+            tf.truncated_normal(
+                [IMAGE_PIXELS * IMAGE_PIXELS, FLAGS.hidden_units],
+                stddev=1.0 / IMAGE_PIXELS),
+            name="hid_w")
+        hid_b = tf.Variable(tf.zeros([FLAGS.hidden_units]), name="hid_b")
+
+        # Variables of the softmax layer
+        sm_w = tf.Variable(
+            tf.truncated_normal(
+                [FLAGS.hidden_units, 10],
+                stddev=1.0 / math.sqrt(FLAGS.hidden_units)),
+            name="sm_w")
+        sm_b = tf.Variable(tf.zeros([10]), name="sm_b")
+
+        # Ops: located on the worker specified with task_index
+        x = tf.placeholder(tf.float32, [None, IMAGE_PIXELS * IMAGE_PIXELS])
+        y_ = tf.placeholder(tf.float32, [None, 10])
+
+        hid_lin = tf.nn.xw_plus_b(x, hid_w, hid_b)
+        hid = tf.nn.relu(hid_lin)
+
+        y = tf.nn.softmax(tf.nn.xw_plus_b(hid, sm_w, sm_b))
+        cross_entropy = -tf.reduce_sum(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)))
+
+        opt = tf.train.AdamOptimizer(FLAGS.learning_rate)
+
+        if FLAGS.sync_replicas:
+            if FLAGS.replicas_to_aggregate is None:
+                replicas_to_aggregate = num_workers
+            else:
+                replicas_to_aggregate = FLAGS.replicas_to_aggregate
+
+            opt = tf.train.SyncReplicasOptimizer(
+                opt,
+                replicas_to_aggregate=replicas_to_aggregate,
+                total_num_replicas=num_workers,
+                name="mnist_sync_replicas")
+
+        train_step = opt.minimize(cross_entropy, global_step=global_step)
+
+        if FLAGS.sync_replicas:
+            local_init_op = opt.local_step_init_op
+            if is_chief:
+                local_init_op = opt.chief_init_op
+
+            ready_for_local_init_op = opt.ready_for_local_init_op
+
+            # Initial token and chief queue runners required by the sync_replicas mode
+            chief_queue_runner = opt.get_chief_queue_runner()
+            sync_init_op = opt.get_init_tokens_op()
+
+        init_op = tf.global_variables_initializer()
+        train_dir = tempfile.mkdtemp()
+
+        if FLAGS.sync_replicas:
+            sv = tf.train.Supervisor(
+                is_chief=is_chief,
+                logdir=train_dir,
+                init_op=init_op,
+                local_init_op=local_init_op,
+                ready_for_local_init_op=ready_for_local_init_op,
+                recovery_wait_secs=1,
+                global_step=global_step)
+        else:
+            sv = tf.train.Supervisor(
+                is_chief=is_chief,
+                logdir=train_dir,
+                init_op=init_op,
+                recovery_wait_secs=1,
+                global_step=global_step)
+
+        sess_config = tf.ConfigProto(
+            allow_soft_placement=True,
+            log_device_placement=False,
+            device_filters=["/job:ps",
+                            "/job:worker/task:%d" % task_index])
+
+        # The chief worker (task_index==0) session will prepare the session,
+        # while the remaining workers will wait for the preparation to complete.
+        if is_chief:
+            print("Worker %d: Initializing session..." % task_index)
+        else:
+            print("Worker %d: Waiting for session to be initialized..." %
+                  task_index)
+
+        if FLAGS.existing_servers:
+            server_grpc_url = "grpc://" + task_index
+            print("Using existing server at: %s" % server_grpc_url)
+
+            sess = sv.prepare_or_wait_for_session(server_grpc_url, config=sess_config)
+        else:
+            sess = sv.prepare_or_wait_for_session(server.target, config=sess_config)
+
+        print("Worker %d: Session initialization complete." % task_index)
+
+        if FLAGS.sync_replicas and is_chief:
+            # Chief worker will start the chief queue runner and call the init op.
+            sess.run(sync_init_op)
+            sv.start_queue_runners(sess, [chief_queue_runner])
+
+        # Perform training
+        time_begin = time.time()
+        print("Training begins @ %f" % time_begin)
+
+        local_step = 0
+        while True:
+            # Training feed
+            batch_xs, batch_ys = mnist.train.next_batch(FLAGS.batch_size)
+            train_feed = {x: batch_xs, y_: batch_ys}
+
+            _, step = sess.run([train_step, global_step], feed_dict=train_feed)
+            local_step += 1
+
+            now = time.time()
+            print("%f: Worker %d: training step %d done (global step: %d)" %
+                  (now, task_index, local_step, step))
+
+            if step >= FLAGS.train_steps:
+                break
+
+        time_end = time.time()
+        print("Training ends @ %f" % time_end)
+        training_time = time_end - time_begin
+        print("Training elapsed time: %f s" % training_time)
+
+        # Validation feed
+        val_feed = {x: mnist.validation.images, y_: mnist.validation.labels}
+        val_xent = sess.run(cross_entropy, feed_dict=val_feed)
+        print("After %d training step(s), validation cross entropy = %g" %
+              (FLAGS.train_steps, val_xent))
+        if job_name == "worker" and task_index == 0:
+            run = Run.get_context()
+            run.log("CrossEntropy", val_xent)
+
+
+if __name__ == "__main__":
+    tf.app.run()
diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/training/train-tensorflow-resume-training/tf_mnist_with_checkpoint.py b/how-to-use-azureml/ml-frameworks/tensorflow/training/train-tensorflow-resume-training/tf_mnist_with_checkpoint.py
new file mode 100644
index 00000000..85e80cbd
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/tensorflow/training/train-tensorflow-resume-training/tf_mnist_with_checkpoint.py
@@ -0,0 +1,123 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import numpy as np
+import argparse
+import os
+import re
+import tensorflow as tf
+
+from azureml.core import Run
+from utils import load_data
+
+print("TensorFlow version:", tf.VERSION)
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--data-folder', type=str, dest='data_folder', help='data folder mounting point')
+
+parser.add_argument('--resume-from', type=str, default=None,
+                    help='location of the model or checkpoint files from where to resume the training')
+args = parser.parse_args()
+
+
+previous_model_location = args.resume_from
+# You can also use environment variable to get the model/checkpoint files location
+# previous_model_location = os.path.expandvars(os.getenv("AZUREML_DATAREFERENCE_MODEL_LOCATION", None))
+
+data_folder = os.path.join(args.data_folder, 'mnist')
+
+print('training dataset is stored here:', data_folder)
+
+X_train = load_data(os.path.join(data_folder, 'train-images.gz'), False) / 255.0
+X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / 255.0
+
+y_train = load_data(os.path.join(data_folder, 'train-labels.gz'), True).reshape(-1)
+y_test = load_data(os.path.join(data_folder, 'test-labels.gz'), True).reshape(-1)
+
+print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep='\n')
+training_set_size = X_train.shape[0]
+
+n_inputs = 28 * 28
+n_h1 = 100
+n_h2 = 100
+n_outputs = 10
+learning_rate = 0.01
+n_epochs = 20
+batch_size = 50
+
+with tf.name_scope('network'):
+    # construct the DNN
+    X = tf.placeholder(tf.float32, shape=(None, n_inputs), name='X')
+    y = tf.placeholder(tf.int64, shape=(None), name='y')
+    h1 = tf.layers.dense(X, n_h1, activation=tf.nn.relu, name='h1')
+    h2 = tf.layers.dense(h1, n_h2, activation=tf.nn.relu, name='h2')
+    output = tf.layers.dense(h2, n_outputs, name='output')
+
+with tf.name_scope('train'):
+    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=output)
+    loss = tf.reduce_mean(cross_entropy, name='loss')
+    optimizer = tf.train.GradientDescentOptimizer(learning_rate)
+    train_op = optimizer.minimize(loss)
+
+with tf.name_scope('eval'):
+    correct = tf.nn.in_top_k(output, y, 1)
+    acc_op = tf.reduce_mean(tf.cast(correct, tf.float32))
+
+init = tf.global_variables_initializer()
+saver = tf.train.Saver()
+
+# start an Azure ML run
+run = Run.get_context()
+
+with tf.Session() as sess:
+    start_epoch = 0
+    if previous_model_location:
+        checkpoint_file_path = tf.train.latest_checkpoint(previous_model_location)
+        saver.restore(sess, checkpoint_file_path)
+        checkpoint_filename = os.path.basename(checkpoint_file_path)
+        num_found = re.search(r'\d+', checkpoint_filename)
+        if num_found:
+            start_epoch = int(num_found.group(0))
+            print("Resuming from epoch {}".format(str(start_epoch)))
+    else:
+        init.run()
+
+    for epoch in range(start_epoch, n_epochs):
+
+        # randomly shuffle training set
+        indices = np.random.permutation(training_set_size)
+        X_train = X_train[indices]
+        y_train = y_train[indices]
+
+        # batch index
+        b_start = 0
+        b_end = b_start + batch_size
+        for _ in range(training_set_size // batch_size):
+            # get a batch
+            X_batch, y_batch = X_train[b_start: b_end], y_train[b_start: b_end]
+
+            # update batch index for the next batch
+            b_start = b_start + batch_size
+            b_end = min(b_start + batch_size, training_set_size)
+
+            # train
+            sess.run(train_op, feed_dict={X: X_batch, y: y_batch})
+        # evaluate training set
+        acc_train = acc_op.eval(feed_dict={X: X_batch, y: y_batch})
+        # evaluate validation set
+        acc_val = acc_op.eval(feed_dict={X: X_test, y: y_test})
+
+        # log accuracies
+        run.log('training_acc', np.float(acc_train))
+        run.log('validation_acc', np.float(acc_val))
+        print(epoch, '-- Training accuracy:', acc_train, '\b Validation accuracy:', acc_val)
+        y_hat = np.argmax(output.eval(feed_dict={X: X_test}), axis=1)
+
+        if epoch % 5 == 0:
+            saver.save(sess, './outputs/', global_step=epoch)
+
+        # saving only half of the model and resuming again from same epoch
+        if not previous_model_location and epoch == 10:
+            break
+
+    run.log('final_acc', np.float(acc_val))
diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/training/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb b/how-to-use-azureml/ml-frameworks/tensorflow/training/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb
new file mode 100644
index 00000000..da294e7d
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/tensorflow/training/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb
@@ -0,0 +1,487 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/tensorflow/training/train-tensorflow-resume-training/train-tensorflow-resume-training.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Resuming Tensorflow training from previous run\n",
+        "In this tutorial, you will resume a mnist model in TensorFlow from a previously submitted run."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n",
+        "* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
+        "    * install the AML SDK\n",
+        "    * create a workspace and its configuration file (`config.json`)\n",
+        "* Review the [tutorial](../train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) on single-node TensorFlow training using the SDK"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Diagnostics\n",
+        "Opt-in diagnostics for better experience, quality, and security of future releases."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "Diagnostics"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.telemetry import set_diagnostics_collection\n",
+        "\n",
+        "set_diagnostics_collection(send_diagnostics=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize workspace\n",
+        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.workspace import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep='\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create or Attach existing AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
+        "\n",
+        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
+        "\n",
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "cluster_name = \"gpu-cluster\"\n",
+        "\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+        "    print('Found existing compute target.')\n",
+        "except ComputeTargetException:\n",
+        "    print('Creating a new compute target...')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
+        "                                                           max_nodes=4)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+        "\n",
+        "    compute_target.wait_for_completion(show_output=True)\n",
+        "\n",
+        "# use get_status() to get a detailed status for the current cluster. \n",
+        "print(compute_target.get_status().serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Upload data to datastore\n",
+        "To make data accessible for remote training, AML provides a convenient way to do so via a [Datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data). The datastore provides a mechanism for you to upload/download data to Azure Storage, and interact with it from your remote compute targets. \n",
+        "\n",
+        "If your data is already stored in Azure, or you download the data as part of your training script, you will not need to do this step. For this tutorial, although you can download the data in your training script, we will demonstrate how to upload the training data to a datastore and access it during training to illustrate the datastore functionality."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First download the data from Yan LeCun's web site directly and save them in a data folder locally."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import urllib\n",
+        "\n",
+        "os.makedirs('./data/mnist', exist_ok=True)\n",
+        "\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz', filename = './data/mnist/train-images.gz')\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz', filename = './data/mnist/train-labels.gz')\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename = './data/mnist/test-images.gz')\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename = './data/mnist/test-labels.gz')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Each workspace is associated with a default datastore. In this tutorial, we will upload the training data to this default datastore."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ds = ws.get_default_datastore()\n",
+        "print(ds.datastore_type, ds.account_name, ds.container_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Upload MNIST data to the default datastore."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ds.upload(src_dir='./data/mnist', target_path='mnist', overwrite=True, show_progress=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For convenience, let's get a reference to the datastore. In the next section, we can then pass this reference to our training script's `--data-folder` argument. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ds_data = ds.as_mount()\n",
+        "print(ds_data)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train model on the remote compute"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a project directory\n",
+        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "script_folder = './tf-resume-training'\n",
+        "os.makedirs(script_folder, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copy the training script `tf_mnist_with_checkpoint.py` into this project directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import shutil\n",
+        "\n",
+        "# the training logic is in the tf_mnist_with_checkpoint.py file.\n",
+        "shutil.copy('./tf_mnist_with_checkpoint.py', script_folder)\n",
+        "\n",
+        "# the utils.py just helps loading data from the downloaded MNIST dataset into numpy arrays.\n",
+        "shutil.copy('./utils.py', script_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create an experiment\n",
+        "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed TensorFlow tutorial. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "experiment_name = 'tf-resume-training'\n",
+        "experiment = Experiment(ws, name=experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a TensorFlow estimator\n",
+        "The AML SDK's TensorFlow estimator enables you to easily submit TensorFlow training jobs for both single-node and distributed runs. For more information on the TensorFlow estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-tensorflow).\n",
+        "\n",
+        "The TensorFlow estimator also takes a `framework_version` parameter -- if no version is provided, the estimator will default to the latest version supported by AzureML. Use `TensorFlow.get_supported_versions()` to get a list of all versions supported by your current SDK version or see the [SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn?view=azure-ml-py) for the versions supported in the most current release."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.dnn import TensorFlow\n",
+        "\n",
+        "script_params={\n",
+        "    '--data-folder': ds_data\n",
+        "}\n",
+        "\n",
+        "estimator= TensorFlow(source_directory=script_folder,\n",
+        "                      compute_target=compute_target,\n",
+        "                      script_params=script_params,\n",
+        "                      entry_script='tf_mnist_with_checkpoint.py')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In the above code, we passed our training data reference `ds_data` to our script's `--data-folder` argument. This will 1) mount our datastore on the remote compute and 2) provide the path to the data zip file on our datastore."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit job\n",
+        "### Run your experiment by submitting your estimator object. Note that this call is asynchronous."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run = experiment.submit(estimator)\n",
+        "print(run)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Monitor your run\n",
+        "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Alternatively, you can block until the script has completed training before running more code."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Now let's resume the training from the above run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First, we will get the DataPath to the outputs directory of the above run which\n",
+        "contains the checkpoint files and/or model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model_location = run._get_outputs_datapath()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, we will create a new TensorFlow estimator and pass in the model location. On passing 'resume_from' parameter, a new entry in script_params is created with key as 'resume_from' and value as the model/checkpoint files location and the location gets automatically mounted on the compute target."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.dnn import TensorFlow\n",
+        "\n",
+        "script_params={\n",
+        "    '--data-folder': ds_data\n",
+        "}\n",
+        "\n",
+        "estimator2 = TensorFlow(source_directory=script_folder,\n",
+        "                      compute_target=compute_target,\n",
+        "                      script_params=script_params,\n",
+        "                      entry_script='tf_mnist_with_checkpoint.py',\n",
+        "                      resume_from=model_location)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now you can submit the experiment and it should resume from previous run's checkpoint files."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run2 = experiment.submit(estimator2)\n",
+        "print(run2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run2.wait_for_completion(show_output=True)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "hesuri"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    },
+    "msauthor": "hesuri"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/training/train-tensorflow-resume-training/train-tensorflow-resume-training.yml b/how-to-use-azureml/ml-frameworks/tensorflow/training/train-tensorflow-resume-training/train-tensorflow-resume-training.yml
new file mode 100644
index 00000000..c814eef5
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/tensorflow/training/train-tensorflow-resume-training/train-tensorflow-resume-training.yml
@@ -0,0 +1,5 @@
+name: train-tensorflow-resume-training
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/training/train-tensorflow-resume-training/utils.py b/how-to-use-azureml/ml-frameworks/tensorflow/training/train-tensorflow-resume-training/utils.py
new file mode 100644
index 00000000..98170ada
--- /dev/null
+++ b/how-to-use-azureml/ml-frameworks/tensorflow/training/train-tensorflow-resume-training/utils.py
@@ -0,0 +1,27 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import gzip
+import numpy as np
+import struct
+
+
+# load compressed MNIST gz files and return numpy arrays
+def load_data(filename, label=False):
+    with gzip.open(filename) as gz:
+        struct.unpack('I', gz.read(4))
+        n_items = struct.unpack('>I', gz.read(4))
+        if not label:
+            n_rows = struct.unpack('>I', gz.read(4))[0]
+            n_cols = struct.unpack('>I', gz.read(4))[0]
+            res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8)
+            res = res.reshape(n_items[0], n_rows * n_cols)
+        else:
+            res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8)
+            res = res.reshape(n_items[0], 1)
+    return res
+
+
+# one-hot encode a 1-D array
+def one_hot_encode(array, num_of_classes):
+    return np.eye(num_of_classes)[array.reshape(-1)]
diff --git a/contrib/datadrift/azure-ml-datadrift.ipynb b/how-to-use-azureml/monitor-models/data-drift/azure-ml-datadrift.ipynb
similarity index 99%
rename from contrib/datadrift/azure-ml-datadrift.ipynb
rename to how-to-use-azureml/monitor-models/data-drift/azure-ml-datadrift.ipynb
index 2bfe28d7..b80973a3 100644
--- a/contrib/datadrift/azure-ml-datadrift.ipynb
+++ b/how-to-use-azureml/monitor-models/data-drift/azure-ml-datadrift.ipynb
@@ -17,7 +17,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/datadrift/azureml-datadrift.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/monitor-models/data-drift/azureml-datadrift.png)"
       ]
     },
     {
@@ -716,7 +716,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.6"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/contrib/datadrift/azure-ml-datadrift.yml b/how-to-use-azureml/monitor-models/data-drift/azure-ml-datadrift.yml
similarity index 100%
rename from contrib/datadrift/azure-ml-datadrift.yml
rename to how-to-use-azureml/monitor-models/data-drift/azure-ml-datadrift.yml
diff --git a/contrib/datadrift/score.py b/how-to-use-azureml/monitor-models/data-drift/score.py
similarity index 100%
rename from contrib/datadrift/score.py
rename to how-to-use-azureml/monitor-models/data-drift/score.py
diff --git a/how-to-use-azureml/track-and-monitor-experiments/README.md b/how-to-use-azureml/track-and-monitor-experiments/README.md
new file mode 100644
index 00000000..6f46406a
--- /dev/null
+++ b/how-to-use-azureml/track-and-monitor-experiments/README.md
@@ -0,0 +1,19 @@
+
+## Follow these sample notebooks to learn:
+
+1. [Logging API](./logging-api/logging-api.ipynb): experiment with various logging functions to create runs and automatically generate graphs.
+2. [Manage runs](./manage-runs/manage-runs.ipynb): learn different ways how to start runs and child runs, monitor them, and cancel them.
+1. [Tensorboard to monitor runs](./tensorboard/tensorboard.ipynb)
+
+## Use MLflow with Azure Machine Learning service (Preview)
+
+[MLflow](https://mlflow.org/) is an open-source platform for tracking machine learning experiments and managing models. You can use MLflow logging APIs with Azure Machine Learning service: the metrics and artifacts are logged to your Azure ML Workspace.
+
+Try out the sample notebooks:
+1. [Use MLflow with Azure Machine Learning for Local Training Run](./train-local/train-local.ipynb)
+1. [Use MLflow with Azure Machine Learning for Remote Training Run](./train-remote/train-remote.ipynb)
+1. [Deploy Model as Azure Machine Learning Web Service using MLflow](./deploy-model/deploy-model.ipynb)
+1. [Train and Deploy PyTorch Image Classifier](./train-deploy-pytorch/train-deploy-pytorch.ipynb)
+
+ ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/track-and-monitor-experiments/README.png)
+
diff --git a/how-to-use-azureml/training/logging-api/img/run_details.PNG b/how-to-use-azureml/track-and-monitor-experiments/logging-api/img/run_details.PNG
similarity index 100%
rename from how-to-use-azureml/training/logging-api/img/run_details.PNG
rename to how-to-use-azureml/track-and-monitor-experiments/logging-api/img/run_details.PNG
diff --git a/how-to-use-azureml/training/logging-api/img/run_history.png b/how-to-use-azureml/track-and-monitor-experiments/logging-api/img/run_history.PNG
similarity index 100%
rename from how-to-use-azureml/training/logging-api/img/run_history.png
rename to how-to-use-azureml/track-and-monitor-experiments/logging-api/img/run_history.PNG
diff --git a/how-to-use-azureml/training/logging-api/logging-api.ipynb b/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb
similarity index 95%
rename from how-to-use-azureml/training/logging-api/logging-api.ipynb
rename to how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb
index 4d9401ae..84264fa6 100644
--- a/how-to-use-azureml/training/logging-api/logging-api.ipynb
+++ b/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb
@@ -1,5 +1,12 @@
 {
   "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -9,13 +16,6 @@
         "Licensed under the MIT License."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/logging-api/logging-api.png)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -100,7 +100,7 @@
         "\n",
         "# Check core SDK version number\n",
         "\n",
-        "print(\"This notebook was created using SDK version 1.0.48\r\n, you are currently running version\", azureml.core.VERSION)"
+        "print(\"This notebook was created using SDK version 1.0.62, you are currently running version\", azureml.core.VERSION)"
       ]
     },
     {
@@ -447,6 +447,22 @@
         "fetched_run.get_metrics()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Call ``run.get_metrics(name = <metric name>)`` to retrieve a metric value by name. Retrieving a single metric can be faster, especially if the run contains many metrics."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "fetched_run.get_metrics(name = \"scale factor\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -522,6 +538,19 @@
         "name": "roastala"
       }
     ],
+    "category": "other",
+    "compute": [
+      "None"
+    ],
+    "datasets": [],
+    "deployment": [
+      "None"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "None"
+    ],
+    "friendly_name": "Logging APIs",
     "kernelspec": {
       "display_name": "Python 3.6",
       "language": "python",
@@ -537,8 +566,14 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.5"
-    }
+      "version": "3.6.8"
+    },
+    "order_index": 1,
+    "star_tag": [],
+    "tags": [
+      "None"
+    ],
+    "task": "Logging APIs and analyzing results"
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/training/logging-api/logging-api.yml b/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.yml
similarity index 100%
rename from how-to-use-azureml/training/logging-api/logging-api.yml
rename to how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.yml
diff --git a/how-to-use-azureml/training/manage-runs/hello.py b/how-to-use-azureml/track-and-monitor-experiments/manage-runs/hello.py
similarity index 100%
rename from how-to-use-azureml/training/manage-runs/hello.py
rename to how-to-use-azureml/track-and-monitor-experiments/manage-runs/hello.py
diff --git a/how-to-use-azureml/training/manage-runs/hello_with_children.py b/how-to-use-azureml/track-and-monitor-experiments/manage-runs/hello_with_children.py
similarity index 100%
rename from how-to-use-azureml/training/manage-runs/hello_with_children.py
rename to how-to-use-azureml/track-and-monitor-experiments/manage-runs/hello_with_children.py
diff --git a/how-to-use-azureml/training/manage-runs/hello_with_delay.py b/how-to-use-azureml/track-and-monitor-experiments/manage-runs/hello_with_delay.py
similarity index 100%
rename from how-to-use-azureml/training/manage-runs/hello_with_delay.py
rename to how-to-use-azureml/track-and-monitor-experiments/manage-runs/hello_with_delay.py
diff --git a/how-to-use-azureml/training/manage-runs/manage-runs.ipynb b/how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.ipynb
similarity index 99%
rename from how-to-use-azureml/training/manage-runs/manage-runs.ipynb
rename to how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.ipynb
index ff618ef2..ae0b0536 100644
--- a/how-to-use-azureml/training/manage-runs/manage-runs.ipynb
+++ b/how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.ipynb
@@ -13,7 +13,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/manage-runs/manage-runs.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.png)"
       ]
     },
     {
diff --git a/how-to-use-azureml/training/manage-runs/manage-runs.yml b/how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.yml
similarity index 100%
rename from how-to-use-azureml/training/manage-runs/manage-runs.yml
rename to how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.yml
diff --git a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.ipynb b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.ipynb
new file mode 100644
index 00000000..2d72f3fd
--- /dev/null
+++ b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.ipynb
@@ -0,0 +1,562 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Tensorboard Integration with Run History\n",
+        "\n",
+        "1. Run a Tensorflow job locally and view its TB output live.\n",
+        "2. The same, for a DSVM.\n",
+        "3. And once more, with an AmlCompute cluster.\n",
+        "4. Finally, we'll collect all of these historical runs together into a single Tensorboard graph."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
+        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) notebook to:\n",
+        "    * install the AML SDK\n",
+        "    * create a workspace and its configuration file (`config.json`)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Diagnostics\n",
+        "Opt-in diagnostics for better experience, quality, and security of future releases."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "Diagnostics"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.telemetry import set_diagnostics_collection\n",
+        "\n",
+        "set_diagnostics_collection(send_diagnostics=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize Workspace\n",
+        "\n",
+        "Initialize a workspace object from persisted configuration."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep='\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Set experiment name and create project\n",
+        "Choose a name for your run history container in the workspace, and create a folder for the project."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from os import path, makedirs\n",
+        "experiment_name = 'tensorboard-demo'\n",
+        "\n",
+        "# experiment folder\n",
+        "exp_dir = './sample_projects/' + experiment_name\n",
+        "\n",
+        "if not path.exists(exp_dir):\n",
+        "    makedirs(exp_dir)\n",
+        "\n",
+        "# runs we started in this session, for the finale\n",
+        "runs = []"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Download Tensorflow Tensorboard demo code\n",
+        "\n",
+        "Tensorflow's repository has an MNIST demo with extensive Tensorboard instrumentation. We'll use it here for our purposes.\n",
+        "\n",
+        "Note that we don't need to make any code changes at all - the code works without modification from the Tensorflow repository."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import requests\n",
+        "import os\n",
+        "\n",
+        "tf_code = requests.get(\"https://raw.githubusercontent.com/tensorflow/tensorflow/r1.8/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py\")\n",
+        "with open(os.path.join(exp_dir, \"mnist_with_summaries.py\"), \"w\") as file:\n",
+        "    file.write(tf_code.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Configure and run locally\n",
+        "\n",
+        "We'll start by running this locally. While it might not initially seem that useful to use this for a local run - why not just run TB against the files generated locally? - even in this case there is some value to using this feature. Your local run will be registered in the run history, and your Tensorboard logs will be uploaded to the artifact store associated with this run. Later, you'll be able to restore the logs from any run, regardless of where it happened.\n",
+        "\n",
+        "Note that for this run, you will need to install Tensorflow on your local machine by yourself. Further, the Tensorboard module (that is, the one included with Tensorflow) must be accessible to this notebook's kernel, as the local machine is what runs Tensorboard."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "\n",
+        "# Create a run configuration.\n",
+        "run_config = RunConfiguration()\n",
+        "run_config.environment.python.user_managed_dependencies = True\n",
+        "\n",
+        "# You can choose a specific Python environment by pointing to a Python path \n",
+        "#run_config.environment.python.interpreter_path = '/home/ninghai/miniconda3/envs/sdk2/bin/python'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "from azureml.core.script_run_config import ScriptRunConfig\n",
+        "\n",
+        "logs_dir = os.path.join(os.curdir, \"logs\")\n",
+        "data_dir = os.path.abspath(os.path.join(os.curdir, \"mnist_data\"))\n",
+        "\n",
+        "if not path.exists(data_dir):\n",
+        "    makedirs(data_dir)\n",
+        "\n",
+        "os.environ[\"TEST_TMPDIR\"] = data_dir\n",
+        "\n",
+        "# Writing logs to ./logs results in their being uploaded to Artifact Service,\n",
+        "# and thus, made accessible to our Tensorboard instance.\n",
+        "arguments_list = [\"--log_dir\", logs_dir]\n",
+        "\n",
+        "# Create an experiment\n",
+        "exp = Experiment(ws, experiment_name)\n",
+        "\n",
+        "# If you would like the run to go for longer, add --max_steps 5000 to the arguments list:\n",
+        "# arguments_list += [\"--max_steps\", \"5000\"]\n",
+        "\n",
+        "script = ScriptRunConfig(exp_dir,\n",
+        "                         script=\"mnist_with_summaries.py\",\n",
+        "                         run_config=run_config,\n",
+        "                         arguments=arguments_list)\n",
+        "\n",
+        "run = exp.submit(script)\n",
+        "# You can also wait for the run to complete\n",
+        "# run.wait_for_completion(show_output=True)\n",
+        "runs.append(run)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Start Tensorboard\n",
+        "\n",
+        "Now, while the run is in progress, we just need to start Tensorboard with the run as its target, and it will begin streaming logs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "tensorboard-sample"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.tensorboard import Tensorboard\n",
+        "\n",
+        "# The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here\n",
+        "tb = Tensorboard([run])\n",
+        "\n",
+        "# If successful, start() returns a string with the URI of the instance.\n",
+        "tb.start()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Stop Tensorboard\n",
+        "\n",
+        "When you're done, make sure to call the `stop()` method of the Tensorboard object, or it will stay running even after your job completes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "tb.stop()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Now, with a DSVM\n",
+        "\n",
+        "Tensorboard uploading works with all compute targets. Here we demonstrate it from a DSVM.\n",
+        "Note that the Tensorboard instance itself will be run by the notebook kernel. Again, this means this notebook's kernel must have access to the Tensorboard module.\n",
+        "\n",
+        "If you are unfamiliar with DSVM configuration, check [Train in a remote VM](../../training/train-on-remote-vm/train-on-remote-vm.ipynb) for a more detailed breakdown.\n",
+        "\n",
+        "**Note**: To streamline the compute that Azure Machine Learning creates, we are making updates to support creating only single to multi-node `AmlCompute`. The `DSVMCompute` class will be deprecated in a later release, but the DSVM can be created using the below single line command and then attached(like any VM) using the sample code below. Also note, that we only support Linux VMs for remote execution from AML and the commands below will spin a Linux VM only.\n",
+        "\n",
+        "```shell\n",
+        "# create a DSVM in your resource group\n",
+        "# note you need to be at least a contributor to the resource group in order to execute this command successfully.\n",
+        "(myenv) $ az vm create --resource-group <resource_group_name> --name <some_vm_name> --image microsoft-dsvm:linux-data-science-vm-ubuntu:linuxdsvmubuntu:latest --admin-username <username> --admin-password <password> --generate-ssh-keys --authentication-type password\n",
+        "```\n",
+        "You can also use [this url](https://portal.azure.com/#create/microsoft-dsvm.linux-data-science-vm-ubuntulinuxdsvmubuntu) to create the VM using the Azure Portal."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, RemoteCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "username = os.getenv('AZUREML_DSVM_USERNAME', default='<my_username>')\n",
+        "address = os.getenv('AZUREML_DSVM_ADDRESS', default='<ip_address_or_fqdn>')\n",
+        "\n",
+        "compute_target_name = 'cpudsvm'\n",
+        "# if you want to connect using SSH key instead of username/password you can provide parameters private_key_file and private_key_passphrase \n",
+        "try:\n",
+        "    attached_dsvm_compute = RemoteCompute(workspace=ws, name=compute_target_name)\n",
+        "    print('found existing:', attached_dsvm_compute.name)\n",
+        "except ComputeTargetException:\n",
+        "    config = RemoteCompute.attach_configuration(username=username,\n",
+        "                                                address=address,\n",
+        "                                                ssh_port=22,\n",
+        "                                                private_key_file='./.ssh/id_rsa')\n",
+        "    attached_dsvm_compute = ComputeTarget.attach(ws, compute_target_name, config)\n",
+        "    \n",
+        "    attached_dsvm_compute.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Submit run using TensorFlow estimator\n",
+        "\n",
+        "Instead of manually configuring the DSVM environment, we can use the TensorFlow estimator and everything is set up automatically."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.dnn import TensorFlow\n",
+        "\n",
+        "script_params = {\"--log_dir\": \"./logs\"}\n",
+        "\n",
+        "# If you want the run to go longer, set --max-steps to a higher number.\n",
+        "# script_params[\"--max_steps\"] = \"5000\"\n",
+        "\n",
+        "tf_estimator = TensorFlow(source_directory=exp_dir,\n",
+        "                          compute_target=attached_dsvm_compute,\n",
+        "                          entry_script='mnist_with_summaries.py',\n",
+        "                          script_params=script_params)\n",
+        "\n",
+        "run = exp.submit(tf_estimator)\n",
+        "\n",
+        "runs.append(run)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Start Tensorboard with this run\n",
+        "\n",
+        "Just like before."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here\n",
+        "tb = Tensorboard([run])\n",
+        "\n",
+        "# If successful, start() returns a string with the URI of the instance.\n",
+        "tb.start()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Stop Tensorboard\n",
+        "\n",
+        "When you're done, make sure to call the `stop()` method of the Tensorboard object, or it will stay running even after your job completes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "tb.stop()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Once more, with an AmlCompute cluster\n",
+        "\n",
+        "Just to prove we can, let's create an AmlCompute CPU cluster, and run our demo there, as well."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "cluster_name = \"cpucluster\"\n",
+        "\n",
+        "cts = ws.compute_targets\n",
+        "found = False\n",
+        "if cluster_name in cts and cts[cluster_name].type == 'AmlCompute':\n",
+        "   found = True\n",
+        "   print('Found existing compute target.')\n",
+        "   compute_target = cts[cluster_name]\n",
+        "if not found:\n",
+        "    print('Creating a new compute target...')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2', \n",
+        "                                                           max_nodes=4)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+        "\n",
+        "compute_target.wait_for_completion(show_output=True, min_node_count=None)\n",
+        "\n",
+        "# use get_status() to get a detailed status for the current cluster. \n",
+        "# print(compute_target.get_status().serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Submit run using TensorFlow estimator\n",
+        "\n",
+        "Again, we can use the TensorFlow estimator and everything is set up automatically."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "script_params = {\"--log_dir\": \"./logs\"}\n",
+        "\n",
+        "# If you want the run to go longer, set --max-steps to a higher number.\n",
+        "# script_params[\"--max_steps\"] = \"5000\"\n",
+        "\n",
+        "tf_estimator = TensorFlow(source_directory=exp_dir,\n",
+        "                          compute_target=compute_target,\n",
+        "                          entry_script='mnist_with_summaries.py',\n",
+        "                          script_params=script_params)\n",
+        "\n",
+        "run = exp.submit(tf_estimator)\n",
+        "\n",
+        "runs.append(run)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Start Tensorboard with this run\n",
+        "\n",
+        "Once more..."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here\n",
+        "tb = Tensorboard([run])\n",
+        "\n",
+        "# If successful, start() returns a string with the URI of the instance.\n",
+        "tb.start()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Stop Tensorboard\n",
+        "\n",
+        "When you're done, make sure to call the `stop()` method of the Tensorboard object, or it will stay running even after your job completes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "tb.stop()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Finale\n",
+        "\n",
+        "If you've paid close attention, you'll have noticed that we've been saving the run objects in an array as we went along. We can start a Tensorboard instance that combines all of these run objects into a single process. This way, you can compare historical runs. You can even do this with live runs; if you made some of those previous runs longer via the `--max_steps` parameter, they might still be running, and you'll see them live in this instance as well."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# The Tensorboard constructor takes an array of runs...\n",
+        "# and it turns out that we have been building one of those all along.\n",
+        "tb = Tensorboard(runs)\n",
+        "\n",
+        "# If successful, start() returns a string with the URI of the instance.\n",
+        "tb.start()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Stop Tensorboard\n",
+        "\n",
+        "As you might already know, make sure to call the `stop()` method of the Tensorboard object, or it will stay running (until you kill the kernel associated with this notebook, at least)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "tb.stop()"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "roastala"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.yml b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.yml
new file mode 100644
index 00000000..de683457
--- /dev/null
+++ b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.yml
@@ -0,0 +1,6 @@
+name: tensorboard
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-tensorboard
+  - tensorflow
diff --git a/how-to-use-azureml/using-mlflow/deploy-model/deploy-model.ipynb b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/deploy-model/deploy-model.ipynb
similarity index 100%
rename from how-to-use-azureml/using-mlflow/deploy-model/deploy-model.ipynb
rename to how-to-use-azureml/track-and-monitor-experiments/using-mlflow/deploy-model/deploy-model.ipynb
diff --git a/how-to-use-azureml/using-mlflow/deploy-model/deploy-model.yml b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/deploy-model/deploy-model.yml
similarity index 100%
rename from how-to-use-azureml/using-mlflow/deploy-model/deploy-model.yml
rename to how-to-use-azureml/track-and-monitor-experiments/using-mlflow/deploy-model/deploy-model.yml
diff --git a/how-to-use-azureml/using-mlflow/train-deploy-pytorch/scripts/train.py b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-deploy-pytorch/scripts/train.py
similarity index 100%
rename from how-to-use-azureml/using-mlflow/train-deploy-pytorch/scripts/train.py
rename to how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-deploy-pytorch/scripts/train.py
diff --git a/how-to-use-azureml/using-mlflow/train-deploy-pytorch/train-and-deploy-pytorch.ipynb b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-deploy-pytorch/train-and-deploy-pytorch.ipynb
similarity index 100%
rename from how-to-use-azureml/using-mlflow/train-deploy-pytorch/train-and-deploy-pytorch.ipynb
rename to how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-deploy-pytorch/train-and-deploy-pytorch.ipynb
diff --git a/how-to-use-azureml/using-mlflow/train-deploy-pytorch/train-and-deploy-pytorch.yml b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-deploy-pytorch/train-and-deploy-pytorch.yml
similarity index 100%
rename from how-to-use-azureml/using-mlflow/train-deploy-pytorch/train-and-deploy-pytorch.yml
rename to how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-deploy-pytorch/train-and-deploy-pytorch.yml
diff --git a/how-to-use-azureml/using-mlflow/train-local/train-local.ipynb b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.ipynb
similarity index 100%
rename from how-to-use-azureml/using-mlflow/train-local/train-local.ipynb
rename to how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.ipynb
diff --git a/how-to-use-azureml/using-mlflow/train-local/train-local.yml b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.yml
similarity index 100%
rename from how-to-use-azureml/using-mlflow/train-local/train-local.yml
rename to how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.yml
diff --git a/how-to-use-azureml/using-mlflow/train-remote/train-remote.ipynb b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.ipynb
similarity index 99%
rename from how-to-use-azureml/using-mlflow/train-remote/train-remote.ipynb
rename to how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.ipynb
index 089406b4..c6da843d 100644
--- a/how-to-use-azureml/using-mlflow/train-remote/train-remote.ipynb
+++ b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.ipynb
@@ -13,7 +13,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/using-mlflow/train-remote/train-remote.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.png)"
       ]
     },
     {
diff --git a/how-to-use-azureml/using-mlflow/train-remote/train-remote.yml b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.yml
similarity index 100%
rename from how-to-use-azureml/using-mlflow/train-remote/train-remote.yml
rename to how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.yml
diff --git a/how-to-use-azureml/using-mlflow/train-remote/train_diabetes.py b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train_diabetes.py
similarity index 100%
rename from how-to-use-azureml/using-mlflow/train-remote/train_diabetes.py
rename to how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train_diabetes.py
diff --git a/how-to-use-azureml/training-with-deep-learning/README.md b/how-to-use-azureml/training-with-deep-learning/README.md
index b5200946..77a64b1b 100644
--- a/how-to-use-azureml/training-with-deep-learning/README.md
+++ b/how-to-use-azureml/training-with-deep-learning/README.md
@@ -3,17 +3,10 @@
 These examples show you:
 
 1. [How to use the Estimator pattern in Azure ML](how-to-use-estimator)
-2. [Train using TensorFlow Estimator and tune hyperparameters using Hyperdrive](train-hyperparameter-tune-deploy-with-tensorflow)
-3. [Train using Pytorch Estimator and tune hyperparameters using Hyperdrive](train-hyperparameter-tune-deploy-with-pytorch)
-4. [Train using Keras and tune hyperparameters using Hyperdrive](train-hyperparameter-tune-deploy-with-keras)
-5. [Train using Chainer Estimator and tune hyperparameters using Hyperdrive](train-hyperparameter-tune-deploy-with-chainer)
-6. [Distributed training using TensorFlow and Parameter Server](distributed-tensorflow-with-parameter-server)
-7. [Distributed training using TensorFlow and Horovod](distributed-tensorflow-with-horovod)
-8. [Distributed training using Pytorch and Horovod](distributed-pytorch-with-horovod)
-9. [Distributed training using CNTK and custom Docker image](distributed-cntk-with-custom-docker)
-10. [Distributed training using Chainer](distributed-chainer)
-11. [Export run history records to Tensorboard](export-run-history-to-tensorboard)
-12. [Use TensorBoard to monitor training execution](tensorboard)
+2. [Train using Keras and tune hyperparameters using Hyperdrive](train-hyperparameter-tune-deploy-with-keras)
+3. [Distributed training using CNTK and custom Docker image](distributed-cntk-with-custom-docker)
+4. [Export run history records to Tensorboard](export-run-history-to-tensorboard)
+5. [Use TensorBoard to monitor training execution](tensorboard)
 
 Learn more about how to use `Estimator` class to  [train deep neural networks with Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-ml-models).
 
diff --git a/how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/distributed-cntk-with-custom-docker.ipynb b/how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/distributed-cntk-with-custom-docker.ipynb
index 32bf1a24..61120774 100644
--- a/how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/distributed-cntk-with-custom-docker.ipynb
+++ b/how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/distributed-cntk-with-custom-docker.ipynb
@@ -286,7 +286,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "estimator-remarks-sample"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.train.estimator import Estimator\n",
diff --git a/how-to-use-azureml/training-with-deep-learning/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb b/how-to-use-azureml/training-with-deep-learning/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb
index 6d15a48c..ff076fb8 100644
--- a/how-to-use-azureml/training-with-deep-learning/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb
+++ b/how-to-use-azureml/training-with-deep-learning/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb
@@ -153,7 +153,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "tensorboard-export-sample"
+        ]
+      },
       "outputs": [],
       "source": [
         "# Export Run History to Tensorboard logs\n",
diff --git a/how-to-use-azureml/training-with-deep-learning/tensorboard/tensorboard.ipynb b/how-to-use-azureml/training-with-deep-learning/tensorboard/tensorboard.ipynb
index 751f6671..2f638e63 100644
--- a/how-to-use-azureml/training-with-deep-learning/tensorboard/tensorboard.ipynb
+++ b/how-to-use-azureml/training-with-deep-learning/tensorboard/tensorboard.ipynb
@@ -227,7 +227,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "tensorboard-sample"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.tensorboard import Tensorboard\n",
diff --git a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/chainer_mnist.py b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/chainer_mnist.py
index 515ce8ba..df2d6a6e 100644
--- a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/chainer_mnist.py
+++ b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/chainer_mnist.py
@@ -1,5 +1,6 @@
 
 import argparse
+import os
 
 import numpy as np
 
@@ -131,6 +132,8 @@ def main():
 
             run.log("Accuracy", np.float(val_accuracy))
 
+    serializers.save_npz(os.path.join(args.output_dir, 'model.npz'), model)
+
 
 if __name__ == '__main__':
     main()
diff --git a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/chainer_score.py b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/chainer_score.py
new file mode 100644
index 00000000..f6ec3a6c
--- /dev/null
+++ b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/chainer_score.py
@@ -0,0 +1,45 @@
+import numpy as np
+import os
+import json
+
+from chainer import serializers, using_config, Variable, datasets
+import chainer.functions as F
+import chainer.links as L
+from chainer import Chain
+
+from azureml.core.model import Model
+
+
+class MyNetwork(Chain):
+
+    def __init__(self, n_mid_units=100, n_out=10):
+        super(MyNetwork, self).__init__()
+        with self.init_scope():
+            self.l1 = L.Linear(None, n_mid_units)
+            self.l2 = L.Linear(n_mid_units, n_mid_units)
+            self.l3 = L.Linear(n_mid_units, n_out)
+
+    def forward(self, x):
+        h = F.relu(self.l1(x))
+        h = F.relu(self.l2(h))
+        return self.l3(h)
+
+
+def init():
+    global model
+
+    model_root = Model.get_model_path('chainer-dnn-mnist')
+
+    # Load our saved artifacts
+    model = MyNetwork()
+    serializers.load_npz(model_root, model)
+
+
+def run(input_data):
+    i = np.array(json.loads(input_data)['data'])
+
+    _, test = datasets.get_mnist()
+    x = Variable(np.asarray([test[i][0]]))
+    y = model(x)
+
+    return np.ndarray.tolist(y.data.argmax(axis=1))
diff --git a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb
index 353294fa..85fd5f53 100644
--- a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb
+++ b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb
@@ -45,6 +45,16 @@
         "print(\"SDK version:\", azureml.core.VERSION)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!jupyter nbextension install --py --user azureml.widgets\n",
+        "!jupyter nbextension enable --py --user azureml.widgets"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -121,6 +131,7 @@
         "except ComputeTargetException:\n",
         "    print('Creating a new compute target...')\n",
         "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
+        "                                                           min_nodes=2,\n",
         "                                                           max_nodes=4)\n",
         "\n",
         "    # create the cluster\n",
@@ -206,7 +217,8 @@
       "source": [
         "import shutil\n",
         "\n",
-        "shutil.copy('chainer_mnist.py', project_folder)"
+        "shutil.copy('chainer_mnist.py', project_folder)\n",
+        "shutil.copy('chainer_score.py', project_folder)"
       ]
     },
     {
@@ -240,7 +252,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "dnn-chainer-remarks-sample"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.train.dnn import Chainer\n",
@@ -353,6 +369,7 @@
         "hyperdrive_config = HyperDriveConfig(estimator=estimator,\n",
         "                                     hyperparameter_sampling=param_sampling, \n",
         "                                     primary_metric_name='Accuracy',\n",
+        "                                     policy=BanditPolicy(evaluation_interval=1, slack_factor=0.1, delay_evaluation=3),\n",
         "                                     primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n",
         "                                     max_total_runs=8,\n",
         "                                     max_concurrent_runs=4)\n"
@@ -398,14 +415,344 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "run.wait_for_completion(show_output=True)"
+        "hyperdrive_run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Find and register best model\n",
+        "When all jobs finish, we can find out the one that has the highest accuracy."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run = hyperdrive_run.get_best_run_by_primary_metric()\n",
+        "print(best_run.get_details()['runDefinition']['arguments'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, let's list the model files uploaded during the run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(best_run.get_file_names())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can then register the folder (and all files in it) as a model named `chainer-dnn-mnist` under the workspace for deployment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model = best_run.register_model(model_name='chainer-dnn-mnist', model_path='outputs/model.npz')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Deploy the model in ACI\n",
+        "Now, we are ready to deploy the model as a web service running in Azure Container Instance, [ACI](https://azure.microsoft.com/en-us/services/container-instances/). Azure Machine Learning accomplishes this by constructing a Docker image with the scoring logic and model baked in.\n",
+        "\n",
+        "### Create scoring script\n",
+        "First, we will create a scoring script that will be invoked by the web service call.\n",
+        "+ Now that the scoring script must have two required functions, `init()` and `run(input_data)`.\n",
+        "    + In `init()`, you typically load the model into a global object. This function is executed only once when the Docker contianer is started.\n",
+        "    + In `run(input_data)`, the model is used to predict a value based on the input data. The input and output to `run` uses NPZ as the serialization and de-serialization format because it is the preferred format for Chainer, but you are not limited to it.\n",
+        "    \n",
+        "Refer to the scoring script `chainer_score.py` for this tutorial. Our web service will use this file to predict. When writing your own scoring script, don't forget to test it locally first before you go and deploy the web service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "shutil.copy('chainer_score.py', project_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create myenv.yml\n",
+        "We also need to create an environment file so that Azure Machine Learning can install the necessary packages in the Docker image which are required by your scoring script. In this case, we need to specify conda packages `numpy` and `chainer`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import CondaDependencies\n",
+        "\n",
+        "cd = CondaDependencies.create()\n",
+        "cd.add_conda_package('numpy')\n",
+        "cd.add_conda_package('chainer')\n",
+        "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",
+        "\n",
+        "print(cd.serialize_to_string())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Deploy to ACI\n",
+        "We are almost ready to deploy. Create a deployment configuration and specify the number of CPUs and gigabytes of RAM needed for your ACI container."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice\n",
+        "\n",
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1,\n",
+        "                                               auth_enabled=True, # this flag generates API keys to secure access\n",
+        "                                               memory_gb=1,\n",
+        "                                               tags={'name': 'mnist', 'framework': 'Chainer'},\n",
+        "                                               description='Chainer DNN with MNIST')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**Deployment Process**\n",
+        "\n",
+        "Now we can deploy. **This cell will run for about 7-8 minutes.** Behind the scenes, it will do the following:\n",
+        "\n",
+        "1. **Build Docker image**\n",
+        "Build a Docker image using the scoring file (chainer_score.py), the environment file (myenv.yml), and the model object.\n",
+        "2. **Register image**\n",
+        "Register that image under the workspace.\n",
+        "3. **Ship to ACI**\n",
+        "And finally ship the image to the ACI infrastructure, start up a container in ACI using that image, and expose an HTTP endpoint to accept REST client calls."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.image import ContainerImage\n",
+        "\n",
+        "imgconfig = ContainerImage.image_configuration(execution_script=\"chainer_score.py\", \n",
+        "                                               runtime=\"python\", \n",
+        "                                               conda_file=\"myenv.yml\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "from azureml.core.webservice import Webservice\n",
+        "\n",
+        "service = Webservice.deploy_from_model(workspace=ws,\n",
+        "                                       name='chainer-mnist-1',\n",
+        "                                       deployment_config=aciconfig,\n",
+        "                                       models=[model],\n",
+        "                                       image_config=imgconfig)\n",
+        "\n",
+        "service.wait_for_deployment(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(service.get_logs())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(service.scoring_uri)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**Tip: If something goes wrong with the deployment, the first thing to look at is the logs from the service by running the following command:** `print(service.get_logs())`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This is the scoring web service endpoint: `print(service.scoring_uri)`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Test the deployed model\n",
+        "Let's test the deployed model. Pick a random sample from the test set, and send it to the web service hosted in ACI for a prediction. Note, here we are using the an HTTP request to invoke the service.\n",
+        "\n",
+        "We can retrieve the API keys used for accessing the HTTP endpoint and construct a raw HTTP request to send to the service. Don't forget to add key to the HTTP header."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# retreive the API keys. two keys were generated.\n",
+        "key1, Key2 = service.get_keys()\n",
+        "print(key1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "import matplotlib.pyplot as plt\n",
+        "import urllib\n",
+        "import gzip\n",
+        "import numpy as np\n",
+        "import struct\n",
+        "import requests\n",
+        "\n",
+        "\n",
+        "# load compressed MNIST gz files and return numpy arrays\n",
+        "def load_data(filename, label=False):\n",
+        "    with gzip.open(filename) as gz:\n",
+        "        struct.unpack('I', gz.read(4))\n",
+        "        n_items = struct.unpack('>I', gz.read(4))\n",
+        "        if not label:\n",
+        "            n_rows = struct.unpack('>I', gz.read(4))[0]\n",
+        "            n_cols = struct.unpack('>I', gz.read(4))[0]\n",
+        "            res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8)\n",
+        "            res = res.reshape(n_items[0], n_rows * n_cols)\n",
+        "        else:\n",
+        "            res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8)\n",
+        "            res = res.reshape(n_items[0], 1)\n",
+        "    return res\n",
+        "\n",
+        "os.makedirs('./data/mnist', exist_ok=True)\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename = './data/mnist/test-images.gz')\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename = './data/mnist/test-labels.gz')\n",
+        "\n",
+        "X_test = load_data('./data/mnist/test-images.gz', False)\n",
+        "y_test = load_data('./data/mnist/test-labels.gz', True).reshape(-1)\n",
+        "\n",
+        "\n",
+        "# send a random row from the test set to score\n",
+        "random_index = np.random.randint(0, len(X_test)-1)\n",
+        "input_data = \"{\\\"data\\\": [\" + str(random_index) + \"]}\"\n",
+        "\n",
+        "headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
+        "\n",
+        "# send sample to service for scoring\n",
+        "resp = requests.post(service.scoring_uri, input_data, headers=headers)\n",
+        "\n",
+        "print(\"label:\", y_test[random_index])\n",
+        "print(\"prediction:\", resp.text[1])\n",
+        "\n",
+        "plt.imshow(X_test[random_index].reshape((28,28)), cmap='gray')\n",
+        "plt.axis('off')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Let's look at the workspace after the web service was deployed. You should see\n",
+        "\n",
+        " + a registered model named 'chainer-dnn-mnist' and with the id 'chainer-dnn-mnist:1'\n",
+        " + an image called 'chainer-mnist-svc' and with a docker image location pointing to your workspace's Azure Container Registry (ACR)\n",
+        " + a webservice called 'chainer-mnist-svc' with some scoring URL"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "models = ws.models\n",
+        "for name, model in models.items():\n",
+        "    print(\"Model: {}, ID: {}\".format(name, model.id))\n",
+        "    \n",
+        "images = ws.images\n",
+        "for name, image in images.items():\n",
+        "    print(\"Image: {}, location: {}\".format(name, image.image_location))\n",
+        "    \n",
+        "webservices = ws.webservices\n",
+        "for name, webservice in webservices.items():\n",
+        "    print(\"Webservice: {}, scoring URI: {}\".format(name, webservice.scoring_uri))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Clean up"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can delete the ACI deployment with a simple delete API call."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "service.delete()"
       ]
     }
   ],
   "metadata": {
     "authors": [
       {
-        "name": "ninhu"
+        "name": "dipeck"
       }
     ],
     "kernelspec": {
@@ -424,7 +771,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.6"
-    }
+    },
+    "msauthor": "dipeck"
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.yml b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.yml
index 4f68f902..6024bba0 100644
--- a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.yml
+++ b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.yml
@@ -4,4 +4,9 @@ dependencies:
   - azureml-sdk
   - azureml-widgets
   - numpy
-  - pytest
+  - matplotlib
+  - json
+  - urllib
+  - gzip
+  - struct
+  - requests
diff --git a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/keras_mnist.py b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/keras_mnist.py
index 9f2529e6..e4d17706 100644
--- a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/keras_mnist.py
+++ b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/keras_mnist.py
@@ -4,6 +4,7 @@
 import numpy as np
 import argparse
 import os
+import glob
 
 import matplotlib.pyplot as plt
 
@@ -36,11 +37,15 @@ data_folder = args.data_folder
 
 print('training dataset is stored here:', data_folder)
 
-X_train = load_data(os.path.join(data_folder, 'train-images.gz'), False) / 255.0
-X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / 255.0
+X_train_path = glob.glob(os.path.join(data_folder, '**/train-images-idx3-ubyte.gz'), recursive=True)[0]
+X_test_path = glob.glob(os.path.join(data_folder, '**/t10k-images-idx3-ubyte.gz'), recursive=True)[0]
+y_train_path = glob.glob(os.path.join(data_folder, '**/train-labels-idx1-ubyte.gz'), recursive=True)[0]
+y_test_path = glob.glob(os.path.join(data_folder, '**/t10k-labels-idx1-ubyte.gz'), recursive=True)[0]
 
-y_train = load_data(os.path.join(data_folder, 'train-labels.gz'), True).reshape(-1)
-y_test = load_data(os.path.join(data_folder, 'test-labels.gz'), True).reshape(-1)
+X_train = load_data(X_train_path, False) / 255.0
+X_test = load_data(X_test_path, False) / 255.0
+y_train = load_data(y_train_path, True).reshape(-1)
+y_test = load_data(y_test_path, True).reshape(-1)
 
 training_set_size = X_train.shape[0]
 
diff --git a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb
index 913fb842..45ae4330 100644
--- a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb
+++ b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb
@@ -132,14 +132,18 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "nbpresent": {
-          "id": "defe921f-8097-44c3-8336-8af6700804a7"
-        }
-      },
+      "metadata": {},
       "source": [
-        "## Download MNIST dataset\n",
-        "In order to train on the MNIST dataset we will first need to download it from Yan LeCun's web site directly and save them in a `data` folder locally."
+        "## Explore data\n",
+        "\n",
+        "Before you train a model, you need to understand the data that you are using to train it. In this section you learn how to:\n",
+        "\n",
+        "* Download the MNIST dataset\n",
+        "* Display some sample images\n",
+        "\n",
+        "### Download the MNIST dataset\n",
+        "\n",
+        "Download the MNIST dataset and save the files into a `data` directory locally.  Images and labels for both training and testing are downloaded."
       ]
     },
     {
@@ -148,47 +152,42 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import urllib\n",
+        "import urllib.request\n",
         "\n",
-        "os.makedirs('./data/mnist', exist_ok=True)\n",
+        "data_folder = os.path.join(os.getcwd(), 'data')\n",
+        "os.makedirs(data_folder, exist_ok=True)\n",
         "\n",
-        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz', filename='./data/mnist/train-images.gz')\n",
-        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz', filename='./data/mnist/train-labels.gz')\n",
-        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename='./data/mnist/test-images.gz')\n",
-        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename='./data/mnist/test-labels.gz')"
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz', filename=os.path.join(data_folder, 'train-images.gz'))\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz', filename=os.path.join(data_folder, 'train-labels.gz'))\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename=os.path.join(data_folder, 'test-images.gz'))\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename=os.path.join(data_folder, 'test-labels.gz'))"
       ]
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "nbpresent": {
-          "id": "c3f2f57c-7454-4d3e-b38d-b0946cf066ea"
-        }
-      },
+      "metadata": {},
       "source": [
-        "## Show some sample images\n",
-        "Let's load the downloaded compressed file into numpy arrays using some utility functions included in the `utils.py` library file from the current folder. Then we use `matplotlib` to plot 30 random images from the dataset along with their labels."
+        "### Display some sample images\n",
+        "\n",
+        "Load the compressed files into `numpy` arrays. Then use `matplotlib` to plot 30 random images from the dataset with their labels above them. Note this step requires a `load_data` function that's included in an `utils.py` file. This file is included in the sample folder. Please make sure it is placed in the same folder as this notebook. The `load_data` function simply parses the compressed files into numpy arrays."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {
-        "nbpresent": {
-          "id": "396d478b-34aa-4afa-9898-cdce8222a516"
-        }
-      },
+      "metadata": {},
       "outputs": [],
       "source": [
+        "# make sure utils.py is in the same directory as this code\n",
         "from utils import load_data, one_hot_encode\n",
         "\n",
-        "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the neural network converge faster.\n",
-        "X_train = load_data('./data/mnist/train-images.gz', False) / 255.0\n",
-        "y_train = load_data('./data/mnist/train-labels.gz', True).reshape(-1)\n",
-        "\n",
-        "X_test = load_data('./data/mnist/test-images.gz', False) / 255.0\n",
-        "y_test = load_data('./data/mnist/test-labels.gz', True).reshape(-1)\n",
+        "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the model converge faster.\n",
+        "X_train = load_data(os.path.join(data_folder, 'train-images.gz'), False) / 255.0\n",
+        "X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / 255.0\n",
+        "y_train = load_data(os.path.join(data_folder, 'train-labels.gz'), True).reshape(-1)\n",
+        "y_test = load_data(os.path.join(data_folder, 'test-labels.gz'), True).reshape(-1)\n",
         "\n",
+        "# now let's show some randomly chosen images from the training set.\n",
         "count = 0\n",
         "sample_size = 30\n",
         "plt.figure(figsize = (16, 6))\n",
@@ -197,8 +196,8 @@
         "    plt.subplot(1, sample_size, count)\n",
         "    plt.axhline('')\n",
         "    plt.axvline('')\n",
-        "    plt.text(x = 10, y = -10, s = y_train[i], fontsize = 18)\n",
-        "    plt.imshow(X_train[i].reshape(28, 28), cmap = plt.cm.Greys)\n",
+        "    plt.text(x=10, y=-10, s=y_train[i], fontsize=18)\n",
+        "    plt.imshow(X_train[i].reshape(28, 28), cmap=plt.cm.Greys)\n",
         "plt.show()"
       ]
     },
@@ -206,8 +205,19 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Upload MNIST dataset to default datastore \n",
-        "A [datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data) is a place where data can be stored that is then made accessible to a Run either by means of mounting or copying the data to the compute target. A datastore can either be backed by an Azure Blob Storage or and Azure File Share (ADLS will be supported in the future). For simple data handling, each workspace provides a default datastore that can be used, in case the data is not already in Blob Storage or File Share."
+        "Now you have an idea of what these images look like and the expected prediction outcome."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nbpresent": {
+          "id": "defe921f-8097-44c3-8336-8af6700804a7"
+        }
+      },
+      "source": [
+        "## Create a FileDataset\n",
+        "A FileDataset references one or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred. [Learn More](https://aka.ms/azureml/howto/createdatasets)"
       ]
     },
     {
@@ -216,14 +226,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ds = ws.get_default_datastore()"
+        "from azureml.core.dataset import Dataset\n",
+        "\n",
+        "web_paths = [\n",
+        "            'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',\n",
+        "            'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz',\n",
+        "            'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',\n",
+        "            'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz'\n",
+        "            ]\n",
+        "dataset = Dataset.File.from_files(path = web_paths)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "In this next step, we will upload the training and test set into the workspace's default datastore, which we will then later be mount on an `AmlCompute` cluster for training."
+        "Use the `register()` method to register datasets to your workspace so they can be shared with others, reused across various experiments, and referred to by name in your training script."
       ]
     },
     {
@@ -232,7 +250,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ds.upload(src_dir='./data/mnist', target_path='mnist', overwrite=True, show_progress=True)"
+        "dataset = dataset.register(workspace = ws,\n",
+        "                           name = 'mnist dataset',\n",
+        "                           description='training and test dataset',\n",
+        "                           create_new_version=True)"
       ]
     },
     {
@@ -345,7 +366,7 @@
       "source": [
         "### Azure ML concepts  \n",
         "Please note the following three things in the code below:\n",
-        "1. The script accepts arguments using the argparse package. In this case there is one argument `--data_folder` which specifies the file system folder in which the script can find the MNIST data\n",
+        "1. The script accepts arguments using the argparse package. In this case there is one argument `--data_folder` which specifies the FileDataset in which the script can find the MNIST data\n",
         "```\n",
         "    parser = argparse.ArgumentParser()\n",
         "    parser.add_argument('--data_folder')\n",
@@ -384,6 +405,36 @@
         "The TensorFlow estimator is providing a simple way of launching a TensorFlow training job on a compute target. It will automatically provide a docker image that has TensorFlow installed. In this case, we add `keras` package (for the Keras framework obviously), and `matplotlib` package for plotting a \"Loss vs. Accuracy\" chart and record it in run history."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.dataset import Dataset\n",
+        "\n",
+        "dataset = Dataset.get_by_name(ws, 'mnist dataset')\n",
+        "\n",
+        "# list the files referenced by mnist dataset\n",
+        "dataset.to_path()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.environment import Environment\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "\n",
+        "# set up environment\n",
+        "env = Environment('my_env')\n",
+        "cd = CondaDependencies.create(pip_packages=['keras','azureml-sdk','tensorflow-gpu','matplotlib','azureml-dataprep[pandas,fuse]>=1.1.14'])\n",
+        "\n",
+        "env.python.conda_dependencies = cd"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -393,7 +444,7 @@
         "from azureml.train.dnn import TensorFlow\n",
         "\n",
         "script_params = {\n",
-        "    '--data-folder': ds.path('mnist').as_mount(),\n",
+        "    '--data-folder': dataset.as_named_input('mnist').as_mount(),\n",
         "    '--batch-size': 50,\n",
         "    '--first-layer-neurons': 300,\n",
         "    '--second-layer-neurons': 100,\n",
@@ -403,25 +454,8 @@
         "est = TensorFlow(source_directory=script_folder,\n",
         "                 script_params=script_params,\n",
         "                 compute_target=compute_target, \n",
-        "                 pip_packages=['keras', 'matplotlib'],\n",
         "                 entry_script='keras_mnist.py', \n",
-        "                 use_gpu=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "And if you are curious, this is what the mounting point looks like:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(ds.path('mnist').as_mount())"
+        "                 environment_definition= env)"
       ]
     },
     {
@@ -698,11 +732,10 @@
       "outputs": [],
       "source": [
         "est = TensorFlow(source_directory=script_folder,\n",
-        "                 script_params={'--data-folder': ds.path('mnist').as_mount()},\n",
+        "                 script_params={'--data-folder': dataset.as_named_input('mnist').as_mount()},\n",
         "                 compute_target=compute_target,\n",
-        "                 pip_packages=['keras', 'matplotlib'],\n",
         "                 entry_script='keras_mnist.py', \n",
-        "                 use_gpu=True)"
+        "                 environment_definition= env)"
       ]
     },
     {
@@ -911,7 +944,7 @@
       "metadata": {},
       "source": [
         "### Deploy to ACI\n",
-        "We are almost ready to deploy. Create a deployment configuration and specify the number of CPUs and gigbyte of RAM needed for your ACI container. "
+        "We are almost ready to deploy. Create the inference configuration and deployment configuration and deploy to ACI. This cell will run for about 7-8 minutes."
       ]
     },
     {
@@ -921,73 +954,35 @@
       "outputs": [],
       "source": [
         "from azureml.core.webservice import AciWebservice\n",
-        "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
-        "                                               auth_enabled=True, # this flag generates API keys to secure access\n",
-        "                                               memory_gb=1, \n",
-        "                                               tags={'name':'mnist', 'framework': 'Keras'},\n",
-        "                                               description='Keras MLP on MNIST')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Deployment Process\n",
-        "Now we can deploy. **This cell will run for about 7-8 minutes**. Behind the scene, it will do the following:\n",
-        "1. **Build Docker image**  \n",
-        "Build a Docker image using the scoring file (`score.py`), the environment file (`myenv.yml`), and the `model` object. \n",
-        "2. **Register image**    \n",
-        "Register that image under the workspace. \n",
-        "3. **Ship to ACI**    \n",
-        "And finally ship the image to the ACI infrastructure, start up a container in ACI using that image, and expose an HTTP endpoint to accept REST client calls."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.image import ContainerImage\n",
-        "\n",
-        "imgconfig = ContainerImage.image_configuration(execution_script=\"score.py\", \n",
-        "                                               runtime=\"python\", \n",
-        "                                               conda_file=\"myenv.yml\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
+        "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.webservice import Webservice\n",
+        "from azureml.core.model import Model\n",
         "\n",
-        "service = Webservice.deploy_from_model(workspace=ws,\n",
-        "                                       name='keras-mnist-svc',\n",
-        "                                       deployment_config=aciconfig,\n",
-        "                                       models=[model],\n",
-        "                                       image_config=imgconfig)\n",
+        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "                                   entry_script=\"score.py\",\n",
+        "                                   conda_file=\"myenv.yml\")\n",
         "\n",
-        "service.wait_for_deployment(show_output=True)"
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1,\n",
+        "                                               auth_enabled=True, # this flag generates API keys to secure access\n",
+        "                                               memory_gb=1,\n",
+        "                                               tags={'name': 'mnist', 'framework': 'Keras'},\n",
+        "                                               description='Keras MLP on MNIST')\n",
+        "\n",
+        "service = Model.deploy(workspace=ws, \n",
+        "                           name='keras-mnist-svc', \n",
+        "                           models=[model], \n",
+        "                           inference_config=inference_config, \n",
+        "                           deployment_config=aciconfig)\n",
+        "\n",
+        "service.wait_for_deployment(True)\n",
+        "print(service.state)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "**Tip: If something goes wrong with the deployment, the first thing to look at is the logs from the service by running the following command:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(service.get_logs())"
+        "**Tip: If something goes wrong with the deployment, the first thing to look at is the logs from the service by running the following command:** `print(service.get_logs())`"
       ]
     },
     {
@@ -1047,7 +1042,7 @@
         "    font_color = 'red' if y_test[s] != result[i] else 'black'\n",
         "    clr_map = plt.cm.gray if y_test[s] != result[i] else plt.cm.Greys\n",
         "    \n",
-        "    plt.text(x=10, y=-10, s=y_hat[s], fontsize=18, color=font_color)\n",
+        "    plt.text(x=10, y=-10, s=y_test[s], fontsize=18, color=font_color)\n",
         "    plt.imshow(X_test[s].reshape(28, 28), cmap=clr_map)\n",
         "    \n",
         "    i = i + 1\n",
@@ -1106,8 +1101,7 @@
       "metadata": {},
       "source": [
         "Let's look at the workspace after the web service was deployed. You should see \n",
-        "* a registered model named 'keras-mlp-mnist' and with the id 'model:1'\n",
-        "* an image called 'keras-mnist-svc' and with a docker image location pointing to your workspace's Azure Container Registry (ACR)  \n",
+        "* a registered model named 'keras-mlp-mnist' and with the id 'model:1'  \n",
         "* a webservice called 'keras-mnist-svc' with some scoring URL"
       ]
     },
@@ -1121,10 +1115,6 @@
         "for name, model in models.items():\n",
         "    print(\"Model: {}, ID: {}\".format(name, model.id))\n",
         "    \n",
-        "images = ws.images\n",
-        "for name, image in images.items():\n",
-        "    print(\"Image: {}, location: {}\".format(name, image.image_location))\n",
-        "    \n",
         "webservices = ws.webservices\n",
         "for name, webservice in webservices.items():\n",
         "    print(\"Webservice: {}, scoring URI: {}\".format(name, webservice.scoring_uri))"
@@ -1169,7 +1159,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.9"
     }
   },
   "nbformat": 4,
diff --git a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/pytorch_score.py b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/pytorch_score.py
index 68512625..5df2d8dc 100644
--- a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/pytorch_score.py
+++ b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/pytorch_score.py
@@ -11,7 +11,7 @@ from azureml.core.model import Model
 
 def init():
     global model
-    model_path = Model.get_model_path('pytorch-hymenoptera')
+    model_path = Model.get_model_path('pytorch-birds')
     model = torch.load(model_path, map_location=lambda storage, loc: storage)
     model.eval()
 
@@ -22,7 +22,7 @@ def run(input_data):
     # get prediction
     with torch.no_grad():
         output = model(input_data)
-        classes = ['ants', 'bees']
+        classes = ['chicken', 'turkey']
         softmax = nn.Softmax(dim=1)
         pred_probs = softmax(output).numpy()[0]
         index = torch.argmax(output, 1)
diff --git a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/pytorch_train.py b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/pytorch_train.py
index d0bc6a1f..733c9a22 100644
--- a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/pytorch_train.py
+++ b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/pytorch_train.py
@@ -165,8 +165,8 @@ def download_data():
     import urllib
     from zipfile import ZipFile
     # download data
-    data_file = './hymenoptera_data.zip'
-    download_url = 'https://download.pytorch.org/tutorial/hymenoptera_data.zip'
+    data_file = './fowl_data.zip'
+    download_url = 'https://msdocsdatasets.blob.core.windows.net/pytorchfowl/fowl_data.zip'
     urllib.request.urlretrieve(download_url, filename=data_file)
 
     # extract files
diff --git a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/test_img.jpg b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/test_img.jpg
index eb0f2c69..f2878b48 100644
Binary files a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/test_img.jpg and b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/test_img.jpg differ
diff --git a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb
index 64f557de..2a73ef9e 100644
--- a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb
+++ b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb
@@ -24,7 +24,7 @@
         "\n",
         "In this tutorial, you will train, hyperparameter tune, and deploy a PyTorch model using the Azure Machine Learning (Azure ML) Python SDK.\n",
         "\n",
-        "This tutorial will train an image classification model using transfer learning, based on PyTorch's [Transfer Learning tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html). The model is trained to classify ants and bees by first using a pretrained ResNet18 model that has been trained on the [ImageNet](http://image-net.org/index) dataset."
+        "This tutorial will train an image classification model using transfer learning, based on PyTorch's [Transfer Learning tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html). The model is trained to classify chickens and turkeys by first using a pretrained ResNet18 model that has been trained on the [ImageNet](http://image-net.org/index) dataset."
       ]
     },
     {
@@ -165,7 +165,7 @@
       "source": [
         "import os\n",
         "\n",
-        "project_folder = './pytorch-hymenoptera'\n",
+        "project_folder = './pytorch-birds'\n",
         "os.makedirs(project_folder, exist_ok=True)"
       ]
     },
@@ -174,7 +174,7 @@
       "metadata": {},
       "source": [
         "### Download training data\n",
-        "The dataset we will use (located [here](https://download.pytorch.org/tutorial/hymenoptera_data.zip) as a zip file) consists of about 120 training images each for ants and bees, with 75 validation images for each class. [Hymenoptera](https://en.wikipedia.org/wiki/Hymenoptera) is the order of insects that includes ants and bees. We will download and extract the dataset as part of our training script `pytorch_train.py`"
+        "The dataset we will use (located on a public blob [here](https://msdocsdatasets.blob.core.windows.net/pytorchfowl/fowl_data.zip) as a zip file) consists of about 120 training images each for turkeys and chickens, with 100 validation images for each class. The images are a subset of the [Open Images v5 Dataset](https://storage.googleapis.com/openimages/web/index.html). We will download and extract the dataset as part of our training script `pytorch_train.py`"
       ]
     },
     {
@@ -235,7 +235,7 @@
       "source": [
         "from azureml.core import Experiment\n",
         "\n",
-        "experiment_name = 'pytorch-hymenoptera'\n",
+        "experiment_name = 'pytorch-birds'\n",
         "experiment = Experiment(ws, name=experiment_name)"
       ]
     },
@@ -250,7 +250,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "dnn-pytorch-remarks-sample"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.train.dnn import PyTorch\n",
@@ -273,7 +277,7 @@
       "metadata": {},
       "source": [
         "The `script_params` parameter is a dictionary containing the command-line arguments to your training script `entry_script`. Please note the following:\n",
-        "- We passed our training data reference `ds_data` to our script's `--data_dir` argument. This will 1) mount our datastore on the remote compute and 2) provide the path to the training data `hymenoptera_data` on our datastore.\n",
+        "- We passed our training data reference `ds_data` to our script's `--data_dir` argument. This will 1) mount our datastore on the remote compute and 2) provide the path to the training data `fowl_data` on our datastore.\n",
         "- We specified the output directory as `./outputs`. The `outputs` directory is specially treated by Azure ML in that all the content in this directory gets uploaded to your workspace as part of your run history. The files written to this directory are therefore accessible even once your remote run is over. In this tutorial, we will save our trained model to this output directory.\n",
         "\n",
         "To leverage the Azure VM's GPU for training, we set `use_gpu=True`."
@@ -481,7 +485,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "model = best_run.register_model(model_name = 'pytorch-hymenoptera', model_path = 'outputs/model.pt')\n",
+        "model = best_run.register_model(model_name = 'pytorch-birds', model_path = 'outputs/model.pt')\n",
         "print(model.name, model.id, model.version, sep = '\\t')"
       ]
     },
@@ -503,7 +507,7 @@
         "* `init()`: In this function, you typically load the model into a `global` object. This function is executed only once when the Docker container is started. \n",
         "* `run(input_data)`: In this function, the model is used to predict a value based on the input data. The input and output typically use JSON as serialization and deserialization format, but you are not limited to that.\n",
         "\n",
-        "Refer to the scoring script `pytorch_score.py` for this tutorial. Our web service will use this file to predict whether an image is an ant or a bee. When writing your own scoring script, don't forget to test it locally first before you go and deploy the web service."
+        "Refer to the scoring script `pytorch_score.py` for this tutorial. Our web service will use this file to predict whether an image is a chicken or a turkey. When writing your own scoring script, don't forget to test it locally first before you go and deploy the web service."
       ]
     },
     {
@@ -549,7 +553,7 @@
         "image_config = ContainerImage.image_configuration(execution_script='pytorch_score.py', \n",
         "                                                  runtime='python', \n",
         "                                                  conda_file='myenv.yml',\n",
-        "                                                  description='Image with hymenoptera model')"
+        "                                                  description='Image with bird model')"
       ]
     },
     {
@@ -570,8 +574,8 @@
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
         "                                               memory_gb=1, \n",
-        "                                               tags={'data': 'hymenoptera',  'method':'transfer learning', 'framework':'pytorch'},\n",
-        "                                               description='Classify ants/bees using transfer learning with PyTorch')"
+        "                                               tags={'data': 'birds',  'method':'transfer learning', 'framework':'pytorch'},\n",
+        "                                               description='Classify turkey/chickens using transfer learning with PyTorch')"
       ]
     },
     {
@@ -591,7 +595,7 @@
         "%%time\n",
         "from azureml.core.webservice import Webservice\n",
         "\n",
-        "service_name = 'aci-hymenoptera'\n",
+        "service_name = 'aci-birds'\n",
         "service = Webservice.deploy_from_model(workspace=ws,\n",
         "                                       name=service_name,\n",
         "                                       models=[model],\n",
@@ -659,6 +663,7 @@
         "from PIL import Image\n",
         "import matplotlib.pyplot as plt\n",
         "\n",
+        "%matplotlib inline\n",
         "plt.imshow(Image.open('test_img.jpg'))"
       ]
     },
diff --git a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb
index e3717d27..8af4e963 100644
--- a/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb
+++ b/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb
@@ -412,7 +412,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "dnn-tensorflow-remarks-sample"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.train.dnn import TensorFlow\n",
diff --git a/how-to-use-azureml/training-with-deep-learning/train-tensorflow-resume-training/tf_mnist_with_checkpoint.py b/how-to-use-azureml/training-with-deep-learning/train-tensorflow-resume-training/tf_mnist_with_checkpoint.py
new file mode 100644
index 00000000..85e80cbd
--- /dev/null
+++ b/how-to-use-azureml/training-with-deep-learning/train-tensorflow-resume-training/tf_mnist_with_checkpoint.py
@@ -0,0 +1,123 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import numpy as np
+import argparse
+import os
+import re
+import tensorflow as tf
+
+from azureml.core import Run
+from utils import load_data
+
+print("TensorFlow version:", tf.VERSION)
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--data-folder', type=str, dest='data_folder', help='data folder mounting point')
+
+parser.add_argument('--resume-from', type=str, default=None,
+                    help='location of the model or checkpoint files from where to resume the training')
+args = parser.parse_args()
+
+
+previous_model_location = args.resume_from
+# You can also use environment variable to get the model/checkpoint files location
+# previous_model_location = os.path.expandvars(os.getenv("AZUREML_DATAREFERENCE_MODEL_LOCATION", None))
+
+data_folder = os.path.join(args.data_folder, 'mnist')
+
+print('training dataset is stored here:', data_folder)
+
+X_train = load_data(os.path.join(data_folder, 'train-images.gz'), False) / 255.0
+X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / 255.0
+
+y_train = load_data(os.path.join(data_folder, 'train-labels.gz'), True).reshape(-1)
+y_test = load_data(os.path.join(data_folder, 'test-labels.gz'), True).reshape(-1)
+
+print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep='\n')
+training_set_size = X_train.shape[0]
+
+n_inputs = 28 * 28
+n_h1 = 100
+n_h2 = 100
+n_outputs = 10
+learning_rate = 0.01
+n_epochs = 20
+batch_size = 50
+
+with tf.name_scope('network'):
+    # construct the DNN
+    X = tf.placeholder(tf.float32, shape=(None, n_inputs), name='X')
+    y = tf.placeholder(tf.int64, shape=(None), name='y')
+    h1 = tf.layers.dense(X, n_h1, activation=tf.nn.relu, name='h1')
+    h2 = tf.layers.dense(h1, n_h2, activation=tf.nn.relu, name='h2')
+    output = tf.layers.dense(h2, n_outputs, name='output')
+
+with tf.name_scope('train'):
+    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=output)
+    loss = tf.reduce_mean(cross_entropy, name='loss')
+    optimizer = tf.train.GradientDescentOptimizer(learning_rate)
+    train_op = optimizer.minimize(loss)
+
+with tf.name_scope('eval'):
+    correct = tf.nn.in_top_k(output, y, 1)
+    acc_op = tf.reduce_mean(tf.cast(correct, tf.float32))
+
+init = tf.global_variables_initializer()
+saver = tf.train.Saver()
+
+# start an Azure ML run
+run = Run.get_context()
+
+with tf.Session() as sess:
+    start_epoch = 0
+    if previous_model_location:
+        checkpoint_file_path = tf.train.latest_checkpoint(previous_model_location)
+        saver.restore(sess, checkpoint_file_path)
+        checkpoint_filename = os.path.basename(checkpoint_file_path)
+        num_found = re.search(r'\d+', checkpoint_filename)
+        if num_found:
+            start_epoch = int(num_found.group(0))
+            print("Resuming from epoch {}".format(str(start_epoch)))
+    else:
+        init.run()
+
+    for epoch in range(start_epoch, n_epochs):
+
+        # randomly shuffle training set
+        indices = np.random.permutation(training_set_size)
+        X_train = X_train[indices]
+        y_train = y_train[indices]
+
+        # batch index
+        b_start = 0
+        b_end = b_start + batch_size
+        for _ in range(training_set_size // batch_size):
+            # get a batch
+            X_batch, y_batch = X_train[b_start: b_end], y_train[b_start: b_end]
+
+            # update batch index for the next batch
+            b_start = b_start + batch_size
+            b_end = min(b_start + batch_size, training_set_size)
+
+            # train
+            sess.run(train_op, feed_dict={X: X_batch, y: y_batch})
+        # evaluate training set
+        acc_train = acc_op.eval(feed_dict={X: X_batch, y: y_batch})
+        # evaluate validation set
+        acc_val = acc_op.eval(feed_dict={X: X_test, y: y_test})
+
+        # log accuracies
+        run.log('training_acc', np.float(acc_train))
+        run.log('validation_acc', np.float(acc_val))
+        print(epoch, '-- Training accuracy:', acc_train, '\b Validation accuracy:', acc_val)
+        y_hat = np.argmax(output.eval(feed_dict={X: X_test}), axis=1)
+
+        if epoch % 5 == 0:
+            saver.save(sess, './outputs/', global_step=epoch)
+
+        # saving only half of the model and resuming again from same epoch
+        if not previous_model_location and epoch == 10:
+            break
+
+    run.log('final_acc', np.float(acc_val))
diff --git a/how-to-use-azureml/training-with-deep-learning/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb b/how-to-use-azureml/training-with-deep-learning/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb
new file mode 100644
index 00000000..94c51ff4
--- /dev/null
+++ b/how-to-use-azureml/training-with-deep-learning/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb
@@ -0,0 +1,487 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training-with-deep-learning/train-tensorflow-resume-training/tensorflow-resume-training.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Resuming Tensorflow training from previous run\n",
+        "In this tutorial, you will resume a mnist model in TensorFlow from a previously submitted run."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n",
+        "* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
+        "    * install the AML SDK\n",
+        "    * create a workspace and its configuration file (`config.json`)\n",
+        "* Review the [tutorial](../train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) on single-node TensorFlow training using the SDK"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Diagnostics\n",
+        "Opt-in diagnostics for better experience, quality, and security of future releases."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "Diagnostics"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.telemetry import set_diagnostics_collection\n",
+        "\n",
+        "set_diagnostics_collection(send_diagnostics=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize workspace\n",
+        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.workspace import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep='\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create or Attach existing AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
+        "\n",
+        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
+        "\n",
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "cluster_name = \"gpu-cluster\"\n",
+        "\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+        "    print('Found existing compute target.')\n",
+        "except ComputeTargetException:\n",
+        "    print('Creating a new compute target...')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
+        "                                                           max_nodes=4)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+        "\n",
+        "    compute_target.wait_for_completion(show_output=True)\n",
+        "\n",
+        "# use get_status() to get a detailed status for the current cluster. \n",
+        "print(compute_target.get_status().serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Upload data to datastore\n",
+        "To make data accessible for remote training, AML provides a convenient way to do so via a [Datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data). The datastore provides a mechanism for you to upload/download data to Azure Storage, and interact with it from your remote compute targets. \n",
+        "\n",
+        "If your data is already stored in Azure, or you download the data as part of your training script, you will not need to do this step. For this tutorial, although you can download the data in your training script, we will demonstrate how to upload the training data to a datastore and access it during training to illustrate the datastore functionality."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First download the data from Yan LeCun's web site directly and save them in a data folder locally."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import urllib\n",
+        "\n",
+        "os.makedirs('./data/mnist', exist_ok=True)\n",
+        "\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz', filename = './data/mnist/train-images.gz')\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz', filename = './data/mnist/train-labels.gz')\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename = './data/mnist/test-images.gz')\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename = './data/mnist/test-labels.gz')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Each workspace is associated with a default datastore. In this tutorial, we will upload the training data to this default datastore."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ds = ws.get_default_datastore()\n",
+        "print(ds.datastore_type, ds.account_name, ds.container_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Upload MNIST data to the default datastore."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ds.upload(src_dir='./data/mnist', target_path='mnist', overwrite=True, show_progress=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For convenience, let's get a reference to the datastore. In the next section, we can then pass this reference to our training script's `--data-folder` argument. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ds_data = ds.as_mount()\n",
+        "print(ds_data)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train model on the remote compute"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a project directory\n",
+        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "script_folder = './tf-resume-training'\n",
+        "os.makedirs(script_folder, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copy the training script `tf_mnist_with_checkpoint.py` into this project directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import shutil\n",
+        "\n",
+        "# the training logic is in the tf_mnist_with_checkpoint.py file.\n",
+        "shutil.copy('./tf_mnist_with_checkpoint.py', script_folder)\n",
+        "\n",
+        "# the utils.py just helps loading data from the downloaded MNIST dataset into numpy arrays.\n",
+        "shutil.copy('./utils.py', script_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create an experiment\n",
+        "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed TensorFlow tutorial. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "experiment_name = 'tf-resume-training'\n",
+        "experiment = Experiment(ws, name=experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a TensorFlow estimator\n",
+        "The AML SDK's TensorFlow estimator enables you to easily submit TensorFlow training jobs for both single-node and distributed runs. For more information on the TensorFlow estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-tensorflow).\n",
+        "\n",
+        "The TensorFlow estimator also takes a `framework_version` parameter -- if no version is provided, the estimator will default to the latest version supported by AzureML. Use `TensorFlow.get_supported_versions()` to get a list of all versions supported by your current SDK version or see the [SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn?view=azure-ml-py) for the versions supported in the most current release."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.dnn import TensorFlow\n",
+        "\n",
+        "script_params={\n",
+        "    '--data-folder': ds_data\n",
+        "}\n",
+        "\n",
+        "estimator= TensorFlow(source_directory=script_folder,\n",
+        "                      compute_target=compute_target,\n",
+        "                      script_params=script_params,\n",
+        "                      entry_script='tf_mnist_with_checkpoint.py')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In the above code, we passed our training data reference `ds_data` to our script's `--data-folder` argument. This will 1) mount our datastore on the remote compute and 2) provide the path to the data zip file on our datastore."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit job\n",
+        "### Run your experiment by submitting your estimator object. Note that this call is asynchronous."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run = experiment.submit(estimator)\n",
+        "print(run)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Monitor your run\n",
+        "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Alternatively, you can block until the script has completed training before running more code."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Now let's resume the training from the above run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First, we will get the DataPath to the outputs directory of the above run which\n",
+        "contains the checkpoint files and/or model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model_location = run._get_outputs_datapath()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, we will create a new TensorFlow estimator and pass in the model location. On passing 'resume_from' parameter, a new entry in script_params is created with key as 'resume_from' and value as the model/checkpoint files location and the location gets automatically mounted on the compute target."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.dnn import TensorFlow\n",
+        "\n",
+        "script_params={\n",
+        "    '--data-folder': ds_data\n",
+        "}\n",
+        "\n",
+        "estimator2 = TensorFlow(source_directory=script_folder,\n",
+        "                      compute_target=compute_target,\n",
+        "                      script_params=script_params,\n",
+        "                      entry_script='tf_mnist_with_checkpoint.py',\n",
+        "                      resume_from=model_location)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now you can submit the experiment and it should resume from previous run's checkpoint files."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run2 = experiment.submit(estimator2)\n",
+        "print(run2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run2.wait_for_completion(show_output=True)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "hesuri"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    },
+    "msauthor": "hesuri"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/training-with-deep-learning/train-tensorflow-resume-training/train-tensorflow-resume-training.yml b/how-to-use-azureml/training-with-deep-learning/train-tensorflow-resume-training/train-tensorflow-resume-training.yml
new file mode 100644
index 00000000..c814eef5
--- /dev/null
+++ b/how-to-use-azureml/training-with-deep-learning/train-tensorflow-resume-training/train-tensorflow-resume-training.yml
@@ -0,0 +1,5 @@
+name: train-tensorflow-resume-training
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
diff --git a/how-to-use-azureml/training-with-deep-learning/train-tensorflow-resume-training/utils.py b/how-to-use-azureml/training-with-deep-learning/train-tensorflow-resume-training/utils.py
new file mode 100644
index 00000000..98170ada
--- /dev/null
+++ b/how-to-use-azureml/training-with-deep-learning/train-tensorflow-resume-training/utils.py
@@ -0,0 +1,27 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import gzip
+import numpy as np
+import struct
+
+
+# load compressed MNIST gz files and return numpy arrays
+def load_data(filename, label=False):
+    with gzip.open(filename) as gz:
+        struct.unpack('I', gz.read(4))
+        n_items = struct.unpack('>I', gz.read(4))
+        if not label:
+            n_rows = struct.unpack('>I', gz.read(4))[0]
+            n_cols = struct.unpack('>I', gz.read(4))[0]
+            res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8)
+            res = res.reshape(n_items[0], n_rows * n_cols)
+        else:
+            res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8)
+            res = res.reshape(n_items[0], 1)
+    return res
+
+
+# one-hot encode a 1-D array
+def one_hot_encode(array, num_of_classes):
+    return np.eye(num_of_classes)[array.reshape(-1)]
diff --git a/how-to-use-azureml/training/README.md b/how-to-use-azureml/training/README.md
index a24ee002..39d2d6b7 100644
--- a/how-to-use-azureml/training/README.md
+++ b/how-to-use-azureml/training/README.md
@@ -7,8 +7,6 @@ Follow these sample notebooks to learn:
 3. [Train on remote VM](train-on-remote-vm): train a model using a remote Azure VM as compute target.
 4. [Train on ML Compute](train-on-amlcompute): train a model using an ML Compute cluster as compute target.
 5. [Train in an HDI Spark cluster](train-in-spark): train a Spark ML model using an HDInsight Spark cluster as compute target.
-6. [Logging API](logging-api): experiment with various logging functions to create runs and automatically generate graphs.
-7. [Manage runs](manage-runs): learn different ways how to start runs and child runs, monitor them, and cancel them.
-8. [Train and hyperparameter tune on Iris Dataset with Scikit-learn](train-hyperparameter-tune-deploy-with-sklearn): train a model using the Scikit-learn estimator and tune hyperparameters with Hyperdrive.
+6. [Train and hyperparameter tune on Iris Dataset with Scikit-learn](train-hyperparameter-tune-deploy-with-sklearn): train a model using the Scikit-learn estimator and tune hyperparameters with Hyperdrive.
 
- ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/README.png)
\ No newline at end of file
+ ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/README.png)
diff --git a/how-to-use-azureml/training/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb b/how-to-use-azureml/training/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb
index cb7fe1fb..d4890cfa 100644
--- a/how-to-use-azureml/training/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb
+++ b/how-to-use-azureml/training/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb
@@ -120,19 +120,42 @@
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_D2_V2` CPU VMs. This process is broken down into 3 steps:\n",
+        "1. create the configuration (this step is local and only takes a second)\n",
+        "2. create the cluster (this step will take about **20 seconds**)\n",
+        "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# choose a name for your cluster\n",
         "cluster_name = \"cpu-cluster\"\n",
         "\n",
-        "compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "print('Found existing compute target.')\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+        "    print('Found existing compute target')\n",
+        "except ComputeTargetException:\n",
+        "    print('Creating a new compute target...')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2', \n",
+        "                                                           max_nodes=4)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+        "\n",
+        "    # can poll for a minimum number of nodes and for a specific timeout. \n",
+        "    # if no min node count is provided it uses the scale settings for the cluster\n",
+        "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
         "\n",
         "# use get_status() to get a detailed status for the current cluster. \n",
         "print(compute_target.get_status().serialize())"
@@ -142,7 +165,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The above code retrieves an existing CPU compute target. Scikit-learn does not support GPU computing."
+        "The above code retrieves a CPU compute target. Scikit-learn does not support GPU computing."
       ]
     },
     {
@@ -275,7 +298,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sklearn-remarks-sample"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.train.sklearn import SKLearn\n",
@@ -289,7 +316,7 @@
         "                    script_params=script_params,\n",
         "                    compute_target=compute_target,\n",
         "                    entry_script='train_iris.py',\n",
-        "                    pip_packages=['joblib']\n",
+        "                    pip_packages=['joblib==0.13.2']\n",
         "                   )"
       ]
     },
@@ -507,7 +534,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "model = best_run.register_model(model_name='sklearn-iris', model_path='model.joblib')"
+        "model = best_run.register_model(model_name='sklearn-iris', model_path='outputs/model.joblib')"
       ]
     }
   ],
diff --git a/how-to-use-azureml/training/train-hyperparameter-tune-deploy-with-sklearn/train_iris.py b/how-to-use-azureml/training/train-hyperparameter-tune-deploy-with-sklearn/train_iris.py
index e41e6d26..bc9099d8 100644
--- a/how-to-use-azureml/training/train-hyperparameter-tune-deploy-with-sklearn/train_iris.py
+++ b/how-to-use-azureml/training/train-hyperparameter-tune-deploy-with-sklearn/train_iris.py
@@ -1,6 +1,7 @@
 # Modified from https://www.geeksforgeeks.org/multiclass-classification-using-scikit-learn/
 
 import argparse
+import os
 
 # importing necessary libraries
 import numpy as np
@@ -50,8 +51,9 @@ def main():
     cm = confusion_matrix(y_test, svm_predictions)
     print(cm)
 
-    # save model
-    joblib.dump(svm_model_linear, 'model.joblib')
+    os.makedirs('outputs', exist_ok=True)
+    # files saved in the "outputs" folder are automatically uploaded into run history
+    joblib.dump(svm_model_linear, 'outputs/model.joblib')
 
 
 if __name__ == '__main__':
diff --git a/how-to-use-azureml/training/using-environments/using-environments.ipynb b/how-to-use-azureml/training/using-environments/using-environments.ipynb
index 03691c36..3f420c8f 100644
--- a/how-to-use-azureml/training/using-environments/using-environments.ipynb
+++ b/how-to-use-azureml/training/using-environments/using-environments.ipynb
@@ -332,7 +332,11 @@
         "\n",
         "* [Train on ML Compute](../../train-on-amlcompute)\n",
         "\n",
-        "* [Train on remote VM](../../train-on-remote-vm)"
+        "* [Train on remote VM](../../train-on-remote-vm)\n",
+        "\n",
+        "Learn more about registering and deploying a model:\n",
+        "\n",
+        "* [Model Register and Deploy](../../deploy-to-cloud/model-register-and-deploy.ipynb)"
       ]
     },
     {
diff --git a/how-to-use-azureml/using-mlflow/README.md b/how-to-use-azureml/using-mlflow/README.md
deleted file mode 100644
index 91eae0d5..00000000
--- a/how-to-use-azureml/using-mlflow/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
-## Use MLflow with Azure Machine Learning service (Preview)
-
-[MLflow](https://mlflow.org/) is an open-source platform for tracking machine learning experiments and managing models. You can use MLflow logging APIs with Azure Machine Learning service: the metrics and artifacts are logged to your Azure ML Workspace.
-
-Try out the sample notebooks:
-
-* [Use MLflow with Azure Machine Learning for Local Training Run](./train-local/train-local.ipynb)
-* [Use MLflow with Azure Machine Learning for Remote Training Run](./train-remote/train-remote.ipynb)
-* [Deploy Model as Azure Machine Learning Web Service using MLflow](./deploy-model/deploy-model.ipynb)
-* [Train and Deploy PyTorch Image Classifier](./train-deploy-pytorch/train-deploy-pytorch.ipynb)
-
-![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/using-mlflow/README..png) 
\ No newline at end of file
diff --git a/how-to-use-azureml/work-with-data/README.md b/how-to-use-azureml/work-with-data/README.md
index 0b2958d0..cfc265b2 100644
--- a/how-to-use-azureml/work-with-data/README.md
+++ b/how-to-use-azureml/work-with-data/README.md
@@ -6,4 +6,4 @@ Azure Machine Learning Datasets (preview) make it easier to access and work with
 - For advanced data preparation examples, see [dataprep](dataprep).
 
 
-![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/README..png) 
\ No newline at end of file
+![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/README..png) 
\ No newline at end of file
diff --git a/how-to-use-azureml/work-with-data/dataprep/README.md b/how-to-use-azureml/work-with-data/dataprep/README.md
index 57c64989..a356d134 100644
--- a/how-to-use-azureml/work-with-data/dataprep/README.md
+++ b/how-to-use-azureml/work-with-data/dataprep/README.md
@@ -31,6 +31,51 @@ If you have any questions or feedback, send us an email at: [askamldataprep@micr
 
 ## Release Notes
 
+### 2019-07-25 (version 1.1.9)
+New features
+- Added support for reading a file directly from a http or https url.
+
+Bug fixes and improvements
+- Improved error message when attempting to read a Parquet Dataset from a remote source (which is not currently supported).
+- Fixed a bug when writing to Parquet file format in ADLS Gen 2, and updating the ADLS Gen 2 container name in the path.
+
+### 2019-07-09 (version 1.1.8)
+
+New features
+- Dataflow objects can now be iterated over, producing a sequence of records. See documentation for `Dataflow.to_record_iterator`.
+
+Bug fixes and improvements
+- Increased the robustness of DataPrep SDK.
+- Improved handling of pandas DataFrames with non-string Column Indexes.
+- Improved the performance of `to_pandas_dataframe` in Datasets.
+- Fixed a bug where Spark execution of Datasets failed when run in a multi-node environment.
+
+### 2019-07-01 (version 1.1.7)
+
+We reverted a change that improved performance, as it was causing issues for some customers using Azure Databricks. If you experienced an issue on Azure Databricks, you can upgrade to version 1.1.7 using one of the methods below:
+1. Run this script to upgrade: `%sh /home/ubuntu/databricks/python/bin/pip install azureml-dataprep==1.1.7`
+2. Recreate the cluster, which will install the latest Data Prep SDK version.
+
+### 2019-06-24 (version 1.1.6)
+
+New features
+- Added summary functions for top values (`SummaryFunction.TOPVALUES`) and bottom values (`SummaryFunction.BOTTOMVALUES`).
+
+Bug fixes and improvements
+- Significantly improved the performance of `read_pandas_dataframe`.
+- Fixed a bug that would cause `get_profile()` on a Dataflow pointing to binary files to fail.
+- Exposed `set_diagnostics_collection()` to allow for programmatic enabling/disabling of the telemetry collection.
+- Changed the behavior of `get_profile()`. NaN values are now ignored for Min, Mean, Std, and Sum, which aligns with the behavior of Pandas.
+
+### 2019-06-10 (version 1.1.5)
+
+Bug fixes and improvements
+- For interpreted datetime values that have a 2-digit year format, the range of valid years has been updated to match Windows May Release. The range has been changed from 1930-2029 to 1950-2049.
+- When reading in a file and setting `handleQuotedLineBreaks=True`, `\r` will be treated as a new line.
+- Fixed a bug that caused `read_pandas_dataframe` to fail in some cases.
+- Improved performance of `get_profile`.
+- Improved error messages.
+
 ### 2019-05-28 (version 1.1.4)
 
 New features
@@ -58,7 +103,7 @@ Bug fixes and improvements
   - Now does not fail when attempting to set a date column to be date type.
 - Improved JoinType types and accompanying reference documentation. When joining two dataflows, you can now specify one of these types of join:
   - NONE, MATCH, INNER, UNMATCHLEFT, LEFTANTI, LEFTOUTER, UNMATCHRIGHT, RIGHTANTI, RIGHTOUTER, FULLANTI, FULL.
-- Improved data type inferencing to recognize more date formats.
+- Improved data type inference to recognize more date formats.
 
 ### 2019-04-17 (version 1.1.2)
 
@@ -252,4 +297,4 @@ IMPORTANT: Please read the notice and find out more about this NYC Taxi and Limo
 
 IMPORTANT: Please read the notice and find out more about this Chicago Police Department dataset here: https://catalog.data.gov/dataset/crimes-2001-to-present-398a4 
 
-![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/README.png) 
+![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/README.png) 
diff --git a/how-to-use-azureml/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi.ipynb b/how-to-use-azureml/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi.ipynb
index 45ab51ca..36dfd27a 100644
--- a/how-to-use-azureml/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi.ipynb
@@ -4,9 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Cleaning up New York Taxi Cab data\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Cleaning up New York Taxi Cab data\n"
       ]
     },
     {
@@ -48,10 +46,9 @@
         "pd.set_option('display.max_columns', None)\n",
         "\n",
         "cache_location = mkdtemp()\n",
-        "dataset_root = \"https://dprepdata.blob.core.windows.net/demo\"\n",
-        "\n",
-        "green_path = \"/\".join([dataset_root, \"green-small/*\"])\n",
-        "yellow_path = \"/\".join([dataset_root, \"yellow-small/*\"])\n",
+        "green_path = \"https://dprepdata.blob.core.windows.net/demo/green-small/*\"\n",
+        "yellow_path = \"https://dprepdata.blob.core.windows.net/demo/yellow-small/*\"\n",
+        "# (optional) Download and view a subset of the data: https://dprepdata.blob.core.windows.net/demo/green-small/green_tripdata_2013-08.csv\n",
         "\n",
         "print(\"Retrieving data from the following two sources:\")\n",
         "print(green_path)\n",
@@ -482,7 +479,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi.png)"
       ]
     }
   ],
@@ -508,7 +505,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi_scale-out.ipynb b/how-to-use-azureml/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi_scale-out.ipynb
index 3830935b..fd69f736 100644
--- a/how-to-use-azureml/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi_scale-out.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi_scale-out.ipynb
@@ -4,9 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Scale-Out Data Preparation\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Scale-Out Data Preparation\n"
       ]
     },
     {
@@ -102,7 +100,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi_scale-out.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi_scale-out.png)"
       ]
     }
   ],
@@ -129,6 +127,7 @@
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
     },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.",
     "skip_execute_as_test": true
   },
   "nbformat": 4,
diff --git a/how-to-use-azureml/work-with-data/dataprep/data/ADLSgen2-datapreptest.crt b/how-to-use-azureml/work-with-data/dataprep/data/ADLSgen2-datapreptest.crt
new file mode 100644
index 00000000..db96837e
--- /dev/null
+++ b/how-to-use-azureml/work-with-data/dataprep/data/ADLSgen2-datapreptest.crt
@@ -0,0 +1,45 @@
+-----BEGIN PRIVATE KEY-----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+-----END PRIVATE KEY-----
+-----BEGIN CERTIFICATE-----
+MIICoTCCAYkCAgPoMA0GCSqGSIb3DQEBBQUAMBQxEjAQBgNVBAMMCUNMSS1Mb2dp
+bjAiGA8yMDE5MDUwMzIwMDIwOVoYDzIwMjAwNTAzMjAwMjExWjAUMRIwEAYDVQQD
+DAlDTEktTG9naW4wggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQC/C0oc
+6vvF1UEcy9JeGDXdtKynG11wTTIHIokFhNinHNSpJBLmNWFyFkqzvjJCPR4kWuqw
+4IXhCS3LVoqRmT680SvUFFF6HnEaa75Bc1YSACn1ZsHuCRGrqO9BaTgt3mM0sRYC
+67+f+W0EtA+k+EA0XnTtDdEBX3RLzvaYAR4yijEHIBQeeNemPYK4msW6Xw67ib1x
+n59blX4Za4Z85FjrekmoTl9493bFj6znDTX6wpKsPF7WLEF9S+oD/Lg4EHBi9Bfe
+fFxQpGZ9FQHToFKyz1tA2iaY/9LjCtJcincMkuXt3KuQA4Nv2GiTzz4+FEy1pOqH
+nyNL2tFR1G5n04BHAgMBAAEwDQYJKoZIhvcNAQEFBQADggEBAGz3pOgNPESr+QoO
+OVCgSS6VtWlmrAcxl5JaiNBFpBGAqfvbfRe1eZY7Rn6fuw1jc3pPBVzNTf8Plel+
+DcuLzDLJAEag2GpRE+Xg57DNSwPqP6jZfHRE/ufLwIRLcNG9wRUwqlBvdAu1Kign
+nlTZvTEAwxlQdvmIIT1XrTLZ+OwtVXcgrf0vInmueZKz/UDqsSDPY+d426S9eOWt
+60h2WgXPU3QvBYfA6Yd2ReeP3+SHwBd4/1ByNFWBytcI9ow3pp2JznU366dfX4IQ
+Q0iOTvHzXbfPmtsxqho6+hBbLvXVNWJMg8e22Pp/TyXYqeV5V09k18EgCnuA/9Gd
+kKDVROA=
+-----END CERTIFICATE-----
diff --git a/how-to-use-azureml/work-with-data/dataprep/data/adls-dpreptestfiles.crt b/how-to-use-azureml/work-with-data/dataprep/data/adls-dpreptestfiles.crt
index 98498f95..d1979611 100644
--- a/how-to-use-azureml/work-with-data/dataprep/data/adls-dpreptestfiles.crt
+++ b/how-to-use-azureml/work-with-data/dataprep/data/adls-dpreptestfiles.crt
@@ -1,45 +1,45 @@
 -----BEGIN PRIVATE KEY-----
-MIIEvwIBADANBgkqhkiG9w0BAQEFAASCBKkwggSlAgEAAoIBAQDmkkyF0BwipZow
-Wd1AMkRkySx0y079JPxpsYhv4i1xXKdoa9bpFqwoXmJpeQM1JWnU4UeZzFeM86qK
-AhQvL4KV4kibcP2ENvu2NKFEdotO3uxPJ+6GlcYwMYzy+tUj008KnnRZfTrR78sJ
-tIl3C6lnVL0ICihksG59P1sskRq3PvOjXLAdEZalwDjZ4ZPoNDZdj6nUjB2l8zqu
-pKAt5mR+bJ9Sox4yrDuNhMmFt5QsRDRe3wUqdV+C9OCWHmjlmsjrYw7p9YmjBDvC
-5U7mF0Mk/XeYFzj0pkXKQVqBL6xqig+q5ob0szYfg19iDeFhS3iIsRcJGEnRVW/A
-NpsBZyKrAgMBAAECggEBANlvP8C1F8NInhZYuIAwpzTQTh86Fxw8g9h8dijkh2wv
-LyQXBk07d1B+aZoDZ5X32UzKwcX04N9obfvFqBkzWZdVFJmZvUmwvEEActBoZkkT
-io+/HX5HweVy5PPCvbsSK6jc8uXtZcnSs4tMeJIOKkvqqnTpd1w00Y1FcQqfMC16
-4p7o8wbt6OFoFAYqcxeVYVwDzCTLZD3+iJaqmntkBkoDndJy52yXQmMq5z1wbQVp
-BL6+L9nTvmouy64jiHVSKOx8nnWThYfHsXoPv+rYywjeuK/v3hyaTAwogs36ooEn
-SnuTBRvJcumN9Q0XIVlxKMVBcGyyAP+0yNKGz5NQgdECgYEA/I/Uq1E3epPJgEWR
-Bub+LpCgwtrw/lgKncb/Q/AiE9qoXobUe4KNU8aGaNMb7uVNLckY7cOluLS6SQb3
-Mzwk2Jl0G3vk8rW46tZWvSYB8+zAR2Rz7seUOT9SE5OmvwpnHrnp3nRr1vvVd2bp
-Q/ypwMLrwWQN51Kr+oTS74bUbrkCgYEA6bXVIUyao7z2Q3qAr6h+6JEWDbkJA7hJ
-BjHIOXvxd1tMoJJX+X9+IE/2XoJaUkGCb0vrM/hi1cyQFmS4Or/J6IWSZu8oBpDr
-EBmIK3PF1nrzNvWD28wM46c6ScehyWSm/u4bJWSm9liTX3dv5Kpa6ym7yLKc3c0B
-ECpSJM+5SoMCgYEAq585Tukzn/IJPUcIk/4nv5C8DW0l0lAVdr2g/JOTNJajTwik
-HwHJ86G1+Elsc9wRpAlBDWCjnm4BIFrBZGl8SEuOoJaCL4PZEotwCbxoG09IIbtb
-JGkuifBDX9Y3ux3gkPqYt3e5SC99EVQ3MuHgoIJUHehVolmFUAkuJWIjvNECgYEA
-5pU0VspRuELzZdgzpxvDOooLDDcHodfslGQBfFXBA1Xc4IACtHMJaa/7D3vkyUtA
-+bYZtQjX2sEdWDq/WZdoCjXfIBfNkczhXt0R8G0lQFvGIu9QzUchYGrZo3mHMkBQ
-Uy1xMw9/e4YgwQwCJcW+Nk7Sq00uX9enuN9IdHFOCykCgYAqAGMK6CH1tlpjvHrf
-k+ZhigYxTXBlsVVvK1BIGGaiwzDpn65zeQp4aLOjSZkI1LuRi3tfTiZ321jRd64J
-4lGk5Jurqv5grDmxROX/U50wEYbI9ncu/thU7syUdxDiqxHPI2RMG50mRcm3a55p
-ZCNSqkMlcXyA0U1z8C1ILNUsbA==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 -----END PRIVATE KEY-----
 -----BEGIN CERTIFICATE-----
 MIICoTCCAYkCAgPoMA0GCSqGSIb3DQEBBQUAMBQxEjAQBgNVBAMMCUNMSS1Mb2dp
-bjAiGA8yMDE4MDcxMzIzMjA0N1oYDzIwMTkwNzEzMjMyMDQ5WjAUMRIwEAYDVQQD
-DAlDTEktTG9naW4wggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQDmkkyF
-0BwipZowWd1AMkRkySx0y079JPxpsYhv4i1xXKdoa9bpFqwoXmJpeQM1JWnU4UeZ
-zFeM86qKAhQvL4KV4kibcP2ENvu2NKFEdotO3uxPJ+6GlcYwMYzy+tUj008KnnRZ
-fTrR78sJtIl3C6lnVL0ICihksG59P1sskRq3PvOjXLAdEZalwDjZ4ZPoNDZdj6nU
-jB2l8zqupKAt5mR+bJ9Sox4yrDuNhMmFt5QsRDRe3wUqdV+C9OCWHmjlmsjrYw7p
-9YmjBDvC5U7mF0Mk/XeYFzj0pkXKQVqBL6xqig+q5ob0szYfg19iDeFhS3iIsRcJ
-GEnRVW/ANpsBZyKrAgMBAAEwDQYJKoZIhvcNAQEFBQADggEBAI4VlaFb9NsXMLdT
-Cw5/pk0Xo2Qi6483RGTy8vzrw88IE7f3juB/JWG+rayjtW5bBRx2fae4/ZIdZ4zg
-N2FDKn2PQPAc9m9pcKyUKUvWOC8ixSkrUmeQew0l1AXU0hsPSlJ7/7ZK4efoyB47
-hj71fsyKdyKbisZDcUFBq/S8PazdPF0YOD1W/4A2tW0cSMg+jmFWynuUTdWt3SU8
-CwBGqdiSKT5faJuYwIWnRXDEQS3ObRn1OFEfFdd4d2sxjxydWKRgnINnGlBdiFAT
-KzCozVr+75cO2ErH6x5C0hLQGG5BxXbaijyxyvaRNokTMVVv6OaDEnjzCGfJ72Yf
-2wgitNc=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 -----END CERTIFICATE-----
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/add-column-using-expression.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/add-column-using-expression.ipynb
index 46b09c65..3fa0e65e 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/add-column-using-expression.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/add-column-using-expression.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/add-column-using-expression.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/add-column-using-expression.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Add Column using Expression\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Add Column using Expression\n"
       ]
     },
     {
@@ -354,7 +352,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/append-columns-and-rows.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/append-columns-and-rows.ipynb
index 1873d4eb..51a55e4a 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/append-columns-and-rows.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/append-columns-and-rows.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/append-columns-and-rows.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/append-columns-and-rows.png)"
       ]
     },
     {
@@ -12,8 +12,6 @@
       "metadata": {},
       "source": [
         "# Append Columns and Rows\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License.<br>\n",
         "\n",
         "Often the data we want does not come in a single dataset: they are coming from different locations, have features that are separated, or are simply not homogeneous. Unsurprisingly, we typically want to work with a single dataset at a time.\n",
         "\n",
@@ -245,7 +243,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/assertions.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/assertions.ipynb
index 6a3fa2c3..b33a30ed 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/assertions.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/assertions.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/assertions.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/assertions.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Assertions\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Assertions\n"
       ]
     },
     {
@@ -127,7 +125,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/auto-read-file.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/auto-read-file.ipynb
index d3008d57..2a12288c 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/auto-read-file.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/auto-read-file.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/auto-read-file.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/auto-read-file.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Auto Read File\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Auto Read File\n"
       ]
     },
     {
@@ -183,7 +181,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/cache.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/cache.ipynb
index a8044902..fd47cf0f 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/cache.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/cache.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/cache.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/cache.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Cache\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Cache\n"
       ]
     },
     {
@@ -188,7 +186,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/column-manipulations.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/column-manipulations.ipynb
index 20003ac5..bf1836f9 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/column-manipulations.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/column-manipulations.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/column-manipulations.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/column-manipulations.png)"
       ]
     },
     {
@@ -12,8 +12,6 @@
       "metadata": {},
       "source": [
         "# Column Manipulations\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License.<br>\n",
         "\n",
         "Azure ML Data Prep has many methods for manipulating columns, including basic CUD operations and several other more complex manipulations.\n",
         "\n",
@@ -557,7 +555,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/column-type-transforms.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/column-type-transforms.ipynb
index 3084c9b0..bfc4e73f 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/column-type-transforms.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/column-type-transforms.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/column-type-transforms.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/column-type-transforms.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Column Type Transforms\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Column Type Transforms\n"
       ]
     },
     {
@@ -467,7 +465,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/custom-python-transforms.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/custom-python-transforms.ipynb
index 99af1fc9..43a3ca62 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/custom-python-transforms.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/custom-python-transforms.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/custom-python-transforms.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/custom-python-transforms.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Custom Python Transforms\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Custom Python Transforms\n"
       ]
     },
     {
@@ -224,8 +222,9 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    }
+      "version": "3.6.4"
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/data-ingestion.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/data-ingestion.ipynb
index 7885f0b2..3f04e85a 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/data-ingestion.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/data-ingestion.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/data-ingestion.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/data-ingestion.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Data Ingestion\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Data Ingestion\n"
       ]
     },
     {
@@ -49,7 +47,9 @@
         "[Read PostgreSQL](#postgresql)<br>\n",
         "[Read From Azure Blob](#azure-blob)<br>\n",
         "[Read From ADLS](#adls)<br>\n",
-        "[Read Pandas DataFrame](#pandas-df)<br>"
+        "[Read From ADLSGen2](#adlsgen2)<br>\n",
+        "[Read Pandas DataFrame](#pandas-df)<br>\n",
+        "[Read From HTTP/HTTPS Link](#http)<br>"
       ]
     },
     {
@@ -317,6 +317,25 @@
         "df"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can see in the results that the FBI Code column now contains some NaN values where before, when calling head, it didn't. By default, `to_pandas_dataframe` attempts to coalesce columns into a single type for better performance and lower memory overhead. This specific column has a mixutre of both numbers and strings and the strings were replaced with NaN values.\n",
+        "\n",
+        "If you wish to keep the mixed-type column in the Pandas DataFrame, you can set the `extended_types` argument to True when calling `to_pandas_dataframe`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "df = dflow_skipped_rows.to_pandas_dataframe(extended_types=True)\n",
+        "df"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -637,7 +656,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "df = dflow.to_pandas_dataframe()\n",
+        "df = dflow.to_pandas_dataframe(extended_types=True)\n",
         "df.dtypes"
       ]
     },
@@ -753,7 +772,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "There are two ways the Data Prep API can acquire the necessary OAuth token to access Azure DataLake Storage:\n",
+        "Data Prep currently supports both ADLS and ADLSGen2. There are two ways the Data Prep API can acquire the necessary OAuth token to access Azure DataLake Storage:\n",
         "1. Retrieve the access token from a recent login session of the user's [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) login.\n",
         "2. Use a ServicePrincipal (SP) and a certificate as a secret."
       ]
@@ -784,7 +803,7 @@
       "metadata": {},
       "source": [
         "```python\n",
-        "dflow = read_csv(path = DataLakeDataSource(path='adl://dpreptestfiles.azuredatalakestore.net/farmers-markets.csv', tenant='microsoft.onmicrosoft.com'))\n",
+        "dflow = read_csv(path = DataLakeDataSource(path='adl://dpreptestfiles.azuredatalakestore.net/crime-spring.csv', tenant='microsoft.onmicrosoft.com'))\n",
         "head = dflow.head(5)\n",
         "head\n",
         "```"
@@ -828,12 +847,12 @@
       "source": [
         "To configure the ACL for the ADLS filesystem, use the objectId of the user or, here, ServicePrincipal:\n",
         "```\n",
-        "az ad sp show --id \"8dd38f34-1fcb-4ff9-accd-7cd60b757174\" --query objectId\n",
+        "az ad sp show --id \"fbc406bf-f7c2-410d-bc26-8b08e4dab1aa\" --query objectId\n",
         "```\n",
         "Configure Read and Execute access for the ADLS file system. Since the underlying HDFS ACL model doesn't support inheritance, folders and files need to be ACL-ed individually.\n",
         "```\n",
-        "az dls fs access set-entry --account dpreptestfiles --acl-spec \"user:e37b9b1f-6a5e-4bee-9def-402b956f4e6f:r-x\" --path /\n",
-        "az dls fs access set-entry --account dpreptestfiles --acl-spec \"user:e37b9b1f-6a5e-4bee-9def-402b956f4e6f:r--\" --path /farmers-markets.csv\n",
+        "az dls fs access set-entry --account dpreptestfiles --acl-spec \"user:999a21ef-75aa-4538-b325-249285672204:r-x\" --path /\n",
+        "az dls fs access set-entry --account dpreptestfiles --acl-spec \"user:999a21ef-75aa-4538-b325-249285672204:r--\" --path /crime-spring.csv\n",
         "```\n",
         "\n",
         "References:\n",
@@ -848,12 +867,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "certThumbprint = 'C2:08:9D:9E:D1:74:FC:EB:E9:7E:63:96:37:1C:13:88:5E:B9:2C:84'\n",
+        "certThumbprint = '84:60:1E:BE:AA:FF:90:A0:7A:73:38:80:F7:D0:12:44:98:70:9C:3A'\n",
         "certificate = ''\n",
         "with open('../data/adls-dpreptestfiles.crt', 'rt', encoding='utf-8') as crtFile:\n",
         "    certificate = crtFile.read()\n",
         "\n",
-        "servicePrincipalAppId = \"8dd38f34-1fcb-4ff9-accd-7cd60b757174\""
+        "servicePrincipalAppId = \"fbc406bf-f7c2-410d-bc26-8b08e4dab1aa\""
       ]
     },
     {
@@ -885,6 +904,70 @@
         "dflow.to_pandas_dataframe().head()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"adlsgen2\"></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Read from ADLSGen2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Please refer to the Read for ADLS section above to get details of how to register a Service Principal and obtain an OAuth access token.[ADLS](http://localhost:8888/notebooks/notebooks/how-to-guides/data-ingestion.ipynb#adls)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Configure ADLSGen2 Account for ServicePrincipal"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "certThumbprint = '23:66:84:6B:3A:14:9E:B1:17:CA:EE:E3:BB:2C:21:2D:20:B0:DF:F2'\n",
+        "certificate = ''\n",
+        "with open('../data/ADLSgen2-datapreptest.crt', 'rt', encoding='utf-8') as crtFile:\n",
+        "    certificate = crtFile.read()\n",
+        "\n",
+        "servicePrincipalAppId = \"127a58c3-f307-46a1-969e-a6b63da3f411\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Acquire an OAuth Access Token for ADLSGen2"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import adal\n",
+        "from azureml.dataprep.api.datasources import ADLSGen2\n",
+        "\n",
+        "ctx = adal.AuthenticationContext('https://login.microsoftonline.com/72f988bf-86f1-41af-91ab-2d7cd011db47')\n",
+        "token = ctx.acquire_token_with_client_certificate('https://storage.azure.com/', servicePrincipalAppId, certificate, certThumbprint)\n",
+        "dflow = dprep.read_csv(path = ADLSGen2(path='https://adlsgen2datapreptest.dfs.core.windows.net/datapreptest/people.csv', accessToken=token['accessToken']))\n",
+        "dflow.to_pandas_dataframe().head()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -925,7 +1008,24 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "After loading in the data you can now do `read_pandas_dataframe`."
+        "After loading in the data you can now do `read_pandas_dataframe`. If you only need to consume the Dataflow created from the current environment, you can read the DataFrame in memory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "dflow_df = dprep.read_pandas_dataframe(df, in_memory=True)\n",
+        "dflow_df.head(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "However, if you intend to use this Dataflow past the end of your current Python session (such as by saving the Dataflow to a file), you can provide a cache directory where the contents of the DataFrame will be stored so they can be retrieved later."
       ]
     },
     {
@@ -948,6 +1048,37 @@
       "source": [
         "dflow_df.head(5)"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"http\"></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Read from HTTP/HTTPS Link"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can pass in an HTTP/HTTPS path when loading remote data source."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "dflow = dprep.read_csv('https://dprepdata.blob.core.windows.net/test/Sample-Spreadsheet-10-rows.csv')\n",
+        "dflow.head(5)"
+      ]
     }
   ],
   "metadata": {
@@ -972,7 +1103,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/data-profile.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/data-profile.ipynb
index c4c17253..97b42ee1 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/data-profile.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/data-profile.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/data-profile.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/data-profile.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Data Profile\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Data Profile\n"
       ]
     },
     {
@@ -173,7 +171,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/datastore.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/datastore.ipynb
index c60ea2a1..76bc0ed4 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/datastore.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/datastore.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/datastore.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/datastore.png)"
       ]
     },
     {
@@ -14,14 +14,6 @@
         "# Reading from and Writing to Datastores"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -191,6 +183,37 @@
         "dflow_adls = dprep.read_csv(path=DataPath(datastore, path_on_datastore='/input/crime0-10.csv'))\n",
         "dflow_adls.head(5)"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now you can read all the files in the `dataprep_adlsgen2` datastore which references an ADLSGen2 Storage account."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# read a file from ADLSGen2\n",
+        "datastore = Datastore(workspace=workspace, name='adlsgen2')\n",
+        "dflow_adlsgen2 = dprep.read_csv(path=DataPath(datastore, path_on_datastore='/testfolder/peopletest.csv'))\n",
+        "dflow_adlsgen2.head(5)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# read all files from ADLSGen2 directory\n",
+        "datastore = Datastore(workspace=workspace, name='adlsgen2')\n",
+        "dflow_adlsgen2 = dprep.read_csv(path=DataPath(datastore, path_on_datastore='/testfolder/testdir'))\n",
+        "dflow_adlsgen2.head()"
+      ]
     }
   ],
   "metadata": {
@@ -215,7 +238,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/derive-column-by-example.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/derive-column-by-example.ipynb
index 47383329..5d1db4ee 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/derive-column-by-example.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/derive-column-by-example.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/derive-column-by-example.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/derive-column-by-example.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Derive Column By Example\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Derive Column By Example\n"
       ]
     },
     {
@@ -181,7 +179,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.8"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/external-references.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/external-references.ipynb
index 61574bd3..579d9087 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/external-references.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/external-references.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/external-references.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/external-references.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# External References\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# External References\n"
       ]
     },
     {
@@ -112,7 +110,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/filtering.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/filtering.ipynb
index 780309bb..545fd3ca 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/filtering.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/filtering.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/filtering.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/filtering.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Filtering\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Filtering\n"
       ]
     },
     {
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/fuzzy-group.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/fuzzy-group.ipynb
index 499977de..53f309a7 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/fuzzy-group.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/fuzzy-group.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/fuzzy-group.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/fuzzy-group.png)"
       ]
     },
     {
@@ -12,8 +12,6 @@
       "metadata": {},
       "source": [
         "# Fuzzy Grouping\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License.<br>\n",
         "\n",
         "Unprepared data often represents the same entity with multiple values; examples include different spellings, varying capitalizations, and abbreviations. This is common when working with data gathered from multiple sources or through human input. One way to canonicalize and reconcile these variants is to use Data Prep's fuzzy_group_column (also known as \"text clustering\") functionality.\n",
         "\n",
@@ -205,7 +203,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/impute-missing-values.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/impute-missing-values.ipynb
index f86a07f3..a1b9e46e 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/impute-missing-values.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/impute-missing-values.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/impute-missing-values.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/impute-missing-values.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Impute missing values\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Impute missing values\n"
       ]
     },
     {
@@ -141,7 +139,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/join.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/join.ipynb
index cb80a337..2f8c2e47 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/join.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/join.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/join.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/join.png)"
       ]
     },
     {
@@ -12,8 +12,6 @@
       "metadata": {},
       "source": [
         "# Join\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License.<br>\n",
         "\n",
         "In Data Prep you can easily join two Dataflows."
       ]
@@ -259,7 +257,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.5"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/label-encoder.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/label-encoder.ipynb
index 8b4a1c69..bc7b78c1 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/label-encoder.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/label-encoder.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/label-encoder.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/label-encoder.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Label Encoder\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License.<br>"
+        "# Label Encoder\n"
       ]
     },
     {
@@ -162,7 +160,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/min-max-scaler.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/min-max-scaler.ipynb
index c6d1db60..a7e5fd65 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/min-max-scaler.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/min-max-scaler.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/min-max-scaler.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/min-max-scaler.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Min-Max Scaler\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Min-Max Scaler\n"
       ]
     },
     {
@@ -233,7 +231,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/one-hot-encoder.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/one-hot-encoder.ipynb
index ad5350ec..72918540 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/one-hot-encoder.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/one-hot-encoder.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/one-hot-encoder.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/one-hot-encoder.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# One Hot Encoder\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
-        "Licensed under the MIT License."
+        "# One Hot Encoder\n"
       ]
     },
     {
@@ -173,7 +171,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/open-save-dataflows.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/open-save-dataflows.ipynb
index b401ae9b..92064377 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/open-save-dataflows.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/open-save-dataflows.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/open-save-dataflows.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/open-save-dataflows.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Opening and Saving Dataflows\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Opening and Saving Dataflows\n"
       ]
     },
     {
@@ -165,7 +163,8 @@
       "display_name": "Python 3.6",
       "language": "python",
       "name": "python36"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/quantile-transformation.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/quantile-transformation.ipynb
index 6fcf082d..883bc5c8 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/quantile-transformation.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/quantile-transformation.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/quantile-transformation.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/quantile-transformation.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Quantile Transformation\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Quantile Transformation\n"
       ]
     },
     {
@@ -85,7 +83,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/random-split.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/random-split.ipynb
index 26b53043..4f87af22 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/random-split.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/random-split.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/random-split.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/random-split.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Random Split\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Random Split\n"
       ]
     },
     {
@@ -139,7 +137,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/replace-datasource-replace-reference.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/replace-datasource-replace-reference.ipynb
index 260d500e..e8d62acf 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/replace-datasource-replace-reference.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/replace-datasource-replace-reference.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/replace-datasource-replace-reference.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/replace-datasource-replace-reference.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Replace DataSource Reference\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Replace DataSource Reference\n"
       ]
     },
     {
@@ -124,7 +122,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/replace-fill-error.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/replace-fill-error.ipynb
index ddef0f4b..04dad995 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/replace-fill-error.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/replace-fill-error.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/replace-fill-error.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/replace-fill-error.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Replace, Fill, Error\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Replace, Fill, Error\n"
       ]
     },
     {
@@ -233,7 +231,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/secrets.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/secrets.ipynb
index 982d66f3..c77169c9 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/secrets.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/secrets.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/secrets.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/secrets.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Providing Secrets\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Providing Secrets\n"
       ]
     },
     {
@@ -134,7 +132,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/semantic-types.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/semantic-types.ipynb
index 5053cbaa..266353df 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/semantic-types.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/semantic-types.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/semantic-types.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/semantic-types.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Semantic Types\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Semantic Types\n"
       ]
     },
     {
@@ -158,7 +156,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/split-column-by-example.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/split-column-by-example.ipynb
index 3d7c62db..02c74746 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/split-column-by-example.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/split-column-by-example.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/split-column-by-example.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/split-column-by-example.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Split column by example\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Split column by example\n"
       ]
     },
     {
@@ -188,7 +186,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Now we have successfully split the data into useful columns through examples. "
+        "Now we have successfully split the data into useful columns through examples."
       ]
     }
   ],
@@ -214,7 +212,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.8"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/subsetting-sampling.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/subsetting-sampling.ipynb
index 833ab7e3..d1abb62f 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/subsetting-sampling.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/subsetting-sampling.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/subsetting-sampling.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/subsetting-sampling.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Sampling and Subsetting\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Sampling and Subsetting\n"
       ]
     },
     {
@@ -211,7 +209,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/summarize.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/summarize.ipynb
index 3aff2b43..56a37bee 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/summarize.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/summarize.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/summarize.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/summarize.png)"
       ]
     },
     {
@@ -12,8 +12,6 @@
       "metadata": {},
       "source": [
         "# Summarize\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License.<br>\n",
         "\n",
         "Azure ML Data Prep can help summarize your data by providing you a synopsis based on aggregates over specific columns.\n",
         "\n",
@@ -584,7 +582,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/working-with-file-streams.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/working-with-file-streams.ipynb
index 28ac3d2f..e92c1e1c 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/working-with-file-streams.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/working-with-file-streams.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/working-with-file-streams.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/working-with-file-streams.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Working With File Streams\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Working With File Streams\n"
       ]
     },
     {
@@ -186,7 +184,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/writing-data.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/writing-data.ipynb
index ae660d30..bfbe3865 100644
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/writing-data.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/writing-data.ipynb
@@ -4,16 +4,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/writing-data.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/writing-data.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Writing Data\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Writing Data\n"
       ]
     },
     {
@@ -177,7 +175,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.4"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/dataprep/tutorials/getting-started/getting-started.ipynb b/how-to-use-azureml/work-with-data/dataprep/tutorials/getting-started/getting-started.ipynb
index 3881b8d8..73514661 100644
--- a/how-to-use-azureml/work-with-data/dataprep/tutorials/getting-started/getting-started.ipynb
+++ b/how-to-use-azureml/work-with-data/dataprep/tutorials/getting-started/getting-started.ipynb
@@ -4,9 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Getting started with Azure ML Data Prep SDK\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
+        "# Getting started with Azure ML Data Prep SDK\n"
       ]
     },
     {
@@ -34,11 +32,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from IPython.display import display\n",
-        "from os import path\n",
-        "from tempfile import mkdtemp\n",
-        "\n",
-        "import pandas as pd\n",
         "import azureml.dataprep as dprep\n",
         "\n",
         "# Paths for datasets\n",
@@ -368,10 +361,7 @@
         "3. Consume directly in Azure Machine Learning models\n",
         "\n",
         "In this quickstart guide, we'll show how you can export to a pandas dataframe.\n",
-        "\n",
-        "__Advanced features:__ \n",
-        "* One of the beautiful features of Azure ML Data Prep is that you only need to write your code once and choose whether to scale up or out.\n",
-        "* You can directly consume your new DataFlow in model builders like Azure Machine Learning's [automated machine learning](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.ipynb)."
+        "\n"
       ]
     },
     {
@@ -409,7 +399,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/tutorials/getting-started/getting-started.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/tutorials/getting-started/getting-started.png)"
       ]
     }
   ],
@@ -435,7 +425,8 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.2"
-    }
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/work-with-data/datasets/README.md b/how-to-use-azureml/work-with-data/datasets/README.md
index daa2269d..2a4adb53 100644
--- a/how-to-use-azureml/work-with-data/datasets/README.md
+++ b/how-to-use-azureml/work-with-data/datasets/README.md
@@ -1,159 +1,22 @@
-# Azure Machine Learning Datasets (preview)
+# Azure Machine Learning datasets (preview)
 
-Azure Machine Learning Datasets (preview) make it easier to access and work with your data. Datasets manage data in various scenarios such as model training and pipeline creation. Using the Azure Machine Learning SDK, you can access underlying storage, explore and prepare data, manage the life cycle of different Dataset definitions, and compare between Datasets used in training and in production.
+Azure Machine Learning datasets (preview) let data scientists and machine learning engineers apply data for ML with confidence. By creating a dataset, you create a reference to the data source location, along with a copy of its metadata. The data remains in its existing location, so no extra storage cost is incurred.
 
-## Create and Register Datasets
+With Azure Machine Learning datasets, you can: 
 
-It's easy to create Datasets from either local files, or Azure Datastores.
+* **Keep a single copy of data in your storage** referenced by datasets. 
 
-```Python
-from azureml.core.workspace import Workspace
-from azureml.core.datastore import Datastore
-from azureml.core.dataset import Dataset
+* **Easily access data during model training** without worrying about connection string or data path.
 
-datastore_name = 'your datastore name'
-
-# get existing workspace
-workspace = Workspace.from_config()
-
-# get Datastore from the workspace
-dstore = Datastore.get(workspace, datastore_name)
-
-# create an in-memory Dataset on your local machine
-dataset = Dataset.from_delimited_files(dstore.path('data/src/crime.csv'))
-```
-
-To consume Datasets across various scenarios in Azure Machine Learning service such as automated machine learning, model training and pipeline creation, you need to register the Datasets with your workspace. By doing so, you can also share and reuse the Datasets within your organization.
-
-```Python
-dataset = dataset.register(workspace = workspace,
-                           name = 'dataset_crime',
-                           description = 'Training data'
-                           )
-```
-
-## Sampling
-
-Sampling can be particular useful with Datasets that are too large to efficiently analyze in full. It enables data scientists to work with a manageable amount of data to build and train machine learning models. At this time, the [`sample()`](https://docs.microsoft.com//python/api/azureml-core/azureml.core.dataset(class)?view=azure-ml-py#sample-sample-strategy--arguments-) method from the Dataset class supports Top N, Simple Random, and Stratified sampling strategies.
-
-After sampling, you can convert your sampled Dataset to pandas DataFrame for training. By using  the native [`sample()`](https://docs.microsoft.com//python/api/azureml-core/azureml.core.dataset(class)?view=azure-ml-py#sample-sample-strategy--arguments-) method from the Dataset class, you will load the sampled data on the fly instead of loading full data into memory.
-
-### Top N sample
-
-For Top N sampling, the first n records of your Dataset are your sample. This is helpful if you are just trying  to get an idea of what your data records look like or to see what fields are in your data.
-
-```Python
-top_n_sample_dataset = dataset.sample('top_n', {'n': 5})
-top_n_sample_dataset.to_pandas_dataframe()
-```
-
-### Simple random sample
-
-In Simple Random sampling, every member of the data population has an equal chance of being selected as a part of the sample. In the `simple_random` sample strategy, the records from your Dataset are selected based on the probability specified and returns a modified Dataset. The seed parameter is optional.
-
-```Python
-simple_random_sample_dataset = dataset.sample('simple_random', {'probability':0.3, 'seed': seed})
-simple_random_sample_dataset.to_pandas_dataframe()
-```
-
-### Stratified sample
-
-Stratified samples ensure that certain groups of a population are represented in the sample. In the `stratified` sample strategy, the population is divided into strata, or subgroups, based on similarities, and records are randomly selected from each strata according to the strata weights indicated by the `fractions` parameter.
-
-In the following example, we group each record by the specified columns, and include said record based on the strata X weight information in `fractions`. If a strata is not specified or the record cannot be grouped, the default weight to sample is 0.
-
-```Python
-# take 50% of records with `Primary Type` as `THEFT` and 20% of records with `Primary Type` as `DECEPTIVE PRACTICE` into sample Dataset
-fractions = {}
-fractions[('THEFT',)] = 0.5
-fractions[('DECEPTIVE PRACTICE',)] = 0.2
-
-sample_dataset = dataset.sample('stratified', {'columns': ['Primary Type'], 'fractions': fractions, 'seed': seed})
-
-sample_dataset.to_pandas_dataframe()
-```
-
-## Explore with summary statistics
-
- Detect anomalies, missing values, or error counts with the [`get_profile()`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#get-profile-arguments-none--generate-if-not-exist-true--workspace-none--compute-target-none-) method. This function gets the profile and summary statistics of your data, which in turn helps determine the necessary data preparation operations to apply.
-
-```Python
-# get pre-calculated profile
-# if there is no precalculated profile available or the precalculated profile is not up-to-date, this method will generate a new profile of the Dataset
-dataset.get_profile()
-```
-
-||Type|Min|Max|Count|Missing Count|Not Missing Count|Percent missing|Error Count|Empty count|0.1% Quantile|1% Quantile|5% Quantile|25% Quantile|50% Quantile|75% Quantile|95% Quantile|99% Quantile|99.9% Quantile|Mean|Standard Deviation|Variance|Skewness|Kurtosis
--|----|---|---|-----|-------------|-----------------|---------------|-----------|-----------|-------------|-----------|-----------|------------|------------|------------|------------|------------|--------------|----|------------------|--------|--------|--------
-ID|FieldType.INTEGER|1.04986e+07|1.05351e+07|10.0|0.0|10.0|0.0|0.0|0.0|1.04986e+07|1.04992e+07|1.04986e+07|1.05166e+07|1.05209e+07|1.05259e+07|1.05351e+07|1.05351e+07|1.05351e+07|1.05195e+07|12302.7|1.51358e+08|-0.495701|-1.02814
-Case Number|FieldType.STRING|HZ239907|HZ278872|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
-Date|FieldType.DATE|2016-04-04 23:56:00+00:00|2016-04-15 17:00:00+00:00|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
-Block|FieldType.STRING|004XX S KILBOURN AVE|113XX S PRAIRIE AVE|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
-IUCR|FieldType.INTEGER|810|1154|10.0|0.0|10.0|0.0|0.0|0.0|810|850|810|890|1136|1153|1154|1154|1154|1058.5|137.285|18847.2|-0.785501|-1.3543
-Primary Type|FieldType.STRING|DECEPTIVE PRACTICE|THEFT|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
-Description|FieldType.STRING|BOGUS CHECK|OVER $500|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
-Location Description|FieldType.STRING||SCHOOL, PUBLIC, BUILDING|10.0|0.0|10.0|0.0|0.0|1.0||||||||||||||
-Arrest|FieldType.BOOLEAN|False|False|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
-Domestic|FieldType.BOOLEAN|False|False|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
-Beat|FieldType.INTEGER|531|2433|10.0|0.0|10.0|0.0|0.0|0.0|531|531|531|614|1318.5|1911|2433|2433|2433|1371.1|692.094|478994|0.105418|-1.60684
-District|FieldType.INTEGER|5|24|10.0|0.0|10.0|0.0|0.0|0.0|5|5|5|6|13|19|24|24|24|13.5|6.94822|48.2778|0.0930109|-1.62325
-Ward|FieldType.INTEGER|1|48|10.0|0.0|10.0|0.0|0.0|0.0|1|5|1|9|22.5|40|48|48|48|24.5|16.2635|264.5|0.173723|-1.51271
-Community Area|FieldType.INTEGER|4|77|10.0|0.0|10.0|0.0|0.0|0.0|4|8.5|4|24|37.5|71|77|77|77|41.2|26.6366|709.511|0.112157|-1.73379
-FBI Code|FieldType.INTEGER|6|11|10.0|0.0|10.0|0.0|0.0|0.0|6|6|6|6|11|11|11|11|11|9.4|2.36643|5.6|-0.702685|-1.59582
-X Coordinate|FieldType.INTEGER|1.16309e+06|1.18336e+06|10.0|7.0|3.0|0.7|0.0|0.0|1.16309e+06|1.16309e+06|1.16309e+06|1.16401e+06|1.16678e+06|1.17921e+06|1.18336e+06|1.18336e+06|1.18336e+06|1.17108e+06|10793.5|1.165e+08|0.335126|-2.33333
-Y Coordinate|FieldType.INTEGER|1.8315e+06|1.908e+06|10.0|7.0|3.0|0.7|0.0|0.0|1.8315e+06|1.8315e+06|1.8315e+06|1.83614e+06|1.85005e+06|1.89352e+06|1.908e+06|1.908e+06|1.908e+06|1.86319e+06|39905.2|1.59243e+09|0.293465|-2.33333
-Year|FieldType.INTEGER|2016|2016|10.0|0.0|10.0|0.0|0.0|0.0|2016|2016|2016|2016|2016|2016|2016|2016|2016|2016|0|0|NaN|NaN
-Updated On|FieldType.DATE|2016-05-11 15:48:00+00:00|2016-05-27 15:45:00+00:00|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
-Latitude|FieldType.DECIMAL|41.6928|41.9032|10.0|7.0|3.0|0.7|0.0|0.0|41.6928|41.6928|41.6928|41.7057|41.7441|41.8634|41.9032|41.9032|41.9032|41.78|0.109695|0.012033|0.292478|-2.33333
-Longitude|FieldType.DECIMAL|-87.6764|-87.6043|10.0|7.0|3.0|0.7|0.0|0.0|-87.6764|-87.6764|-87.6764|-87.6734|-87.6645|-87.6194|-87.6043|-87.6043|-87.6043|-87.6484|0.0386264|0.001492|0.344429|-2.33333
-Location|FieldType.STRING||(41.903206037, -87.676361925)|10.0|0.0|10.0|0.0|0.0|7.0||||||||||||||
-
-
-## Training with Dataset
-
-Now that you have registered your Dataset, you can call up the Dataset and convert it to pandas DataFrame or Spark DataFrame easily in your train.py script.
-
-```Python
-# Sample train.py script
-import azureml.core
-import pandas as pd
-import datetime
-import shutil
-from azureml.core import Workspace, Datastore, Dataset, Experiment, Run
-from sklearn.model_selection import train_test_split
-from azureml.core.compute import ComputeTarget, AmlCompute
-from azureml.core.compute_target import ComputeTargetException
-from sklearn.tree import DecisionTreeClassifier
-
-run = Run.get_context()
-workspace = run.experiment.workspace
-
-# Access Dataset registered with the workspace by name
-dataset_name = 'training_data'
-dataset = Dataset.get(workspace=workspace, name=dataset_name)
-
-ds_def = dataset.get_definition()
-dataset_val, dataset_train = ds_def.random_split(percentage=0.3)
-y_df = dataset_train.keep_columns(['HasDetections']).to_pandas_dataframe()
-x_df = dataset_train.drop_columns(['HasDetections']).to_pandas_dataframe()
-y_val = dataset_val.keep_columns(['HasDetections']).to_pandas_dataframe()
-x_val = dataset_val.drop_columns(['HasDetections']).to_pandas_dataframe()
-
-data = {"train": {"X": x_df, "y": y_df},
-        "validation": {"X": x_val, "y": y_val}}
-
-clf = DecisionTreeClassifier().fit(data["train"]["X"], data["train"]["y"])
-print('Accuracy of Decision Tree classifier on training set: {:.2f}'.format(clf.score(x_df, y_df)))
-print('Accuracy of Decision Tree classifier on validation set: {:.2f}'.format(clf.score(x_val, y_val)))
-```
-
-For an end-to-end tutorial, you may refer to [Dataset tutorial](datasets-tutorial.ipynb). You will learn how to:
-- Explore and prepare data for training the model.
-- Register the Dataset in your workspace for easy access in training.
-- Take snapshots of data to ensure models can be trained with the same data every time.
-- Use registered Dataset in your training script.
-- Create and use multiple Dataset definitions to ensure that updates to the definition don't break existing pipelines/scripts.
+* **Share data & collaborate** with other users.
 
+## Learn how to use Azure Machine Learning datasets:
+* [Create and register datasets](https://aka.ms/azureml/howto/createdatasets)
+* Use [Datasets in training](datasets-tutorial/train-with-datasets.ipynb)
+* Use TabularDatasets in [automated machine learning training](https://aka.ms/automl-dataset)
+* Use FileDatasets in [image classification](https://aka.ms/filedataset-samplenotebook)
+* Use FileDatasets in [deep learning with hyperparameter tuning](https://aka.ms/filedataset-hyperdrive)
+* For existing Dataset users: [Dataset API change notice](dataset-api-change-notice.md)
  
 
-![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/datasets/README.png) 
\ No newline at end of file
+![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/datasets/README.png) 
\ No newline at end of file
diff --git a/how-to-use-azureml/work-with-data/datasets/dataset-api-change-notice.md b/how-to-use-azureml/work-with-data/datasets/dataset-api-change-notice.md
new file mode 100644
index 00000000..3d1b2683
--- /dev/null
+++ b/how-to-use-azureml/work-with-data/datasets/dataset-api-change-notice.md
@@ -0,0 +1,55 @@
+# Dataset API change notice
+
+## Why are Dataset API changes essential?
+
+The existing Dataset class only supports data in tabular format. In order to support binary data and address a wider range of machine learning scenarios including deep learning, we will introduce Dataset types. Datasets are categorized into various types based on how users consume them in training. List of Dataset types:
+- **TabularDataset**: Represents data in a tabular format by parsing the provided file or list of files. TabularDataset can be created from csv, tsv, parquet files, SQL query results etc. For the complete list, please visit our [documentation](https://aka.ms/tabulardataset-api-reference). It provides you with the ability to materialize the data into a pandas DataFrame.
+- **FileDataset**: References single or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute.
+
+In order to transit from the current Dataset design to typed Dataset, we will deprecate the following methods over time.
+
+## Which methods on Dataset class will be deprecated in upcoming releases?
+Methods to be deprecated|Replacement in the new version|
+----|--------
+[Dataset.get()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#get-workspace--name-none--id-none-)|[Dataset.get_by_name()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#get-by-name-workspace--name--version--latest--)
+[Dataset.from_pandas_dataframe()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#from-pandas-dataframe-dataframe--path-none--in-memory-false-)|Creating a Dataset from in-memory DataFrame or local files will cause errors in training on remote compute. Therefore, the new Dataset design will only support creating Datasets from paths in datastores or public web urls. If you are using pandas, you can write the DataFrame into a parquet file, upload it to the cloud, and create a TabularDataset referencing the parquet file using [Dataset.Tabular.from_parquet_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#from-parquet-files-path--validate-true--include-path-false--set-column-types-none-)
+[Dataset.from_delimited_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#from-delimited-files-path--separator------header--promoteheadersbehavior-all-files-have-same-headers--3---encoding--fileencoding-utf8--0---quoting-false--infer-column-types-true--skip-rows-0--skip-mode--skiplinesbehavior-no-rows--0---comment-none--include-path-false--archive-options-none--partition-format-none-)|[Dataset.Tabular.from_delimited_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#from-delimited-files-path--validate-true--include-path-false--infer-column-types-true--set-column-types-none--separator------header--promoteheadersbehavior-all-files-have-same-headers--3--)
+[Dataset.auto_read_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#auto-read-files-path--include-path-false--partition-format-none-)|`auto_read_files` does not always produce results that match with users' expectation. To avoid confusion, this method is not introduced with TabularDataset for now. Please use [Dataset.Tabular.from_parquet_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#from-parquet-files-path--validate-true--include-path-false--set-column-types-none-) or [Dataset.Tabular.from_delimited_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#from-delimited-files-path--validate-true--include-path-false--infer-column-types-true--set-column-types-none--separator------header--promoteheadersbehavior-all-files-have-same-headers--3--) depending on your file format.
+[Dataset.from_parquet_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#from-parquet-files-path--include-path-false--partition-format-none-)|[Dataset.Tabular.from_parquet_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#from-parquet-files-path--validate-true--include-path-false--set-column-types-none-)
+[Dataset.from_sql_query()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#from-sql-query-data-source--query-)|[Dataset.Tabular.from_sql_query()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#from-sql-query-query--validate-true--set-column-types-none-)
+[Dataset.from_excel_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#from-excel-files-path--sheet-name-none--use-column-headers-false--skip-rows-0--include-path-false--infer-column-types-true--partition-format-none-)|We will support creating a TabularDataset from Excel files in a future release.
+[Dataset.from_json_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#from-json-files-path--encoding--fileencoding-utf8--0---flatten-nested-arrays-false--include-path-false--partition-format-none-)| We will support creating a TabularDataset from json files in a future release.
+[Dataset.to_pandas_dataframe()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#to-pandas-dataframe--)|[TabularDataset.to_pandas_dataframe()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py#to-pandas-dataframe--)
+[Dataset.to_spark_dataframe()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#to-spark-dataframe--)|[TabularDataset.to_spark_dataframe()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py#to-spark-dataframe--)
+[Dataset.head(3)](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#head-count-)|[TabularDataset.take(3).to_pandas_dataframe()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py#take-count-)
+[Dataset.sample()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#sample-sample-strategy--arguments-)|[TabularDataset.take_sample()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py#take-sample-probability--seed-none-)
+[Dataset.from_binary_files()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#from-binary-files-path-)|`Dataset.File.from_files()`
+
+
+## Why should I use the new Dataset API if I'm only dealing with tabular data?
+The current Dataset will be kept around for backward compatibility, but we strongly encourage you to move to TabularDataset for the new capabilities listed below: 
+
+- You are able to use TabularDatasets as automated ML input. [Learn How](https://aka.ms/automl-dataset)
+- You are able to version the new typed Datasets. [Learn How](https://aka.ms/azureml/howto/createdatasets)
+- You will be able to use the new typed Datasets as ScriptRun, Estimator, HyperDrive input.
+- You will be able to use the new typed Datasets in Azure Machine Learning Pipelines.
+- You will be able to track the lineage of new typed Datasets for model reproducibility.
+
+
+## How to migrate registered Datasets to new typed Datasets?
+If you have registered Datasets created using the old API, you can easily migrate these old Datasets to the new typed Datasets using the following code.
+```Python
+from azureml.core.workspace import Workspace
+from azureml.core.dataset import Dataset
+
+# get existing workspace
+workspace = Workspace.from_config()
+# This method will convert old Dataset without type to either a TabularDataset or a FileDataset object automatically.
+new_ds = Dataset.get_by_name(workspace, 'old_ds_name')
+
+# register the new typed Dataset with the workspace
+new_ds.register(workspace, 'new_ds_name')
+```
+
+## How to provide feedback?
+If you have any feedback about our product, or if there is any missing capability that is essential for you to use new Dataset API, please email us at [AskAzureMLData@microsoft.com](mailto:AskAzureMLData@microsoft.com).
\ No newline at end of file
diff --git a/how-to-use-azureml/work-with-data/datasets/datasets-tutorial.ipynb b/how-to-use-azureml/work-with-data/datasets/datasets-tutorial.ipynb
deleted file mode 100644
index cf865ca0..00000000
--- a/how-to-use-azureml/work-with-data/datasets/datasets-tutorial.ipynb
+++ /dev/null
@@ -1,437 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Tutorial: Learn how to use Datasets in Azure ML"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In this tutorial, you learn how to use Azure ML Datasets to train a regression model with the Azure Machine Learning SDK for Python. You will\n",
-        "\n",
-        "* Explore and prepare data for training the model\n",
-        "* Register the Dataset in your workspace to share it with others\n",
-        "* Take snapshots of data to ensure models can be trained with the same data every time\n",
-        "* Create and use multiple Dataset definitions to ensure that updates to the definition don't break existing pipelines/scripts\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In this tutorial, you:\n",
-        "\n",
-        "&#x2611; Setup a Python environment and import packages\n",
-        "\n",
-        "&#x2611; Load the Titanic data from your Azure Blob Storage. (The [original data](https://www.kaggle.com/c/titanic/data) can be found on Kaggle)\n",
-        "\n",
-        "&#x2611; Explore and cleanse the data to remove anomalies\n",
-        "\n",
-        "&#x2611; Register the Dataset in your workspace, allowing you to use it in model training \n",
-        "\n",
-        "&#x2611; Take a Dataset snapshot for repeatability and train a model with the snapshot\n",
-        "\n",
-        "&#x2611; Make changes to the dataset's definition without breaking the production model or the daily data pipeline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Pre-requisites:\n",
-        "Skip to Set up your development environment to read through the notebook steps, or use the instructions below to get the notebook and run it on Azure Notebooks or your own notebook server. To run the notebook you will need:\n",
-        "\n",
-        "A Python 3.6 notebook server with the following installed:\n",
-        "* The Azure Machine Learning SDK for Python\n",
-        "* The Azure Machine Learning Data Prep SDK for Python\n",
-        "* The tutorial notebook\n",
-        "\n",
-        "Data and train.py script to store in your Azure Blob Storage Account.\n",
-        " * [Titanic data](./train-dataset/Titanic.csv)\n",
-        " * [train.py](./train-dataset/train.py)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To create and register Datasets you need:\n",
-        "\n",
-        "   * An Azure subscription. If you don\u00e2\u20ac\u2122t have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning service](https://aka.ms/AMLFree) today.\n",
-        "\n",
-        "   * An Azure Machine Learning service workspace. See the [Create an Azure Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/setup-create-workspace?branch=release-build-amls).\n",
-        "\n",
-        "   * The Azure Machine Learning SDK for Python (version 1.0.21 or later). To install or update to the latest version of the SDK, see [Install or update the SDK](https://docs.microsoft.com/python/api/overview/azure/ml/install?view=azure-ml-py).\n",
-        "\n",
-        "\n",
-        "For more information on how to set up your workspace, see the [Create an Azure Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/setup-create-workspace?branch=release-build-amls).\n",
-        "\n",
-        "The first part that needs to be done is setting up your python environment. You will need to import all of your python packages including `azureml.dataprep` and `azureml.core.dataset`. Then access your workspace through your Azure subscription and set up your compute target. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "import azureml.core\n",
-        "import pandas as pd\n",
-        "import logging\n",
-        "import os\n",
-        "import shutil\n",
-        "from azureml.core import Workspace, Datastore, Dataset\n",
-        "\n",
-        "# Get existing workspace from config.json file in the same folder as the tutorial notebook\n",
-        "# You can download the config file from your workspace\n",
-        "workspace = Workspace.from_config()\n",
-        "print(\"Workspace\")\n",
-        "print(workspace)\n",
-        "print(\"Compute targets\")\n",
-        "print(workspace.compute_targets)\n",
-        "\n",
-        "# Get compute target that has already been attached to the workspace\n",
-        "# Pick the right compute target from the list of computes attached to your workspace\n",
-        "\n",
-        "compute_target_name = 'dataset-test'\n",
-        "remote_compute_target = workspace.compute_targets[compute_target_name]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To load data to your dataset, you will access the data through your datastore. After you create your dataset, you can use `get_profile()` to see your data's statistics.\n",
-        "\n",
-        "We will now upload the [original data](https://www.kaggle.com/c/titanic/data) to the default datastore(blob) within your workspace.."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "datastore = workspace.get_default_datastore()\n",
-        "datastore.upload_files(files=['./train-dataset/Titanic.csv'],\n",
-        "                       target_path='train-dataset/',\n",
-        "                       overwrite=True,\n",
-        "                       show_progress=True)\n",
-        "\n",
-        "dataset = Dataset.auto_read_files(path=datastore.path('train-dataset/Titanic.csv'))\n",
-        "\n",
-        "#Display Dataset Profile of the Titanic Dataset\n",
-        "dataset.get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To predict if a person survived the Titanic's sinking or not, the columns that are relevant to train the model are 'Survived','Pclass', 'Sex','SibSp', and 'Parch'. You can update your dataset's deinition and only keep these columns you will need. You will also need to convert values (\"male\",\"female\") in the \"Sex\" column to 0 or 1, because the algorithm in the train.py file will be using numeric values instead of strings.\n",
-        "\n",
-        "For more examples of preparing data with Datasets, see [Explore and prepare data with the Dataset class](aka.ms/azureml/howto/exploreandpreparedata)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ds_def = dataset.get_definition()\n",
-        "ds_def = ds_def.keep_columns(['Survived','Pclass', 'Sex','SibSp', 'Parch', 'Fare'])\n",
-        "ds_def = ds_def.replace('Sex','male', 0)\n",
-        "ds_def = ds_def.replace('Sex','female', 1)\n",
-        "ds_def.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once you have cleaned your data, you can register your dataset in your workspace. \n",
-        "\n",
-        "Registering your dataset allows you to easily have access to your processed data and share it with other people in your organization using the same workspace. It can be accessed in any notebook or script that is connected to your workspace."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dataset = dataset.update_definition(ds_def, 'Cleaned Data')\n",
-        "\n",
-        "dataset.generate_profile(compute_target='local').get_result()\n",
-        "\n",
-        "dataset_name = 'clean_Titanic_tutorial'\n",
-        "dataset = dataset.register(workspace=workspace,\n",
-        "                           name=dataset_name,\n",
-        "                           description='training dataset',\n",
-        "                           tags = {'year':'2019', 'month':'Apr'},\n",
-        "                           exist_ok=True)\n",
-        "workspace.datasets"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also take a snapshot of your dataset. This makes for easily reproducing your data as it is in that moment. Even if you changed the definition of your dataset, or have data that refreshes regularly, you can always go back to your snapshot to compare. Since this snapshot is being created on a compute in your workspace, it may take a signficant amount of time to provision the compute before running the action itself."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(dataset.get_all_snapshots())\n",
-        "snapshot_name = 'train_snapshot'\n",
-        "\n",
-        "print(\"Compute target status\")\n",
-        "print(remote_compute_target.get_status().provisioning_state)\n",
-        "\n",
-        "snapshot = dataset.create_snapshot(snapshot_name=snapshot_name, \n",
-        "                                   compute_target=remote_compute_target, \n",
-        "                                   create_data_snapshot=True)\n",
-        "snapshot.wait_for_completion()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now that you have registered your dataset and created a snapshot, you can call up the dataset and it's snapshot to use it in your train.py script.\n",
-        "\n",
-        "The following code snippit will train your model locally using the train.py script."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Experiment, RunConfiguration\n",
-        "\n",
-        "experiment_name = 'training-datasets'\n",
-        "experiment = Experiment(workspace = workspace, name = experiment_name)\n",
-        "project_folder = './train-dataset/'\n",
-        "\n",
-        "# create a new RunConfig object\n",
-        "run_config = RunConfiguration()\n",
-        "\n",
-        "run_config.environment.python.user_managed_dependencies = True\n",
-        "\n",
-        "from azureml.core import Run\n",
-        "from azureml.core import ScriptRunConfig\n",
-        "\n",
-        "src = ScriptRunConfig(source_directory=project_folder, \n",
-        "                      script='train.py', \n",
-        "                      run_config=run_config) \n",
-        "run = experiment.submit(config=src)\n",
-        "run.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also use the same script with your dataset snapshot for your Pipeline's Python Script Step.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.pipeline.core import Pipeline, PipelineData\n",
-        "from azureml.pipeline.steps import PythonScriptStep\n",
-        "from azureml.data.data_reference import DataReference\n",
-        "\n",
-        "trainStep = PythonScriptStep(script_name=\"train.py\",\n",
-        "                             compute_target=remote_compute_target,\n",
-        "                             source_directory=project_folder)\n",
-        "\n",
-        "pipeline = Pipeline(workspace=workspace,\n",
-        "                    steps=trainStep)\n",
-        "\n",
-        "pipeline_run = experiment.submit(pipeline)\n",
-        "pipeline_run.wait_for_completion()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "During any point of your workflow, you can get a previous snapshot of your dataset and use that version in your pipeline to quickly see how different versions of your data can effect your model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "snapshot = dataset.get_snapshot(snapshot_name=snapshot_name)\n",
-        "snapshot.to_pandas_dataframe().head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can make changes to the dataset's definition without breaking the production model or the daily data pipeline. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can call get_definitions to see that there are several versions. After each change to a dataset's version, another one is added."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dataset.get_definitions()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dataset = Dataset.get(workspace=workspace, name=dataset_name)\n",
-        "ds_def = dataset.get_definition()\n",
-        "ds_def = ds_def.drop_columns(['Fare'])\n",
-        "dataset = dataset.update_definition(ds_def, 'Dropping Fare as PClass and Fare are strongly correlated')\n",
-        "dataset.generate_profile(compute_target='local').get_result()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Dataset definitions can be deprecated when usage is no longer recommended and a replacement is available. When a deprecated dataset definition is used in an AML Experimentation/Pipeline scenario, a warning message gets returned but execution will not be blocked. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Deprecate dataset definition 1 by the 2nd definition\n",
-        "ds_def = dataset.get_definition('1')\n",
-        "ds_def.deprecate(deprecate_by_dataset_id=dataset._id, deprecated_by_definition_version='2')\n",
-        "dataset.get_definitions()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Dataset definitions can be archived when definitions are not supposed to be used for any reasons (such as underlying data no longer available). When an archived dataset definition is used in an AML Experimentation/Pipeline scenario, execution will be blocked with error. No further actions can be performed on archived Dataset definitions, but the references will be kept intact. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Archive the deprecated dataset definition #1\n",
-        "ds_def = dataset.get_definition('1')\n",
-        "ds_def.archive()\n",
-        "dataset.get_definitions()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also reactivate any defition that you archived for later use."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ds_def = dataset.get_definition('1')\n",
-        "ds_def.reactivate()\n",
-        "dataset.get_definitions()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now delete the current snapshot name to clean up your resource's space."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dataset.delete_snapshot(snapshot_name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You have now finished using a dataset from start to finish of your experiment!"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/datasets/datasets-tutorial.png)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "cforbe"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/file-dataset-img-classification.ipynb b/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/file-dataset-img-classification.ipynb
new file mode 100644
index 00000000..d2d99569
--- /dev/null
+++ b/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/file-dataset-img-classification.ipynb
@@ -0,0 +1,716 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Train an image classification model with Azure Machine Learning\n",
+        " \n",
+        "This tutorial trains a simple logistic regression using the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset and [scikit-learn](http://scikit-learn.org) with Azure Machine Learning.  MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing a number from 0 to 9. The goal is to create a multi-class classifier to identify the digit a given image represents. \n",
+        "\n",
+        "Learn how to:\n",
+        "\n",
+        "> * Set up your development environment\n",
+        "> * Access and examine the data via AzureML FileDataset\n",
+        "> * Train a simple logistic regression model on a remote cluster\n",
+        "> * Review training results, find and register the best model\n",
+        "\n",
+        "## Prerequisites\n",
+        "\n",
+        "See prerequisites in the [Azure Machine Learning documentation](https://docs.microsoft.com/azure/machine-learning/service/tutorial-train-models-with-aml#prerequisites)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Set up your development environment\n",
+        "\n",
+        "All the setup for your development work can be accomplished in a Python notebook.  Setup includes:\n",
+        "\n",
+        "* Importing Python packages\n",
+        "* Connecting to a workspace to enable communication between your local computer and remote resources\n",
+        "* Creating an experiment to track all your runs\n",
+        "* Creating a remote compute target to use for training\n",
+        "\n",
+        "### Import packages\n",
+        "\n",
+        "Import Python packages you need in this session. Also display the Azure Machine Learning SDK version."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "check version"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "import azureml.core\n",
+        "from azureml.core import Workspace\n",
+        "\n",
+        "# check core SDK version number\n",
+        "print(\"Azure ML SDK Version: \", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Connect to workspace\n",
+        "\n",
+        "Create a workspace object from the existing workspace. `Workspace.from_config()` reads the file **config.json** and loads the details into an object named `ws`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "load workspace"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "# load workspace configuration from the config.json file in the current folder.\n",
+        "workspace = Workspace.from_config()\n",
+        "print(workspace.name, workspace.location, workspace.resource_group, workspace.location, sep='\\t')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create experiment\n",
+        "\n",
+        "Create an experiment to track the runs in your workspace. A workspace can have muliple experiments. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "create experiment"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "experiment_name = 'sklearn-mnist'\n",
+        "\n",
+        "from azureml.core import Experiment\n",
+        "exp = Experiment(workspace=workspace, name=experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create or Attach existing compute resource\n",
+        "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n",
+        "\n",
+        "**Creation of compute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace the code will skip the creation process."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "create mlc",
+          "amlcompute"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import AmlCompute\n",
+        "from azureml.core.compute import ComputeTarget\n",
+        "\n",
+        "# Choose a name for your cluster.\n",
+        "amlcompute_cluster_name = \"azureml-compute\"\n",
+        "\n",
+        "found = False\n",
+        "# Check if this compute target already exists in the workspace.\n",
+        "cts = workspace.compute_targets\n",
+        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
+        "    found = True\n",
+        "    print('Found existing compute target.')\n",
+        "    compute_target = cts[amlcompute_cluster_name]\n",
+        "\n",
+        "if not found:\n",
+        "    print('Creating a new compute target...')\n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
+        "                                                                #vm_priority = 'lowpriority', # optional\n",
+        "                                                                max_nodes = 6)\n",
+        "\n",
+        "    # Create the cluster.\\n\",\n",
+        "    compute_target = ComputeTarget.create(workspace, amlcompute_cluster_name, provisioning_config)\n",
+        "\n",
+        "print('Checking cluster status...')\n",
+        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
+        "\n",
+        "# For a more detailed view of current AmlCompute status, use get_status()."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You now have the necessary packages and compute resources to train a model in the cloud. \n",
+        "\n",
+        "## Explore data\n",
+        "\n",
+        "Before you train a model, you need to understand the data that you are using to train it.  You also need to copy the data into the cloud so it can be accessed by your cloud training environment.  In this section you learn how to:\n",
+        "\n",
+        "* Download the MNIST dataset\n",
+        "* Display some sample images\n",
+        "* Upload data to the cloud\n",
+        "\n",
+        "### Download the MNIST dataset\n",
+        "\n",
+        "Download the MNIST dataset and save the files into a `data` directory locally.  Images and labels for both training and testing are downloaded."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import urllib.request\n",
+        "\n",
+        "data_folder = os.path.join(os.getcwd(), 'data')\n",
+        "os.makedirs(data_folder, exist_ok=True)\n",
+        "\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz', filename=os.path.join(data_folder, 'train-images.gz'))\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz', filename=os.path.join(data_folder, 'train-labels.gz'))\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename=os.path.join(data_folder, 'test-images.gz'))\n",
+        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename=os.path.join(data_folder, 'test-labels.gz'))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Display some sample images\n",
+        "\n",
+        "Load the compressed files into `numpy` arrays. Then use `matplotlib` to plot 30 random images from the dataset with their labels above them. Note this step requires a `load_data` function that's included in an `utils.py` file. This file is included in the sample folder. Please make sure it is placed in the same folder as this notebook. The `load_data` function simply parses the compresse files into numpy arrays."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# make sure utils.py is in the same directory as this code\n",
+        "from utils import load_data\n",
+        "\n",
+        "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the model converge faster.\n",
+        "X_train = load_data(os.path.join(data_folder, 'train-images.gz'), False) / 255.0\n",
+        "X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / 255.0\n",
+        "y_train = load_data(os.path.join(data_folder, 'train-labels.gz'), True).reshape(-1)\n",
+        "y_test = load_data(os.path.join(data_folder, 'test-labels.gz'), True).reshape(-1)\n",
+        "\n",
+        "# now let's show some randomly chosen images from the traininng set.\n",
+        "count = 0\n",
+        "sample_size = 30\n",
+        "plt.figure(figsize = (16, 6))\n",
+        "for i in np.random.permutation(X_train.shape[0])[:sample_size]:\n",
+        "    count = count + 1\n",
+        "    plt.subplot(1, sample_size, count)\n",
+        "    plt.axhline('')\n",
+        "    plt.axvline('')\n",
+        "    plt.text(x=10, y=-10, s=y_train[i], fontsize=18)\n",
+        "    plt.imshow(X_train[i].reshape(28, 28), cmap=plt.cm.Greys)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now you have an idea of what these images look like and the expected prediction outcome.\n",
+        "\n",
+        "### Upload data to the cloud\n",
+        "\n",
+        "Now make the data accessible remotely by uploading that data from your local machine into Azure so it can be accessed for remote training. The datastore is a convenient construct associated with your workspace for you to upload/download data, and interact with it from your remote compute targets. It is backed by Azure blob storage account.\n",
+        "\n",
+        "The MNIST files are uploaded into a directory named `mnist` at the root of the datastore. See [access data from your datastores](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data) for more information."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "use datastore"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "datastore = workspace.get_default_datastore()\n",
+        "print(datastore.datastore_type, datastore.account_name, datastore.container_name)\n",
+        "\n",
+        "datastore.upload(src_dir=data_folder, target_path='mnist', overwrite=True, show_progress=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create a FileDataset\n",
+        "A FileDataset references single or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred. [Learn More](https://aka.ms/azureml/howto/createdatasets)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.dataset import Dataset\n",
+        "\n",
+        "datastore = workspace.get_default_datastore()\n",
+        "dataset = Dataset.File.from_files(path = [(datastore, 'mnist/')])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Use the `register()` method to register datasets to your workspace so they can be shared with others, reused across various experiments, and refered to by name in your training script."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "dataset = dataset.register(workspace = workspace,\n",
+        "                           name = 'mnist dataset',\n",
+        "                           description='training and test dataset',\n",
+        "                           create_new_version=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train on a remote cluster\n",
+        "\n",
+        "For this task, submit the job to the remote training cluster you set up earlier.  To submit a job you:\n",
+        "* Create a directory\n",
+        "* Create a training script\n",
+        "* Create an estimator object\n",
+        "* Submit the job \n",
+        "\n",
+        "### Create a directory\n",
+        "\n",
+        "Create a directory to deliver the necessary code from your computer to the remote resource."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "script_folder = os.path.join(os.getcwd(), \"sklearn-mnist\")\n",
+        "os.makedirs(script_folder, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a training script\n",
+        "\n",
+        "To submit the job to the cluster, first create a training script. Run the following code to create the training script called `train.py` in the directory you just created. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile $script_folder/train.py\n",
+        "\n",
+        "import argparse\n",
+        "import os\n",
+        "import numpy as np\n",
+        "\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.externals import joblib\n",
+        "\n",
+        "from azureml.core import Run, Dataset\n",
+        "from utils import load_data\n",
+        "from uuid import uuid4\n",
+        "\n",
+        "# let user feed in the regularization rate of the logistic regression model as an argument\n",
+        "parser = argparse.ArgumentParser()\n",
+        "parser.add_argument('--dataset-name', dest='ds_name', help='the name of dataset')\n",
+        "parser.add_argument('--regularization', type=float, dest='reg', default=0.01, help='regularization rate')\n",
+        "args = parser.parse_args()\n",
+        "\n",
+        "# get hold of the current run\n",
+        "run = Run.get_context()\n",
+        "\n",
+        "workspace = run.experiment.workspace\n",
+        "dataset_name = args.ds_name\n",
+        "dataset = Dataset.get_by_name(workspace=workspace, name=dataset_name)\n",
+        "\n",
+        "# create a folder on the compute that we will mount the dataset to\n",
+        "data_folder = '/tmp/mnist/{}'.format(uuid4())\n",
+        "os.makedirs(data_folder)\n",
+        "\n",
+        "with dataset.mount(data_folder):\n",
+        "    import glob\n",
+        "    X_train_path = glob.glob(os.path.join(data_folder, '**/train-images.gz'), recursive=True)[0]\n",
+        "    X_test_path = glob.glob(os.path.join(data_folder, '**/test-images.gz'), recursive=True)[0]\n",
+        "    y_train_path = glob.glob(os.path.join(data_folder, '**/train-labels.gz'), recursive=True)[0]\n",
+        "    y_test_path = glob.glob(os.path.join(data_folder, '**/test-labels.gz'), recursive=True)[0]\n",
+        "    # load train and test set into numpy arrays\n",
+        "    # note we scale the pixel intensity values to 0-1 (by dividing it with 255.0) so the model can converge faster.\n",
+        "    X_train = load_data(X_train_path, False) / 255.0\n",
+        "    X_test = load_data(X_test_path, False) / 255.0\n",
+        "    y_train = load_data(y_train_path, True).reshape(-1)\n",
+        "    y_test = load_data(y_test_path, True).reshape(-1)\n",
+        "    print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep = '\\n')\n",
+        "\n",
+        "    print('Train a logistic regression model with regularization rate of', args.reg)\n",
+        "    clf = LogisticRegression(C=1.0/args.reg, solver=\"liblinear\", multi_class=\"auto\", random_state=42)\n",
+        "    clf.fit(X_train, y_train)\n",
+        "\n",
+        "    print('Predict the test set')\n",
+        "    y_hat = clf.predict(X_test)\n",
+        "\n",
+        "    # calculate accuracy on the prediction\n",
+        "    acc = np.average(y_hat == y_test)\n",
+        "    print('Accuracy is', acc)\n",
+        "\n",
+        "    run.log('regularization rate', np.float(args.reg))\n",
+        "    run.log('accuracy', np.float(acc))\n",
+        "\n",
+        "    os.makedirs('outputs', exist_ok=True)\n",
+        "    # note file saved in the outputs folder is automatically uploaded into experiment record\n",
+        "    joblib.dump(value=clf, filename='outputs/sklearn_mnist_model.pkl')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Notice how the script gets data and saves models:\n",
+        "\n",
+        "+ The training script gets the mnist dataset registered with the workspace through the Run object, then uses the FileDataset to download file streams defined by it to a target path (data_folder) on the compute."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n",
+        "+ The training script saves your model into a directory named outputs. <br/>\n",
+        "`joblib.dump(value=clf, filename='outputs/sklearn_mnist_model.pkl')`<br/>\n",
+        "Anything written in this directory is automatically uploaded into your workspace. You'll access your model from this directory later in the tutorial."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The file `utils.py` is referenced from the training script to load the dataset correctly.  Copy this script into the script folder so that it can be accessed along with the training script on the remote resource."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import shutil\n",
+        "shutil.copy('utils.py', script_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create an estimator\n",
+        "\n",
+        "An estimator object is used to submit the run. Azure Machine Learning has pre-configured estimators for common machine learning frameworks, as well as generic Estimator. Create SKLearn estimator for scikit-learn model, by specifying\n",
+        "\n",
+        "* The name of the estimator object, `est`\n",
+        "* The directory that contains your scripts. All the files in this directory are uploaded into the cluster nodes for execution. \n",
+        "* The compute target.  In this case you will use the AmlCompute you created\n",
+        "* The training script name, train.py\n",
+        "* Parameters required from the training script \n",
+        "\n",
+        "In this tutorial, this target is AmlCompute. All files in the script folder are uploaded into the cluster nodes for execution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.environment import Environment\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "\n",
+        "env = Environment('my_env')\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk', 'pandas','scikit-learn','azureml-dataprep[pandas,fuse]==1.1.14'])\n",
+        "\n",
+        "env.python.conda_dependencies = cd"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "configure estimator"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.train.sklearn import SKLearn\n",
+        "\n",
+        "script_params = {\n",
+        "    '--dataset-name': 'mnist dataset',\n",
+        "    '--regularization': 0.5\n",
+        "}\n",
+        "\n",
+        "est = SKLearn(source_directory=script_folder,\n",
+        "              script_params=script_params,\n",
+        "              compute_target=compute_target,\n",
+        "              environment_definition = env,\n",
+        "              entry_script='train.py')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit the job to the cluster\n",
+        "\n",
+        "Run the experiment by submitting the estimator object. And you can navigate to Azure portal to monitor the run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "remote run",
+          "amlcompute",
+          "scikit-learn"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "run = exp.submit(config=est)\n",
+        "run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Since the call is asynchronous, it returns a **Preparing** or **Running** state as soon as the job is started.\n",
+        "\n",
+        "## Monitor a remote run\n",
+        "\n",
+        "In total, the first run takes **approximately 10 minutes**.\n",
+        "\n",
+        "Here is what's happening while you wait:\n",
+        "\n",
+        "- **Image creation**: A Docker image is created matching the Python environment specified by the estimator. The image is built and stored in the ACR (Azure Container Registry) associated with your workspace. Image creation and uploading takes **about 5 minutes**. \n",
+        "\n",
+        "  This stage happens once for each Python environment since the container is cached for subsequent runs.  During image creation, logs are streamed to the run history. You can monitor the image creation progress using these logs.\n",
+        "\n",
+        "- **Scaling**: If the remote cluster requires more nodes to execute the run than currently available, additional nodes are added automatically. Scaling typically takes **about 5 minutes.**\n",
+        "\n",
+        "- **Running**: In this stage, the necessary scripts and files are sent to the compute target, then data stores are mounted/copied, then the entry_script is run. While the job is running, stdout and the files in the ./logs directory are streamed to the run history. You can monitor the run's progress using these logs.\n",
+        "\n",
+        "- **Post-Processing**: The ./outputs directory of the run is copied over to the run history in your workspace so you can access these results.\n",
+        "\n",
+        "\n",
+        "You can check the progress of a running job in multiple ways. This tutorial uses a Jupyter widget as well as a `wait_for_completion` method. \n",
+        "\n",
+        "### Jupyter widget\n",
+        "\n",
+        "Watch the progress of the run with a Jupyter widget.  Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "use notebook widget"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "By the way, if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Get log results upon completion\n",
+        "\n",
+        "Model training happens in the background. You can use `wait_for_completion` to block and wait until the model has completed training before running more code. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "remote run",
+          "amlcompute",
+          "scikit-learn"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "# specify show_output to True for a verbose log\n",
+        "run.wait_for_completion(show_output=True) "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Display run results\n",
+        "\n",
+        "You now have a model trained on a remote cluster.  Retrieve all the metrics logged during the run, including the accuracy of the model:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "get metrics"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "print(run.get_metrics())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Register model\n",
+        "\n",
+        "The last step in the training script wrote the file `outputs/sklearn_mnist_model.pkl` in a directory named `outputs` in the VM of the cluster where the job is executed. `outputs` is a special directory in that all content in this  directory is automatically uploaded to your workspace.  This content appears in the run record in the experiment under your workspace. Hence, the model file is now also available in your workspace.\n",
+        "\n",
+        "You can see files associated with that run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "query history"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "print(run.get_file_names())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Register the model in the workspace so that you (or other collaborators) can later query, examine, and deploy this model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "register model from history"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "# register model \n",
+        "model = run.register_model(model_name='sklearn_mnist', model_path='outputs/sklearn_mnist_model.pkl')\n",
+        "print(model.name, model.id, model.version, sep='\\t')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/datasets/datasets-tutorial/filedatasets-tutorial.png)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "roastala"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.4"
+    },
+    "msauthor": "sihhu"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/tabular-dataset-tutorial.ipynb b/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/tabular-dataset-tutorial.ipynb
new file mode 100644
index 00000000..07d03723
--- /dev/null
+++ b/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/tabular-dataset-tutorial.ipynb
@@ -0,0 +1,312 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Tutorial: Learn how to use TabularDatasets in Azure Machine Learning"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this tutorial, you will learn how to use Azure Machine Learning Datasets to train a classification model with the Azure Machine Learning SDK for Python. You will:\n",
+        "\n",
+        "&#x2611; Setup a Python environment and import packages\n",
+        "\n",
+        "&#x2611; Load the Titanic data from your Azure Blob Storage. (The [original data](https://www.kaggle.com/c/titanic/data) can be found on Kaggle)\n",
+        "\n",
+        "&#x2611; Create and register a TabularDataset in your workspace\n",
+        "\n",
+        "&#x2611; Train a classification model using the TabularDataset"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Pre-requisites:\n",
+        "To create and work with datasets, you need:\n",
+        "* An Azure subscription. If you don\u00e2\u20ac\u2122t have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning service](https://aka.ms/AMLFree) today.\n",
+        "* An [Azure Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace)\n",
+        "* The [Azure Machine Learning SDK for Python installed](https://docs.microsoft.com/python/api/overview/azure/ml/install?view=azure-ml-py), which includes the azureml-datasets package.\n",
+        "\n",
+        "Data and train.py script to store in your Azure Blob Storage Account.\n",
+        " * [Titanic data](./train-dataset/Titanic.csv)\n",
+        " * [train.py](./train-dataset/train.py)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize a Workspace\n",
+        "\n",
+        "Initialize a workspace object from persisted configuration"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "from azureml.core import Workspace, Datastore, Dataset\n",
+        "\n",
+        "# Get existing workspace from config.json file in the same folder as the tutorial notebook\n",
+        "# You can download the config file from your workspace\n",
+        "workspace = Workspace.from_config()\n",
+        "print(workspace)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create a TabularDataset\n",
+        "\n",
+        "Datasets are categorized into various types based on how users consume them in training. In this tutorial, you will create and use a TabularDataset in training. A TabularDataset represents data in a tabular format by parsing the provided file or list of files. TabularDataset can be created from csv, tsv, parquet files, SQL query results etc. For the complete list, please visit our [documentation](https://aka.ms/tabulardataset-api-reference). It provides you with the ability to materialize the data into a pandas DataFrame."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "By creating a dataset, you create a reference to the data source location, along with a copy of its metadata. The data remains in  its existing location, so no extra storage cost is incurred.\n",
+        "\n",
+        "We will now upload the [Titanic data](./train-dataset/Titanic.csv) to the default datastore(blob) within your workspace.."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "datastore = workspace.get_default_datastore()\n",
+        "datastore.upload_files(files = ['./train-dataset/Titanic.csv'],\n",
+        "                       target_path = 'train-dataset/',\n",
+        "                       overwrite = True,\n",
+        "                       show_progress = True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Then we will create an unregistered TabularDataset pointing to the path in the datastore. We also support create a Dataset from multiple paths. [learn more](https://aka.ms/azureml/howto/createdatasets) "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, 'train-dataset/Titanic.csv')])\n",
+        "\n",
+        "#preview the first 3 rows of the dataset\n",
+        "dataset.take(3).to_pandas_dataframe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Use the `register()` method to register datasets to your workspace so they can be shared with others, reused across various experiments, and refered to by name in your training script."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "dataset = dataset.register(workspace = workspace,\n",
+        "                           name = 'titanic dataset',\n",
+        "                           description='training dataset')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create or Attach existing AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your training. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
+        "\n",
+        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
+        "\n",
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import AmlCompute\n",
+        "from azureml.core.compute import ComputeTarget\n",
+        "\n",
+        "# Choose a name for your cluster.\n",
+        "amlcompute_cluster_name = \"your cluster name\"\n",
+        "\n",
+        "found = False\n",
+        "# Check if this compute target already exists in the workspace.\n",
+        "cts = workspace.compute_targets\n",
+        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
+        "    found = True\n",
+        "    print('Found existing compute target.')\n",
+        "    compute_target = cts[amlcompute_cluster_name]\n",
+        "\n",
+        "if not found:\n",
+        "    print('Creating a new compute target...')\n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
+        "                                                                #vm_priority = 'lowpriority', # optional\n",
+        "                                                                max_nodes = 6)\n",
+        "\n",
+        "    # Create the cluster.\\n\",\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
+        "\n",
+        "print('Checking cluster status...')\n",
+        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
+        "\n",
+        "# For a more detailed view of current AmlCompute status, use get_status()."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create an Experiment\n",
+        "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "experiment_name = 'training-datasets'\n",
+        "experiment = Experiment(workspace = workspace, name = experiment_name)\n",
+        "project_folder = './train-dataset/'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Configure & Run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "import pkg_resources\n",
+        "\n",
+        "# create a new RunConfig object\n",
+        "conda_run_config = RunConfiguration(framework=\"python\")\n",
+        "\n",
+        "# Set compute target to AmlCompute\n",
+        "conda_run_config.target = compute_target\n",
+        "conda_run_config.environment.docker.enabled = True\n",
+        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
+        "\n",
+        "dprep_dependency = 'azureml-dataprep==' + pkg_resources.get_distribution(\"azureml-dataprep\").version\n",
+        "\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk', 'scikit-learn', 'pandas', dprep_dependency])\n",
+        "conda_run_config.environment.python.conda_dependencies = cd"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# create a new RunConfig object\n",
+        "run_config = RunConfiguration()\n",
+        "\n",
+        "run_config.environment.python.user_managed_dependencies = True\n",
+        "\n",
+        "from azureml.core import Run\n",
+        "from azureml.core import ScriptRunConfig\n",
+        "\n",
+        "src = ScriptRunConfig(source_directory=project_folder, \n",
+        "                      script='train.py', \n",
+        "                      run_config=conda_run_config) \n",
+        "run = experiment.submit(config=src)\n",
+        "run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## View run history details"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You have now finished using a dataset from start to finish of your experiment!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/datasets/datasets-tutorial/datasets-tutorial.png)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "cforbe"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/tabular-timeseries-dataset-filtering.ipynb b/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/tabular-timeseries-dataset-filtering.ipynb
new file mode 100644
index 00000000..0672e5d5
--- /dev/null
+++ b/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/tabular-timeseries-dataset-filtering.ipynb
@@ -0,0 +1,544 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Tabular Time Series Related API Demo with NOAA Weather Data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved. <br>\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this notebook, you will learn how to use the Tabular Time Series related API to filter the data by time windows for sample data uploaded to Azure blob storage. \n",
+        "\n",
+        "The detailed APIs to be demoed in this script are:\n",
+        "- Create Tabular Dataset instance\n",
+        "- Assign fine timestamp column and coarse timestamp column for Tabular Dataset to activate Time Series related APIs\n",
+        "- Clear fine timestamp column and coarse timestamp column\n",
+        "- Filter in data before a specific time\n",
+        "- Filter in data after a specific time\n",
+        "- Filter in data in a specific time range\n",
+        "- Filter in data for recent time range\n",
+        "\n",
+        "Besides above APIs, you'll also see:\n",
+        "- Create and load a Workspace\n",
+        "- Load National Oceanic & Atmospheric (NOAA) weather data into Azure blob storage\n",
+        "- Create and register NOAA weather data as a Tabular dataset\n",
+        "- Re-load Tabular Dataset from your Workspace"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Import Dependencies\n",
+        "\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, run the cells below to install the Azure Machine Learning Python SDK and create an Azure ML Workspace that's required for this demo."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prepare Environment"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Print out your version of the Azure ML Python SDK. Version 1.0.60 or above is required for TabularDataset with timeseries attribute. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "azureml.data.__version__"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Import Packages"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# import packages\n",
+        "import os\n",
+        "\n",
+        "import pandas as pd\n",
+        "\n",
+        "from calendar import monthrange\n",
+        "from datetime import datetime, timedelta\n",
+        "\n",
+        "from azureml.core import Dataset, Datastore, Workspace, Run\n",
+        "from azureml.opendatasets import NoaaIsdWeather"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Set up Configuraton and Create Azure ML Workspace\n",
+        "\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) first if you haven't already to establish your connection to the Azure ML Workspace."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws     = Workspace.from_config()\n",
+        "dstore = ws.get_default_datastore()\n",
+        "\n",
+        "dset_name = 'weather-data-florida'\n",
+        "\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, dstore.name, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load Data to Blob Storage\n",
+        "\n",
+        "This demo uses public NOAA weather data. You can replace this data with your own. The first cell below creates a Pandas Dataframe object with the first 6 months of 2019 NOAA weather data. The last cell saves the data to a CSV file and uploads the CSV file to Azure blob storage to the location specified in the datapath variable. Currently, the Dataset class only reads uploaded files from blob storage. \n",
+        "\n",
+        "**NOTE:** to reduce the size of data, we will only keep specific rows with a given stationName."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "target_years = [2019]\n",
+        "\n",
+        "for year in target_years:\n",
+        "    for month in range(1, 12+1):\n",
+        "        path = 'data/{}/{:02d}/'.format(year, month)\n",
+        "        \n",
+        "        try:  \n",
+        "            start = datetime(year, month, 1)\n",
+        "            end   = datetime(year, month, monthrange(year, month)[1]) + timedelta(days=1)\n",
+        "            isd   = NoaaIsdWeather(start, end).to_pandas_dataframe()\n",
+        "            isd   = isd[isd['stationName'].str.contains('FLORIDA', regex=True, na=False)]\n",
+        "            \n",
+        "            os.makedirs(path, exist_ok=True)\n",
+        "            isd.to_parquet(path + 'data.parquet')\n",
+        "        except Exception as e:\n",
+        "            print('Month {} in year {} likely has no data.\\n'.format(month, year))\n",
+        "            print('Exception: {}'.format(e))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Upload data to blob storage so it can be used as a Dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "dstore.upload('data', dset_name, overwrite=True, show_progress=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create & Register Tabular Dataset with time-series trait from Blob\n",
+        "\n",
+        "The API on Tabular datasets with time-series trait is specially designed to handle Tabular time-series data and time related operations more efficiently. By registering your time-series dataset, you are publishing your dataset to your workspace so that it is accessible to anyone with the same subscription id. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create Tabular Dataset instance from blob storage datapath.\n",
+        "\n",
+        "**TIP:** you can set virtual columns in the partition_format. I.e. if you partition the weather data by state and city, the path can be '/{STATE}/{CITY}/{coarse_time:yyy/MM}/data.parquet'. STATE and CITY would then appear as virtual columns in the dataset, allowing for efficient filtering by these grains. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "datastore_path = [(dstore, dset_name + '/*/*/data.parquet')]\n",
+        "dataset        = Dataset.Tabular.from_parquet_files(path=datastore_path, partition_format = dset_name + '/{coarse_time:yyyy/MM}/data.parquet')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Assign fine timestamp column for Tabular Dataset to activate Time Series related APIs. The column to be assigned should be a Date type, otherwise the assigning will fail."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# for this demo, leave out coarse_time so fine_grain_timestamp is used\n",
+        "tsd = dataset.with_timestamp_columns(fine_grain_timestamp='datetime') # , coarse_grain_timestamp='coarse_time')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Register the dataset for easy access from anywhere in Azure ML and to keep track of versions, lineage. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# register dataset to Workspace\n",
+        "registered_ds = tsd.register(ws, dset_name, create_new_version=True, description='Data for Tabular Dataset with time-series trait demo.', tags={ 'type': 'TabularDataset' })"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Reload the Dataset from Workspace"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# get dataset by dataset name\n",
+        "tsd = Dataset.get_by_name(ws, name=dset_name)\n",
+        "tsd.to_pandas_dataframe().head(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Filter Data by Time Windows\n",
+        "\n",
+        "Once your data has been loaded into the notebook, you can query by time using the time_before(), time_after(), time_between(), and time_recent() functions. You can also choose to drop or keep certain columns.  "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Before Time Input"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# select data that occurs before a specified date\n",
+        "tsd2 = tsd.time_before(datetime(2019, 6, 12))\n",
+        "tsd2.to_pandas_dataframe().tail(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## After Time Input"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# select data that occurs after a specified date\n",
+        "tsd2 = tsd.time_after(datetime(2019, 5, 30))\n",
+        "tsd2.to_pandas_dataframe().head(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Before & After Time Inputs\n",
+        "\n",
+        "You can chain time functions together."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**NOTE:** You must set the coarse_grain_timestamp to None to filter on the fine_grain_timestamp. The below cell will fail unless the second line is uncommented "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# select data that occurs within a given time range\n",
+        "#tsd  = tsd.with_timestamp_columns(fine_grain_timestamp='datetime', coarse_grain_timestamp=None)\n",
+        "tsd2 = tsd.time_after(datetime(2019, 1, 2)).time_before(datetime(2019, 1, 10))\n",
+        "tsd2.to_pandas_dataframe().head(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Time Range Input"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# another way to select data that occurs within a given time range\n",
+        "tsd2 = tsd.time_between(start_time=datetime(2019, 1, 31, 23, 59, 59), end_time=datetime(2019, 2, 7))\n",
+        "tsd2.to_pandas_dataframe().head(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Time Recent Input"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This function takes in a datetime.timedelta and returns a dataset containing the data from datetime.now()-timedelta() to datetime.now()."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "tsd2 = tsd.time_recent(timedelta(weeks=5, days=0))\n",
+        "tsd2.to_pandas_dataframe().head(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**NOTE:** This will return an empty dataframe there is no data within the last 2 days."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "tsd2 = tsd.time_recent(timedelta(days=2))\n",
+        "tsd2.to_pandas_dataframe().tail(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Drop Columns"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<font color=red>The columns to be dropped should NOT include timstamp columns.</font><br>Below operation will lead to exception."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    tsd2 = tsd.drop_columns(columns=['snowDepth', 'version', 'datetime'])\n",
+        "except Exception as e:\n",
+        "    print('Expected exception : {}'.format(str(e)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Drop will succeed if modify column list to exclude timestamp columns."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "tsd2 = tsd.drop_columns(columns=['snowDepth', 'version', 'upload_date'])\n",
+        "tsd2.take(5).to_pandas_dataframe().sort_values(by='datetime')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Keep Columns"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<font color=red>The columns to be kept should ALWAYS include timstamp columns.</font><br>Below operation will lead to exception."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    tsd2 = tsd.keep_columns(columns=['snowDepth'], validate=False)\n",
+        "except Exception as e:\n",
+        "    print('Expected exception : {}'.format(str(e)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Keep will succeed if modify column list to include timestamp columns."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "tsd2 = tsd.keep_columns(columns=['snowDepth', 'datetime', 'coarse_time'], validate=False)\n",
+        "tsd2.to_pandas_dataframe().tail()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Resetting Timestamp Columns"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Rules for reseting are:\n",
+        "- You cannot assign 'None' to fine_grain_timestamp while assign a valid column name to coarse_grain_timestamp because coarse_grain_timestamp is optional while fine_grain_timestamp is mandatory for Tabular time series data.\n",
+        "- If you assign 'None' to fine_grain_timestamp, then both fine_grain_timestamp and coarse_grain_timestamp will all be cleared.\n",
+        "- If you assign only 'None' to coarse_grain_timestamp, then only coarse_grain_timestamp will be cleared."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Illegal clearing, exception is expected.\n",
+        "try:\n",
+        "    tsd2 = tsd.with_timestamp_columns(fine_grain_timestamp=None, coarse_grain_timestamp='coarse_time')\n",
+        "except Exception as e:\n",
+        "    print('Cleaning not allowed because {}'.format(str(e)))\n",
+        "\n",
+        "# clear both\n",
+        "tsd2 = tsd.with_timestamp_columns(fine_grain_timestamp=None, coarse_grain_timestamp=None)\n",
+        "print('after clean both with None/None, timestamp columns are: {}'.format(tsd2.timestamp_columns))\n",
+        "\n",
+        "# clear coarse_grain_timestamp only and assign 'datetime' as fine timestamp column\n",
+        "tsd2 = tsd2.with_timestamp_columns(fine_grain_timestamp='datetime', coarse_grain_timestamp=None)\n",
+        "print('after clean coarse timestamp column, timestamp columns are: {}'.format(tsd2.timestamp_columns))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/datasets/datasets-tutorial/datasets-tutorial.png)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "copeters"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/train-dataset/Titanic.csv b/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/train-dataset/Titanic.csv
new file mode 100644
index 00000000..50801331
--- /dev/null
+++ b/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/train-dataset/Titanic.csv
@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",0,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",1,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",1,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",1,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",0,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",0,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",0,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",0,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",1,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",1,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",1,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",1,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",0,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",0,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",1,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",1,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",0,2,4,1,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",0,,0,0,244373,13,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",1,31,1,0,345763,18,,S
+20,1,3,"Masselmani, Mrs. Fatima",1,,0,0,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",0,35,0,0,239865,26,,S
+22,1,2,"Beesley, Mr. Lawrence",0,34,0,0,248698,13,D56,S
+23,1,3,"McGowan, Miss. Anna ""Annie""",1,15,0,0,330923,8.0292,,Q
+24,1,1,"Sloper, Mr. William Thompson",0,28,0,0,113788,35.5,A6,S
+25,0,3,"Palsson, Miss. Torborg Danira",1,8,3,1,349909,21.075,,S
+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",1,38,1,5,347077,31.3875,,S
+27,0,3,"Emir, Mr. Farred Chehab",0,,0,0,2631,7.225,,C
+28,0,1,"Fortune, Mr. Charles Alexander",0,19,3,2,19950,263,C23 C25 C27,S
+29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",1,,0,0,330959,7.8792,,Q
+30,0,3,"Todoroff, Mr. Lalio",0,,0,0,349216,7.8958,,S
+31,0,1,"Uruchurtu, Don. Manuel E",0,40,0,0,PC 17601,27.7208,,C
+32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",1,,1,0,PC 17569,146.5208,B78,C
+33,1,3,"Glynn, Miss. Mary Agatha",1,,0,0,335677,7.75,,Q
+34,0,2,"Wheadon, Mr. Edward H",0,66,0,0,C.A. 24579,10.5,,S
+35,0,1,"Meyer, Mr. Edgar Joseph",0,28,1,0,PC 17604,82.1708,,C
+36,0,1,"Holverson, Mr. Alexander Oskar",0,42,1,0,113789,52,,S
+37,1,3,"Mamee, Mr. Hanna",0,,0,0,2677,7.2292,,C
+38,0,3,"Cann, Mr. Ernest Charles",0,21,0,0,A./5. 2152,8.05,,S
+39,0,3,"Vander Planke, Miss. Augusta Maria",1,18,2,0,345764,18,,S
+40,1,3,"Nicola-Yarred, Miss. Jamila",1,14,1,0,2651,11.2417,,C
+41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",1,40,1,0,7546,9.475,,S
+42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",1,27,1,0,11668,21,,S
+43,0,3,"Kraeff, Mr. Theodor",0,,0,0,349253,7.8958,,C
+44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",1,3,1,2,SC/Paris 2123,41.5792,,C
+45,1,3,"Devaney, Miss. Margaret Delia",1,19,0,0,330958,7.8792,,Q
+46,0,3,"Rogers, Mr. William John",0,,0,0,S.C./A.4. 23567,8.05,,S
+47,0,3,"Lennon, Mr. Denis",0,,1,0,370371,15.5,,Q
+48,1,3,"O'Driscoll, Miss. Bridget",1,,0,0,14311,7.75,,Q
+49,0,3,"Samaan, Mr. Youssef",0,,2,0,2662,21.6792,,C
+50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",1,18,1,0,349237,17.8,,S
+51,0,3,"Panula, Master. Juha Niilo",0,7,4,1,3101295,39.6875,,S
+52,0,3,"Nosworthy, Mr. Richard Cater",0,21,0,0,A/4. 39886,7.8,,S
+53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",1,49,1,0,PC 17572,76.7292,D33,C
+54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",1,29,1,0,2926,26,,S
+55,0,1,"Ostby, Mr. Engelhart Cornelius",0,65,0,1,113509,61.9792,B30,C
+56,1,1,"Woolner, Mr. Hugh",0,,0,0,19947,35.5,C52,S
+57,1,2,"Rugg, Miss. Emily",1,21,0,0,C.A. 31026,10.5,,S
+58,0,3,"Novel, Mr. Mansouer",0,28.5,0,0,2697,7.2292,,C
+59,1,2,"West, Miss. Constance Mirium",1,5,1,2,C.A. 34651,27.75,,S
+60,0,3,"Goodwin, Master. William Frederick",0,11,5,2,CA 2144,46.9,,S
+61,0,3,"Sirayanian, Mr. Orsen",0,22,0,0,2669,7.2292,,C
+62,1,1,"Icard, Miss. Amelie",1,38,0,0,113572,80,B28,
+63,0,1,"Harris, Mr. Henry Birkhardt",0,45,1,0,36973,83.475,C83,S
+64,0,3,"Skoog, Master. Harald",0,4,3,2,347088,27.9,,S
+65,0,1,"Stewart, Mr. Albert A",0,,0,0,PC 17605,27.7208,,C
+66,1,3,"Moubarek, Master. Gerios",0,,1,1,2661,15.2458,,C
+67,1,2,"Nye, Mrs. (Elizabeth Ramell)",1,29,0,0,C.A. 29395,10.5,F33,S
+68,0,3,"Crease, Mr. Ernest James",0,19,0,0,S.P. 3464,8.1583,,S
+69,1,3,"Andersson, Miss. Erna Alexandra",1,17,4,2,3101281,7.925,,S
+70,0,3,"Kink, Mr. Vincenz",0,26,2,0,315151,8.6625,,S
+71,0,2,"Jenkin, Mr. Stephen Curnow",0,32,0,0,C.A. 33111,10.5,,S
+72,0,3,"Goodwin, Miss. Lillian Amy",1,16,5,2,CA 2144,46.9,,S
+73,0,2,"Hood, Mr. Ambrose Jr",0,21,0,0,S.O.C. 14879,73.5,,S
+74,0,3,"Chronopoulos, Mr. Apostolos",0,26,1,0,2680,14.4542,,C
+75,1,3,"Bing, Mr. Lee",0,32,0,0,1601,56.4958,,S
+76,0,3,"Moen, Mr. Sigurd Hansen",0,25,0,0,348123,7.65,F G73,S
+77,0,3,"Staneff, Mr. Ivan",0,,0,0,349208,7.8958,,S
+78,0,3,"Moutal, Mr. Rahamin Haim",0,,0,0,374746,8.05,,S
+79,1,2,"Caldwell, Master. Alden Gates",0,0.83,0,2,248738,29,,S
+80,1,3,"Dowdell, Miss. Elizabeth",1,30,0,0,364516,12.475,,S
+81,0,3,"Waelens, Mr. Achille",0,22,0,0,345767,9,,S
+82,1,3,"Sheerlinck, Mr. Jan Baptist",0,29,0,0,345779,9.5,,S
+83,1,3,"McDermott, Miss. Brigdet Delia",1,,0,0,330932,7.7875,,Q
+84,0,1,"Carrau, Mr. Francisco M",0,28,0,0,113059,47.1,,S
+85,1,2,"Ilett, Miss. Bertha",1,17,0,0,SO/C 14885,10.5,,S
+86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",1,33,3,0,3101278,15.85,,S
+87,0,3,"Ford, Mr. William Neal",0,16,1,3,W./C. 6608,34.375,,S
+88,0,3,"Slocovski, Mr. Selman Francis",0,,0,0,SOTON/OQ 392086,8.05,,S
+89,1,1,"Fortune, Miss. Mabel Helen",1,23,3,2,19950,263,C23 C25 C27,S
+90,0,3,"Celotti, Mr. Francesco",0,24,0,0,343275,8.05,,S
+91,0,3,"Christmann, Mr. Emil",0,29,0,0,343276,8.05,,S
+92,0,3,"Andreasson, Mr. Paul Edvin",0,20,0,0,347466,7.8542,,S
+93,0,1,"Chaffee, Mr. Herbert Fuller",0,46,1,0,W.E.P. 5734,61.175,E31,S
+94,0,3,"Dean, Mr. Bertram Frank",0,26,1,2,C.A. 2315,20.575,,S
+95,0,3,"Coxon, Mr. Daniel",0,59,0,0,364500,7.25,,S
+96,0,3,"Shorney, Mr. Charles Joseph",0,,0,0,374910,8.05,,S
+97,0,1,"Goldschmidt, Mr. George B",0,71,0,0,PC 17754,34.6542,A5,C
+98,1,1,"Greenfield, Mr. William Bertram",0,23,0,1,PC 17759,63.3583,D10 D12,C
+99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",1,34,0,1,231919,23,,S
+100,0,2,"Kantor, Mr. Sinai",0,34,1,0,244367,26,,S
+101,0,3,"Petranec, Miss. Matilda",1,28,0,0,349245,7.8958,,S
+102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",0,,0,0,349215,7.8958,,S
+103,0,1,"White, Mr. Richard Frasar",0,21,0,1,35281,77.2875,D26,S
+104,0,3,"Johansson, Mr. Gustaf Joel",0,33,0,0,7540,8.6542,,S
+105,0,3,"Gustafsson, Mr. Anders Vilhelm",0,37,2,0,3101276,7.925,,S
+106,0,3,"Mionoff, Mr. Stoytcho",0,28,0,0,349207,7.8958,,S
+107,1,3,"Salkjelsvik, Miss. Anna Kristine",1,21,0,0,343120,7.65,,S
+108,1,3,"Moss, Mr. Albert Johan",0,,0,0,312991,7.775,,S
+109,0,3,"Rekic, Mr. Tido",0,38,0,0,349249,7.8958,,S
+110,1,3,"Moran, Miss. Bertha",1,,1,0,371110,24.15,,Q
+111,0,1,"Porter, Mr. Walter Chamberlain",0,47,0,0,110465,52,C110,S
+112,0,3,"Zabour, Miss. Hileni",1,14.5,1,0,2665,14.4542,,C
+113,0,3,"Barton, Mr. David John",0,22,0,0,324669,8.05,,S
+114,0,3,"Jussila, Miss. Katriina",1,20,1,0,4136,9.825,,S
+115,0,3,"Attalah, Miss. Malake",1,17,0,0,2627,14.4583,,C
+116,0,3,"Pekoniemi, Mr. Edvard",0,21,0,0,STON/O 2. 3101294,7.925,,S
+117,0,3,"Connors, Mr. Patrick",0,70.5,0,0,370369,7.75,,Q
+118,0,2,"Turpin, Mr. William John Robert",0,29,1,0,11668,21,,S
+119,0,1,"Baxter, Mr. Quigg Edmond",0,24,0,1,PC 17558,247.5208,B58 B60,C
+120,0,3,"Andersson, Miss. Ellis Anna Maria",1,2,4,2,347082,31.275,,S
+121,0,2,"Hickman, Mr. Stanley George",0,21,2,0,S.O.C. 14879,73.5,,S
+122,0,3,"Moore, Mr. Leonard Charles",0,,0,0,A4. 54510,8.05,,S
+123,0,2,"Nasser, Mr. Nicholas",0,32.5,1,0,237736,30.0708,,C
+124,1,2,"Webber, Miss. Susan",1,32.5,0,0,27267,13,E101,S
+125,0,1,"White, Mr. Percival Wayland",0,54,0,1,35281,77.2875,D26,S
+126,1,3,"Nicola-Yarred, Master. Elias",0,12,1,0,2651,11.2417,,C
+127,0,3,"McMahon, Mr. Martin",0,,0,0,370372,7.75,,Q
+128,1,3,"Madsen, Mr. Fridtjof Arne",0,24,0,0,C 17369,7.1417,,S
+129,1,3,"Peter, Miss. Anna",1,,1,1,2668,22.3583,F E69,C
+130,0,3,"Ekstrom, Mr. Johan",0,45,0,0,347061,6.975,,S
+131,0,3,"Drazenoic, Mr. Jozef",0,33,0,0,349241,7.8958,,C
+132,0,3,"Coelho, Mr. Domingos Fernandeo",0,20,0,0,SOTON/O.Q. 3101307,7.05,,S
+133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",1,47,1,0,A/5. 3337,14.5,,S
+134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",1,29,1,0,228414,26,,S
+135,0,2,"Sobey, Mr. Samuel James Hayden",0,25,0,0,C.A. 29178,13,,S
+136,0,2,"Richard, Mr. Emile",0,23,0,0,SC/PARIS 2133,15.0458,,C
+137,1,1,"Newsom, Miss. Helen Monypeny",1,19,0,2,11752,26.2833,D47,S
+138,0,1,"Futrelle, Mr. Jacques Heath",0,37,1,0,113803,53.1,C123,S
+139,0,3,"Osen, Mr. Olaf Elon",0,16,0,0,7534,9.2167,,S
+140,0,1,"Giglio, Mr. Victor",0,24,0,0,PC 17593,79.2,B86,C
+141,0,3,"Boulos, Mrs. Joseph (Sultana)",1,,0,2,2678,15.2458,,C
+142,1,3,"Nysten, Miss. Anna Sofia",1,22,0,0,347081,7.75,,S
+143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",1,24,1,0,STON/O2. 3101279,15.85,,S
+144,0,3,"Burke, Mr. Jeremiah",0,19,0,0,365222,6.75,,Q
+145,0,2,"Andrew, Mr. Edgardo Samuel",0,18,0,0,231945,11.5,,S
+146,0,2,"Nicholls, Mr. Joseph Charles",0,19,1,1,C.A. 33112,36.75,,S
+147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",0,27,0,0,350043,7.7958,,S
+148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",1,9,2,2,W./C. 6608,34.375,,S
+149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",0,36.5,0,2,230080,26,F2,S
+150,0,2,"Byles, Rev. Thomas Roussel Davids",0,42,0,0,244310,13,,S
+151,0,2,"Bateman, Rev. Robert James",0,51,0,0,S.O.P. 1166,12.525,,S
+152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",1,22,1,0,113776,66.6,C2,S
+153,0,3,"Meo, Mr. Alfonzo",0,55.5,0,0,A.5. 11206,8.05,,S
+154,0,3,"van Billiard, Mr. Austin Blyler",0,40.5,0,2,A/5. 851,14.5,,S
+155,0,3,"Olsen, Mr. Ole Martin",0,,0,0,Fa 265302,7.3125,,S
+156,0,1,"Williams, Mr. Charles Duane",0,51,0,1,PC 17597,61.3792,,C
+157,1,3,"Gilnagh, Miss. Katherine ""Katie""",1,16,0,0,35851,7.7333,,Q
+158,0,3,"Corn, Mr. Harry",0,30,0,0,SOTON/OQ 392090,8.05,,S
+159,0,3,"Smiljanic, Mr. Mile",0,,0,0,315037,8.6625,,S
+160,0,3,"Sage, Master. Thomas Henry",0,,8,2,CA. 2343,69.55,,S
+161,0,3,"Cribb, Mr. John Hatfield",0,44,0,1,371362,16.1,,S
+162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",1,40,0,0,C.A. 33595,15.75,,S
+163,0,3,"Bengtsson, Mr. John Viktor",0,26,0,0,347068,7.775,,S
+164,0,3,"Calic, Mr. Jovo",0,17,0,0,315093,8.6625,,S
+165,0,3,"Panula, Master. Eino Viljami",0,1,4,1,3101295,39.6875,,S
+166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",0,9,0,2,363291,20.525,,S
+167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",1,,0,1,113505,55,E33,S
+168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",1,45,1,4,347088,27.9,,S
+169,0,1,"Baumann, Mr. John D",0,,0,0,PC 17318,25.925,,S
+170,0,3,"Ling, Mr. Lee",0,28,0,0,1601,56.4958,,S
+171,0,1,"Van der hoef, Mr. Wyckoff",0,61,0,0,111240,33.5,B19,S
+172,0,3,"Rice, Master. Arthur",0,4,4,1,382652,29.125,,Q
+173,1,3,"Johnson, Miss. Eleanor Ileen",1,1,1,1,347742,11.1333,,S
+174,0,3,"Sivola, Mr. Antti Wilhelm",0,21,0,0,STON/O 2. 3101280,7.925,,S
+175,0,1,"Smith, Mr. James Clinch",0,56,0,0,17764,30.6958,A7,C
+176,0,3,"Klasen, Mr. Klas Albin",0,18,1,1,350404,7.8542,,S
+177,0,3,"Lefebre, Master. Henry Forbes",0,,3,1,4133,25.4667,,S
+178,0,1,"Isham, Miss. Ann Elizabeth",1,50,0,0,PC 17595,28.7125,C49,C
+179,0,2,"Hale, Mr. Reginald",0,30,0,0,250653,13,,S
+180,0,3,"Leonard, Mr. Lionel",0,36,0,0,LINE,0,,S
+181,0,3,"Sage, Miss. Constance Gladys",1,,8,2,CA. 2343,69.55,,S
+182,0,2,"Pernot, Mr. Rene",0,,0,0,SC/PARIS 2131,15.05,,C
+183,0,3,"Asplund, Master. Clarence Gustaf Hugo",0,9,4,2,347077,31.3875,,S
+184,1,2,"Becker, Master. Richard F",0,1,2,1,230136,39,F4,S
+185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",1,4,0,2,315153,22.025,,S
+186,0,1,"Rood, Mr. Hugh Roscoe",0,,0,0,113767,50,A32,S
+187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",1,,1,0,370365,15.5,,Q
+188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",0,45,0,0,111428,26.55,,S
+189,0,3,"Bourke, Mr. John",0,40,1,1,364849,15.5,,Q
+190,0,3,"Turcin, Mr. Stjepan",0,36,0,0,349247,7.8958,,S
+191,1,2,"Pinsky, Mrs. (Rosa)",1,32,0,0,234604,13,,S
+192,0,2,"Carbines, Mr. William",0,19,0,0,28424,13,,S
+193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",1,19,1,0,350046,7.8542,,S
+194,1,2,"Navratil, Master. Michel M",0,3,1,1,230080,26,F2,S
+195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",1,44,0,0,PC 17610,27.7208,B4,C
+196,1,1,"Lurette, Miss. Elise",1,58,0,0,PC 17569,146.5208,B80,C
+197,0,3,"Mernagh, Mr. Robert",0,,0,0,368703,7.75,,Q
+198,0,3,"Olsen, Mr. Karl Siegwart Andreas",0,42,0,1,4579,8.4042,,S
+199,1,3,"Madigan, Miss. Margaret ""Maggie""",1,,0,0,370370,7.75,,Q
+200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",1,24,0,0,248747,13,,S
+201,0,3,"Vande Walle, Mr. Nestor Cyriel",0,28,0,0,345770,9.5,,S
+202,0,3,"Sage, Mr. Frederick",0,,8,2,CA. 2343,69.55,,S
+203,0,3,"Johanson, Mr. Jakob Alfred",0,34,0,0,3101264,6.4958,,S
+204,0,3,"Youseff, Mr. Gerious",0,45.5,0,0,2628,7.225,,C
+205,1,3,"Cohen, Mr. Gurshon ""Gus""",0,18,0,0,A/5 3540,8.05,,S
+206,0,3,"Strom, Miss. Telma Matilda",1,2,0,1,347054,10.4625,G6,S
+207,0,3,"Backstrom, Mr. Karl Alfred",0,32,1,0,3101278,15.85,,S
+208,1,3,"Albimona, Mr. Nassef Cassem",0,26,0,0,2699,18.7875,,C
+209,1,3,"Carr, Miss. Helen ""Ellen""",1,16,0,0,367231,7.75,,Q
+210,1,1,"Blank, Mr. Henry",0,40,0,0,112277,31,A31,C
+211,0,3,"Ali, Mr. Ahmed",0,24,0,0,SOTON/O.Q. 3101311,7.05,,S
+212,1,2,"Cameron, Miss. Clear Annie",1,35,0,0,F.C.C. 13528,21,,S
+213,0,3,"Perkin, Mr. John Henry",0,22,0,0,A/5 21174,7.25,,S
+214,0,2,"Givard, Mr. Hans Kristensen",0,30,0,0,250646,13,,S
+215,0,3,"Kiernan, Mr. Philip",0,,1,0,367229,7.75,,Q
+216,1,1,"Newell, Miss. Madeleine",1,31,1,0,35273,113.275,D36,C
+217,1,3,"Honkanen, Miss. Eliina",1,27,0,0,STON/O2. 3101283,7.925,,S
+218,0,2,"Jacobsohn, Mr. Sidney Samuel",0,42,1,0,243847,27,,S
+219,1,1,"Bazzani, Miss. Albina",1,32,0,0,11813,76.2917,D15,C
+220,0,2,"Harris, Mr. Walter",0,30,0,0,W/C 14208,10.5,,S
+221,1,3,"Sunderland, Mr. Victor Francis",0,16,0,0,SOTON/OQ 392089,8.05,,S
+222,0,2,"Bracken, Mr. James H",0,27,0,0,220367,13,,S
+223,0,3,"Green, Mr. George Henry",0,51,0,0,21440,8.05,,S
+224,0,3,"Nenkoff, Mr. Christo",0,,0,0,349234,7.8958,,S
+225,1,1,"Hoyt, Mr. Frederick Maxfield",0,38,1,0,19943,90,C93,S
+226,0,3,"Berglund, Mr. Karl Ivar Sven",0,22,0,0,PP 4348,9.35,,S
+227,1,2,"Mellors, Mr. William John",0,19,0,0,SW/PP 751,10.5,,S
+228,0,3,"Lovell, Mr. John Hall (""Henry"")",0,20.5,0,0,A/5 21173,7.25,,S
+229,0,2,"Fahlstrom, Mr. Arne Jonas",0,18,0,0,236171,13,,S
+230,0,3,"Lefebre, Miss. Mathilde",1,,3,1,4133,25.4667,,S
+231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",1,35,1,0,36973,83.475,C83,S
+232,0,3,"Larsson, Mr. Bengt Edvin",0,29,0,0,347067,7.775,,S
+233,0,2,"Sjostedt, Mr. Ernst Adolf",0,59,0,0,237442,13.5,,S
+234,1,3,"Asplund, Miss. Lillian Gertrud",1,5,4,2,347077,31.3875,,S
+235,0,2,"Leyson, Mr. Robert William Norman",0,24,0,0,C.A. 29566,10.5,,S
+236,0,3,"Harknett, Miss. Alice Phoebe",1,,0,0,W./C. 6609,7.55,,S
+237,0,2,"Hold, Mr. Stephen",0,44,1,0,26707,26,,S
+238,1,2,"Collyer, Miss. Marjorie ""Lottie""",1,8,0,2,C.A. 31921,26.25,,S
+239,0,2,"Pengelly, Mr. Frederick William",0,19,0,0,28665,10.5,,S
+240,0,2,"Hunt, Mr. George Henry",0,33,0,0,SCO/W 1585,12.275,,S
+241,0,3,"Zabour, Miss. Thamine",1,,1,0,2665,14.4542,,C
+242,1,3,"Murphy, Miss. Katherine ""Kate""",1,,1,0,367230,15.5,,Q
+243,0,2,"Coleridge, Mr. Reginald Charles",0,29,0,0,W./C. 14263,10.5,,S
+244,0,3,"Maenpaa, Mr. Matti Alexanteri",0,22,0,0,STON/O 2. 3101275,7.125,,S
+245,0,3,"Attalah, Mr. Sleiman",0,30,0,0,2694,7.225,,C
+246,0,1,"Minahan, Dr. William Edward",0,44,2,0,19928,90,C78,Q
+247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",1,25,0,0,347071,7.775,,S
+248,1,2,"Hamalainen, Mrs. William (Anna)",1,24,0,2,250649,14.5,,S
+249,1,1,"Beckwith, Mr. Richard Leonard",0,37,1,1,11751,52.5542,D35,S
+250,0,2,"Carter, Rev. Ernest Courtenay",0,54,1,0,244252,26,,S
+251,0,3,"Reed, Mr. James George",0,,0,0,362316,7.25,,S
+252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",1,29,1,1,347054,10.4625,G6,S
+253,0,1,"Stead, Mr. William Thomas",0,62,0,0,113514,26.55,C87,S
+254,0,3,"Lobb, Mr. William Arthur",0,30,1,0,A/5. 3336,16.1,,S
+255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",1,41,0,2,370129,20.2125,,S
+256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",1,29,0,2,2650,15.2458,,C
+257,1,1,"Thorne, Mrs. Gertrude Maybelle",1,,0,0,PC 17585,79.2,,C
+258,1,1,"Cherry, Miss. Gladys",1,30,0,0,110152,86.5,B77,S
+259,1,1,"Ward, Miss. Anna",1,35,0,0,PC 17755,512.3292,,C
+260,1,2,"Parrish, Mrs. (Lutie Davis)",1,50,0,1,230433,26,,S
+261,0,3,"Smith, Mr. Thomas",0,,0,0,384461,7.75,,Q
+262,1,3,"Asplund, Master. Edvin Rojj Felix",0,3,4,2,347077,31.3875,,S
+263,0,1,"Taussig, Mr. Emil",0,52,1,1,110413,79.65,E67,S
+264,0,1,"Harrison, Mr. William",0,40,0,0,112059,0,B94,S
+265,0,3,"Henry, Miss. Delia",1,,0,0,382649,7.75,,Q
+266,0,2,"Reeves, Mr. David",0,36,0,0,C.A. 17248,10.5,,S
+267,0,3,"Panula, Mr. Ernesti Arvid",0,16,4,1,3101295,39.6875,,S
+268,1,3,"Persson, Mr. Ernst Ulrik",0,25,1,0,347083,7.775,,S
+269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",1,58,0,1,PC 17582,153.4625,C125,S
+270,1,1,"Bissette, Miss. Amelia",1,35,0,0,PC 17760,135.6333,C99,S
+271,0,1,"Cairns, Mr. Alexander",0,,0,0,113798,31,,S
+272,1,3,"Tornquist, Mr. William Henry",0,25,0,0,LINE,0,,S
+273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",1,41,0,1,250644,19.5,,S
+274,0,1,"Natsch, Mr. Charles H",0,37,0,1,PC 17596,29.7,C118,C
+275,1,3,"Healy, Miss. Hanora ""Nora""",1,,0,0,370375,7.75,,Q
+276,1,1,"Andrews, Miss. Kornelia Theodosia",1,63,1,0,13502,77.9583,D7,S
+277,0,3,"Lindblom, Miss. Augusta Charlotta",1,45,0,0,347073,7.75,,S
+278,0,2,"Parkes, Mr. Francis ""Frank""",0,,0,0,239853,0,,S
+279,0,3,"Rice, Master. Eric",0,7,4,1,382652,29.125,,Q
+280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",1,35,1,1,C.A. 2673,20.25,,S
+281,0,3,"Duane, Mr. Frank",0,65,0,0,336439,7.75,,Q
+282,0,3,"Olsson, Mr. Nils Johan Goransson",0,28,0,0,347464,7.8542,,S
+283,0,3,"de Pelsmaeker, Mr. Alfons",0,16,0,0,345778,9.5,,S
+284,1,3,"Dorking, Mr. Edward Arthur",0,19,0,0,A/5. 10482,8.05,,S
+285,0,1,"Smith, Mr. Richard William",0,,0,0,113056,26,A19,S
+286,0,3,"Stankovic, Mr. Ivan",0,33,0,0,349239,8.6625,,C
+287,1,3,"de Mulder, Mr. Theodore",0,30,0,0,345774,9.5,,S
+288,0,3,"Naidenoff, Mr. Penko",0,22,0,0,349206,7.8958,,S
+289,1,2,"Hosono, Mr. Masabumi",0,42,0,0,237798,13,,S
+290,1,3,"Connolly, Miss. Kate",1,22,0,0,370373,7.75,,Q
+291,1,1,"Barber, Miss. Ellen ""Nellie""",1,26,0,0,19877,78.85,,S
+292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",1,19,1,0,11967,91.0792,B49,C
+293,0,2,"Levy, Mr. Rene Jacques",0,36,0,0,SC/Paris 2163,12.875,D,C
+294,0,3,"Haas, Miss. Aloisia",1,24,0,0,349236,8.85,,S
+295,0,3,"Mineff, Mr. Ivan",0,24,0,0,349233,7.8958,,S
+296,0,1,"Lewy, Mr. Ervin G",0,,0,0,PC 17612,27.7208,,C
+297,0,3,"Hanna, Mr. Mansour",0,23.5,0,0,2693,7.2292,,C
+298,0,1,"Allison, Miss. Helen Loraine",1,2,1,2,113781,151.55,C22 C26,S
+299,1,1,"Saalfeld, Mr. Adolphe",0,,0,0,19988,30.5,C106,S
+300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",1,50,0,1,PC 17558,247.5208,B58 B60,C
+301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",1,,0,0,9234,7.75,,Q
+302,1,3,"McCoy, Mr. Bernard",0,,2,0,367226,23.25,,Q
+303,0,3,"Johnson, Mr. William Cahoone Jr",0,19,0,0,LINE,0,,S
+304,1,2,"Keane, Miss. Nora A",1,,0,0,226593,12.35,E101,Q
+305,0,3,"Williams, Mr. Howard Hugh ""Harry""",0,,0,0,A/5 2466,8.05,,S
+306,1,1,"Allison, Master. Hudson Trevor",0,0.92,1,2,113781,151.55,C22 C26,S
+307,1,1,"Fleming, Miss. Margaret",1,,0,0,17421,110.8833,,C
+308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",1,17,1,0,PC 17758,108.9,C65,C
+309,0,2,"Abelson, Mr. Samuel",0,30,1,0,P/PP 3381,24,,C
+310,1,1,"Francatelli, Miss. Laura Mabel",1,30,0,0,PC 17485,56.9292,E36,C
+311,1,1,"Hays, Miss. Margaret Bechstein",1,24,0,0,11767,83.1583,C54,C
+312,1,1,"Ryerson, Miss. Emily Borie",1,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C
+313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",1,26,1,1,250651,26,,S
+314,0,3,"Hendekovic, Mr. Ignjac",0,28,0,0,349243,7.8958,,S
+315,0,2,"Hart, Mr. Benjamin",0,43,1,1,F.C.C. 13529,26.25,,S
+316,1,3,"Nilsson, Miss. Helmina Josefina",1,26,0,0,347470,7.8542,,S
+317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",1,24,1,0,244367,26,,S
+318,0,2,"Moraweck, Dr. Ernest",0,54,0,0,29011,14,,S
+319,1,1,"Wick, Miss. Mary Natalie",1,31,0,2,36928,164.8667,C7,S
+320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",1,40,1,1,16966,134.5,E34,C
+321,0,3,"Dennis, Mr. Samuel",0,22,0,0,A/5 21172,7.25,,S
+322,0,3,"Danoff, Mr. Yoto",0,27,0,0,349219,7.8958,,S
+323,1,2,"Slayter, Miss. Hilda Mary",1,30,0,0,234818,12.35,,Q
+324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",1,22,1,1,248738,29,,S
+325,0,3,"Sage, Mr. George John Jr",0,,8,2,CA. 2343,69.55,,S
+326,1,1,"Young, Miss. Marie Grice",1,36,0,0,PC 17760,135.6333,C32,C
+327,0,3,"Nysveen, Mr. Johan Hansen",0,61,0,0,345364,6.2375,,S
+328,1,2,"Ball, Mrs. (Ada E Hall)",1,36,0,0,28551,13,D,S
+329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",1,31,1,1,363291,20.525,,S
+330,1,1,"Hippach, Miss. Jean Gertrude",1,16,0,1,111361,57.9792,B18,C
+331,1,3,"McCoy, Miss. Agnes",1,,2,0,367226,23.25,,Q
+332,0,1,"Partner, Mr. Austen",0,45.5,0,0,113043,28.5,C124,S
+333,0,1,"Graham, Mr. George Edward",0,38,0,1,PC 17582,153.4625,C91,S
+334,0,3,"Vander Planke, Mr. Leo Edmondus",0,16,2,0,345764,18,,S
+335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",1,,1,0,PC 17611,133.65,,S
+336,0,3,"Denkoff, Mr. Mitto",0,,0,0,349225,7.8958,,S
+337,0,1,"Pears, Mr. Thomas Clinton",0,29,1,0,113776,66.6,C2,S
+338,1,1,"Burns, Miss. Elizabeth Margaret",1,41,0,0,16966,134.5,E40,C
+339,1,3,"Dahl, Mr. Karl Edwart",0,45,0,0,7598,8.05,,S
+340,0,1,"Blackwell, Mr. Stephen Weart",0,45,0,0,113784,35.5,T,S
+341,1,2,"Navratil, Master. Edmond Roger",0,2,1,1,230080,26,F2,S
+342,1,1,"Fortune, Miss. Alice Elizabeth",1,24,3,2,19950,263,C23 C25 C27,S
+343,0,2,"Collander, Mr. Erik Gustaf",0,28,0,0,248740,13,,S
+344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",0,25,0,0,244361,13,,S
+345,0,2,"Fox, Mr. Stanley Hubert",0,36,0,0,229236,13,,S
+346,1,2,"Brown, Miss. Amelia ""Mildred""",1,24,0,0,248733,13,F33,S
+347,1,2,"Smith, Miss. Marion Elsie",1,40,0,0,31418,13,,S
+348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",1,,1,0,386525,16.1,,S
+349,1,3,"Coutts, Master. William Loch ""William""",0,3,1,1,C.A. 37671,15.9,,S
+350,0,3,"Dimic, Mr. Jovan",0,42,0,0,315088,8.6625,,S
+351,0,3,"Odahl, Mr. Nils Martin",0,23,0,0,7267,9.225,,S
+352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",0,,0,0,113510,35,C128,S
+353,0,3,"Elias, Mr. Tannous",0,15,1,1,2695,7.2292,,C
+354,0,3,"Arnold-Franchi, Mr. Josef",0,25,1,0,349237,17.8,,S
+355,0,3,"Yousif, Mr. Wazli",0,,0,0,2647,7.225,,C
+356,0,3,"Vanden Steen, Mr. Leo Peter",0,28,0,0,345783,9.5,,S
+357,1,1,"Bowerman, Miss. Elsie Edith",1,22,0,1,113505,55,E33,S
+358,0,2,"Funk, Miss. Annie Clemmer",1,38,0,0,237671,13,,S
+359,1,3,"McGovern, Miss. Mary",1,,0,0,330931,7.8792,,Q
+360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",1,,0,0,330980,7.8792,,Q
+361,0,3,"Skoog, Mr. Wilhelm",0,40,1,4,347088,27.9,,S
+362,0,2,"del Carlo, Mr. Sebastiano",0,29,1,0,SC/PARIS 2167,27.7208,,C
+363,0,3,"Barbara, Mrs. (Catherine David)",1,45,0,1,2691,14.4542,,C
+364,0,3,"Asim, Mr. Adola",0,35,0,0,SOTON/O.Q. 3101310,7.05,,S
+365,0,3,"O'Brien, Mr. Thomas",0,,1,0,370365,15.5,,Q
+366,0,3,"Adahl, Mr. Mauritz Nils Martin",0,30,0,0,C 7076,7.25,,S
+367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",1,60,1,0,110813,75.25,D37,C
+368,1,3,"Moussa, Mrs. (Mantoura Boulos)",1,,0,0,2626,7.2292,,C
+369,1,3,"Jermyn, Miss. Annie",1,,0,0,14313,7.75,,Q
+370,1,1,"Aubart, Mme. Leontine Pauline",1,24,0,0,PC 17477,69.3,B35,C
+371,1,1,"Harder, Mr. George Achilles",0,25,1,0,11765,55.4417,E50,C
+372,0,3,"Wiklund, Mr. Jakob Alfred",0,18,1,0,3101267,6.4958,,S
+373,0,3,"Beavan, Mr. William Thomas",0,19,0,0,323951,8.05,,S
+374,0,1,"Ringhini, Mr. Sante",0,22,0,0,PC 17760,135.6333,,C
+375,0,3,"Palsson, Miss. Stina Viola",1,3,3,1,349909,21.075,,S
+376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",1,,1,0,PC 17604,82.1708,,C
+377,1,3,"Landergren, Miss. Aurora Adelia",1,22,0,0,C 7077,7.25,,S
+378,0,1,"Widener, Mr. Harry Elkins",0,27,0,2,113503,211.5,C82,C
+379,0,3,"Betros, Mr. Tannous",0,20,0,0,2648,4.0125,,C
+380,0,3,"Gustafsson, Mr. Karl Gideon",0,19,0,0,347069,7.775,,S
+381,1,1,"Bidois, Miss. Rosalie",1,42,0,0,PC 17757,227.525,,C
+382,1,3,"Nakid, Miss. Maria (""Mary"")",1,1,0,2,2653,15.7417,,C
+383,0,3,"Tikkanen, Mr. Juho",0,32,0,0,STON/O 2. 3101293,7.925,,S
+384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",1,35,1,0,113789,52,,S
+385,0,3,"Plotcharsky, Mr. Vasil",0,,0,0,349227,7.8958,,S
+386,0,2,"Davies, Mr. Charles Henry",0,18,0,0,S.O.C. 14879,73.5,,S
+387,0,3,"Goodwin, Master. Sidney Leonard",0,1,5,2,CA 2144,46.9,,S
+388,1,2,"Buss, Miss. Kate",1,36,0,0,27849,13,,S
+389,0,3,"Sadlier, Mr. Matthew",0,,0,0,367655,7.7292,,Q
+390,1,2,"Lehmann, Miss. Bertha",1,17,0,0,SC 1748,12,,C
+391,1,1,"Carter, Mr. William Ernest",0,36,1,2,113760,120,B96 B98,S
+392,1,3,"Jansson, Mr. Carl Olof",0,21,0,0,350034,7.7958,,S
+393,0,3,"Gustafsson, Mr. Johan Birger",0,28,2,0,3101277,7.925,,S
+394,1,1,"Newell, Miss. Marjorie",1,23,1,0,35273,113.275,D36,C
+395,1,3,"Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)",1,24,0,2,PP 9549,16.7,G6,S
+396,0,3,"Johansson, Mr. Erik",0,22,0,0,350052,7.7958,,S
+397,0,3,"Olsson, Miss. Elina",1,31,0,0,350407,7.8542,,S
+398,0,2,"McKane, Mr. Peter David",0,46,0,0,28403,26,,S
+399,0,2,"Pain, Dr. Alfred",0,23,0,0,244278,10.5,,S
+400,1,2,"Trout, Mrs. William H (Jessie L)",1,28,0,0,240929,12.65,,S
+401,1,3,"Niskanen, Mr. Juha",0,39,0,0,STON/O 2. 3101289,7.925,,S
+402,0,3,"Adams, Mr. John",0,26,0,0,341826,8.05,,S
+403,0,3,"Jussila, Miss. Mari Aina",1,21,1,0,4137,9.825,,S
+404,0,3,"Hakkarainen, Mr. Pekka Pietari",0,28,1,0,STON/O2. 3101279,15.85,,S
+405,0,3,"Oreskovic, Miss. Marija",1,20,0,0,315096,8.6625,,S
+406,0,2,"Gale, Mr. Shadrach",0,34,1,0,28664,21,,S
+407,0,3,"Widegren, Mr. Carl/Charles Peter",0,51,0,0,347064,7.75,,S
+408,1,2,"Richards, Master. William Rowe",0,3,1,1,29106,18.75,,S
+409,0,3,"Birkeland, Mr. Hans Martin Monsen",0,21,0,0,312992,7.775,,S
+410,0,3,"Lefebre, Miss. Ida",1,,3,1,4133,25.4667,,S
+411,0,3,"Sdycoff, Mr. Todor",0,,0,0,349222,7.8958,,S
+412,0,3,"Hart, Mr. Henry",0,,0,0,394140,6.8583,,Q
+413,1,1,"Minahan, Miss. Daisy E",1,33,1,0,19928,90,C78,Q
+414,0,2,"Cunningham, Mr. Alfred Fleming",0,,0,0,239853,0,,S
+415,1,3,"Sundman, Mr. Johan Julian",0,44,0,0,STON/O 2. 3101269,7.925,,S
+416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",1,,0,0,343095,8.05,,S
+417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",1,34,1,1,28220,32.5,,S
+418,1,2,"Silven, Miss. Lyyli Karoliina",1,18,0,2,250652,13,,S
+419,0,2,"Matthews, Mr. William John",0,30,0,0,28228,13,,S
+420,0,3,"Van Impe, Miss. Catharina",1,10,0,2,345773,24.15,,S
+421,0,3,"Gheorgheff, Mr. Stanio",0,,0,0,349254,7.8958,,C
+422,0,3,"Charters, Mr. David",0,21,0,0,A/5. 13032,7.7333,,Q
+423,0,3,"Zimmerman, Mr. Leo",0,29,0,0,315082,7.875,,S
+424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",1,28,1,1,347080,14.4,,S
+425,0,3,"Rosblom, Mr. Viktor Richard",0,18,1,1,370129,20.2125,,S
+426,0,3,"Wiseman, Mr. Phillippe",0,,0,0,A/4. 34244,7.25,,S
+427,1,2,"Clarke, Mrs. Charles V (Ada Maria Winfield)",1,28,1,0,2003,26,,S
+428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",1,19,0,0,250655,26,,S
+429,0,3,"Flynn, Mr. James",0,,0,0,364851,7.75,,Q
+430,1,3,"Pickard, Mr. Berk (Berk Trembisky)",0,32,0,0,SOTON/O.Q. 392078,8.05,E10,S
+431,1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",0,28,0,0,110564,26.55,C52,S
+432,1,3,"Thorneycroft, Mrs. Percival (Florence Kate White)",1,,1,0,376564,16.1,,S
+433,1,2,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",1,42,1,0,SC/AH 3085,26,,S
+434,0,3,"Kallio, Mr. Nikolai Erland",0,17,0,0,STON/O 2. 3101274,7.125,,S
+435,0,1,"Silvey, Mr. William Baird",0,50,1,0,13507,55.9,E44,S
+436,1,1,"Carter, Miss. Lucile Polk",1,14,1,2,113760,120,B96 B98,S
+437,0,3,"Ford, Miss. Doolina Margaret ""Daisy""",1,21,2,2,W./C. 6608,34.375,,S
+438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",1,24,2,3,29106,18.75,,S
+439,0,1,"Fortune, Mr. Mark",0,64,1,4,19950,263,C23 C25 C27,S
+440,0,2,"Kvillner, Mr. Johan Henrik Johannesson",0,31,0,0,C.A. 18723,10.5,,S
+441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",1,45,1,1,F.C.C. 13529,26.25,,S
+442,0,3,"Hampe, Mr. Leon",0,20,0,0,345769,9.5,,S
+443,0,3,"Petterson, Mr. Johan Emil",0,25,1,0,347076,7.775,,S
+444,1,2,"Reynaldo, Ms. Encarnacion",1,28,0,0,230434,13,,S
+445,1,3,"Johannesen-Bratthammer, Mr. Bernt",0,,0,0,65306,8.1125,,S
+446,1,1,"Dodge, Master. Washington",0,4,0,2,33638,81.8583,A34,S
+447,1,2,"Mellinger, Miss. Madeleine Violet",1,13,0,1,250644,19.5,,S
+448,1,1,"Seward, Mr. Frederic Kimber",0,34,0,0,113794,26.55,,S
+449,1,3,"Baclini, Miss. Marie Catherine",1,5,2,1,2666,19.2583,,C
+450,1,1,"Peuchen, Major. Arthur Godfrey",0,52,0,0,113786,30.5,C104,S
+451,0,2,"West, Mr. Edwy Arthur",0,36,1,2,C.A. 34651,27.75,,S
+452,0,3,"Hagland, Mr. Ingvald Olai Olsen",0,,1,0,65303,19.9667,,S
+453,0,1,"Foreman, Mr. Benjamin Laventall",0,30,0,0,113051,27.75,C111,C
+454,1,1,"Goldenberg, Mr. Samuel L",0,49,1,0,17453,89.1042,C92,C
+455,0,3,"Peduzzi, Mr. Joseph",0,,0,0,A/5 2817,8.05,,S
+456,1,3,"Jalsevac, Mr. Ivan",0,29,0,0,349240,7.8958,,C
+457,0,1,"Millet, Mr. Francis Davis",0,65,0,0,13509,26.55,E38,S
+458,1,1,"Kenyon, Mrs. Frederick R (Marion)",1,,1,0,17464,51.8625,D21,S
+459,1,2,"Toomey, Miss. Ellen",1,50,0,0,F.C.C. 13531,10.5,,S
+460,0,3,"O'Connor, Mr. Maurice",0,,0,0,371060,7.75,,Q
+461,1,1,"Anderson, Mr. Harry",0,48,0,0,19952,26.55,E12,S
+462,0,3,"Morley, Mr. William",0,34,0,0,364506,8.05,,S
+463,0,1,"Gee, Mr. Arthur H",0,47,0,0,111320,38.5,E63,S
+464,0,2,"Milling, Mr. Jacob Christian",0,48,0,0,234360,13,,S
+465,0,3,"Maisner, Mr. Simon",0,,0,0,A/S 2816,8.05,,S
+466,0,3,"Goncalves, Mr. Manuel Estanslas",0,38,0,0,SOTON/O.Q. 3101306,7.05,,S
+467,0,2,"Campbell, Mr. William",0,,0,0,239853,0,,S
+468,0,1,"Smart, Mr. John Montgomery",0,56,0,0,113792,26.55,,S
+469,0,3,"Scanlan, Mr. James",0,,0,0,36209,7.725,,Q
+470,1,3,"Baclini, Miss. Helene Barbara",1,0.75,2,1,2666,19.2583,,C
+471,0,3,"Keefe, Mr. Arthur",0,,0,0,323592,7.25,,S
+472,0,3,"Cacic, Mr. Luka",0,38,0,0,315089,8.6625,,S
+473,1,2,"West, Mrs. Edwy Arthur (Ada Mary Worth)",1,33,1,2,C.A. 34651,27.75,,S
+474,1,2,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",1,23,0,0,SC/AH Basle 541,13.7917,D,C
+475,0,3,"Strandberg, Miss. Ida Sofia",1,22,0,0,7553,9.8375,,S
+476,0,1,"Clifford, Mr. George Quincy",0,,0,0,110465,52,A14,S
+477,0,2,"Renouf, Mr. Peter Henry",0,34,1,0,31027,21,,S
+478,0,3,"Braund, Mr. Lewis Richard",0,29,1,0,3460,7.0458,,S
+479,0,3,"Karlsson, Mr. Nils August",0,22,0,0,350060,7.5208,,S
+480,1,3,"Hirvonen, Miss. Hildur E",1,2,0,1,3101298,12.2875,,S
+481,0,3,"Goodwin, Master. Harold Victor",0,9,5,2,CA 2144,46.9,,S
+482,0,2,"Frost, Mr. Anthony Wood ""Archie""",0,,0,0,239854,0,,S
+483,0,3,"Rouse, Mr. Richard Henry",0,50,0,0,A/5 3594,8.05,,S
+484,1,3,"Turkula, Mrs. (Hedwig)",1,63,0,0,4134,9.5875,,S
+485,1,1,"Bishop, Mr. Dickinson H",0,25,1,0,11967,91.0792,B49,C
+486,0,3,"Lefebre, Miss. Jeannie",1,,3,1,4133,25.4667,,S
+487,1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",1,35,1,0,19943,90,C93,S
+488,0,1,"Kent, Mr. Edward Austin",0,58,0,0,11771,29.7,B37,C
+489,0,3,"Somerton, Mr. Francis William",0,30,0,0,A.5. 18509,8.05,,S
+490,1,3,"Coutts, Master. Eden Leslie ""Neville""",0,9,1,1,C.A. 37671,15.9,,S
+491,0,3,"Hagland, Mr. Konrad Mathias Reiersen",0,,1,0,65304,19.9667,,S
+492,0,3,"Windelov, Mr. Einar",0,21,0,0,SOTON/OQ 3101317,7.25,,S
+493,0,1,"Molson, Mr. Harry Markland",0,55,0,0,113787,30.5,C30,S
+494,0,1,"Artagaveytia, Mr. Ramon",0,71,0,0,PC 17609,49.5042,,C
+495,0,3,"Stanley, Mr. Edward Roland",0,21,0,0,A/4 45380,8.05,,S
+496,0,3,"Yousseff, Mr. Gerious",0,,0,0,2627,14.4583,,C
+497,1,1,"Eustis, Miss. Elizabeth Mussey",1,54,1,0,36947,78.2667,D20,C
+498,0,3,"Shellard, Mr. Frederick William",0,,0,0,C.A. 6212,15.1,,S
+499,0,1,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",1,25,1,2,113781,151.55,C22 C26,S
+500,0,3,"Svensson, Mr. Olof",0,24,0,0,350035,7.7958,,S
+501,0,3,"Calic, Mr. Petar",0,17,0,0,315086,8.6625,,S
+502,0,3,"Canavan, Miss. Mary",1,21,0,0,364846,7.75,,Q
+503,0,3,"O'Sullivan, Miss. Bridget Mary",1,,0,0,330909,7.6292,,Q
+504,0,3,"Laitinen, Miss. Kristina Sofia",1,37,0,0,4135,9.5875,,S
+505,1,1,"Maioni, Miss. Roberta",1,16,0,0,110152,86.5,B79,S
+506,0,1,"Penasco y Castellana, Mr. Victor de Satode",0,18,1,0,PC 17758,108.9,C65,C
+507,1,2,"Quick, Mrs. Frederick Charles (Jane Richards)",1,33,0,2,26360,26,,S
+508,1,1,"Bradley, Mr. George (""George Arthur Brayton"")",0,,0,0,111427,26.55,,S
+509,0,3,"Olsen, Mr. Henry Margido",0,28,0,0,C 4001,22.525,,S
+510,1,3,"Lang, Mr. Fang",0,26,0,0,1601,56.4958,,S
+511,1,3,"Daly, Mr. Eugene Patrick",0,29,0,0,382651,7.75,,Q
+512,0,3,"Webber, Mr. James",0,,0,0,SOTON/OQ 3101316,8.05,,S
+513,1,1,"McGough, Mr. James Robert",0,36,0,0,PC 17473,26.2875,E25,S
+514,1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",1,54,1,0,PC 17603,59.4,,C
+515,0,3,"Coleff, Mr. Satio",0,24,0,0,349209,7.4958,,S
+516,0,1,"Walker, Mr. William Anderson",0,47,0,0,36967,34.0208,D46,S
+517,1,2,"Lemore, Mrs. (Amelia Milley)",1,34,0,0,C.A. 34260,10.5,F33,S
+518,0,3,"Ryan, Mr. Patrick",0,,0,0,371110,24.15,,Q
+519,1,2,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",1,36,1,0,226875,26,,S
+520,0,3,"Pavlovic, Mr. Stefo",0,32,0,0,349242,7.8958,,S
+521,1,1,"Perreault, Miss. Anne",1,30,0,0,12749,93.5,B73,S
+522,0,3,"Vovk, Mr. Janko",0,22,0,0,349252,7.8958,,S
+523,0,3,"Lahoud, Mr. Sarkis",0,,0,0,2624,7.225,,C
+524,1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",1,44,0,1,111361,57.9792,B18,C
+525,0,3,"Kassem, Mr. Fared",0,,0,0,2700,7.2292,,C
+526,0,3,"Farrell, Mr. James",0,40.5,0,0,367232,7.75,,Q
+527,1,2,"Ridsdale, Miss. Lucy",1,50,0,0,W./C. 14258,10.5,,S
+528,0,1,"Farthing, Mr. John",0,,0,0,PC 17483,221.7792,C95,S
+529,0,3,"Salonen, Mr. Johan Werner",0,39,0,0,3101296,7.925,,S
+530,0,2,"Hocking, Mr. Richard George",0,23,2,1,29104,11.5,,S
+531,1,2,"Quick, Miss. Phyllis May",1,2,1,1,26360,26,,S
+532,0,3,"Toufik, Mr. Nakli",0,,0,0,2641,7.2292,,C
+533,0,3,"Elias, Mr. Joseph Jr",0,17,1,1,2690,7.2292,,C
+534,1,3,"Peter, Mrs. Catherine (Catherine Rizk)",1,,0,2,2668,22.3583,,C
+535,0,3,"Cacic, Miss. Marija",1,30,0,0,315084,8.6625,,S
+536,1,2,"Hart, Miss. Eva Miriam",1,7,0,2,F.C.C. 13529,26.25,,S
+537,0,1,"Butt, Major. Archibald Willingham",0,45,0,0,113050,26.55,B38,S
+538,1,1,"LeRoy, Miss. Bertha",1,30,0,0,PC 17761,106.425,,C
+539,0,3,"Risien, Mr. Samuel Beard",0,,0,0,364498,14.5,,S
+540,1,1,"Frolicher, Miss. Hedwig Margaritha",1,22,0,2,13568,49.5,B39,C
+541,1,1,"Crosby, Miss. Harriet R",1,36,0,2,WE/P 5735,71,B22,S
+542,0,3,"Andersson, Miss. Ingeborg Constanzia",1,9,4,2,347082,31.275,,S
+543,0,3,"Andersson, Miss. Sigrid Elisabeth",1,11,4,2,347082,31.275,,S
+544,1,2,"Beane, Mr. Edward",0,32,1,0,2908,26,,S
+545,0,1,"Douglas, Mr. Walter Donald",0,50,1,0,PC 17761,106.425,C86,C
+546,0,1,"Nicholson, Mr. Arthur Ernest",0,64,0,0,693,26,,S
+547,1,2,"Beane, Mrs. Edward (Ethel Clarke)",1,19,1,0,2908,26,,S
+548,1,2,"Padro y Manent, Mr. Julian",0,,0,0,SC/PARIS 2146,13.8625,,C
+549,0,3,"Goldsmith, Mr. Frank John",0,33,1,1,363291,20.525,,S
+550,1,2,"Davies, Master. John Morgan Jr",0,8,1,1,C.A. 33112,36.75,,S
+551,1,1,"Thayer, Mr. John Borland Jr",0,17,0,2,17421,110.8833,C70,C
+552,0,2,"Sharp, Mr. Percival James R",0,27,0,0,244358,26,,S
+553,0,3,"O'Brien, Mr. Timothy",0,,0,0,330979,7.8292,,Q
+554,1,3,"Leeni, Mr. Fahim (""Philip Zenni"")",0,22,0,0,2620,7.225,,C
+555,1,3,"Ohman, Miss. Velin",1,22,0,0,347085,7.775,,S
+556,0,1,"Wright, Mr. George",0,62,0,0,113807,26.55,,S
+557,1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",1,48,1,0,11755,39.6,A16,C
+558,0,1,"Robbins, Mr. Victor",0,,0,0,PC 17757,227.525,,C
+559,1,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",1,39,1,1,110413,79.65,E67,S
+560,1,3,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",1,36,1,0,345572,17.4,,S
+561,0,3,"Morrow, Mr. Thomas Rowan",0,,0,0,372622,7.75,,Q
+562,0,3,"Sivic, Mr. Husein",0,40,0,0,349251,7.8958,,S
+563,0,2,"Norman, Mr. Robert Douglas",0,28,0,0,218629,13.5,,S
+564,0,3,"Simmons, Mr. John",0,,0,0,SOTON/OQ 392082,8.05,,S
+565,0,3,"Meanwell, Miss. (Marion Ogden)",1,,0,0,SOTON/O.Q. 392087,8.05,,S
+566,0,3,"Davies, Mr. Alfred J",0,24,2,0,A/4 48871,24.15,,S
+567,0,3,"Stoytcheff, Mr. Ilia",0,19,0,0,349205,7.8958,,S
+568,0,3,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",1,29,0,4,349909,21.075,,S
+569,0,3,"Doharr, Mr. Tannous",0,,0,0,2686,7.2292,,C
+570,1,3,"Jonsson, Mr. Carl",0,32,0,0,350417,7.8542,,S
+571,1,2,"Harris, Mr. George",0,62,0,0,S.W./PP 752,10.5,,S
+572,1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",1,53,2,0,11769,51.4792,C101,S
+573,1,1,"Flynn, Mr. John Irwin (""Irving"")",0,36,0,0,PC 17474,26.3875,E25,S
+574,1,3,"Kelly, Miss. Mary",1,,0,0,14312,7.75,,Q
+575,0,3,"Rush, Mr. Alfred George John",0,16,0,0,A/4. 20589,8.05,,S
+576,0,3,"Patchett, Mr. George",0,19,0,0,358585,14.5,,S
+577,1,2,"Garside, Miss. Ethel",1,34,0,0,243880,13,,S
+578,1,1,"Silvey, Mrs. William Baird (Alice Munger)",1,39,1,0,13507,55.9,E44,S
+579,0,3,"Caram, Mrs. Joseph (Maria Elias)",1,,1,0,2689,14.4583,,C
+580,1,3,"Jussila, Mr. Eiriik",0,32,0,0,STON/O 2. 3101286,7.925,,S
+581,1,2,"Christy, Miss. Julie Rachel",1,25,1,1,237789,30,,S
+582,1,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",1,39,1,1,17421,110.8833,C68,C
+583,0,2,"Downton, Mr. William James",0,54,0,0,28403,26,,S
+584,0,1,"Ross, Mr. John Hugo",0,36,0,0,13049,40.125,A10,C
+585,0,3,"Paulner, Mr. Uscher",0,,0,0,3411,8.7125,,C
+586,1,1,"Taussig, Miss. Ruth",1,18,0,2,110413,79.65,E68,S
+587,0,2,"Jarvis, Mr. John Denzil",0,47,0,0,237565,15,,S
+588,1,1,"Frolicher-Stehli, Mr. Maxmillian",0,60,1,1,13567,79.2,B41,C
+589,0,3,"Gilinski, Mr. Eliezer",0,22,0,0,14973,8.05,,S
+590,0,3,"Murdlin, Mr. Joseph",0,,0,0,A./5. 3235,8.05,,S
+591,0,3,"Rintamaki, Mr. Matti",0,35,0,0,STON/O 2. 3101273,7.125,,S
+592,1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",1,52,1,0,36947,78.2667,D20,C
+593,0,3,"Elsbury, Mr. William James",0,47,0,0,A/5 3902,7.25,,S
+594,0,3,"Bourke, Miss. Mary",1,,0,2,364848,7.75,,Q
+595,0,2,"Chapman, Mr. John Henry",0,37,1,0,SC/AH 29037,26,,S
+596,0,3,"Van Impe, Mr. Jean Baptiste",0,36,1,1,345773,24.15,,S
+597,1,2,"Leitch, Miss. Jessie Wills",1,,0,0,248727,33,,S
+598,0,3,"Johnson, Mr. Alfred",0,49,0,0,LINE,0,,S
+599,0,3,"Boulos, Mr. Hanna",0,,0,0,2664,7.225,,C
+600,1,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",0,49,1,0,PC 17485,56.9292,A20,C
+601,1,2,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",1,24,2,1,243847,27,,S
+602,0,3,"Slabenoff, Mr. Petco",0,,0,0,349214,7.8958,,S
+603,0,1,"Harrington, Mr. Charles H",0,,0,0,113796,42.4,,S
+604,0,3,"Torber, Mr. Ernst William",0,44,0,0,364511,8.05,,S
+605,1,1,"Homer, Mr. Harry (""Mr E Haven"")",0,35,0,0,111426,26.55,,C
+606,0,3,"Lindell, Mr. Edvard Bengtsson",0,36,1,0,349910,15.55,,S
+607,0,3,"Karaic, Mr. Milan",0,30,0,0,349246,7.8958,,S
+608,1,1,"Daniel, Mr. Robert Williams",0,27,0,0,113804,30.5,,S
+609,1,2,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",1,22,1,2,SC/Paris 2123,41.5792,,C
+610,1,1,"Shutes, Miss. Elizabeth W",1,40,0,0,PC 17582,153.4625,C125,S
+611,0,3,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",1,39,1,5,347082,31.275,,S
+612,0,3,"Jardin, Mr. Jose Neto",0,,0,0,SOTON/O.Q. 3101305,7.05,,S
+613,1,3,"Murphy, Miss. Margaret Jane",1,,1,0,367230,15.5,,Q
+614,0,3,"Horgan, Mr. John",0,,0,0,370377,7.75,,Q
+615,0,3,"Brocklebank, Mr. William Alfred",0,35,0,0,364512,8.05,,S
+616,1,2,"Herman, Miss. Alice",1,24,1,2,220845,65,,S
+617,0,3,"Danbom, Mr. Ernst Gilbert",0,34,1,1,347080,14.4,,S
+618,0,3,"Lobb, Mrs. William Arthur (Cordelia K Stanlick)",1,26,1,0,A/5. 3336,16.1,,S
+619,1,2,"Becker, Miss. Marion Louise",1,4,2,1,230136,39,F4,S
+620,0,2,"Gavey, Mr. Lawrence",0,26,0,0,31028,10.5,,S
+621,0,3,"Yasbeck, Mr. Antoni",0,27,1,0,2659,14.4542,,C
+622,1,1,"Kimball, Mr. Edwin Nelson Jr",0,42,1,0,11753,52.5542,D19,S
+623,1,3,"Nakid, Mr. Sahid",0,20,1,1,2653,15.7417,,C
+624,0,3,"Hansen, Mr. Henry Damsgaard",0,21,0,0,350029,7.8542,,S
+625,0,3,"Bowen, Mr. David John ""Dai""",0,21,0,0,54636,16.1,,S
+626,0,1,"Sutton, Mr. Frederick",0,61,0,0,36963,32.3208,D50,S
+627,0,2,"Kirkland, Rev. Charles Leonard",0,57,0,0,219533,12.35,,Q
+628,1,1,"Longley, Miss. Gretchen Fiske",1,21,0,0,13502,77.9583,D9,S
+629,0,3,"Bostandyeff, Mr. Guentcho",0,26,0,0,349224,7.8958,,S
+630,0,3,"O'Connell, Mr. Patrick D",0,,0,0,334912,7.7333,,Q
+631,1,1,"Barkworth, Mr. Algernon Henry Wilson",0,80,0,0,27042,30,A23,S
+632,0,3,"Lundahl, Mr. Johan Svensson",0,51,0,0,347743,7.0542,,S
+633,1,1,"Stahelin-Maeglin, Dr. Max",0,32,0,0,13214,30.5,B50,C
+634,0,1,"Parr, Mr. William Henry Marsh",0,,0,0,112052,0,,S
+635,0,3,"Skoog, Miss. Mabel",1,9,3,2,347088,27.9,,S
+636,1,2,"Davis, Miss. Mary",1,28,0,0,237668,13,,S
+637,0,3,"Leinonen, Mr. Antti Gustaf",0,32,0,0,STON/O 2. 3101292,7.925,,S
+638,0,2,"Collyer, Mr. Harvey",0,31,1,1,C.A. 31921,26.25,,S
+639,0,3,"Panula, Mrs. Juha (Maria Emilia Ojala)",1,41,0,5,3101295,39.6875,,S
+640,0,3,"Thorneycroft, Mr. Percival",0,,1,0,376564,16.1,,S
+641,0,3,"Jensen, Mr. Hans Peder",0,20,0,0,350050,7.8542,,S
+642,1,1,"Sagesser, Mlle. Emma",1,24,0,0,PC 17477,69.3,B35,C
+643,0,3,"Skoog, Miss. Margit Elizabeth",1,2,3,2,347088,27.9,,S
+644,1,3,"Foo, Mr. Choong",0,,0,0,1601,56.4958,,S
+645,1,3,"Baclini, Miss. Eugenie",1,0.75,2,1,2666,19.2583,,C
+646,1,1,"Harper, Mr. Henry Sleeper",0,48,1,0,PC 17572,76.7292,D33,C
+647,0,3,"Cor, Mr. Liudevit",0,19,0,0,349231,7.8958,,S
+648,1,1,"Simonius-Blumer, Col. Oberst Alfons",0,56,0,0,13213,35.5,A26,C
+649,0,3,"Willey, Mr. Edward",0,,0,0,S.O./P.P. 751,7.55,,S
+650,1,3,"Stanley, Miss. Amy Zillah Elsie",1,23,0,0,CA. 2314,7.55,,S
+651,0,3,"Mitkoff, Mr. Mito",0,,0,0,349221,7.8958,,S
+652,1,2,"Doling, Miss. Elsie",1,18,0,1,231919,23,,S
+653,0,3,"Kalvik, Mr. Johannes Halvorsen",0,21,0,0,8475,8.4333,,S
+654,1,3,"O'Leary, Miss. Hanora ""Norah""",1,,0,0,330919,7.8292,,Q
+655,0,3,"Hegarty, Miss. Hanora ""Nora""",1,18,0,0,365226,6.75,,Q
+656,0,2,"Hickman, Mr. Leonard Mark",0,24,2,0,S.O.C. 14879,73.5,,S
+657,0,3,"Radeff, Mr. Alexander",0,,0,0,349223,7.8958,,S
+658,0,3,"Bourke, Mrs. John (Catherine)",1,32,1,1,364849,15.5,,Q
+659,0,2,"Eitemiller, Mr. George Floyd",0,23,0,0,29751,13,,S
+660,0,1,"Newell, Mr. Arthur Webster",0,58,0,2,35273,113.275,D48,C
+661,1,1,"Frauenthal, Dr. Henry William",0,50,2,0,PC 17611,133.65,,S
+662,0,3,"Badt, Mr. Mohamed",0,40,0,0,2623,7.225,,C
+663,0,1,"Colley, Mr. Edward Pomeroy",0,47,0,0,5727,25.5875,E58,S
+664,0,3,"Coleff, Mr. Peju",0,36,0,0,349210,7.4958,,S
+665,1,3,"Lindqvist, Mr. Eino William",0,20,1,0,STON/O 2. 3101285,7.925,,S
+666,0,2,"Hickman, Mr. Lewis",0,32,2,0,S.O.C. 14879,73.5,,S
+667,0,2,"Butler, Mr. Reginald Fenton",0,25,0,0,234686,13,,S
+668,0,3,"Rommetvedt, Mr. Knud Paust",0,,0,0,312993,7.775,,S
+669,0,3,"Cook, Mr. Jacob",0,43,0,0,A/5 3536,8.05,,S
+670,1,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",1,,1,0,19996,52,C126,S
+671,1,2,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",1,40,1,1,29750,39,,S
+672,0,1,"Davidson, Mr. Thornton",0,31,1,0,F.C. 12750,52,B71,S
+673,0,2,"Mitchell, Mr. Henry Michael",0,70,0,0,C.A. 24580,10.5,,S
+674,1,2,"Wilhelms, Mr. Charles",0,31,0,0,244270,13,,S
+675,0,2,"Watson, Mr. Ennis Hastings",0,,0,0,239856,0,,S
+676,0,3,"Edvardsson, Mr. Gustaf Hjalmar",0,18,0,0,349912,7.775,,S
+677,0,3,"Sawyer, Mr. Frederick Charles",0,24.5,0,0,342826,8.05,,S
+678,1,3,"Turja, Miss. Anna Sofia",1,18,0,0,4138,9.8417,,S
+679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",1,43,1,6,CA 2144,46.9,,S
+680,1,1,"Cardeza, Mr. Thomas Drake Martinez",0,36,0,1,PC 17755,512.3292,B51 B53 B55,C
+681,0,3,"Peters, Miss. Katie",1,,0,0,330935,8.1375,,Q
+682,1,1,"Hassab, Mr. Hammad",0,27,0,0,PC 17572,76.7292,D49,C
+683,0,3,"Olsvigen, Mr. Thor Anderson",0,20,0,0,6563,9.225,,S
+684,0,3,"Goodwin, Mr. Charles Edward",0,14,5,2,CA 2144,46.9,,S
+685,0,2,"Brown, Mr. Thomas William Solomon",0,60,1,1,29750,39,,S
+686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",0,25,1,2,SC/Paris 2123,41.5792,,C
+687,0,3,"Panula, Mr. Jaako Arnold",0,14,4,1,3101295,39.6875,,S
+688,0,3,"Dakic, Mr. Branko",0,19,0,0,349228,10.1708,,S
+689,0,3,"Fischer, Mr. Eberhard Thelander",0,18,0,0,350036,7.7958,,S
+690,1,1,"Madill, Miss. Georgette Alexandra",1,15,0,1,24160,211.3375,B5,S
+691,1,1,"Dick, Mr. Albert Adrian",0,31,1,0,17474,57,B20,S
+692,1,3,"Karun, Miss. Manca",1,4,0,1,349256,13.4167,,C
+693,1,3,"Lam, Mr. Ali",0,,0,0,1601,56.4958,,S
+694,0,3,"Saad, Mr. Khalil",0,25,0,0,2672,7.225,,C
+695,0,1,"Weir, Col. John",0,60,0,0,113800,26.55,,S
+696,0,2,"Chapman, Mr. Charles Henry",0,52,0,0,248731,13.5,,S
+697,0,3,"Kelly, Mr. James",0,44,0,0,363592,8.05,,S
+698,1,3,"Mullens, Miss. Katherine ""Katie""",1,,0,0,35852,7.7333,,Q
+699,0,1,"Thayer, Mr. John Borland",0,49,1,1,17421,110.8833,C68,C
+700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",0,42,0,0,348121,7.65,F G63,S
+701,1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",1,18,1,0,PC 17757,227.525,C62 C64,C
+702,1,1,"Silverthorne, Mr. Spencer Victor",0,35,0,0,PC 17475,26.2875,E24,S
+703,0,3,"Barbara, Miss. Saiide",1,18,0,1,2691,14.4542,,C
+704,0,3,"Gallagher, Mr. Martin",0,25,0,0,36864,7.7417,,Q
+705,0,3,"Hansen, Mr. Henrik Juul",0,26,1,0,350025,7.8542,,S
+706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",0,39,0,0,250655,26,,S
+707,1,2,"Kelly, Mrs. Florence ""Fannie""",1,45,0,0,223596,13.5,,S
+708,1,1,"Calderhead, Mr. Edward Pennington",0,42,0,0,PC 17476,26.2875,E24,S
+709,1,1,"Cleaver, Miss. Alice",1,22,0,0,113781,151.55,,S
+710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",0,,1,1,2661,15.2458,,C
+711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",1,24,0,0,PC 17482,49.5042,C90,C
+712,0,1,"Klaber, Mr. Herman",0,,0,0,113028,26.55,C124,S
+713,1,1,"Taylor, Mr. Elmer Zebley",0,48,1,0,19996,52,C126,S
+714,0,3,"Larsson, Mr. August Viktor",0,29,0,0,7545,9.4833,,S
+715,0,2,"Greenberg, Mr. Samuel",0,52,0,0,250647,13,,S
+716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",0,19,0,0,348124,7.65,F G73,S
+717,1,1,"Endres, Miss. Caroline Louise",1,38,0,0,PC 17757,227.525,C45,C
+718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",1,27,0,0,34218,10.5,E101,S
+719,0,3,"McEvoy, Mr. Michael",0,,0,0,36568,15.5,,Q
+720,0,3,"Johnson, Mr. Malkolm Joackim",0,33,0,0,347062,7.775,,S
+721,1,2,"Harper, Miss. Annie Jessie ""Nina""",1,6,0,1,248727,33,,S
+722,0,3,"Jensen, Mr. Svend Lauritz",0,17,1,0,350048,7.0542,,S
+723,0,2,"Gillespie, Mr. William Henry",0,34,0,0,12233,13,,S
+724,0,2,"Hodges, Mr. Henry Price",0,50,0,0,250643,13,,S
+725,1,1,"Chambers, Mr. Norman Campbell",0,27,1,0,113806,53.1,E8,S
+726,0,3,"Oreskovic, Mr. Luka",0,20,0,0,315094,8.6625,,S
+727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",1,30,3,0,31027,21,,S
+728,1,3,"Mannion, Miss. Margareth",1,,0,0,36866,7.7375,,Q
+729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",0,25,1,0,236853,26,,S
+730,0,3,"Ilmakangas, Miss. Pieta Sofia",1,25,1,0,STON/O2. 3101271,7.925,,S
+731,1,1,"Allen, Miss. Elisabeth Walton",1,29,0,0,24160,211.3375,B5,S
+732,0,3,"Hassan, Mr. Houssein G N",0,11,0,0,2699,18.7875,,C
+733,0,2,"Knight, Mr. Robert J",0,,0,0,239855,0,,S
+734,0,2,"Berriman, Mr. William John",0,23,0,0,28425,13,,S
+735,0,2,"Troupiansky, Mr. Moses Aaron",0,23,0,0,233639,13,,S
+736,0,3,"Williams, Mr. Leslie",0,28.5,0,0,54636,16.1,,S
+737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",1,48,1,3,W./C. 6608,34.375,,S
+738,1,1,"Lesurer, Mr. Gustave J",0,35,0,0,PC 17755,512.3292,B101,C
+739,0,3,"Ivanoff, Mr. Kanio",0,,0,0,349201,7.8958,,S
+740,0,3,"Nankoff, Mr. Minko",0,,0,0,349218,7.8958,,S
+741,1,1,"Hawksford, Mr. Walter James",0,,0,0,16988,30,D45,S
+742,0,1,"Cavendish, Mr. Tyrell William",0,36,1,0,19877,78.85,C46,S
+743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",1,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C
+744,0,3,"McNamee, Mr. Neal",0,24,1,0,376566,16.1,,S
+745,1,3,"Stranden, Mr. Juho",0,31,0,0,STON/O 2. 3101288,7.925,,S
+746,0,1,"Crosby, Capt. Edward Gifford",0,70,1,1,WE/P 5735,71,B22,S
+747,0,3,"Abbott, Mr. Rossmore Edward",0,16,1,1,C.A. 2673,20.25,,S
+748,1,2,"Sinkkonen, Miss. Anna",1,30,0,0,250648,13,,S
+749,0,1,"Marvin, Mr. Daniel Warner",0,19,1,0,113773,53.1,D30,S
+750,0,3,"Connaghton, Mr. Michael",0,31,0,0,335097,7.75,,Q
+751,1,2,"Wells, Miss. Joan",1,4,1,1,29103,23,,S
+752,1,3,"Moor, Master. Meier",0,6,0,1,392096,12.475,E121,S
+753,0,3,"Vande Velde, Mr. Johannes Joseph",0,33,0,0,345780,9.5,,S
+754,0,3,"Jonkoff, Mr. Lalio",0,23,0,0,349204,7.8958,,S
+755,1,2,"Herman, Mrs. Samuel (Jane Laver)",1,48,1,2,220845,65,,S
+756,1,2,"Hamalainen, Master. Viljo",0,0.67,1,1,250649,14.5,,S
+757,0,3,"Carlsson, Mr. August Sigfrid",0,28,0,0,350042,7.7958,,S
+758,0,2,"Bailey, Mr. Percy Andrew",0,18,0,0,29108,11.5,,S
+759,0,3,"Theobald, Mr. Thomas Leonard",0,34,0,0,363294,8.05,,S
+760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",1,33,0,0,110152,86.5,B77,S
+761,0,3,"Garfirth, Mr. John",0,,0,0,358585,14.5,,S
+762,0,3,"Nirva, Mr. Iisakki Antino Aijo",0,41,0,0,SOTON/O2 3101272,7.125,,S
+763,1,3,"Barah, Mr. Hanna Assi",0,20,0,0,2663,7.2292,,C
+764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",1,36,1,2,113760,120,B96 B98,S
+765,0,3,"Eklund, Mr. Hans Linus",0,16,0,0,347074,7.775,,S
+766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",1,51,1,0,13502,77.9583,D11,S
+767,0,1,"Brewe, Dr. Arthur Jackson",0,,0,0,112379,39.6,,C
+768,0,3,"Mangan, Miss. Mary",1,30.5,0,0,364850,7.75,,Q
+769,0,3,"Moran, Mr. Daniel J",0,,1,0,371110,24.15,,Q
+770,0,3,"Gronnestad, Mr. Daniel Danielsen",0,32,0,0,8471,8.3625,,S
+771,0,3,"Lievens, Mr. Rene Aime",0,24,0,0,345781,9.5,,S
+772,0,3,"Jensen, Mr. Niels Peder",0,48,0,0,350047,7.8542,,S
+773,0,2,"Mack, Mrs. (Mary)",1,57,0,0,S.O./P.P. 3,10.5,E77,S
+774,0,3,"Elias, Mr. Dibo",0,,0,0,2674,7.225,,C
+775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",1,54,1,3,29105,23,,S
+776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",0,18,0,0,347078,7.75,,S
+777,0,3,"Tobin, Mr. Roger",0,,0,0,383121,7.75,F38,Q
+778,1,3,"Emanuel, Miss. Virginia Ethel",1,5,0,0,364516,12.475,,S
+779,0,3,"Kilgannon, Mr. Thomas J",0,,0,0,36865,7.7375,,Q
+780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",1,43,0,1,24160,211.3375,B3,S
+781,1,3,"Ayoub, Miss. Banoura",1,13,0,0,2687,7.2292,,C
+782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",1,17,1,0,17474,57,B20,S
+783,0,1,"Long, Mr. Milton Clyde",0,29,0,0,113501,30,D6,S
+784,0,3,"Johnston, Mr. Andrew G",0,,1,2,W./C. 6607,23.45,,S
+785,0,3,"Ali, Mr. William",0,25,0,0,SOTON/O.Q. 3101312,7.05,,S
+786,0,3,"Harmer, Mr. Abraham (David Lishin)",0,25,0,0,374887,7.25,,S
+787,1,3,"Sjoblom, Miss. Anna Sofia",1,18,0,0,3101265,7.4958,,S
+788,0,3,"Rice, Master. George Hugh",0,8,4,1,382652,29.125,,Q
+789,1,3,"Dean, Master. Bertram Vere",0,1,1,2,C.A. 2315,20.575,,S
+790,0,1,"Guggenheim, Mr. Benjamin",0,46,0,0,PC 17593,79.2,B82 B84,C
+791,0,3,"Keane, Mr. Andrew ""Andy""",0,,0,0,12460,7.75,,Q
+792,0,2,"Gaskell, Mr. Alfred",0,16,0,0,239865,26,,S
+793,0,3,"Sage, Miss. Stella Anna",1,,8,2,CA. 2343,69.55,,S
+794,0,1,"Hoyt, Mr. William Fisher",0,,0,0,PC 17600,30.6958,,C
+795,0,3,"Dantcheff, Mr. Ristiu",0,25,0,0,349203,7.8958,,S
+796,0,2,"Otter, Mr. Richard",0,39,0,0,28213,13,,S
+797,1,1,"Leader, Dr. Alice (Farnham)",1,49,0,0,17465,25.9292,D17,S
+798,1,3,"Osman, Mrs. Mara",1,31,0,0,349244,8.6833,,S
+799,0,3,"Ibrahim Shawah, Mr. Yousseff",0,30,0,0,2685,7.2292,,C
+800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",1,30,1,1,345773,24.15,,S
+801,0,2,"Ponesell, Mr. Martin",0,34,0,0,250647,13,,S
+802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",1,31,1,1,C.A. 31921,26.25,,S
+803,1,1,"Carter, Master. William Thornton II",0,11,1,2,113760,120,B96 B98,S
+804,1,3,"Thomas, Master. Assad Alexander",0,0.42,0,1,2625,8.5167,,C
+805,1,3,"Hedman, Mr. Oskar Arvid",0,27,0,0,347089,6.975,,S
+806,0,3,"Johansson, Mr. Karl Johan",0,31,0,0,347063,7.775,,S
+807,0,1,"Andrews, Mr. Thomas Jr",0,39,0,0,112050,0,A36,S
+808,0,3,"Pettersson, Miss. Ellen Natalia",1,18,0,0,347087,7.775,,S
+809,0,2,"Meyer, Mr. August",0,39,0,0,248723,13,,S
+810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",1,33,1,0,113806,53.1,E8,S
+811,0,3,"Alexander, Mr. William",0,26,0,0,3474,7.8875,,S
+812,0,3,"Lester, Mr. James",0,39,0,0,A/4 48871,24.15,,S
+813,0,2,"Slemen, Mr. Richard James",0,35,0,0,28206,10.5,,S
+814,0,3,"Andersson, Miss. Ebba Iris Alfrida",1,6,4,2,347082,31.275,,S
+815,0,3,"Tomlin, Mr. Ernest Portage",0,30.5,0,0,364499,8.05,,S
+816,0,1,"Fry, Mr. Richard",0,,0,0,112058,0,B102,S
+817,0,3,"Heininen, Miss. Wendla Maria",1,23,0,0,STON/O2. 3101290,7.925,,S
+818,0,2,"Mallet, Mr. Albert",0,31,1,1,S.C./PARIS 2079,37.0042,,C
+819,0,3,"Holm, Mr. John Fredrik Alexander",0,43,0,0,C 7075,6.45,,S
+820,0,3,"Skoog, Master. Karl Thorsten",0,10,3,2,347088,27.9,,S
+821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",1,52,1,1,12749,93.5,B69,S
+822,1,3,"Lulic, Mr. Nikola",0,27,0,0,315098,8.6625,,S
+823,0,1,"Reuchlin, Jonkheer. John George",0,38,0,0,19972,0,,S
+824,1,3,"Moor, Mrs. (Beila)",1,27,0,1,392096,12.475,E121,S
+825,0,3,"Panula, Master. Urho Abraham",0,2,4,1,3101295,39.6875,,S
+826,0,3,"Flynn, Mr. John",0,,0,0,368323,6.95,,Q
+827,0,3,"Lam, Mr. Len",0,,0,0,1601,56.4958,,S
+828,1,2,"Mallet, Master. Andre",0,1,0,2,S.C./PARIS 2079,37.0042,,C
+829,1,3,"McCormack, Mr. Thomas Joseph",0,,0,0,367228,7.75,,Q
+830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",1,62,0,0,113572,80,B28,
+831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",1,15,1,0,2659,14.4542,,C
+832,1,2,"Richards, Master. George Sibley",0,0.83,1,1,29106,18.75,,S
+833,0,3,"Saad, Mr. Amin",0,,0,0,2671,7.2292,,C
+834,0,3,"Augustsson, Mr. Albert",0,23,0,0,347468,7.8542,,S
+835,0,3,"Allum, Mr. Owen George",0,18,0,0,2223,8.3,,S
+836,1,1,"Compton, Miss. Sara Rebecca",1,39,1,1,PC 17756,83.1583,E49,C
+837,0,3,"Pasic, Mr. Jakob",0,21,0,0,315097,8.6625,,S
+838,0,3,"Sirota, Mr. Maurice",0,,0,0,392092,8.05,,S
+839,1,3,"Chip, Mr. Chang",0,32,0,0,1601,56.4958,,S
+840,1,1,"Marechal, Mr. Pierre",0,,0,0,11774,29.7,C47,C
+841,0,3,"Alhomaki, Mr. Ilmari Rudolf",0,20,0,0,SOTON/O2 3101287,7.925,,S
+842,0,2,"Mudd, Mr. Thomas Charles",0,16,0,0,S.O./P.P. 3,10.5,,S
+843,1,1,"Serepeca, Miss. Augusta",1,30,0,0,113798,31,,C
+844,0,3,"Lemberopolous, Mr. Peter L",0,34.5,0,0,2683,6.4375,,C
+845,0,3,"Culumovic, Mr. Jeso",0,17,0,0,315090,8.6625,,S
+846,0,3,"Abbing, Mr. Anthony",0,42,0,0,C.A. 5547,7.55,,S
+847,0,3,"Sage, Mr. Douglas Bullen",0,,8,2,CA. 2343,69.55,,S
+848,0,3,"Markoff, Mr. Marin",0,35,0,0,349213,7.8958,,C
+849,0,2,"Harper, Rev. John",0,28,0,1,248727,33,,S
+850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",1,,1,0,17453,89.1042,C92,C
+851,0,3,"Andersson, Master. Sigvard Harald Elias",0,4,4,2,347082,31.275,,S
+852,0,3,"Svensson, Mr. Johan",0,74,0,0,347060,7.775,,S
+853,0,3,"Boulos, Miss. Nourelain",1,9,1,1,2678,15.2458,,C
+854,1,1,"Lines, Miss. Mary Conover",1,16,0,1,PC 17592,39.4,D28,S
+855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",1,44,1,0,244252,26,,S
+856,1,3,"Aks, Mrs. Sam (Leah Rosen)",1,18,0,1,392091,9.35,,S
+857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",1,45,1,1,36928,164.8667,,S
+858,1,1,"Daly, Mr. Peter Denis ",0,51,0,0,113055,26.55,E17,S
+859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",1,24,0,3,2666,19.2583,,C
+860,0,3,"Razi, Mr. Raihed",0,,0,0,2629,7.2292,,C
+861,0,3,"Hansen, Mr. Claus Peter",0,41,2,0,350026,14.1083,,S
+862,0,2,"Giles, Mr. Frederick Edward",0,21,1,0,28134,11.5,,S
+863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",1,48,0,0,17466,25.9292,D17,S
+864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",1,,8,2,CA. 2343,69.55,,S
+865,0,2,"Gill, Mr. John William",0,24,0,0,233866,13,,S
+866,1,2,"Bystrom, Mrs. (Karolina)",1,42,0,0,236852,13,,S
+867,1,2,"Duran y More, Miss. Asuncion",1,27,1,0,SC/PARIS 2149,13.8583,,C
+868,0,1,"Roebling, Mr. Washington Augustus II",0,31,0,0,PC 17590,50.4958,A24,S
+869,0,3,"van Melkebeke, Mr. Philemon",0,,0,0,345777,9.5,,S
+870,1,3,"Johnson, Master. Harold Theodor",0,4,1,1,347742,11.1333,,S
+871,0,3,"Balkic, Mr. Cerin",0,26,0,0,349248,7.8958,,S
+872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",1,47,1,1,11751,52.5542,D35,S
+873,0,1,"Carlsson, Mr. Frans Olof",0,33,0,0,695,5,B51 B53 B55,S
+874,0,3,"Vander Cruyssen, Mr. Victor",0,47,0,0,345765,9,,S
+875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",1,28,1,0,P/PP 3381,24,,C
+876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",1,15,0,0,2667,7.225,,C
+877,0,3,"Gustafsson, Mr. Alfred Ossian",0,20,0,0,7534,9.8458,,S
+878,0,3,"Petroff, Mr. Nedelio",0,19,0,0,349212,7.8958,,S
+879,0,3,"Laleff, Mr. Kristo",0,,0,0,349217,7.8958,,S
+880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",1,56,0,1,11767,83.1583,C50,C
+881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",1,25,0,1,230433,26,,S
+882,0,3,"Markun, Mr. Johann",0,33,0,0,349257,7.8958,,S
+883,0,3,"Dahlberg, Miss. Gerda Ulrika",1,22,0,0,7552,10.5167,,S
+884,0,2,"Banfield, Mr. Frederick James",0,28,0,0,C.A./SOTON 34068,10.5,,S
+885,0,3,"Sutehall, Mr. Henry Jr",0,25,0,0,SOTON/OQ 392076,7.05,,S
+886,0,3,"Rice, Mrs. William (Margaret Norton)",1,39,0,5,382652,29.125,,Q
+887,0,2,"Montvila, Rev. Juozas",0,27,0,0,211536,13,,S
+888,1,1,"Graham, Miss. Margaret Edith",1,19,0,0,112053,30,B42,S
+889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",1,,1,2,W./C. 6607,23.45,,S
+890,1,1,"Behr, Mr. Karl Howell",0,26,0,0,111369,30,C148,C
+891,0,3,"Dooley, Mr. Patrick",0,32,0,0,370376,7.75,,Q
diff --git a/work-with-data/datasets/datasets-tutorial/train-dataset/train.py b/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/train-dataset/train.py
similarity index 77%
rename from work-with-data/datasets/datasets-tutorial/train-dataset/train.py
rename to how-to-use-azureml/work-with-data/datasets/datasets-tutorial/train-dataset/train.py
index 9fbca891..785c8e74 100644
--- a/work-with-data/datasets/datasets-tutorial/train-dataset/train.py
+++ b/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/train-dataset/train.py
@@ -11,13 +11,14 @@ from sklearn.model_selection import train_test_split
 from azureml.core.compute import ComputeTarget, AmlCompute
 from azureml.core.compute_target import ComputeTargetException
 from sklearn.tree import DecisionTreeClassifier
+from sklearn.externals import joblib
 
 run = Run.get_context()
 workspace = run.experiment.workspace
 
-dataset_name = 'clean_Titanic_tutorial'
+dataset_name = 'titanic dataset'
 
-dataset = Dataset.get(workspace=workspace, name=dataset_name)
+dataset = Dataset.get_by_name(workspace=workspace, name=dataset_name)
 df = dataset.to_pandas_dataframe()
 
 x_col = ['Pclass', 'Sex', 'SibSp', 'Parch']
@@ -32,6 +33,11 @@ data = {"train": {"X": x_train, "y": y_train},
         "test": {"X": x_test, "y": y_test}}
 
 clf = DecisionTreeClassifier().fit(data["train"]["X"], data["train"]["y"])
+model_file_name = 'decision_tree.pkl'
 
 print('Accuracy of Decision Tree classifier on training set: {:.2f}'.format(clf.score(x_train, y_train)))
 print('Accuracy of Decision Tree classifier on test set: {:.2f}'.format(clf.score(x_test, y_test)))
+
+os.makedirs('./outputs', exist_ok=True)
+with open(model_file_name, "wb") as file:
+    joblib.dump(value=clf, filename='outputs/' + model_file_name)
diff --git a/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/train-with-datasets.ipynb b/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/train-with-datasets.ipynb
new file mode 100644
index 00000000..42b183e7
--- /dev/null
+++ b/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/train-with-datasets.ipynb
@@ -0,0 +1,620 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/train-with-datasets.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Train with Azure Machine Learning Datasets\n",
+        "Datasets are categorized into TabularDataset and FileDataset based on how users consume them in training. \n",
+        "* A TabularDataset represents data in a tabular format by parsing the provided file or list of files. TabularDataset can be created from csv, tsv, parquet files, SQL query results etc. For the complete list, please visit our [documentation](https://aka.ms/tabulardataset-api-reference). It provides you with the ability to materialize the data into a pandas DataFrame.\n",
+        "* A FileDataset references single or multiple files in your datastores or public urls. This provides you with the ability to download or mount the files to your compute. The files can be of any format, which enables a wider range of machine learning scenarios including deep learning.\n",
+        "\n",
+        "In this tutorial, you will learn how to train with Azure Machine Learning Datasets:\n",
+        "\n",
+        "&#x2611; Use Datasets directly in your training script\n",
+        "\n",
+        "&#x2611; Use Datasets to mount files to a remote compute"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) first if you haven't already established your connection to the AzureML Workspace."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print('SDK version:', azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize Workspace\n",
+        "\n",
+        "Initialize a workspace object from persisted configuration."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create Experiment\n",
+        "\n",
+        "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "experiment_name = 'train-with-datasets'\n",
+        "\n",
+        "from azureml.core import Experiment\n",
+        "exp = Experiment(workspace=ws, name=experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create or Attach existing compute resource\n",
+        "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n",
+        "\n",
+        "**Creation of compute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace the code will skip the creation process."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import AmlCompute\n",
+        "from azureml.core.compute import ComputeTarget\n",
+        "import os\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "compute_name = os.environ.get('AML_COMPUTE_CLUSTER_NAME', 'cpu-cluster')\n",
+        "compute_min_nodes = os.environ.get('AML_COMPUTE_CLUSTER_MIN_NODES', 0)\n",
+        "compute_max_nodes = os.environ.get('AML_COMPUTE_CLUSTER_MAX_NODES', 4)\n",
+        "\n",
+        "# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6\n",
+        "vm_size = os.environ.get('AML_COMPUTE_CLUSTER_SKU', 'STANDARD_D2_V2')\n",
+        "\n",
+        "\n",
+        "if compute_name in ws.compute_targets:\n",
+        "    compute_target = ws.compute_targets[compute_name]\n",
+        "    if compute_target and type(compute_target) is AmlCompute:\n",
+        "        print('found compute target. just use it. ' + compute_name)\n",
+        "else:\n",
+        "    print('creating a new compute target...')\n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size=vm_size,\n",
+        "                                                                min_nodes=compute_min_nodes, \n",
+        "                                                                max_nodes=compute_max_nodes)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n",
+        "    \n",
+        "    # can poll for a minimum number of nodes and for a specific timeout. \n",
+        "    # if no min node count is provided it will use the scale settings for the cluster\n",
+        "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
+        "    \n",
+        "     # For a more detailed view of current AmlCompute status, use get_status()\n",
+        "    print(compute_target.get_status().serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You now have the necessary packages and compute resources to train a model in the cloud.\n",
+        "## Use Datasets directly in training\n",
+        "\n",
+        "### Create a TabularDataset\n",
+        "By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred. \n",
+        "\n",
+        "Every workspace comes with a default [datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data) (and you can register more) which is backed by the Azure blob storage account associated with the workspace. We can use it to transfer data from local to the cloud, and create Dataset from it.We will now upload the [Titanic data](./train-dataset/Titanic.csv) to the default datastore (blob) within your workspace."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "datastore = ws.get_default_datastore()\n",
+        "datastore.upload_files(files = ['./train-dataset/Titanic.csv'],\n",
+        "                       target_path = 'train-dataset/tabular/',\n",
+        "                       overwrite = True,\n",
+        "                       show_progress = True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Then we will create an unregistered TabularDataset pointing to the path in the datastore. You can also create a Dataset from multiple paths. [learn more](https://aka.ms/azureml/howto/createdatasets) "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Dataset\n",
+        "dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, 'train-dataset/tabular/Titanic.csv')])\n",
+        "\n",
+        "# preview the first 3 rows of the dataset\n",
+        "dataset.take(3).to_pandas_dataframe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a training script\n",
+        "\n",
+        "To submit the job to the cluster, first create a training script. Run the following code to create the training script called `train_titanic.py` in the script_folder. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "script_folder = os.path.join(os.getcwd(), 'train-dataset')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile $script_folder/train_titanic.py\n",
+        "\n",
+        "import os\n",
+        "\n",
+        "from azureml.core import Dataset, Run\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "from sklearn.externals import joblib\n",
+        "\n",
+        "run = Run.get_context()\n",
+        "# get input dataset by name\n",
+        "dataset = run.input_datasets['titanic']\n",
+        "\n",
+        "df = dataset.to_pandas_dataframe()\n",
+        "\n",
+        "x_col = ['Pclass', 'Sex', 'SibSp', 'Parch']\n",
+        "y_col = ['Survived']\n",
+        "x_df = df.loc[:, x_col]\n",
+        "y_df = df.loc[:, y_col]\n",
+        "\n",
+        "x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.2, random_state=223)\n",
+        "\n",
+        "data = {'train': {'X': x_train, 'y': y_train},\n",
+        "\n",
+        "        'test': {'X': x_test, 'y': y_test}}\n",
+        "\n",
+        "clf = DecisionTreeClassifier().fit(data['train']['X'], data['train']['y'])\n",
+        "model_file_name = 'decision_tree.pkl'\n",
+        "\n",
+        "print('Accuracy of Decision Tree classifier on training set: {:.2f}'.format(clf.score(x_train, y_train)))\n",
+        "print('Accuracy of Decision Tree classifier on test set: {:.2f}'.format(clf.score(x_test, y_test)))\n",
+        "\n",
+        "os.makedirs('./outputs', exist_ok=True)\n",
+        "with open(model_file_name, 'wb') as file:\n",
+        "    joblib.dump(value=clf, filename='outputs/' + model_file_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Configure and use Datasets as the input to Estimator"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can ask the system to build a conda environment based on your dependency specification. Once the environment is built, and if you don't change your dependencies, it will be reused in subsequent runs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "\n",
+        "conda_env = Environment('conda-env')\n",
+        "conda_env.python.conda_dependencies = CondaDependencies.create(pip_packages=['azureml-sdk','azureml-dataprep[pandas,fuse]>=1.1.','scikit-learn'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "An estimator object is used to submit the run. Azure Machine Learning has pre-configured estimators for common machine learning frameworks, as well as generic Estimator. Create a generic estimator for by specifying\n",
+        "\n",
+        "* The name of the estimator object, `est`\n",
+        "* The directory that contains your scripts. All the files in this directory are uploaded into the cluster nodes for execution. \n",
+        "* The training script name, train_titanic.py\n",
+        "* The input Dataset for training\n",
+        "* The compute target.  In this case you will use the AmlCompute you created\n",
+        "* The environment definition for the experiment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.estimator import Estimator\n",
+        "\n",
+        "est = Estimator(source_directory=script_folder, \n",
+        "                entry_script='train_titanic.py', \n",
+        "                # pass dataset object as an input with name 'titanic'\n",
+        "                inputs=[dataset.as_named_input('titanic')],\n",
+        "                compute_target=compute_target,\n",
+        "                environment_definition= conda_env) "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit job to run\n",
+        "Submit the estimator to the Azure ML experiment to kick off the execution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run = exp.submit(est)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "\n",
+        "# monitor the run\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Use Datasets to mount files to a remote compute\n",
+        "\n",
+        "You can use the Dataset object to mount or download files referred by it. When you mount a file system, you attach that file system to a directory (mount point) and make it available to the system. Because mounting load files at the time of processing, it is usually faster than download.<br> \n",
+        "Note: mounting is only available for Linux-based compute (DSVM/VM, AMLCompute, HDInsights)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Upload data files into datastore\n",
+        "We will first load diabetes data from `scikit-learn` to the train-dataset folder."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.datasets import load_diabetes\n",
+        "import numpy as np\n",
+        "\n",
+        "training_data = load_diabetes()\n",
+        "np.save(file='train-dataset/features.npy', arr=training_data['data'])\n",
+        "np.save(file='train-dataset/labels.npy', arr=training_data['target'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now let's upload the 2 files into the default datastore under a path named `diabetes`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "datastore.upload_files(['train-dataset/features.npy', 'train-dataset/labels.npy'], target_path='diabetes', overwrite=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a FileDataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.dataset import Dataset\n",
+        "\n",
+        "dataset = Dataset.File.from_files(path = [(datastore, 'diabetes/')])\n",
+        "\n",
+        "# see a list of files referenced by dataset\n",
+        "dataset.to_path()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a training script\n",
+        "\n",
+        "To submit the job to the cluster, first create a training script. Run the following code to create the training script called `train_diabetes.py` in the script_folder. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile $script_folder/train_diabetes.py\n",
+        "\n",
+        "import os\n",
+        "import glob\n",
+        "\n",
+        "from sklearn.linear_model import Ridge\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from azureml.core.run import Run\n",
+        "from sklearn.externals import joblib\n",
+        "\n",
+        "import numpy as np\n",
+        "\n",
+        "os.makedirs('./outputs', exist_ok=True)\n",
+        "\n",
+        "run = Run.get_context()\n",
+        "base_path = run.input_datasets['diabetes']\n",
+        "\n",
+        "X = np.load(glob.glob(os.path.join(base_path, '**/features.npy'), recursive=True)[0])\n",
+        "y = np.load(glob.glob(os.path.join(base_path, '**/labels.npy'), recursive=True)[0])\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(\n",
+        "    X, y, test_size=0.2, random_state=0)\n",
+        "data = {'train': {'X': X_train, 'y': y_train},\n",
+        "        'test': {'X': X_test, 'y': y_test}}\n",
+        "\n",
+        "# list of numbers from 0.0 to 1.0 with a 0.05 interval\n",
+        "alphas = np.arange(0.0, 1.0, 0.05)\n",
+        "\n",
+        "for alpha in alphas:\n",
+        "    # use Ridge algorithm to create a regression model\n",
+        "    reg = Ridge(alpha=alpha)\n",
+        "    reg.fit(data['train']['X'], data['train']['y'])\n",
+        "\n",
+        "    preds = reg.predict(data['test']['X'])\n",
+        "    mse = mean_squared_error(preds, data['test']['y'])\n",
+        "    run.log('alpha', alpha)\n",
+        "    run.log('mse', mse)\n",
+        "\n",
+        "    model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)\n",
+        "    with open(model_file_name, 'wb') as file:\n",
+        "        joblib.dump(value=reg, filename='outputs/' + model_file_name)\n",
+        "\n",
+        "    print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Configure & Run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import ScriptRunConfig\n",
+        "\n",
+        "src = ScriptRunConfig(source_directory=script_folder, \n",
+        "                      script='train_diabetes.py', \n",
+        "                      # to mount the dataset on the remote compute and pass the mounted path as an argument to the training script\n",
+        "                      arguments =[dataset.as_named_input('diabetes').as_mount('tmp/dataset')])\n",
+        "\n",
+        "src.run_config.framework = 'python'\n",
+        "src.run_config.environment = conda_env\n",
+        "src.run_config.target = compute_target.name"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run = exp.submit(config=src)\n",
+        "\n",
+        "# monitor the run\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Display run results\n",
+        "You now have a model trained on a remote cluster. Retrieve all the metrics logged during the run, including the accuracy of the model:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(run.get_metrics())\n",
+        "metrics = run.get_metrics()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Register Datasets\n",
+        "Use the register() method to register datasets to your workspace so they can be shared with others, reused across various experiments, and referred to by name in your training script."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "dataset = dataset.register(workspace = ws,\n",
+        "                           name = 'diabetes dataset',\n",
+        "                           description='training dataset',\n",
+        "                           create_new_version=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Register models with Datasets\n",
+        "The last step in the training script wrote the model files in a directory named `outputs` in the VM of the cluster where the job is executed. `outputs` is a special directory in that all content in this directory is automatically uploaded to your workspace. This content appears in the run record in the experiment under your workspace. Hence, the model file is now also available in your workspace.\n",
+        "\n",
+        "You can register models with Datasets for reproducibility and auditing purpose."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# find the index where MSE is the smallest\n",
+        "indices = list(range(0, len(metrics['mse'])))\n",
+        "min_mse_index = min(indices, key=lambda x: metrics['mse'][x])\n",
+        "\n",
+        "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
+        "    metrics['mse'][min_mse_index], \n",
+        "    metrics['alpha'][min_mse_index]\n",
+        "))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# find the best model\n",
+        "best_alpha = metrics['alpha'][min_mse_index]\n",
+        "model_file_name = 'ridge_{0:.2f}.pkl'.format(best_alpha)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# register the best model with the input dataset\n",
+        "model = run.register_model(model_name='sklearn_diabetes', model_path=os.path.join('outputs', model_file_name),\n",
+        "                           datasets =[('training data',dataset)])"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "sihhu"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.9"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/utils.py b/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/utils.py
new file mode 100644
index 00000000..98170ada
--- /dev/null
+++ b/how-to-use-azureml/work-with-data/datasets/datasets-tutorial/utils.py
@@ -0,0 +1,27 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import gzip
+import numpy as np
+import struct
+
+
+# load compressed MNIST gz files and return numpy arrays
+def load_data(filename, label=False):
+    with gzip.open(filename) as gz:
+        struct.unpack('I', gz.read(4))
+        n_items = struct.unpack('>I', gz.read(4))
+        if not label:
+            n_rows = struct.unpack('>I', gz.read(4))[0]
+            n_cols = struct.unpack('>I', gz.read(4))[0]
+            res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8)
+            res = res.reshape(n_items[0], n_rows * n_cols)
+        else:
+            res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8)
+            res = res.reshape(n_items[0], 1)
+    return res
+
+
+# one-hot encode a 1-D array
+def one_hot_encode(array, num_of_classes):
+    return np.eye(num_of_classes)[array.reshape(-1)]
diff --git a/how-to-use-azureml/work-with-data/datasets/train-dataset/Titanic.csv b/how-to-use-azureml/work-with-data/datasets/train-dataset/Titanic.csv
deleted file mode 100644
index 5cc466e9..00000000
--- a/how-to-use-azureml/work-with-data/datasets/train-dataset/Titanic.csv
+++ /dev/null
@@ -1,892 +0,0 @@
-PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
-1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
-2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
-3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
-4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
-5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
-6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
-7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
-8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
-9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
-10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
-11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
-12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
-13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
-14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
-15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
-16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
-17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
-18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
-19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
-20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
-21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
-22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
-23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
-24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
-25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
-26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
-27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
-28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
-29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
-30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
-31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
-32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
-33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
-34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
-35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
-36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
-37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
-38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
-39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
-40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
-41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
-42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
-43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
-44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
-45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
-46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
-47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
-48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
-49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
-50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
-51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
-52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
-53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
-54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
-55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
-56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
-57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
-58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
-59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
-60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
-61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
-62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
-63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
-64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
-65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
-66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
-67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
-68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
-69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
-70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
-71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
-72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
-73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
-74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
-75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
-76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
-77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
-78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
-79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
-80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
-81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
-82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
-83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
-84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
-85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
-86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
-87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
-88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
-89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
-90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
-91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
-92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
-93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
-94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
-95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
-96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
-97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
-98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
-99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
-100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
-101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
-102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
-103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S
-104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S
-105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S
-106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
-107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
-108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
-109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
-110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
-111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
-112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
-113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
-114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
-115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
-116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
-117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
-118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
-119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
-120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
-121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
-122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
-123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
-124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
-125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
-126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
-127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
-128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
-129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
-130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
-131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
-132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
-133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S
-134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S
-135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S
-136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C
-137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S
-138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S
-139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S
-140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C
-141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
-142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
-143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
-144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
-145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
-146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
-147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S
-148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S
-149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S
-150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S
-151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S
-152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S
-153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S
-154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S
-155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S
-156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
-157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q
-158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S
-159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S
-160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S
-161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S
-162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S
-163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S
-164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S
-165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S
-166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S
-167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S
-168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S
-169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S
-170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S
-171,0,1,"Van der hoef, Mr. Wyckoff",male,61,0,0,111240,33.5,B19,S
-172,0,3,"Rice, Master. Arthur",male,4,4,1,382652,29.125,,Q
-173,1,3,"Johnson, Miss. Eleanor Ileen",female,1,1,1,347742,11.1333,,S
-174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S
-175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C
-176,0,3,"Klasen, Mr. Klas Albin",male,18,1,1,350404,7.8542,,S
-177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S
-178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C
-179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S
-180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S
-181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S
-182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C
-183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S
-184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S
-185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4,0,2,315153,22.025,,S
-186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50,A32,S
-187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q
-188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S
-189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q
-190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S
-191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S
-192,0,2,"Carbines, Mr. William",male,19,0,0,28424,13,,S
-193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19,1,0,350046,7.8542,,S
-194,1,2,"Navratil, Master. Michel M",male,3,1,1,230080,26,F2,S
-195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44,0,0,PC 17610,27.7208,B4,C
-196,1,1,"Lurette, Miss. Elise",female,58,0,0,PC 17569,146.5208,B80,C
-197,0,3,"Mernagh, Mr. Robert",male,,0,0,368703,7.75,,Q
-198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42,0,1,4579,8.4042,,S
-199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q
-200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S
-201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28,0,0,345770,9.5,,S
-202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S
-203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S
-204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C
-205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S
-206,0,3,"Strom, Miss. Telma Matilda",female,2,0,1,347054,10.4625,G6,S
-207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S
-208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C
-209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q
-210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C
-211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S
-212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S
-213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S
-214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S
-215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q
-216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C
-217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S
-218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42,1,0,243847,27,,S
-219,1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C
-220,0,2,"Harris, Mr. Walter",male,30,0,0,W/C 14208,10.5,,S
-221,1,3,"Sunderland, Mr. Victor Francis",male,16,0,0,SOTON/OQ 392089,8.05,,S
-222,0,2,"Bracken, Mr. James H",male,27,0,0,220367,13,,S
-223,0,3,"Green, Mr. George Henry",male,51,0,0,21440,8.05,,S
-224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S
-225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38,1,0,19943,90,C93,S
-226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22,0,0,PP 4348,9.35,,S
-227,1,2,"Mellors, Mr. William John",male,19,0,0,SW/PP 751,10.5,,S
-228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S
-229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18,0,0,236171,13,,S
-230,0,3,"Lefebre, Miss. Mathilde",female,,3,1,4133,25.4667,,S
-231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35,1,0,36973,83.475,C83,S
-232,0,3,"Larsson, Mr. Bengt Edvin",male,29,0,0,347067,7.775,,S
-233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59,0,0,237442,13.5,,S
-234,1,3,"Asplund, Miss. Lillian Gertrud",female,5,4,2,347077,31.3875,,S
-235,0,2,"Leyson, Mr. Robert William Norman",male,24,0,0,C.A. 29566,10.5,,S
-236,0,3,"Harknett, Miss. Alice Phoebe",female,,0,0,W./C. 6609,7.55,,S
-237,0,2,"Hold, Mr. Stephen",male,44,1,0,26707,26,,S
-238,1,2,"Collyer, Miss. Marjorie ""Lottie""",female,8,0,2,C.A. 31921,26.25,,S
-239,0,2,"Pengelly, Mr. Frederick William",male,19,0,0,28665,10.5,,S
-240,0,2,"Hunt, Mr. George Henry",male,33,0,0,SCO/W 1585,12.275,,S
-241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C
-242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q
-243,0,2,"Coleridge, Mr. Reginald Charles",male,29,0,0,W./C. 14263,10.5,,S
-244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22,0,0,STON/O 2. 3101275,7.125,,S
-245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C
-246,0,1,"Minahan, Dr. William Edward",male,44,2,0,19928,90,C78,Q
-247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25,0,0,347071,7.775,,S
-248,1,2,"Hamalainen, Mrs. William (Anna)",female,24,0,2,250649,14.5,,S
-249,1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S
-250,0,2,"Carter, Rev. Ernest Courtenay",male,54,1,0,244252,26,,S
-251,0,3,"Reed, Mr. James George",male,,0,0,362316,7.25,,S
-252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S
-253,0,1,"Stead, Mr. William Thomas",male,62,0,0,113514,26.55,C87,S
-254,0,3,"Lobb, Mr. William Arthur",male,30,1,0,A/5. 3336,16.1,,S
-255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S
-256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C
-257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C
-258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S
-259,1,1,"Ward, Miss. Anna",female,35,0,0,PC 17755,512.3292,,C
-260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S
-261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q
-262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S
-263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S
-264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S
-265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q
-266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S
-267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S
-268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S
-269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S
-270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S
-271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S
-272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S
-273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S
-274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C
-275,1,3,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q
-276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S
-277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S
-278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S
-279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q
-280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S
-281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q
-282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S
-283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S
-284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S
-285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S
-286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C
-287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S
-288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S
-289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S
-290,1,3,"Connolly, Miss. Kate",female,22,0,0,370373,7.75,,Q
-291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S
-292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C
-293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C
-294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S
-295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S
-296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C
-297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C
-298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S
-299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S
-300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C
-301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q
-302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q
-303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S
-304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q
-305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S
-306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S
-307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C
-308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17,1,0,PC 17758,108.9,C65,C
-309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C
-310,1,1,"Francatelli, Miss. Laura Mabel",female,30,0,0,PC 17485,56.9292,E36,C
-311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C
-312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C
-313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S
-314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S
-315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S
-316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S
-317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S
-318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S
-319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S
-320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C
-321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S
-322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S
-323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q
-324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S
-325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S
-326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C
-327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S
-328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S
-329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S
-330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C
-331,1,3,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q
-332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S
-333,0,1,"Graham, Mr. George Edward",male,38,0,1,PC 17582,153.4625,C91,S
-334,0,3,"Vander Planke, Mr. Leo Edmondus",male,16,2,0,345764,18,,S
-335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S
-336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S
-337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S
-338,1,1,"Burns, Miss. Elizabeth Margaret",female,41,0,0,16966,134.5,E40,C
-339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S
-340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S
-341,1,2,"Navratil, Master. Edmond Roger",male,2,1,1,230080,26,F2,S
-342,1,1,"Fortune, Miss. Alice Elizabeth",female,24,3,2,19950,263,C23 C25 C27,S
-343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S
-344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S
-345,0,2,"Fox, Mr. Stanley Hubert",male,36,0,0,229236,13,,S
-346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S
-347,1,2,"Smith, Miss. Marion Elsie",female,40,0,0,31418,13,,S
-348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S
-349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S
-350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S
-351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S
-352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S
-353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C
-354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S
-355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C
-356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S
-357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S
-358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S
-359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q
-360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q
-361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S
-362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C
-363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C
-364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S
-365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q
-366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S
-367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60,1,0,110813,75.25,D37,C
-368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C
-369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q
-370,1,1,"Aubart, Mme. Leontine Pauline",female,24,0,0,PC 17477,69.3,B35,C
-371,1,1,"Harder, Mr. George Achilles",male,25,1,0,11765,55.4417,E50,C
-372,0,3,"Wiklund, Mr. Jakob Alfred",male,18,1,0,3101267,6.4958,,S
-373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S
-374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C
-375,0,3,"Palsson, Miss. Stina Viola",female,3,3,1,349909,21.075,,S
-376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C
-377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S
-378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C
-379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C
-380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S
-381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C
-382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C
-383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S
-384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35,1,0,113789,52,,S
-385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S
-386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S
-387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S
-388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S
-389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q
-390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C
-391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S
-392,1,3,"Jansson, Mr. Carl Olof",male,21,0,0,350034,7.7958,,S
-393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S
-394,1,1,"Newell, Miss. Marjorie",female,23,1,0,35273,113.275,D36,C
-395,1,3,"Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)",female,24,0,2,PP 9549,16.7,G6,S
-396,0,3,"Johansson, Mr. Erik",male,22,0,0,350052,7.7958,,S
-397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S
-398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S
-399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S
-400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S
-401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S
-402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S
-403,0,3,"Jussila, Miss. Mari Aina",female,21,1,0,4137,9.825,,S
-404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S
-405,0,3,"Oreskovic, Miss. Marija",female,20,0,0,315096,8.6625,,S
-406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S
-407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.75,,S
-408,1,2,"Richards, Master. William Rowe",male,3,1,1,29106,18.75,,S
-409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S
-410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S
-411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S
-412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q
-413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q
-414,0,2,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0,,S
-415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S
-416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S
-417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S
-418,1,2,"Silven, Miss. Lyyli Karoliina",female,18,0,2,250652,13,,S
-419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S
-420,0,3,"Van Impe, Miss. Catharina",female,10,0,2,345773,24.15,,S
-421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C
-422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q
-423,0,3,"Zimmerman, Mr. Leo",male,29,0,0,315082,7.875,,S
-424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S
-425,0,3,"Rosblom, Mr. Viktor Richard",male,18,1,1,370129,20.2125,,S
-426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S
-427,1,2,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28,1,0,2003,26,,S
-428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S
-429,0,3,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q
-430,1,3,"Pickard, Mr. Berk (Berk Trembisky)",male,32,0,0,SOTON/O.Q. 392078,8.05,E10,S
-431,1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28,0,0,110564,26.55,C52,S
-432,1,3,"Thorneycroft, Mrs. Percival (Florence Kate White)",female,,1,0,376564,16.1,,S
-433,1,2,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",female,42,1,0,SC/AH 3085,26,,S
-434,0,3,"Kallio, Mr. Nikolai Erland",male,17,0,0,STON/O 2. 3101274,7.125,,S
-435,0,1,"Silvey, Mr. William Baird",male,50,1,0,13507,55.9,E44,S
-436,1,1,"Carter, Miss. Lucile Polk",female,14,1,2,113760,120,B96 B98,S
-437,0,3,"Ford, Miss. Doolina Margaret ""Daisy""",female,21,2,2,W./C. 6608,34.375,,S
-438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S
-439,0,1,"Fortune, Mr. Mark",male,64,1,4,19950,263,C23 C25 C27,S
-440,0,2,"Kvillner, Mr. Johan Henrik Johannesson",male,31,0,0,C.A. 18723,10.5,,S
-441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45,1,1,F.C.C. 13529,26.25,,S
-442,0,3,"Hampe, Mr. Leon",male,20,0,0,345769,9.5,,S
-443,0,3,"Petterson, Mr. Johan Emil",male,25,1,0,347076,7.775,,S
-444,1,2,"Reynaldo, Ms. Encarnacion",female,28,0,0,230434,13,,S
-445,1,3,"Johannesen-Bratthammer, Mr. Bernt",male,,0,0,65306,8.1125,,S
-446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S
-447,1,2,"Mellinger, Miss. Madeleine Violet",female,13,0,1,250644,19.5,,S
-448,1,1,"Seward, Mr. Frederic Kimber",male,34,0,0,113794,26.55,,S
-449,1,3,"Baclini, Miss. Marie Catherine",female,5,2,1,2666,19.2583,,C
-450,1,1,"Peuchen, Major. Arthur Godfrey",male,52,0,0,113786,30.5,C104,S
-451,0,2,"West, Mr. Edwy Arthur",male,36,1,2,C.A. 34651,27.75,,S
-452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S
-453,0,1,"Foreman, Mr. Benjamin Laventall",male,30,0,0,113051,27.75,C111,C
-454,1,1,"Goldenberg, Mr. Samuel L",male,49,1,0,17453,89.1042,C92,C
-455,0,3,"Peduzzi, Mr. Joseph",male,,0,0,A/5 2817,8.05,,S
-456,1,3,"Jalsevac, Mr. Ivan",male,29,0,0,349240,7.8958,,C
-457,0,1,"Millet, Mr. Francis Davis",male,65,0,0,13509,26.55,E38,S
-458,1,1,"Kenyon, Mrs. Frederick R (Marion)",female,,1,0,17464,51.8625,D21,S
-459,1,2,"Toomey, Miss. Ellen",female,50,0,0,F.C.C. 13531,10.5,,S
-460,0,3,"O'Connor, Mr. Maurice",male,,0,0,371060,7.75,,Q
-461,1,1,"Anderson, Mr. Harry",male,48,0,0,19952,26.55,E12,S
-462,0,3,"Morley, Mr. William",male,34,0,0,364506,8.05,,S
-463,0,1,"Gee, Mr. Arthur H",male,47,0,0,111320,38.5,E63,S
-464,0,2,"Milling, Mr. Jacob Christian",male,48,0,0,234360,13,,S
-465,0,3,"Maisner, Mr. Simon",male,,0,0,A/S 2816,8.05,,S
-466,0,3,"Goncalves, Mr. Manuel Estanslas",male,38,0,0,SOTON/O.Q. 3101306,7.05,,S
-467,0,2,"Campbell, Mr. William",male,,0,0,239853,0,,S
-468,0,1,"Smart, Mr. John Montgomery",male,56,0,0,113792,26.55,,S
-469,0,3,"Scanlan, Mr. James",male,,0,0,36209,7.725,,Q
-470,1,3,"Baclini, Miss. Helene Barbara",female,0.75,2,1,2666,19.2583,,C
-471,0,3,"Keefe, Mr. Arthur",male,,0,0,323592,7.25,,S
-472,0,3,"Cacic, Mr. Luka",male,38,0,0,315089,8.6625,,S
-473,1,2,"West, Mrs. Edwy Arthur (Ada Mary Worth)",female,33,1,2,C.A. 34651,27.75,,S
-474,1,2,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23,0,0,SC/AH Basle 541,13.7917,D,C
-475,0,3,"Strandberg, Miss. Ida Sofia",female,22,0,0,7553,9.8375,,S
-476,0,1,"Clifford, Mr. George Quincy",male,,0,0,110465,52,A14,S
-477,0,2,"Renouf, Mr. Peter Henry",male,34,1,0,31027,21,,S
-478,0,3,"Braund, Mr. Lewis Richard",male,29,1,0,3460,7.0458,,S
-479,0,3,"Karlsson, Mr. Nils August",male,22,0,0,350060,7.5208,,S
-480,1,3,"Hirvonen, Miss. Hildur E",female,2,0,1,3101298,12.2875,,S
-481,0,3,"Goodwin, Master. Harold Victor",male,9,5,2,CA 2144,46.9,,S
-482,0,2,"Frost, Mr. Anthony Wood ""Archie""",male,,0,0,239854,0,,S
-483,0,3,"Rouse, Mr. Richard Henry",male,50,0,0,A/5 3594,8.05,,S
-484,1,3,"Turkula, Mrs. (Hedwig)",female,63,0,0,4134,9.5875,,S
-485,1,1,"Bishop, Mr. Dickinson H",male,25,1,0,11967,91.0792,B49,C
-486,0,3,"Lefebre, Miss. Jeannie",female,,3,1,4133,25.4667,,S
-487,1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35,1,0,19943,90,C93,S
-488,0,1,"Kent, Mr. Edward Austin",male,58,0,0,11771,29.7,B37,C
-489,0,3,"Somerton, Mr. Francis William",male,30,0,0,A.5. 18509,8.05,,S
-490,1,3,"Coutts, Master. Eden Leslie ""Neville""",male,9,1,1,C.A. 37671,15.9,,S
-491,0,3,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S
-492,0,3,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.25,,S
-493,0,1,"Molson, Mr. Harry Markland",male,55,0,0,113787,30.5,C30,S
-494,0,1,"Artagaveytia, Mr. Ramon",male,71,0,0,PC 17609,49.5042,,C
-495,0,3,"Stanley, Mr. Edward Roland",male,21,0,0,A/4 45380,8.05,,S
-496,0,3,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C
-497,1,1,"Eustis, Miss. Elizabeth Mussey",female,54,1,0,36947,78.2667,D20,C
-498,0,3,"Shellard, Mr. Frederick William",male,,0,0,C.A. 6212,15.1,,S
-499,0,1,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25,1,2,113781,151.55,C22 C26,S
-500,0,3,"Svensson, Mr. Olof",male,24,0,0,350035,7.7958,,S
-501,0,3,"Calic, Mr. Petar",male,17,0,0,315086,8.6625,,S
-502,0,3,"Canavan, Miss. Mary",female,21,0,0,364846,7.75,,Q
-503,0,3,"O'Sullivan, Miss. Bridget Mary",female,,0,0,330909,7.6292,,Q
-504,0,3,"Laitinen, Miss. Kristina Sofia",female,37,0,0,4135,9.5875,,S
-505,1,1,"Maioni, Miss. Roberta",female,16,0,0,110152,86.5,B79,S
-506,0,1,"Penasco y Castellana, Mr. Victor de Satode",male,18,1,0,PC 17758,108.9,C65,C
-507,1,2,"Quick, Mrs. Frederick Charles (Jane Richards)",female,33,0,2,26360,26,,S
-508,1,1,"Bradley, Mr. George (""George Arthur Brayton"")",male,,0,0,111427,26.55,,S
-509,0,3,"Olsen, Mr. Henry Margido",male,28,0,0,C 4001,22.525,,S
-510,1,3,"Lang, Mr. Fang",male,26,0,0,1601,56.4958,,S
-511,1,3,"Daly, Mr. Eugene Patrick",male,29,0,0,382651,7.75,,Q
-512,0,3,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.05,,S
-513,1,1,"McGough, Mr. James Robert",male,36,0,0,PC 17473,26.2875,E25,S
-514,1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54,1,0,PC 17603,59.4,,C
-515,0,3,"Coleff, Mr. Satio",male,24,0,0,349209,7.4958,,S
-516,0,1,"Walker, Mr. William Anderson",male,47,0,0,36967,34.0208,D46,S
-517,1,2,"Lemore, Mrs. (Amelia Milley)",female,34,0,0,C.A. 34260,10.5,F33,S
-518,0,3,"Ryan, Mr. Patrick",male,,0,0,371110,24.15,,Q
-519,1,2,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36,1,0,226875,26,,S
-520,0,3,"Pavlovic, Mr. Stefo",male,32,0,0,349242,7.8958,,S
-521,1,1,"Perreault, Miss. Anne",female,30,0,0,12749,93.5,B73,S
-522,0,3,"Vovk, Mr. Janko",male,22,0,0,349252,7.8958,,S
-523,0,3,"Lahoud, Mr. Sarkis",male,,0,0,2624,7.225,,C
-524,1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44,0,1,111361,57.9792,B18,C
-525,0,3,"Kassem, Mr. Fared",male,,0,0,2700,7.2292,,C
-526,0,3,"Farrell, Mr. James",male,40.5,0,0,367232,7.75,,Q
-527,1,2,"Ridsdale, Miss. Lucy",female,50,0,0,W./C. 14258,10.5,,S
-528,0,1,"Farthing, Mr. John",male,,0,0,PC 17483,221.7792,C95,S
-529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S
-530,0,2,"Hocking, Mr. Richard George",male,23,2,1,29104,11.5,,S
-531,1,2,"Quick, Miss. Phyllis May",female,2,1,1,26360,26,,S
-532,0,3,"Toufik, Mr. Nakli",male,,0,0,2641,7.2292,,C
-533,0,3,"Elias, Mr. Joseph Jr",male,17,1,1,2690,7.2292,,C
-534,1,3,"Peter, Mrs. Catherine (Catherine Rizk)",female,,0,2,2668,22.3583,,C
-535,0,3,"Cacic, Miss. Marija",female,30,0,0,315084,8.6625,,S
-536,1,2,"Hart, Miss. Eva Miriam",female,7,0,2,F.C.C. 13529,26.25,,S
-537,0,1,"Butt, Major. Archibald Willingham",male,45,0,0,113050,26.55,B38,S
-538,1,1,"LeRoy, Miss. Bertha",female,30,0,0,PC 17761,106.425,,C
-539,0,3,"Risien, Mr. Samuel Beard",male,,0,0,364498,14.5,,S
-540,1,1,"Frolicher, Miss. Hedwig Margaritha",female,22,0,2,13568,49.5,B39,C
-541,1,1,"Crosby, Miss. Harriet R",female,36,0,2,WE/P 5735,71,B22,S
-542,0,3,"Andersson, Miss. Ingeborg Constanzia",female,9,4,2,347082,31.275,,S
-543,0,3,"Andersson, Miss. Sigrid Elisabeth",female,11,4,2,347082,31.275,,S
-544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S
-545,0,1,"Douglas, Mr. Walter Donald",male,50,1,0,PC 17761,106.425,C86,C
-546,0,1,"Nicholson, Mr. Arthur Ernest",male,64,0,0,693,26,,S
-547,1,2,"Beane, Mrs. Edward (Ethel Clarke)",female,19,1,0,2908,26,,S
-548,1,2,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C
-549,0,3,"Goldsmith, Mr. Frank John",male,33,1,1,363291,20.525,,S
-550,1,2,"Davies, Master. John Morgan Jr",male,8,1,1,C.A. 33112,36.75,,S
-551,1,1,"Thayer, Mr. John Borland Jr",male,17,0,2,17421,110.8833,C70,C
-552,0,2,"Sharp, Mr. Percival James R",male,27,0,0,244358,26,,S
-553,0,3,"O'Brien, Mr. Timothy",male,,0,0,330979,7.8292,,Q
-554,1,3,"Leeni, Mr. Fahim (""Philip Zenni"")",male,22,0,0,2620,7.225,,C
-555,1,3,"Ohman, Miss. Velin",female,22,0,0,347085,7.775,,S
-556,0,1,"Wright, Mr. George",male,62,0,0,113807,26.55,,S
-557,1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48,1,0,11755,39.6,A16,C
-558,0,1,"Robbins, Mr. Victor",male,,0,0,PC 17757,227.525,,C
-559,1,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",female,39,1,1,110413,79.65,E67,S
-560,1,3,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",female,36,1,0,345572,17.4,,S
-561,0,3,"Morrow, Mr. Thomas Rowan",male,,0,0,372622,7.75,,Q
-562,0,3,"Sivic, Mr. Husein",male,40,0,0,349251,7.8958,,S
-563,0,2,"Norman, Mr. Robert Douglas",male,28,0,0,218629,13.5,,S
-564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S
-565,0,3,"Meanwell, Miss. (Marion Ogden)",female,,0,0,SOTON/O.Q. 392087,8.05,,S
-566,0,3,"Davies, Mr. Alfred J",male,24,2,0,A/4 48871,24.15,,S
-567,0,3,"Stoytcheff, Mr. Ilia",male,19,0,0,349205,7.8958,,S
-568,0,3,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",female,29,0,4,349909,21.075,,S
-569,0,3,"Doharr, Mr. Tannous",male,,0,0,2686,7.2292,,C
-570,1,3,"Jonsson, Mr. Carl",male,32,0,0,350417,7.8542,,S
-571,1,2,"Harris, Mr. George",male,62,0,0,S.W./PP 752,10.5,,S
-572,1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53,2,0,11769,51.4792,C101,S
-573,1,1,"Flynn, Mr. John Irwin (""Irving"")",male,36,0,0,PC 17474,26.3875,E25,S
-574,1,3,"Kelly, Miss. Mary",female,,0,0,14312,7.75,,Q
-575,0,3,"Rush, Mr. Alfred George John",male,16,0,0,A/4. 20589,8.05,,S
-576,0,3,"Patchett, Mr. George",male,19,0,0,358585,14.5,,S
-577,1,2,"Garside, Miss. Ethel",female,34,0,0,243880,13,,S
-578,1,1,"Silvey, Mrs. William Baird (Alice Munger)",female,39,1,0,13507,55.9,E44,S
-579,0,3,"Caram, Mrs. Joseph (Maria Elias)",female,,1,0,2689,14.4583,,C
-580,1,3,"Jussila, Mr. Eiriik",male,32,0,0,STON/O 2. 3101286,7.925,,S
-581,1,2,"Christy, Miss. Julie Rachel",female,25,1,1,237789,30,,S
-582,1,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",female,39,1,1,17421,110.8833,C68,C
-583,0,2,"Downton, Mr. William James",male,54,0,0,28403,26,,S
-584,0,1,"Ross, Mr. John Hugo",male,36,0,0,13049,40.125,A10,C
-585,0,3,"Paulner, Mr. Uscher",male,,0,0,3411,8.7125,,C
-586,1,1,"Taussig, Miss. Ruth",female,18,0,2,110413,79.65,E68,S
-587,0,2,"Jarvis, Mr. John Denzil",male,47,0,0,237565,15,,S
-588,1,1,"Frolicher-Stehli, Mr. Maxmillian",male,60,1,1,13567,79.2,B41,C
-589,0,3,"Gilinski, Mr. Eliezer",male,22,0,0,14973,8.05,,S
-590,0,3,"Murdlin, Mr. Joseph",male,,0,0,A./5. 3235,8.05,,S
-591,0,3,"Rintamaki, Mr. Matti",male,35,0,0,STON/O 2. 3101273,7.125,,S
-592,1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52,1,0,36947,78.2667,D20,C
-593,0,3,"Elsbury, Mr. William James",male,47,0,0,A/5 3902,7.25,,S
-594,0,3,"Bourke, Miss. Mary",female,,0,2,364848,7.75,,Q
-595,0,2,"Chapman, Mr. John Henry",male,37,1,0,SC/AH 29037,26,,S
-596,0,3,"Van Impe, Mr. Jean Baptiste",male,36,1,1,345773,24.15,,S
-597,1,2,"Leitch, Miss. Jessie Wills",female,,0,0,248727,33,,S
-598,0,3,"Johnson, Mr. Alfred",male,49,0,0,LINE,0,,S
-599,0,3,"Boulos, Mr. Hanna",male,,0,0,2664,7.225,,C
-600,1,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",male,49,1,0,PC 17485,56.9292,A20,C
-601,1,2,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",female,24,2,1,243847,27,,S
-602,0,3,"Slabenoff, Mr. Petco",male,,0,0,349214,7.8958,,S
-603,0,1,"Harrington, Mr. Charles H",male,,0,0,113796,42.4,,S
-604,0,3,"Torber, Mr. Ernst William",male,44,0,0,364511,8.05,,S
-605,1,1,"Homer, Mr. Harry (""Mr E Haven"")",male,35,0,0,111426,26.55,,C
-606,0,3,"Lindell, Mr. Edvard Bengtsson",male,36,1,0,349910,15.55,,S
-607,0,3,"Karaic, Mr. Milan",male,30,0,0,349246,7.8958,,S
-608,1,1,"Daniel, Mr. Robert Williams",male,27,0,0,113804,30.5,,S
-609,1,2,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",female,22,1,2,SC/Paris 2123,41.5792,,C
-610,1,1,"Shutes, Miss. Elizabeth W",female,40,0,0,PC 17582,153.4625,C125,S
-611,0,3,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",female,39,1,5,347082,31.275,,S
-612,0,3,"Jardin, Mr. Jose Neto",male,,0,0,SOTON/O.Q. 3101305,7.05,,S
-613,1,3,"Murphy, Miss. Margaret Jane",female,,1,0,367230,15.5,,Q
-614,0,3,"Horgan, Mr. John",male,,0,0,370377,7.75,,Q
-615,0,3,"Brocklebank, Mr. William Alfred",male,35,0,0,364512,8.05,,S
-616,1,2,"Herman, Miss. Alice",female,24,1,2,220845,65,,S
-617,0,3,"Danbom, Mr. Ernst Gilbert",male,34,1,1,347080,14.4,,S
-618,0,3,"Lobb, Mrs. William Arthur (Cordelia K Stanlick)",female,26,1,0,A/5. 3336,16.1,,S
-619,1,2,"Becker, Miss. Marion Louise",female,4,2,1,230136,39,F4,S
-620,0,2,"Gavey, Mr. Lawrence",male,26,0,0,31028,10.5,,S
-621,0,3,"Yasbeck, Mr. Antoni",male,27,1,0,2659,14.4542,,C
-622,1,1,"Kimball, Mr. Edwin Nelson Jr",male,42,1,0,11753,52.5542,D19,S
-623,1,3,"Nakid, Mr. Sahid",male,20,1,1,2653,15.7417,,C
-624,0,3,"Hansen, Mr. Henry Damsgaard",male,21,0,0,350029,7.8542,,S
-625,0,3,"Bowen, Mr. David John ""Dai""",male,21,0,0,54636,16.1,,S
-626,0,1,"Sutton, Mr. Frederick",male,61,0,0,36963,32.3208,D50,S
-627,0,2,"Kirkland, Rev. Charles Leonard",male,57,0,0,219533,12.35,,Q
-628,1,1,"Longley, Miss. Gretchen Fiske",female,21,0,0,13502,77.9583,D9,S
-629,0,3,"Bostandyeff, Mr. Guentcho",male,26,0,0,349224,7.8958,,S
-630,0,3,"O'Connell, Mr. Patrick D",male,,0,0,334912,7.7333,,Q
-631,1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80,0,0,27042,30,A23,S
-632,0,3,"Lundahl, Mr. Johan Svensson",male,51,0,0,347743,7.0542,,S
-633,1,1,"Stahelin-Maeglin, Dr. Max",male,32,0,0,13214,30.5,B50,C
-634,0,1,"Parr, Mr. William Henry Marsh",male,,0,0,112052,0,,S
-635,0,3,"Skoog, Miss. Mabel",female,9,3,2,347088,27.9,,S
-636,1,2,"Davis, Miss. Mary",female,28,0,0,237668,13,,S
-637,0,3,"Leinonen, Mr. Antti Gustaf",male,32,0,0,STON/O 2. 3101292,7.925,,S
-638,0,2,"Collyer, Mr. Harvey",male,31,1,1,C.A. 31921,26.25,,S
-639,0,3,"Panula, Mrs. Juha (Maria Emilia Ojala)",female,41,0,5,3101295,39.6875,,S
-640,0,3,"Thorneycroft, Mr. Percival",male,,1,0,376564,16.1,,S
-641,0,3,"Jensen, Mr. Hans Peder",male,20,0,0,350050,7.8542,,S
-642,1,1,"Sagesser, Mlle. Emma",female,24,0,0,PC 17477,69.3,B35,C
-643,0,3,"Skoog, Miss. Margit Elizabeth",female,2,3,2,347088,27.9,,S
-644,1,3,"Foo, Mr. Choong",male,,0,0,1601,56.4958,,S
-645,1,3,"Baclini, Miss. Eugenie",female,0.75,2,1,2666,19.2583,,C
-646,1,1,"Harper, Mr. Henry Sleeper",male,48,1,0,PC 17572,76.7292,D33,C
-647,0,3,"Cor, Mr. Liudevit",male,19,0,0,349231,7.8958,,S
-648,1,1,"Simonius-Blumer, Col. Oberst Alfons",male,56,0,0,13213,35.5,A26,C
-649,0,3,"Willey, Mr. Edward",male,,0,0,S.O./P.P. 751,7.55,,S
-650,1,3,"Stanley, Miss. Amy Zillah Elsie",female,23,0,0,CA. 2314,7.55,,S
-651,0,3,"Mitkoff, Mr. Mito",male,,0,0,349221,7.8958,,S
-652,1,2,"Doling, Miss. Elsie",female,18,0,1,231919,23,,S
-653,0,3,"Kalvik, Mr. Johannes Halvorsen",male,21,0,0,8475,8.4333,,S
-654,1,3,"O'Leary, Miss. Hanora ""Norah""",female,,0,0,330919,7.8292,,Q
-655,0,3,"Hegarty, Miss. Hanora ""Nora""",female,18,0,0,365226,6.75,,Q
-656,0,2,"Hickman, Mr. Leonard Mark",male,24,2,0,S.O.C. 14879,73.5,,S
-657,0,3,"Radeff, Mr. Alexander",male,,0,0,349223,7.8958,,S
-658,0,3,"Bourke, Mrs. John (Catherine)",female,32,1,1,364849,15.5,,Q
-659,0,2,"Eitemiller, Mr. George Floyd",male,23,0,0,29751,13,,S
-660,0,1,"Newell, Mr. Arthur Webster",male,58,0,2,35273,113.275,D48,C
-661,1,1,"Frauenthal, Dr. Henry William",male,50,2,0,PC 17611,133.65,,S
-662,0,3,"Badt, Mr. Mohamed",male,40,0,0,2623,7.225,,C
-663,0,1,"Colley, Mr. Edward Pomeroy",male,47,0,0,5727,25.5875,E58,S
-664,0,3,"Coleff, Mr. Peju",male,36,0,0,349210,7.4958,,S
-665,1,3,"Lindqvist, Mr. Eino William",male,20,1,0,STON/O 2. 3101285,7.925,,S
-666,0,2,"Hickman, Mr. Lewis",male,32,2,0,S.O.C. 14879,73.5,,S
-667,0,2,"Butler, Mr. Reginald Fenton",male,25,0,0,234686,13,,S
-668,0,3,"Rommetvedt, Mr. Knud Paust",male,,0,0,312993,7.775,,S
-669,0,3,"Cook, Mr. Jacob",male,43,0,0,A/5 3536,8.05,,S
-670,1,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",female,,1,0,19996,52,C126,S
-671,1,2,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",female,40,1,1,29750,39,,S
-672,0,1,"Davidson, Mr. Thornton",male,31,1,0,F.C. 12750,52,B71,S
-673,0,2,"Mitchell, Mr. Henry Michael",male,70,0,0,C.A. 24580,10.5,,S
-674,1,2,"Wilhelms, Mr. Charles",male,31,0,0,244270,13,,S
-675,0,2,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0,,S
-676,0,3,"Edvardsson, Mr. Gustaf Hjalmar",male,18,0,0,349912,7.775,,S
-677,0,3,"Sawyer, Mr. Frederick Charles",male,24.5,0,0,342826,8.05,,S
-678,1,3,"Turja, Miss. Anna Sofia",female,18,0,0,4138,9.8417,,S
-679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43,1,6,CA 2144,46.9,,S
-680,1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36,0,1,PC 17755,512.3292,B51 B53 B55,C
-681,0,3,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q
-682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C
-683,0,3,"Olsvigen, Mr. Thor Anderson",male,20,0,0,6563,9.225,,S
-684,0,3,"Goodwin, Mr. Charles Edward",male,14,5,2,CA 2144,46.9,,S
-685,0,2,"Brown, Mr. Thomas William Solomon",male,60,1,1,29750,39,,S
-686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25,1,2,SC/Paris 2123,41.5792,,C
-687,0,3,"Panula, Mr. Jaako Arnold",male,14,4,1,3101295,39.6875,,S
-688,0,3,"Dakic, Mr. Branko",male,19,0,0,349228,10.1708,,S
-689,0,3,"Fischer, Mr. Eberhard Thelander",male,18,0,0,350036,7.7958,,S
-690,1,1,"Madill, Miss. Georgette Alexandra",female,15,0,1,24160,211.3375,B5,S
-691,1,1,"Dick, Mr. Albert Adrian",male,31,1,0,17474,57,B20,S
-692,1,3,"Karun, Miss. Manca",female,4,0,1,349256,13.4167,,C
-693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S
-694,0,3,"Saad, Mr. Khalil",male,25,0,0,2672,7.225,,C
-695,0,1,"Weir, Col. John",male,60,0,0,113800,26.55,,S
-696,0,2,"Chapman, Mr. Charles Henry",male,52,0,0,248731,13.5,,S
-697,0,3,"Kelly, Mr. James",male,44,0,0,363592,8.05,,S
-698,1,3,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q
-699,0,1,"Thayer, Mr. John Borland",male,49,1,1,17421,110.8833,C68,C
-700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42,0,0,348121,7.65,F G63,S
-701,1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18,1,0,PC 17757,227.525,C62 C64,C
-702,1,1,"Silverthorne, Mr. Spencer Victor",male,35,0,0,PC 17475,26.2875,E24,S
-703,0,3,"Barbara, Miss. Saiide",female,18,0,1,2691,14.4542,,C
-704,0,3,"Gallagher, Mr. Martin",male,25,0,0,36864,7.7417,,Q
-705,0,3,"Hansen, Mr. Henrik Juul",male,26,1,0,350025,7.8542,,S
-706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39,0,0,250655,26,,S
-707,1,2,"Kelly, Mrs. Florence ""Fannie""",female,45,0,0,223596,13.5,,S
-708,1,1,"Calderhead, Mr. Edward Pennington",male,42,0,0,PC 17476,26.2875,E24,S
-709,1,1,"Cleaver, Miss. Alice",female,22,0,0,113781,151.55,,S
-710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C
-711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C
-712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S
-713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S
-714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S
-715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S
-716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S
-717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C
-718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S
-719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q
-720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S
-721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S
-722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S
-723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S
-724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S
-725,1,1,"Chambers, Mr. Norman Campbell",male,27,1,0,113806,53.1,E8,S
-726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S
-727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S
-728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q
-729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S
-730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S
-731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S
-732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C
-733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S
-734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S
-735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S
-736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S
-737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S
-738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C
-739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S
-740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S
-741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S
-742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S
-743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C
-744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S
-745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S
-746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S
-747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S
-748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S
-749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S
-750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q
-751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S
-752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S
-753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S
-754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S
-755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S
-756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S
-757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S
-758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S
-759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S
-760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S
-761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S
-762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S
-763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C
-764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36,1,2,113760,120,B96 B98,S
-765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S
-766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S
-767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C
-768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q
-769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q
-770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S
-771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S
-772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S
-773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S
-774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C
-775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S
-776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S
-777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q
-778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S
-779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q
-780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S
-781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C
-782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S
-783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S
-784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S
-785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S
-786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S
-787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S
-788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q
-789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S
-790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C
-791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q
-792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S
-793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S
-794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C
-795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S
-796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S
-797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S
-798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S
-799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C
-800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S
-801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S
-802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S
-803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S
-804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C
-805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S
-806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S
-807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S
-808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S
-809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S
-810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S
-811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S
-812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S
-813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S
-814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S
-815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S
-816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S
-817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S
-818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C
-819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S
-820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S
-821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
-822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
-823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S
-824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S
-825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
-826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
-827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
-828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C
-829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
-830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
-831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C
-832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
-833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
-834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S
-835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S
-836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
-837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
-838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
-839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
-840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
-841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
-842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
-843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
-844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
-845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
-846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
-847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
-848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
-849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
-850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
-851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
-852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
-853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
-854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
-855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
-856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
-857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
-858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
-859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
-860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
-861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
-862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
-863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
-864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
-865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
-866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
-867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
-868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
-869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
-870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
-871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
-872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
-873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
-874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
-875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
-876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
-877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
-878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
-879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
-880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
-881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
-882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
-883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
-884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
-885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
-886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
-887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
-888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
-889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
-890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
-891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q
diff --git a/how-to-use-azureml/work-with-data/datasets/train-dataset/train.py b/how-to-use-azureml/work-with-data/datasets/train-dataset/train.py
deleted file mode 100644
index ab48c02f..00000000
--- a/how-to-use-azureml/work-with-data/datasets/train-dataset/train.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import azureml.dataprep as dprep
-import azureml.core
-import pandas as pd
-import logging
-import os
-import datetime
-import shutil
-
-from azureml.core import Workspace, Datastore, Dataset, Experiment, Run
-from sklearn.model_selection import train_test_split
-from azureml.core.compute import ComputeTarget, AmlCompute
-from azureml.core.compute_target import ComputeTargetException
-from sklearn.tree import DecisionTreeClassifier
-
-run = Run.get_context()
-workspace = run.experiment.workspace
-
-dataset_name = 'clean_Titanic_tutorial'
-
-snapshot_name = 'train_snapshot'
-
-dataset = Dataset.get(workspace=workspace, name=dataset_name)
-df = dataset.get_snapshot(snapshot_name=snapshot_name).to_pandas_dataframe()
-
-x_col = ['Pclass', 'Sex', 'SibSp', 'Parch']
-y_col = ['Survived']
-x_df = df.loc[:, x_col]
-y_df = df.loc[:, y_col]
-
-x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.2, random_state=223)
-
-data = {"train": {"X": x_train, "y": y_train},
-
-        "test": {"X": x_test, "y": y_test}}
-
-clf = DecisionTreeClassifier().fit(data["train"]["X"], data["train"]["y"])
-
-print('Accuracy of Decision Tree classifier on training set: {:.2f}'.format(clf.score(x_train, y_train)))
-print('Accuracy of Decision Tree classifier on test set: {:.2f}'.format(clf.score(x_test, y_test)))
diff --git a/index.md b/index.md
new file mode 100644
index 00000000..51fe0a68
--- /dev/null
+++ b/index.md
@@ -0,0 +1,206 @@
+
+# Index
+Azure Machine Learning is a cloud service that you use to train, deploy, automate,
+and manage machine learning models. This index should assist in navigating the Azure
+Machine Learning notebook samples and encourage efficient retrieval of topics and content.
+![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/Index.png)
+
+## Getting Started
+
+|Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
+|:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
+
+
+## Tutorials
+
+|Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
+|:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
+| :star:[Use pipelines for batch scoring](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/tutorial-pipeline-batch-scoring-classification.ipynb) | Batch scoring | None | AmlCompute | Published pipeline | Azure ML Pipelines | None |
+
+
+## Training
+
+|Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
+|:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
+
+
+
+## Deployment
+
+
+|Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
+|:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
+
+
+
+## Other Notebooks
+|Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
+|:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
+| [Logging APIs](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb) | Logging APIs and analyzing results | None | None | None | None | None |
+| [configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) |  |  |  |  |  |  |
+| [azure-ml-with-nvidia-rapids](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb) |  |  |  |  |  |  |
+| [auto-ml-classification](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb) |  |  |  |  |  |  |
+| [auto-ml-classification-bank-marketing](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.ipynb) |  |  |  |  |  |  |
+| [auto-ml-classification-credit-card-fraud](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb) |  |  |  |  |  |  |
+| [auto-ml-classification-with-deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb) |  |  |  |  |  |  |
+| [auto-ml-classification-with-onnx](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.ipynb) |  |  |  |  |  |  |
+| [auto-ml-classification-with-whitelisting](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb) |  |  |  |  |  |  |
+| [auto-ml-dataset](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/dataset/auto-ml-dataset.ipynb) |  |  |  |  |  |  |
+| [auto-ml-dataset-remote-execution](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/dataset-remote-execution/auto-ml-dataset-remote-execution.ipynb) |  |  |  |  |  |  |
+| [auto-ml-exploring-previous-runs](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb) |  |  |  |  |  |  |
+| [auto-ml-forecasting-bike-share](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb) |  |  |  |  |  |  |
+| [auto-ml-forecasting-energy-demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb) |  |  |  |  |  |  |
+| [auto-ml-forecasting-orange-juice-sales](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb) |  |  |  |  |  |  |
+| [auto-ml-missing-data-blacklist-early-termination](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb) |  |  |  |  |  |  |
+| [auto-ml-model-explanation](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.ipynb) |  |  |  |  |  |  |
+| [auto-ml-regression](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb) |  |  |  |  |  |  |
+| [auto-ml-regression-concrete-strength](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/regression-concrete-strength/auto-ml-regression-concrete-strength.ipynb) |  |  |  |  |  |  |
+| [auto-ml-regression-hardware-performance](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/regression-hardware-performance/auto-ml-regression-hardware-performance.ipynb) |  |  |  |  |  |  |
+| [auto-ml-remote-amlcompute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/remote-amlcompute/auto-ml-remote-amlcompute.ipynb) |  |  |  |  |  |  |
+| [auto-ml-remote-amlcompute-with-onnx](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/remote-amlcompute-with-onnx/auto-ml-remote-amlcompute-with-onnx.ipynb) |  |  |  |  |  |  |
+| [auto-ml-sample-weight](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/sample-weight/auto-ml-sample-weight.ipynb) |  |  |  |  |  |  |
+| [auto-ml-sparse-data-train-test-split](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/sparse-data-train-test-split/auto-ml-sparse-data-train-test-split.ipynb) |  |  |  |  |  |  |
+| [auto-ml-sql-energy-demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/sql-server/energy-demand/auto-ml-sql-energy-demand.ipynb) |  |  |  |  |  |  |
+| [auto-ml-sql-setup](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/sql-server/setup/auto-ml-sql-setup.ipynb) |  |  |  |  |  |  |
+| [auto-ml-subsampling-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/subsampling/auto-ml-subsampling-local.ipynb) |  |  |  |  |  |  |
+| [build-model-run-history-03](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/amlsdk/build-model-run-history-03.ipynb) |  |  |  |  |  |  |
+| [deploy-to-aci-04](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aci-04.ipynb) |  |  |  |  |  |  |
+| [deploy-to-aks-existingimage-05](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aks-existingimage-05.ipynb) |  |  |  |  |  |  |
+| [ingest-data-02](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/amlsdk/ingest-data-02.ipynb) |  |  |  |  |  |  |
+| [installation-and-configuration-01](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/amlsdk/installation-and-configuration-01.ipynb) |  |  |  |  |  |  |
+| [automl-databricks-local-01](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb) |  |  |  |  |  |  |
+| [automl-databricks-local-with-deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb) |  |  |  |  |  |  |
+| [aml-pipelines-use-databricks-as-compute-target](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb) |  |  |  |  |  |  |
+| [automl_hdi_local_classification](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-hdi/automl_hdi_local_classification.ipynb) |  |  |  |  |  |  |
+| [model-register-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deploy-to-cloud/model-register-and-deploy.ipynb) |  |  |  |  |  |  |
+| [register-model-deploy-local-advanced](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deploy-to-local/register-model-deploy-local-advanced.ipynb) |  |  |  |  |  |  |
+| [register-model-deploy-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deploy-to-local/register-model-deploy-local.ipynb) |  |  |  |  |  |  |
+| [accelerated-models-object-detection](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.ipynb) |  |  |  |  |  |  |
+| [accelerated-models-quickstart](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb) |  |  |  |  |  |  |
+| [accelerated-models-training](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb) |  |  |  |  |  |  |
+| [model-register-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb) |  |  |  |  |  |  |
+| [register-model-deploy-local-advanced](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb) |  |  |  |  |  |  |
+| [register-model-deploy-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb) |  |  |  |  |  |  |
+| [enable-app-insights-in-production-service](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb) |  |  |  |  |  |  |
+| [enable-data-collection-for-models-in-aks](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb) |  |  |  |  |  |  |
+| [onnx-convert-aml-deploy-tinyyolo](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb) |  |  |  |  |  |  |
+| [onnx-inference-facial-expression-recognition-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb) |  |  |  |  |  |  |
+| [onnx-inference-mnist-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb) |  |  |  |  |  |  |
+| [onnx-modelzoo-aml-deploy-resnet50](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb) |  |  |  |  |  |  |
+| [onnx-train-pytorch-aml-deploy-mnist](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb) |  |  |  |  |  |  |
+| [production-deploy-to-aks](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb) |  |  |  |  |  |  |
+| [production-deploy-to-aks-gpu](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb) |  |  |  |  |  |  |
+| [register-model-create-image-deploy-service](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb) |  |  |  |  |  |  |
+| [explain-model-on-amlcompute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb) |  |  |  |  |  |  |
+| [save-retrieve-explanations-run-history](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb) |  |  |  |  |  |  |
+| [train-explain-model-locally-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb) |  |  |  |  |  |  |
+| [train-explain-model-on-amlcompute-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb) |  |  |  |  |  |  |
+| [advanced-feature-transformations-explain-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/tabular-data/advanced-feature-transformations-explain-local.ipynb) |  |  |  |  |  |  |
+| [explain-binary-classification-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/tabular-data/explain-binary-classification-local.ipynb) |  |  |  |  |  |  |
+| [explain-multiclass-classification-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/tabular-data/explain-multiclass-classification-local.ipynb) |  |  |  |  |  |  |
+| [explain-regression-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/tabular-data/explain-regression-local.ipynb) |  |  |  |  |  |  |
+| [simple-feature-transformations-explain-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/tabular-data/simple-feature-transformations-explain-local.ipynb) |  |  |  |  |  |  |
+| [aml-pipelines-data-transfer](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb) |  |  |  |  |  |  |
+| [aml-pipelines-getting-started](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb) |  |  |  |  |  |  |
+| [aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb) |  |  |  |  |  |  |
+| [aml-pipelines-how-to-use-estimatorstep](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-estimatorstep.ipynb) |  |  |  |  |  |  |
+| [aml-pipelines-how-to-use-pipeline-drafts](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb) |  |  |  |  |  |  |
+| [aml-pipelines-parameter-tuning-with-hyperdrive](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb) |  |  |  |  |  |  |
+| [aml-pipelines-publish-and-run-using-rest-endpoint](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb) |  |  |  |  |  |  |
+| [aml-pipelines-setup-schedule-for-a-published-pipeline](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb) |  |  |  |  |  |  |
+| [aml-pipelines-setup-versioned-pipeline-endpoints](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb) |  |  |  |  |  |  |
+| [aml-pipelines-showcasing-datapath-and-pipelineparameter](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb) |  |  |  |  |  |  |
+| [aml-pipelines-use-adla-as-compute-target](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb) |  |  |  |  |  |  |
+| [aml-pipelines-use-databricks-as-compute-target](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb) |  |  |  |  |  |  |
+| [aml-pipelines-with-automated-machine-learning-step](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb) |  |  |  |  |  |  |
+| [aml-pipelines-with-data-dependency-steps](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb) |  |  |  |  |  |  |
+| [nyc-taxi-data-regression-model-building](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb) |  |  |  |  |  |  |
+| [pipeline-batch-scoring](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb) |  |  |  |  |  |  |
+| [pipeline-style-transfer](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.ipynb) |  |  |  |  |  |  |
+| [authentication-in-azureml](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb) |  |  |  |  |  |  |
+| [train-hyperparameter-tune-deploy-with-chainer](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb) |  |  |  |  |  |  |
+| [distributed-chainer](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/chainer/training/distributed-chainer/distributed-chainer.ipynb) |  |  |  |  |  |  |
+| [train-hyperparameter-tune-deploy-with-pytorch](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) |  |  |  |  |  |  |
+| [distributed-pytorch-with-horovod](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb) |  |  |  |  |  |  |
+| [distributed-pytorch-with-nccl-gloo](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/pytorch/training/distributed-pytorch-with-nccl-gloo/distributed-pytorch-with-nccl-gloo.ipynb) |  |  |  |  |  |  |
+| [train-hyperparameter-tune-deploy-with-sklearn](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/scikit-learn/training/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb) |  |  |  |  |  |  |
+| [train-hyperparameter-tune-deploy-with-tensorflow](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) |  |  |  |  |  |  |
+| [distributed-tensorflow-with-horovod](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb) |  |  |  |  |  |  |
+| [distributed-tensorflow-with-parameter-server](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/training/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb) |  |  |  |  |  |  |
+| [train-tensorflow-resume-training](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/training/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb) |  |  |  |  |  |  |
+| [azure-ml-datadrift](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/monitor-models/data-drift/azure-ml-datadrift.ipynb) |  |  |  |  |  |  |
+| [manage-runs](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.ipynb) |  |  |  |  |  |  |
+| [tensorboard](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.ipynb) |  |  |  |  |  |  |
+| [deploy-model](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/deploy-model/deploy-model.ipynb) |  |  |  |  |  |  |
+| [train-and-deploy-pytorch](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-deploy-pytorch/train-and-deploy-pytorch.ipynb) |  |  |  |  |  |  |
+| [train-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.ipynb) |  |  |  |  |  |  |
+| [train-remote](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.ipynb) |  |  |  |  |  |  |
+| [logging-api](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/logging-api/logging-api.ipynb) |  |  |  |  |  |  |
+| [manage-runs](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/manage-runs/manage-runs.ipynb) |  |  |  |  |  |  |
+| [train-hyperparameter-tune-deploy-with-sklearn](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb) |  |  |  |  |  |  |
+| [train-in-spark](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb) |  |  |  |  |  |  |
+| [train-on-amlcompute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb) |  |  |  |  |  |  |
+| [train-on-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-on-local/train-on-local.ipynb) |  |  |  |  |  |  |
+| [train-on-remote-vm](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb) |  |  |  |  |  |  |
+| [train-within-notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb) |  |  |  |  |  |  |
+| [using-environments](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/using-environments/using-environments.ipynb) |  |  |  |  |  |  |
+| [distributed-chainer](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/distributed-chainer/distributed-chainer.ipynb) |  |  |  |  |  |  |
+| [distributed-cntk-with-custom-docker](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/distributed-cntk-with-custom-docker.ipynb) |  |  |  |  |  |  |
+| [distributed-pytorch-with-horovod](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb) |  |  |  |  |  |  |
+| [distributed-tensorflow-with-horovod](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb) |  |  |  |  |  |  |
+| [distributed-tensorflow-with-parameter-server](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb) |  |  |  |  |  |  |
+| [export-run-history-to-tensorboard](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb) |  |  |  |  |  |  |
+| [how-to-use-estimator](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/how-to-use-estimator/how-to-use-estimator.ipynb) |  |  |  |  |  |  |
+| [notebook_example](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/how-to-use-estimator/notebook_example.ipynb) |  |  |  |  |  |  |
+| [tensorboard](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/tensorboard/tensorboard.ipynb) |  |  |  |  |  |  |
+| [train-hyperparameter-tune-deploy-with-chainer](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb) |  |  |  |  |  |  |
+| [train-hyperparameter-tune-deploy-with-keras](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb) |  |  |  |  |  |  |
+| [train-hyperparameter-tune-deploy-with-pytorch](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) |  |  |  |  |  |  |
+| [train-hyperparameter-tune-deploy-with-tensorflow](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) |  |  |  |  |  |  |
+| [train-tensorflow-resume-training](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb) |  |  |  |  |  |  |
+| [new-york-taxi](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi.ipynb) |  |  |  |  |  |  |
+| [new-york-taxi_scale-out](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi_scale-out.ipynb) |  |  |  |  |  |  |
+| [add-column-using-expression](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/add-column-using-expression.ipynb) |  |  |  |  |  |  |
+| [append-columns-and-rows](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/append-columns-and-rows.ipynb) |  |  |  |  |  |  |
+| [assertions](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/assertions.ipynb) |  |  |  |  |  |  |
+| [auto-read-file](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/auto-read-file.ipynb) |  |  |  |  |  |  |
+| [cache](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/cache.ipynb) |  |  |  |  |  |  |
+| [column-manipulations](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/column-manipulations.ipynb) |  |  |  |  |  |  |
+| [column-type-transforms](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/column-type-transforms.ipynb) |  |  |  |  |  |  |
+| [custom-python-transforms](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/custom-python-transforms.ipynb) |  |  |  |  |  |  |
+| [data-ingestion](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/data-ingestion.ipynb) |  |  |  |  |  |  |
+| [data-profile](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/data-profile.ipynb) |  |  |  |  |  |  |
+| [datastore](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/datastore.ipynb) |  |  |  |  |  |  |
+| [derive-column-by-example](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/derive-column-by-example.ipynb) |  |  |  |  |  |  |
+| [external-references](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/external-references.ipynb) |  |  |  |  |  |  |
+| [filtering](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/filtering.ipynb) |  |  |  |  |  |  |
+| [fuzzy-group](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/fuzzy-group.ipynb) |  |  |  |  |  |  |
+| [impute-missing-values](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/impute-missing-values.ipynb) |  |  |  |  |  |  |
+| [join](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/join.ipynb) |  |  |  |  |  |  |
+| [label-encoder](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/label-encoder.ipynb) |  |  |  |  |  |  |
+| [min-max-scaler](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/min-max-scaler.ipynb) |  |  |  |  |  |  |
+| [one-hot-encoder](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/one-hot-encoder.ipynb) |  |  |  |  |  |  |
+| [open-save-dataflows](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/open-save-dataflows.ipynb) |  |  |  |  |  |  |
+| [quantile-transformation](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/quantile-transformation.ipynb) |  |  |  |  |  |  |
+| [random-split](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/random-split.ipynb) |  |  |  |  |  |  |
+| [replace-datasource-replace-reference](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/replace-datasource-replace-reference.ipynb) |  |  |  |  |  |  |
+| [replace-fill-error](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/replace-fill-error.ipynb) |  |  |  |  |  |  |
+| [secrets](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/secrets.ipynb) |  |  |  |  |  |  |
+| [semantic-types](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/semantic-types.ipynb) |  |  |  |  |  |  |
+| [split-column-by-example](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/split-column-by-example.ipynb) |  |  |  |  |  |  |
+| [subsetting-sampling](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/subsetting-sampling.ipynb) |  |  |  |  |  |  |
+| [summarize](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/summarize.ipynb) |  |  |  |  |  |  |
+| [working-with-file-streams](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/working-with-file-streams.ipynb) |  |  |  |  |  |  |
+| [writing-data](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/how-to-guides/writing-data.ipynb) |  |  |  |  |  |  |
+| [getting-started](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/dataprep/tutorials/getting-started/getting-started.ipynb) |  |  |  |  |  |  |
+| [datasets-diff](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets/datasets-diff/datasets-diff.ipynb) |  |  |  |  |  |  |
+| [file-dataset-img-classification](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets/datasets-tutorial/file-dataset-img-classification.ipynb) |  |  |  |  |  |  |
+| [tabular-dataset-tutorial](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets/datasets-tutorial/tabular-dataset-tutorial.ipynb) |  |  |  |  |  |  |
+| [tabular-timeseries-dataset-filtering](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets/datasets-tutorial/tabular-timeseries-dataset-filtering.ipynb) |  |  |  |  |  |  |
+| [train-with-datasets](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets/datasets-tutorial/train-with-datasets.ipynb) |  |  |  |  |  |  |
+| [configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master//setup-environment/configuration.ipynb) |  |  |  |  |  |  |
+| [img-classification-part1-training](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/img-classification-part1-training.ipynb) |  |  |  |  |  |  |
+| [img-classification-part2-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/img-classification-part2-deploy.ipynb) |  |  |  |  |  |  |
+| [regression-automated-ml](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/regression-automated-ml.ipynb) |  |  |  |  |  |  |
+| [tutorial-1st-experiment-sdk-train](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/tutorial-1st-experiment-sdk-train.ipynb) |  |  |  |  |  |  |
+
diff --git a/setup-environment/configuration.ipynb b/setup-environment/configuration.ipynb
index a197eafd..1425c490 100644
--- a/setup-environment/configuration.ipynb
+++ b/setup-environment/configuration.ipynb
@@ -102,7 +102,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.0.48\r\n of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.0.62 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
diff --git a/training/README.md b/training/README.md
deleted file mode 100644
index e69de29b..00000000
diff --git a/tutorials/README.md b/tutorials/README.md
index dbcb5c1e..07575bc0 100644
--- a/tutorials/README.md
+++ b/tutorials/README.md
@@ -8,15 +8,20 @@ two sets of tutorial articles for:
 
 If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
 
+### Create first ML experiment
+
+* [Part 1](https://docs.microsoft.com/azure/machine-learning/service/tutorial-quickstart-setup): Set up workspace & dev environment
+* [Part 2](tutorial-quickstart-train-model.ipynb): Learn the foundational design patterns in Azure Machine Learning service, and train a simple scikit-learn model based on the diabetes data set
+
 ### Image classification
 
  * [Part 1](img-classification-part1-training.ipynb): Train an image classification model with Azure Machine Learning.
  * [Part 2](img-classification-part2-deploy.ipynb): Deploy an image classification model from first tutorial in Azure Container Instance (ACI).
 
  ### Regression
- * [Part 1](regression-part1-data-prep.ipynb): Prepare the data using Azure Machine Learning Data Prep SDK. 
+ * [Part 1](regression-part1-data-prep.ipynb): Prepare the data using Azure Machine Learning Data Prep SDK.
  * [Part 2](regression-part2-automated-ml.ipynb): Train a model using Automated Machine Learning.
 
  Also find quickstarts and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
 
-![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/README.png) 
\ No newline at end of file
+![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/README.png)
\ No newline at end of file
diff --git a/tutorials/dflows.dprep b/tutorials/dflows.dprep
deleted file mode 100644
index 2ff862f2..00000000
--- a/tutorials/dflows.dprep
+++ /dev/null
@@ -1,3025 +0,0 @@
-{
-  "blocks": [
-    {
-      "id": "01111501-eb7e-49e1-9f50-4b1cfa86a785",
-      "type": "Microsoft.DPrep.GetFilesBlock",
-      "arguments": {
-        "isArchive": false,
-        "path": {
-          "target": 1,
-          "resourceDetails": [
-            {
-              "path": "https://dprepdata.blob.core.windows.net/demo/green-small/*",
-              "sas": null,
-              "storageAccountName": null,
-              "storageAccountKey": null
-            }
-          ]
-        }
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "90f226e9-b393-4690-b85c-f90aeb6b3afc",
-      "type": "Microsoft.DPrep.ParseDelimitedBlock",
-      "arguments": {
-        "columnHeadersMode": 2,
-        "fileEncoding": 0,
-        "handleQuotedLineBreaks": false,
-        "preview": false,
-        "separator": ",",
-        "skipRows": 0,
-        "skipRowsMode": 0
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "4292a198-4a35-4e88-be22-cec950aa7586",
-      "type": "Microsoft.DPrep.DropColumnsBlock",
-      "arguments": {
-        "columns": {
-          "type": 0,
-          "details": {
-            "selectedColumns": [
-              "Path"
-            ]
-          }
-        }
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "4e59beb2-ea20-4528-86cd-1ed4ca98ee7d",
-      "type": "Microsoft.DPrep.ReplaceNaBlock",
-      "arguments": {
-        "columns": {
-          "type": 1,
-          "details": {
-            "ignoreCase": false,
-            "term": ".*",
-            "useRegex": true,
-            "matchWholeWord": false,
-            "invert": false
-          }
-        },
-        "useDefaultNaList": true,
-        "useEmptyStringAsNa": true,
-        "useNanAsNa": true
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "a8fa1654-7a5a-43b1-8354-4f98288f9b64",
-      "type": "Microsoft.DPrep.DropNullsBlock",
-      "arguments": {
-        "columnRelationship": 0,
-        "columns": {
-          "type": 1,
-          "details": {
-            "ignoreCase": false,
-            "term": ".*",
-            "useRegex": true,
-            "matchWholeWord": false,
-            "invert": false
-          }
-        }
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "e95b3a50-3bea-4548-9972-f2be1d30737b",
-      "type": "Microsoft.DPrep.RenameColumnsBlock",
-      "arguments": {
-        "columnPairs": [
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "VendorID"
-              }
-            },
-            "newColumnId": "vendor"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "lpep_pickup_datetime"
-              }
-            },
-            "newColumnId": "pickup_datetime"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Lpep_dropoff_datetime"
-              }
-            },
-            "newColumnId": "dropoff_datetime"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "lpep_dropoff_datetime"
-              }
-            },
-            "newColumnId": "dropoff_datetime"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Store_and_fwd_flag"
-              }
-            },
-            "newColumnId": "store_forward"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "store_and_fwd_flag"
-              }
-            },
-            "newColumnId": "store_forward"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Pickup_longitude"
-              }
-            },
-            "newColumnId": "pickup_longitude"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Pickup_latitude"
-              }
-            },
-            "newColumnId": "pickup_latitude"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Dropoff_longitude"
-              }
-            },
-            "newColumnId": "dropoff_longitude"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Dropoff_latitude"
-              }
-            },
-            "newColumnId": "dropoff_latitude"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Passenger_count"
-              }
-            },
-            "newColumnId": "passengers"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Fare_amount"
-              }
-            },
-            "newColumnId": "cost"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Trip_distance"
-              }
-            },
-            "newColumnId": "distance"
-          }
-        ]
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "51c214cd-d34c-46b6-8f22-c38b983e5574",
-      "type": "Microsoft.DPrep.KeepColumnsBlock",
-      "arguments": {
-        "columns": {
-          "type": 0,
-          "details": {
-            "selectedColumns": [
-              "cost",
-              "distance",
-              "dropoff_datetime",
-              "dropoff_latitude",
-              "dropoff_longitude",
-              "passengers",
-              "pickup_datetime",
-              "pickup_latitude",
-              "pickup_longitude",
-              "store_forward",
-              "vendor"
-            ]
-          }
-        }
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "48f6db5e-335b-4ce1-8477-e831e8254a33",
-      "type": "Microsoft.DPrep.AppendRowsBlock",
-      "arguments": {
-        "dataflows": [
-          {
-            "referenceType": 2,
-            "referenceContainerPath": null,
-            "referencedStep": null,
-            "anonymousSteps": [
-              {
-                "id": "34e25357-2ca3-43a1-ab24-0bf90688b18a",
-                "description": {
-                  "localDataProperties": [],
-                  "blockGroup": 0,
-                  "supportedTargets": [
-                    4
-                  ],
-                  "supportedFieldTypes": null,
-                  "supportedMessages": [],
-                  "requiresInitialization": false,
-                  "allowDefaultAdd": true,
-                  "documentation": "Expands the path specified by reading globs and files in folders and outputs one record per file found.",
-                  "type": "Microsoft.DPrep.GetFilesBlock",
-                  "propertyDescriptions": [
-                    {
-                      "name": "path",
-                      "type": 11,
-                      "multipleValues": false,
-                      "domain": {
-                        "type": 7,
-                        "details": {
-                          "allowMultiple": true,
-                          "allowedSources": [
-                            0,
-                            1,
-                            2
-                          ],
-                          "allowedSourceType": 2
-                        }
-                      },
-                      "multiValueDetails": null,
-                      "isRequired": true,
-                      "origin": 1,
-                      "defaultValue": null,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 1,
-                      "condition": null,
-                      "documentation": "The path or paths to expand."
-                    },
-                    {
-                      "name": "isArchive",
-                      "type": 4,
-                      "multipleValues": false,
-                      "domain": null,
-                      "multiValueDetails": null,
-                      "isRequired": false,
-                      "origin": 1,
-                      "defaultValue": false,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": null,
-                      "documentation": "Whether the path or paths point to archive files."
-                    },
-                    {
-                      "name": "archiveOptions",
-                      "type": 7,
-                      "multipleValues": false,
-                      "domain": {
-                        "type": 0,
-                        "details": [
-                          {
-                            "name": "archiveType",
-                            "type": 6,
-                            "multipleValues": false,
-                            "domain": {
-                              "type": 1,
-                              "details": {
-                                "enumName": "ArchiveType",
-                                "enumType": "Microsoft.DPrep.Engine.ArchiveType, Microsoft.DPrep.EngineAPI, Version=0.1.1903.18033, Culture=neutral, PublicKeyToken=null",
-                                "enumValues": [
-                                  0
-                                ]
-                              }
-                            },
-                            "multiValueDetails": null,
-                            "isRequired": true,
-                            "origin": 1,
-                            "defaultValue": null,
-                            "initializeFromProperty": null,
-                            "telemetryStrategy": 0,
-                            "condition": null,
-                            "documentation": "The type of archive file"
-                          },
-                          {
-                            "name": "entryGlob",
-                            "type": 1,
-                            "multipleValues": false,
-                            "domain": null,
-                            "multiValueDetails": null,
-                            "isRequired": false,
-                            "origin": 0,
-                            "defaultValue": null,
-                            "initializeFromProperty": null,
-                            "telemetryStrategy": 0,
-                            "condition": null,
-                            "documentation": "The glob pattern for entries in archive file"
-                          }
-                        ]
-                      },
-                      "multiValueDetails": null,
-                      "isRequired": false,
-                      "origin": 1,
-                      "defaultValue": null,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": {
-                        "condition": {
-                          "property": {
-                            "propertyPath": [
-                              "isArchive"
-                            ]
-                          },
-                          "comparison": 0,
-                          "value": true
-                        },
-                        "aggregate": null
-                      },
-                      "documentation": "Options for archive file"
-                    }
-                  ],
-                  "dataEffectDetails": {
-                    "dataEffect": 9
-                  }
-                },
-                "type": "Microsoft.DPrep.GetFilesBlock",
-                "dataEffectDetails": {
-                  "dataEffect": 9
-                },
-                "propertyDescriptions": [
-                  {
-                    "name": "path",
-                    "type": 11,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 7,
-                      "details": {
-                        "allowMultiple": true,
-                        "allowedSources": [
-                          0,
-                          1,
-                          2
-                        ],
-                        "allowedSourceType": 2
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The path or paths to expand."
-                  },
-                  {
-                    "name": "isArchive",
-                    "type": 4,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": false,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Whether the path or paths point to archive files."
-                  },
-                  {
-                    "name": "archiveOptions",
-                    "type": 7,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 0,
-                      "details": [
-                        {
-                          "name": "archiveType",
-                          "type": 6,
-                          "multipleValues": false,
-                          "domain": {
-                            "type": 1,
-                            "details": {
-                              "enumName": "ArchiveType",
-                              "enumType": "Microsoft.DPrep.Engine.ArchiveType, Microsoft.DPrep.EngineAPI, Version=0.1.1903.18033, Culture=neutral, PublicKeyToken=null",
-                              "enumValues": [
-                                0
-                              ]
-                            }
-                          },
-                          "multiValueDetails": null,
-                          "isRequired": true,
-                          "origin": 1,
-                          "defaultValue": null,
-                          "initializeFromProperty": null,
-                          "telemetryStrategy": 0,
-                          "condition": null,
-                          "documentation": "The type of archive file"
-                        },
-                        {
-                          "name": "entryGlob",
-                          "type": 1,
-                          "multipleValues": false,
-                          "domain": null,
-                          "multiValueDetails": null,
-                          "isRequired": false,
-                          "origin": 0,
-                          "defaultValue": null,
-                          "initializeFromProperty": null,
-                          "telemetryStrategy": 0,
-                          "condition": null,
-                          "documentation": "The glob pattern for entries in archive file"
-                        }
-                      ]
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": {
-                      "condition": {
-                        "property": {
-                          "propertyPath": [
-                            "isArchive"
-                          ]
-                        },
-                        "comparison": 0,
-                        "value": true
-                      },
-                      "aggregate": null
-                    },
-                    "documentation": "Options for archive file"
-                  }
-                ],
-                "arguments": {
-                  "isArchive": false,
-                  "path": {
-                    "target": 1,
-                    "resourceDetails": [
-                      {
-                        "path": "https://dprepdata.blob.core.windows.net/demo/yellow-small/*",
-                        "sas": null,
-                        "storageAccountName": null,
-                        "storageAccountKey": null
-                      }
-                    ]
-                  }
-                }
-              },
-              {
-                "id": "b0198a55-5142-4bc5-a7f5-f4439480f4d2",
-                "description": {
-                  "localDataProperties": [],
-                  "blockGroup": 0,
-                  "supportedTargets": [
-                    5
-                  ],
-                  "supportedFieldTypes": null,
-                  "supportedMessages": [],
-                  "requiresInitialization": false,
-                  "allowDefaultAdd": true,
-                  "documentation": null,
-                  "type": "Microsoft.DPrep.ParseDelimitedBlock",
-                  "propertyDescriptions": [
-                    {
-                      "name": "maxRows",
-                      "type": 2,
-                      "multipleValues": false,
-                      "domain": null,
-                      "multiValueDetails": null,
-                      "isRequired": false,
-                      "origin": 2,
-                      "defaultValue": null,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": null,
-                      "documentation": null
-                    },
-                    {
-                      "name": "preview",
-                      "type": 4,
-                      "multipleValues": false,
-                      "domain": null,
-                      "multiValueDetails": null,
-                      "isRequired": false,
-                      "origin": 2,
-                      "defaultValue": false,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": null,
-                      "documentation": null
-                    },
-                    {
-                      "name": "columnHeadersMode",
-                      "type": 6,
-                      "multipleValues": false,
-                      "domain": {
-                        "type": 1,
-                        "details": {
-                          "enumName": "PromoteHeadersMode",
-                          "enumType": "Microsoft.DPrep.Engine.PromoteHeadersMode, Microsoft.DPrep.EngineAPI, Version=0.1.1903.18033, Culture=neutral, PublicKeyToken=null",
-                          "enumValues": [
-                            0,
-                            1,
-                            2,
-                            3
-                          ]
-                        }
-                      },
-                      "multiValueDetails": null,
-                      "isRequired": true,
-                      "origin": 1,
-                      "defaultValue": 1,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": null,
-                      "documentation": null
-                    },
-                    {
-                      "name": "separator",
-                      "type": 0,
-                      "multipleValues": false,
-                      "domain": {
-                        "type": 6,
-                        "details": {
-                          "values": [
-                            {
-                              "value": ",",
-                              "id": 0
-                            },
-                            {
-                              "value": "\t",
-                              "id": 1
-                            },
-                            {
-                              "value": ":",
-                              "id": 2
-                            },
-                            {
-                              "value": ";",
-                              "id": 3
-                            },
-                            {
-                              "value": " ",
-                              "id": 5
-                            },
-                            {
-                              "value": "=",
-                              "id": 4
-                            },
-                            {
-                              "value": "—",
-                              "id": 7
-                            }
-                          ],
-                          "allowCustom": true,
-                          "customEnumValue": 6
-                        }
-                      },
-                      "multiValueDetails": null,
-                      "isRequired": true,
-                      "origin": 1,
-                      "defaultValue": ",",
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": null,
-                      "documentation": null
-                    },
-                    {
-                      "name": "commentLineCharacter",
-                      "type": 0,
-                      "multipleValues": false,
-                      "domain": null,
-                      "multiValueDetails": null,
-                      "isRequired": false,
-                      "origin": 1,
-                      "defaultValue": null,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": null,
-                      "documentation": null
-                    },
-                    {
-                      "name": "fileEncoding",
-                      "type": 6,
-                      "multipleValues": false,
-                      "domain": {
-                        "type": 1,
-                        "details": {
-                          "enumName": "FileEncoding",
-                          "enumType": "Microsoft.DPrep.Engine.FileEncoding, Microsoft.DPrep.EngineAPI, Version=0.1.1903.18033, Culture=neutral, PublicKeyToken=null",
-                          "enumValues": [
-                            0,
-                            1,
-                            2,
-                            3,
-                            4,
-                            5,
-                            6,
-                            7
-                          ]
-                        }
-                      },
-                      "multiValueDetails": null,
-                      "isRequired": true,
-                      "origin": 1,
-                      "defaultValue": 0,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": null,
-                      "documentation": null
-                    },
-                    {
-                      "name": "skipRowsMode",
-                      "type": 6,
-                      "multipleValues": false,
-                      "domain": {
-                        "type": 1,
-                        "details": {
-                          "enumName": "SkipMode",
-                          "enumType": "Microsoft.DPrep.Engine.SkipMode, Microsoft.DPrep.EngineAPI, Version=0.1.1903.18033, Culture=neutral, PublicKeyToken=null",
-                          "enumValues": [
-                            0,
-                            1,
-                            2
-                          ]
-                        }
-                      },
-                      "multiValueDetails": null,
-                      "isRequired": true,
-                      "origin": 1,
-                      "defaultValue": 0,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": null,
-                      "documentation": null
-                    },
-                    {
-                      "name": "skipRows",
-                      "type": 2,
-                      "multipleValues": false,
-                      "domain": {
-                        "type": 4,
-                        "details": {
-                          "min": 0.0,
-                          "max": 1.7976931348623157E+308
-                        }
-                      },
-                      "multiValueDetails": null,
-                      "isRequired": false,
-                      "origin": 1,
-                      "defaultValue": null,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": {
-                        "condition": {
-                          "property": {
-                            "propertyPath": [
-                              "skipRowsMode"
-                            ]
-                          },
-                          "comparison": 1,
-                          "value": 0
-                        },
-                        "aggregate": null
-                      },
-                      "documentation": null
-                    },
-                    {
-                      "name": "handleQuotedLineBreaks",
-                      "type": 4,
-                      "multipleValues": false,
-                      "domain": null,
-                      "multiValueDetails": null,
-                      "isRequired": true,
-                      "origin": 1,
-                      "defaultValue": false,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": null,
-                      "documentation": null
-                    }
-                  ],
-                  "dataEffectDetails": {
-                    "dataEffect": 1
-                  }
-                },
-                "type": "Microsoft.DPrep.ParseDelimitedBlock",
-                "dataEffectDetails": {
-                  "dataEffect": 1
-                },
-                "propertyDescriptions": [
-                  {
-                    "name": "maxRows",
-                    "type": 2,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 2,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": null
-                  },
-                  {
-                    "name": "preview",
-                    "type": 4,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 2,
-                    "defaultValue": false,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": null
-                  },
-                  {
-                    "name": "columnHeadersMode",
-                    "type": 6,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 1,
-                      "details": {
-                        "enumName": "PromoteHeadersMode",
-                        "enumType": "Microsoft.DPrep.Engine.PromoteHeadersMode, Microsoft.DPrep.EngineAPI, Version=0.1.1903.18033, Culture=neutral, PublicKeyToken=null",
-                        "enumValues": [
-                          0,
-                          1,
-                          2,
-                          3
-                        ]
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 1,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": null
-                  },
-                  {
-                    "name": "separator",
-                    "type": 0,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 6,
-                      "details": {
-                        "values": [
-                          {
-                            "value": ",",
-                            "id": 0
-                          },
-                          {
-                            "value": "\t",
-                            "id": 1
-                          },
-                          {
-                            "value": ":",
-                            "id": 2
-                          },
-                          {
-                            "value": ";",
-                            "id": 3
-                          },
-                          {
-                            "value": " ",
-                            "id": 5
-                          },
-                          {
-                            "value": "=",
-                            "id": 4
-                          },
-                          {
-                            "value": "—",
-                            "id": 7
-                          }
-                        ],
-                        "allowCustom": true,
-                        "customEnumValue": 6
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": ",",
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": null
-                  },
-                  {
-                    "name": "commentLineCharacter",
-                    "type": 0,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": null
-                  },
-                  {
-                    "name": "fileEncoding",
-                    "type": 6,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 1,
-                      "details": {
-                        "enumName": "FileEncoding",
-                        "enumType": "Microsoft.DPrep.Engine.FileEncoding, Microsoft.DPrep.EngineAPI, Version=0.1.1903.18033, Culture=neutral, PublicKeyToken=null",
-                        "enumValues": [
-                          0,
-                          1,
-                          2,
-                          3,
-                          4,
-                          5,
-                          6,
-                          7
-                        ]
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 0,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": null
-                  },
-                  {
-                    "name": "skipRowsMode",
-                    "type": 6,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 1,
-                      "details": {
-                        "enumName": "SkipMode",
-                        "enumType": "Microsoft.DPrep.Engine.SkipMode, Microsoft.DPrep.EngineAPI, Version=0.1.1903.18033, Culture=neutral, PublicKeyToken=null",
-                        "enumValues": [
-                          0,
-                          1,
-                          2
-                        ]
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 0,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": null
-                  },
-                  {
-                    "name": "skipRows",
-                    "type": 2,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 4,
-                      "details": {
-                        "min": 0.0,
-                        "max": 1.7976931348623157E+308
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": {
-                      "condition": {
-                        "property": {
-                          "propertyPath": [
-                            "skipRowsMode"
-                          ]
-                        },
-                        "comparison": 1,
-                        "value": 0
-                      },
-                      "aggregate": null
-                    },
-                    "documentation": null
-                  },
-                  {
-                    "name": "handleQuotedLineBreaks",
-                    "type": 4,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": false,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": null
-                  }
-                ],
-                "arguments": {
-                  "columnHeadersMode": 3,
-                  "fileEncoding": 0,
-                  "handleQuotedLineBreaks": false,
-                  "preview": false,
-                  "separator": ",",
-                  "skipRows": 0,
-                  "skipRowsMode": 0
-                }
-              },
-              {
-                "id": "b3a40b63-acb6-467c-8eb0-6718a9e52e22",
-                "description": {
-                  "localDataProperties": [],
-                  "blockGroup": 6,
-                  "supportedTargets": [
-                    2,
-                    3
-                  ],
-                  "supportedFieldTypes": null,
-                  "supportedMessages": [],
-                  "requiresInitialization": false,
-                  "allowDefaultAdd": true,
-                  "documentation": "Drops the specified columns.",
-                  "type": "Microsoft.DPrep.DropColumnsBlock",
-                  "propertyDescriptions": [
-                    {
-                      "name": "columns",
-                      "type": 5,
-                      "multipleValues": false,
-                      "domain": {
-                        "type": 12,
-                        "details": {
-                          "selectorDomainType": 1,
-                          "fieldTypes": []
-                        }
-                      },
-                      "multiValueDetails": null,
-                      "isRequired": true,
-                      "origin": 0,
-                      "defaultValue": null,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 1,
-                      "condition": null,
-                      "documentation": "The source columns."
-                    }
-                  ],
-                  "dataEffectDetails": {
-                    "columnsRemovedProperties": [
-                      {
-                        "propertyPath": [
-                          "columns"
-                        ]
-                      }
-                    ],
-                    "dataEffect": 4
-                  }
-                },
-                "type": "Microsoft.DPrep.DropColumnsBlock",
-                "dataEffectDetails": {
-                  "columnsRemovedProperties": [
-                    {
-                      "propertyPath": [
-                        "columns"
-                      ]
-                    }
-                  ],
-                  "dataEffect": 4
-                },
-                "propertyDescriptions": [
-                  {
-                    "name": "columns",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 1,
-                        "fieldTypes": []
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source columns."
-                  }
-                ],
-                "arguments": {
-                  "columns": {
-                    "type": 0,
-                    "details": {
-                      "selectedColumns": [
-                        "Path"
-                      ]
-                    }
-                  }
-                }
-              },
-              {
-                "id": "378efb1b-9db2-4c76-a8e1-ad653493551a",
-                "description": {
-                  "localDataProperties": [],
-                  "blockGroup": 7,
-                  "supportedTargets": [
-                    5
-                  ],
-                  "supportedFieldTypes": null,
-                  "supportedMessages": [],
-                  "requiresInitialization": false,
-                  "allowDefaultAdd": true,
-                  "documentation": null,
-                  "type": "Microsoft.DPrep.SetColumnTypesBlock",
-                  "propertyDescriptions": [
-                    {
-                      "name": "columnConversion",
-                      "type": 7,
-                      "multipleValues": true,
-                      "domain": {
-                        "type": 0,
-                        "details": [
-                          {
-                            "name": "column",
-                            "type": 5,
-                            "multipleValues": false,
-                            "domain": {
-                              "type": 12,
-                              "details": {
-                                "selectorDomainType": 0,
-                                "fieldTypes": []
-                              }
-                            },
-                            "multiValueDetails": null,
-                            "isRequired": true,
-                            "origin": 0,
-                            "defaultValue": null,
-                            "initializeFromProperty": null,
-                            "telemetryStrategy": 1,
-                            "condition": null,
-                            "documentation": "The source column."
-                          },
-                          {
-                            "name": "typeProperty",
-                            "type": 6,
-                            "multipleValues": false,
-                            "domain": {
-                              "type": 1,
-                              "details": {
-                                "enumName": "FieldType",
-                                "enumType": "Microsoft.DPrep.Engine.FieldType, Microsoft.DPrep.EngineAPI, Version=0.1.1903.18033, Culture=neutral, PublicKeyToken=null",
-                                "enumValues": [
-                                  0,
-                                  1,
-                                  3,
-                                  4,
-                                  10
-                                ]
-                              }
-                            },
-                            "multiValueDetails": null,
-                            "isRequired": true,
-                            "origin": 0,
-                            "defaultValue": 0,
-                            "initializeFromProperty": null,
-                            "telemetryStrategy": 0,
-                            "condition": null,
-                            "documentation": null
-                          },
-                          {
-                            "name": "typeArguments",
-                            "type": 7,
-                            "multipleValues": false,
-                            "domain": {
-                              "type": 0,
-                              "details": [
-                                {
-                                  "name": "dateTimeFormats",
-                                  "type": 1,
-                                  "multipleValues": true,
-                                  "domain": null,
-                                  "multiValueDetails": {
-                                    "isOrdered": false
-                                  },
-                                  "isRequired": false,
-                                  "origin": 1,
-                                  "defaultValue": null,
-                                  "initializeFromProperty": null,
-                                  "telemetryStrategy": 0,
-                                  "condition": null,
-                                  "documentation": null
-                                }
-                              ]
-                            },
-                            "multiValueDetails": null,
-                            "isRequired": false,
-                            "origin": 0,
-                            "defaultValue": null,
-                            "initializeFromProperty": null,
-                            "telemetryStrategy": 0,
-                            "condition": null,
-                            "documentation": null
-                          }
-                        ]
-                      },
-                      "multiValueDetails": {
-                        "isOrdered": false
-                      },
-                      "isRequired": false,
-                      "origin": 0,
-                      "defaultValue": null,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": null,
-                      "documentation": null
-                    }
-                  ],
-                  "dataEffectDetails": {
-                    "dataEffect": 1
-                  }
-                },
-                "type": "Microsoft.DPrep.SetColumnTypesBlock",
-                "dataEffectDetails": {
-                  "dataEffect": 1
-                },
-                "propertyDescriptions": [
-                  {
-                    "name": "columnConversion",
-                    "type": 7,
-                    "multipleValues": true,
-                    "domain": {
-                      "type": 0,
-                      "details": [
-                        {
-                          "name": "column",
-                          "type": 5,
-                          "multipleValues": false,
-                          "domain": {
-                            "type": 12,
-                            "details": {
-                              "selectorDomainType": 0,
-                              "fieldTypes": []
-                            }
-                          },
-                          "multiValueDetails": null,
-                          "isRequired": true,
-                          "origin": 0,
-                          "defaultValue": null,
-                          "initializeFromProperty": null,
-                          "telemetryStrategy": 1,
-                          "condition": null,
-                          "documentation": "The source column."
-                        },
-                        {
-                          "name": "typeProperty",
-                          "type": 6,
-                          "multipleValues": false,
-                          "domain": {
-                            "type": 1,
-                            "details": {
-                              "enumName": "FieldType",
-                              "enumType": "Microsoft.DPrep.Engine.FieldType, Microsoft.DPrep.EngineAPI, Version=0.1.1903.18033, Culture=neutral, PublicKeyToken=null",
-                              "enumValues": [
-                                0,
-                                1,
-                                3,
-                                4,
-                                10
-                              ]
-                            }
-                          },
-                          "multiValueDetails": null,
-                          "isRequired": true,
-                          "origin": 0,
-                          "defaultValue": 0,
-                          "initializeFromProperty": null,
-                          "telemetryStrategy": 0,
-                          "condition": null,
-                          "documentation": null
-                        },
-                        {
-                          "name": "typeArguments",
-                          "type": 7,
-                          "multipleValues": false,
-                          "domain": {
-                            "type": 0,
-                            "details": [
-                              {
-                                "name": "dateTimeFormats",
-                                "type": 1,
-                                "multipleValues": true,
-                                "domain": null,
-                                "multiValueDetails": {
-                                  "isOrdered": false
-                                },
-                                "isRequired": false,
-                                "origin": 1,
-                                "defaultValue": null,
-                                "initializeFromProperty": null,
-                                "telemetryStrategy": 0,
-                                "condition": null,
-                                "documentation": null
-                              }
-                            ]
-                          },
-                          "multiValueDetails": null,
-                          "isRequired": false,
-                          "origin": 0,
-                          "defaultValue": null,
-                          "initializeFromProperty": null,
-                          "telemetryStrategy": 0,
-                          "condition": null,
-                          "documentation": null
-                        }
-                      ]
-                    },
-                    "multiValueDetails": {
-                      "isOrdered": false
-                    },
-                    "isRequired": false,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": null
-                  }
-                ],
-                "arguments": {
-                  "columnConversion": [
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "End_Lon"
-                        }
-                      },
-                      "typeProperty": 0
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Trip_Pickup_DateTime"
-                        }
-                      },
-                      "typeProperty": 0
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Tip_Amt"
-                        }
-                      },
-                      "typeProperty": 0
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Passenger_Count"
-                        }
-                      },
-                      "typeProperty": 0
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Fare_Amt"
-                        }
-                      },
-                      "typeProperty": 0
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "vendor_name"
-                        }
-                      },
-                      "typeProperty": 0
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Start_Lat"
-                        }
-                      },
-                      "typeProperty": 0
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Tolls_Amt"
-                        }
-                      },
-                      "typeProperty": 0
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "End_Lat"
-                        }
-                      },
-                      "typeProperty": 0
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "surcharge"
-                        }
-                      },
-                      "typeProperty": 0
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Trip_Dropoff_DateTime"
-                        }
-                      },
-                      "typeProperty": 0
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Total_Amt"
-                        }
-                      },
-                      "typeProperty": 0
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "store_and_forward"
-                        }
-                      },
-                      "typeProperty": 0
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Payment_Type"
-                        }
-                      },
-                      "typeProperty": 0
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Rate_Code"
-                        }
-                      },
-                      "typeProperty": 0
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Trip_Distance"
-                        }
-                      },
-                      "typeProperty": 0
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Start_Lon"
-                        }
-                      },
-                      "typeProperty": 0
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "mta_tax"
-                        }
-                      },
-                      "typeProperty": 0
-                    }
-                  ]
-                }
-              },
-              {
-                "id": "1569498c-6f81-43e2-881b-e11587ae60e2",
-                "description": {
-                  "localDataProperties": [],
-                  "blockGroup": 3,
-                  "supportedTargets": [
-                    2,
-                    3
-                  ],
-                  "supportedFieldTypes": [
-                    1,
-                    4,
-                    3,
-                    2,
-                    0,
-                    5
-                  ],
-                  "supportedMessages": [],
-                  "requiresInitialization": false,
-                  "allowDefaultAdd": true,
-                  "documentation": "Replaces values in the specified columns with nulls. You can choose to use the default list, supply your own, or both.",
-                  "type": "Microsoft.DPrep.ReplaceNaBlock",
-                  "propertyDescriptions": [
-                    {
-                      "name": "useDefaultNaList",
-                      "type": 4,
-                      "multipleValues": false,
-                      "domain": null,
-                      "multiValueDetails": null,
-                      "isRequired": false,
-                      "origin": 1,
-                      "defaultValue": true,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": null,
-                      "documentation": "Use the default list and replace 'null', 'NaN', 'NA', and 'N/A' with null."
-                    },
-                    {
-                      "name": "useEmptyStringAsNa",
-                      "type": 4,
-                      "multipleValues": false,
-                      "domain": null,
-                      "multiValueDetails": null,
-                      "isRequired": false,
-                      "origin": 1,
-                      "defaultValue": true,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": null,
-                      "documentation": "Replace empty strings with null."
-                    },
-                    {
-                      "name": "useNanAsNa",
-                      "type": 4,
-                      "multipleValues": false,
-                      "domain": null,
-                      "multiValueDetails": null,
-                      "isRequired": false,
-                      "origin": 1,
-                      "defaultValue": true,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": null,
-                      "documentation": "Replace NaNs with Null."
-                    },
-                    {
-                      "name": "customNaList",
-                      "type": 1,
-                      "multipleValues": false,
-                      "domain": null,
-                      "multiValueDetails": null,
-                      "isRequired": false,
-                      "origin": 1,
-                      "defaultValue": null,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": null,
-                      "documentation": "Provide a comma separated list of values to replace with null."
-                    },
-                    {
-                      "name": "columns",
-                      "type": 5,
-                      "multipleValues": false,
-                      "domain": {
-                        "type": 12,
-                        "details": {
-                          "selectorDomainType": 1,
-                          "fieldTypes": []
-                        }
-                      },
-                      "multiValueDetails": null,
-                      "isRequired": true,
-                      "origin": 0,
-                      "defaultValue": null,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 1,
-                      "condition": null,
-                      "documentation": "The source columns."
-                    }
-                  ],
-                  "dataEffectDetails": {
-                    "columnsTransformedProperties": [
-                      {
-                        "propertyPath": [
-                          "columns"
-                        ]
-                      }
-                    ],
-                    "dataEffect": 5
-                  }
-                },
-                "type": "Microsoft.DPrep.ReplaceNaBlock",
-                "dataEffectDetails": {
-                  "columnsTransformedProperties": [
-                    {
-                      "propertyPath": [
-                        "columns"
-                      ]
-                    }
-                  ],
-                  "dataEffect": 5
-                },
-                "propertyDescriptions": [
-                  {
-                    "name": "useDefaultNaList",
-                    "type": 4,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": true,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Use the default list and replace 'null', 'NaN', 'NA', and 'N/A' with null."
-                  },
-                  {
-                    "name": "useEmptyStringAsNa",
-                    "type": 4,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": true,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Replace empty strings with null."
-                  },
-                  {
-                    "name": "useNanAsNa",
-                    "type": 4,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": true,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Replace NaNs with Null."
-                  },
-                  {
-                    "name": "customNaList",
-                    "type": 1,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Provide a comma separated list of values to replace with null."
-                  },
-                  {
-                    "name": "columns",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 1,
-                        "fieldTypes": []
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source columns."
-                  }
-                ],
-                "arguments": {
-                  "columns": {
-                    "type": 1,
-                    "details": {
-                      "ignoreCase": false,
-                      "term": ".*",
-                      "useRegex": true,
-                      "matchWholeWord": false,
-                      "invert": false
-                    }
-                  },
-                  "useDefaultNaList": true,
-                  "useEmptyStringAsNa": true,
-                  "useNanAsNa": true
-                }
-              },
-              {
-                "id": "5c88808e-dd28-4d79-be9c-06773aae56e8",
-                "description": {
-                  "localDataProperties": [],
-                  "blockGroup": 5,
-                  "supportedTargets": [
-                    2,
-                    3
-                  ],
-                  "supportedFieldTypes": null,
-                  "supportedMessages": [],
-                  "requiresInitialization": false,
-                  "allowDefaultAdd": false,
-                  "documentation": "Drops rows where all or any of the selected columns are null.",
-                  "type": "Microsoft.DPrep.DropNullsBlock",
-                  "propertyDescriptions": [
-                    {
-                      "name": "columns",
-                      "type": 5,
-                      "multipleValues": false,
-                      "domain": {
-                        "type": 12,
-                        "details": {
-                          "selectorDomainType": 1,
-                          "fieldTypes": []
-                        }
-                      },
-                      "multiValueDetails": null,
-                      "isRequired": true,
-                      "origin": 0,
-                      "defaultValue": null,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 1,
-                      "condition": null,
-                      "documentation": "The source columns."
-                    },
-                    {
-                      "name": "columnRelationship",
-                      "type": 6,
-                      "multipleValues": false,
-                      "domain": {
-                        "type": 1,
-                        "details": {
-                          "enumName": "ColumnRelationship",
-                          "enumType": "Microsoft.DPrep.Engine.ColumnRelationship, Microsoft.DPrep.EngineAPI, Version=0.1.1903.18033, Culture=neutral, PublicKeyToken=null",
-                          "enumValues": [
-                            0,
-                            1
-                          ]
-                        }
-                      },
-                      "multiValueDetails": null,
-                      "isRequired": true,
-                      "origin": 1,
-                      "defaultValue": 0,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": null,
-                      "documentation": "Whether all or any of the selected columns must be null."
-                    }
-                  ],
-                  "dataEffectDetails": {
-                    "dataEffect": 6
-                  }
-                },
-                "type": "Microsoft.DPrep.DropNullsBlock",
-                "dataEffectDetails": {
-                  "dataEffect": 6
-                },
-                "propertyDescriptions": [
-                  {
-                    "name": "columns",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 1,
-                        "fieldTypes": []
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source columns."
-                  },
-                  {
-                    "name": "columnRelationship",
-                    "type": 6,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 1,
-                      "details": {
-                        "enumName": "ColumnRelationship",
-                        "enumType": "Microsoft.DPrep.Engine.ColumnRelationship, Microsoft.DPrep.EngineAPI, Version=0.1.1903.18033, Culture=neutral, PublicKeyToken=null",
-                        "enumValues": [
-                          0,
-                          1
-                        ]
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 0,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Whether all or any of the selected columns must be null."
-                  }
-                ],
-                "arguments": {
-                  "columnRelationship": 0,
-                  "columns": {
-                    "type": 1,
-                    "details": {
-                      "ignoreCase": false,
-                      "term": ".*",
-                      "useRegex": true,
-                      "matchWholeWord": false,
-                      "invert": false
-                    }
-                  }
-                }
-              },
-              {
-                "id": "507b229d-a8bd-4517-8984-d5750b4443e9",
-                "description": {
-                  "localDataProperties": [],
-                  "blockGroup": 6,
-                  "supportedTargets": [
-                    2,
-                    3
-                  ],
-                  "supportedFieldTypes": null,
-                  "supportedMessages": [],
-                  "requiresInitialization": false,
-                  "allowDefaultAdd": true,
-                  "documentation": "Renames the specified columns.",
-                  "type": "Microsoft.DPrep.RenameColumnsBlock",
-                  "propertyDescriptions": [
-                    {
-                      "name": "columnPairs",
-                      "type": 7,
-                      "multipleValues": true,
-                      "domain": {
-                        "type": 0,
-                        "details": [
-                          {
-                            "name": "column",
-                            "type": 5,
-                            "multipleValues": false,
-                            "domain": {
-                              "type": 12,
-                              "details": {
-                                "selectorDomainType": 0,
-                                "fieldTypes": []
-                              }
-                            },
-                            "multiValueDetails": null,
-                            "isRequired": true,
-                            "origin": 0,
-                            "defaultValue": null,
-                            "initializeFromProperty": null,
-                            "telemetryStrategy": 1,
-                            "condition": null,
-                            "documentation": "The source column."
-                          },
-                          {
-                            "name": "newColumnId",
-                            "type": 1,
-                            "multipleValues": false,
-                            "domain": {
-                              "type": 3,
-                              "details": null
-                            },
-                            "multiValueDetails": null,
-                            "isRequired": true,
-                            "origin": 1,
-                            "defaultValue": null,
-                            "initializeFromProperty": null,
-                            "telemetryStrategy": 0,
-                            "condition": null,
-                            "documentation": null
-                          }
-                        ]
-                      },
-                      "multiValueDetails": {
-                        "isOrdered": false
-                      },
-                      "isRequired": true,
-                      "origin": 1,
-                      "defaultValue": null,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 0,
-                      "condition": null,
-                      "documentation": "The columns to rename and the desired new names."
-                    }
-                  ],
-                  "dataEffectDetails": {
-                    "columnsChangedProperties": [
-                      {
-                        "propertyPath": [
-                          "columnPairs",
-                          "column"
-                        ]
-                      },
-                      {
-                        "propertyPath": [
-                          "columnPairs",
-                          "newColumnId"
-                        ]
-                      }
-                    ],
-                    "dataEffect": 2
-                  }
-                },
-                "type": "Microsoft.DPrep.RenameColumnsBlock",
-                "dataEffectDetails": {
-                  "columnsChangedProperties": [
-                    {
-                      "propertyPath": [
-                        "columnPairs",
-                        "column"
-                      ]
-                    },
-                    {
-                      "propertyPath": [
-                        "columnPairs",
-                        "newColumnId"
-                      ]
-                    }
-                  ],
-                  "dataEffect": 2
-                },
-                "propertyDescriptions": [
-                  {
-                    "name": "columnPairs",
-                    "type": 7,
-                    "multipleValues": true,
-                    "domain": {
-                      "type": 0,
-                      "details": [
-                        {
-                          "name": "column",
-                          "type": 5,
-                          "multipleValues": false,
-                          "domain": {
-                            "type": 12,
-                            "details": {
-                              "selectorDomainType": 0,
-                              "fieldTypes": []
-                            }
-                          },
-                          "multiValueDetails": null,
-                          "isRequired": true,
-                          "origin": 0,
-                          "defaultValue": null,
-                          "initializeFromProperty": null,
-                          "telemetryStrategy": 1,
-                          "condition": null,
-                          "documentation": "The source column."
-                        },
-                        {
-                          "name": "newColumnId",
-                          "type": 1,
-                          "multipleValues": false,
-                          "domain": {
-                            "type": 3,
-                            "details": null
-                          },
-                          "multiValueDetails": null,
-                          "isRequired": true,
-                          "origin": 1,
-                          "defaultValue": null,
-                          "initializeFromProperty": null,
-                          "telemetryStrategy": 0,
-                          "condition": null,
-                          "documentation": null
-                        }
-                      ]
-                    },
-                    "multiValueDetails": {
-                      "isOrdered": false
-                    },
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "The columns to rename and the desired new names."
-                  }
-                ],
-                "arguments": {
-                  "columnPairs": [
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "vendor_name"
-                        }
-                      },
-                      "newColumnId": "vendor"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "VendorID"
-                        }
-                      },
-                      "newColumnId": "vendor"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "vendor_id"
-                        }
-                      },
-                      "newColumnId": "vendor"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Trip_Pickup_DateTime"
-                        }
-                      },
-                      "newColumnId": "pickup_datetime"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "tpep_pickup_datetime"
-                        }
-                      },
-                      "newColumnId": "pickup_datetime"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Trip_Dropoff_DateTime"
-                        }
-                      },
-                      "newColumnId": "dropoff_datetime"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "tpep_dropoff_datetime"
-                        }
-                      },
-                      "newColumnId": "dropoff_datetime"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "store_and_forward"
-                        }
-                      },
-                      "newColumnId": "store_forward"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "store_and_fwd_flag"
-                        }
-                      },
-                      "newColumnId": "store_forward"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Start_Lon"
-                        }
-                      },
-                      "newColumnId": "pickup_longitude"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Start_Lat"
-                        }
-                      },
-                      "newColumnId": "pickup_latitude"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "End_Lon"
-                        }
-                      },
-                      "newColumnId": "dropoff_longitude"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "End_Lat"
-                        }
-                      },
-                      "newColumnId": "dropoff_latitude"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Passenger_Count"
-                        }
-                      },
-                      "newColumnId": "passengers"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "passenger_count"
-                        }
-                      },
-                      "newColumnId": "passengers"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Fare_Amt"
-                        }
-                      },
-                      "newColumnId": "cost"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "fare_amount"
-                        }
-                      },
-                      "newColumnId": "cost"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "Trip_Distance"
-                        }
-                      },
-                      "newColumnId": "distance"
-                    },
-                    {
-                      "column": {
-                        "type": 2,
-                        "details": {
-                          "selectedColumn": "trip_distance"
-                        }
-                      },
-                      "newColumnId": "distance"
-                    }
-                  ]
-                }
-              },
-              {
-                "id": "f1dff530-60d8-467a-b24f-246eb2a27893",
-                "description": {
-                  "localDataProperties": [],
-                  "blockGroup": 6,
-                  "supportedTargets": [
-                    2,
-                    3
-                  ],
-                  "supportedFieldTypes": null,
-                  "supportedMessages": [],
-                  "requiresInitialization": false,
-                  "allowDefaultAdd": true,
-                  "documentation": "Keeps the specified columns and drops all others.",
-                  "type": "Microsoft.DPrep.KeepColumnsBlock",
-                  "propertyDescriptions": [
-                    {
-                      "name": "columns",
-                      "type": 5,
-                      "multipleValues": false,
-                      "domain": {
-                        "type": 12,
-                        "details": {
-                          "selectorDomainType": 1,
-                          "fieldTypes": []
-                        }
-                      },
-                      "multiValueDetails": null,
-                      "isRequired": true,
-                      "origin": 0,
-                      "defaultValue": null,
-                      "initializeFromProperty": null,
-                      "telemetryStrategy": 1,
-                      "condition": null,
-                      "documentation": "The source columns."
-                    }
-                  ],
-                  "dataEffectDetails": {
-                    "dataEffect": 1
-                  }
-                },
-                "type": "Microsoft.DPrep.KeepColumnsBlock",
-                "dataEffectDetails": {
-                  "dataEffect": 1
-                },
-                "propertyDescriptions": [
-                  {
-                    "name": "columns",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 1,
-                        "fieldTypes": []
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source columns."
-                  }
-                ],
-                "arguments": {
-                  "columns": {
-                    "type": 0,
-                    "details": {
-                      "selectedColumns": [
-                        "cost",
-                        "distance",
-                        "dropoff_datetime",
-                        "dropoff_latitude",
-                        "dropoff_longitude",
-                        "passengers",
-                        "pickup_datetime",
-                        "pickup_latitude",
-                        "pickup_longitude",
-                        "store_forward",
-                        "vendor"
-                      ]
-                    }
-                  }
-                }
-              }
-            ]
-          }
-        ]
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "9a72242d-1823-41a0-9588-e416d621bc75",
-      "type": "Microsoft.DPrep.SetColumnTypesBlock",
-      "arguments": {
-        "columnConversion": [
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "pickup_longitude"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "pickup_latitude"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "dropoff_longitude"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "dropoff_latitude"
-              }
-            },
-            "typeProperty": 3
-          }
-        ]
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "69c0d1c3-1628-4147-890d-1a19dc4f7fc7",
-      "type": "Microsoft.DPrep.DropNullsBlock",
-      "arguments": {
-        "columnRelationship": 1,
-        "columns": {
-          "type": 0,
-          "details": {
-            "selectedColumns": [
-              "pickup_longitude",
-              "pickup_latitude",
-              "dropoff_longitude",
-              "dropoff_latitude"
-            ]
-          }
-        }
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "6919ef99-c7c0-4895-a9d2-af1abd09740f",
-      "type": "Microsoft.DPrep.ExpressionFilterBlock",
-      "arguments": {
-        "expression": {"r":["And",[{"r":["And",[{"r":["And",[{"r":["And",[{"r":["And",[{"r":["And",[{"r":["And",[{"r":["Invoke",[{"r":["Identifier","Value_LE"]},[{"r":["RecordField",[{"r":["Identifier","row"]},"pickup_longitude"]]},-73.72]]]},{"r":["Invoke",[{"r":["Identifier","Value_GE"]},[{"r":["RecordField",[{"r":["Identifier","row"]},"pickup_longitude"]]},-74.09]]]}]]},{"r":["Invoke",[{"r":["Identifier","Value_LE"]},[{"r":["RecordField",[{"r":["Identifier","row"]},"pickup_latitude"]]},40.88]]]}]]},{"r":["Invoke",[{"r":["Identifier","Value_GE"]},[{"r":["RecordField",[{"r":["Identifier","row"]},"pickup_latitude"]]},40.53]]]}]]},{"r":["Invoke",[{"r":["Identifier","Value_LE"]},[{"r":["RecordField",[{"r":["Identifier","row"]},"dropoff_longitude"]]},-73.72]]]}]]},{"r":["Invoke",[{"r":["Identifier","Value_GE"]},[{"r":["RecordField",[{"r":["Identifier","row"]},"dropoff_longitude"]]},-74.09]]]}]]},{"r":["Invoke",[{"r":["Identifier","Value_LE"]},[{"r":["RecordField",[{"r":["Identifier","row"]},"dropoff_latitude"]]},40.88]]]}]]},{"r":["Invoke",[{"r":["Identifier","Value_GE"]},[{"r":["RecordField",[{"r":["Identifier","row"]},"dropoff_latitude"]]},40.53]]]}]]}
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "98ede915-38ed-4fc2-bdc5-31fd43a8ad69",
-      "type": "Microsoft.DPrep.ReplaceBlock",
-      "arguments": {
-        "booleanReplaceWith": 1,
-        "booleanValueToFind": 1,
-        "columns": {
-          "type": 0,
-          "details": {
-            "selectedColumns": [
-              "store_forward"
-            ]
-          }
-        },
-        "replaceWithType": 0,
-        "stringReplaceWith": "N",
-        "stringValueToFind": "0",
-        "valueToFindType": 0
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "a72da6d6-7594-4118-90ee-e7107b6c9d7a",
-      "type": "Microsoft.DPrep.FillNullsBlock",
-      "arguments": {
-        "booleanReplaceWith": 1,
-        "columns": {
-          "type": 0,
-          "details": {
-            "selectedColumns": [
-              "store_forward"
-            ]
-          }
-        },
-        "replaceWithType": 0,
-        "stringReplaceWith": "N"
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "35290d77-a442-4ace-8f88-bed7fb153a57",
-      "type": "Microsoft.DPrep.ReplaceBlock",
-      "arguments": {
-        "booleanReplaceWith": 1,
-        "booleanValueToFind": 1,
-        "columns": {
-          "type": 0,
-          "details": {
-            "selectedColumns": [
-              "distance"
-            ]
-          }
-        },
-        "doubleReplaceWith": 0.0,
-        "replaceWithType": 3,
-        "stringValueToFind": ".00",
-        "valueToFindType": 0
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "c98fdba9-f82e-4587-bef3-a4f7f9b2dc47",
-      "type": "Microsoft.DPrep.FillNullsBlock",
-      "arguments": {
-        "booleanReplaceWith": 1,
-        "columns": {
-          "type": 0,
-          "details": {
-            "selectedColumns": [
-              "distance"
-            ]
-          }
-        },
-        "doubleReplaceWith": 0.0,
-        "replaceWithType": 3
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "c84749ff-b6e8-41f7-b696-51c2399fe5bc",
-      "type": "Microsoft.DPrep.ToNumberBlock",
-      "arguments": {
-        "columns": {
-          "type": 0,
-          "details": {
-            "selectedColumns": [
-              "distance"
-            ]
-          }
-        },
-        "decimalPoint": 0
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "46d344f7-dfe4-4305-ba49-c101572c4e87",
-      "type": "Microsoft.DPrep.SplitColumnByExampleBlock",
-      "arguments": {
-        "column": {
-          "type": 2,
-          "details": {
-            "selectedColumn": "pickup_datetime"
-          }
-        },
-        "delimiters": [],
-        "dsl": "PABOAG8AbgB0AGUAcgBtAGkAbgBhAGwATgBvAGQAZQAgAHMAeQBtAGIAbwBsAD0AIgByAGUAZwBpAG8AbgBTAHAAbABpAHQAIgAgAHIAdQBsAGUAPQAiAFMAcABsAGkAdABSAGUAZwBpAG8AbgAiAD4APABWAGEAcgBpAGEAYgBsAGUATgBvAGQAZQAgAHMAeQBtAGIAbwBsAD0AIgB2ACIAIAAvAD4APABOAG8AbgB0AGUAcgBtAGkAbgBhAGwATgBvAGQAZQAgAHMAeQBtAGIAbwBsAD0AIgBzAHAAbABpAHQATQBhAHQAYwBoAGUAcwAiACAAcgB1AGwAZQA9ACIAfgBjAG8AbgB2AGUAcgB0AF8AcwBwAGwAaQB0AE0AYQB0AGMAaABlAHMAXwBtAHUAbAB0AGkAcABsAGUATQBhAHQAYwBoAGUAcwAiAD4APABOAG8AbgB0AGUAcgBtAGkAbgBhAGwATgBvAGQAZQAgAHMAeQBtAGIAbwBsAD0AIgBtAHUAbAB0AGkAcABsAGUATQBhAHQAYwBoAGUAcwAiACAAcgB1AGwAZQA9ACIAfgBjAG8AbgB2AGUAcgB0AF8AbQB1AGwAdABpAHAAbABlAE0AYQB0AGMAaABlAHMAXwBkACIAPgA8AE4AbwBuAHQAZQByAG0AaQBuAGEAbABOAG8AZABlACAAcwB5AG0AYgBvAGwAPQAiAGQAIgAgAHIAdQBsAGUAPQAiAEwAbwBvAGsAQQByAG8AdQBuAGQAIgA+ADwATgBvAG4AdABlAHIAbQBpAG4AYQBsAE4AbwBkAGUAIABzAHkAbQBiAG8AbAA9ACIAcgAiACAAcgB1AGwAZQA9ACIARQBtAHAAdAB5ACIAPgA8AFYAYQByAGkAYQBiAGwAZQBOAG8AZABlACAAcwB5AG0AYgBvAGwAPQAiAHYAIgAgAC8APgA8AC8ATgBvAG4AdABlAHIAbQBpAG4AYQBsAE4AbwBkAGUAPgA8AE4AbwBuAHQAZQByAG0AaQBuAGEAbABOAG8AZABlACAAcwB5AG0AYgBvAGwAPQAiAGMAIgAgAHIAdQBsAGUAPQAiAEMAbwBuAHMAdABTAHQAcgAiAD4APABWAGEAcgBpAGEAYgBsAGUATgBvAGQAZQAgAHMAeQBtAGIAbwBsAD0AIgB2ACIAIAAvAD4APABMAGkAdABlAHIAYQBsAE4AbwBkAGUAIABzAHkAbQBiAG8AbAA9ACIAcwAiAD4APAAhAFsAQwBEAEEAVABBAFsAIgAgACIAXQBdAD4APAAvAEwAaQB0AGUAcgBhAGwATgBvAGQAZQA+ADwALwBOAG8AbgB0AGUAcgBtAGkAbgBhAGwATgBvAGQAZQA+ADwATgBvAG4AdABlAHIAbQBpAG4AYQBsAE4AbwBkAGUAIABzAHkAbQBiAG8AbAA9ACIAcgAiACAAcgB1AGwAZQA9ACIARQBtAHAAdAB5ACIAPgA8AFYAYQByAGkAYQBiAGwAZQBOAG8AZABlACAAcwB5AG0AYgBvAGwAPQAiAHYAIgAgAC8APgA8AC8ATgBvAG4AdABlAHIAbQBpAG4AYQBsAE4AbwBkAGUAPgA8AC8ATgBvAG4AdABlAHIAbQBpAG4AYQBsAE4AbwBkAGUAPgA8AC8ATgBvAG4AdABlAHIAbQBpAG4AYQBsAE4AbwBkAGUAPgA8AC8ATgBvAG4AdABlAHIAbQBpAG4AYQBsAE4AbwBkAGUAPgA8AEwAaQB0AGUAcgBhAGwATgBvAGQAZQAgAHMAeQBtAGIAbwBsAD0AIgBpAGcAbgBvAHIAZQBJAG4AZABlAHgAZQBzACIAPgA8AEEAcgByAGEAeQAgAHMAaQB6AGUAPQAiADAAIgAgAC8APgA8AC8ATABpAHQAZQByAGEAbABOAG8AZABlAD4APABMAGkAdABlAHIAYQBsAE4AbwBkAGUAIABzAHkAbQBiAG8AbAA9ACIAbgB1AG0AUwBwAGwAaQB0AHMAIgA+ADwAIQBbAEMARABBAFQAQQBbADIAXQBdAD4APAAvAEwAaQB0AGUAcgBhAGwATgBvAGQAZQA+ADwATABpAHQAZQByAGEAbABOAG8AZABlACAAcwB5AG0AYgBvAGwAPQAiAGQAZQBsAGkAbQBpAHQAZQByAFMAdABhAHIAdAAiAD4APAAhAFsAQwBEAEEAVABBAFsAZgBhAGwAcwBlAF0AXQA+ADwALwBMAGkAdABlAHIAYQBsAE4AbwBkAGUAPgA8AEwAaQB0AGUAcgBhAGwATgBvAGQAZQAgAHMAeQBtAGIAbwBsAD0AIgBkAGUAbABpAG0AaQB0AGUAcgBFAG4AZAAiAD4APAAhAFsAQwBEAEEAVABBAFsAZgBhAGwAcwBlAF0AXQA+ADwALwBMAGkAdABlAHIAYQBsAE4AbwBkAGUAPgA8AEwAaQB0AGUAcgBhAGwATgBvAGQAZQAgAHMAeQBtAGIAbwBsAD0AIgBpAG4AYwBsAHUAZABlAEQAZQBsAGkAbQBpAHQAZQByAHMAIgA+ADwAIQBbAEMARABBAFQAQQBbAGYAYQBsAHMAZQBdAF0APgA8AC8ATABpAHQAZQByAGEAbABOAG8AZABlAD4APABMAGkAdABlAHIAYQBsAE4AbwBkAGUAIABzAHkAbQBiAG8AbAA9ACIAZgBpAGwAbABTAHQAcgBhAHQAZQBnAHkAIgA+ADwAIQBbAEMARABBAFQAQQBbAE4AdQBsAGwAXQBdAD4APAAvAEwAaQB0AGUAcgBhAGwATgBvAGQAZQA+ADwALwBOAG8AbgB0AGUAcgBtAGkAbgBhAGwATgBvAGQAZQA+AA==",
-        "examples": "[]",
-        "fillStrategy": 0,
-        "keepDelimiter": false,
-        "newColumnsBaseName": "pickup_datetime_"
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "b62a7aaa-6c70-4d9b-a3ec-7eaad3dfa170",
-      "type": "Microsoft.DPrep.SplitColumnByExampleBlock",
-      "arguments": {
-        "column": {
-          "type": 2,
-          "details": {
-            "selectedColumn": "dropoff_datetime"
-          }
-        },
-        "delimiters": [],
-        "dsl": "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",
-        "examples": "[]",
-        "fillStrategy": 0,
-        "keepDelimiter": false,
-        "newColumnsBaseName": "dropoff_datetime_"
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "987b78c5-bb25-44ee-ae5c-e18302e2f8d4",
-      "type": "Microsoft.DPrep.RenameColumnsBlock",
-      "arguments": {
-        "columnPairs": [
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "pickup_datetime_1"
-              }
-            },
-            "newColumnId": "pickup_date"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "pickup_datetime_2"
-              }
-            },
-            "newColumnId": "pickup_time"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "dropoff_datetime_1"
-              }
-            },
-            "newColumnId": "dropoff_date"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "dropoff_datetime_2"
-              }
-            },
-            "newColumnId": "dropoff_time"
-          }
-        ]
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "56d8eeb5-ad5b-46a5-b4af-d6e492c75b00",
-      "type": "Microsoft.DPrep.DeriveColumnByExample",
-      "arguments": {
-        "anchorColumnId": "pickup_date",
-        "columnId": "pickup_weekday",
-        "dsl": "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",
-        "examples": "{\"-1\": {\"row\": -1, \"sourceData\": {\"pickup_date\": \"2009-01-04\"}, \"example\": \"Sunday\"}, \"-2\": {\"row\": -2, \"sourceData\": {\"pickup_date\": \"2013-08-22\"}, \"example\": \"Thursday\"}}",
-        "outputType": 2,
-        "priorColumnIds": {
-          "type": 0,
-          "details": {
-            "selectedColumns": [
-              "pickup_date"
-            ]
-          }
-        },
-        "sourceColumnIds": [
-          "pickup_date"
-        ]
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "a81d2481-ec3e-4445-81a4-7f99aa8f449b",
-      "type": "Microsoft.DPrep.DeriveColumnByExample",
-      "arguments": {
-        "anchorColumnId": "dropoff_date",
-        "columnId": "dropoff_weekday",
-        "dsl": "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",
-        "examples": "{\"-1\": {\"row\": -1, \"sourceData\": {\"dropoff_date\": \"2013-08-22\"}, \"example\": \"Thursday\"}, \"-2\": {\"row\": -2, \"sourceData\": {\"dropoff_date\": \"2013-11-03\"}, \"example\": \"Sunday\"}}",
-        "outputType": 2,
-        "priorColumnIds": {
-          "type": 0,
-          "details": {
-            "selectedColumns": [
-              "dropoff_date"
-            ]
-          }
-        },
-        "sourceColumnIds": [
-          "dropoff_date"
-        ]
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "4699c028-36b8-4c7a-826f-5b64ec7af309",
-      "type": "Microsoft.DPrep.SplitColumnByExampleBlock",
-      "arguments": {
-        "column": {
-          "type": 2,
-          "details": {
-            "selectedColumn": "pickup_time"
-          }
-        },
-        "delimiters": [],
-        "dsl": "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",
-        "examples": "[]",
-        "fillStrategy": 0,
-        "keepDelimiter": false,
-        "newColumnsBaseName": "pickup_time_"
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "bb5e7ef8-0763-414a-b36f-ecb5d74806da",
-      "type": "Microsoft.DPrep.SplitColumnByExampleBlock",
-      "arguments": {
-        "column": {
-          "type": 2,
-          "details": {
-            "selectedColumn": "dropoff_time"
-          }
-        },
-        "delimiters": [],
-        "dsl": "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",
-        "examples": "[]",
-        "fillStrategy": 0,
-        "keepDelimiter": false,
-        "newColumnsBaseName": "dropoff_time_"
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "ee2d7138-8867-4ede-830c-e37f01a83f75",
-      "type": "Microsoft.DPrep.SplitColumnByExampleBlock",
-      "arguments": {
-        "column": {
-          "type": 2,
-          "details": {
-            "selectedColumn": "pickup_time_1"
-          }
-        },
-        "delimiters": [],
-        "dsl": "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",
-        "examples": "[]",
-        "fillStrategy": 0,
-        "keepDelimiter": false,
-        "newColumnsBaseName": "pickup_time_1_"
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "7f09be9f-372f-4c35-bde5-91d133bfed14",
-      "type": "Microsoft.DPrep.SplitColumnByExampleBlock",
-      "arguments": {
-        "column": {
-          "type": 2,
-          "details": {
-            "selectedColumn": "dropoff_time_1"
-          }
-        },
-        "delimiters": [],
-        "dsl": "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",
-        "examples": "[]",
-        "fillStrategy": 0,
-        "keepDelimiter": false,
-        "newColumnsBaseName": "dropoff_time_1_"
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "46c61672-c91e-467a-b157-e06643e5c93b",
-      "type": "Microsoft.DPrep.DropColumnsBlock",
-      "arguments": {
-        "columns": {
-          "type": 0,
-          "details": {
-            "selectedColumns": [
-              "pickup_date",
-              "pickup_time",
-              "dropoff_date",
-              "dropoff_time",
-              "pickup_date_1",
-              "dropoff_date_1",
-              "pickup_time_1",
-              "dropoff_time_1"
-            ]
-          }
-        }
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "45756d2e-176c-4a33-b65b-dfbbf72a82f6",
-      "type": "Microsoft.DPrep.RenameColumnsBlock",
-      "arguments": {
-        "columnPairs": [
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "pickup_date_2"
-              }
-            },
-            "newColumnId": "pickup_month"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "pickup_date_3"
-              }
-            },
-            "newColumnId": "pickup_monthday"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "pickup_time_1_1"
-              }
-            },
-            "newColumnId": "pickup_hour"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "pickup_time_1_2"
-              }
-            },
-            "newColumnId": "pickup_minute"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "pickup_time_2"
-              }
-            },
-            "newColumnId": "pickup_second"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "dropoff_date_2"
-              }
-            },
-            "newColumnId": "dropoff_month"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "dropoff_date_3"
-              }
-            },
-            "newColumnId": "dropoff_monthday"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "dropoff_time_1_1"
-              }
-            },
-            "newColumnId": "dropoff_hour"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "dropoff_time_1_2"
-              }
-            },
-            "newColumnId": "dropoff_minute"
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "dropoff_time_2"
-              }
-            },
-            "newColumnId": "dropoff_second"
-          }
-        ]
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "c9ed92e3-464e-4706-9e91-a39f8d2a5504",
-      "type": "Microsoft.DPrep.DropColumnsBlock",
-      "arguments": {
-        "columns": {
-          "type": 0,
-          "details": {
-            "selectedColumns": [
-              "pickup_datetime",
-              "dropoff_datetime"
-            ]
-          }
-        }
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "7b9401a8-61bd-410f-a63b-a9f1a2432d03",
-      "type": "Microsoft.DPrep.SetColumnTypesBlock",
-      "arguments": {
-        "columnConversion": [
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "vendor"
-              }
-            },
-            "typeProperty": 0
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "passengers"
-              }
-            },
-            "typeProperty": 2
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "store_forward"
-              }
-            },
-            "typeProperty": 1
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "pickup_minute"
-              }
-            },
-            "typeProperty": 2
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "dropoff_latitude"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "cost"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "pickup_weekday"
-              }
-            },
-            "typeProperty": 0
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "pickup_hour"
-              }
-            },
-            "typeProperty": 2
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "dropoff_longitude"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "dropoff_weekday"
-              }
-            },
-            "typeProperty": 0
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "dropoff_second"
-              }
-            },
-            "typeProperty": 2
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "dropoff_hour"
-              }
-            },
-            "typeProperty": 2
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "dropoff_minute"
-              }
-            },
-            "typeProperty": 2
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "pickup_longitude"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "pickup_second"
-              }
-            },
-            "typeProperty": 2
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "pickup_latitude"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "distance"
-              }
-            },
-            "typeProperty": 3
-          }
-        ]
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "0cf8ee9c-d0b3-47fa-8b88-8d40393476b0",
-      "type": "Microsoft.DPrep.ExpressionFilterBlock",
-      "arguments": {
-        "expression": {"r":["Invoke",[{"r":["Identifier","Value_GT"]},[{"r":["RecordField",[{"r":["Identifier","row"]},"distance"]]},0]]]}
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "d413f9c1-e33b-4c7a-8e44-a789af2dbf14",
-      "type": "Microsoft.DPrep.ExpressionFilterBlock",
-      "arguments": {
-        "expression": {"r":["Invoke",[{"r":["Identifier","Value_GT"]},[{"r":["RecordField",[{"r":["Identifier","row"]},"cost"]]},0]]]}
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    }
-  ],
-  "inspectors": []
-}
\ No newline at end of file
diff --git a/tutorials/img-classification-part1-training.ipynb b/tutorials/img-classification-part1-training.ipynb
index 81fd73e1..87c60300 100644
--- a/tutorials/img-classification-part1-training.ipynb
+++ b/tutorials/img-classification-part1-training.ipynb
@@ -93,7 +93,7 @@
       "source": [
         "# load workspace configuration from the config.json file in the current folder.\n",
         "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.location, ws.resource_group, ws.location, sep='\\t')"
+        "print(ws.name, ws.location, ws.resource_group, sep='\\t')"
       ]
     },
     {
@@ -184,11 +184,10 @@
         "\n",
         "## Explore data\n",
         "\n",
-        "Before you train a model, you need to understand the data that you are using to train it.  You also need to copy the data into the cloud so it can be accessed by your cloud training environment.  In this section you learn how to:\n",
+        "Before you train a model, you need to understand the data that you are using to train it. In this section you learn how to:\n",
         "\n",
         "* Download the MNIST dataset\n",
         "* Display some sample images\n",
-        "* Upload data to the cloud\n",
         "\n",
         "### Download the MNIST dataset\n",
         "\n",
@@ -218,7 +217,7 @@
       "source": [
         "### Display some sample images\n",
         "\n",
-        "Load the compressed files into `numpy` arrays. Then use `matplotlib` to plot 30 random images from the dataset with their labels above them. Note this step requires a `load_data` function that's included in an `util.py` file. This file is included in the sample folder. Please make sure it is placed in the same folder as this notebook. The `load_data` function simply parses the compresse files into numpy arrays."
+        "Load the compressed files into `numpy` arrays. Then use `matplotlib` to plot 30 random images from the dataset with their labels above them. Note this step requires a `load_data` function that's included in an `utils.py` file. This file is included in the sample folder. Please make sure it is placed in the same folder as this notebook. The `load_data` function simply parses the compresse files into numpy arrays."
       ]
     },
     {
@@ -254,13 +253,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Now you have an idea of what these images look like and the expected prediction outcome.\n",
-        "\n",
-        "### Upload data to the cloud\n",
-        "\n",
-        "Now make the data accessible remotely by uploading that data from your local machine into Azure so it can be accessed for remote training. The datastore is a convenient construct associated with your workspace for you to upload/download data, and interact with it from your remote compute targets. It is backed by Azure blob storage account.\n",
-        "\n",
-        "The MNIST files are uploaded into a directory named `mnist` at the root of the datastore."
+        "## Create a FileDataset\n",
+        "A FileDataset references single or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred. [Learn More](https://aka.ms/azureml/howto/createdatasets)"
       ]
     },
     {
@@ -273,10 +267,44 @@
       },
       "outputs": [],
       "source": [
-        "ds = ws.get_default_datastore()\n",
-        "print(ds.datastore_type, ds.account_name, ds.container_name)\n",
+        "from azureml.core.dataset import Dataset\n",
         "\n",
-        "ds.upload(src_dir=data_folder, target_path='mnist', overwrite=True, show_progress=True)"
+        "web_paths = [\n",
+        "            'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',\n",
+        "            'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz',\n",
+        "            'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',\n",
+        "            'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz'\n",
+        "            ]\n",
+        "dataset = Dataset.File.from_files(path = web_paths)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Use the `register()` method to register datasets to your workspace so they can be shared with others, reused across various experiments, and referred to by name in your training script."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "dataset = dataset.register(workspace = ws,\n",
+        "                           name = 'mnist dataset',\n",
+        "                           description='training and test dataset',\n",
+        "                           create_new_version=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# list the files referenced by dataset\n",
+        "dataset.to_path()"
       ]
     },
     {
@@ -327,6 +355,7 @@
         "import argparse\n",
         "import os\n",
         "import numpy as np\n",
+        "import glob\n",
         "\n",
         "from sklearn.linear_model import LogisticRegression\n",
         "from sklearn.externals import joblib\n",
@@ -334,7 +363,7 @@
         "from azureml.core import Run\n",
         "from utils import load_data\n",
         "\n",
-        "# let user feed in 2 parameters, the location of the data files (from datastore), and the regularization rate of the logistic regression model\n",
+        "# let user feed in 2 parameters, the dataset to mount or download, and the regularization rate of the logistic regression model\n",
         "parser = argparse.ArgumentParser()\n",
         "parser.add_argument('--data-folder', type=str, dest='data_folder', help='data folder mounting point')\n",
         "parser.add_argument('--regularization', type=float, dest='reg', default=0.01, help='regularization rate')\n",
@@ -345,10 +374,11 @@
         "\n",
         "# load train and test set into numpy arrays\n",
         "# note we scale the pixel intensity values to 0-1 (by dividing it with 255.0) so the model can converge faster.\n",
-        "X_train = load_data(os.path.join(data_folder, 'train-images.gz'), False) / 255.0\n",
-        "X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / 255.0\n",
-        "y_train = load_data(os.path.join(data_folder, 'train-labels.gz'), True).reshape(-1)\n",
-        "y_test = load_data(os.path.join(data_folder, 'test-labels.gz'), True).reshape(-1)\n",
+        "X_train = load_data(glob.glob(os.path.join(data_folder, '**/train-images-idx3-ubyte.gz'), recursive=True)[0], False) / 255.0\n",
+        "X_test = load_data(glob.glob(os.path.join(data_folder, '**/t10k-images-idx3-ubyte.gz'), recursive=True)[0], False) / 255.0\n",
+        "y_train = load_data(glob.glob(os.path.join(data_folder, '**/train-labels-idx1-ubyte.gz'), recursive=True)[0], True).reshape(-1)\n",
+        "y_test = load_data(glob.glob(os.path.join(data_folder, '**/t10k-labels-idx1-ubyte.gz'), recursive=True)[0], True).reshape(-1)\n",
+        "\n",
         "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep = '\\n')\n",
         "\n",
         "# get hold of the current run\n",
@@ -379,7 +409,7 @@
       "source": [
         "Notice how the script gets data and saves models:\n",
         "\n",
-        "+ The training script reads an argument to find the directory containing the data.  When you submit the job later, you point to the datastore for this argument:\n",
+        "+ The training script reads an argument to find the directory containing the data.  When you submit the job later, you point to the dataset for this argument:\n",
         "`parser.add_argument('--data-folder', type=str, dest='data_folder', help='data directory mounting point')`"
       ]
     },
@@ -424,7 +454,23 @@
         "* The training script name, train.py\n",
         "* Parameters required from the training script \n",
         "\n",
-        "In this tutorial, this target is AmlCompute. All files in the script folder are uploaded into the cluster nodes for execution. The data_folder is set to use the datastore (`ds.path('mnist').as_mount()`)."
+        "In this tutorial, the target is AmlCompute. All files in the script folder are uploaded into the cluster nodes for execution. The data_folder is set to use the dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.environment import Environment\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "\n",
+        "# to install required packages\n",
+        "env = Environment('my_env')\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk','scikit-learn','azureml-dataprep[pandas,fuse]>=1.1.14'])\n",
+        "\n",
+        "env.python.conda_dependencies = cd"
       ]
     },
     {
@@ -440,30 +486,16 @@
         "from azureml.train.sklearn import SKLearn\n",
         "\n",
         "script_params = {\n",
-        "    '--data-folder': ds.path('mnist').as_mount(),\n",
+        "    # to mount files referenced by mnist dataset\n",
+        "    '--data-folder': dataset.as_named_input('mnist').as_mount(),\n",
         "    '--regularization': 0.5\n",
         "}\n",
         "\n",
         "est = SKLearn(source_directory=script_folder,\n",
-        "                script_params=script_params,\n",
-        "                compute_target=compute_target,\n",
-        "                entry_script='train.py')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This is what the mounting point looks like:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(ds.path('mnist').as_mount())"
+        "              script_params=script_params,\n",
+        "              compute_target=compute_target,\n",
+        "              environment_definition=env,\n",
+        "              entry_script='train.py')"
       ]
     },
     {
@@ -674,6 +706,18 @@
       "language": "python",
       "name": "python36"
     },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.9"
+    },
     "msauthor": "roastala"
   },
   "nbformat": 4,
diff --git a/tutorials/img-classification-part2-deploy.ipynb b/tutorials/img-classification-part2-deploy.ipynb
index acb0f951..c01b3149 100644
--- a/tutorials/img-classification-part2-deploy.ipynb
+++ b/tutorials/img-classification-part2-deploy.ipynb
@@ -412,18 +412,17 @@
       "source": [
         "%%time\n",
         "from azureml.core.webservice import Webservice\n",
-        "from azureml.core.image import ContainerImage\n",
+        "from azureml.core.model import InferenceConfig\n",
         "\n",
-        "# configure the image\n",
-        "image_config = ContainerImage.image_configuration(execution_script=\"score.py\", \n",
-        "                                                  runtime=\"python\", \n",
-        "                                                  conda_file=\"myenv.yml\")\n",
+        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "                                   entry_script=\"score.py\",\n",
+        "                                   conda_file=\"myenv.yml\")\n",
         "\n",
-        "service = Webservice.deploy_from_model(workspace=ws,\n",
-        "                                       name='sklearn-mnist-svc',\n",
-        "                                       deployment_config=aciconfig,\n",
-        "                                       models=[model],\n",
-        "                                       image_config=image_config)\n",
+        "service = Model.deploy(workspace=ws, \n",
+        "                       name='sklearn-mnist-svc', \n",
+        "                       models=[model], \n",
+        "                       inference_config=inference_config, \n",
+        "                       deployment_config=aciconfig)\n",
         "\n",
         "service.wait_for_deployment(show_output=True)"
       ]
diff --git a/tutorials/imgs/experiment_main.png b/tutorials/imgs/experiment_main.png
new file mode 100644
index 00000000..bb3e51af
Binary files /dev/null and b/tutorials/imgs/experiment_main.png differ
diff --git a/tutorials/imgs/flow2.png b/tutorials/imgs/flow2.png
new file mode 100644
index 00000000..f5c8968b
Binary files /dev/null and b/tutorials/imgs/flow2.png differ
diff --git a/tutorials/imgs/model_download.png b/tutorials/imgs/model_download.png
new file mode 100644
index 00000000..e07fc1db
Binary files /dev/null and b/tutorials/imgs/model_download.png differ
diff --git a/tutorials/regression-automated-ml.ipynb b/tutorials/regression-automated-ml.ipynb
new file mode 100644
index 00000000..6482feb1
--- /dev/null
+++ b/tutorials/regression-automated-ml.ipynb
@@ -0,0 +1,654 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/regression-part2-automated-ml.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Tutorial: Use automated machine learning to predict taxi fares"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this tutorial, you use automated machine learning in Azure Machine Learning service to create a regression model to predict NYC taxi fare prices. This process accepts training data and configuration settings, and automatically iterates through combinations of different feature normalization/standardization methods, models, and hyperparameter settings to arrive at the best model.\n",
+        "\n",
+        "In this tutorial you learn the following tasks:\n",
+        "\n",
+        "* Download, transform, and clean data using Azure Open Datasets\n",
+        "* Train an automated machine learning regression model\n",
+        "* Calculate model accuracy\n",
+        "\n",
+        "If you don\u00e2\u20ac\u2122t have an Azure subscription, create a free account before you begin. Try the [free or paid version](https://aka.ms/AMLFree) of Azure Machine Learning service today."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "* Complete the [setup tutorial](https://docs.microsoft.com/azure/machine-learning/service/tutorial-1st-experiment-sdk-setup) if you don't already have an Azure Machine Learning service workspace or notebook virtual machine.\n",
+        "* After you complete the setup tutorial, open the **tutorials/regression-automated-ml.ipynb** notebook using the same notebook server.\n",
+        "\n",
+        "This tutorial is also available on [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/tutorials) if you wish to run it in your own [local environment](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment#local). Run `pip install azureml-sdk[automl] azureml-opendatasets azureml-widgets` to get the required packages."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Download and prepare data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Import the necessary packages. The Open Datasets package contains a class representing each data source (`NycTlcGreen` for example) to easily filter date parameters before downloading."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.opendatasets import NycTlcGreen\n",
+        "import pandas as pd\n",
+        "from datetime import datetime\n",
+        "from dateutil.relativedelta import relativedelta"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Begin by creating a dataframe to hold the taxi data. When working in a non-Spark environment, Open Datasets only allows downloading one month of data at a time with certain classes to avoid `MemoryError` with large datasets. To download taxi data, iteratively fetch one month at a time, and before appending it to `green_taxi_df` randomly sample 2,000 records from each month to avoid bloating the dataframe. Then preview the data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "green_taxi_df = pd.DataFrame([])\n",
+        "start = datetime.strptime(\"1/1/2015\",\"%m/%d/%Y\")\n",
+        "end = datetime.strptime(\"1/31/2015\",\"%m/%d/%Y\")\n",
+        "\n",
+        "for sample_month in range(12):\n",
+        "    temp_df_green = NycTlcGreen(start + relativedelta(months=sample_month), end + relativedelta(months=sample_month)) \\\n",
+        "        .to_pandas_dataframe()\n",
+        "    green_taxi_df = green_taxi_df.append(temp_df_green.sample(2000))\n",
+        "    \n",
+        "green_taxi_df.head(10)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that the initial data is loaded, define a function to create various time-based features from the pickup datetime field. This will create new fields for the month number, day of month, day of week, and hour of day, and will allow the model to factor in time-based seasonality. \n",
+        "\n",
+        "Use the `apply()` function on the dataframe to iteratively apply the `build_time_features()` function to each row in the taxi data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def build_time_features(vector):\n",
+        "    pickup_datetime = vector[0]\n",
+        "    month_num = pickup_datetime.month\n",
+        "    day_of_month = pickup_datetime.day\n",
+        "    day_of_week = pickup_datetime.weekday()\n",
+        "    hour_of_day = pickup_datetime.hour\n",
+        "    \n",
+        "    return pd.Series((month_num, day_of_month, day_of_week, hour_of_day))\n",
+        "\n",
+        "green_taxi_df[[\"month_num\", \"day_of_month\",\"day_of_week\", \"hour_of_day\"]] = green_taxi_df[[\"lpepPickupDatetime\"]].apply(build_time_features, axis=1)\n",
+        "green_taxi_df.head(10)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Remove some of the columns that you won't need for training or additional feature building."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "columns_to_remove = [\"lpepPickupDatetime\", \"lpepDropoffDatetime\", \"puLocationId\", \"doLocationId\", \"extra\", \"mtaTax\",\n",
+        "                     \"improvementSurcharge\", \"tollsAmount\", \"ehailFee\", \"tripType\", \"rateCodeID\", \n",
+        "                     \"storeAndFwdFlag\", \"paymentType\", \"fareAmount\", \"tipAmount\"\n",
+        "                    ]\n",
+        "for col in columns_to_remove:\n",
+        "    green_taxi_df.pop(col)\n",
+        "    \n",
+        "green_taxi_df.head(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Cleanse data "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Run the `describe()` function on the new dataframe to see summary statistics for each field."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "green_taxi_df.describe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "From the summary statistics, you see that there are several fields that have outliers or values that will reduce model accuracy. First filter the lat/long fields to be within the bounds of the Manhattan area. This will filter out longer taxi trips or trips that are outliers in respect to their relationship with other features. \n",
+        "\n",
+        "Additionally filter the `tripDistance` field to be greater than zero but less than 31 miles (the haversine distance between the two lat/long pairs). This eliminates long outlier trips that have inconsistent trip cost.\n",
+        "\n",
+        "Lastly, the `totalAmount` field has negative values for the taxi fares, which don't make sense in the context of our model, and the `passengerCount` field has bad data with the minimum values being zero.\n",
+        "\n",
+        "Filter out these anomalies using query functions, and then remove the last few columns unnecessary for training."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "final_df = green_taxi_df.query(\"pickupLatitude>=40.53 and pickupLatitude<=40.88\")\n",
+        "final_df = final_df.query(\"pickupLongitude>=-74.09 and pickupLongitude<=-73.72\")\n",
+        "final_df = final_df.query(\"tripDistance>=0.25 and tripDistance<31\")\n",
+        "final_df = final_df.query(\"passengerCount>0 and totalAmount>0\")\n",
+        "\n",
+        "columns_to_remove_for_training = [\"pickupLongitude\", \"pickupLatitude\", \"dropoffLongitude\", \"dropoffLatitude\"]\n",
+        "for col in columns_to_remove_for_training:\n",
+        "    final_df.pop(col)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Call `describe()` again on the data to ensure cleansing worked as expected. You now have a prepared and cleansed set of taxi, holiday, and weather data to use for machine learning model training."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "final_df.describe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Configure workspace\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create a workspace object from the existing workspace. A [Workspace](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace.workspace?view=azure-ml-py) is a class that accepts your Azure subscription and resource information. It also creates a cloud resource to monitor and track your model runs. `Workspace.from_config()` reads the file **config.json** and loads the authentication details into an object named `ws`. `ws` is used throughout the rest of the code in this tutorial."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.workspace import Workspace\n",
+        "ws = Workspace.from_config()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Split the data into train and test sets"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Split the data into training and test sets by using the `train_test_split` function in the `scikit-learn` library. This function segregates the data into the x (**features**) data set for model training and the y (**values to predict**) data set for testing. The `test_size` parameter determines the percentage of data to allocate to testing. The `random_state` parameter sets a seed to the random generator, so that your train-test splits are deterministic."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "y_df = final_df.pop(\"totalAmount\")\n",
+        "x_df = final_df\n",
+        "\n",
+        "x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.2, random_state=223)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The purpose of this step is to have data points to test the finished model that haven't been used to train the model, in order to measure true accuracy. \n",
+        "\n",
+        "In other words, a well-trained model should be able to accurately make predictions from data it hasn't already seen. You now have data prepared for auto-training a machine learning model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Automatically train a model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To automatically train a model, take the following steps:\n",
+        "1. Define settings for the experiment run. Attach your training data to the configuration, and modify settings that control the training process.\n",
+        "1. Submit the experiment for model tuning. After submitting the experiment, the process iterates through different machine learning algorithms and hyperparameter settings, adhering to your defined constraints. It chooses the best-fit model by optimizing an accuracy metric."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Define training settings"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Define the experiment parameter and model settings for training. View the full list of [settings](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train). Submitting the experiment with these default settings will take approximately 5-10 min, but if you want a shorter run time, reduce the `iterations` parameter.\n",
+        "\n",
+        "\n",
+        "|Property| Value in this tutorial |Description|\n",
+        "|----|----|---|\n",
+        "|**iteration_timeout_minutes**|2|Time limit in minutes for each iteration. Reduce this value to decrease total runtime.|\n",
+        "|**iterations**|20|Number of iterations. In each iteration, a new machine learning model is trained with your data. This is the primary value that affects total run time.|\n",
+        "|**primary_metric**| spearman_correlation | Metric that you want to optimize. The best-fit model will be chosen based on this metric.|\n",
+        "|**preprocess**| True | By using **True**, the experiment can preprocess the input data (handling missing data, converting text to numeric, etc.)|\n",
+        "|**verbosity**| logging.INFO | Controls the level of logging.|\n",
+        "|**n_cross_validations**|5|Number of cross-validation splits to perform when validation data is not specified.|"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import logging\n",
+        "\n",
+        "automl_settings = {\n",
+        "    \"iteration_timeout_minutes\": 2,\n",
+        "    \"iterations\": 20,\n",
+        "    \"primary_metric\": 'spearman_correlation',\n",
+        "    \"preprocess\": True,\n",
+        "    \"verbosity\": logging.INFO,\n",
+        "    \"n_cross_validations\": 5\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Use your defined training settings as a `**kwargs` parameter to an `AutoMLConfig` object. Additionally, specify your training data and the type of model, which is `regression` in this case."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.automl import AutoMLConfig\n",
+        "\n",
+        "automl_config = AutoMLConfig(task='regression',\n",
+        "                             debug_log='automated_ml_errors.log',\n",
+        "                             X=x_train.values,\n",
+        "                             y=y_train.values.flatten(),\n",
+        "                             **automl_settings)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Automated machine learning pre-processing steps (feature normalization, handling missing data, converting text to numeric, etc.) become part of the underlying model. When using the model for predictions, the same pre-processing steps applied during training are applied to your input data automatically."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Train the automatic regression model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create an experiment object in your workspace. An experiment acts as a container for your individual runs. Pass the defined `automl_config` object to the experiment, and set the output to `True` to view progress during the run. \n",
+        "\n",
+        "After starting the experiment, the output shown updates live as the experiment runs. For each iteration, you see the model type, the run duration, and the training accuracy. The field `BEST` tracks the best running training score based on your metric type."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.experiment import Experiment\n",
+        "experiment = Experiment(ws, \"taxi-experiment\")\n",
+        "local_run = experiment.submit(automl_config, show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Explore the results"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Explore the results of automatic training with a [Jupyter widget](https://docs.microsoft.com/python/api/azureml-widgets/azureml.widgets?view=azure-ml-py). The widget allows you to see a graph and table of all individual run iterations, along with training accuracy metrics and metadata. Additionally, you can filter on different accuracy metrics than your primary metric with the dropdown selector."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(local_run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrieve the best model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Select the best model from your iterations. The `get_output` function returns the best run and the fitted model for the last fit invocation. By using the overloads on `get_output`, you can retrieve the best run and fitted model for any logged metric or a particular iteration."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run, fitted_model = local_run.get_output()\n",
+        "print(best_run)\n",
+        "print(fitted_model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Test the best model accuracy"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Use the best model to run predictions on the test data set to predict taxi fares. The function `predict` uses the best model and predicts the values of y, **trip cost**, from the `x_test` data set. Print the first 10 predicted cost values from `y_predict`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "y_predict = fitted_model.predict(x_test.values)\n",
+        "print(y_predict[:10])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Calculate the `root mean squared error` of the results. Convert the `y_test` dataframe to a list to compare to the predicted values. The function `mean_squared_error` takes two arrays of values and calculates the average squared error between them. Taking the square root of the result gives an error in the same units as the y variable, **cost**. It indicates roughly how far the taxi fare predictions are from the actual fares."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import mean_squared_error\n",
+        "from math import sqrt\n",
+        "\n",
+        "y_actual = y_test.values.flatten().tolist()\n",
+        "rmse = sqrt(mean_squared_error(y_actual, y_predict))\n",
+        "rmse"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Run the following code to calculate mean absolute percent error (MAPE) by using the full `y_actual` and `y_predict` data sets. This metric calculates an absolute difference between each predicted and actual value and sums all the differences. Then it expresses that sum as a percent of the total of the actual values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sum_actuals = sum_errors = 0\n",
+        "\n",
+        "for actual_val, predict_val in zip(y_actual, y_predict):\n",
+        "    abs_error = actual_val - predict_val\n",
+        "    if abs_error < 0:\n",
+        "        abs_error = abs_error * -1\n",
+        "\n",
+        "    sum_errors = sum_errors + abs_error\n",
+        "    sum_actuals = sum_actuals + actual_val\n",
+        "\n",
+        "mean_abs_percent_error = sum_errors / sum_actuals\n",
+        "print(\"Model MAPE:\")\n",
+        "print(mean_abs_percent_error)\n",
+        "print()\n",
+        "print(\"Model Accuracy:\")\n",
+        "print(1 - mean_abs_percent_error)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "From the two prediction accuracy metrics, you see that the model is fairly good at predicting taxi fares from the data set's features, typically within +- $4.00, and approximately 15% error. \n",
+        "\n",
+        "The traditional machine learning model development process is highly resource-intensive, and requires significant domain knowledge and time investment to run and compare the results of dozens of models. Using automated machine learning is a great way to rapidly test many different models for your scenario."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Clean up resources"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Do not complete this section if you plan on running other Azure Machine Learning service tutorials."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Stop the notebook VM"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If you used a cloud notebook server, stop the VM when you are not using it to reduce cost."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "1. In your workspace, select **Notebook VMs**.\n",
+        "1. From the list, select the VM.\n",
+        "1. Select **Stop**.\n",
+        "1. When you're ready to use the server again, select **Start**."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Delete everything"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If you don't plan to use the resources you created, delete them, so you don't incur any charges."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "1. In the Azure portal, select **Resource groups** on the far left.\n",
+        "1. From the list, select the resource group you created.\n",
+        "1. Select **Delete resource group**.\n",
+        "1. Enter the resource group name. Then select **Delete**.\n",
+        "\n",
+        "You can also keep the resource group but delete a single workspace. Display the workspace properties and select **Delete**."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next steps"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this automated machine learning tutorial, you did the following tasks:\n",
+        "\n",
+        "> * Configured a workspace and prepared data for an experiment.\n",
+        "> * Trained by using an automated regression model locally with custom parameters.\n",
+        "> * Explored and reviewed training results.\n",
+        "\n",
+        "[Deploy your model](https://docs.microsoft.com/azure/machine-learning/service/tutorial-deploy-models-with-aml) with Azure Machine Learning service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "jeffshep"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.7"
+    },
+    "msauthor": "trbye"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/tutorials/regression-automated-ml.yml b/tutorials/regression-automated-ml.yml
new file mode 100644
index 00000000..f971e01d
--- /dev/null
+++ b/tutorials/regression-automated-ml.yml
@@ -0,0 +1,7 @@
+name: regression-automated-ml
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - azureml-opendatasets
diff --git a/tutorials/regression-part1-data-prep.ipynb b/tutorials/regression-part1-data-prep.ipynb
deleted file mode 100644
index 5813cdfc..00000000
--- a/tutorials/regression-part1-data-prep.ipynb
+++ /dev/null
@@ -1,638 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Tutorial: Prepare data for regression modeling"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In this tutorial, you learn how to prepare data for regression modeling by using the Azure Machine Learning Data Prep SDK. You run various transformations to filter and combine two different NYC taxi data sets.\n",
-        "\n",
-        "This tutorial is **part one of a two-part tutorial series**. After you complete the tutorial series, you can predict the cost of a taxi trip by training a model on data features. These features include the pickup day and time, the number of passengers, and the pickup location.\n",
-        "\n",
-        "In this tutorial, you:\n",
-        "\n",
-        "\n",
-        "> * Setup a Python environment and import packages\n",
-        "> * Load two datasets with different field names\n",
-        "> * Cleanse data to remove anomalies\n",
-        "> * Transform data using intelligent transforms to create new features\n",
-        "> * Save your dataflow object to use in a regression model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "\n",
-        "To run the notebook you will need:\n",
-        "\n",
-        "* A Python 3.6 notebook server with the following installed:\n",
-        "    * The Azure Machine Learning Data Prep SDK for Python\n",
-        "* The tutorial notebook\n",
-        "\n",
-        "Navigate back to the [tutorial page](https://docs.microsoft.com/azure/machine-learning/service/tutorial-data-prep) for specific environment setup instructions.\n",
-        "\n",
-        "## <a name=\"start\"></a>Set up your development environment\n",
-        "\n",
-        "All the setup for your development work can be accomplished in a Python notebook. Setup includes the following actions:\n",
-        "\n",
-        "* Install the SDK\n",
-        "* Import Python packages\n",
-        "\n",
-        "### Install and import packages\n",
-        "\n",
-        "Use the following to install necessary packages if you don't already have them.\n",
-        "\n",
-        "```shell\n",
-        "pip install \"azureml-dataprep[pandas]>=1.1.2,<1.2.0\"\n",
-        "```\n",
-        "\n",
-        "Import the SDK."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load data\n",
-        "Download two different NYC Taxi data sets into dataflow objects. These datasets contain slightly different fields. The method `auto_read_file()` automatically recognizes the input file type."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from IPython.display import display\n",
-        "dataset_root = \"https://dprepdata.blob.core.windows.net/demo\"\n",
-        "\n",
-        "green_path = \"/\".join([dataset_root, \"green-small/*\"])\n",
-        "yellow_path = \"/\".join([dataset_root, \"yellow-small/*\"])\n",
-        "\n",
-        "green_df_raw = dprep.read_csv(path=green_path, header=dprep.PromoteHeadersMode.GROUPED)\n",
-        "# auto_read_file automatically identifies and parses the file type, which is useful when you don't know the file type.\n",
-        "yellow_df_raw = dprep.auto_read_file(path=yellow_path)\n",
-        "\n",
-        "display(green_df_raw.head(5))\n",
-        "display(yellow_df_raw.head(5))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "A `Dataflow` object is similar to a dataframe, and represents a series of lazily-evaluated, immutable operations on data. Operations can be added by invoking the different transformation and filtering methods available. The result of adding an operation to a `Dataflow` is always a new `Dataflow` object."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Cleanse data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now you populate some variables with shortcut transforms to apply to all dataflows. The `drop_if_all_null` variable is used to delete records where all fields are null. The `useful_columns` variable holds an array of column descriptions that are kept in each dataflow."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "all_columns = dprep.ColumnSelector(term=\".*\", use_regex=True)\n",
-        "drop_if_all_null = [all_columns, dprep.ColumnRelationship(dprep.ColumnRelationship.ALL)]\n",
-        "useful_columns = [\n",
-        "    \"cost\", \"distance\", \"dropoff_datetime\", \"dropoff_latitude\", \"dropoff_longitude\",\n",
-        "    \"passengers\", \"pickup_datetime\", \"pickup_latitude\", \"pickup_longitude\", \"store_forward\", \"vendor\"\n",
-        "]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You first work with the green taxi data to get it into a valid shape that can be combined with the yellow taxi data. Call the `replace_na()`, `drop_nulls()`, and `keep_columns()` functions by using the shortcut transform variables you created. Additionally, rename all the columns in the dataframe to match the names in the `useful_columns` variable."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "green_df = (green_df_raw\n",
-        "    .replace_na(columns=all_columns)\n",
-        "    .drop_nulls(*drop_if_all_null)\n",
-        "    .rename_columns(column_pairs={\n",
-        "        \"VendorID\": \"vendor\",\n",
-        "        \"lpep_pickup_datetime\": \"pickup_datetime\",\n",
-        "        \"Lpep_dropoff_datetime\": \"dropoff_datetime\",\n",
-        "        \"lpep_dropoff_datetime\": \"dropoff_datetime\",\n",
-        "        \"Store_and_fwd_flag\": \"store_forward\",\n",
-        "        \"store_and_fwd_flag\": \"store_forward\",\n",
-        "        \"Pickup_longitude\": \"pickup_longitude\",\n",
-        "        \"Pickup_latitude\": \"pickup_latitude\",\n",
-        "        \"Dropoff_longitude\": \"dropoff_longitude\",\n",
-        "        \"Dropoff_latitude\": \"dropoff_latitude\",\n",
-        "        \"Passenger_count\": \"passengers\",\n",
-        "        \"Fare_amount\": \"cost\",\n",
-        "        \"Trip_distance\": \"distance\"\n",
-        "     })\n",
-        "    .keep_columns(columns=useful_columns))\n",
-        "green_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Run the same transformation steps on the yellow taxi data. These functions ensure that null data is removed from the data set, which will help increase machine learning model accuracy."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "yellow_df = (yellow_df_raw\n",
-        "    .replace_na(columns=all_columns)\n",
-        "    .drop_nulls(*drop_if_all_null)\n",
-        "    .rename_columns(column_pairs={\n",
-        "        \"vendor_name\": \"vendor\",\n",
-        "        \"VendorID\": \"vendor\",\n",
-        "        \"vendor_id\": \"vendor\",\n",
-        "        \"Trip_Pickup_DateTime\": \"pickup_datetime\",\n",
-        "        \"tpep_pickup_datetime\": \"pickup_datetime\",\n",
-        "        \"Trip_Dropoff_DateTime\": \"dropoff_datetime\",\n",
-        "        \"tpep_dropoff_datetime\": \"dropoff_datetime\",\n",
-        "        \"store_and_forward\": \"store_forward\",\n",
-        "        \"store_and_fwd_flag\": \"store_forward\",\n",
-        "        \"Start_Lon\": \"pickup_longitude\",\n",
-        "        \"Start_Lat\": \"pickup_latitude\",\n",
-        "        \"End_Lon\": \"dropoff_longitude\",\n",
-        "        \"End_Lat\": \"dropoff_latitude\",\n",
-        "        \"Passenger_Count\": \"passengers\",\n",
-        "        \"passenger_count\": \"passengers\",\n",
-        "        \"Fare_Amt\": \"cost\",\n",
-        "        \"fare_amount\": \"cost\",\n",
-        "        \"Trip_Distance\": \"distance\",\n",
-        "        \"trip_distance\": \"distance\"\n",
-        "    })\n",
-        "    .keep_columns(columns=useful_columns))\n",
-        "yellow_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Call the `append_rows()` function on the green taxi data to append the yellow taxi data. A new combined dataframe is created."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "combined_df = green_df.append_rows([yellow_df])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Convert types and filter "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Examine the pickup and drop-off coordinates summary statistics to see how the data is distributed. First, define a `TypeConverter` object to change the latitude and longitude fields to decimal type. Next, call the `keep_columns()` function to restrict output to only the latitude and longitude fields, and then call the `get_profile()` function. These function calls create a condensed view of the dataflow to just show the lat/long fields, which makes it easier to evaluate missing or out-of-scope coordinates."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "decimal_type = dprep.TypeConverter(data_type=dprep.FieldType.DECIMAL)\n",
-        "combined_df = combined_df.set_column_types(type_conversions={\n",
-        "    \"pickup_longitude\": decimal_type,\n",
-        "    \"pickup_latitude\": decimal_type,\n",
-        "    \"dropoff_longitude\": decimal_type,\n",
-        "    \"dropoff_latitude\": decimal_type\n",
-        "})\n",
-        "combined_df.keep_columns(columns=[\n",
-        "    \"pickup_longitude\", \"pickup_latitude\",\n",
-        "    \"dropoff_longitude\", \"dropoff_latitude\"\n",
-        "]).get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "From the summary statistics output, you see there are missing coordinates and coordinates that aren't in New York City (this is determined from subjective analysis). Filter out coordinates for locations that are outside the city border. Chain the column filter commands within the `filter()` function and define the minimum and maximum bounds for each field. Then call the `get_profile()` function again to verify the transformation."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "latlong_filtered_df = (combined_df\n",
-        "    .drop_nulls(\n",
-        "        columns=[\"pickup_longitude\", \"pickup_latitude\", \"dropoff_longitude\", \"dropoff_latitude\"],\n",
-        "        column_relationship=dprep.ColumnRelationship(dprep.ColumnRelationship.ANY)\n",
-        "    )\n",
-        "    .filter(dprep.f_and(\n",
-        "        dprep.col(\"pickup_longitude\") <= -73.72,\n",
-        "        dprep.col(\"pickup_longitude\") >= -74.09,\n",
-        "        dprep.col(\"pickup_latitude\") <= 40.88,\n",
-        "        dprep.col(\"pickup_latitude\") >= 40.53,\n",
-        "        dprep.col(\"dropoff_longitude\") <= -73.72,\n",
-        "        dprep.col(\"dropoff_longitude\") >= -74.09,\n",
-        "        dprep.col(\"dropoff_latitude\") <= 40.88,\n",
-        "        dprep.col(\"dropoff_latitude\") >= 40.53\n",
-        "    )))\n",
-        "latlong_filtered_df.keep_columns(columns=[\n",
-        "    \"pickup_longitude\", \"pickup_latitude\",\n",
-        "    \"dropoff_longitude\", \"dropoff_latitude\"\n",
-        "]).get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Split and rename columns"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Look at the data profile for the `store_forward` column. This field is a boolean flag that is `Y` when the taxi did not have a connection to the server after the trip, and thus had to store the trip data in memory, and later forward it to the server when connected."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "latlong_filtered_df.keep_columns(columns='store_forward').get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Notice that the data profile output in the `store_forward` column shows that the data is inconsistent and there are missing or null values. Use the `replace()` and `fill_nulls()` functions to replace these values with the string \"N\":"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "replaced_stfor_vals_df = latlong_filtered_df.replace(columns=\"store_forward\", find=\"0\", replace_with=\"N\").fill_nulls(\"store_forward\", \"N\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Execute the `replace` function on the `distance` field. The function reformats distance values that are incorrectly labeled as `.00`, and fills any nulls with zeros. Convert the `distance` field to numerical format. These incorrect data points are likely anomolies in the data collection system on the taxi cabs."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "replaced_distance_vals_df = replaced_stfor_vals_df.replace(columns=\"distance\", find=\".00\", replace_with=0).fill_nulls(\"distance\", 0)\n",
-        "replaced_distance_vals_df = replaced_distance_vals_df.to_number([\"distance\"])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Split the pickup and dropoff datetime values into the respective date and time columns. Use the `split_column_by_example()` function to make the split. In this case, the optional `example` parameter of the `split_column_by_example()` function is omitted. Therefore, the function automatically determines where to split based on the data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "time_split_df = (replaced_distance_vals_df\n",
-        "    .split_column_by_example(source_column=\"pickup_datetime\")\n",
-        "    .split_column_by_example(source_column=\"dropoff_datetime\"))\n",
-        "time_split_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Rename the columns generated by `split_column_by_example()` into meaningful names."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "renamed_col_df = (time_split_df\n",
-        "    .rename_columns(column_pairs={\n",
-        "        \"pickup_datetime_1\": \"pickup_date\",\n",
-        "        \"pickup_datetime_2\": \"pickup_time\",\n",
-        "        \"dropoff_datetime_1\": \"dropoff_date\",\n",
-        "        \"dropoff_datetime_2\": \"dropoff_time\"\n",
-        "    }))\n",
-        "renamed_col_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Call the `get_profile()` function to see the full summary statistics after all cleansing steps."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "renamed_col_df.get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Transform data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Split the pickup and dropoff date further into the day of the week, day of the month, and month values. To get the day of the week value, use the `derive_column_by_example()` function. The function takes an array parameter of example objects that define the input data, and the preferred output. The function automatically determines your preferred transformation. For the pickup and dropoff time columns, split the time into the hour, minute, and second by using the `split_column_by_example()` function with no example parameter.\n",
-        "\n",
-        "After you generate the new features, use the `drop_columns()` function to delete the original fields as the newly generated features are preferred. Rename the rest of the fields to use meaningful descriptions.\n",
-        "\n",
-        "Transforming the data in this way to create new time-based features will improve machine learning model accuracy. For example, generating a new feature for the weekday will help establish a relationship between the day of the week and the taxi fare price, which is often more expensive on certain days of the week due to high demand."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "transformed_features_df = (renamed_col_df\n",
-        "    .derive_column_by_example(\n",
-        "        source_columns=\"pickup_date\",\n",
-        "        new_column_name=\"pickup_weekday\",\n",
-        "        example_data=[(\"2009-01-04\", \"Sunday\"), (\"2013-08-22\", \"Thursday\")]\n",
-        "    )\n",
-        "    .derive_column_by_example(\n",
-        "        source_columns=\"dropoff_date\",\n",
-        "        new_column_name=\"dropoff_weekday\",\n",
-        "        example_data=[(\"2013-08-22\", \"Thursday\"), (\"2013-11-03\", \"Sunday\")]\n",
-        "    )\n",
-        "\n",
-        "    .split_column_by_example(source_column=\"pickup_time\")\n",
-        "    .split_column_by_example(source_column=\"dropoff_time\")\n",
-        "    # The following two calls to split_column_by_example reference the column names generated from the previous two calls.\n",
-        "    .split_column_by_example(source_column=\"pickup_time_1\")\n",
-        "    .split_column_by_example(source_column=\"dropoff_time_1\")\n",
-        "    .drop_columns(columns=[\n",
-        "        \"pickup_date\", \"pickup_time\", \"dropoff_date\", \"dropoff_time\",\n",
-        "        \"pickup_date_1\", \"dropoff_date_1\", \"pickup_time_1\", \"dropoff_time_1\"\n",
-        "    ])\n",
-        "\n",
-        "    .rename_columns(column_pairs={\n",
-        "        \"pickup_date_2\": \"pickup_month\",\n",
-        "        \"pickup_date_3\": \"pickup_monthday\",\n",
-        "        \"pickup_time_1_1\": \"pickup_hour\",\n",
-        "        \"pickup_time_1_2\": \"pickup_minute\",\n",
-        "        \"pickup_time_2\": \"pickup_second\",\n",
-        "        \"dropoff_date_2\": \"dropoff_month\",\n",
-        "        \"dropoff_date_3\": \"dropoff_monthday\",\n",
-        "        \"dropoff_time_1_1\": \"dropoff_hour\",\n",
-        "        \"dropoff_time_1_2\": \"dropoff_minute\",\n",
-        "        \"dropoff_time_2\": \"dropoff_second\"\n",
-        "    }))\n",
-        "\n",
-        "transformed_features_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Notice that the data shows that the pickup and dropoff date and time components produced from the derived transformations are correct. Drop the `pickup_datetime` and `dropoff_datetime` columns because they're no longer needed (granular time features like hour, minute and second are more useful for model training)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "processed_df = transformed_features_df.drop_columns(columns=[\"pickup_datetime\", \"dropoff_datetime\"])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Use the type inference functionality to automatically check the data type of each field, and display the inference results."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "type_infer = processed_df.builders.set_column_types()\n",
-        "type_infer.learn()\n",
-        "type_infer"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The inference results look correct based on the data. Now apply the type conversions to the dataflow."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "type_converted_df = type_infer.to_dataflow()\n",
-        "type_converted_df.get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Before you package the dataflow, run two final filters on the data set. To eliminate incorrectly captured data points, filter the dataflow on records where both the `cost` and `distance` variable values are greater than zero. This step will significantly improve machine learning model accuracy, because data points with a zero cost or distance represent major outliers that throw off prediction accuracy."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "final_df = type_converted_df.filter(dprep.col(\"distance\") > 0)\n",
-        "final_df = final_df.filter(dprep.col(\"cost\") > 0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You now have a fully transformed and prepared dataflow object to use in a machine learning model. The SDK includes object serialization functionality, which is used as shown in the following code."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "file_path = os.path.join(os.getcwd(), \"dflows.dprep\")\n",
-        "\n",
-        "final_df.save(file_path)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Clean up resources"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To continue with part two of the tutorial, you need the **dflows.dprep** file in the current directory.\n",
-        "\n",
-        "If you don't plan to continue to part two, delete the **dflows.dprep** file in your current directory. Delete this file whether you're running the execution locally or in [Azure Notebooks](https://notebooks.azure.com/)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Next steps"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In this Azure Machine Learning Data Prep SDK tutorial, you:\n",
-        "\n",
-        "> * Set up your development environment\n",
-        "> * Loaded and cleansed data sets\n",
-        "> * Used smart transforms to predict your logic based on an example\n",
-        "> * Merged and packaged datasets for machine learning training\n",
-        "\n",
-        "You are ready to use this training data in the next part of the tutorial series:\n",
-        "\n",
-        "\n",
-        "> [Tutorial #2: Train regression model](regression-part2-automated-ml.ipynb)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/regression-part1-data-prep.png)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "cforbe"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.7"
-    },
-    "msauthor": "trbye"
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/tutorials/regression-part1-data-prep.yml b/tutorials/regression-part1-data-prep.yml
deleted file mode 100644
index 4cc6d99f..00000000
--- a/tutorials/regression-part1-data-prep.yml
+++ /dev/null
@@ -1,5 +0,0 @@
-name: regression-part1-data-prep
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-dataprep[pandas]>=1.1.2,<1.2.0
diff --git a/tutorials/regression-part2-automated-ml.ipynb b/tutorials/regression-part2-automated-ml.ipynb
deleted file mode 100644
index 10a9d354..00000000
--- a/tutorials/regression-part2-automated-ml.ipynb
+++ /dev/null
@@ -1,549 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Tutorial: Use automated machine learning to build your regression model\n",
-        "\n",
-        "This tutorial is **part two of a two-part tutorial series**. In the previous tutorial, you [prepared the NYC taxi data for regression modeling](regression-part1-data-prep.ipynb).\n",
-        "\n",
-        "Now you're ready to start building your model with Azure Machine Learning service. In this part of the tutorial, you use the prepared data and automatically generate a regression model to predict taxi fare prices. By using the automated machine learning capabilities of the service, you define your machine learning goals and constraints. You launch the automated machine learning process. Then allow the algorithm selection and hyperparameter tuning to happen for you. The automated machine learning technique iterates over many combinations of algorithms and hyperparameters until it finds the best model based on your criterion.\n",
-        "\n",
-        "In this tutorial, you learn the following tasks:\n",
-        "\n",
-        "> * Set up a Python environment and import the SDK packages\n",
-        "> * Configure an Azure Machine Learning service workspace\n",
-        "> * Auto-train a regression model \n",
-        "> * Run the model locally with custom parameters\n",
-        "> * Explore the results\n",
-        "\n",
-        "If you do not have an Azure subscription, create a [free account](https://aka.ms/AMLfree) before you begin. \n",
-        "\n",
-        "> Code in this article was tested with Azure Machine Learning SDK version 1.0.0\n",
-        "\n",
-        "\n",
-        "## Prerequisites\n",
-        "\n",
-        "To run the notebook you will need:\n",
-        "\n",
-        "* [Run the data preparation tutorial](regression-part1-data-prep.ipynb).\n",
-        "* A Python 3.6 notebook server with the following installed:\n",
-        "    * The Azure Machine Learning SDK for Python with `automl` and `notebooks` extras\n",
-        "    * `matplotlib`\n",
-        "* The tutorial notebook\n",
-        "* A machine learning workspace\n",
-        "* The configuration file for the workspace in the same directory as the notebook\n",
-        "\n",
-        "Navigate back to the [tutorial page](https://docs.microsoft.com/azure/machine-learning/service/tutorial-auto-train-models) for specific environment setup instructions.\n",
-        "\n",
-        "## <a name=\"start\"></a>Set up your development environment\n",
-        "\n",
-        "All the setup for your development work can be accomplished in a Python notebook. Setup includes the following actions:\n",
-        "\n",
-        "* Install the SDK\n",
-        "* Import Python packages\n",
-        "* Configure your workspace"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Install and import packages\n",
-        "\n",
-        "If you are following the tutorial in your own Python environment, use the following to install necessary packages.\n",
-        "\n",
-        "```shell\n",
-        "pip install azureml-sdk[automl,notebooks] matplotlib\n",
-        "```\n",
-        "\n",
-        "Import the Python packages you need in this tutorial:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "import pandas as pd\n",
-        "from azureml.core.workspace import Workspace\n",
-        "import logging\n",
-        "import os"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Configure workspace\n",
-        "\n",
-        "Create a workspace object from the existing workspace. A `Workspace` is a class that accepts your Azure subscription and resource information. It also creates a cloud resource to monitor and track your model runs.\n",
-        "\n",
-        "`Workspace.from_config()` reads the file **aml_config/config.json** and loads the details into an object named `ws`.  `ws` is used throughout the rest of the code in this tutorial.\n",
-        "\n",
-        "After you have a workspace object, specify a name for the experiment. Create and register a local directory with the workspace. The history of all runs is recorded under the specified experiment and in the [Azure portal](https://portal.azure.com)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "# choose a name for the run history container in the workspace\n",
-        "experiment_name = 'automated-ml-regression'\n",
-        "# project folder\n",
-        "project_folder = './automated-ml-regression'\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
-        "outputDf.T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explore data\n",
-        "\n",
-        "Use the data flow object created in the previous tutorial. To summarize, part 1 of this tutorial cleaned the NYC Taxi data so it could be used in a machine learning model. Now, you use various features from the data set and allow an automated model to build relationships between the features and the price of a taxi trip. Open and run the data flow and review the results:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "\n",
-        "file_path = os.path.join(os.getcwd(), \"dflows.dprep\")\n",
-        "\n",
-        "dflow_prepared = dprep.Dataflow.open(file_path)\n",
-        "dflow_prepared.get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You prepare the data for the experiment by adding columns to `dflow_x` to be features for our model creation. You define `dflow_y` to be our prediction value, **cost**:\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_X = dflow_prepared.keep_columns(['pickup_weekday','pickup_hour', 'distance','passengers', 'vendor'])\n",
-        "dflow_y = dflow_prepared.keep_columns('cost')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Split data into train and test sets\n",
-        "\n",
-        "Now you split the data into training and test sets by using the `train_test_split` function in the `sklearn` library. This function segregates the data into the x, **features**, dataset for model training and the y, **values to predict**, dataset for testing. The `test_size` parameter determines the percentage of data to allocate to testing. The `random_state` parameter sets a seed to the random generator, so that your train-test splits are always deterministic:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.model_selection import train_test_split\n",
-        "\n",
-        "\n",
-        "x_df = dflow_X.to_pandas_dataframe()\n",
-        "y_df = dflow_y.to_pandas_dataframe()\n",
-        "\n",
-        "x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.2, random_state=223)\n",
-        "# flatten y_train to 1d array\n",
-        "y_train.values.flatten()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The purpose of this step is to have data points to test the finished model that haven't been used to train the model, in order to measure true accuracy. In other words, a well-trained model should be able to accurately make predictions from data it hasn't already seen. You now have the necessary packages and data ready for autotraining your model.\n",
-        "\n",
-        "## Automatically train a model\n",
-        "\n",
-        "To automatically train a model, take the following steps:\n",
-        "1. Define settings for the experiment run. Attach your training data to the configuration, and modify settings that control the training process.\n",
-        "1. Submit the experiment for model tuning. After submitting the experiment, the process iterates through different machine learning algorithms and hyperparameter settings, adhering to your defined constraints. It chooses the best-fit model by optimizing an accuracy metric.\n",
-        "\n",
-        "\n",
-        "### Define settings for autogeneration and tuning\n",
-        "\n",
-        "Define the experiment parameters and models settings for autogeneration and tuning. View the full list of [settings](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train). Submitting the experiment with these default settings will take approximately 10-15 min, but if you want a shorter run time, reduce either `iterations` or `iteration_timeout_minutes`.\n",
-        "\n",
-        "\n",
-        "|Property| Value in this tutorial |Description|\n",
-        "|----|----|---|\n",
-        "|**iteration_timeout_minutes**|10|Time limit in minutes for each iteration. Reduce this value to decrease total runtime.|\n",
-        "|**iterations**|30|Number of iterations. In each iteration, a new machine learning model is trained with your data. This is the primary value that affects total run time.|\n",
-        "|**primary_metric**|spearman_correlation | Metric that you want to optimize. The best-fit model will be chosen based on this metric.|\n",
-        "|**preprocess**| True | By using **True**, the experiment can preprocess the input data (handling missing data, converting text to numeric, etc.)|\n",
-        "|**verbosity**| logging.INFO | Controls the level of logging.|\n",
-        "|**n_cross_validationss**|5| Number of cross-validation splits to perform when validation data is not specified.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_settings = {\n",
-        "    \"iteration_timeout_minutes\" : 10,\n",
-        "    \"iterations\" : 30,\n",
-        "    \"primary_metric\" : 'spearman_correlation',\n",
-        "    \"preprocess\" : True,\n",
-        "    \"verbosity\" : logging.INFO,\n",
-        "    \"n_cross_validations\": 5\n",
-        "}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Use your defined training settings as a parameter to an `AutoMLConfig` object. Additionally, specify your training data and the type of model, which is `regression` in this case."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "configure automl"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.train.automl import AutoMLConfig\n",
-        "\n",
-        "# local compute \n",
-        "automated_ml_config = AutoMLConfig(task = 'regression',\n",
-        "                             debug_log = 'automated_ml_errors.log',\n",
-        "                             path = project_folder,\n",
-        "                             X = x_train.values,\n",
-        "                             y = y_train.values.flatten(),\n",
-        "                             **automl_settings)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Train the automatic regression model\n",
-        "\n",
-        "Start the experiment to run locally. Pass the defined `automated_ml_config` object to the experiment. Set the output to `True` to view progress during the experiment:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "local submitted run",
-          "automl"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.experiment import Experiment\n",
-        "experiment=Experiment(ws, experiment_name)\n",
-        "local_run = experiment.submit(automated_ml_config, show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The output shown updates live as the experiment runs. For each iteration, you see the model type, the run duration, and the training accuracy. The field `BEST` tracks the best running training score based on your metric type."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explore the results\n",
-        "\n",
-        "Explore the results of automatic training with a Jupyter widget or by examining the experiment history.\n",
-        "\n",
-        "### Option 1: Add a Jupyter widget to see results\n",
-        "\n",
-        "If you use a Jupyter notebook, use this Jupyter notebook widget to see a graph and a table of all results:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "use notebook widget"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(local_run).show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Option 2: Get and examine all run iterations in Python\n",
-        "\n",
-        "You can also retrieve the history of each experiment and explore the individual metrics for each iteration run. By examining RMSE (root_mean_squared_error) for each individual model run, you see that most iterations are predicting the taxi fair cost within a reasonable margin ($3-4).\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "get metrics",
-          "query history"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "children = list(local_run.get_children())\n",
-        "metricslist = {}\n",
-        "for run in children:\n",
-        "    properties = run.get_properties()\n",
-        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
-        "    metricslist[int(properties['iteration'])] = metrics\n",
-        "\n",
-        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-        "rundata"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Retrieve the best model\n",
-        "\n",
-        "Select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last fit invocation. By using the overloads on `get_output`, you can retrieve the best run and fitted model for any logged metric or a particular iteration:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "best_run, fitted_model = local_run.get_output()\n",
-        "print(best_run)\n",
-        "print(fitted_model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Test the best model accuracy\n",
-        "\n",
-        "Use the best model to run predictions on the test dataset to predict taxi fares. The function `predict` uses the best model and predicts the values of y, **trip cost**, from the `x_test` dataset. Print the first 10 predicted cost values from `y_predict`:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "y_predict = fitted_model.predict(x_test.values) \n",
-        "print(y_predict[:10])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Create a scatter plot to visualize the predicted cost values compared to the actual cost values. The following code uses the `distance` feature as the x-axis and trip `cost` as the y-axis. To compare the variance of predicted cost at each trip distance value, the first 100 predicted and actual cost values are created as separate series. Examining the plot shows that the distance/cost relationship is nearly linear, and the predicted cost values are in most cases very close to the actual cost values for the same trip distance."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "fig = plt.figure(figsize=(14, 10))\n",
-        "ax1 = fig.add_subplot(111)\n",
-        "\n",
-        "distance_vals = [x[4] for x in x_test.values]\n",
-        "y_actual = y_test.values.flatten().tolist()\n",
-        "\n",
-        "ax1.scatter(distance_vals[:100], y_predict[:100], s=18, c='b', marker=\"s\", label='Predicted')\n",
-        "ax1.scatter(distance_vals[:100], y_actual[:100], s=18, c='r', marker=\"o\", label='Actual')\n",
-        "\n",
-        "ax1.set_xlabel('distance (mi)')\n",
-        "ax1.set_title('Predicted and Actual Cost/Distance')\n",
-        "ax1.set_ylabel('Cost ($)')\n",
-        "\n",
-        "plt.legend(loc='upper left', prop={'size': 12})\n",
-        "plt.rcParams.update({'font.size': 14})\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        " Calculate the `root mean squared error` of the results. Use the `y_test` dataframe. Convert it to a list to compare to the predicted values. The function `mean_squared_error` takes two arrays of values and calculates the average squared error between them. Taking the square root of the result gives an error in the same units as the y variable, **cost**. It indicates roughly how far the taxi fare predictions are from the actual fares:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.metrics import mean_squared_error\n",
-        "from math import sqrt\n",
-        "\n",
-        "rmse = sqrt(mean_squared_error(y_actual, y_predict))\n",
-        "rmse"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Run the following code to calculate mean absolute percent error (MAPE) by using the full `y_actual` and `y_predict` datasets. This metric calculates an absolute difference between each predicted and actual value and sums all the differences. Then it expresses that sum as a percent of the total of the actual values:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "sum_actuals = sum_errors = 0\n",
-        "\n",
-        "for actual_val, predict_val in zip(y_actual, y_predict):\n",
-        "    abs_error = actual_val - predict_val\n",
-        "    if abs_error < 0:\n",
-        "        abs_error = abs_error * -1\n",
-        "    \n",
-        "    sum_errors = sum_errors + abs_error\n",
-        "    sum_actuals = sum_actuals + actual_val\n",
-        "    \n",
-        "mean_abs_percent_error = sum_errors / sum_actuals\n",
-        "print(\"Model MAPE:\")\n",
-        "print(mean_abs_percent_error)\n",
-        "print()\n",
-        "print(\"Model Accuracy:\")\n",
-        "print(1 - mean_abs_percent_error)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "From the final prediction accuracy metrics, you see that the model is fairly good at predicting taxi fares from the data set's features, typically within +- $3.00. The traditional machine learning model development process is highly resource-intensive, and requires significant domain knowledge and time investment to run and compare the results of dozens of models. Using automated machine learning is a great way to rapidly test many different models for your scenario."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Clean up resources\n",
-        "\n",
-        ">The resources you created can be used as prerequisites to other Azure Machine Learning service tutorials and how-to articles. \n",
-        "\n",
-        "\n",
-        "If you do not plan to use the resources you created, delete them, so you do not incur any charges:\n",
-        "\n",
-        "1. In the Azure portal, select **Resource groups** on the far left.\n",
-        "\n",
-        "1. From the list, select the resource group you created.\n",
-        "\n",
-        "1. Select **Delete resource group**.\n",
-        "\n",
-        "1. Enter the resource group name. Then select **Delete**."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Next steps\n",
-        "\n",
-        "In this automated machine learning tutorial, you did the following tasks:\n",
-        "\n",
-        "* Configured a workspace and prepared data for an experiment.\n",
-        "* Trained by using an automated regression model locally with custom parameters.\n",
-        "* Explored and reviewed training results.\n",
-        "\n",
-        "[Deploy your model](https://docs.microsoft.com/azure/machine-learning/service/tutorial-deploy-models-with-aml) with Azure Machine Learning."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/regression-part2-automated-ml.png)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "jeffshep"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.7"
-    },
-    "msauthor": "sgilley"
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/tutorials/tutorial-1st-experiment-sdk-train.ipynb b/tutorials/tutorial-1st-experiment-sdk-train.ipynb
new file mode 100644
index 00000000..6a5dc7e2
--- /dev/null
+++ b/tutorials/tutorial-1st-experiment-sdk-train.ipynb
@@ -0,0 +1,385 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/tutorial-quickstart-train-model.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Tutorial: Train your first model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This tutorial is **part two of a two-part tutorial series**. In the previous tutorial, you created a workspace and chose a development environment. In this tutorial, you learn the foundational design patterns in Azure Machine Learning service, and train a simple scikit-learn model based on the diabetes data set. After completing this tutorial, you will have the practical knowledge of the SDK to scale up to developing more-complex experiments and workflows. \n",
+        "\n",
+        "In this tutorial, you learn the following tasks:\n",
+        "\n",
+        "> * Connect your workspace and create an experiment \n",
+        "> * Load data and train a scikit-learn model\n",
+        "> * View training results in the portal\n",
+        "> * Retrieve the best model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "\n",
+        "The only prerequisite is to run the previous tutorial, Setup environment and workspace."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Connect workspace and create experiment"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Import the `Workspace` class, and load your subscription information from the file `config.json` using the function `from_config().` This looks for the JSON file in the current directory by default, but you can also specify a path parameter to point to the file using `from_config(path=\"your/file/path\")`. If you are running this notebook in a cloud notebook server in your workspace, the file is automatically in the root directory.\n",
+        "\n",
+        "If the following code asks for additional authentication, simply paste the link in a browser and enter the authentication token."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "ws = Workspace.from_config()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now create an experiment in your workspace. An experiment is another foundational cloud resource that represents a collection of trials (individual model runs). In this tutorial you use the experiment to create runs and track your model training in the Azure Portal. Parameters include your workspace reference, and a string name for the experiment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "experiment = Experiment(workspace=ws, name=\"diabetes-experiment\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load data and prepare for training"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For this tutorial, you use the diabetes data set, which is a pre-normalized data set included in scikit-learn. This data set uses features like age, gender, and BMI to predict diabetes disease progression. Load the data from the `load_diabetes()` static function, and split it into training and test sets using `train_test_split()`. This function segregates the data so the model has unseen data to use for testing following training."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.datasets import load_diabetes\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "X, y = load_diabetes(return_X_y = True)\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=66)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train a model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Training a simple scikit-learn model can easily be done locally for small-scale training, but when training many iterations with dozens of different feature permutations and hyperparameter settings, it is easy to lose track of what models you've trained and how you trained them. The following design pattern shows how to leverage the SDK to easily keep track of your training in the cloud.\n",
+        "\n",
+        "Build a script that trains ridge models in a loop through different hyperparameter alpha values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.linear_model import Ridge\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "from sklearn.externals import joblib\n",
+        "import math\n",
+        "\n",
+        "alphas = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]\n",
+        "\n",
+        "for alpha in alphas:\n",
+        "    run = experiment.start_logging()\n",
+        "    run.log(\"alpha_value\", alpha)\n",
+        "    \n",
+        "    model = Ridge(alpha=alpha)\n",
+        "    model.fit(X=X_train, y=y_train)\n",
+        "    y_pred = model.predict(X=X_test)\n",
+        "    rmse = math.sqrt(mean_squared_error(y_true=y_test, y_pred=y_pred))\n",
+        "    run.log(\"rmse\", rmse)\n",
+        "    \n",
+        "    model_name = \"model_alpha_\" + str(alpha) + \".pkl\"\n",
+        "    filename = \"outputs/\" + model_name\n",
+        "    \n",
+        "    joblib.dump(value=model, filename=filename)\n",
+        "    run.upload_file(name=model_name, path_or_stream=filename)\n",
+        "    run.complete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The above code accomplishes the following:\n",
+        "\n",
+        "1. For each alpha hyperparameter value in the `alphas` array, a new run is created within the experiment. The alpha value is logged to differentiate between each run.\n",
+        "1. In each run, a Ridge model is instantiated, trained, and used to run predictions. The root-mean-squared-error is calculated for the actual versus predicted values, and then logged to the run. At this point the run has metadata attached for both the alpha value and the rmse accuracy.\n",
+        "1. Next, the model for each run is serialized and uploaded to the run. This allows you to download the model file from the run in the portal.\n",
+        "1. At the end of each iteration the run is completed by calling `run.complete()`.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "After the training has completed, call the `experiment` variable to fetch a link to the experiment in the portal."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "experiment"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## View training results in portal"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Following the **Link to Azure Portal** takes you to the main experiment page. Here you see all the individual runs in the experiment. Any custom-logged values (`alpha_value` and `rmse`, in this case) become fields for each run, and also become available for the charts and tiles at the top of the experiment page. To add a logged metric to a chart or tile, hover over it, click the edit button, and find your custom-logged metric.\n",
+        "\n",
+        "When training models at scale over hundreds and thousands of runs, this page makes it easy to see every model you trained, specifically how they were trained, and how your unique metrics have changed over time."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Main Experiment page in Portal](imgs/experiment_main.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Clicking on a run number link in the `RUN NUMBER` column takes you to the page for each individual run. The default tab **Details** shows you more-detailed information on each run. Navigate to the **Outputs** tab, and you see the `.pkl` file for the model that was uploaded to the run during each training iteration. Here you can download the model file, rather than having to retrain it manually."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Run details page in Portal](imgs/model_download.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Get the best model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In addition to being able to download model files from the experiment in the portal, you can also download them programmatically. The following code iterates through each run in the experiment, and accesses both the logged run metrics and the run details (which contains the run_id). This keeps track of the best run, in this case the run with the lowest root-mean-squared-error."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "minimum_rmse_runid = None\n",
+        "minimum_rmse = None\n",
+        "\n",
+        "for run in experiment.get_runs():\n",
+        "    run_metrics = run.get_metrics()\n",
+        "    run_details = run.get_details()\n",
+        "    # each logged metric becomes a key in this returned dict\n",
+        "    run_rmse = run_metrics[\"rmse\"]\n",
+        "    run_id = run_details[\"runId\"]\n",
+        "    \n",
+        "    if minimum_rmse is None:\n",
+        "        minimum_rmse = run_rmse\n",
+        "        minimum_rmse_runid = run_id\n",
+        "    else:\n",
+        "        if run_rmse < minimum_rmse:\n",
+        "            minimum_rmse = run_rmse\n",
+        "            minimum_rmse_runid = run_id\n",
+        "\n",
+        "print(\"Best run_id: \" + minimum_rmse_runid)\n",
+        "print(\"Best run_id rmse: \" + str(minimum_rmse))    "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Use the best run id to fetch the individual run using the `Run` constructor along with the experiment object. Then call `get_file_names()` to see all the files available for download from this run. In this case, you only uploaded one file for each run during training."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Run\n",
+        "best_run = Run(experiment=experiment, run_id=minimum_rmse_runid)\n",
+        "print(best_run.get_file_names())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Call `download()` on the run object, specifying the model file name to download. By default this function downloads to the current directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run.download_file(name=\"model_alpha_0.1.pkl\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Clean up resources\n",
+        "\n",
+        "Do not complete this section if you plan on running other Azure Machine Learning service tutorials.\n",
+        "\n",
+        "### Stop the notebook VM\n",
+        "\n",
+        "If you used a cloud notebook server, stop the VM when you are not using it to reduce cost.\n",
+        "\n",
+        "1. In your workspace, select **Notebook VMs**.\n",
+        "\n",
+        "1. From the list, select the VM.\n",
+        "\n",
+        "1. Select **Stop**.\n",
+        "\n",
+        "1. When you're ready to use the server again, select **Start**.\n",
+        "\n",
+        "### Delete everything\n",
+        "\n",
+        "If you don't plan to use the resources you created, delete them, so you don't incur any charges:\n",
+        "\n",
+        "1. In the Azure portal, select **Resource groups** on the far left.\n",
+        "\n",
+        "1. From the list, select the resource group you created.\n",
+        "\n",
+        "1. Select **Delete resource group**.\n",
+        "\n",
+        "1. Enter the resource group name. Then select **Delete**.\n",
+        "\n",
+        "You can also keep the resource group but delete a single workspace. Display the workspace properties and select **Delete**."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next steps\n",
+        "\n",
+        "In this tutorial, you did the following tasks:\n",
+        "\n",
+        "> * Connected your workspace and created an experiment\n",
+        "> * Loaded data and trained scikit-learn models\n",
+        "> * Viewed training results in the portal and retrieved models\n",
+        "\n",
+        "[Deploy your model](https://docs.microsoft.com/azure/machine-learning/service/tutorial-deploy-models-with-aml) with Azure Machine Learning.\n",
+        "Learn how to develop [automated machine learning](https://docs.microsoft.com/azure/machine-learning/service/tutorial-auto-train-models) experiments."
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "trbye"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.5"
+    },
+    "msauthor": "trbye"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/tutorials/tutorial-1st-experiment-sdk-train.yml b/tutorials/tutorial-1st-experiment-sdk-train.yml
new file mode 100644
index 00000000..ae943e7b
--- /dev/null
+++ b/tutorials/tutorial-1st-experiment-sdk-train.yml
@@ -0,0 +1,5 @@
+name: tutorial-1st-experiment-sdk-train
+dependencies:
+- pip:
+  - azureml-sdk
+  - sklearn
diff --git a/tutorials/tutorial-pipeline-batch-scoring-classification.ipynb b/tutorials/tutorial-pipeline-batch-scoring-classification.ipynb
new file mode 100644
index 00000000..72762315
--- /dev/null
+++ b/tutorials/tutorial-pipeline-batch-scoring-classification.ipynb
@@ -0,0 +1,738 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Use Azure Machine Learning Pipelines for batch prediction\n",
+        "\n",
+        "In this tutorial, you use Azure Machine Learning service pipelines to run a batch scoring image classification job. The example job uses the pre-trained [Inception-V3](https://arxiv.org/abs/1512.00567) CNN (convolutional neural network) Tensorflow model to classify unlabeled images. Machine learning pipelines optimize your workflow with speed, portability, and reuse so you can focus on your expertise, machine learning, rather than on infrastructure and automation. After building and publishing a pipeline, you can configure a REST endpoint to enable triggering the pipeline from any HTTP library on any platform.\n",
+        "\n",
+        "\n",
+        "In this tutorial, you learn the following tasks:\n",
+        "\n",
+        "> * Configure workspace and download sample data\n",
+        "> * Create data objects to fetch and output data\n",
+        "> * Download, prepare, and register the model to your workspace\n",
+        "> * Provision compute targets and create a scoring script\n",
+        "> * Build, run, and publish a pipeline\n",
+        "> * Enable a REST endpoint for the pipeline\n",
+        "\n",
+        "If you don\u00e2\u20ac\u2122t have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning service](https://aka.ms/AMLFree) today."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "\n",
+        "* Complete the [setup tutorial](https://docs.microsoft.com/azure/machine-learning/service/tutorial-1st-experiment-sdk-setup) if you don't already have an Azure Machine Learning service workspace or notebook virtual machine.\n",
+        "* After you complete the setup tutorial, open the **tutorials/tutorial-pipeline-batch-scoring-classification.ipynb** notebook using the same notebook server.\n",
+        "\n",
+        "This tutorial is also available on [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/tutorials) if you wish to run it in your own [local environment](how-to-configure-environment.md#local). Run `pip install azureml-sdk[notebooks] azureml-pipeline-core azureml-pipeline-steps pandas requests` to get the required packages."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Configure workspace and create datastore"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create a workspace object from the existing workspace. A [Workspace](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace.workspace?view=azure-ml-py) is a class that accepts your Azure subscription and resource information. It also creates a cloud resource to monitor and track your model runs. `Workspace.from_config()` reads the file **config.json** and loads the authentication details into an object named `ws`. `ws` is used throughout the rest of the code in this tutorial."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "ws = Workspace.from_config()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a datastore for sample images\n",
+        "\n",
+        "Get the ImageNet evaluation public data sample from the public blob container `sampledata` on the account `pipelinedata`. Calling `register_azure_blob_container()` makes the data available to the workspace under the name `images_datastore`. Then specify the workspace default datastore as the output datastore, which you use for scoring output in the pipeline."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.datastore import Datastore\n",
+        "\n",
+        "batchscore_blob = Datastore.register_azure_blob_container(ws, \n",
+        "                      datastore_name=\"images_datastore\", \n",
+        "                      container_name=\"sampledata\", \n",
+        "                      account_name=\"pipelinedata\", \n",
+        "                      overwrite=True)\n",
+        "\n",
+        "def_data_store = ws.get_default_datastore()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create data objects\n",
+        "\n",
+        "When building pipelines, `DataReference` objects are used for reading data from workspace datastores, and `PipelineData` objects are used for transferring intermediate data between pipeline steps.\n",
+        "\n",
+        "This batch scoring example only uses one pipeline step, but in use-cases with multiple steps, the typical flow will include:\n",
+        "\n",
+        "1. Using `DataReference` objects as **inputs** to fetch raw data, performing some transformations, then **outputting** a `PipelineData` object.\n",
+        "1. Use the previous step's `PipelineData` **output object** as an *input object*, repeated for subsequent steps.\n",
+        "\n",
+        "For this scenario you create `DataReference` objects corresponding to the datastore directories for both the input images and the classification labels (y-test values). You also create a `PipelineData` object for the batch scoring output data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.data.data_reference import DataReference\n",
+        "from azureml.pipeline.core import PipelineData\n",
+        "\n",
+        "input_images = DataReference(datastore=batchscore_blob, \n",
+        "                             data_reference_name=\"input_images\",\n",
+        "                             path_on_datastore=\"batchscoring/images\",\n",
+        "                             mode=\"download\"\n",
+        "                            )\n",
+        "\n",
+        "label_dir = DataReference(datastore=batchscore_blob, \n",
+        "                          data_reference_name=\"input_labels\",\n",
+        "                          path_on_datastore=\"batchscoring/labels\",\n",
+        "                          mode=\"download\"                          \n",
+        "                         )\n",
+        "\n",
+        "output_dir = PipelineData(name=\"scores\", \n",
+        "                          datastore=def_data_store, \n",
+        "                          output_path_on_compute=\"batchscoring/results\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Download and register the model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Download the pre-trained Tensorflow model to use it for batch scoring in the pipeline. First create a local directory where you store the model, then download and extract it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import tarfile\n",
+        "import urllib.request\n",
+        "\n",
+        "if not os.path.isdir(\"models\"):\n",
+        "    os.mkdir(\"models\")\n",
+        "    \n",
+        "response = urllib.request.urlretrieve(\"http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz\", \"model.tar.gz\")\n",
+        "tar = tarfile.open(\"model.tar.gz\", \"r:gz\")\n",
+        "tar.extractall(\"models\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now you register the model to your workspace, which allows you to easily retrieve it in the pipeline process. In the `register()` static function, the `model_name` parameter is the key you use to locate your model throughout the SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import Model\n",
+        " \n",
+        "model = Model.register(model_path=\"models/inception_v3.ckpt\",\n",
+        "                       model_name=\"inception\",\n",
+        "                       tags={\"pretrained\": \"inception\"},\n",
+        "                       description=\"Imagenet trained tensorflow inception\",\n",
+        "                       workspace=ws)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create and attach remote compute target\n",
+        "\n",
+        "Azure Machine Learning service pipelines cannot be run locally, and only run on cloud resources. Remote compute targets are reusable virtual compute environments where you run experiments and work-flows. Run the following code to create a GPU-enabled [`AmlCompute`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.compute.amlcompute.amlcompute?view=azure-ml-py) target, and attach it to your workspace. See the [conceptual article](https://docs.microsoft.com/azure/machine-learning/service/concept-compute-target) for more information on compute targets."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
+        "from azureml.exceptions import ComputeTargetException\n",
+        "compute_name = \"gpu-cluster\"\n",
+        "\n",
+        "# checks to see if compute target already exists in workspace, else create it\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=compute_name)\n",
+        "except ComputeTargetException:\n",
+        "    config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n",
+        "                                                   vm_priority=\"lowpriority\", \n",
+        "                                                   min_nodes=0, \n",
+        "                                                   max_nodes=1)\n",
+        "\n",
+        "    compute_target = ComputeTarget.create(workspace=ws, name=compute_name, provisioning_configuration=config)\n",
+        "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Write a scoring script"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To do the scoring, you create a batch scoring script `batch_scoring.py`, and write it to the current directory. The script takes input images, applies the classification model, and outputs the predictions to a results file.\n",
+        "\n",
+        "The script `batch_scoring.py` takes the following parameters, which get passed from the `PythonScriptStep` that you create later:\n",
+        "\n",
+        "- `--model_name`: the name of the model being used\n",
+        "- `--label_dir` : the directory holding the `labels.txt` file \n",
+        "- `--dataset_path`: the directory containing the input images\n",
+        "- `--output_dir` : the script will run the model on the data and output a `results-label.txt` to this directory\n",
+        "- `--batch_size` : the batch size used in running the model\n",
+        "\n",
+        "The pipelines infrastructure uses the `ArgumentParser` class to pass parameters into pipeline steps. For example, in the code below the first argument `--model_name` is given the property identifier `model_name`. In the `main()` function, this property is accessed using `Model.get_model_path(args.model_name)`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile batch_scoring.py\n",
+        "\n",
+        "import os\n",
+        "import argparse\n",
+        "import datetime\n",
+        "import time\n",
+        "import tensorflow as tf\n",
+        "from math import ceil\n",
+        "import numpy as np\n",
+        "import shutil\n",
+        "from tensorflow.contrib.slim.python.slim.nets import inception_v3\n",
+        "from azureml.core.model import Model\n",
+        "\n",
+        "slim = tf.contrib.slim\n",
+        "\n",
+        "parser = argparse.ArgumentParser(description=\"Start a tensorflow model serving\")\n",
+        "parser.add_argument('--model_name', dest=\"model_name\", required=True)\n",
+        "parser.add_argument('--label_dir', dest=\"label_dir\", required=True)\n",
+        "parser.add_argument('--dataset_path', dest=\"dataset_path\", required=True)\n",
+        "parser.add_argument('--output_dir', dest=\"output_dir\", required=True)\n",
+        "parser.add_argument('--batch_size', dest=\"batch_size\", type=int, required=True)\n",
+        "\n",
+        "args = parser.parse_args()\n",
+        "\n",
+        "image_size = 299\n",
+        "num_channel = 3\n",
+        "\n",
+        "# create output directory if it does not exist\n",
+        "os.makedirs(args.output_dir, exist_ok=True)\n",
+        "\n",
+        "\n",
+        "def get_class_label_dict(label_file):\n",
+        "    label = []\n",
+        "    proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()\n",
+        "    for l in proto_as_ascii_lines:\n",
+        "        label.append(l.rstrip())\n",
+        "    return label\n",
+        "\n",
+        "\n",
+        "class DataIterator:\n",
+        "    def __init__(self, data_dir):\n",
+        "        self.file_paths = []\n",
+        "        image_list = os.listdir(data_dir)\n",
+        "        self.file_paths = [data_dir + '/' + file_name.rstrip() for file_name in image_list]\n",
+        "\n",
+        "        self.labels = [1 for file_name in self.file_paths]\n",
+        "\n",
+        "    @property\n",
+        "    def size(self):\n",
+        "        return len(self.labels)\n",
+        "\n",
+        "    def input_pipeline(self, batch_size):\n",
+        "        images_tensor = tf.convert_to_tensor(self.file_paths, dtype=tf.string)\n",
+        "        labels_tensor = tf.convert_to_tensor(self.labels, dtype=tf.int64)\n",
+        "        input_queue = tf.train.slice_input_producer([images_tensor, labels_tensor], shuffle=False)\n",
+        "        labels = input_queue[1]\n",
+        "        images_content = tf.read_file(input_queue[0])\n",
+        "\n",
+        "        image_reader = tf.image.decode_jpeg(images_content, channels=num_channel, name=\"jpeg_reader\")\n",
+        "        float_caster = tf.cast(image_reader, tf.float32)\n",
+        "        new_size = tf.constant([image_size, image_size], dtype=tf.int32)\n",
+        "        images = tf.image.resize_images(float_caster, new_size)\n",
+        "        images = tf.divide(tf.subtract(images, [0]), [255])\n",
+        "\n",
+        "        image_batch, label_batch = tf.train.batch([images, labels], batch_size=batch_size, capacity=5 * batch_size)\n",
+        "        return image_batch\n",
+        "\n",
+        "\n",
+        "def main(_):\n",
+        "    label_file_name = os.path.join(args.label_dir, \"labels.txt\")\n",
+        "    label_dict = get_class_label_dict(label_file_name)\n",
+        "    classes_num = len(label_dict)\n",
+        "    test_feeder = DataIterator(data_dir=args.dataset_path)\n",
+        "    total_size = len(test_feeder.labels)\n",
+        "    count = 0\n",
+        "    \n",
+        "    # get model from model registry\n",
+        "    model_path = Model.get_model_path(args.model_name)\n",
+        "    \n",
+        "    with tf.Session() as sess:\n",
+        "        test_images = test_feeder.input_pipeline(batch_size=args.batch_size)\n",
+        "        with slim.arg_scope(inception_v3.inception_v3_arg_scope()):\n",
+        "            input_images = tf.placeholder(tf.float32, [args.batch_size, image_size, image_size, num_channel])\n",
+        "            logits, _ = inception_v3.inception_v3(input_images,\n",
+        "                                                  num_classes=classes_num,\n",
+        "                                                  is_training=False)\n",
+        "            probabilities = tf.argmax(logits, 1)\n",
+        "\n",
+        "        sess.run(tf.global_variables_initializer())\n",
+        "        sess.run(tf.local_variables_initializer())\n",
+        "        coord = tf.train.Coordinator()\n",
+        "        threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n",
+        "        saver = tf.train.Saver()\n",
+        "        saver.restore(sess, model_path)\n",
+        "        out_filename = os.path.join(args.output_dir, \"result-labels.txt\")\n",
+        "        with open(out_filename, \"w\") as result_file:\n",
+        "            i = 0\n",
+        "            while count < total_size and not coord.should_stop():\n",
+        "                test_images_batch = sess.run(test_images)\n",
+        "                file_names_batch = test_feeder.file_paths[i * args.batch_size:\n",
+        "                                                          min(test_feeder.size, (i + 1) * args.batch_size)]\n",
+        "                results = sess.run(probabilities, feed_dict={input_images: test_images_batch})\n",
+        "                new_add = min(args.batch_size, total_size - count)\n",
+        "                count += new_add\n",
+        "                i += 1\n",
+        "                for j in range(new_add):\n",
+        "                    result_file.write(os.path.basename(file_names_batch[j]) + \": \" + label_dict[results[j]] + \"\\n\")\n",
+        "                result_file.flush()\n",
+        "            coord.request_stop()\n",
+        "            coord.join(threads)\n",
+        "\n",
+        "        shutil.copy(out_filename, \"./outputs/\")\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    tf.app.run()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The pipeline in this tutorial only has one step and writes the output to a file, but for multi-step pipelines, you also use `ArgumentParser` to define a directory to write output data for input to subsequent steps. See the [notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb) for an example of passing data between multiple pipeline steps using the `ArgumentParser` design pattern."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Build and run the pipeline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Before running the pipeline, you create an object that defines the python environment and dependencies needed by your script `batch_scoring.py`. The main dependency required is Tensorflow, but you also install `azureml-defaults` for background processes from the SDK. Create a `RunConfiguration` object using the dependencies, and also specify Docker and Docker-GPU support."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import DEFAULT_GPU_IMAGE\n",
+        "from azureml.core.runconfig import CondaDependencies, RunConfiguration\n",
+        "\n",
+        "cd = CondaDependencies.create(pip_packages=[\"tensorflow-gpu==1.13.1\", \"azureml-defaults\"])\n",
+        "\n",
+        "amlcompute_run_config = RunConfiguration(conda_dependencies=cd)\n",
+        "amlcompute_run_config.environment.docker.enabled = True\n",
+        "amlcompute_run_config.environment.docker.base_image = DEFAULT_GPU_IMAGE\n",
+        "amlcompute_run_config.environment.spark.precache_packages = False"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Parameterize the pipeline\n",
+        "\n",
+        "Define a custom parameter for the pipeline to control the batch size. After the pipeline has been published and exposed via a REST endpoint, any configured parameters are also exposed and can be specified in the JSON payload when rerunning the pipeline with an HTTP request.\n",
+        "\n",
+        "Create a `PipelineParameter` object to enable this behavior, and define a name and default value."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core.graph import PipelineParameter\n",
+        "batch_size_param = PipelineParameter(name=\"param_batch_size\", default_value=20)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create the pipeline step\n",
+        "\n",
+        "A pipeline step is an object that encapsulates everything you need for running a pipeline including:\n",
+        "\n",
+        "* environment and dependency settings\n",
+        "* the compute resource to run the pipeline on\n",
+        "* input and output data, and any custom parameters\n",
+        "* reference to a script or SDK-logic to run during the step\n",
+        "\n",
+        "There are multiple classes that inherit from the parent class [`PipelineStep`](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.builder.pipelinestep?view=azure-ml-py) to assist with building a step using certain frameworks and stacks. In this example, you use the [`PythonScriptStep`](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.python_script_step.pythonscriptstep?view=azure-ml-py) class to define your step logic using a custom python script. Note that if an argument to your script is either an input to the step or output of the step, it must be defined **both** in the `arguments` array, **as well as** in either the `input` or `output` parameter, respectively. \n",
+        "\n",
+        "An object reference in the `outputs` array becomes available as an **input** for a subsequent pipeline step, for scenarios where there is more than one step."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.steps import PythonScriptStep\n",
+        "\n",
+        "batch_score_step = PythonScriptStep(\n",
+        "    name=\"batch_scoring\",\n",
+        "    script_name=\"batch_scoring.py\",\n",
+        "    arguments=[\"--dataset_path\", input_images, \n",
+        "               \"--model_name\", \"inception\",\n",
+        "               \"--label_dir\", label_dir, \n",
+        "               \"--output_dir\", output_dir, \n",
+        "               \"--batch_size\", batch_size_param],\n",
+        "    compute_target=compute_target,\n",
+        "    inputs=[input_images, label_dir],\n",
+        "    outputs=[output_dir],\n",
+        "    runconfig=amlcompute_run_config\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For a list of all classes for different step types, see the [steps package](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps?view=azure-ml-py)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Run the pipeline\n",
+        "\n",
+        "Now you run the pipeline. First create a `Pipeline` object with your workspace reference and the pipeline step you created. The `steps` parameter is an array of steps, and in this case there is only one step for batch scoring. To build pipelines with multiple steps, you place the steps in order in this array.\n",
+        "\n",
+        "Next use the `Experiment.submit()` function to submit the pipeline for execution. You also specify the custom parameter `param_batch_size`. The `wait_for_completion` function will output logs during the pipeline build process, which allows you to see current progress.\n",
+        "\n",
+        "Note: The first pipeline run takes roughly **15 minutes**, as all dependencies must be downloaded, a Docker image is created, and the Python environment is provisioned/created. Running it again takes significantly less time as those resources are reused. However, total run time depends on the workload of your scripts and processes running in each pipeline step."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "from azureml.pipeline.core import Pipeline\n",
+        "\n",
+        "pipeline = Pipeline(workspace=ws, steps=[batch_score_step])\n",
+        "pipeline_run = Experiment(ws, 'batch_scoring').submit(pipeline, pipeline_parameters={\"param_batch_size\": 20})\n",
+        "pipeline_run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Download and review output"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Run the following code to download the output file created from the `batch_scoring.py` script, then explore the scoring results."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "step_run = list(pipeline_run.get_children())[0]\n",
+        "step_run.download_file(\"./outputs/result-labels.txt\")\n",
+        "\n",
+        "df = pd.read_csv(\"result-labels.txt\", delimiter=\":\", header=None)\n",
+        "df.columns = [\"Filename\", \"Prediction\"]\n",
+        "df.head(10)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Publish and run from REST endpoint"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Run the following code to publish the pipeline to your workspace. In your workspace in the portal, you can see metadata for the pipeline including run history and durations. You can also run the pipeline manually from the portal.\n",
+        "\n",
+        "Additionally, publishing the pipeline enables a REST endpoint to rerun the pipeline from any HTTP library on any platform."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "published_pipeline = pipeline_run.publish_pipeline(\n",
+        "    name=\"Inception_v3_scoring\", description=\"Batch scoring using Inception v3 model\", version=\"1.0\")\n",
+        "\n",
+        "published_pipeline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To run the pipeline from the REST endpoint, you first need an OAuth2 Bearer-type authentication header. This example uses interactive authentication for illustration purposes, but for most production scenarios requiring automated or headless authentication, use service principle authentication as [described in this notebook](https://aka.ms/pl-restep-auth).\n",
+        "\n",
+        "Service principle authentication involves creating an **App Registration** in **Azure Active Directory**, generating a client secret, and then granting your service principal **role access** to your machine learning workspace. You then use the [`ServicePrincipalAuthentication`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.authentication.serviceprincipalauthentication?view=azure-ml-py) class to manage your auth flow. \n",
+        "\n",
+        "Both `InteractiveLoginAuthentication` and `ServicePrincipalAuthentication` inherit from `AbstractAuthentication`, and in both cases you use the `get_authentication_header()` function in the same way to fetch the header."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
+        "\n",
+        "interactive_auth = InteractiveLoginAuthentication()\n",
+        "auth_header = interactive_auth.get_authentication_header()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Get the REST url from the `endpoint` property of the published pipeline object. You can also find the REST url in your workspace in the portal. Build an HTTP POST request to the endpoint, specifying your authentication header. Additionally, add a JSON payload object with the experiment name and the batch size parameter. As a reminder, the `param_batch_size` is passed through to your `batch_scoring.py` script because you defined it as a `PipelineParameter` object in the step configuration.\n",
+        "\n",
+        "Make the request to trigger the run. Access the `Id` key from the response dict to get the value of the run id."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import requests\n",
+        "\n",
+        "rest_endpoint = published_pipeline.endpoint\n",
+        "response = requests.post(rest_endpoint, \n",
+        "                         headers=auth_header, \n",
+        "                         json={\"ExperimentName\": \"batch_scoring\",\n",
+        "                               \"ParameterAssignments\": {\"param_batch_size\": 50}})\n",
+        "run_id = response.json()[\"Id\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Use the run id to monitor the status of the new run. This will take another 10-15 min to run and will look similar to the previous pipeline run, so if you don't need to see another pipeline run, you can skip watching the full output."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core.run import PipelineRun\n",
+        "from azureml.widgets import RunDetails\n",
+        "\n",
+        "published_pipeline_run = PipelineRun(ws.experiments[\"batch_scoring\"], run_id)\n",
+        "RunDetails(published_pipeline_run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Clean up resources\n",
+        "\n",
+        "Do not complete this section if you plan on running other Azure Machine Learning service tutorials.\n",
+        "\n",
+        "### Stop the notebook VM\n",
+        "\n",
+        "If you used a cloud notebook server, stop the VM when you are not using it to reduce cost.\n",
+        "\n",
+        "1. In your workspace, select **Notebook VMs**.\n",
+        "1. From the list, select the VM.\n",
+        "1. Select **Stop**.\n",
+        "1. When you're ready to use the server again, select **Start**.\n",
+        "\n",
+        "### Delete everything\n",
+        "\n",
+        "If you don't plan to use the resources you created, delete them, so you don't incur any charges.\n",
+        "\n",
+        "1. In the Azure portal, select **Resource groups** on the far left.\n",
+        "1. From the list, select the resource group you created.\n",
+        "1. Select **Delete resource group**.\n",
+        "1. Enter the resource group name. Then select **Delete**.\n",
+        "\n",
+        "You can also keep the resource group but delete a single workspace. Display the workspace properties and select **Delete**."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next steps\n",
+        "\n",
+        "In this machine learning pipelines tutorial, you did the following tasks:\n",
+        "\n",
+        "> * Built a pipeline with environment dependencies to run on a remote GPU compute resource\n",
+        "> * Created a scoring script to run batch predictions with a pre-trained Tensorflow model\n",
+        "> * Published a pipeline and enabled it to be run from a REST endpoint\n",
+        "\n",
+        "See the [how-to](https://docs.microsoft.com/azure/machine-learning/service/how-to-create-your-first-pipeline?view=azure-devops) for additional detail on building pipelines with the machine learning SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "sanpil"
+      }
+    ],
+    "friendly_name": "Use pipelines for batch scoring",
+    "exclude_from_index": false,
+    "index_order": 1,
+    "category": "tutorial",
+    "star_tag": [
+      "featured"
+    ],
+    "task": "Batch scoring",
+    "datasets": [
+      "None"
+    ],
+    "compute": [
+      "AmlCompute"
+    ],
+    "deployment": [
+      "Published pipeline"
+    ],
+    "framework": [
+      "Azure ML Pipelines"
+    ],
+    "tags": [
+      "None"
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.5"
+    },
+    "msauthor": "trbye"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/tutorials/tutorial-pipeline-batch-scoring-classification.yml b/tutorials/tutorial-pipeline-batch-scoring-classification.yml
new file mode 100644
index 00000000..8a0dc35b
--- /dev/null
+++ b/tutorials/tutorial-pipeline-batch-scoring-classification.yml
@@ -0,0 +1,9 @@
+name: tutorial-pipeline-batch-scoring-classification
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
+  - azureml-pipeline-core
+  - azureml-pipeline-steps
+  - pandas
+  - requests
diff --git a/work-with-data/README.md b/work-with-data/README.md
deleted file mode 100644
index cfc265b2..00000000
--- a/work-with-data/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# Work With Data Using Azure Machine Learning Service
-
-Azure Machine Learning Datasets (preview) make it easier to access and work with your data. Datasets manage data in various scenarios such as model training and pipeline creation. Using the Azure Machine Learning SDK, you can access underlying storage, explore and prepare data, manage the life cycle of different Dataset definitions, and compare between Datasets used in training and in production.
-
-- For an example of using Datasets, see the [sample](datasets).
-- For advanced data preparation examples, see [dataprep](dataprep).
-
-
-![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/README..png) 
\ No newline at end of file
diff --git a/work-with-data/dataprep/README.md b/work-with-data/dataprep/README.md
deleted file mode 100644
index 4346cd20..00000000
--- a/work-with-data/dataprep/README.md
+++ /dev/null
@@ -1,255 +0,0 @@
-# Azure Machine Learning Data Prep SDK
-
-The Azure Machine Learning Data Prep SDK helps data scientists explore, cleanse and transform data for machine learning workflows in any Python environment.
-
-Key benefits to the SDK:
-- Cross-platform functionality. Write with a single SDK and run it on Windows, macOS, or Linux.
-- Intelligent transformations powered by AI, including grouping similar values to their canonical form and deriving columns by examples without custom code.
-- Capability to work with large, multiple files of different schema.
-- Scalability on a single machine by streaming data during processing rather than loading into memory.
-- Seamless integration with other Azure Machine Learning services. You can simply pass your prepared data file into `AutoMLConfig` object for automated machine learning training.
-
-You will find in this repo:
-- [Getting Started Tutorial](tutorials/getting-started/getting-started.ipynb) for a quick introduction to the main features of Data Prep SDK.
-- [Case Study Notebooks](case-studies/new-york-taxi) that present an end-to-end data preparation tutorial where users start with small dataset, profile data with statistics summary, cleanse and perform feature engineering. All transformation steps are saved in a dataflow object. Users can easily reapply the same steps on the full dataset, and run it on Spark.
-- [How-To Guide Notebooks](how-to-guides) for more in-depth sample code at feature level.
-
-## Installation
-Here are the [SDK installation steps](https://aka.ms/aml-data-prep-installation).
-
-## Documentation 
-Here is more information on how to use the new Data Prep SDK:
-- [SDK overview and API reference docs](http://aka.ms/data-prep-sdk) that show different classes, methods, and function parameters for the SDK.
-- [Tutorial: Prep NYC taxi data](https://docs.microsoft.com/azure/machine-learning/service/tutorial-data-prep) for regression modeling and then run automated machine learning to build the model.
-- [How to load data](https://docs.microsoft.com/azure/machine-learning/service/how-to-load-data) is an overview guide on how to load data using the Data Prep SDK.
-- [How to transform data](https://docs.microsoft.com/azure/machine-learning/service/how-to-transform-data) is an overview guide on how to transform data. 
-- [How to write data](https://docs.microsoft.com/azure/machine-learning/service/how-to-write-data) is an overview guide on how to write data to different storage locations. 
-
-## Support
-
-If you have any questions or feedback, send us an email at: [askamldataprep@microsoft.com](mailto:askamldataprep@microsoft.com).
-
-## Release Notes
-
-### 2019-05-28 (version 1.1.4)
-
-New features
-- You can now use the following expression language functions to extract and parse datetime values into new columns.
-  - `RegEx.extract_record()` extracts datetime elements into a new column.
-  - `create_datetime()` creates datetime objects from separate datetime elements.
-- When calling `get_profile()`, you can now see that quantile columns are labeled as (est.) to clearly indicate that the values are approximations.
-- You can now use ** globbing when reading from Azure Blob Storage.
-  - e.g. `dprep.read_csv(path='https://yourblob.blob.core.windows.net/yourcontainer/**/data/*.csv')`
-
-Bug fixes
-- Fixed a bug related to reading a Parquet file from a remote source (Azure Blob).
-
-### 2019-05-08 (version 1.1.3)
-
-New features
-- Added support to read from a PostgresSQL database, either by calling `read_postgresql` or using a Datastore.
-  - See examples in how-to guides:
-    - [Data Ingestion notebook](https://aka.ms/aml-data-prep-ingestion-nb)
-    - [Datastore notebook](https://aka.ms/aml-data-prep-datastore-nb)
-
-Bug fixes and improvements
-- Fixed issues with column type conversion:
-  - Now correctly converts a boolean or numeric column to a boolean column.
-  - Now does not fail when attempting to set a date column to be date type.
-- Improved JoinType types and accompanying reference documentation. When joining two dataflows, you can now specify one of these types of join:
-  - NONE, MATCH, INNER, UNMATCHLEFT, LEFTANTI, LEFTOUTER, UNMATCHRIGHT, RIGHTANTI, RIGHTOUTER, FULLANTI, FULL.
-- Improved data type inference to recognize more date formats.
-
-### 2019-04-17 (version 1.1.2)
-
-Note: Data Prep Python SDK will no longer install `numpy` and `pandas` packages. See [updated installation instructions](https://aka.ms/aml-data-prep-installation).
-
-New features
-- You can now use the Pivot transform.
-  - How-to guide: [Pivot notebook](https://aka.ms/aml-data-prep-pivot-nb)
-- You can now use regular expressions in native functions.
-  - Examples:
-    - `dflow.filter(dprep.RegEx('pattern').is_match(dflow['column_name']))`
-    - `dflow.assert_value('column_name', dprep.RegEx('pattern').is_match(dprep.value))`
-- You can now use `to_upper` and `to_lower` functions in expression language.
-- You can now see the number of unique values of each column in a data profile.
-- For some of the commonly used reader steps, you can now pass in the `infer_column_types` argument. If it is set to `True`, Data Prep will attempt to detect and automatically convert column types.
-  - `inference_arguments` is now deprecated.
-- You can now call `Dataflow.shape`.
-
-Bug fixes and improvements
-- `keep_columns` now accepts an additional optional argument `validate_column_exists`, which checks if the result of `keep_columns` will contain any columns.
-- All reader steps (which read from a file) now accept an additional optional argument `verify_exists`.
-- Improved performance of reading from pandas dataframe and getting data profiles.
-- Fixed a bug where slicing a single step from a Dataflow failed with a single index.
-
-### 2019-04-08 (version 1.1.1)
-
-New features
-- You can read multiple Datastore/DataPath/DataReference sources using read_* transforms.
-- You can perform the following operations on columns to create a new column: division, floor, modulo, power, length.
-- Data Prep is now part of the Azure ML diagnostics suite and will log diagnostic information by default.
-  - To turn this off, set this environment variable to true: DISABLE_DPREP_LOGGER
-
-Bug fixes and improvements
-- Improved code documentation for commonly used classes and functions.
-- Fixed a bug in auto_read_file that failed to read Excel files.
-- Added option to overwrite the folder in read_pandas_dataframe.
-- Improved performance of dotnetcore2 dependency installation, and added support for Fedora 27/28 and Ubuntu 1804.
-- Improved the performance of reading from Azure Blobs.
-- Column type detection now supports columns of type Long.
-- Fixed a bug where some date values were being displayed as timestamps instead of Python datetime objects.
-- Fixed a bug where some type counts were being displayed as doubles instead of integers.
-
-### 2019-03-25 (version 1.1.0)
-
-Breaking changes
-- The concept of the Data Prep Package has been deprecated and is no longer supported. Instead of persisting multiple Dataflows in one Package, you can persist Dataflows individually.
-  - How-to guide: [Opening and Saving Dataflows notebook](https://aka.ms/aml-data-prep-open-save-dataflows-nb)
-
-New features
-- Data Prep can now recognize columns that match a particular Semantic Type, and split accordingly. The STypes currently supported include: email address, geographic coordinates (latitude & longitude), IPv4 and IPv6 addresses, US phone number, and US zip code.
-  - How-to guide: [Semantic Types notebook](https://aka.ms/aml-data-prep-semantic-types-nb)
-- Data Prep now supports the following operations to generate a resultant column from two numeric columns: subtract, multiply, divide, and modulo.
-- You can call `verify_has_data()` on a Dataflow to check whether the Dataflow would produce records if executed.
-
-Bug fixes and improvements
-- You can now specify the number of bins to use in a histogram for numeric column profiles.
-- The `read_pandas_dataframe` transform now requires the DataFrame to have string- or byte- typed column names.
-- Fixed a bug in the `fill_nulls` transform, where values were not correctly filled in if the column was missing.
-
-### 2019-03-11 (version 1.0.17)
-
-New features
-- Now supports adding two numeric columns to generate a resultant column using the expression language.
-
-Bug fixes and improvements
-- Improved the documentation and parameter checking for random_split.
-
-### 2019-02-27 (version 1.0.16)
-
-Bug fix
-- Fixed a Service Principal authentication issue that was caused by an API change.
-
-### 2019-02-25 (version 1.0.15)
-
-New features
-- Data Prep now supports writing file streams from a dataflow. Also provides the ability to manipulate the file stream names to create new file names.
-  - How-to guide: [Working With File Streams notebook](https://aka.ms/aml-data-prep-file-stream-nb)
- 
-Bug fixes and improvements
-- Improved performance of t-Digest on large data sets.
-- Data Prep now supports reading data from a DataPath.
-- One hot encoding now works on boolean and numeric columns.
-- Other miscellaneous bug fixes.
-
-### 2019-02-11 (version 1.0.12)
-
-New features
-- Data Prep now supports reading from an Azure SQL database using Datastore.
- 
-Changes
-- Significantly improved the memory performance of certain operations on large data.
-- `read_pandas_dataframe()` now requires `temp_folder` to be specified.
-- The `name` property on `ColumnProfile` has been deprecated - use `column_name` instead.
-
-### 2019-01-28 (version 1.0.8)
-
-Bug fixes
-- Significantly improved the performance of getting data profiles.
-- Fixed minor bugs related to error reporting.
-
-### 2019-01-14 (version 1.0.7)
-
-New features
-- Datastore improvements (documented in [Datastore how-to-guide](https://aka.ms/aml-data-prep-datastore-nb))
-  - Added ability to read from and write to Azure File Share and ADLS Datastores in scale-up.
-  - When using Datastores, Data Prep now supports using service principal authentication instead of interactive authentication.
-  - Added support for wasb and wasbs urls.
-
-### 2019-01-09 (version 1.0.6)
-
-Bug fixes
-- Fixed bug with reading from public readable Azure Blob containers on Spark.
-
-### 2018-12-19 (version 1.0.4)
-
-New features
-- `to_bool` function now allows mismatched values to be converted to Error values. This is the new default mismatch behavior for `to_bool` and `set_column_types`, whereas the previous default behavior was to convert mismatched values to False.
-- When calling `to_pandas_dataframe`, there is a new option to interpret null/missing values in numeric columns as NaN.
-- Added ability to check the return type of some expressions to ensure type consistency and fail early.
-- You can now call `parse_json` to parse values in a column as JSON objects and expand them into multiple columns.
-
-Bug fixes
-- Fixed a bug that crashed `set_column_types` in Python 3.5.2.
-- Fixed a bug that crashed when connecting to Datastore using an AML image.
-
-### 2018-12-07 (version 0.5.3)
-
-Fixed missing dependency issue for .NET Core2 on Ubuntu 16.
-
-### 2018-12-03 (version 0.5.2)
-
-Breaking changes
-- `SummaryFunction.N` was renamed to `SummaryFunction.Count`.
-  
-Bug fixes
-- Use latest AML Run Token when reading from and writing to datastores on remote runs. Previously, if the AML Run Token is updated in Python, the Data Prep runtime will not be updated with the updated AML Run Token.
-- Additional clearer error messages
-- to_spark_dataframe() will no longer crash when Spark uses Kryo serialization
-- Value Count Inspector can now show more than 1000 unique values
-- Random Split no longer fails if the original Dataflow doesn’t have a name  
-
-### 2018-11-19 (version 0.5.0)
-
-New features
-- Created a new DataPrep CLI to execute DataPrep packages and view the data profile for a dataset or dataflow
-- Redesigned SetColumnType API to improve usability
-- Renamed smart_read_file to auto_read_file
-- Now includes skew and kurtosis in the Data Profile
-- Can sample with stratified sampling
-- Can read from zip files that contain CSV files
-- Can split datasets row-wise with Random Split (e.g. into test-train sets)
-- Can get all the column data types from a dataflow or a data profile by calling .dtypes
-- Can get the row count from a dataflow or a data profile by calling .row_count
-
-Bug fixes
-- Fixed long to double conversion 
-- Fixed assert after any add column 
-- Fixed an issue with FuzzyGrouping, where it would not detect groups in some cases
-- Fixed sort function to respect multi-column sort order
-- Fixed and/or expressions to be similar to how Pandas handles them
-- Fixed reading from dbfs path.
-- Made error messages more understandable 
-- Now no longer fails when reading on remote compute target using AML token
-- Now no longer fails on Linux DSVM
-- Now no longer crashes when non-string values are in string predicates
-- Now handles assertion errors when Dataflow should fail correctly
-- Now supports dbutils mounted storage locations on Azure Databricks
-
-### 2018-11-05 (version 0.4.0)
-
-New features
-- Type Count added to Data Profile
-- Value Count and Histogram is now available
-- More percentiles in Data Profile
-- The Median is available in Summarize
-- Python 3.7 is now supported
-- When you save a dataflow that contains datastores to a Data Prep package, the datastore information will be persisted as part of the Data Prep package
-- Writing to datastore is now supported
- 
-Bug fixes
-- 64bit unsigned integer overflows are now handled properly on Linux 
-- Fixed incorrect text label for plain text files in smart_read
-- String column type now shows up in metrics view
-- Type count now is fixed to show ValueKinds mapped to single FieldType instead of individual ones
-- Write_to_csv no longer fails when path is provided as a string
-- When using Replace, leaving “find” blank will no longer fail
-
-## Datasets License Information
-
-IMPORTANT: Please read the notice and find out more about this NYC Taxi and Limousine Commission dataset here: http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml 
-
-IMPORTANT: Please read the notice and find out more about this Chicago Police Department dataset here: https://catalog.data.gov/dataset/crimes-2001-to-present-398a4 
-
-![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/README.png) 
diff --git a/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi.ipynb b/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi.ipynb
deleted file mode 100644
index cdfb6d70..00000000
--- a/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi.ipynb
+++ /dev/null
@@ -1,514 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Cleaning up New York Taxi Cab data\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's use DataPrep to clean and featurize the data which can then be used to predict taxi trip duration. We will not use the For Hire Vehicle (FHV) datasets as they are not really taxi rides and they don't provide drop-off time and geo-coordinates."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from IPython.display import display\n",
-        "from os import path\n",
-        "from tempfile import mkdtemp\n",
-        "\n",
-        "import pandas as pd\n",
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's take a quick peek at yellow cab data and green cab data to see what the data looks like. DataPrep supports globing, so you will notice below that we have added a `*` in the path.\n",
-        "\n",
-        "*We are using a small sample of the taxi data for this demo. You can find a bigger sample ~6GB by changing \"green-small\" to \"green-sample\" and \"yellow-small\" to \"yellow-sample\" in the paths below.*"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "pd.set_option('display.max_columns', None)\n",
-        "\n",
-        "cache_location = mkdtemp()\n",
-        "dataset_root = \"https://dprepdata.blob.core.windows.net/demo\"\n",
-        "\n",
-        "green_path = \"/\".join([dataset_root, \"green-small/*\"])\n",
-        "yellow_path = \"/\".join([dataset_root, \"yellow-small/*\"])\n",
-        "\n",
-        "print(\"Retrieving data from the following two sources:\")\n",
-        "print(green_path)\n",
-        "print(yellow_path)\n",
-        "\n",
-        "green_df = dprep.read_csv(path=green_path, header=dprep.PromoteHeadersMode.GROUPED)\n",
-        "yellow_df = dprep.auto_read_file(path=yellow_path)\n",
-        "\n",
-        "display(green_df.head(5))\n",
-        "display(yellow_df.head(5))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Data Cleanup"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's define some shortcut transforms that will apply to all Dataflows."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "all_columns = dprep.ColumnSelector(term=\".*\", use_regex=True)\n",
-        "drop_if_all_null = [all_columns, dprep.ColumnRelationship(dprep.ColumnRelationship.ALL)]\n",
-        "useful_columns = [\n",
-        "    \"cost\", \"distance\"\"distance\", \"dropoff_datetime\", \"dropoff_latitude\", \"dropoff_longitude\",\n",
-        "    \"passengers\", \"pickup_datetime\", \"pickup_latitude\", \"pickup_longitude\", \"store_forward\", \"vendor\"\n",
-        "]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's first work with the green taxi data and get it into a good shape that then can be combined with the yellow taxi data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tmp_df = (green_df\n",
-        "    .replace_na(columns=all_columns)\n",
-        "    .drop_nulls(*drop_if_all_null)\n",
-        "    .rename_columns(column_pairs={\n",
-        "        \"VendorID\": \"vendor\",\n",
-        "        \"lpep_pickup_datetime\": \"pickup_datetime\",\n",
-        "        \"Lpep_dropoff_datetime\": \"dropoff_datetime\",\n",
-        "        \"lpep_dropoff_datetime\": \"dropoff_datetime\",\n",
-        "        \"Store_and_fwd_flag\": \"store_forward\",\n",
-        "        \"store_and_fwd_flag\": \"store_forward\",\n",
-        "        \"Pickup_longitude\": \"pickup_longitude\",\n",
-        "        \"Pickup_latitude\": \"pickup_latitude\",\n",
-        "        \"Dropoff_longitude\": \"dropoff_longitude\",\n",
-        "        \"Dropoff_latitude\": \"dropoff_latitude\",\n",
-        "        \"Passenger_count\": \"passengers\",\n",
-        "        \"Fare_amount\": \"cost\",\n",
-        "        \"Trip_distance\": \"distance\"\n",
-        "     })\n",
-        "    .keep_columns(columns=useful_columns))\n",
-        "tmp_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "green_df = tmp_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's do the same thing to yellow taxi data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tmp_df = (yellow_df\n",
-        "    .replace_na(columns=all_columns)\n",
-        "    .drop_nulls(*drop_if_all_null)\n",
-        "    .rename_columns(column_pairs={\n",
-        "        \"vendor_name\": \"vendor\",\n",
-        "        \"VendorID\": \"vendor\",\n",
-        "        \"vendor_id\": \"vendor\",\n",
-        "        \"Trip_Pickup_DateTime\": \"pickup_datetime\",\n",
-        "        \"tpep_pickup_datetime\": \"pickup_datetime\",\n",
-        "        \"Trip_Dropoff_DateTime\": \"dropoff_datetime\",\n",
-        "        \"tpep_dropoff_datetime\": \"dropoff_datetime\",\n",
-        "        \"store_and_forward\": \"store_forward\",\n",
-        "        \"store_and_fwd_flag\": \"store_forward\",\n",
-        "        \"Start_Lon\": \"pickup_longitude\",\n",
-        "        \"Start_Lat\": \"pickup_latitude\",\n",
-        "        \"End_Lon\": \"dropoff_longitude\",\n",
-        "        \"End_Lat\": \"dropoff_latitude\",\n",
-        "        \"Passenger_Count\": \"passengers\",\n",
-        "        \"passenger_count\": \"passengers\",\n",
-        "        \"Fare_Amt\": \"cost\",\n",
-        "        \"fare_amount\": \"cost\",\n",
-        "        \"Trip_Distance\": \"distance\",\n",
-        "        \"trip_distance\": \"distance\"\n",
-        "    })\n",
-        "    .keep_columns(columns=useful_columns))\n",
-        "tmp_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "yellow_df = tmp_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's now append the rows from the `yellow_df` to `green_df`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "combined_df = green_df.append_rows(dataflows=[yellow_df])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's take a look at the pickup and drop-off coordinates' data profile to see how the data is distributed."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "decimal_type = dprep.TypeConverter(data_type=dprep.FieldType.DECIMAL)\n",
-        "combined_df = combined_df.set_column_types(type_conversions={\n",
-        "    \"pickup_longitude\": decimal_type,\n",
-        "    \"pickup_latitude\": decimal_type,\n",
-        "    \"dropoff_longitude\": decimal_type,\n",
-        "    \"dropoff_latitude\": decimal_type\n",
-        "})\n",
-        "combined_df.keep_columns(columns=[\n",
-        "    \"pickup_longitude\", \"pickup_latitude\", \n",
-        "    \"dropoff_longitude\", \"dropoff_latitude\"\n",
-        "]).get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "From the data profile, we can see that there are coordinates that are missing and coordinates that are not in New York. Let's filter out coordinates not in the [city border](https://mapmakerapp.com?map=5b60a055a191245990310739f658)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tmp_df = (combined_df\n",
-        "    .drop_nulls(\n",
-        "        columns=[\"pickup_longitude\", \"pickup_latitude\", \"dropoff_longitude\", \"dropoff_latitude\"],\n",
-        "        column_relationship=dprep.ColumnRelationship(dprep.ColumnRelationship.ANY)\n",
-        "    ) \n",
-        "    .filter(dprep.f_and(\n",
-        "        dprep.col(\"pickup_longitude\") <= -73.72,\n",
-        "        dprep.col(\"pickup_longitude\") >= -74.09,\n",
-        "        dprep.col(\"pickup_latitude\") <= 40.88,\n",
-        "        dprep.col(\"pickup_latitude\") >= 40.53,\n",
-        "        dprep.col(\"dropoff_longitude\") <= -73.72,\n",
-        "        dprep.col(\"dropoff_longitude\") >= -74.09,\n",
-        "        dprep.col(\"dropoff_latitude\") <= 40.88,\n",
-        "        dprep.col(\"dropoff_latitude\") >= 40.53\n",
-        "    )))\n",
-        "tmp_df.keep_columns(columns=[\n",
-        "    \"pickup_longitude\", \"pickup_latitude\", \n",
-        "    \"dropoff_longitude\", \"dropoff_latitude\"\n",
-        "]).get_profile()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "combined_df = tmp_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's take a look at the data profile for the `store_forward` column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "combined_df.keep_columns(columns='store_forward').get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "From the data profile of `store_forward` above, we can see that the data is inconsistent and there are missing values. Let's fix them."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "combined_df = combined_df.replace(columns=\"store_forward\", find=\"0\", replace_with=\"N\").fill_nulls(\"store_forward\", \"N\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's now split the pick up and drop off datetimes into a date column and a time column. We will use `split_column_by_example` to perform the split. If the `example` parameter of `split_column_by_example` is omitted, we will automatically try to figure out where to split based on the data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tmp_df = (combined_df\n",
-        "    .split_column_by_example(source_column=\"pickup_datetime\")\n",
-        "    .split_column_by_example(source_column=\"dropoff_datetime\"))\n",
-        "tmp_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "combined_df = tmp_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's rename the columns generated by `split_column_by_example` into meaningful names."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tmp_df = (combined_df\n",
-        "    .rename_columns(column_pairs={\n",
-        "        \"pickup_datetime_1\": \"pickup_date\",\n",
-        "        \"pickup_datetime_2\": \"pickup_time\",\n",
-        "        \"dropoff_datetime_1\": \"dropoff_date\",\n",
-        "        \"dropoff_datetime_2\": \"dropoff_time\"\n",
-        "    }))\n",
-        "tmp_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "combined_df = tmp_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Feature Engineering"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Datetime features"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's split the pickup and drop-off date further into day of week, day of month, and month. For pickup and drop-off time columns, we will split it into hour, minute, and second."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tmp_df = (combined_df\n",
-        "    .derive_column_by_example(\n",
-        "        source_columns=\"pickup_date\", \n",
-        "        new_column_name=\"pickup_weekday\", \n",
-        "        example_data=[(\"2009-01-04\", \"Sunday\"), (\"2013-08-22\", \"Thursday\")]\n",
-        "    )\n",
-        "    .derive_column_by_example(\n",
-        "        source_columns=\"dropoff_date\",\n",
-        "        new_column_name=\"dropoff_weekday\",\n",
-        "        example_data=[(\"2013-08-22\", \"Thursday\"), (\"2013-11-03\", \"Sunday\")]\n",
-        "    )\n",
-        "    .split_column_by_example(source_column=\"pickup_date\")\n",
-        "    .split_column_by_example(source_column=\"pickup_time\")\n",
-        "    .split_column_by_example(source_column=\"dropoff_date\")\n",
-        "    .split_column_by_example(source_column=\"dropoff_time\")\n",
-        "    .split_column_by_example(source_column=\"pickup_time_1\")\n",
-        "    .split_column_by_example(source_column=\"dropoff_time_1\")\n",
-        "    .drop_columns(columns=[\n",
-        "        \"pickup_date\", \"pickup_time\", \"dropoff_date\", \"dropoff_time\", \n",
-        "        \"pickup_date_1\", \"dropoff_date_1\", \"pickup_time_1\", \"dropoff_time_1\"\n",
-        "    ])\n",
-        "    .rename_columns(column_pairs={\n",
-        "        \"pickup_date_2\": \"pickup_month\",\n",
-        "        \"pickup_date_3\": \"pickup_monthday\",\n",
-        "        \"pickup_time_1_1\": \"pickup_hour\",\n",
-        "        \"pickup_time_1_2\": \"pickup_minute\",\n",
-        "        \"pickup_time_2\": \"pickup_second\",\n",
-        "        \"dropoff_date_2\": \"dropoff_month\",\n",
-        "        \"dropoff_date_3\": \"dropoff_monthday\",\n",
-        "        \"dropoff_time_1_1\": \"dropoff_hour\",\n",
-        "        \"dropoff_time_1_2\": \"dropoff_minute\",\n",
-        "        \"dropoff_time_2\": \"dropoff_second\"\n",
-        "    }))\n",
-        "tmp_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "combined_df = tmp_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "From the data above, we can see that the pickup and drop-off date and time components produced from the transforms above looks good. Let's drop the `pickup_datetime` and `dropoff_datetime` columns as they are no longer needed."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tmp_df = combined_df.drop_columns(columns=[\"pickup_datetime\", \"dropoff_datetime\"])\n",
-        "tmp_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "combined_df = tmp_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's now save the transformation steps into a DataPrep package so we can use it to to run on spark."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_path = path.join(mkdtemp(), \"new_york_taxi.dprep\")\n",
-        "combined_df.save(file_path=dflow_path)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi.png)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi_scale-out.ipynb b/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi_scale-out.ipynb
deleted file mode 100644
index fd69f736..00000000
--- a/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi_scale-out.ipynb
+++ /dev/null
@@ -1,135 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Scale-Out Data Preparation\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once we are done with preparing and featurizing the data locally, we can run the same steps on the full dataset in scale-out mode. The new york taxi cab data is about 300GB in total, which is perfect for scale-out. Let's start by downloading the package we saved earlier to disk. Feel free to run the `new_york_taxi_cab.ipynb` notebook to generate the package yourself, in which case you may comment out the download code and set the `package_path` to where the package is saved."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from tempfile import mkdtemp\n",
-        "from os import path\n",
-        "from urllib.request import urlretrieve\n",
-        "\n",
-        "dflow_root = mkdtemp()\n",
-        "dflow_path = path.join(dflow_root, \"new_york_taxi.dprep\")\n",
-        "print(\"Downloading Dataflow to: {}\".format(dflow_path))\n",
-        "urlretrieve(\"https://dprepdata.blob.core.windows.net/demo/new_york_taxi_v2.dprep\", dflow_path)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's load the package we just downloaded."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "\n",
-        "df = dprep.Dataflow.open(dflow_path)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's replace the datasources with the full dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from uuid import uuid4\n",
-        "\n",
-        "other_step = df._get_steps()[7].arguments['dataflows'][0]['anonymousSteps'][0]\n",
-        "other_step['id'] = str(uuid4())\n",
-        "other_step['arguments']['path']['target'] = 1\n",
-        "other_step['arguments']['path']['resourceDetails'][0]['path'] = 'https://wranglewestus.blob.core.windows.net/nyctaxi/yellow_tripdata*'"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "green_dsource = dprep.BlobDataSource(\"https://wranglewestus.blob.core.windows.net/nyctaxi/green_tripdata*\")\n",
-        "df = df.replace_datasource(green_dsource)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once we have replaced the datasource, we can now run the same steps on the full dataset. We will print the first 5 rows of the spark DataFrame. Since we are running on the full dataset, this might take a little while depending on your spark cluster's size."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "spark_df = df.to_spark_dataframe()\n",
-        "spark_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi_scale-out.png)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.",
-    "skip_execute_as_test": true
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/data/adls-dpreptestfiles.crt b/work-with-data/dataprep/data/adls-dpreptestfiles.crt
deleted file mode 100644
index 98498f95..00000000
--- a/work-with-data/dataprep/data/adls-dpreptestfiles.crt
+++ /dev/null
@@ -1,45 +0,0 @@
------BEGIN PRIVATE KEY-----
-MIIEvwIBADANBgkqhkiG9w0BAQEFAASCBKkwggSlAgEAAoIBAQDmkkyF0BwipZow
-Wd1AMkRkySx0y079JPxpsYhv4i1xXKdoa9bpFqwoXmJpeQM1JWnU4UeZzFeM86qK
-AhQvL4KV4kibcP2ENvu2NKFEdotO3uxPJ+6GlcYwMYzy+tUj008KnnRZfTrR78sJ
-tIl3C6lnVL0ICihksG59P1sskRq3PvOjXLAdEZalwDjZ4ZPoNDZdj6nUjB2l8zqu
-pKAt5mR+bJ9Sox4yrDuNhMmFt5QsRDRe3wUqdV+C9OCWHmjlmsjrYw7p9YmjBDvC
-5U7mF0Mk/XeYFzj0pkXKQVqBL6xqig+q5ob0szYfg19iDeFhS3iIsRcJGEnRVW/A
-NpsBZyKrAgMBAAECggEBANlvP8C1F8NInhZYuIAwpzTQTh86Fxw8g9h8dijkh2wv
-LyQXBk07d1B+aZoDZ5X32UzKwcX04N9obfvFqBkzWZdVFJmZvUmwvEEActBoZkkT
-io+/HX5HweVy5PPCvbsSK6jc8uXtZcnSs4tMeJIOKkvqqnTpd1w00Y1FcQqfMC16
-4p7o8wbt6OFoFAYqcxeVYVwDzCTLZD3+iJaqmntkBkoDndJy52yXQmMq5z1wbQVp
-BL6+L9nTvmouy64jiHVSKOx8nnWThYfHsXoPv+rYywjeuK/v3hyaTAwogs36ooEn
-SnuTBRvJcumN9Q0XIVlxKMVBcGyyAP+0yNKGz5NQgdECgYEA/I/Uq1E3epPJgEWR
-Bub+LpCgwtrw/lgKncb/Q/AiE9qoXobUe4KNU8aGaNMb7uVNLckY7cOluLS6SQb3
-Mzwk2Jl0G3vk8rW46tZWvSYB8+zAR2Rz7seUOT9SE5OmvwpnHrnp3nRr1vvVd2bp
-Q/ypwMLrwWQN51Kr+oTS74bUbrkCgYEA6bXVIUyao7z2Q3qAr6h+6JEWDbkJA7hJ
-BjHIOXvxd1tMoJJX+X9+IE/2XoJaUkGCb0vrM/hi1cyQFmS4Or/J6IWSZu8oBpDr
-EBmIK3PF1nrzNvWD28wM46c6ScehyWSm/u4bJWSm9liTX3dv5Kpa6ym7yLKc3c0B
-ECpSJM+5SoMCgYEAq585Tukzn/IJPUcIk/4nv5C8DW0l0lAVdr2g/JOTNJajTwik
-HwHJ86G1+Elsc9wRpAlBDWCjnm4BIFrBZGl8SEuOoJaCL4PZEotwCbxoG09IIbtb
-JGkuifBDX9Y3ux3gkPqYt3e5SC99EVQ3MuHgoIJUHehVolmFUAkuJWIjvNECgYEA
-5pU0VspRuELzZdgzpxvDOooLDDcHodfslGQBfFXBA1Xc4IACtHMJaa/7D3vkyUtA
-+bYZtQjX2sEdWDq/WZdoCjXfIBfNkczhXt0R8G0lQFvGIu9QzUchYGrZo3mHMkBQ
-Uy1xMw9/e4YgwQwCJcW+Nk7Sq00uX9enuN9IdHFOCykCgYAqAGMK6CH1tlpjvHrf
-k+ZhigYxTXBlsVVvK1BIGGaiwzDpn65zeQp4aLOjSZkI1LuRi3tfTiZ321jRd64J
-4lGk5Jurqv5grDmxROX/U50wEYbI9ncu/thU7syUdxDiqxHPI2RMG50mRcm3a55p
-ZCNSqkMlcXyA0U1z8C1ILNUsbA==
------END PRIVATE KEY-----
------BEGIN CERTIFICATE-----
-MIICoTCCAYkCAgPoMA0GCSqGSIb3DQEBBQUAMBQxEjAQBgNVBAMMCUNMSS1Mb2dp
-bjAiGA8yMDE4MDcxMzIzMjA0N1oYDzIwMTkwNzEzMjMyMDQ5WjAUMRIwEAYDVQQD
-DAlDTEktTG9naW4wggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQDmkkyF
-0BwipZowWd1AMkRkySx0y079JPxpsYhv4i1xXKdoa9bpFqwoXmJpeQM1JWnU4UeZ
-zFeM86qKAhQvL4KV4kibcP2ENvu2NKFEdotO3uxPJ+6GlcYwMYzy+tUj008KnnRZ
-fTrR78sJtIl3C6lnVL0ICihksG59P1sskRq3PvOjXLAdEZalwDjZ4ZPoNDZdj6nU
-jB2l8zqupKAt5mR+bJ9Sox4yrDuNhMmFt5QsRDRe3wUqdV+C9OCWHmjlmsjrYw7p
-9YmjBDvC5U7mF0Mk/XeYFzj0pkXKQVqBL6xqig+q5ob0szYfg19iDeFhS3iIsRcJ
-GEnRVW/ANpsBZyKrAgMBAAEwDQYJKoZIhvcNAQEFBQADggEBAI4VlaFb9NsXMLdT
-Cw5/pk0Xo2Qi6483RGTy8vzrw88IE7f3juB/JWG+rayjtW5bBRx2fae4/ZIdZ4zg
-N2FDKn2PQPAc9m9pcKyUKUvWOC8ixSkrUmeQew0l1AXU0hsPSlJ7/7ZK4efoyB47
-hj71fsyKdyKbisZDcUFBq/S8PazdPF0YOD1W/4A2tW0cSMg+jmFWynuUTdWt3SU8
-CwBGqdiSKT5faJuYwIWnRXDEQS3ObRn1OFEfFdd4d2sxjxydWKRgnINnGlBdiFAT
-KzCozVr+75cO2ErH6x5C0hLQGG5BxXbaijyxyvaRNokTMVVv6OaDEnjzCGfJ72Yf
-2wgitNc=
------END CERTIFICATE-----
diff --git a/work-with-data/dataprep/data/chicago-aldermen-2015.csv b/work-with-data/dataprep/data/chicago-aldermen-2015.csv
deleted file mode 100644
index a0cae0ba..00000000
--- a/work-with-data/dataprep/data/chicago-aldermen-2015.csv
+++ /dev/null
@@ -1,54 +0,0 @@
-"Retrieved from https://en.wikipedia.org/wiki/Chicago_City_Council on November 6, 2018"
-
-
-Ward,Name,Took Office,Party
-1,Proco Joe Moreno,2010*,Dem
-2,Brian Hopkins,2015,Dem
-3,Pat Dowell,2007,Dem
-4,Sophia King,2016*,Dem
-5,Leslie Hairston,1999,Dem
-6,Roderick Sawyer,2011,Dem
-7,Gregory Mitchell,2015,Dem
-8,Michelle A. Harris,2006*,Dem
-9,Anthony Beale,1999,Dem
-10,Susie Sadlowski Garza,2015,Dem
-11,Patrick Daley Thompson,2015,Dem
-12,George Cardenas,2003,Dem
-13,Marty Quinn,2011,Dem
-14,Edward M. Burke,1969,Dem
-15,Raymond Lopez,2015,Dem
-16,Toni Foulkes,2007,Dem
-17,David H. Moore,2015,Dem
-18,Derrick Curtis,2015,Dem
-19,Matthew O'Shea,2011,Dem
-20,Willie Cochran,2007,Dem
-21,Howard Brookins Jr.,2003,Dem
-22,Ricardo Muñoz,1993*,Dem
-23,Silvana Tabares,2018*,Dem
-24,"Michael Scott, Jr.",2015,Dem
-25,Daniel Solis,1996*,Dem
-26,Roberto Maldonado,2009*,Dem
-27,"Walter Burnett, Jr.",1995,Dem
-28,Jason Ervin,2011*,Dem
-29,Chris Taliaferro,2015,Dem
-30,Ariel Reboyras,2003,Dem
-31,Milly Santiago,2015,Dem
-32,Scott Waguespack,2007,Dem
-33,Deb Mell,2013*,Dem
-34,Carrie Austin,1994*,Dem
-35,Carlos Ramirez-Rosa,2015,Dem
-36,Gilbert Villegas,2015,Dem
-37,Emma Mitts,2000*,Dem
-38,Nicholas Sposato,2011,Ind
-39,Margaret Laurino,1994*,Dem
-40,Patrick J. O'Connor,1983,Dem
-41,Anthony Napolitano,2015,Rep
-42,Brendan Reilly,2007,Dem
-43,Michele Smith,2011,Dem
-44,Thomas M. Tunney,2002*,Dem
-45,John Arena,2011,Dem
-46,James Cappleman,2011,Dem
-47,Ameya Pawar,2011,Dem
-48,Harry Osterman,2011,Dem
-49,Joe Moore,1991,Dem
-50,Debra Silverstein,2011,Dem
diff --git a/work-with-data/dataprep/data/crime-dirty.csv b/work-with-data/dataprep/data/crime-dirty.csv
deleted file mode 100644
index ef7beb0b..00000000
--- a/work-with-data/dataprep/data/crime-dirty.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-File updated 11/2/2018
-
-
-
-ID|Case Number|Date|Block|IUCR|Primary Type|Description|Location Description|Arrest|Domestic|Beat|District|Ward|Community Area|FBI Code|X Coordinate|Y Coordinate|Year|Updated On|Latitude|Longitude|Location
-10140490|HY329907|07/05/2015 11:50:00 PM|050XX N NEWLAND AVE|0820|THEFT|$500 AND UNDER|STREET|false|false|1613|016|41|10|06|1129230|1933315|2015|07/12/2015 12:42:46 PM|41.973309466|-87.800174996|(41.973309466, -87.800174996)
-10139776|HY329265|07/05/2015 11:30:00 PM|011XX W MORSE AVE|0460|BATTERY|SIMPLE|STREET|false|true|2431|024|49|1|08B|1167370|1946271|2015|07/12/2015 12:42:46 PM|42.008124017|-87.65955018|(42.008124017, -87.65955018)
-10140270|HY329253|07/05/2015 11:20:00 PM|121XX S FRONT AVE|0486|BATTERY|DOMESTIC BATTERY SIMPLE|STREET|false|true|0532||9|53|08B|||2015|07/12/2015 12:42:46 PM|||
-10139885|HY329308|07/05/2015 11:19:00 PM|051XX W DIVISION ST|0610|BURGLARY|FORCIBLE ENTRY|SMALL RETAIL STORE|false|false|1531|015|37|25|05|1141721|1907465|2015|07/12/2015 12:42:46 PM|41.902152027|-87.754883404|(41.902152027, -87.754883404)
-10140379|HY329556|07/05/2015 11:00:00 PM|012XX W LAKE ST|0930|MOTOR VEHICLE THEFT|THEFT/RECOVERY: AUTOMOBILE|STREET|false|false|1215|012|27|28|07|1168413|1901632|2015|07/12/2015 12:42:46 PM|41.885610142|-87.657008701|(41.885610142, -87.657008701)
-10140868|HY330421|07/05/2015 10:54:00 PM|118XX S PEORIA ST|1320|CRIMINAL DAMAGE|TO VEHICLE|VEHICLE NON-COMMERCIAL|false|false|0524|005|34|53|14|1172409|1826485|2015|07/12/2015 12:42:46 PM|41.6793109|-87.644545209|(41.6793109, -87.644545209)
-10139762|HY329232|07/05/2015 10:42:00 PM|026XX W 37TH PL|1020|ARSON|BY FIRE|VACANT LOT/LAND|false|false|0911|009|12|58|09|1159436|1879658|2015|07/12/2015 12:42:46 PM|41.825500607|-87.690578042|(41.825500607, -87.690578042)
-10139722|HY329228|07/05/2015 10:30:00 PM|016XX S CENTRAL PARK AVE|1811|NARCOTICS|POSS: CANNABIS 30GMS OR LESS|ALLEY|true|false|1021|010|24|29|18|1152687|1891389|2015|07/12/2015 12:42:46 PM|41.857827814|-87.715028789|(41.857827814, -87.715028789)
-10139774|HY329209|07/05/2015 10:15:00 PM|048XX N ASHLAND AVE|1310|CRIMINAL DAMAGE|TO PROPERTY|APARTMENT|false|false|2032|020|46|3|14|1164821|1932394|2015|07/12/2015 12:42:46 PM|41.970099796|-87.669324377|(41.970099796, -87.669324377)
-10139697|HY329177|07/05/2015 10:10:00 PM|058XX S ARTESIAN AVE|1320|CRIMINAL DAMAGE|TO VEHICLE|ALLEY|false|false|0824|008|16|63|14|1160997|1865851|2015|07/12/2015 12:42:46 PM|41.787580282|-87.685233078|(41.787580282, -87.685233078)
diff --git a/work-with-data/dataprep/data/crime-full.csv b/work-with-data/dataprep/data/crime-full.csv
deleted file mode 100644
index e46bda18..00000000
--- a/work-with-data/dataprep/data/crime-full.csv
+++ /dev/null
@@ -1,1001 +0,0 @@
-ID,Case Number,Date,Block,IUCR,Primary Type,Description,Location Description,Damage Cost,Arrest,Domestic,Beat,District,Ward,Community Area,FBI Code,X Coordinate,Y Coordinate,Year,Updated On,Latitude,Longitude,Location
-2705685,HJ329710,2003-04-26 00:00:00,105XX S WOOD ST,0460,BATTERY,SIMPLE,RESIDENCE,4615,False,False,2212,NA,19,72,08B,1166261,1834693,2003,ERROR,41.701967722,-87.666817049,"(41.701967722, -87.666817049)"
-8367900,HT600798,2011-11-22 15:15:00,063XX S PULASKI RD,0890,THEFT,FROM BUILDING,LIBRARY,3882,False,False,813,8,13,65,06,1150746,1862490,2011,ERROR,41.778563069,-87.722907063,"(41.778563069, -87.722907063)"
-8726660,HV402430,2012-07-26 19:30:00,019XX W BELMONT AVE,0610,BURGLARY,FORCIBLE ENTRY,SMALL RETAIL STORE,4566,False,False,1931,19,32,5,05,1162975,1921223,2012,ERROR,41.939485082,-87.676427052,"(41.939485082, -87.676427052)"
-9103375,HW247768,2013-04-25 14:20:00,047XX S ASHLAND AVE,0460,BATTERY,SIMPLE,SIDEWALK,2469,False,False,931,9,20,61,08B,1166425,1873469,2013,ERROR,41.808370972,-87.665113707,"(41.808370972, -87.665113707)"
-3807148,HL176623,2005-02-12 01:24:03,078XX S EAST END AVE,0920,MOTOR VEHICLE THEFT,ATT: AUTOMOBILE,ALLEY,989,False,False,414,4,8,43,07,1188944,1853066,2005,ERROR,41.751873397,-87.583173082,"(41.751873397, -87.583173082)"
-4241820,HL534761,2005-08-07 21:12:26,068XX S MARSHFIELD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,3807,False,False,725,7,17,67,08B,1166478,1859475,2005,ERROR,41.769968592,-87.665318117,"(41.769968592, -87.665318117)"
-4824796,HM433465,2006-06-23 22:15:00,105XX S STATE ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,1504,False,False,512,5,34,49,14,1178075,1835093,2006,ERROR,41.702806335,-87.623545729,"(41.702806335, -87.623545729)"
-8172753,HT407165,2011-07-20 17:50:00,021XX W 21ST PL,0820,THEFT,$500 AND UNDER,RESIDENCE,262,False,False,1223,12,25,31,06,1162541,1889761,2011,2011-03-08 09:56:44,41.853160001,-87.678904133,"(41.853160001, -87.678904133)"
-8154927,HT390078,2011-07-10 15:50:00,046XX W NORTH AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,2972,True,False,2533,25,37,25,06,1144852,1910260,2011,2011-11-07 10:34:01,41.909763298,-87.743312036,"(41.909763298, -87.743312036)"
-6944181,HR349635,2009-05-30 18:13:00,015XX W 63RD ST,031A,ROBBERY,ARMED: HANDGUN,ALLEY,1296,False,False,713,7,16,67,03,1167116,1862990,2009,2009-12-06 18:29:17,41.779600581,-87.662879029,"(41.779600581, -87.662879029)"
-5836733,HN643782,2007-10-12 14:00:00,027XX W SUMMERDALE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,3105,False,False,2011,20,40,4,14,1157009,1935459,2007,ERROR,41.978672864,-87.697965904,"(41.978672864, -87.697965904)"
-7740163,HS548309,2010-10-04 21:30:00,057XX N WASHTENAW AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,APARTMENT,594,True,False,2011,20,40,2,18,1157257,1938221,2010,2010-04-10 23:26:24,41.986246871,-87.696978408,"(41.986246871, -87.696978408)"
-4984650,HM597309,2006-09-11 18:30:00,052XX S STATE ST,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",1591,False,False,232,2,3,40,06,1177212,1869954,2006,ERROR,41.798488523,-87.625655865,"(41.798488523, -87.625655865)"
-3941717,HL312501,2005-04-22 08:00:00,069XX S EBERHART AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,1397,False,True,322,3,6,69,08B,1180740,1858964,2005,ERROR,41.76825049,-87.613055702,"(41.76825049, -87.613055702)"
-5084563,HM687682,2006-10-28 14:30:00,022XX S PULASKI RD,0560,ASSAULT,SIMPLE,STREET,2224,False,False,1013,10,22,29,08A,1150013,1889063,2006,ERROR,41.851497405,-87.724904562,"(41.851497405, -87.724904562)"
-8136835,HT371007,2011-06-28 23:20:00,011XX N CHRISTIANA AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,538,True,False,1121,11,26,23,18,1153742,1907328,2011,ERROR,41.901545171,-87.710731851,"(41.901545171, -87.710731851)"
-1815636,G631826,2001-10-20 18:30:00,046XX S HALSTED ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,2501,True,False,921,NA,NA,NA,26,1171705,1874218,2001,ERROR,41.810312019,-87.645725963,"(41.810312019, -87.645725963)"
-9276577,HW420770,2013-08-23 16:40:00,007XX S ALBANY AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,658,True,False,1134,11,24,27,18,1155766,1896834,2013,ERROR,41.872708075,-87.703580324,"(41.872708075, -87.703580324)"
-1603336,G368431,2001-06-24 19:52:14,001XX W CHICAGO AV,5001,OTHER OFFENSE,OTHER CRIME INVOLVING PROPERTY,PARKING LOT/GARAGE(NON.RESID.),3948,True,False,1832,NA,NA,NA,26,1175257,1905662,2001,ERROR,41.896517917,-87.631755414,"(41.896517917, -87.631755414)"
-7210353,HR608426,2009-10-26 08:40:00,015XX S PULASKI RD,2024,NARCOTICS,POSS: HEROIN(WHITE),ABANDONED BUILDING,1124,True,False,1012,10,24,29,18,1149936,1891869,2009,2009-04-11 09:53:16,41.859198911,-87.725114223,"(41.859198911, -87.725114223)"
-4471848,HL301576,2005-04-17 20:50:03,006XX E GRAND AVE,1330,CRIMINAL TRESPASS,TO LAND,OTHER,3257,False,False,1834,18,42,8,26,1180772,1904096,2005,2007-11-06 15:52:33,41.892095212,-87.611548469,"(41.892095212, -87.611548469)"
-7637700,HS441827,2010-08-02 12:45:00,028XX W 22ND PL,1570,SEX OFFENSE,PUBLIC INDECENCY,SIDEWALK,2297,False,False,1033,10,12,30,17,1157857,1888970,2010,ERROR,41.851086084,-87.696117577,"(41.851086084, -87.696117577)"
-4703044,HM309199,2006-04-24 00:00:00,012XX N CLARK ST,0820,THEFT,$500 AND UNDER,STREET,20,False,False,1821,18,42,8,06,1175274,1908508,2006,2014-04-12 12:43:35,41.904327102,-87.631607477,"(41.904327102, -87.631607477)"
-3961270,HL324634,2005-04-29 08:30:00,005XX W DIVISION ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,CHA HALLWAY/STAIRWELL/ELEVATOR,4726,False,False,1821,18,27,8,04B,1172300,1908305,2005,ERROR,41.903836297,-87.642537685,"(41.903836297, -87.642537685)"
-2281333,HH546276,2002-07-30 13:30:00,069XX S EMERALD AVE,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),SIDEWALK,864,True,False,732,NA,6,68,18,1172538,1859044,2002,ERROR,41.76865457,-87.643117449,"(41.76865457, -87.643117449)"
-1837655,G672065,2001-11-07 21:45:40,062XX S ST LAWRENCE AV,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,4001,True,False,313,NA,NA,NA,14,1181266,1863792,2001,ERROR,41.78148689,-87.610979035,"(41.78148689, -87.610979035)"
-1842160,G679483,2001-11-11 03:30:00,021XX N CAMPBELL AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,1986,False,False,1431,NA,NA,NA,07,1159571,1914405,2001,ERROR,41.920846924,-87.689126006,"(41.920846924, -87.689126006)"
-2636288,HJ216645,2003-03-03 20:48:00,045XX W WASHINGTON BLVD,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,1393,True,False,1113,NA,28,26,16,1146373,1900156,2003,ERROR,41.882007967,-87.737982025,"(41.882007967, -87.737982025)"
-1496183,G233892,2001-04-24 09:00:00,004XX E MC FETRIDGE DR,0820,THEFT,$500 AND UNDER,PARK PROPERTY,187,False,False,133,NA,NA,NA,06,1179031,1894246,2001,2014-04-12 12:43:35,41.865106307,-87.618243723,"(41.865106307, -87.618243723)"
-3931945,HL305935,2005-04-19 16:00:00,015XX N ARTESIAN AVE,0810,THEFT,OVER $500,RESIDENTIAL YARD (FRONT/BACK),1386,False,False,1423,14,1,24,06,1159834,1910187,2005,2014-04-12 12:43:35,41.909266991,-87.688276249,"(41.909266991, -87.688276249)"
-5621453,HN427508,2007-06-25 13:25:00,040XX W DICKENS AVE,2022,NARCOTICS,POSS: COCAINE,STREET,2783,True,False,2525,25,30,20,18,1149318,1913614,2007,2007-01-07 02:00:46,41.918881522,-87.726818542,"(41.918881522, -87.726818542)"
-1709847,G508926,2001-08-26 02:00:00,010XX N MONTICELLO AV,0460,BATTERY,SIMPLE,RESIDENCE,3761,False,True,1112,NA,NA,NA,08B,1151889,1906869,2001,ERROR,41.900322332,-87.717550266999993,"(41.900322332, -87.717550267)"
-2306701,HH595350,2002-08-21 05:45:00,001XX W POLK ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,1890,False,False,131,NA,2,32,07,1175097,1896746,2002,ERROR,41.872055484,-87.63261045,"(41.872055484, -87.63261045)"
-6581196,HP653741,2008-10-07 03:00:00,034XX W SCHOOL ST,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),312,False,False,1732,17,35,21,06,1152747,1921690,2008,ERROR,41.940975485,-87.714005626,"(41.940975485, -87.714005626)"
-5443942,HN272087,2007-04-08 15:44:18,059XX S LAFLIN ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,1938,False,False,713,7,16,67,14,1167399,1865013,2007,ERROR,41.785145883,-87.661783592,"(41.785145883, -87.661783592)"
-3792137,HK836619,2004-12-30 18:30:00,131XX S VERNON AVE,2027,NARCOTICS,POSS: CRACK,RESIDENCE PORCH/HALLWAY,4455,True,False,533,5,9,54,18,1181519,1818462,2004,ERROR,41.657089903,-87.61144499,"(41.657089903, -87.61144499)"
-9206054,HW352076,2013-07-07 15:00:00,016XX W 38TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,975,True,True,912,9,11,59,08B,1165971,1879568,2013,2013-08-07 12:07:35,41.825116974,-87.666605306,"(41.825116974, -87.666605306)"
-4720801,HM327230,2006-05-03 05:03:58,026XX E 73RD ST,0334,ROBBERY,ATTEMPT: ARMED-KNIFE/CUT INSTR,SIDEWALK,4270,False,False,334,3,7,43,03,1194873,1857481,2006,2006-07-05 03:57:33,41.763844577,-87.561301231,"(41.763844577, -87.561301231)"
-7034576,HR441456,2009-07-21 21:05:00,107XX S HALSTED ST,0326,ROBBERY,AGGRAVATED VEHICULAR HIJACKING,GAS STATION,1442,True,False,2233,22,34,75,03,1172840,1833829,2009,ERROR,41.699454527,-87.642752065,"(41.699454527, -87.642752065)"
-8348862,HT582100,2011-11-09 15:00:00,108XX S BENSLEY AVE,0560,ASSAULT,SIMPLE,SIDEWALK,844,False,False,434,4,10,51,08A,1194612,1833847,2011,ERROR,41.698997218,-87.563033068,"(41.698997218, -87.563033068)"
-2882234,HJ552553,2003-08-08 22:00:00,109XX S WABASH AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,664,False,False,513,5,9,49,14,1178490,1832061,2003,ERROR,41.694476704,-87.622117791,"(41.694476704, -87.622117791)"
-8571720,HV245210,2011-12-26 09:00:00,038XX W NORTH AVE,0842,THEFT,AGG: FINANCIAL ID THEFT,BANK,4830,False,False,2535,25,30,23,06,1150240,1910394,2011,ERROR,41.910027597,-87.723515111,"(41.910027597, -87.723515111)"
-3479397,HK551539,2004-08-11 11:00:00,014XX S CHRISTIANA AVE,0810,THEFT,OVER $500,STREET,3767,False,False,1021,10,24,29,06,1154306,1892825,2004,2014-04-12 12:43:35,41.861736212,-87.709047718,"(41.861736212, -87.709047718)"
-6571624,HP644036,2008-10-23 18:10:00,003XX S PLYMOUTH CT,0340,ROBBERY,ATTEMPT: STRONGARM-NO WEAPON,STREET,3266,False,False,123,1,2,32,03,1176091,1898936,2008,2008-06-11 15:42:26,41.878042652,-87.628895109,"(41.878042652, -87.628895109)"
-3425217,HK489162,2004-07-11 19:46:59,060XX S MICHIGAN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,1195,False,True,311,3,20,40,08B,1178229,1865098,2004,ERROR,41.785140156,-87.62207368,"(41.785140156, -87.62207368)"
-9297900,HW443063,2013-09-08 14:30:00,073XX S MICHIGAN AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,3308,False,False,323,3,6,69,03,1178393,1856302,2013,2013-02-10 11:54:38,41.760999284,-87.621739229,"(41.760999284, -87.621739229)"
-5343960,HM744635,2006-11-28 07:45:00,042XX S CALUMET AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,2763,True,False,214,2,3,38,16,1179097,1876982,2006,ERROR,41.81773117,-87.61852889,"(41.81773117, -87.61852889)"
-7231348,HR644530,2009-11-15 13:15:00,048XX N KENMORE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,2613,True,True,2024,20,46,3,08B,1168339,1932383,2009,ERROR,41.969994063,-87.656388912,"(41.969994063, -87.656388912)"
-8276922,HT511056,2011-09-23 18:15:00,076XX S CICERO AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,3184,True,False,833,8,13,65,06,1145766,1853738,2011,ERROR,41.75464162,-87.741385158,"(41.75464162, -87.741385158)"
-7096630,HR505081,2009-08-26 18:00:00,081XX S PAULINA ST,0460,BATTERY,SIMPLE,RESIDENCE PORCH/HALLWAY,2842,False,False,614,6,18,71,08B,1166381,1850921,2009,ERROR,41.746497306,-87.66591694,"(41.746497306, -87.66591694)"
-3509426,HK586658,2004-08-28 00:00:00,083XX S KINGSTON AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,606,False,False,423,4,7,46,07,1194581,1850155,2004,ERROR,41.743748652,-87.562611996,"(41.743748652, -87.562611996)"
-9478072,HX130921,2014-01-21 17:00:00,068XX S WOLCOTT AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,1761,False,True,726,7,17,67,26,1164830,1859219,2014,ERROR,41.769301059,-87.671366222,"(41.769301059, -87.671366222)"
-9320325,HW464485,2013-09-23 21:30:00,016XX E 77TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,1715,False,True,414,4,8,43,14,1188749,1854298,2013,ERROR,41.755258781,-87.583848327,"(41.755258781, -87.583848327)"
-8528788,HV205948,2012-03-20 09:50:00,111XX S EWING AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE-GARAGE,1676,False,False,433,4,10,52,14,1202171,1831712,2012,ERROR,41.692949738,-87.535428598,"(41.692949738, -87.535428598)"
-4290691,HL605721,2005-09-11 14:00:00,001XX N DEARBORN ST,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),2621,False,False,122,1,42,32,06,1175948,1901601,2005,2014-04-12 12:43:35,41.885358785,-87.629339896,"(41.885358785, -87.629339896)"
-9191309,HW336113,2013-06-25 11:28:00,013XX N ASHLAND AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,3808,True,False,1433,14,1,24,06,1165434,1909298,2013,ERROR,41.906710108,-87.66772974,"(41.906710108, -87.66772974)"
-6943981,HR348351,2009-05-29 22:00:00,057XX N MC VICKER AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,1332,False,False,1622,16,45,10,14,1134948,1938056,2009,ERROR,41.986219618,-87.779035389,"(41.986219618, -87.779035389)"
-1432501,G153916,2001-03-18 01:15:59,023XX N HAMLIN AV,0460,BATTERY,SIMPLE,RESIDENCE,2320,True,False,2525,NA,NA,NA,08B,1150616,1915362,2001,ERROR,41.923652901,-87.722003735,"(41.923652901, -87.722003735)"
-8288964,HT523191,2011-10-01 16:10:00,021XX E 70TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,4671,True,False,331,3,5,43,18,1191556,1859009,2011,2011-01-10 19:38:17,41.768118581,-87.573409074,"(41.768118581, -87.573409074)"
-7910721,HT140388,2011-01-28 23:30:00,029XX W ADAMS ST,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,APARTMENT,4639,False,False,1124,11,2,27,04A,1156613,1898917,2011,ERROR,41.878406942,-87.700414194,"(41.878406942, -87.700414194)"
-2089112,HH309698,2002-04-15 20:15:00,010XX N LAWLER AV,2027,NARCOTICS,POSS: CRACK,SIDEWALK,1200,True,False,1531,NA,NA,NA,18,1142456,1906817,2002,ERROR,41.900360209,-87.752199731,"(41.900360209, -87.752199731)"
-9419206,HW562896,2013-12-08 01:39:00,011XX S CANAL ST,0810,THEFT,OVER $500,RESTAURANT,4414,False,False,124,1,2,28,06,1173358,1895046,2013,ERROR,41.867429345,-87.639045471,"(41.867429345, -87.639045471)"
-8846059,HV519322,2012-10-14 20:00:00,018XX W 103RD ST,0610,BURGLARY,FORCIBLE ENTRY,OTHER,4604,False,False,2213,22,19,72,05,1165953,1836420,2012,ERROR,41.70671343,-87.667895994,"(41.70671343, -87.667895994)"
-6454875,HP535283,2008-08-26 03:22:00,019XX W 19TH ST,0560,ASSAULT,SIMPLE,APARTMENT,1503,False,False,1223,12,25,31,08A,1163581,1890708,2008,ERROR,41.855736847,-87.675060338,"(41.855736847, -87.675060338)"
-8223778,HT457480,2011-08-20 15:15:00,007XX E 43RD ST,0560,ASSAULT,SIMPLE,CHA PARKING LOT/GROUNDS,4240,False,False,213,2,4,38,08A,1182085,1876707,2011,ERROR,41.816907818,-87.607576648,"(41.816907818, -87.607576648)"
-1622655,G400595,2001-07-09 14:55:00,035XX N HERMITAGE AV,0560,ASSAULT,SIMPLE,ALLEY,2256,False,False,1923,NA,NA,NA,08A,1164066,1923605,2001,ERROR,41.945998404,-87.672349817,"(41.945998404, -87.672349817)"
-7843346,HS655355,2010-12-10 22:15:00,028XX S SPAULDING AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,2971,True,False,1032,10,22,30,15,1154782,1885145,2010,ERROR,41.840651893,-87.707505856,"(41.840651893, -87.707505856)"
-6043466,HP146623,2008-01-28 00:00:07,091XX S STONY ISLAND AVE,031A,ROBBERY,ARMED: HANDGUN,SMALL RETAIL STORE,3209,False,False,413,4,8,48,03,1188345,1844791,2008,2008-08-02 08:13:34,41.729180293,-87.585631578,"(41.729180293, -87.585631578)"
-9633417,HX284237,2014-05-31 15:00:00,083XX S INGLESIDE AVE,0820,THEFT,$500 AND UNDER,APARTMENT,380,False,False,632,6,8,44,06,1183965,1849926,2014,2014-07-06 12:40:43,41.743374586,-87.601516477,"(41.743374586, -87.601516477)"
-9050651,HW196019,2013-03-16 14:30:00,037XX N SEMINARY AVE,0810,THEFT,OVER $500,OTHER,3835,False,False,1923,19,44,6,06,1168275,1925255,2013,ERROR,41.950435964,-87.656831163,"(41.950435964, -87.656831163)"
-1724326,G530529,2001-08-24 09:00:00,068XX N FRANCISCO AV,0810,THEFT,OVER $500,CONSTRUCTION SITE,4601,False,False,2411,NA,NA,NA,06,1155826,1945498,2001,2014-04-12 12:43:35,42.006244328,-87.70204429,"(42.006244328, -87.70204429)"
-7313924,HS117962,2009-11-23 09:18:00,001XX W JACKSON BLVD,1110,DECEPTIVE PRACTICE,BOGUS CHECK,BANK,2873,True,False,112,1,2,32,11,1175229,1898922,2009,2011-03-01 19:34:38,41.878023606,-87.632060555,"(41.878023606, -87.632060555)"
-8348182,HT581427,2011-11-08 17:45:00,049XX S WABASH AVE,0820,THEFT,$500 AND UNDER,"SCHOOL, PUBLIC, BUILDING",404,True,False,231,2,3,38,06,1177513,1872176,2011,2011-09-11 12:54:43,41.804579093,-87.624484847,"(41.804579093, -87.624484847)"
-8457513,HV134404,2012-01-27 10:40:00,095XX S LAFAYETTE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,4775,False,False,511,5,21,49,07,1177575,1841925,2012,ERROR,41.721565569,-87.625170844,"(41.721565569, -87.625170844)"
-3991779,HL277064,2005-04-05 22:00:00,004XX S CICERO AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,4442,True,False,1533,15,24,25,16,1144422,1897554,2005,ERROR,41.874904679,-87.745211632,"(41.874904679, -87.745211632)"
-8231576,HT460583,2011-08-03 11:00:00,025XX W 63RD ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,OTHER,2479,False,False,825,8,15,66,26,1160154,1862826,2011,ERROR,41.779296661,-87.688407212,"(41.779296661, -87.688407212)"
-4496745,HL799743,2005-12-20 09:05:00,073XX S SACRAMENTO AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,4808,False,False,835,8,18,66,05,1157607,1855700,2005,ERROR,41.75979389,-87.697937855,"(41.75979389, -87.697937855)"
-5033246,HM641247,2006-10-04 20:00:00,080XX S LAFLIN ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,4181,False,False,612,6,21,71,05,1167772,1851438,2006,ERROR,41.747886306,-87.660805171,"(41.747886306, -87.660805171)"
-5208608,HM778355,2006-12-16 19:10:00,068XX S HALSTED ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,TAVERN/LIQUOR STORE,3007,True,False,723,7,6,68,15,1172116,1859172,2006,2007-01-01 07:32:02,41.7690151,-87.644660529,"(41.7690151, -87.644660529)"
-2528826,HJ107508,2003-01-04 22:02:46,030XX W 47TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,4398,False,False,912,NA,14,58,14,1157054,1873373,2003,ERROR,41.808302333,-87.699487137,"(41.808302333, -87.699487137)"
-1613083,G380569,2001-06-30 03:34:50,010XX E 133 ST,0560,ASSAULT,SIMPLE,CHA APARTMENT,4421,True,False,533,NA,NA,NA,08A,1185848,1817236,2001,ERROR,41.653625,-87.595643042,"(41.653625, -87.595643042)"
-5350380,HN205637,2007-03-03 10:00:00,032XX W 63RD ST,0460,BATTERY,SIMPLE,APARTMENT,1902,False,False,823,8,15,66,08B,1155671,1862624,2007,2007-08-03 21:51:06,41.77883343,-87.704847919,"(41.77883343, -87.704847919)"
-5570467,HN377874,2007-04-02 12:00:00,064XX S LANGLEY AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,4605,False,True,312,3,20,42,26,1181963,1862332,2007,ERROR,41.777464417,-87.608468819,"(41.777464417, -87.608468819)"
-3864729,HL237399,2005-03-16 10:00:00,046XX S HALSTED ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,3954,True,False,921,9,11,61,06,1171713,1873921,2005,ERROR,41.809496845,-87.645705337,"(41.809496845, -87.645705337)"
-4208971,HL527767,2005-07-04 22:30:00,056XX S MICHIGAN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,1863,False,True,233,2,20,40,08B,1178081,1867582,2005,ERROR,41.791959854,-87.622541015,"(41.791959854, -87.622541015)"
-4616724,HM210940,2006-03-03 10:26:14,0000X W RANDOLPH ST,1330,CRIMINAL TRESPASS,TO LAND,RESTAURANT,1416,True,False,122,1,42,32,26,1175995,1901321,2006,2006-07-03 03:46:57,41.884589391,-87.629175742,"(41.884589391, -87.629175742)"
-9574311,HX225021,2014-04-15 20:00:00,088XX S LOOMIS ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,4758,False,False,2222,22,21,71,07,1168581,1846266,2014,2014-06-05 00:39:53,41.733676216,-87.657989464,"(41.733676216, -87.657989464)"
-3138687,HK129812,2004-01-16 21:30:00,115XX S YALE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,4990,False,True,522,5,34,53,08B,1176719,1828319,2004,ERROR,41.684248043,-87.628713951,"(41.684248043, -87.628713951)"
-8344443,HT528932,2011-10-05 05:00:00,052XX W SCHOOL ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,2954,False,False,1634,16,38,15,14,1140574,1921391,2011,2011-08-11 10:43:52,41.940387619,-87.758753823,"(41.940387619, -87.758753823)"
-1796827,G622864,2001-10-15 20:30:00,030XX S GRATTEN AV,0810,THEFT,OVER $500,OTHER,2409,False,False,923,NA,NA,NA,06,1169291,1884678,2001,2014-04-12 12:43:35,41.839067958,-87.654276965,"(41.839067958, -87.654276965)"
-6321465,HP407211,2008-06-21 01:00:00,013XX S KEDZIE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,1039,False,False,1022,10,24,29,08B,1155195,1893805,2008,ERROR,41.864407654,-87.705758038,"(41.864407654, -87.705758038)"
-4048269,HL397796,2005-06-04 01:00:00,106XX S HALSTED ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,VEHICLE NON-COMMERCIAL,1253,False,True,2233,22,34,73,08B,1172820,1834491,2005,ERROR,41.701271596,-87.642805861,"(41.701271596, -87.642805861)"
-3192419,HK199140,2004-02-23 08:15:00,068XX S STEWART AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",2632,True,False,722,7,6,68,08B,1174671,1859859,2004,ERROR,41.770843786,-87.635274724,"(41.770843786, -87.635274724)"
-2000440,HH198576,2002-02-20 16:32:03,064XX W DIVERSEY AV,0620,BURGLARY,UNLAWFUL ENTRY,APPLIANCE STORE,4375,False,False,2512,NA,NA,NA,05,NA,NA,2002,ERROR,NA,NA,
-9584763,HX234897,2014-04-23 21:50:00,026XX N RUTHERFORD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,1333,False,False,2512,25,36,18,08B,1130931,1916809,2014,ERROR,41.927985963,-87.794301643,"(41.927985963, -87.794301643)"
-6633395,HP703001,2008-11-26 10:25:55,001XX N STATE ST,0850,THEFT,ATTEMPT THEFT,SIDEWALK,1180,True,False,122,1,42,32,06,1176393,1900887,2008,2008-01-12 07:38:55,41.8833895,-87.627727352,"(41.8833895, -87.627727352)"
-10025851,HY215334,2015-04-09 08:09:00,037XX N KILPATRICK AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,4106,True,True,1731,17,38,15,08B,1144437,1924607,2015,ERROR,41.949140682,-87.744474617,"(41.949140682, -87.744474617)"
-4257303,HL576564,2005-08-28 00:01:00,018XX N KEDVALE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,1447,False,False,2534,25,30,20,14,1148380,1912201,2005,ERROR,41.915022267,-87.730301385,"(41.915022267, -87.730301385)"
-5994905,HP103223,2008-01-02 20:45:00,029XX W WARREN BLVD,2027,NARCOTICS,POSS: CRACK,SIDEWALK,2969,True,False,1331,12,2,27,18,1156699,1900170,2008,2008-03-01 08:43:16,41.881843556,-87.700064472,"(41.881843556, -87.700064472)"
-6422343,HP506170,2008-08-05 23:00:00,008XX E 101ST ST,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,111,False,False,511,5,8,50,06,1183373,1838023,2008,2008-11-08 08:34:38,41.710725164,-87.604055073,"(41.710725164, -87.604055073)"
-20991,HW370238,2013-07-19 21:43:00,038XX W HIRSCH ST,0110,HOMICIDE,FIRST DEGREE MURDER,STREET,3308,False,False,2535,25,30,23,01A,1150202,1908997,2013,2013-03-10 07:24:17,41.906194835,-87.723691183,"(41.906194835, -87.723691183)"
-6084521,HP178194,2008-02-16 03:00:00,066XX S ASHLAND AVE,0313,ROBBERY,ARMED: OTHER DANGEROUS WEAPON,SIDEWALK,3004,False,False,725,7,17,67,03,1166856,1860593,2008,ERROR,41.773028465,-87.663900629,"(41.773028465, -87.663900629)"
-10080403,HY268040,2015-05-19 11:00:00,049XX W IRVING PARK RD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,3481,False,True,1624,16,45,15,08B,1142440,1926175,2015,ERROR,41.95348083,-87.751776252,"(41.95348083, -87.751776252)"
-6924204,HR316222,2009-05-12 09:00:00,0000X W 95TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,CTA GARAGE / OTHER PROPERTY,3480,True,False,634,6,21,49,18,1177743,1841988,2009,ERROR,41.721734655,-87.624553594,"(41.721734655, -87.624553594)"
-6386680,HP461038,2008-07-18 22:43:31,045XX W HARRISON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,2401,True,False,1131,11,24,26,18,1146348,1896935,2008,ERROR,41.873169637,-87.738155845,"(41.873169637, -87.738155845)"
-6784849,HR199128,2009-03-04 20:12:00,066XX N CLARK ST,2170,NARCOTICS,POSSESSION OF DRUG EQUIPMENT,ALLEY,3928,True,False,2432,24,40,1,18,1164013,1944171,2009,2009-04-03 20:55:02,42.002433404,-87.671961019,"(42.002433404, -87.671961019)"
-7712756,HS519770,2010-09-17 12:11:00,060XX S WESTERN AVE,0820,THEFT,$500 AND UNDER,SMALL RETAIL STORE,37,False,False,825,8,15,66,06,1161368,1864419,2010,ERROR,41.783642999,-87.683912427,"(41.783642999, -87.683912427)"
-7906876,HT136770,2011-01-26 15:55:00,0000X S KOSTNER AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,4097,True,False,1113,11,28,26,26,1147028,1899356,2011,ERROR,41.87980018,-87.735597307,"(41.87980018, -87.735597307)"
-7991866,HT223888,2011-03-28 19:15:00,110XX S PRINCETON AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,544,True,False,513,5,34,49,18,1176291,1831621,2011,ERROR,41.693318823,-87.630182058,"(41.693318823, -87.630182058)"
-7123089,HR531884,2009-09-11 18:13:00,013XX S MILLARD AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,STREET,4647,True,False,1011,10,24,29,04A,1152210,1893660,2009,ERROR,41.864069113,-87.71671981,"(41.864069113, -87.71671981)"
-2000324,HH200986,2002-02-21 18:47:45,014XX N AVERS AV,0460,BATTERY,SIMPLE,RESIDENCE,3701,False,True,2535,NA,NA,NA,08B,1150481,1909461,2002,ERROR,41.907462651,-87.722654169,"(41.907462651, -87.722654169)"
-8240399,HT466105,2011-08-25 22:55:00,021XX N CICERO AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,2564,True,False,2522,25,31,19,16,1144003,1913897,2011,2011-02-09 09:05:11,41.919759591,-87.746339498,"(41.919759591, -87.746339498)"
-1514753,G260660,2001-05-06 18:53:39,028XX W 19 ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,3078,True,False,1022,NA,NA,NA,15,1157943,1890640,2001,2010-02-06 10:34:17,41.855666993,-87.695756427,"(41.855666993, -87.695756427)"
-3724031,HK768759,2004-11-12 13:00:00,012XX S ASHLAND AVE,1120,DECEPTIVE PRACTICE,FORGERY,RESIDENCE,1595,False,False,1224,12,2,28,10,1165874,1894646,2004,ERROR,41.866494521,-87.666531674,"(41.866494521, -87.666531674)"
-9095092,HW222941,2013-04-07 10:05:00,039XX S CALUMET AVE,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),SIDEWALK,3181,True,False,213,2,3,38,18,1179129,1878622,2013,2013-03-05 09:26:15,41.822230731,-87.618361446,"(41.822230731, -87.618361446)"
-9606372,HX256322,2014-05-10 14:15:00,017XX S ASHLAND AVE,0860,THEFT,RETAIL THEFT,OTHER,824,False,False,1233,12,25,31,06,1166044,1891531,2014,ERROR,41.857943062,-87.665996477,"(41.857943062, -87.665996477)"
-2080168,HH302067,2002-04-11 17:30:00,005XX S CENTRAL AV,0460,BATTERY,SIMPLE,RESIDENCE,830,False,False,1522,NA,NA,NA,08B,1139180,1897038,2002,ERROR,41.873585657,-87.764470903,"(41.873585657, -87.764470903)"
-2538562,HJ119047,2003-01-10 17:30:00,030XX N MOBILE AVE,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",1800,False,False,2511,NA,36,19,06,1133838,1919613,2003,ERROR,41.935629815,-87.783553233,"(41.935629815, -87.783553233)"
-5728743,HN535876,2007-08-18 03:00:00,055XX W DAKIN ST,0460,BATTERY,SIMPLE,ALLEY,3956,False,False,1633,16,38,15,08B,1138581,1925659,2007,ERROR,41.952135858,-87.765975048,"(41.952135858, -87.765975048)"
-4533362,HM121306,2006-01-05 13:00:00,112XX S PERRY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,4073,False,True,522,5,34,49,08B,1177464,1830657,2006,ERROR,41.690647109,-87.62591646,"(41.690647109, -87.62591646)"
-2940341,HJ623964,2003-09-11 22:52:01,079XX S JUSTINE ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,3976,False,False,612,6,21,71,14,1167342,1852128,2003,ERROR,41.749788975,-87.662361113,"(41.749788975, -87.662361113)"
-4022229,HL375934,2005-05-23 16:30:00,043XX W IRVING PARK RD,1320,CRIMINAL DAMAGE,TO VEHICLE,OTHER,547,False,False,1722,17,38,16,14,1146478,1926264,2005,ERROR,41.953648902,-87.736929761,"(41.953648902, -87.736929761)"
-8856428,HV530199,2012-10-22 19:10:00,001XX N LA CROSSE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,RESIDENCE-GARAGE,3046,False,False,1532,15,28,25,07,1144050,1900929,2012,ERROR,41.884173077,-87.746492747,"(41.884173077, -87.746492747)"
-10126995,HY315210,2015-04-01 09:00:00,099XX S WINSTON AVE,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,RESIDENCE,3601,False,False,2213,22,21,73,11,1168775,1838907,2015,ERROR,41.713477841,-87.6574904,"(41.713477841, -87.6574904)"
-7059731,HR465421,2009-08-04 10:00:00,050XX W ADDISON ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,3296,False,False,1634,16,38,15,05,1142074,1923423,2009,2009-03-09 12:45:23,41.945935898,-87.753190197,"(41.945935898, -87.753190197)"
-4369628,HL653099,2005-10-04 14:15:00,0000X E 68TH ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,4938,False,False,322,3,20,69,05,1178251,1859995,2005,ERROR,41.7711365,-87.622147753,"(41.7711365, -87.622147753)"
-2085336,HH300310,2002-04-11 14:22:32,052XX S ASHLAND AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,2024,True,False,932,NA,NA,NA,18,1166514,1870200,2002,ERROR,41.79939856,-87.664880524,"(41.79939856, -87.664880524)"
-2837739,HJ500794,2003-07-17 12:00:00,064XX S ASHLAND AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),327,False,False,725,7,15,67,06,1166729,1862274,2003,2014-04-12 12:43:35,41.777644057,-87.664318242,"(41.777644057, -87.664318242)"
-9359551,HW502824,2013-10-16 13:00:00,087XX S CONSTANCE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,SIDEWALK,4794,False,False,412,4,8,48,14,1189994,1847553,2013,ERROR,41.736720026,-87.579502355,"(41.736720026, -87.579502355)"
-4269836,HL586435,2005-09-01 00:00:00,099XX S BEVERLY AVE,0890,THEFT,FROM BUILDING,RESIDENCE,2317,False,True,2213,22,21,72,06,1168186,1838983,2005,ERROR,41.713699063,-87.659645355,"(41.713699063, -87.659645355)"
-3579880,HK667872,2004-10-05 09:30:00,053XX S COTTAGE GROVE AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,3907,False,False,2131,2,5,41,05,1182540,1869405,2004,ERROR,41.796859993,-87.606134249,"(41.796859993, -87.606134249)"
-1396555,G081867,2001-02-09 10:00:00,070XX S SANGAMON ST,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,STREET,2543,True,False,733,NA,NA,NA,18,1171146,1858301,2001,ERROR,41.766646226,-87.648241507,"(41.766646226, -87.648241507)"
-6210516,HP298503,2008-04-23 19:00:00,088XX S COLFAX AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,3830,False,False,423,4,7,46,05,1195065,1846888,2008,2008-08-05 07:57:25,41.734771822,-87.560946053,"(41.734771822, -87.560946053)"
-8335396,HT565225,2011-10-29 12:22:19,069XX S LAFAYETTE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESTAURANT,4794,False,True,731,7,6,69,08B,1176984,1859255,2011,2011-07-11 12:45:02,41.769134515,-87.626814372,"(41.769134515, -87.626814372)"
-4575638,HL791694,2005-12-15 20:55:00,006XX N TRUMBULL AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,1930,True,False,1121,11,27,23,18,1153311,1903852,2005,ERROR,41.892015265,-87.712407374,"(41.892015265, -87.712407374)"
-9527333,HX181612,2014-03-12 10:50:00,081XX S VINCENNES AVE,0545,ASSAULT,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",1878,True,False,622,6,21,44,08A,1174722,1850745,2014,ERROR,41.745832739,-87.635358835,"(41.745832739, -87.635358835)"
-2802818,HJ444885,2003-06-21 19:30:00,029XX S STATE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA PARKING LOT/GROUNDS,4371,False,True,2113,1,3,35,08B,1176715,1885559,2003,ERROR,41.841321197,-87.627008027,"(41.841321197, -87.627008027)"
-8240826,HT474337,2011-08-31 10:04:00,070XX S CLYDE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,ALLEY,3112,True,False,331,3,5,43,08B,1191389,1858691,2011,2011-01-09 06:32:18,41.767250011,-87.574031486,"(41.767250011, -87.574031486)"
-8766702,HV441335,2012-08-21 22:30:00,023XX N MILWAUKEE AVE,0620,BURGLARY,UNLAWFUL ENTRY,OTHER,4672,False,False,1414,14,35,22,05,1157125,1915354,2012,2012-03-09 18:32:27,41.923501123,-87.698087331,"(41.923501123, -87.698087331)"
-9497756,HX152161,2014-02-15 11:52:00,071XX S CONSTANCE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,4239,False,True,324,3,5,43,08B,1189655,1857726,2014,ERROR,41.764643801,-87.580418177,"(41.764643801, -87.580418177)"
-7234599,HR643869,2009-11-15 00:30:00,002XX W NORTH AVE,0890,THEFT,FROM BUILDING,OTHER,3514,False,False,1814,18,43,7,06,1174055,1911024,2009,2010-05-01 11:53:51,41.911258405,-87.636010015,"(41.911258405, -87.636010015)"
-3179722,HK182024,2004-02-13 23:00:00,013XX W IRVING PARK RD,0810,THEFT,OVER $500,STREET,4624,False,False,1923,19,47,6,06,1166363,1926630,2004,2014-04-12 12:43:35,41.954250222,-87.663819997,"(41.954250222, -87.663819997)"
-4222202,HL539526,2005-08-10 01:50:00,043XX S LAMON AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,CHA PARKING LOT/GROUNDS,4720,False,False,814,8,23,56,14,1144637,1875298,2005,ERROR,41.813827102,-87.744982032,"(41.813827102, -87.744982032)"
-7219347,HR634999,2009-11-09 19:00:00,052XX S RACINE AVE,0560,ASSAULT,SIMPLE,APARTMENT,2784,False,True,934,9,16,61,08A,1169250,1870146,2009,ERROR,41.799191555,-87.654848486,"(41.799191555, -87.654848486)"
-7793169,HS594082,2010-11-01 18:00:00,019XX W 95TH ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,LIBRARY,553,False,False,2221,22,19,72,26,1164769,1841686,2010,2010-07-11 08:22:48,41.721189215,-87.672083692,"(41.721189215, -87.672083692)"
-2103641,HH335780,2002-04-28 01:50:00,014XX W MARQUETTE RD,0560,ASSAULT,SIMPLE,STREET,4679,False,False,725,NA,17,67,08A,1167596,1860349,2002,ERROR,41.772343059,-87.661194963,"(41.772343059, -87.661194963)"
-8223696,HT457644,2011-08-20 17:25:00,098XX S HALSTED ST,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,1911,True,False,2223,22,21,73,03,1172676,1839503,2011,ERROR,41.715028437,-87.643186054,"(41.715028437, -87.643186054)"
-7585036,HS388608,2010-07-01 06:25:00,003XX N OAKLEY BLVD,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,2283,False,False,1332,12,27,28,07,1160973,1902387,2010,2010-02-07 11:19:23,41.887839582,-87.684308839,"(41.887839582, -87.684308839)"
-5931509,HN704677,2007-11-12 23:30:00,079XX S STATE ST,0320,ROBBERY,STRONGARM - NO WEAPON,CTA PLATFORM,3105,False,False,623,6,6,44,03,1177618,1852608,2007,2007-12-12 01:05:15,41.750880081,-87.624691153,"(41.750880081, -87.624691153)"
-5710926,HN519237,2007-08-09 16:00:00,012XX N ROCKWELL ST,0890,THEFT,FROM BUILDING,RESIDENCE,3673,False,False,1423,14,26,24,06,1158874,1908481,2007,ERROR,41.904605346,-87.691849732,"(41.904605346, -87.691849732)"
-3611691,HK705389,2004-10-23 13:30:00,010XX S WELLS ST,0810,THEFT,OVER $500,STREET,2968,False,False,131,1,2,32,06,1174825,1895931,2004,2014-04-12 12:43:35,41.86982516,-87.633633456,"(41.86982516, -87.633633456)"
-4713760,HM209421,2006-03-02 13:48:00,072XX S PAULINA ST,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,STREET,2117,True,False,735,7,17,67,18,1166303,1856763,2006,ERROR,41.762530229,-87.666036716,"(41.762530229, -87.666036716)"
-2607516,HJ206193,2003-02-26 14:20:00,034XX S DR MARTIN LUTHER KING JR DR,1330,CRIMINAL TRESPASS,TO LAND,DRUG STORE,3921,False,False,2122,NA,4,35,26,1179511,1882404,2003,ERROR,41.832600096,-87.616844359,"(41.832600096, -87.616844359)"
-6611578,HP684171,2008-11-15 03:25:00,009XX W BELMONT AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESTAURANT,1170,True,False,1924,19,44,6,14,1169428,1921475,2008,ERROR,41.940038439,-87.652703194,"(41.940038439, -87.652703194)"
-8020502,HT251222,2011-04-14 22:00:00,066XX S TALMAN AVE,0820,THEFT,$500 AND UNDER,STREET,490,False,False,831,8,15,66,06,1159896,1860350,2011,ERROR,41.772507471,-87.689421041,"(41.772507471, -87.689421041)"
-3795590,HL160354,2005-01-28 23:00:00,036XX N MARSHFIELD AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,2815,False,False,1923,19,47,6,14,1164641,1923969,2005,ERROR,41.946985052,-87.670225966,"(41.946985052, -87.670225966)"
-4925940,HM538019,2006-08-13 16:09:24,060XX S VERNON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,OTHER,3128,True,True,313,3,20,42,08B,1180344,1865263,2006,ERROR,41.785544659,-87.61431417,"(41.785544659, -87.61431417)"
-8500347,HV177396,2012-02-29 11:20:00,001XX W LAKE ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA GARAGE / OTHER PROPERTY,4130,True,False,113,1,42,32,11,1175354,1901695,2012,2012-01-03 08:18:16,41.885630077,-87.631518326,"(41.885630077, -87.631518326)"
-2617371,HJ216694,2003-03-03 18:00:00,073XX S SOUTH SHORE DR,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,1366,False,False,334,NA,7,43,26,1195387,1857193,2003,ERROR,41.76304161,-87.559426843,"(41.76304161, -87.559426843)"
-7970463,HT202960,2011-02-15 17:00:00,077XX S NORMAL AVE,1110,DECEPTIVE PRACTICE,BOGUS CHECK,APARTMENT,4925,False,False,621,6,17,69,11,1174337,1853795,2011,ERROR,41.754210881,-87.636679059,"(41.754210881, -87.636679059)"
-6529891,HP602686,2008-10-01 02:46:00,002XX E 35TH ST,0820,THEFT,$500 AND UNDER,SIDEWALK,496,False,False,2112,2,2,35,06,1178613,1881880,2008,ERROR,41.831182706,-87.620155187,"(41.831182706, -87.620155187)"
-7957427,HT189220,2011-03-05 02:00:00,044XX N MILWAUKEE AVE,0460,BATTERY,SIMPLE,SIDEWALK,4218,False,False,1623,16,45,15,08B,1141633,1928786,2011,2011-08-03 11:36:40,41.960660635,-87.754678083,"(41.960660635, -87.754678083)"
-8157874,HT373208,2011-06-30 09:40:00,038XX W MADISON ST,2027,NARCOTICS,POSS: CRACK,GROCERY FOOD STORE,4416,True,False,1122,11,28,26,18,1150456,1899690,2011,ERROR,41.880650508,-87.723001331,"(41.880650508, -87.723001331)"
-3216861,HK232167,2004-03-10 18:30:00,0000X W 111TH ST,0890,THEFT,FROM BUILDING,HOSPITAL BUILDING/GROUNDS,2419,False,False,522,5,34,49,06,1177728,1831317,2004,ERROR,41.692452292,-87.624930074,"(41.692452292, -87.624930074)"
-2177171,HH431013,2002-06-10 05:51:58,046XX W 82ND ST,0460,BATTERY,SIMPLE,APARTMENT,4946,False,False,834,NA,13,70,08B,1146910,1849636,2002,ERROR,41.743363318,-87.737296807,"(41.743363318, -87.737296807)"
-8393994,HT626631,2011-12-10 20:40:00,076XX S NORMAL AVE,0460,BATTERY,SIMPLE,STREET,3091,False,False,621,6,17,69,08B,1174322,1854368,2011,ERROR,41.755783597,-87.636717026,"(41.755783597, -87.636717026)"
-3445477,HK002362,2004-07-23 21:30:00,021XX W SUPERIOR ST,0820,THEFT,$500 AND UNDER,RESIDENCE,26,False,False,1313,12,1,24,06,1162023,1904942,2004,2014-04-12 12:43:35,41.894828854,-87.680381499,"(41.894828854, -87.680381499)"
-1393385,G083313,2001-02-09 22:06:39,063XX S ALBANY AV,2027,NARCOTICS,POSS: CRACK,STREET,1258,True,False,823,NA,NA,NA,18,1156765,1862558,2001,ERROR,41.778630305,-87.700838972,"(41.778630305, -87.700838972)"
-8144026,HT378531,2011-07-02 23:00:00,033XX W 63RD ST,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,2973,False,False,823,8,15,66,14,1155411,1862617,2011,2011-05-07 14:12:04,41.778819432,-87.705801296,"(41.778819432, -87.705801296)"
-1971985,HH155823,2002-01-29 15:25:27,033XX W FLOURNOY ST,0460,BATTERY,SIMPLE,STREET,4324,False,False,1134,NA,NA,NA,08B,1154134,1896793,2002,ERROR,41.872628267,-87.709573266,"(41.872628267, -87.709573266)"
-8586946,HV261160,2012-04-25 19:30:00,103XX S TORRENCE AVE,0810,THEFT,OVER $500,RESIDENCE,3718,False,False,434,4,10,51,06,1195470,1836864,2012,2012-10-05 12:01:25,41.707255067,-87.559792308,"(41.707255067, -87.559792308)"
-4342220,HL629648,2005-09-22 17:00:00,021XX N LAWLER AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,3886,True,False,2522,25,31,19,08B,1142418,1913633,2005,ERROR,41.919064771,-87.752169679,"(41.919064771, -87.752169679)"
-9280006,HW424844,2013-08-25 21:30:00,001XX W 95TH ST,0890,THEFT,FROM BUILDING,RESTAURANT,4034,False,False,634,6,21,49,06,1176654,1841992,2013,ERROR,41.721770166,-87.628542273,"(41.721770166, -87.628542273)"
-2813480,HJ464372,2003-06-30 18:40:01,005XX S COLUMBUS DR,0820,THEFT,$500 AND UNDER,PARK PROPERTY,143,True,False,124,1,2,32,06,1178319,1898125,2003,2014-04-12 12:43:35,41.875766752,-87.620739245,"(41.875766752, -87.620739245)"
-3561152,HK648531,2004-09-26 04:30:45,110XX S HALSTED ST,0560,ASSAULT,SIMPLE,OTHER,4272,True,False,2233,22,34,75,08A,1172920,1831338,2004,ERROR,41.692617079,-87.642532293,"(41.692617079, -87.642532293)"
-10050076,HY238761,2015-04-28 07:55:00,082XX S MARYLAND AVE,031A,ROBBERY,ARMED: HANDGUN,ALLEY,3860,False,False,631,6,8,44,03,1183383,1850376,2015,2015-05-05 12:47:16,41.744622997,-87.603634954,"(41.744622997, -87.603634954)"
-8001148,HT233085,2011-04-03 23:15:00,107XX S BENSLEY AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,638,False,True,434,4,10,51,26,1194521,1834420,2011,2011-06-04 13:32:13,41.700571822,-87.563347504,"(41.700571822, -87.563347504)"
-6228780,HP311138,2008-05-01 15:18:59,0000X E GARFIELD BLVD,0560,ASSAULT,SIMPLE,RESTAURANT,1289,False,False,233,2,20,40,08A,1177260,1868390,2008,ERROR,41.794195672,-87.625527066,"(41.794195672, -87.625527066)"
-6779900,HR184949,2009-02-23 16:00:00,010XX W GARFIELD BLVD,0820,THEFT,$500 AND UNDER,RESIDENCE,477,False,False,712,7,16,68,06,1170399,1868173,2009,2009-06-04 15:49:19,41.79375246,-87.65069229,"(41.79375246, -87.65069229)"
-7426826,HS229492,2010-03-28 01:59:00,066XX N OLMSTED AVE,0460,BATTERY,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),643,True,False,1612,16,41,9,08B,1124889,1943652,2010,ERROR,42.001748276,-87.815908566,"(42.001748276, -87.815908566)"
-2466429,HH796219,2002-11-22 20:00:00,074XX S INDIANA AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,4674,False,True,323,NA,6,69,26,1178843,1855836,2002,ERROR,41.759710302,-87.620104123,"(41.759710302, -87.620104123)"
-4607990,HM202545,2006-02-26 12:00:00,069XX S CORNELL AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,1838,False,False,332,3,5,43,05,1188596,1859060,2006,2006-05-03 04:52:17,41.768329782,-87.584257056,"(41.768329782, -87.584257056)"
-4704001,HM309457,2006-04-22 00:00:00,043XX W 59TH ST,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENTIAL YARD (FRONT/BACK),3755,False,False,813,8,13,62,14,1148566,1865172,2006,ERROR,41.785965095,-87.730830299,"(41.785965095, -87.730830299)"
-4742590,HM348904,2006-05-13 16:30:00,029XX E 80TH ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,693,True,False,422,4,7,46,15,1196921,1852523,2006,ERROR,41.75018879,-87.553959645,"(41.75018879, -87.553959645)"
-6917090,HR321572,2009-05-14 22:30:00,022XX N NAGLE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,3547,True,True,2512,25,36,19,08B,1133001,1914445,2009,2009-03-06 14:53:51,41.921462904,-87.786750394,"(41.921462904, -87.786750394)"
-6072784,HP171029,2008-02-11 20:52:00,013XX S CANAL ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,GROCERY FOOD STORE,3576,False,False,131,1,2,28,11,1173296,1893975,2008,ERROR,41.864491821,-87.639304867,"(41.864491821, -87.639304867)"
-8412665,HT645859,2011-12-24 07:30:00,021XX N CALIFORNIA AVE,0340,ROBBERY,ATTEMPT: STRONGARM-NO WEAPON,SIDEWALK,1132,False,False,1431,14,1,22,03,1157384,1914494,2011,2012-12-01 19:07:21,41.921135948,-87.69715911,"(41.921135948, -87.69715911)"
-3341445,HK378381,2004-05-20 22:30:00,002XX E OHIO ST,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),3433,False,False,1834,18,42,8,06,1178331,1904297,2004,2014-04-12 12:43:35,41.892702762,-87.620506953,"(41.892702762, -87.620506953)"
-6468782,HP546433,2008-09-01 02:01:30,037XX N HALSTED ST,0460,BATTERY,SIMPLE,SIDEWALK,4456,False,False,2324,19,46,6,08B,1170291,1925093,2008,2008-09-10 20:44:50,41.949947525,-87.649425319,"(41.949947525, -87.649425319)"
-8339679,HT573293,2011-11-03 13:20:00,007XX E 60TH ST,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,4190,False,False,313,3,20,42,26,1182232,1865350,2011,2011-04-11 10:08:19,41.785739862,-87.607389268,"(41.785739862, -87.607389268)"
-1950234,HH139194,2002-01-18 17:00:00,009XX W HURON ST,0810,THEFT,OVER $500,OTHER,4875,False,False,1323,NA,NA,NA,06,1169707,1905109,2002,2014-04-12 12:43:35,41.895123178,-87.652155546,"(41.895123178, -87.652155546)"
-7041908,HR449221,2009-07-26 01:00:00,015XX N OAKLEY BLVD,0810,THEFT,OVER $500,STREET,2985,False,False,1424,14,1,24,06,1160755,1910100,2009,ERROR,41.9090092,-87.684895322,"(41.9090092, -87.684895322)"
-6768049,HR182077,2009-02-21 17:10:00,119XX S MICHIGAN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,1367,False,False,532,5,9,53,14,1178941,1825981,2009,ERROR,41.677782065,-87.620650719,"(41.677782065, -87.620650719)"
-3758036,HL123864,2005-01-14 07:45:00,068XX S CARPENTER ST,031A,ROBBERY,ARMED: HANDGUN,STREET,3785,False,False,724,7,17,68,03,1170445,1859718,2005,ERROR,41.770549943,-87.65076972,"(41.770549943, -87.65076972)"
-9421792,HW565556,2013-12-10 12:10:00,065XX S SANGAMON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,3539,False,True,723,7,17,68,08B,1171144,1861336,2013,ERROR,41.774974679,-87.648160214,"(41.774974679, -87.648160214)"
-7746209,HS553647,2010-10-08 08:30:00,075XX S KINGSTON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,1033,False,True,421,4,7,43,14,1194540,1855586,2010,ERROR,41.758652752,-87.562583957,"(41.758652752, -87.562583957)"
-3024764,HJ733776,2003-11-01 00:00:00,086XX S HERMITAGE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,4585,False,False,614,6,18,71,05,1166139,1847570,2003,ERROR,41.73730682,-87.666898779,"(41.73730682, -87.666898779)"
-6155450,HP244652,2008-03-26 09:00:00,047XX S KEDVALE AVE,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,11,False,False,815,8,23,57,06,1149453,1872819,2008,ERROR,41.806932525,-87.727380443,"(41.806932525, -87.727380443)"
-2806099,HJ459080,2003-06-28 11:01:33,052XX S MARSHFIELD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,3964,False,False,932,9,16,61,08B,1166277,1869726,2003,ERROR,41.798102902,-87.665763163,"(41.798102902, -87.665763163)"
-1454319,G168841,2001-03-24 21:20:00,014XX S LOOMIS ST,2024,NARCOTICS,POSS: HEROIN(WHITE),CHA PARKING LOT/GROUNDS,4558,True,False,1231,NA,NA,NA,18,1167225,1893379,2001,ERROR,41.862988863,-87.661608429,"(41.862988863, -87.661608429)"
-5328640,HN178376,2007-02-15 17:43:21,0000X W 47TH ST,1330,CRIMINAL TRESPASS,TO LAND,PARK PROPERTY,1763,True,False,231,2,3,38,26,1176599,1873813,2007,ERROR,41.809091808,-87.627787677,"(41.809091808, -87.627787677)"
-5030418,HM631435,2006-09-30 04:01:00,012XX S SAWYER AVE,0560,ASSAULT,SIMPLE,RESIDENCE,1035,True,False,1022,10,24,29,08A,1154859,1893942,2006,2006-06-10 04:52:45,41.86479033,-87.706987824,"(41.86479033, -87.706987824)"
-9694653,HX345023,2014-07-14 16:10:00,038XX W CHICAGO AVE,2028,NARCOTICS,POSS: SYNTHETIC DRUGS,OTHER,1780,True,False,1112,11,27,23,18,1150812,1905031,2014,ERROR,41.89529982,-87.7215543,"(41.89529982, -87.7215543)"
-2506466,HH847658,2002-12-19 09:26:25,010XX S LOOMIS ST,0460,BATTERY,SIMPLE,SIDEWALK,2190,False,False,1231,NA,2,28,08B,1167166,1895406,2002,ERROR,41.868552388,-87.661766802,"(41.868552388, -87.661766802)"
-3995692,HL357532,2005-05-14 23:00:00,106XX S MACKINAW AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,4448,False,False,432,4,10,52,14,1200233,1834857,2005,ERROR,41.70162895,-87.542418081,"(41.70162895, -87.542418081)"
-1406698,G119938,2001-03-01 16:31:26,023XX W MADISON ST,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,1768,False,False,1332,NA,NA,NA,03,1161051,1900007,2001,ERROR,41.881307035,-87.684088522,"(41.881307035, -87.684088522)"
-2063190,HH283739,2002-04-03 19:45:00,040XX N LINCOLN AV,1330,CRIMINAL TRESPASS,TO LAND,DRUG STORE,622,True,False,1912,NA,NA,NA,26,1162045,1927127,2002,ERROR,41.955705485,-87.679679583,"(41.955705485, -87.679679583)"
-2187834,HH444731,2002-06-15 21:22:16,004XX E 75TH ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,1461,False,False,323,NA,6,69,07,1180456,1855421,2002,ERROR,41.758534647,-87.61420526,"(41.758534647, -87.61420526)"
-4856312,HM458067,2006-07-06 11:34:00,037XX S WESTERN AVE,0460,BATTERY,SIMPLE,CTA BUS,2800,False,False,913,9,12,59,08B,1161018,1879907,2006,ERROR,41.826151267,-87.684767164,"(41.826151267, -87.684767164)"
-4948851,HM562638,2006-08-25 20:00:00,069XX S LOOMIS BLVD,0890,THEFT,FROM BUILDING,RESIDENCE,3312,False,False,734,7,17,67,06,1168156,1858673,2006,ERROR,41.767731872,-87.65919032,"(41.767731872, -87.65919032)"
-6687849,HR102460,2009-01-02 15:29:00,105XX S EGGLESTON AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,1562,False,False,2233,22,34,49,18,1175109,1835065,2009,2009-02-01 16:25:12,41.702796067,-87.634407275,"(41.702796067, -87.634407275)"
-7550137,HS346624,2010-06-07 05:46:00,007XX W GARFIELD BLVD,0610,BURGLARY,FORCIBLE ENTRY,SMALL RETAIL STORE,4436,False,False,934,9,3,61,05,1172153,1868433,2010,ERROR,41.794427528,-87.644252869,"(41.794427528, -87.644252869)"
-8453368,HV131074,2012-01-23 22:00:00,068XX N SHERIDAN RD,0820,THEFT,$500 AND UNDER,STREET,44,False,False,2431,24,49,1,06,1167006,1945516,2012,ERROR,42.006060138,-87.660911233,"(42.006060138, -87.660911233)"
-1754134,G565506,2001-09-20 22:05:00,007XX E 111 ST,0460,BATTERY,SIMPLE,POLICE FACILITY/VEH PARKING LOT,715,False,False,531,NA,NA,NA,08B,1183305,1831462,2001,ERROR,41.692722515,-87.604507439,"(41.692722515, -87.604507439)"
-2522777,HJ100697,2002-12-31 23:30:00,116XX S VINCENNES AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,2478,False,False,2234,NA,34,75,14,1165846,1827777,2002,ERROR,41.682997842,-87.668532113,"(41.682997842, -87.668532113)"
-1864439,G708189,2001-11-25 13:30:00,007XX N LARAMIE AV,0325,ROBBERY,VEHICULAR HIJACKING,ALLEY,1350,False,False,1524,NA,NA,NA,03,1141513,1904551,2001,ERROR,41.894159515,-87.755719492,"(41.894159515, -87.755719492)"
-6934825,HR340739,2009-05-25 21:45:00,061XX S RACINE AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,1582,True,False,713,7,16,67,18,1169326,1864268,2009,ERROR,41.783059997,-87.654739922,"(41.783059997, -87.654739922)"
-7981176,HT213299,2011-03-21 13:59:00,001XX W 35TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,CTA TRAIN,4539,True,False,924,9,11,34,18,1175731,1881812,2011,ERROR,41.831061278,-87.630731407,"(41.831061278, -87.630731407)"
-4065862,HL408235,2005-06-08 22:48:24,002XX W 87TH ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,WAREHOUSE,3963,False,False,622,6,21,44,06,1176403,1847255,2005,ERROR,41.736218161,-87.629303966,"(41.736218161, -87.629303966)"
-2034759,HH247819,2002-03-16 10:00:00,018XX N DRAKE AV,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,776,False,False,1422,NA,NA,NA,07,1152570,1911949,2002,ERROR,41.914248882,-87.714914388,"(41.914248882, -87.714914388)"
-5544920,HN356358,2007-05-21 17:00:00,090XX S LAFLIN ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,3133,False,False,2222,22,21,73,26,1167966,1844587,2007,ERROR,41.729082013,-87.660290623,"(41.729082013, -87.660290623)"
-4959301,HM571754,2006-08-29 22:30:00,015XX E 87TH ST,0610,BURGLARY,FORCIBLE ENTRY,SMALL RETAIL STORE,1634,True,False,412,4,8,48,05,1187761,1847553,2006,2008-02-05 01:04:48,41.736773407,-87.587683232,"(41.736773407, -87.587683232)"
-7521660,HS325090,2010-05-25 00:00:00,030XX N PARKSIDE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,3579,False,False,2514,25,31,19,14,1138129,1919810,2010,ERROR,41.93609381,-87.767778496,"(41.93609381, -87.767778496)"
-4982342,HM596076,2006-09-12 00:00:00,014XX N DAMEN AVE,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,35,False,False,1424,14,1,24,06,1162762,1909616,2006,2014-04-12 12:43:35,41.907639198,-87.677536132,"(41.907639198, -87.677536132)"
-7403488,HS205438,2010-03-13 04:25:00,080XX S MAY ST,0810,THEFT,OVER $500,STREET,2170,False,False,612,6,21,71,06,1170015,1851280,2010,ERROR,41.74740433,-87.652590689,"(41.74740433, -87.652590689)"
-3140084,HK132250,2004-01-18 11:06:46,024XX N RACINE AVE,0460,BATTERY,SIMPLE,STREET,2504,False,False,1933,19,32,7,08B,1167856,1916386,2004,ERROR,41.926108075,-87.658627954,"(41.926108075, -87.658627954)"
-5902358,HN700127,2007-11-10 11:00:00,003XX S WACKER DR,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),749,False,False,112,1,2,32,06,1174015,1898799,2007,2014-04-12 12:43:35,41.877713219,-87.636521697,"(41.877713219, -87.636521697)"
-4572931,HM163096,2006-02-04 14:00:00,045XX S WESTERN AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),42,False,False,914,9,12,61,06,1161174,1874190,2006,2014-04-12 12:43:35,41.810459905,-87.684353246,"(41.810459905, -87.684353246)"
-10012442,HY202116,2015-03-28 21:12:00,050XX N FRANCISCO AVE,2093,NARCOTICS,FOUND SUSPECT NARCOTICS,APARTMENT,1022,True,False,2031,20,40,4,26,1156167,1933292,2015,2015-04-04 12:43:24,41.972743598,-87.701121216,"(41.972743598, -87.701121216)"
-6554328,HN700877,2007-11-11 04:00:00,076XX S RHODES AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,4899,False,False,624,6,6,69,04B,1181189,1854379,2007,ERROR,41.755658442,-87.611550936,"(41.755658442, -87.611550936)"
-4398984,HL638310,2005-10-05 11:25:00,011XX N MONTICELLO AVE,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,STREET,3766,True,False,1112,11,27,23,18,1151799,1907151,2005,ERROR,41.901097939,-87.717873412,"(41.901097939, -87.717873412)"
-2797787,HJ448611,2003-06-23 07:00:00,057XX S ABERDEEN ST,0890,THEFT,FROM BUILDING,STREET,1540,False,False,712,7,16,68,06,1169915,1867003,2003,ERROR,41.79055238,-87.652501059,"(41.79055238, -87.652501059)"
-4471342,HL761655,2005-11-29 09:57:17,071XX S LAFAYETTE AVE,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,RESIDENCE,3232,True,False,731,7,6,69,20,1177037,1857432,2005,ERROR,41.764130805,-87.626675,"(41.764130805, -87.626675)"
-2751617,HJ389272,2003-05-27 08:16:40,028XX W 24TH BLVD,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",2758,False,False,1033,NA,12,30,08B,1157546,1887831,2003,ERROR,41.847966869,-87.697289982,"(41.847966869, -87.697289982)"
-8965579,HW113481,2013-01-11 03:00:00,023XX N LOWELL AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,3640,False,False,2522,25,31,20,26,1146957,1915184,2013,2013-05-02 14:37:42,41.923235246,-87.735452966,"(41.923235246, -87.735452966)"
-5171598,HM762872,2006-12-08 13:45:00,044XX W GLADYS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,1177,False,True,1131,11,24,26,08B,1146659,1898021,2006,ERROR,41.876143827,-87.736986299,"(41.876143827, -87.736986299)"
-5510052,HN329067,2007-05-08 00:30:00,049XX W BELDEN AVE,0890,THEFT,FROM BUILDING,APARTMENT,2656,False,True,2522,25,31,19,06,1142729,1914878,2007,ERROR,41.922475392,-87.750995953,"(41.922475392, -87.750995953)"
-6808651,HR218704,2009-03-16 14:17:54,004XX N CENTRAL AVE,0460,BATTERY,SIMPLE,SIDEWALK,2744,False,False,1512,15,29,25,08B,1138919,1902473,2009,ERROR,41.888504748,-87.765297105,"(41.888504748, -87.765297105)"
-4991519,HM449098,2006-07-01 22:02:41,071XX S MERRILL AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,4231,True,False,333,3,5,43,18,1191746,1858246,2006,ERROR,41.766020241,-87.572737379,"(41.766020241, -87.572737379)"
-4052767,HL403567,2005-06-06 19:30:00,025XX S WASHTENAW AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,3538,False,False,1034,10,12,30,04B,1158738,1886739,2005,ERROR,41.844945991,-87.692945166,"(41.844945991, -87.692945166)"
-8032674,HT264580,2011-04-21 13:30:00,098XX S ELLIS AVE,0890,THEFT,FROM BUILDING,RESIDENCE,4298,False,False,511,5,8,50,06,1184583,1839875,2011,ERROR,41.715779068,-87.599566057,"(41.715779068, -87.599566057)"
-6241707,HP331800,2008-05-12 08:00:00,047XX W HARRISON ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,3322,False,False,1131,11,24,25,26,1144898,1896892,2008,ERROR,41.873079106,-87.743480642,"(41.873079106, -87.743480642)"
-3134450,HK125197,2004-01-14 12:00:00,064XX S WINCHESTER AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,3991,True,False,726,7,15,67,07,1164415,1862214,2004,ERROR,41.777528496,-87.672803106,"(41.777528496, -87.672803106)"
-1329888,G024689,2001-01-12 11:00:00,109XX S EDBROOKE AV,4650,OTHER OFFENSE,SEX OFFENDER: FAIL TO REGISTER,RESIDENCE,3264,True,False,513,NA,NA,NA,26,1179220,1832688,2001,ERROR,41.696180708,-87.619426047,"(41.696180708, -87.619426047)"
-5756722,HN564951,2007-09-02 06:30:00,014XX W 77TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,2012,False,False,612,6,17,71,14,1167956,1853723,2007,2007-06-09 01:56:11,41.754152713,-87.660065416,"(41.754152713, -87.660065416)"
-8280908,HT515268,2011-09-26 15:06:00,057XX S SACRAMENTO AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,4992,False,False,824,8,14,63,05,1157396,1866517,2011,2011-01-10 09:51:22,41.789481609,-87.69841849,"(41.789481609, -87.69841849)"
-7835980,HS637015,2010-11-28 22:35:00,103XX S ALBANY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,4609,False,True,2211,22,19,74,08B,1157431,1835903,2010,2010-08-12 16:32:38,41.705471163,-87.699117536,"(41.705471163, -87.699117536)"
-3607193,HK688263,2004-10-15 11:56:00,081XX S WOODLAWN AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,756,False,False,411,4,8,45,07,1185584,1851347,2004,ERROR,41.74723602,-87.595539795,"(41.74723602, -87.595539795)"
-9468000,HX121120,2014-01-18 23:00:00,015XX S KEDZIE AVE,0820,THEFT,$500 AND UNDER,RESIDENCE,155,False,False,1022,10,24,29,06,1155311,1892547,2014,ERROR,41.860953237,-87.705365981,"(41.860953237, -87.705365981)"
-6576153,HP648649,2008-10-25 10:30:00,041XX W POTOMAC AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,4740,False,False,2534,25,37,23,14,1148653,1908301,2008,ERROR,41.904315009,-87.729399290999993,"(41.904315009, -87.729399291)"
-3864612,HL239886,2005-01-21 00:01:00,100XX W OHARE ST,0890,THEFT,FROM BUILDING,AIRPORT/AIRCRAFT,4122,False,False,1651,16,41,76,06,1100635,1934208,2005,ERROR,41.976200173,-87.905312411,"(41.976200173, -87.905312411)"
-6466118,HP543667,2008-08-30 13:24:00,001XX N STATE ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,3056,True,False,122,1,42,32,06,1176390,1900949,2008,2008-02-09 08:07:10,41.883559699,-87.627736496,"(41.883559699, -87.627736496)"
-5519425,HL290196,2005-04-12 11:30:00,010XX N RIDGEWAY AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,ALLEY,2680,True,False,1112,NA,27,23,26,NA,NA,2005,2007-08-08 02:59:32,NA,NA,
-2939383,HJ625230,2003-09-11 10:00:00,021XX N DAMEN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,3838,False,False,1432,14,32,22,26,1162713,1914372,2003,ERROR,41.92069102,-87.677582552,"(41.92069102, -87.677582552)"
-2424495,HH744202,2001-10-19 13:00:00,073XX S HALSTED ST,1120,DECEPTIVE PRACTICE,FORGERY,CURRENCY EXCHANGE,4149,False,False,733,NA,17,68,10,1172207,1855826,2001,ERROR,41.759831266,-87.644425197,"(41.759831266, -87.644425197)"
-9212260,HW358008,2013-07-11 15:00:00,052XX W LE MOYNE ST,0810,THEFT,OVER $500,APARTMENT,4849,False,True,2532,25,37,25,06,1141354,1909518,2013,2013-12-07 09:06:47,41.907792476,-87.756180729,"(41.907792476, -87.756180729)"
-4506402,HL736000,2005-11-15 03:42:50,0000X E BURTON PL,2022,NARCOTICS,POSS: COCAINE,STREET,2650,True,False,1824,18,43,8,18,1176556,1910448,2005,ERROR,41.909621673,-87.626839707,"(41.909621673, -87.626839707)"
-1344183,G043772,2001-01-21 16:50:00,042XX S WENTWORTH AV,1330,CRIMINAL TRESPASS,TO LAND,GAS STATION,3960,False,False,935,NA,NA,NA,26,1175608,1876581,2001,ERROR,41.816709722,-87.631339516,"(41.816709722, -87.631339516)"
-1563501,G320369,2001-06-03 04:40:00,035XX S FEDERAL ST,0460,BATTERY,SIMPLE,CHA APARTMENT,791,False,True,211,NA,NA,NA,08B,1176255,1881268,2001,ERROR,41.829556721,-87.628825201,"(41.829556721, -87.628825201)"
-3340028,HK377408,2004-05-20 20:54:54,007XX S WESTERN AVE,1330,CRIMINAL TRESPASS,TO LAND,GROCERY FOOD STORE,555,True,False,1224,12,2,28,26,1160541,1896784,2004,ERROR,41.872473399,-87.68605047,"(41.872473399, -87.68605047)"
-9985418,HY175099,2015-03-06 23:40:00,015XX N WELLS ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESTAURANT,3210,False,False,1821,18,43,8,11,1174460,1910637,2015,ERROR,41.910187414,-87.634533777,"(41.910187414, -87.634533777)"
-5212650,HM793429,2006-12-24 11:45:00,041XX W VAN BUREN ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,1567,False,False,1132,11,24,26,14,1148616,1897735,2006,2007-02-01 06:08:05,41.875321473,-87.729808188,"(41.875321473, -87.729808188)"
-10002890,HY192231,2015-03-20 16:27:00,033XX W 60TH ST,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,509,False,False,822,8,16,66,26,1154876,1864671,2015,ERROR,41.784466611,-87.70770788,"(41.784466611, -87.70770788)"
-6237879,HP323989,2008-05-08 10:35:00,053XX S WESTERN BLVD,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),2025,False,False,915,9,16,63,07,1161471,1869210,2008,2008-11-05 13:01:30,41.796788001,-87.683402001,"(41.796788001, -87.683402001)"
-9198183,HW343691,2013-07-01 12:30:00,078XX S LAFLIN ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,1113,False,False,612,6,17,71,14,1167515,1852777,2013,2013-02-07 08:31:12,41.751566217,-87.661708602,"(41.751566217, -87.661708602)"
-9914993,HY104705,2015-01-05 05:00:00,045XX S DREXEL BLVD,0820,THEFT,$500 AND UNDER,APARTMENT,127,False,False,221,2,4,39,06,1182916,1875077,2015,2015-12-01 12:48:04,41.812415676,-87.604579073,"(41.812415676, -87.604579073)"
-5812687,HN606869,2007-06-14 15:00:00,056XX S DORCHESTER AVE,0890,THEFT,FROM BUILDING,GAS STATION,2970,False,False,2133,2,5,41,06,1186553,1867773,2007,2007-10-10 01:49:09,41.792287561,-87.591469992,"(41.792287561, -87.591469992)"
-1758189,G568662,2001-09-22 11:17:43,031XX W 26 ST,5001,OTHER OFFENSE,OTHER CRIME INVOLVING PROPERTY,PARKING LOT/GARAGE(NON.RESID.),992,True,False,1033,NA,NA,NA,26,1156100,1886534,2001,ERROR,41.844437017,-87.70263184,"(41.844437017, -87.70263184)"
-6402809,HP474280,2008-07-23 16:00:00,089XX S YATES BLVD,0842,THEFT,AGG: FINANCIAL ID THEFT,RESIDENCE,1990,False,False,413,4,7,48,06,1193691,1845865,2008,2008-08-08 18:07:56,41.731998345,-87.566013112,"(41.731998345, -87.566013112)"
-5475427,HN298043,2007-04-22 08:35:00,053XX S KEDZIE AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,1380,True,False,911,9,14,63,16,1155993,1869052,2007,ERROR,41.796466324,-87.703494798,"(41.796466324, -87.703494798)"
-1523992,G255343,2001-05-04 09:55:00,056XX S ROCKWELL ST,141C,WEAPONS VIOLATION,UNLAWFUL USE OTHER DANG WEAPON,"SCHOOL, PUBLIC, BUILDING",3922,True,False,824,NA,NA,NA,15,1159962,1867094,2001,ERROR,41.791012589,-87.688993852,"(41.791012589, -87.688993852)"
-2921705,HJ600586,2003-09-01 01:00:00,023XX N KARLOV AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,3929,False,True,2525,25,31,20,08B,1148703,1915194,2003,ERROR,41.923229102,-87.729037228,"(41.923229102, -87.729037228)"
-1854395,G695770,2001-11-19 10:10:00,066XX S PULASKI RD,0810,THEFT,OVER $500,COMMERCIAL / BUSINESS OFFICE,3532,False,False,833,NA,NA,NA,06,1150890,1860070,2001,2014-04-12 12:43:35,41.771919396,-87.722442165,"(41.771919396, -87.722442165)"
-1908796,G765379,2001-12-23 04:20:00,055XX W VAN BUREN ST,0460,BATTERY,SIMPLE,RESIDENCE,1688,False,False,1522,NA,NA,NA,08B,1139450,1897421,2001,ERROR,41.874631745,-87.763470245,"(41.874631745, -87.763470245)"
-8164922,HT399379,2011-07-15 23:40:00,076XX S CICERO AVE,0560,ASSAULT,SIMPLE,STREET,1389,False,False,833,8,13,65,08A,1145766,1853738,2011,ERROR,41.75464162,-87.741385158,"(41.75464162, -87.741385158)"
-3254937,HK279665,2004-04-03 10:00:00,065XX N BOSWORTH AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,3491,False,False,2432,24,40,1,05,1164802,1943333,2004,ERROR,42.000117152,-87.669082287,"(42.000117152, -87.669082287)"
-5390304,HN233940,2007-03-18 19:50:00,006XX N ALBANY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,VEHICLE NON-COMMERCIAL,1362,False,True,1313,12,27,23,08B,1155550,1904304,2007,ERROR,41.893210839,-87.704172295,"(41.893210839, -87.704172295)"
-8413638,HT647149,2011-12-25 17:30:00,069XX S CAMPBELL AVE,0330,ROBBERY,AGGRAVATED,SIDEWALK,1377,False,False,832,8,15,66,03,1160874,1858299,2011,ERROR,41.766859084,-87.685892542,"(41.766859084, -87.685892542)"
-9527581,HX181802,2014-03-12 12:15:00,054XX S WINCHESTER AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,1147,False,True,932,9,16,61,08B,1164316,1868765,2014,ERROR,41.795507366,-87.672981591,"(41.795507366, -87.672981591)"
-5133690,HM731231,2006-11-20 14:00:00,073XX N HARLEM AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,2303,False,False,1611,16,41,9,26,1127349,1948389,2006,ERROR,42.014705871,-87.806751157,"(42.014705871, -87.806751157)"
-1995039,HH153002,2002-01-28 07:00:00,074XX N DAMEN AV,0460,BATTERY,SIMPLE,RESIDENCE,794,False,True,2424,NA,NA,NA,08B,1161699,1949553,2002,ERROR,42.01725048,-87.680322966,"(42.01725048, -87.680322966)"
-4296516,HL531043,2005-08-05 23:09:43,006XX N CENTRAL PARK AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,2630,True,False,1122,11,27,23,18,1152231,1903962,2005,ERROR,41.892338494,-87.716370862,"(41.892338494, -87.716370862)"
-5458813,HN287100,2007-04-16 17:45:00,051XX S ASHLAND AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,PARKING LOT/GARAGE(NON.RESID.),4687,True,True,932,9,16,61,08B,1166579,1870797,2007,ERROR,41.801035411,-87.66462512,"(41.801035411, -87.66462512)"
-1556046,G312776,2001-05-30 16:45:00,014XX E 69 ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE PORCH/HALLWAY,1587,False,False,332,NA,NA,NA,14,1187298,1859575,2001,ERROR,41.769773909,-87.588998442,"(41.769773909, -87.588998442)"
-6713807,HR129777,2009-01-19 21:00:00,020XX N MOZART ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,4049,False,False,1414,14,35,22,14,1157024,1913723,2009,ERROR,41.919027585,-87.698502808,"(41.919027585, -87.698502808)"
-1940740,HH119927,2002-01-11 12:00:00,047XX S UNION AV,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",3229,True,False,935,NA,NA,NA,08B,1172475,1873203,2002,ERROR,41.807509816,-87.642931616,"(41.807509816, -87.642931616)"
-3362955,HK409648,2004-06-04 17:00:00,055XX W QUINCY ST,051A,ASSAULT,AGGRAVATED: HANDGUN,STREET,3449,False,False,1522,15,29,25,04A,1139492,1898492,2004,ERROR,41.877569945,-87.763289921,"(41.877569945, -87.763289921)"
-1892993,G744161,2001-12-12 11:40:00,077XX W HOWARD ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,2824,False,False,1611,NA,NA,NA,06,1123943,1949779,2001,ERROR,42.018576967,-87.819253498,"(42.018576967, -87.819253498)"
-4987543,HM599912,2006-09-13 23:41:57,032XX S LITUANICA AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,PARKING LOT/GARAGE(NON.RESID.),2748,True,False,924,9,11,60,14,1170787,1883697,2006,ERROR,41.836343419,-87.648816066,"(41.836343419, -87.648816066)"
-8978243,HW122178,2013-01-04 11:00:00,017XX N LOTUS AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,2463,False,False,2532,25,37,25,26,1139764,1910834,2013,2013-12-02 12:25:23,41.91143294,-87.761989425,"(41.91143294, -87.761989425)"
-9287934,HW432629,2013-09-01 03:30:00,023XX S HOMAN AVE,0460,BATTERY,SIMPLE,SIDEWALK,2341,False,False,1024,10,22,30,08B,1154021,1888439,2013,2013-02-09 12:27:03,41.849706206,-87.710210751,"(41.849706206, -87.710210751)"
-3601383,HK694936,2004-09-18 00:00:00,002XX N KILBOURN AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,3628,False,False,1113,11,28,26,11,1146400,1900624,2004,ERROR,41.883291699,-87.737870956,"(41.883291699, -87.737870956)"
-7719567,HS527233,2010-09-21 20:50:00,051XX W WELLINGTON AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,3751,False,True,2521,25,31,19,26,1141674,1919503,2010,2010-11-10 15:54:50,41.935186459,-87.754757729,"(41.935186459, -87.754757729)"
-6550412,HP624139,2008-10-13 00:00:37,052XX S MARSHFIELD AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,2524,True,False,932,9,16,61,15,1166269,1870015,2008,ERROR,41.798896123,-87.66578427,"(41.798896123, -87.66578427)"
-2902949,HJ579061,2003-07-16 15:58:00,046XX W 55TH ST,1206,DECEPTIVE PRACTICE,"THEFT BY LESSEE,MOTOR VEH",OTHER,4441,True,False,813,8,23,56,11,1146337,1867704,2003,ERROR,41.792955883,-87.738938839,"(41.792955883, -87.738938839)"
-3505136,HK581743,2004-08-25 18:04:00,071XX W 64TH PL,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,4733,False,False,812,8,23,64,26,1129743,1860965,2004,ERROR,41.774761494,-87.799941841,"(41.774761494, -87.799941841)"
-8081212,HT310002,2011-05-23 11:30:00,046XX N SHERIDAN RD,0460,BATTERY,SIMPLE,SIDEWALK,3485,False,False,2312,19,46,3,08B,1168819,1931361,2011,ERROR,41.967179243,-87.654653712,"(41.967179243, -87.654653712)"
-1652291,G430211,2001-07-22 12:15:00,012XX N PARKSIDE AV,2230,LIQUOR LAW VIOLATION,ILLEGAL CONSUMPTION BY MINOR,STREET,1369,True,False,2531,NA,NA,NA,22,1138416,1907506,2001,ERROR,41.902325047,-87.767022333,"(41.902325047, -87.767022333)"
-7784284,HS576050,2010-10-21 19:27:56,008XX E 89TH ST,2027,NARCOTICS,POSS: CRACK,APARTMENT,4100,True,False,632,6,8,44,18,1183562,1846188,2010,2010-10-11 15:04:15,41.733126496,-87.603109289,"(41.733126496, -87.603109289)"
-4392495,HL685535,2005-10-18 07:00:00,008XX N LATROBE AVE,0810,THEFT,OVER $500,STREET,4453,False,False,1524,15,37,25,06,1141162,1905112,2005,2014-04-12 12:43:35,41.895705443,-87.756994782,"(41.895705443, -87.756994782)"
-8322510,HT548001,2011-10-17 17:30:00,067XX S CLYDE AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,4763,False,False,331,3,5,43,26,1191419,1860438,2011,ERROR,41.772043187,-87.573864982,"(41.772043187, -87.573864982)"
-5784220,HN581658,2007-06-08 23:40:00,003XX S WASHTENAW AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,2376,False,True,1125,11,2,27,08B,1158395,1898272,2007,ERROR,41.876600753,-87.693888703,"(41.876600753, -87.693888703)"
-1473650,G192572,2001-04-05 10:10:00,028XX W ARTHINGTON ST,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,1925,True,False,1135,NA,NA,NA,18,1157618,1895874,2001,ERROR,41.870036252,-87.6968069,"(41.870036252, -87.6968069)"
-7157534,HR565848,2009-10-01 15:40:00,079XX S DAMEN AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,531,False,False,611,6,18,71,03,1164362,1852231,2009,2009-10-10 16:21:59,41.750134873,-87.673278237,"(41.750134873, -87.673278237)"
-3712271,HK684159,2004-10-13 13:15:00,072XX S SOUTH CHICAGO AVE,5011,OTHER OFFENSE,LICENSE VIOLATION,RESIDENCE-GARAGE,824,False,False,323,3,5,69,26,1183382,1857501,2004,2007-11-06 15:52:33,41.764174754,-87.603417126,"(41.764174754, -87.603417126)"
-1741181,G550640,2001-09-12 14:00:00,021XX W RANDOLPH ST,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,RESIDENCE,4543,False,True,1332,NA,NA,NA,20,1162226,1901155,2001,ERROR,41.884432772,-87.679741878,"(41.884432772, -87.679741878)"
-6882076,HR288440,2009-04-25 18:00:00,046XX S RICHMOND ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,4485,False,False,912,9,14,58,14,1157528,1873541,2009,ERROR,41.80875374,-87.697744048,"(41.80875374, -87.697744048)"
-8303183,HT520944,2011-09-30 03:15:00,002XX E 121ST PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,2521,False,False,532,5,9,53,08B,1180264,1824414,2011,ERROR,41.673451854,-87.615855891,"(41.673451854, -87.615855891)"
-9010374,HW157593,2013-02-15 12:30:00,051XX S CALIFORNIA AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),94,False,False,923,9,14,63,06,1158620,1870342,2013,ERROR,41.799953035,-87.693826098,"(41.799953035, -87.693826098)"
-7689122,HS495203,2010-08-31 19:00:00,003XX E OHIO ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,4329,False,False,1834,18,42,8,11,1178952,1904236,2010,ERROR,41.892521195,-87.618228164,"(41.892521195, -87.618228164)"
-1438982,G153765,2001-03-17 23:38:07,062XX S ST LAWRENCE AV,1821,NARCOTICS,MANU/DEL:CANNABIS 10GM OR LESS,STREET,4128,True,False,313,NA,NA,NA,18,1181276,1863401,2001,ERROR,41.780413719,-87.610954415,"(41.780413719, -87.610954415)"
-9340354,HW484281,2013-10-07 23:00:00,096XX S WENTWORTH AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,2824,False,False,511,5,21,49,05,1176693,1840729,2013,ERROR,41.718303448,-87.628437301,"(41.718303448, -87.628437301)"
-3044554,HJ746479,2003-11-08 10:25:00,018XX W 51ST ST,2017,NARCOTICS,MANU/DELIVER:CRACK,SIDEWALK,1194,True,False,932,9,16,61,18,1165175,1870833,2003,ERROR,41.801164057,-87.669773064,"(41.801164057, -87.669773064)"
-6080834,HP159980,2008-02-05 10:10:00,010XX N HUDSON AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,964,True,False,1823,18,27,8,18,1172947,1907120,2008,ERROR,41.900570272,-87.640196289,"(41.900570272, -87.640196289)"
-4594393,HM187557,2006-02-18 03:50:00,027XX N MILWAUKEE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,4685,False,False,1412,14,35,22,14,1153678,1918136,2006,2014-04-12 12:43:35,41.931204528,-87.710678708,"(41.931204528, -87.710678708)"
-9681055,HX331130,2014-07-04 15:00:00,021XX W PETERSON AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,4484,True,False,2413,24,40,2,06,1161031,1939882,2014,2014-11-07 12:39:31,41.990726931,-87.683051337,"(41.990726931, -87.683051337)"
-6696343,HR110836,2009-01-07 19:30:00,021XX W SHAKESPEARE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,3344,False,False,1432,14,32,22,03,1161425,1914413,2009,2009-03-02 19:02:28,41.920830456,-87.682313785,"(41.920830456, -87.682313785)"
-7313029,HS117588,2010-01-13 00:21:00,032XX N CLARK ST,2022,NARCOTICS,POSS: COCAINE,STREET,4814,True,False,1924,19,44,6,18,1169902,1921495,2010,ERROR,41.94008298,-87.650960519,"(41.94008298, -87.650960519)"
-8914447,HV587762,2012-12-03 19:40:00,077XX S ASHLAND AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,4861,True,False,612,6,17,71,18,1167067,1853091,2012,2012-03-12 20:28:11,41.752437462,-87.663341352,"(41.752437462, -87.663341352)"
-8813683,HV486788,2012-09-22 09:00:00,017XX E 56TH ST,0890,THEFT,FROM BUILDING,APARTMENT,3006,False,False,235,2,5,41,06,1188630,1868244,2012,ERROR,41.793530583,-87.583839014,"(41.793530583, -87.583839014)"
-2915956,HJ592169,2003-08-28 08:00:00,078XX S GREENWOOD AVE,0560,ASSAULT,SIMPLE,RESIDENCE,3119,False,False,624,6,8,69,08A,1184870,1853297,2003,2007-11-06 15:52:33,41.752603788,-87.598095,"(41.752603788, -87.598095)"
-5426156,HN256370,2007-03-29 18:30:00,0000X W CERMAK RD,0810,THEFT,OVER $500,STREET,1448,False,False,134,1,3,33,06,1176378,1889737,2007,2014-04-12 12:43:35,41.852793534,-87.628118788,"(41.852793534, -87.628118788)"
-1331639,G028143,2001-01-12 21:00:00,034XX S KING DR,0820,THEFT,$500 AND UNDER,DRUG STORE,282,True,False,2122,NA,NA,NA,06,1179512,1882402,2001,2014-04-12 12:43:35,41.832594585,-87.616840751,"(41.832594585, -87.616840751)"
-1947050,HH132691,2002-01-17 18:49:57,005XX E 44 PL,0460,BATTERY,SIMPLE,RESIDENCE,4529,False,False,222,NA,NA,NA,08B,1180563,1875665,2002,ERROR,41.814083626,-87.613191703,"(41.814083626, -87.613191703)"
-2490949,HH828905,2002-12-07 18:00:00,008XX W 53RD ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,2258,False,False,934,NA,3,61,11,1171449,1869703,2002,ERROR,41.797928002,-87.646797215,"(41.797928002, -87.646797215)"
-5985175,HN780490,2007-12-27 17:30:00,019XX E 79TH ST,031A,ROBBERY,ARMED: HANDGUN,OTHER,2491,True,False,414,4,8,43,03,1190626,1853018,2007,ERROR,41.751701272,-87.577010969,"(41.751701272, -87.577010969)"
-2817048,HJ473101,2003-07-04 23:55:30,050XX N WESTERN AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,1995,False,False,2031,20,47,4,05,1159496,1933143,2003,ERROR,41.972266669,-87.688883926,"(41.972266669, -87.688883926)"
-9456119,HX108965,2014-01-10 01:04:00,075XX S LAFAYETTE AVE,2028,NARCOTICS,POSS: SYNTHETIC DRUGS,STREET,1608,True,False,623,6,6,69,18,1177099,1855191,2014,2014-12-01 00:40:28,41.757979849,-87.626515246,"(41.757979849, -87.626515246)"
-2798720,HJ444156,2003-06-21 15:58:37,001XX W LAKE ST,0312,ROBBERY,ARMED:KNIFE/CUTTING INSTRUMENT,CTA PLATFORM,1268,False,False,113,1,42,32,03,1175497,1901776,2003,ERROR,41.885849135,-87.630990773,"(41.885849135, -87.630990773)"
-7499449,HS302738,2010-05-11 17:56:00,045XX N BROADWAY,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,2688,False,False,2311,19,46,3,08B,1168079,1930569,2010,2010-12-05 14:32:35,41.965022021,-87.65739756,"(41.965022021, -87.65739756)"
-3967996,HL332690,2005-05-03 13:20:00,014XX S CHRISTIANA AVE,0545,ASSAULT,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",2513,True,False,1021,10,24,29,08A,1154231,1892646,2005,ERROR,41.861246512,-87.709327807,"(41.861246512, -87.709327807)"
-8209042,HT442926,2011-08-11 14:41:00,068XX S WOLCOTT AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,1871,True,False,726,7,17,67,18,1164911,1859184,2011,2011-11-08 15:42:15,41.769203304,-87.6710703,"(41.769203304, -87.6710703)"
-4973670,HM585353,2006-09-06 07:50:00,094XX S LAFAYETTE AVE,0810,THEFT,OVER $500,STREET,4120,False,False,634,6,21,49,06,1177555,1842613,2006,2014-04-12 12:43:35,41.723453982,-87.625223371,"(41.723453982, -87.625223371)"
-1393322,G107984,2001-02-22 22:15:00,087XX S PEORIA ST,0271,CRIM SEXUAL ASSAULT,ATTEMPT AGG: HANDGUN,STREET,1517,False,False,2222,NA,NA,NA,02,1171783,1847121,2001,ERROR,41.735952909,-87.646233916,"(41.735952909, -87.646233916)"
-9297259,HW442278,2013-09-07 15:30:00,059XX S ELIZABETH ST,0560,ASSAULT,SIMPLE,RESIDENCE,1658,False,False,713,7,16,67,08A,1168974,1865056,2013,2013-10-09 09:12:36,41.785229979,-87.656007708,"(41.785229979, -87.656007708)"
-8764812,HV435925,2012-08-17 15:00:00,053XX S MONITOR AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,2173,False,False,811,8,23,56,07,1138309,1868694,2012,ERROR,41.795821159,-87.768353304,"(41.795821159, -87.768353304)"
-6872272,HR278952,2009-04-21 02:08:00,083XX S KEDZIE AVE,0610,BURGLARY,FORCIBLE ENTRY,BARBERSHOP,2575,False,False,834,8,18,70,05,1156456,1849198,2009,ERROR,41.741974607,-87.702331067,"(41.741974607, -87.702331067)"
-3886460,HL262763,2005-03-29 08:00:00,062XX W DICKENS AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,926,False,False,2512,25,29,19,07,1134702,1913339,2005,ERROR,41.918397987,-87.780526574,"(41.918397987, -87.780526574)"
-9247581,HW393661,2013-08-04 23:33:00,083XX S DORCHESTER AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,2059,True,True,412,4,8,45,08B,1187024,1850166,2013,2013-05-08 05:38:09,41.743961235,-87.590300673,"(41.743961235, -87.590300673)"
-6240467,HP328601,2008-05-09 00:00:00,038XX N PACIFIC AVE,0890,THEFT,FROM BUILDING,RESIDENCE,957,False,False,1631,16,36,17,06,1122068,1924371,2008,2008-11-05 08:02:48,41.948885353,-87.82670641,"(41.948885353, -87.82670641)"
-2325815,HH620831,2002-08-29 18:30:00,009XX E 82ND ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,1157,False,False,631,NA,8,44,14,1184144,1850866,2002,ERROR,41.745949866,-87.600831299,"(41.745949866, -87.600831299)"
-1481452,G218910,2001-04-14 10:15:00,017XX W 43 ST,0820,THEFT,$500 AND UNDER,STREET,248,False,False,914,NA,NA,NA,06,1165712,1876148,2001,2014-04-12 12:43:35,41.815737633,-87.667652714,"(41.815737633, -87.667652714)"
-2193379,HH452636,2002-06-19 11:09:24,027XX E 76TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,509,False,False,421,NA,7,43,14,1195835,1855198,2002,ERROR,41.757556115,-87.557850794,"(41.757556115, -87.557850794)"
-6970254,HR375030,2009-06-14 14:15:00,052XX W WOLFRAM ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,683,False,False,2514,25,31,19,05,1140877,1918484,2009,ERROR,41.932404942,-87.757711919,"(41.932404942, -87.757711919)"
-7626585,HS431129,2010-07-26 19:16:00,012XX W WASHBURNE AVE,2220,LIQUOR LAW VIOLATION,ILLEGAL POSSESSION BY MINOR,STREET,4211,True,False,1231,12,2,28,22,1168368,1894493,2010,ERROR,41.866021153,-87.65738041,"(41.866021153, -87.65738041)"
-8990632,HW137651,2013-01-30 12:12:00,060XX S TALMAN AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,1914,True,False,825,8,15,66,05,1159702,1864538,2013,2013-05-02 08:59:06,41.784003919,-87.690017346,"(41.784003919, -87.690017346)"
-3647659,HK742033,2004-11-10 09:15:00,062XX S COTTAGE GROVE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,3824,False,True,313,3,20,42,08B,1182595,1863841,2004,ERROR,41.781590611,-87.606105147,"(41.781590611, -87.606105147)"
-6003770,HP103458,2008-01-03 00:06:00,056XX S TRIPP AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,2815,True,False,813,8,13,62,15,1148949,1867064,2008,2008-09-01 10:07:35,41.791149667,-87.729377285,"(41.791149667, -87.729377285)"
-6025842,HP129074,2008-01-02 16:00:00,069XX S KIMBARK AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,3731,False,False,321,3,5,69,14,1185895,1859153,2008,ERROR,41.768649107,-87.59415445,"(41.768649107, -87.59415445)"
-4800609,HM411215,2006-05-17 20:00:00,114XX S DR MARTIN LUTHER KING JR DR,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,3162,False,True,531,5,9,49,26,1180987,1829335,2006,ERROR,41.686939238,-87.613059096,"(41.686939238, -87.613059096)"
-1666539,G443084,2001-07-27 21:11:26,048XX W FLETCHER ST,0460,BATTERY,SIMPLE,RESIDENCE,2067,True,False,2521,NA,NA,NA,08B,1143266,1920538,2001,ERROR,41.937996967,-87.748881061,"(41.937996967, -87.748881061)"
-5182497,HM771133,2006-12-13 02:45:33,037XX W ROOSEVELT RD,0460,BATTERY,SIMPLE,STREET,1387,False,False,1011,10,24,29,08B,1151547,1894410,2006,2007-11-06 15:52:33,41.866140241,-87.719133978,"(41.866140241, -87.719133978)"
-2653415,HJ264288,2003-03-27 17:55:45,035XX S RHODES AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,1255,False,False,212,NA,4,35,08B,1180131,1881336,2003,ERROR,41.829655213,-87.614602295,"(41.829655213, -87.614602295)"
-1351172,G053225,2001-01-25 20:00:00,023XX W CULLERTON ST,0820,THEFT,$500 AND UNDER,STREET,16,False,False,1223,NA,NA,NA,06,1161315,1890317,2001,2014-04-12 12:43:35,41.854711274,-87.683388522,"(41.854711274, -87.683388522)"
-10107538,HY295865,2015-06-10 18:55:00,031XX W VAN BUREN ST,2024,NARCOTICS,POSS: HEROIN(WHITE),PARK PROPERTY,515,True,False,1134,11,28,27,18,1155706,1897956,2015,ERROR,41.875788166,-87.703770398,"(41.875788166, -87.703770398)"
-2379764,HH688374,2002-10-02 22:17:34,012XX E 93RD ST,0560,ASSAULT,SIMPLE,OTHER,3430,False,True,413,NA,8,47,08A,1186314,1843608,2002,ERROR,41.725982183,-87.593108809,"(41.725982183, -87.593108809)"
-2267732,HH544517,2002-07-29 18:20:00,024XX N MONTICELLO AVE,0460,BATTERY,SIMPLE,APARTMENT,1972,False,False,2524,NA,35,22,08B,1151599,1915885,2002,ERROR,41.925068771,-87.718378015,"(41.925068771, -87.718378015)"
-9401609,HW544488,2013-11-22 15:00:00,022XX W 47TH ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,1599,False,True,931,9,12,61,26,1161952,1873419,2013,ERROR,41.80832803,-87.681521068,"(41.80832803, -87.681521068)"
-1724872,G526711,2001-09-02 23:30:00,008XX W BARRY AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,1898,False,False,1932,NA,NA,NA,07,1170165,1920835,2001,ERROR,41.938266163,-87.650013252,"(41.938266163, -87.650013252)"
-7236503,HR651141,2009-11-19 12:30:00,045XX N KEDZIE AVE,0843,THEFT,ATTEMPT FINANCIAL IDENTITY THEFT,RESIDENCE,1452,False,False,1724,17,33,14,06,1154268,1930085,2009,ERROR,41.963981641,-87.708190292,"(41.963981641, -87.708190292)"
-4531738,HM116986,2006-01-10 17:40:00,018XX W ADDISON ST,0460,BATTERY,SIMPLE,STREET,1107,False,False,1923,19,47,5,08B,1163300,1923889,2006,ERROR,41.94679389,-87.675157351,"(41.94679389, -87.675157351)"
-6323346,HP407292,2008-06-21 01:08:00,027XX W 79TH ST,1220,DECEPTIVE PRACTICE,THEFT OF LOST/MISLAID PROP,ALLEY,1728,True,False,835,8,18,70,11,1159359,1852062,2008,ERROR,41.749774969,-87.691616189,"(41.749774969, -87.691616189)"
-2811252,HJ435689,2003-06-17 19:01:00,007XX N HARDING AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,3835,True,False,1112,11,27,23,18,1149954,1904935,2003,ERROR,41.895053131,-87.724708046,"(41.895053131, -87.724708046)"
-10109799,HY298102,2015-06-12 13:35:00,055XX W BELMONT AVE,0560,ASSAULT,SIMPLE,ALLEY,4032,False,False,2514,25,30,19,08A,1138524,1920677,2015,ERROR,41.938465788,-87.766305737,"(41.938465788, -87.766305737)"
-2122047,HH360853,2002-05-08 03:00:00,035XX W 115TH PL,0810,THEFT,OVER $500,STREET,586,False,False,2211,NA,19,74,06,1155019,1827742,2002,2014-04-12 12:43:35,41.683124226,-87.70816705,"(41.683124226, -87.70816705)"
-5459179,HN286810,2007-04-14 11:00:00,017XX N SEDGWICK ST,0610,BURGLARY,FORCIBLE ENTRY,CONSTRUCTION SITE,4496,False,False,1813,18,43,7,05,1173287,1911789,2007,2007-01-05 07:13:54,41.913374701,-87.638808607,"(41.913374701, -87.638808607)"
-9427739,HW571708,2013-12-15 12:19:00,038XX W 65TH PL,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,RESIDENCE,2456,True,False,833,8,13,65,15,1151877,1860935,2013,ERROR,41.774273798,-87.718801431,"(41.774273798, -87.718801431)"
-9549159,HX200896,2014-03-23 14:00:00,0000X W WASHINGTON ST,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,BANK,506,False,False,111,1,42,32,11,1175824,1900858,2014,ERROR,41.883322741,-87.629817609,"(41.883322741, -87.629817609)"
-3085284,HJ808984,2003-12-10 01:30:00,015XX W MADISON ST,031A,ROBBERY,ARMED: HANDGUN,STREET,1775,False,False,1211,12,2,28,03,1165905,1900059,2003,ERROR,41.881347579,-87.666263434,"(41.881347579, -87.666263434)"
-4372149,HL665453,2005-10-10 19:30:00,037XX N RECREATION DR,0820,THEFT,$500 AND UNDER,PARK PROPERTY,375,False,False,2323,19,46,6,06,1171814,1925403,2005,2014-04-12 12:43:35,41.950764691,-87.643817819,"(41.950764691, -87.643817819)"
-8955831,HW104841,2013-01-01 20:21:00,027XX W 56TH ST,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,4110,False,False,824,8,16,63,26,1159094,1867451,2013,2013-10-01 14:22:16,41.792010053,-87.692166871,"(41.792010053, -87.692166871)"
-7846213,HS658466,2010-12-13 10:50:00,032XX W 26TH ST,031A,ROBBERY,ARMED: HANDGUN,SMALL RETAIL STORE,4184,True,False,1024,10,22,30,03,1155078,1886589,2010,2012-06-02 11:35:31,41.844608478,-87.706380961,"(41.844608478, -87.706380961)"
-7357351,HS158950,2010-02-10 08:10:00,003XX S SACRAMENTO BLVD,033A,ROBBERY,ATTEMPT: ARMED-HANDGUN,STREET,3954,False,False,1124,11,28,27,03,1156394,1898275,2010,2010-06-07 20:35:42,41.876649659,-87.701235678,"(41.876649659, -87.701235678)"
-8459205,HV136620,2012-01-28 22:45:00,008XX N TRIPP AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,2553,False,False,1111,11,37,23,14,1147864,1905093,2012,ERROR,41.895527127,-87.732380072,"(41.895527127, -87.732380072)"
-1389813,G101962,2001-02-19 18:05:00,016XX E 74 PL,0560,ASSAULT,SIMPLE,STREET,681,False,True,324,NA,NA,NA,08A,1188468,1855950,2001,ERROR,41.75979873,-87.584825427,"(41.75979873, -87.584825427)"
-7865849,HS679407,2010-12-28 12:40:00,031XX W MADISON ST,0560,ASSAULT,SIMPLE,APARTMENT,1924,False,False,1124,11,28,27,08A,1155534,1899811,2010,ERROR,41.880881934,-87.704352007,"(41.880881934, -87.704352007)"
-4757789,HM369541,2006-05-23 16:00:00,017XX N WOLCOTT AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,2770,False,True,1434,14,32,24,26,1163450,1911711,2006,ERROR,41.913373563,-87.674949723,"(41.913373563, -87.674949723)"
-3189601,HK196709,2004-02-21 18:00:00,025XX N HARLEM AVE,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),1669,False,False,2512,25,36,18,06,1127702,1916103,2004,2014-04-12 12:43:35,41.9261037,-87.806183216,"(41.9261037, -87.806183216)"
-4409636,HL704892,2005-10-30 15:03:19,083XX S BOND AVE,0454,BATTERY,AGG PO HANDS NO/MIN INJURY,RESIDENCE,4370,True,False,424,4,10,46,08B,1198647,1849847,2005,ERROR,41.742802579,-87.547724486,"(41.742802579, -87.547724486)"
-1505171,G248949,2001-05-01 12:00:00,002XX S MICHIGAN AV,0560,ASSAULT,SIMPLE,STREET,861,False,False,123,NA,NA,NA,08A,1177284,1899473,2001,ERROR,41.879489257,-87.624498463,"(41.879489257, -87.624498463)"
-4586711,HM178303,2006-02-13 02:00:53,003XX N LARAMIE AVE,0460,BATTERY,SIMPLE,RESTAURANT,1167,False,False,1532,15,28,25,08B,1141691,1901663,2006,ERROR,41.886231203,-87.755137217,"(41.886231203, -87.755137217)"
-1989552,HH189521,2002-02-15 19:50:00,075XX S SAGINAW AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,1219,True,False,421,NA,NA,NA,07,1195277,1855344,2002,ERROR,41.757970535,-87.559890935,"(41.757970535, -87.559890935)"
-3644751,HK740805,2004-11-08 17:00:00,018XX S KARLOV AVE,0810,THEFT,OVER $500,STREET,788,False,False,1012,10,24,29,06,1149319,1890548,2004,2014-04-12 12:43:35,41.855585896,-87.727413274,"(41.855585896, -87.727413274)"
-8573990,HV248559,2012-04-18 23:00:00,004XX N CENTRAL PARK BLVD,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,602,False,False,1122,11,27,23,07,1152144,1902675,2012,ERROR,41.888808551,-87.716724345,"(41.888808551, -87.716724345)"
-3524215,HK604674,2004-09-05 19:15:00,036XX W SHAKESPEARE AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,4206,True,False,2525,25,26,22,18,1151518,1913999,2004,2007-11-06 15:52:33,41.919895016,-87.718725318,"(41.919895016, -87.718725318)"
-1847188,G685045,2001-11-14 09:00:00,050XX S LAWNDALE AV,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,3981,False,False,821,NA,NA,NA,05,1152515,1871081,2001,ERROR,41.802103414,-87.716195637,"(41.802103414, -87.716195637)"
-7342528,HS143733,2010-01-30 11:35:00,010XX W FOSTER AVE,0340,ROBBERY,ATTEMPT: STRONGARM-NO WEAPON,ALLEY,1187,False,False,2023,20,48,77,03,1168536,1934754,2010,2010-11-02 08:18:49,41.976495882,-87.655595568,"(41.976495882, -87.655595568)"
-7778066,HS583693,2010-10-26 13:20:00,003XX W 117TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,4154,False,True,522,5,34,53,08B,1176304,1827385,2010,ERROR,41.681694308,-87.630261037,"(41.681694308, -87.630261037)"
-1348067,G033281,2001-01-16 13:30:00,011XX N WASHTENAW AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,783,True,False,1311,NA,NA,NA,18,1158142,1907777,2001,ERROR,41.902688501,-87.694557839,"(41.902688501, -87.694557839)"
-9251968,HW395546,2013-05-19 00:01:00,005XX E 32ND ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,2608,False,False,211,2,4,35,06,1180472,1883635,2013,2013-08-08 11:12:43,41.835955998,-87.613280481,"(41.835955998, -87.613280481)"
-1866081,G710432,2001-08-30 04:15:00,055XX W NORTH AV,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,3533,False,False,2532,NA,NA,NA,05,1138844,1910120,2001,ERROR,41.909490409,-87.765386627,"(41.909490409, -87.765386627)"
-3462034,HK533284,2004-08-01 00:00:00,004XX N OAKLEY BLVD,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),2427,False,False,1313,12,27,24,06,1161044,1902666,2004,2014-04-12 12:43:35,41.888603708,-87.684040351,"(41.888603708, -87.684040351)"
-8200080,HT434131,2011-08-05 01:00:00,043XX S HONORE ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,3797,False,False,914,9,12,61,07,1164697,1876102,2011,2011-06-08 10:43:36,41.81563292,-87.67137723,"(41.81563292, -87.67137723)"
-1532170,G282968,2001-05-15 17:00:00,008XX W CUYLER AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,589,False,False,2322,NA,NA,NA,07,1169953,1927283,2001,ERROR,41.955964358,-87.650603629,"(41.955964358, -87.650603629)"
-8748519,HV423660,2012-08-09 23:43:00,003XX N DEARBORN ST,0460,BATTERY,SIMPLE,BAR OR TAVERN,4895,True,False,1831,18,42,8,08B,1175921,1902673,2012,2012-10-08 09:09:49,41.888301019,-87.629406752,"(41.888301019, -87.629406752)"
-3064212,HJ783363,2003-11-26 12:45:00,003XX N MICHIGAN AVE,0890,THEFT,FROM BUILDING,RESTAURANT,4952,False,False,122,1,42,32,06,1177207,1902277,2003,ERROR,41.887185329,-87.624696174,"(41.887185329, -87.624696174)"
-9266858,HW411605,2013-08-16 21:00:00,026XX N MOZART ST,0820,THEFT,$500 AND UNDER,STREET,2,False,False,1411,14,35,22,06,1156956,1917741,2013,ERROR,41.930054663,-87.69864338,"(41.930054663, -87.69864338)"
-2563813,HJ151075,2003-01-24 17:00:00,037XX S DR MARTIN LUTHER KING JR DR,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,995,False,False,211,NA,3,35,05,1179437,1879984,2003,ERROR,41.825961127,-87.617189893,"(41.825961127, -87.617189893)"
-6272130,HP360251,2008-05-27 23:19:23,078XX S CLYDE AVE,0495,BATTERY,AGGRAVATED OF A SENIOR CITIZEN,RESIDENCE,1708,True,False,414,4,8,43,04B,1191603,1853399,2008,ERROR,41.752723145,-87.573418451,"(41.752723145, -87.573418451)"
-1694291,G483134,2001-08-14 15:00:00,063XX S ELLIS AV,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,1918,False,False,314,NA,NA,NA,04B,1184186,1863155,2001,ERROR,41.779671095,-87.600293674,"(41.779671095, -87.600293674)"
-9180052,HW325256,2013-06-19 00:05:00,063XX S DR MARTIN LUTHER KING JR DR,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,2418,True,False,312,3,20,69,11,1179964,1863307,2013,ERROR,41.780185918,-87.615767262,"(41.780185918, -87.615767262)"
-9280642,HW425506,2013-08-26 22:00:00,018XX N LARRABEE ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,2483,False,False,1813,18,43,7,05,1171935,1912782,2013,ERROR,41.916129478,-87.643746195,"(41.916129478, -87.643746195)"
-4593336,HM184287,2006-02-16 10:20:00,008XX E 103RD ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",3892,False,False,512,5,9,50,08B,1183714,1836812,2006,2006-06-03 03:53:14,41.707394098,-87.602843897,"(41.707394098, -87.602843897)"
-8204247,HT438156,2011-08-08 14:10:00,092XX S CLYDE AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,845,False,False,413,4,8,48,07,1191741,1844312,2011,2011-09-08 09:44:36,41.727784247,-87.573206878,"(41.727784247, -87.573206878)"
-7787558,HS589500,2010-10-27 10:00:00,070XX S PERRY AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,3600,False,False,731,7,6,69,07,1176581,1858104,2010,2010-05-11 08:26:29,41.765985117,-87.628326152,"(41.765985117, -87.628326152)"
-5607127,HN408670,2007-06-16 08:15:00,002XX W 106TH PL,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,4526,False,True,512,5,34,49,14,1176238,1834270,2007,ERROR,41.700589255,-87.630296938,"(41.700589255, -87.630296938)"
-8287357,HT521174,2011-09-30 11:00:00,052XX W JACKSON BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,2963,False,False,1522,15,29,25,14,1141368,1898125,2011,2011-01-10 10:34:37,41.876528452,-87.756410705,"(41.876528452, -87.756410705)"
-2090773,HH320445,2002-04-20 19:30:00,054XX W JACKSON BL,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,3689,False,False,1522,NA,NA,NA,26,1140018,1898171,2002,ERROR,41.876679476,-87.761366414,"(41.876679476, -87.761366414)"
-9718774,HX368460,2014-07-31 11:30:00,042XX S CALIFORNIA AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,4785,False,False,921,9,12,58,05,1158367,1876251,2014,2014-07-08 12:40:12,41.816173258,-87.694592874,"(41.816173258, -87.694592874)"
-5349618,HN204723,2007-03-02 20:20:00,036XX W CORTLAND ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,3550,True,False,2535,25,26,22,08B,1151444,1912332,2007,2007-08-03 21:51:06,41.915322073,-87.719041079,"(41.915322073, -87.719041079)"
-9883576,HX534835,2014-12-08 06:30:00,026XX N OAK PARK AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,1739,False,False,2512,25,36,18,14,1130669,1917134,2014,ERROR,41.928882318,-87.79525692,"(41.928882318, -87.79525692)"
-2130685,HH371576,2002-05-14 14:00:00,034XX N NARRAGANSETT AVE,0810,THEFT,OVER $500,"SCHOOL, PUBLIC, BUILDING",2624,False,False,1632,NA,36,17,06,1133059,1921942,2002,2014-04-12 12:43:35,41.942034525,-87.786361549,"(41.942034525, -87.786361549)"
-1450362,G178877,2001-03-29 17:50:47,003XX W 52 PL,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,3979,False,False,934,NA,NA,NA,04B,1174942,1870105,2001,ERROR,41.798953863,-87.633975858,"(41.798953863, -87.633975858)"
-3802924,HL107794,2005-01-05 11:00:00,051XX S CICERO AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,4516,True,False,814,8,23,56,16,1145182,1870371,2005,ERROR,41.800296394,-87.743107012,"(41.800296394, -87.743107012)"
-6423969,HP506680,2008-08-08 17:00:00,105XX S VINCENNES AVE,0890,THEFT,FROM BUILDING,CONSTRUCTION SITE,3187,False,False,2212,22,19,72,06,1168786,1835112,2008,2008-06-09 10:53:12,41.703063528,-87.657559187,"(41.703063528, -87.657559187)"
-4619517,HM216542,2006-03-06 13:00:00,032XX S KEDZIE AVE,0890,THEFT,FROM BUILDING,RESTAURANT,4455,False,False,1033,10,12,30,06,1155585,1883125,2006,2006-08-03 04:01:50,41.835092676,-87.704613393,"(41.835092676, -87.704613393)"
-3896698,HL272683,2005-04-03 19:00:00,028XX N NARRAGANSETT AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),40,False,False,2511,25,36,19,06,1133225,1917988,2005,2014-04-12 12:43:35,41.931181393,-87.785844226,"(41.931181393, -87.785844226)"
-8760257,HV434693,2012-08-17 15:30:00,051XX N MILWAUKEE AVE,1345,CRIMINAL DAMAGE,TO CITY OF CHICAGO PROPERTY,POLICE FACILITY/VEH PARKING LOT,653,True,False,1623,16,45,11,14,1138481,1933660,2012,ERROR,41.974093149,-87.766147965,"(41.974093149, -87.766147965)"
-5284889,HN142213,2007-01-24 07:45:00,003XX W 83RD ST,0810,THEFT,OVER $500,CONSTRUCTION SITE,3850,False,False,622,6,21,44,06,1175483,1849872,2007,2014-04-12 12:43:35,41.743420145,-87.632596441,"(41.743420145, -87.632596441)"
-6146157,HP237155,2008-03-21 16:00:00,055XX W NORTH AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),2698,False,False,2532,25,37,25,14,1138898,1910041,2008,ERROR,41.909272642,-87.765190174,"(41.909272642, -87.765190174)"
-5828373,HN638214,2007-10-09 16:00:00,013XX E 62ND ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,STREET,4548,True,False,314,3,20,42,26,1185883,1864191,2007,ERROR,41.782474101,-87.594039684,"(41.782474101, -87.594039684)"
-9962492,HY151390,2015-02-13 20:00:00,019XX W MORSE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,OTHER,581,False,False,2424,24,49,1,14,1161994,1946107,2015,ERROR,42.007788395,-87.679334315,"(42.007788395, -87.679334315)"
-6576639,HP649030,2008-10-26 14:44:24,026XX W CHICAGO AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,1628,False,False,1313,12,26,24,05,1158778,1905190,2008,2009-01-08 20:33:55,41.895576534999996,-87.692292699,"(41.895576535, -87.692292699)"
-2773697,HJ394838,2003-05-29 17:10:00,008XX N AVERS AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,1426,True,False,1112,NA,27,23,18,1150597,1905710,2003,ERROR,41.897167269,-87.722326187,"(41.897167269, -87.722326187)"
-3455350,HK523596,2004-07-29 00:00:00,068XX W SHAKESPEARE AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,876,False,False,2512,25,36,25,07,1130189,1913566,2004,ERROR,41.919099535,-87.797102792,"(41.919099535, -87.797102792)"
-1510568,G238792,2001-04-26 20:58:13,047XX W LAKE ST,2024,NARCOTICS,POSS: HEROIN(WHITE),ALLEY,1889,True,False,1113,NA,NA,NA,18,1144354,1901858,2001,ERROR,41.886716653,-87.745353037,"(41.886716653, -87.745353037)"
-10002833,HY192117,2015-03-20 16:50:00,109XX S MICHIGAN AVE,0560,ASSAULT,SIMPLE,STREET,2360,False,False,513,5,9,49,08A,1178806,1832645,2015,ERROR,41.696072117,-87.620943149,"(41.696072117, -87.620943149)"
-9628747,HX278675,2014-05-27 15:00:00,027XX N CLYBOURN AVE,0890,THEFT,FROM BUILDING,GROCERY FOOD STORE,2623,False,False,1931,19,32,7,06,1162878,1918140,2014,2014-03-06 12:44:29,41.931027188,-87.676870318,"(41.931027188, -87.676870318)"
-9380277,HW523315,2013-11-06 16:00:00,072XX S MAPLEWOOD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,3549,False,False,832,8,18,66,08B,1160671,1856428,2013,ERROR,41.761728977,-87.686688178,"(41.761728977, -87.686688178)"
-3098367,HJ826189,2003-12-19 03:20:00,087XX S SAGINAW AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,RESIDENCE,1560,True,False,423,4,7,46,15,1195358,1847409,2003,ERROR,41.736194269,-87.559855496,"(41.736194269, -87.559855496)"
-8700475,HV376946,2012-07-11 02:30:00,013XX W LOYOLA AVE,0820,THEFT,$500 AND UNDER,RESIDENCE,179,False,False,2432,24,40,1,06,1165965,1943761,2012,ERROR,42.001266756,-87.664791595,"(42.001266756, -87.664791595)"
-6309518,HP399117,2008-06-16 06:00:00,047XX S WOLCOTT AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,2208,False,False,931,9,20,61,05,1164528,1873118,2008,ERROR,41.80744805,-87.672081377,"(41.80744805, -87.672081377)"
-2246591,HH520136,2002-07-19 00:30:00,032XX W BELLE PLAINE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,1923,False,False,1724,NA,33,16,14,1153950,1927112,2002,ERROR,41.955829898,-87.709439072,"(41.955829898, -87.709439072)"
-1703748,G500124,2001-08-22 02:45:00,059XX W FULTON ST,0460,BATTERY,SIMPLE,APARTMENT,1254,False,True,1512,NA,NA,NA,08B,1136489,1901353,2001,ERROR,41.885475115,-87.774247889,"(41.885475115, -87.774247889)"
-2097320,HH313066,2002-04-17 12:39:00,044XX S FEDERAL ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,4334,True,False,221,NA,NA,NA,26,1176474,1875517,2002,ERROR,41.813770553,-87.628194868,"(41.813770553, -87.628194868)"
-2304088,HH586111,2002-08-17 09:18:40,014XX S SPRINGFIELD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,3059,False,False,1011,NA,24,29,14,1150654,1892710,2002,ERROR,41.861492727,-87.722456686,"(41.861492727, -87.722456686)"
-2503528,HH844467,2002-12-17 17:20:00,086XX S COMMERCIAL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,VEHICLE NON-COMMERCIAL,2687,False,False,424,NA,10,46,08B,1197693,1847869,2002,ERROR,41.737398641,-87.551285747,"(41.737398641, -87.551285747)"
-7702717,HS509807,2010-09-11 03:30:00,057XX S DR MARTIN LUTHER KING JR DR,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,2786,False,False,234,2,20,40,14,1179868,1866840,2010,2010-11-09 10:33:16,41.789883007,-87.616011151,"(41.789883007, -87.616011151)"
-4497359,HL799972,2005-12-20 14:10:00,0000X N STATE ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,687,True,False,122,1,42,32,06,1176321,1900593,2005,ERROR,41.882584372,-87.628000611,"(41.882584372, -87.628000611)"
-1913830,G774488,2001-11-21 17:00:00,004XX S DEARBORN ST,0810,THEFT,OVER $500,COMMERCIAL / BUSINESS OFFICE,4896,False,False,132,NA,NA,NA,06,1176036,1898446,2001,2014-04-12 12:43:35,41.876699301,-87.629111816,"(41.876699301, -87.629111816)"
-7752781,HS561321,2010-10-12 19:00:00,023XX N ORCHARD ST,0810,THEFT,OVER $500,STREET,3795,False,False,1812,18,43,7,06,1171165,1916071,2010,ERROR,41.925171608,-87.646478328,"(41.925171608, -87.646478328)"
-1495352,G236482,2001-04-25 08:00:00,054XX N SHERIDAN RD,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,1411,False,False,2023,NA,NA,NA,26,1168688,1936590,2001,ERROR,41.981530612,-87.654983135,"(41.981530612, -87.654983135)"
-4269646,HL579828,2005-08-29 19:47:25,029XX N MONITOR AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,1280,False,True,2514,25,30,19,08B,1136910,1919097,2005,ERROR,41.934159241,-87.772275653,"(41.934159241, -87.772275653)"
-8734967,HV410309,2012-06-06 09:00:00,030XX N ELSTON AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,OTHER,2621,False,False,1411,14,1,21,26,1158467,1919885,2012,2014-03-02 14:06:09,41.935907119,-87.693031988,"(41.935907119, -87.693031988)"
-6183611,HP273306,2008-04-10 19:00:00,031XX N MELVINA AVE,0810,THEFT,OVER $500,STREET,2739,False,False,2511,25,36,19,06,1134469,1920529,2008,2008-12-04 08:34:56,41.938132293,-87.781212545,"(41.938132293, -87.781212545)"
-7078825,HR486686,2009-08-16 11:45:00,049XX W AUGUSTA BLVD,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,RESIDENTIAL YARD (FRONT/BACK),3558,True,False,1531,15,37,25,24,1143223,1906157,2009,ERROR,41.898534802,-87.749398973,"(41.898534802, -87.749398973)"
-5779480,HN586714,2007-09-13 10:30:14,006XX W 61ST ST,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,SIDEWALK,2183,True,False,711,7,16,68,18,1172737,1864389,2007,ERROR,41.783317484,-87.642230568,"(41.783317484, -87.642230568)"
-9870258,HX520700,2014-11-25 22:12:00,062XX W 64TH ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,990,False,False,812,8,23,64,07,1135733,1861466,2014,2014-02-12 12:51:55,41.776032284,-87.777971032,"(41.776032284, -87.777971032)"
-8288909,HT523203,2011-10-01 17:20:00,048XX W FLOURNOY ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,2283,True,False,1533,15,24,25,18,1144395,1896558,2011,2011-01-10 18:31:03,41.872172039,-87.745335817,"(41.872172039, -87.745335817)"
-8102316,HT336736,2011-06-08 11:03:00,042XX S ASHLAND AVE,0850,THEFT,ATTEMPT THEFT,PARKING LOT/GARAGE(NON.RESID.),4936,True,False,914,9,12,61,06,1166338,1876850,2011,2011-09-06 11:16:04,41.817650668,-87.665336409,"(41.817650668, -87.665336409)"
-7465353,HS267327,2010-04-20 07:15:00,059XX S INDIANA AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,4064,False,False,233,2,20,40,05,1178571,1865818,2010,2010-03-07 11:45:21,41.787108136,-87.620797891,"(41.787108136, -87.620797891)"
-2448483,HH774056,2002-11-12 03:30:00,027XX S EMERALD AVE,0820,THEFT,$500 AND UNDER,STREET,467,False,False,923,NA,11,60,06,1171708,1886483,2002,2014-04-12 12:43:35,41.843968255,-87.645354708,"(41.843968255, -87.645354708)"
-7555079,HS359022,2010-06-14 11:45:00,021XX N HUMBOLDT BLVD,0810,THEFT,OVER $500,SIDEWALK,576,False,False,1414,14,35,22,06,1156190,1914398,2010,ERROR,41.920896739,-87.701548757,"(41.920896739, -87.701548757)"
-10102657,HY291732,2015-04-15 12:30:00,098XX S CHARLES ST,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,RESIDENCE,1478,False,False,2213,22,19,72,11,1167555,1839283,2015,ERROR,41.714535835,-87.661947749,"(41.714535835, -87.661947749)"
-9023020,HW170629,2013-02-26 04:45:00,119XX S PERRY AVE,031A,ROBBERY,ARMED: HANDGUN,STREET,1991,False,False,522,5,9,53,03,1177683,1825981,2013,2013-02-04 12:18:39,41.677810527,-87.625255393,"(41.677810527, -87.625255393)"
-4650295,HM247617,2006-03-23 06:22:00,027XX N RUTHERFORD AVE,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,3249,True,False,2512,25,36,18,07,1130917,1917271,2006,ERROR,41.929253988,-87.794342417,"(41.929253988, -87.794342417)"
-7862271,HS676968,2010-12-26 15:15:00,072XX S ROCKWELL ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,3255,False,True,831,8,18,66,26,1160255,1856471,2010,2011-01-01 13:13:56,41.761855547,-87.688211689,"(41.761855547, -87.688211689)"
-1549513,G304867,2001-05-26 17:00:00,064XX S COTTAGE GROVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,"SCHOOL, PUBLIC, GROUNDS",4371,False,False,312,NA,NA,NA,07,1182638,1862119,2001,ERROR,41.776864286,-87.606000899,"(41.776864286, -87.606000899)"
-1455026,G184846,2001-04-01 17:12:49,083XX S VERNON AV,0560,ASSAULT,SIMPLE,RESIDENCE,544,False,False,632,NA,NA,NA,08A,1180775,1849537,2001,ERROR,41.742380976,-87.613216643,"(41.742380976, -87.613216643)"
-4427823,HL724219,2005-11-08 00:00:00,014XX E 54TH ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,2419,False,False,2132,2,4,41,05,1186925,1869842,2005,2006-04-02 03:37:59,41.797956235,-87.590040347,"(41.797956235, -87.590040347)"
-5679423,HN487410,2007-07-25 02:40:00,059XX S CAMPBELL AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,2902,True,False,824,8,16,66,14,1160764,1865105,2007,ERROR,41.785537974,-87.686107985,"(41.785537974, -87.686107985)"
-7495732,HS275457,2010-04-25 14:45:00,043XX S DR MARTIN LUTHER KING JR DR,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,1747,False,False,222,2,3,38,03,1179615,1876521,2010,2010-04-06 18:55:38,41.816454308,-87.616642833,"(41.816454308, -87.616642833)"
-7803721,HS613635,2010-11-13 12:10:00,005XX S PULASKI RD,031A,ROBBERY,ARMED: HANDGUN,STREET,2845,False,False,1132,11,24,26,03,1149769,1897321,2010,ERROR,41.874163085,-87.725585552,"(41.874163085, -87.725585552)"
-1710750,G511505,2001-08-25 23:00:00,007XX W WAVELAND AV,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),949,False,False,2323,NA,NA,NA,06,1170739,1924926,2001,2014-04-12 12:43:35,41.94947945,-87.647783443,"(41.94947945, -87.647783443)"
-2224263,HH493721,2002-07-07 18:09:00,104XX S WABASH AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,1931,False,False,512,NA,9,49,14,1178385,1835579,2002,ERROR,41.70413297,-87.622395901,"(41.70413297, -87.622395901)"
-6935102,HR333609,2009-05-21 15:30:00,100XX W OHARE ST,0560,ASSAULT,SIMPLE,AIRPORT/AIRCRAFT,3283,False,False,1651,16,41,76,08A,1100635,1934208,2009,ERROR,41.976200173,-87.905312411,"(41.976200173, -87.905312411)"
-5382077,HN231591,2007-03-08 10:10:00,048XX S CHICAGO BEACH DR,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,1299,False,False,2132,2,4,39,26,1187822,1873221,2007,ERROR,41.807207115,-87.5866433,"(41.807207115, -87.5866433)"
-2472765,HH804536,2002-11-26 22:00:00,045XX N MILWAUKEE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,OTHER,4091,False,False,1623,NA,45,15,14,1140801,1930053,2002,ERROR,41.96415277,-87.757705682,"(41.96415277, -87.757705682)"
-8663325,HV338303,2012-06-16 12:00:00,079XX S MAY ST,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,4672,False,False,612,6,17,71,26,1169994,1852029,2012,ERROR,41.749460143,-87.652645927,"(41.749460143, -87.652645927)"
-3682557,HK717624,2004-10-29 10:00:00,018XX S KARLOV AVE,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,CHA PARKING LOT/GROUNDS,594,True,False,1012,10,24,29,18,1149325,1890347,2004,ERROR,41.855034211,-87.727396457,"(41.855034211, -87.727396457)"
-8506092,HV183131,2012-03-04 16:20:00,085XX S UNIVERSITY AVE,0560,ASSAULT,SIMPLE,RESIDENCE PORCH/HALLWAY,2189,False,False,412,4,8,45,08A,1185352,1848596,2012,2012-06-03 14:26:12,41.739692446,-87.596476215,"(41.739692446, -87.596476215)"
-1536639,G279063,2001-05-15 01:15:00,069XX S MICHIGAN AV,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,APARTMENT,3491,False,False,322,NA,NA,NA,04B,1178326,1858850,2001,ERROR,41.767992795,-87.621907549,"(41.767992795, -87.621907549)"
-8590655,HV265015,2012-04-30 16:07:00,020XX W 63RD ST,1330,CRIMINAL TRESPASS,TO LAND,RESTAURANT,3738,True,False,726,7,15,67,26,1163970,1862819,2012,2012-01-05 05:05:59,41.779198065,-87.674417489,"(41.779198065, -87.674417489)"
-3957643,HL229706,2005-03-12 02:50:00,038XX W 45TH PL,2022,NARCOTICS,POSS: COCAINE,SIDEWALK,1543,True,False,821,8,14,57,18,1151741,1874241,2005,ERROR,41.810790121,-87.718951399,"(41.810790121, -87.718951399)"
-5944517,HN743446,2007-12-01 08:00:00,052XX W HUTCHINSON ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,1515,False,False,1624,16,38,15,07,1140768,1927717,2007,2007-10-12 01:03:40,41.957743188,-87.757884694,"(41.957743188, -87.757884694)"
-8314963,HT548064,2011-10-18 11:20:00,039XX W WILCOX ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,2006,False,False,1122,11,28,26,04A,1150350,1899010,2011,ERROR,41.878786581,-87.723408302,"(41.878786581, -87.723408302)"
-2262015,HH538299,2002-07-27 00:01:00,070XX S PULASKI RD,1310,CRIMINAL DAMAGE,TO PROPERTY,SMALL RETAIL STORE,4192,False,False,833,NA,13,65,14,1150947,1857820,2002,ERROR,41.765743919,-87.722291814,"(41.765743919, -87.722291814)"
-3238230,HK259979,2004-03-24 11:30:00,019XX W PRYOR AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,692,False,False,2212,22,19,75,26,1165739,1831480,2004,ERROR,41.69316178,-87.668819238,"(41.69316178, -87.668819238)"
-8307337,HT541535,2011-10-13 19:10:00,016XX S DRAKE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,RESIDENCE PORCH/HALLWAY,3686,True,False,1021,10,24,29,18,1153013,1891601,2011,ERROR,41.858403118,-87.713826549,"(41.858403118, -87.713826549)"
-8075720,HT308470,2011-05-22 12:18:00,030XX N LECLAIRE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,2281,True,False,2521,25,31,19,18,1141796,1919925,2011,ERROR,41.936342208,-87.754298893,"(41.936342208, -87.754298893)"
-1831471,G661456,2001-11-02 23:30:30,006XX E 46 ST,0810,THEFT,OVER $500,APARTMENT,3210,False,False,222,NA,NA,NA,06,1181656,1874705,2001,2014-04-12 12:43:35,41.811424106,-87.609212194,"(41.811424106, -87.609212194)"
-1577474,G323442,2001-06-04 17:25:00,005XX S DESPLAINES ST,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,4460,True,False,131,NA,NA,NA,18,1172055,1897694,2001,ERROR,41.874724464,-87.643750852,"(41.874724464, -87.643750852)"
-6763360,HR180663,2009-02-20 17:00:00,028XX N CLARK ST,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),2537,False,False,1932,19,44,6,06,1171377,1919102,2009,ERROR,41.933484144,-87.645610016,"(41.933484144, -87.645610016)"
-6307441,HP396661,2008-06-14 20:00:00,105XX S PERRY AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,4564,False,False,512,5,34,49,14,1177515,1834741,2008,ERROR,41.701853045,-87.625606891,"(41.701853045, -87.625606891)"
-1963559,HH143797,2002-01-23 15:00:00,005XX W HARRISON ST,2111,NARCOTICS,SALE/DEL HYPODERMIC NEEDLE,HOTEL/MOTEL,4004,True,False,131,NA,NA,NA,26,1173114,1897625,2002,ERROR,41.874511711,-87.639864736,"(41.874511711, -87.639864736)"
-4056395,HL405242,2005-06-07 01:00:00,060XX S MARSHFIELD AVE,0820,THEFT,$500 AND UNDER,CHURCH/SYNAGOGUE/PLACE OF WORSHIP,154,False,False,714,7,15,67,06,1166409,1864855,2005,2014-04-12 12:43:35,41.784733473,-87.665417872,"(41.784733473, -87.665417872)"
-6587922,HP660944,2008-11-01 23:35:00,069XX N GLENWOOD AVE,0460,BATTERY,SIMPLE,SIDEWALK,4341,False,False,2431,24,49,1,08B,1165536,1945973,2008,2008-03-11 08:27:31,42.007345709,-87.666306413,"(42.007345709, -87.666306413)"
-7794546,HS604138,2010-11-07 23:30:00,025XX S CALIFORNIA AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,4457,False,True,1033,10,12,30,08B,1158063,1887206,2010,ERROR,41.846241276,-87.695409603,"(41.846241276, -87.695409603)"
-9556953,HX208548,2014-04-02 10:30:00,023XX N KEDZIE BLVD,0890,THEFT,FROM BUILDING,RESIDENCE,1548,False,False,1413,14,26,22,06,1154602,1915578,2014,2014-05-04 00:39:46,41.924166711,-87.707351793,"(41.924166711, -87.707351793)"
-4911586,HM527657,2006-08-08 09:00:00,013XX W HASTINGS ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,615,False,False,1231,12,2,28,11,1167818,1893895,2006,2006-09-09 04:19:56,41.864392058,-87.659416725,"(41.864392058, -87.659416725)"
-8530103,HV207352,2012-03-21 03:28:00,037XX N DRAKE AVE,041A,BATTERY,AGGRAVATED: HANDGUN,ALLEY,1354,False,False,1732,17,35,16,04B,1152101,1924527,2012,ERROR,41.948773209,-87.716304871,"(41.948773209, -87.716304871)"
-3202279,HK212812,2004-03-01 04:50:00,119XX S MICHIGAN AVE,031A,ROBBERY,ARMED: HANDGUN,STREET,4345,False,False,532,5,9,53,03,1178938,1826078,2004,ERROR,41.678048316,-87.620658763,"(41.678048316, -87.620658763)"
-1535129,G277552,2001-05-14 13:00:20,070XX S RHODES AV,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",895,False,False,322,NA,NA,NA,08B,1181167,1858324,2001,ERROR,41.766484442,-87.611510246,"(41.766484442, -87.611510246)"
-9439829,HW583592,2013-12-22 09:00:00,010XX W COLUMBIA AVE,0890,THEFT,FROM BUILDING,RESIDENCE PORCH/HALLWAY,4348,True,False,2432,24,49,1,06,1167737,1945049,2013,ERROR,42.004762897,-87.658235361,"(42.004762897, -87.658235361)"
-1486467,G222730,2001-04-19 14:30:00,007XX N MICHIGAN AV,0820,THEFT,$500 AND UNDER,DEPARTMENT STORE,255,True,False,1834,NA,NA,NA,06,1177338,1905329,2001,2014-04-12 12:43:35,41.8955572,-87.624122473,"(41.8955572, -87.624122473)"
-3473591,HK542000,2004-08-07 02:10:00,0000X W MAPLE ST,0460,BATTERY,SIMPLE,STREET,4558,False,False,1824,18,42,8,08B,1176112,1907663,2004,ERROR,41.90198953,-87.628554812,"(41.90198953, -87.628554812)"
-6110065,HP205470,2008-03-01 12:00:00,051XX S HYDE PARK BLVD,0810,THEFT,OVER $500,STREET,2074,False,False,2132,2,4,41,06,1188191,1871483,2008,2008-05-03 12:11:15,41.80242912,-87.585345416,"(41.80242912, -87.585345416)"
-2531598,HJ111717,2002-12-27 14:00:00,005XX N STATE ST,0890,THEFT,FROM BUILDING,COMMERCIAL / BUSINESS OFFICE,3490,False,False,1834,NA,42,8,06,1176317,1903817,2002,ERROR,41.891431292,-87.62791798,"(41.891431292, -87.62791798)"
-8908679,HV581869,2012-11-29 17:00:00,029XX S DR MARTIN LUTHER KING JR DR,0880,THEFT,PURSE-SNATCHING,RESIDENCE PORCH/HALLWAY,2582,False,False,133,1,4,35,06,1179425,1885823,2012,2012-02-12 14:35:58,41.841984044,-87.617055292,"(41.841984044, -87.617055292)"
-8613270,HV287141,2012-02-24 09:00:00,039XX W 79TH ST,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",4666,False,False,834,8,18,70,06,1151305,1851848,2012,ERROR,41.749348763,-87.721135378,"(41.749348763, -87.721135378)"
-7961115,HT185399,2011-03-02 11:00:00,017XX N WHIPPLE ST,0460,BATTERY,SIMPLE,STREET,631,False,False,1421,14,26,23,08B,1155646,1911652,2011,ERROR,41.91337247,-87.703621623,"(41.91337247, -87.703621623)"
-7919740,HT148765,2011-02-05 14:25:00,075XX S YALE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,3762,False,True,623,6,17,69,08B,1175854,1854934,2011,2011-08-02 15:08:26,41.75730259,-87.631085699,"(41.75730259, -87.631085699)"
-2663371,HJ278006,2003-04-01 19:15:00,067XX S WOLCOTT AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,1220,False,False,726,NA,15,67,26,1164809,1859980,2003,ERROR,41.77138979,-87.67142173,"(41.77138979, -87.67142173)"
-6550942,HP624443,2008-10-12 17:30:00,071XX S LAWNDALE AVE,0810,THEFT,OVER $500,RESIDENTIAL YARD (FRONT/BACK),1714,False,False,833,8,13,65,06,1152981,1856803,2008,ERROR,41.762913214,-87.714863238,"(41.762913214, -87.714863238)"
-9052143,HW197487,2013-03-18 16:14:00,006XX W CHICAGO AVE,0460,BATTERY,SIMPLE,COMMERCIAL / BUSINESS OFFICE,1207,False,True,1822,18,27,8,08B,1172179,1905661,2013,ERROR,41.896583685,-87.643060288,"(41.896583685, -87.643060288)"
-2128982,HH359279,2002-05-09 08:20:00,040XX S FEDERAL ST,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,CHA PARKING LOT/GROUNDS,2837,True,False,214,NA,3,38,18,1176399,1878070,2002,ERROR,41.820777898,-87.628393146,"(41.820777898, -87.628393146)"
-9466436,HX119098,2014-01-18 12:15:00,016XX N MAPLEWOOD AVE,0560,ASSAULT,SIMPLE,RESIDENCE,3224,True,False,1434,14,1,24,08A,1159155,1910783,2014,ERROR,41.910916447,-87.690754192,"(41.910916447, -87.690754192)"
-6833293,HR212717,2009-03-12 23:27:00,067XX S PARNELL AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,4242,False,False,722,7,6,68,14,1173780,1859903,2009,2009-02-04 16:12:49,41.770984325,-87.638539479,"(41.770984325, -87.638539479)"
-5816963,HN621314,2007-09-30 22:18:00,033XX W 65TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,3009,True,False,823,8,15,66,14,1155444,1861367,2007,ERROR,41.775388583,-87.705713759,"(41.775388583, -87.705713759)"
-8683873,HV358753,2012-06-29 14:40:00,062XX S EBERHART AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,2697,True,False,313,3,20,42,18,1180607,1863977,2012,ERROR,41.782009714,-87.613389379,"(41.782009714, -87.613389379)"
-9332406,HW476255,2013-10-02 09:15:00,003XX E 51ST ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,1444,True,False,231,2,3,40,06,1179083,1871232,2013,2013-02-10 12:40:29,41.801952997,-87.618755627,"(41.801952997, -87.618755627)"
-6106681,HP199670,2008-02-27 19:00:00,003XX W 45TH ST,0890,THEFT,FROM BUILDING,PARK PROPERTY,1877,False,False,935,9,3,37,06,1174657,1874998,2008,2008-01-03 09:26:24,41.812387089,-87.634875192,"(41.812387089, -87.634875192)"
-8322735,HT556921,2011-10-24 09:45:00,016XX W MONROE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,2411,False,True,1211,12,2,28,14,1165749,1899667,2011,2011-10-11 16:19:44,41.880275226,-87.666847435,"(41.880275226, -87.666847435)"
-8806688,HV480499,2012-09-17 23:00:00,066XX S GREENWOOD AVE,0560,ASSAULT,SIMPLE,APARTMENT,661,True,True,321,3,5,42,08A,1184405,1861060,2012,ERROR,41.773917101,-87.599556337,"(41.773917101, -87.599556337)"
-5649250,HN423753,2007-06-23 12:55:00,033XX W FULLERTON AVE,0820,THEFT,$500 AND UNDER,OTHER,203,False,False,1413,14,35,22,06,1153552,1915794,2007,2014-04-12 12:43:35,41.924780401,-87.711204182,"(41.924780401, -87.711204182)"
-9481419,HX134548,2014-01-31 16:30:00,008XX N RIDGEWAY AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,535,True,False,1112,11,27,23,18,1151276,1905276,2014,2014-05-02 00:38:42,41.895963033,-87.719843697,"(41.895963033, -87.719843697)"
-7452945,HS254403,2010-04-12 18:00:00,011XX S DELANO CT E,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,4917,False,True,131,1,2,32,26,1175238,1895318,2010,ERROR,41.8681338,-87.632135611,"(41.8681338, -87.632135611)"
-9863463,HX513290,2014-11-20 01:26:00,028XX W LAWRENCE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,3692,True,False,1911,19,47,4,08B,1156822,1931722,2014,ERROR,41.968422144,-87.698755406,"(41.968422144, -87.698755406)"
-3888951,HL158249,2005-02-02 13:25:00,035XX W CHICAGO AVE,2027,NARCOTICS,POSS: CRACK,RESIDENCE,2282,True,False,1121,11,27,23,18,1152516,1905145,2005,ERROR,41.895579135,-87.715292874,"(41.895579135, -87.715292874)"
-5869284,HN668807,2007-10-24 23:00:00,049XX W WEST END AVE,0326,ROBBERY,AGGRAVATED VEHICULAR HIJACKING,STREET,934,False,False,1532,15,28,25,03,1143315,1900566,2007,ERROR,41.883190721,-87.749200857,"(41.883190721, -87.749200857)"
-2703495,HJ328167,2003-04-27 16:00:00,053XX S MAY ST,0610,BURGLARY,FORCIBLE ENTRY,ABANDONED BUILDING,3328,False,False,934,NA,16,61,05,1169605,1869148,2003,ERROR,41.796445235,-87.653575559,"(41.796445235, -87.653575559)"
-4257280,HL574185,2005-08-27 01:30:00,084XX S ELIZABETH ST,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,2999,False,False,613,6,21,71,05,1169506,1848666,2005,ERROR,41.740242188,-87.65453137,"(41.740242188, -87.65453137)"
-5932502,HN731979,2007-11-28 09:30:00,077XX S BURNHAM AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",3095,False,False,421,4,7,43,08B,1196020,1854375,2007,2007-11-12 01:04:21,41.755293165,-87.557200019,"(41.755293165, -87.557200019)"
-6238887,HP326210,2008-05-09 08:00:00,011XX N CLARK ST,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),1292,False,False,1824,18,42,8,06,1175376,1907750,2008,2008-12-05 11:05:25,41.902244823,-87.631255595,"(41.902244823, -87.631255595)"
-1875426,G723872,2001-12-03 03:00:00,043XX S SAWYER AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,4250,False,False,821,NA,NA,NA,07,1155459,1875390,2001,ERROR,41.813869382,-87.705283202,"(41.813869382, -87.705283202)"
-7725847,HS533443,2010-09-25 13:18:00,023XX N LONG AVE,0810,THEFT,OVER $500,PARK PROPERTY,2755,False,False,2515,25,37,19,06,1139957,1914995,2010,ERROR,41.922847656,-87.761178414,"(41.922847656, -87.761178414)"
-3451995,HK003019,2004-07-24 01:30:00,013XX N HALSTED ST,2027,NARCOTICS,POSS: CRACK,STREET,687,True,False,1822,18,27,8,18,1170807,1909089,2004,ERROR,41.906020499,-87.647998785,"(41.906020499, -87.647998785)"
-4626034,HM223084,2006-03-09 20:19:52,060XX S HARPER AVE,0890,THEFT,FROM BUILDING,APARTMENT,985,False,True,314,3,5,42,06,1187444,1865041,2006,ERROR,41.784769597,-87.588289691,"(41.784769597, -87.588289691)"
-2635210,HJ244826,2003-03-17 22:30:00,041XX W NORTH AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,4737,False,False,2534,25,30,23,07,1148753,1910358,2003,ERROR,41.90995769,-87.728978726,"(41.90995769, -87.728978726)"
-4038854,HL311429,2005-04-22 14:30:00,055XX N LINCOLN AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,HOTEL/MOTEL,2883,True,False,2011,20,40,4,16,1158216,1936790,2005,ERROR,41.982300536,-87.693490542,"(41.982300536, -87.693490542)"
-4031728,HL384570,2005-05-28 07:00:00,003XX N LATROBE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,4941,False,False,1523,15,28,25,07,1141271,1901864,2005,2007-11-06 15:52:33,41.886790527,-87.756674616,"(41.886790527, -87.756674616)"
-9537378,HX190485,2014-03-19 09:02:00,118XX S PERRY AVE,0497,BATTERY,AGGRAVATED DOMESTIC BATTERY: OTHER DANG WEAPON,SIDEWALK,2791,False,True,522,5,34,53,04B,1177670,1826389,2014,2014-04-04 00:38:54,41.678930434,-87.625290705,"(41.678930434, -87.625290705)"
-7080558,HR488689,2009-08-17 13:55:00,012XX W ARTHUR AVE,0850,THEFT,ATTEMPT THEFT,SIDEWALK,2422,False,False,2432,24,40,1,06,1166693,1943419,2009,ERROR,42.000312672,-87.662123274,"(42.000312672, -87.662123274)"
-6119164,HP210946,2008-03-06 13:17:00,020XX N SEDGWICK ST,0460,BATTERY,SIMPLE,STREET,3739,True,False,1814,18,43,7,08B,1173293,1914116,2008,2008-10-03 08:28:03,41.919759963,-87.638717325,"(41.919759963, -87.638717325)"
-5259619,HN135255,2007-01-20 17:00:00,041XX N CLARENDON AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,2690,False,False,2322,19,46,3,05,1170144,1927369,2007,2007-12-02 06:20:13,41.956196167,-87.649898952,"(41.956196167, -87.649898952)"
-9177023,HW321951,2013-06-16 23:00:00,002XX W 103RD ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,4706,True,False,511,5,9,49,18,1176620,1836684,2013,ERROR,41.707205049,-87.628825892,"(41.707205049, -87.628825892)"
-2827497,HJ477324,2003-07-07 01:35:00,046XX N CLIFTON AVE,0460,BATTERY,SIMPLE,STREET,1471,False,False,2311,19,46,3,08B,1167640,1930699,2003,ERROR,41.965388238,-87.659007885,"(41.965388238, -87.659007885)"
-3012718,HJ716464,2003-10-25 02:42:00,042XX W KAMERLING AVE,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,1750,False,False,2534,25,37,23,06,1147558,1908601,2003,2014-04-12 12:43:35,41.905159324,-87.733413851,"(41.905159324, -87.733413851)"
-3294036,HK326749,2004-04-25 21:00:00,051XX W BLOOMINGDALE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,4527,False,False,2533,25,37,25,05,1141931,1911525,2004,ERROR,41.913289233,-87.754011314,"(41.913289233, -87.754011314)"
-6803317,HR213727,2009-03-12 16:00:00,0000X N STATE ST,0890,THEFT,FROM BUILDING,SMALL RETAIL STORE,4883,False,False,122,1,42,32,06,1176328,1900431,2009,ERROR,41.882139677,-87.627979796,"(41.882139677, -87.627979796)"
-9971989,HY161202,2015-02-23 14:56:00,004XX E 48TH ST,0820,THEFT,$500 AND UNDER,APARTMENT,404,False,True,223,2,3,38,06,1180243,1873338,2015,2015-06-03 12:42:07,41.807705499,-87.614436883,"(41.807705499, -87.614436883)"
-6271204,HP353232,2008-05-23 19:45:00,094XX S BURNSIDE AVE,0560,ASSAULT,SIMPLE,RESIDENCE,3954,False,False,633,6,9,44,08A,1182714,1842560,2008,2008-02-06 06:39:38,41.72319054,-87.60632814,"(41.72319054, -87.60632814)"
-2826618,HJ482716,2003-04-01 13:00:00,034XX W CARROLL AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,3974,False,False,1123,11,28,27,05,1153347,1902226,2003,ERROR,41.887552644,-87.712318379,"(41.887552644, -87.712318379)"
-7953888,HT186007,2011-03-02 19:00:00,007XX N WABASH AVE,0820,THEFT,$500 AND UNDER,STREET,92,False,False,1834,18,42,8,06,1176565,1905227,2011,2011-03-03 06:43:40,41.895294805,-87.626964572,"(41.895294805, -87.626964572)"
-3979852,HL343037,2005-05-07 19:00:00,016XX E 50TH ST,0810,THEFT,OVER $500,STREET,749,False,False,2132,2,4,39,06,1188465,1872158,2005,2014-04-12 12:43:35,41.804274819,-87.584318985,"(41.804274819, -87.584318985)"
-4101174,HL441592,2005-06-24 16:00:00,061XX S WESTERN AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),44,False,False,825,8,15,66,06,1161471,1863600,2005,2014-04-12 12:43:35,41.781393417,-87.683557482,"(41.781393417, -87.683557482)"
-2613348,HJ215734,2003-01-16 16:00:00,077XX S WESTERN AVE,0820,THEFT,$500 AND UNDER,STREET,157,False,False,835,NA,18,70,06,1161675,1853495,2003,2014-04-12 12:43:35,41.753659623,-87.683089644,"(41.753659623, -87.683089644)"
-5184536,HM766200,2006-12-09 17:00:00,031XX W 31ST ST,0820,THEFT,$500 AND UNDER,STREET,311,False,False,1033,10,12,30,06,1155985,1883878,2006,2014-04-12 12:43:35,41.837150955,-87.703125393,"(41.837150955, -87.703125393)"
-8230238,HT464106,2011-08-24 19:15:00,113XX S STEWART AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,RESIDENCE,3176,False,False,2233,22,34,49,04B,1175621,1829403,2011,ERROR,41.687247278,-87.632701111,"(41.687247278, -87.632701111)"
-7374070,HS176750,2010-02-22 20:50:00,019XX S PULASKI RD,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,4263,True,False,1014,10,24,29,15,1150062,1890214,2010,ERROR,41.854654939,-87.724694776,"(41.854654939, -87.724694776)"
-5274149,HN143339,2007-01-25 19:00:00,079XX S COTTAGE GROVE AVE,1330,CRIMINAL TRESPASS,TO LAND,TAVERN/LIQUOR STORE,1997,True,False,624,6,8,44,26,1182965,1852736,2007,ERROR,41.751108806,-87.605093358,"(41.751108806, -87.605093358)"
-5487050,HN286402,2007-04-16 14:00:00,039XX N LAWNDALE AVE,2890,PUBLIC PEACE VIOLATION,OTHER VIOLATION,"SCHOOL, PUBLIC, BUILDING",640,True,False,1732,17,39,16,26,1150988,1925736,2007,ERROR,41.95211271,-87.720364302,"(41.95211271, -87.720364302)"
-9176249,HW319713,2013-06-15 10:45:00,069XX S CRANDON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE PORCH/HALLWAY,2794,False,True,331,3,5,43,08B,1192569,1859188,2013,ERROR,41.76858515,-87.569690199,"(41.76858515, -87.569690199)"
-5970003,HN765590,2007-12-18 14:00:00,028XX N MILWAUKEE AVE,0820,THEFT,$500 AND UNDER,VEHICLE-COMMERCIAL,352,False,False,1412,14,35,21,06,1152714,1918810,2007,2014-04-12 12:43:35,41.933073195,-87.71420336,"(41.933073195, -87.71420336)"
-4936885,HM544641,2006-08-16 20:30:00,095XX S INDIANA AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,4828,True,False,511,5,6,49,03,1179217,1841960,2006,ERROR,41.72162439,-87.619155472,"(41.72162439, -87.619155472)"
-9474839,HX128512,2014-01-26 16:00:00,019XX W PETERSON AVE,0850,THEFT,ATTEMPT THEFT,RESTAURANT,3213,True,False,2413,24,40,2,06,1162040,1939914,2014,ERROR,41.990793656,-87.679339104,"(41.990793656, -87.679339104)"
-7802326,HS611894,2010-11-11 18:45:00,004XX S KEDZIE AVE,0820,THEFT,$500 AND UNDER,SMALL RETAIL STORE,174,False,False,1134,11,28,27,06,1155148,1897810,2010,ERROR,41.87539874,-87.705823098,"(41.87539874, -87.705823098)"
-7731223,HS539017,2010-09-28 22:26:00,007XX S CALIFORNIA AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,4969,True,False,1135,11,2,27,18,1157851,1896880,2010,ERROR,41.872792072,-87.695924058,"(41.872792072, -87.695924058)"
-5221374,HN105437,2007-01-04 02:13:58,088XX S MARSHFIELD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,600,True,True,2221,22,21,71,08B,1166921,1846213,2007,2014-04-12 12:43:35,41.733566359,-87.664072408,"(41.733566359, -87.664072408)"
-4542176,HL749208,2005-11-22 06:19:20,045XX W CERMAK RD,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,2808,True,False,1012,10,24,29,16,1146657,1889031,2005,ERROR,41.851474184,-87.737222838,"(41.851474184, -87.737222838)"
-4562689,HM152355,2006-01-29 19:30:00,020XX N LAWLER AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,3832,False,False,2522,25,31,19,05,1142381,1913021,2006,ERROR,41.917386066,-87.752320857,"(41.917386066, -87.752320857)"
-9707558,HX357398,2014-07-23 17:30:00,020XX E 71ST ST,0554,ASSAULT,AGG PO HANDS NO/MIN INJURY,SMALL RETAIL STORE,1641,True,False,331,3,5,43,08A,1191387,1858371,2014,ERROR,41.766371955,-87.574049171,"(41.766371955, -87.574049171)"
-4714041,HM227293,2006-03-11 20:25:00,065XX S WOLCOTT AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,622,True,False,726,7,15,67,18,1164765,1861497,2006,ERROR,41.775553569,-87.671540228,"(41.775553569, -87.671540228)"
-5921937,HN718848,2007-11-20 17:36:30,059XX S PAULINA ST,0560,ASSAULT,SIMPLE,RESIDENCE PORCH/HALLWAY,3639,False,False,714,7,15,67,08A,1165991,1865160,2007,2007-02-12 01:04:24,41.785579331,-87.66694177,"(41.785579331, -87.66694177)"
-3218316,HK228080,2004-03-03 15:00:00,039XX W 79TH ST,0330,ROBBERY,AGGRAVATED,"SCHOOL, PUBLIC, GROUNDS",4389,False,False,834,8,18,70,03,1151161,1851841,2004,ERROR,41.749332365,-87.721663241,"(41.749332365, -87.721663241)"
-3487515,HK557821,2004-08-14 06:20:00,006XX N LONG AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,4892,False,False,1524,15,37,25,14,1140252,1903605,2004,ERROR,41.891586777,-87.760374016,"(41.891586777, -87.760374016)"
-2451451,HH777241,2002-11-13 04:00:00,084XX S DREXEL AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,3522,False,False,632,6,8,44,07,1183741,1848976,2002,ERROR,41.740772908,-87.602366784,"(41.740772908, -87.602366784)"
-4502366,HL808084,2005-12-24 13:00:00,066XX N ASHLAND AVE,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,2954,False,False,2432,24,40,1,26,1164366,1944194,2005,ERROR,42.002489027,-87.670661713,"(42.002489027, -87.670661713)"
-4931618,HM406308,2006-06-10 20:45:38,026XX W 47TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,3205,True,False,911,9,12,58,18,1159669,1873369,2006,ERROR,41.808238032,-87.689895989,"(41.808238032, -87.689895989)"
-3614068,HK707958,2004-10-24 23:00:00,066XX S MAY ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,3727,False,True,724,7,17,68,08B,1169839,1860579,2004,ERROR,41.77292581,-87.652966109,"(41.77292581, -87.652966109)"
-3751122,HK756140,2004-11-17 11:30:00,071XX S VERNON AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,2489,True,False,323,3,6,69,18,1180514,1858040,2004,ERROR,41.765720129,-87.613912421,"(41.765720129, -87.613912421)"
-8775540,HV450164,2012-08-28 03:10:00,040XX S CALUMET AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,3867,True,True,213,2,3,38,08B,1179057,1878335,2012,ERROR,41.821444824,-87.618634338,"(41.821444824, -87.618634338)"
-3361951,HK409235,2004-06-04 00:01:00,022XX S WASHTENAW AVE,0820,THEFT,$500 AND UNDER,STREET,380,False,False,1034,10,28,30,06,1158665,1889279,2004,2014-04-12 12:43:35,41.851917522,-87.693143577,"(41.851917522, -87.693143577)"
-6028483,HP130477,2008-01-18 08:00:00,054XX S BISHOP ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,4831,False,False,933,9,16,61,05,1167547,1868813,2008,2008-10-02 09:00:51,41.795570365,-87.661132024,"(41.795570365, -87.661132024)"
-3491821,HK563513,2004-08-17 14:22:57,115XX S CHURCH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,DRIVEWAY - RESIDENTIAL,1773,False,False,2212,22,34,75,14,1165666,1828209,2004,ERROR,41.684187135,-87.669178838,"(41.684187135, -87.669178838)"
-3018372,HJ720023,2003-10-25 01:00:00,038XX W 31ST ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,2821,False,False,1031,10,22,30,08B,1151504,1883738,2003,ERROR,41.836855819,-87.719571859,"(41.836855819, -87.719571859)"
-1354914,G057397,2001-01-28 06:00:00,105XX S TRUMBULL AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,1221,False,False,2211,NA,NA,NA,14,1155237,1834766,2001,ERROR,41.702395083,-87.707182113,"(41.702395083, -87.707182113)"
-4569529,HM158752,2006-01-23 08:00:00,072XX S RHODES AVE,0560,ASSAULT,SIMPLE,APARTMENT,3176,False,True,323,3,6,69,08A,1181195,1857297,2006,ERROR,41.763665603,-87.611439209,"(41.763665603, -87.611439209)"
-6806076,HR217090,2009-03-15 12:05:00,054XX N LINCOLN AVE,0820,THEFT,$500 AND UNDER,RESTAURANT,346,False,False,2011,20,40,4,06,1158464,1936018,2009,ERROR,41.980177046,-87.692599697,"(41.980177046, -87.692599697)"
-6083955,HP180648,2008-02-17 16:45:01,049XX S LA CROSSE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,3195,False,False,814,8,23,56,14,1144901,1871417,2008,ERROR,41.803172073,-87.744111245,"(41.803172073, -87.744111245)"
-8807854,HV481114,2012-09-18 08:00:00,006XX E GRAND AVE,0810,THEFT,OVER $500,OTHER,674,False,False,1834,18,42,8,06,1180766,1904096,2012,ERROR,41.89209535,-87.611570504,"(41.89209535, -87.611570504)"
-2338089,HH593976,2002-08-20 19:30:00,029XX N CENTRAL PARK AVE,2210,LIQUOR LAW VIOLATION,SELL/GIVE/DEL LIQUOR TO MINOR,TAVERN/LIQUOR STORE,3797,True,False,2523,NA,35,21,22,1151842,1919206,2002,ERROR,41.934177086,-87.717397454,"(41.934177086, -87.717397454)"
-8452974,HV130354,2012-01-24 16:11:00,048XX N SHERIDAN RD,0560,ASSAULT,SIMPLE,CURRENCY EXCHANGE,2398,False,True,2024,20,46,3,08A,1168724,1932205,2012,ERROR,41.96949727,-87.654978443,"(41.96949727, -87.654978443)"
-5324749,HN183465,2007-02-18 09:00:00,020XX W 70TH PL,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,2289,True,False,735,7,17,67,26,1163866,1857860,2007,ERROR,41.765592081,-87.674937943,"(41.765592081, -87.674937943)"
-1623626,G373657,2001-06-27 02:16:41,005XX W DIVISION ST,2027,NARCOTICS,POSS: CRACK,STREET,3021,True,False,1823,NA,NA,NA,18,1172307,1908305,2001,ERROR,41.903836143,-87.642511973,"(41.903836143, -87.642511973)"
-6375121,HP457261,2008-07-17 01:28:39,042XX W VAN BUREN ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,4906,False,False,1132,11,24,26,04B,1148391,1897730,2008,2008-01-08 22:09:13,41.875312091,-87.730634436,"(41.875312091, -87.730634436)"
-3783641,HK789089,2004-12-04 10:37:29,029XX S DEARBORN ST,1822,NARCOTICS,MANU/DEL:CANNABIS OVER 10 GMS,CHA APARTMENT,2874,True,False,2113,1,3,35,18,1176498,1885030,2004,ERROR,41.839874474,-87.627820281,"(41.839874474, -87.627820281)"
-4549735,HM139445,2006-01-22 23:10:00,019XX S RACINE AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE PORCH/HALLWAY,1515,True,False,1233,12,25,31,26,1168712,1890864,2006,ERROR,41.856055437,-87.656222636,"(41.856055437, -87.656222636)"
-2457835,HH783440,2002-11-10 12:30:00,042XX W 25TH PL,0810,THEFT,OVER $500,STREET,4867,False,False,1013,NA,22,30,06,1148465,1886754,2002,2014-04-12 12:43:35,41.845191169,-87.730645627,"(41.845191169, -87.730645627)"
-6612069,HP685482,2008-11-15 20:00:00,037XX W LEXINGTON ST,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,4044,False,False,1133,11,24,27,26,1151664,1896487,2008,ERROR,41.871837466,-87.718649859,"(41.871837466, -87.718649859)"
-3543604,HK627013,2004-09-16 09:20:00,047XX N WINTHROP AVE,0560,ASSAULT,SIMPLE,SIDEWALK,3752,False,True,2312,19,46,3,08A,1168071,1931384,2004,ERROR,41.967258582,-87.657403334,"(41.967258582, -87.657403334)"
-5036221,HM519102,2006-08-04 11:47:26,051XX W MAYPOLE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,OTHER,1726,True,False,1532,15,28,25,18,1141785,1900930,2006,ERROR,41.884218022,-87.754810169,"(41.884218022, -87.754810169)"
-8417213,HT650314,2011-12-28 12:50:00,023XX W MADISON ST,2170,NARCOTICS,POSSESSION OF DRUG EQUIPMENT,STREET,2061,True,False,1332,12,2,28,18,1160516,1899992,2011,ERROR,41.881276963,-87.686053431,"(41.881276963, -87.686053431)"
-1406729,G971618,2001-02-23 11:00:00,0000X W HUBBARD ST,0820,THEFT,$500 AND UNDER,TAXICAB,182,False,False,1831,NA,NA,NA,06,1176066,1903352,2001,2014-04-12 12:43:35,41.890160967,-87.628853795,"(41.890160967, -87.628853795)"
-5919963,HN717978,2007-11-20 10:45:00,011XX N KEYSTONE AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,1751,True,False,1111,11,27,23,26,1149217,1907290,2007,ERROR,41.901529809,-87.727353784,"(41.901529809, -87.727353784)"
-2685011,HJ285981,2003-04-07 17:30:00,002XX W 24TH PL,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,4426,True,False,2111,NA,25,34,18,1174709,1888096,2003,ERROR,41.848327956,-87.634293516,"(41.848327956, -87.634293516)"
-7647393,HS452564,2010-08-08 11:11:00,044XX N BROADWAY,0870,THEFT,POCKET-PICKING,SMALL RETAIL STORE,644,False,False,2311,19,46,3,06,1168390,1929931,2010,2010-12-08 18:54:25,41.96326459,-87.656272632,"(41.96326459, -87.656272632)"
-9876690,HX527338,2014-12-02 10:50:00,116XX S WALLACE ST,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,4273,True,False,524,5,34,53,26,1174437,1827665,2014,2014-09-12 12:42:40,41.682504278,-87.637086989,"(41.682504278, -87.637086989)"
-8735782,HV411396,2012-08-01 22:30:00,013XX W PRATT BLVD,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,STREET,4850,True,False,2432,24,49,1,18,1166147,1945262,2012,2012-02-08 02:34:01,42.005381627,-87.664078885,"(42.005381627, -87.664078885)"
-1815802,G640754,2001-10-24 17:35:00,072XX S RIDGELAND AV,0460,BATTERY,SIMPLE,SIDEWALK,3648,False,False,324,NA,NA,NA,08B,1189086,1857179,2001,ERROR,41.763156434,-87.582521182,"(41.763156434, -87.582521182)"
-9048414,HW192963,2013-03-13 16:10:00,035XX E 114TH ST,0460,BATTERY,SIMPLE,STREET,1436,False,False,433,4,10,52,08B,1201622,1829943,2013,ERROR,41.688109378,-87.537498377,"(41.688109378, -87.537498377)"
-4252619,HL568353,2005-08-24 10:51:19,027XX E 83RD ST,0610,BURGLARY,FORCIBLE ENTRY,SMALL RETAIL STORE,1402,False,False,423,4,7,46,05,1195865,1850505,2005,ERROR,41.744677425,-87.557895859,"(41.744677425, -87.557895859)"
-7015773,HR422984,2009-07-11 07:45:00,060XX N BROADWAY,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,3873,True,False,2433,24,48,77,06,1167253,1940103,2009,ERROR,41.991201436,-87.660159064,"(41.991201436, -87.660159064)"
-7085175,HR493489,2009-08-20 03:00:00,042XX S COTTAGE GROVE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,STREET,3060,False,False,213,2,4,38,14,1182265,1877306,2009,ERROR,41.818547345,-87.606897794,"(41.818547345, -87.606897794)"
-3872852,HL239738,2005-03-11 19:30:00,007XX W 112TH ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,840,False,False,2233,22,34,49,07,1173427,1830631,2005,ERROR,41.690665788,-87.640696894,"(41.690665788, -87.640696894)"
-4784156,HM394717,2006-06-05 08:00:00,058XX N LINCOLN AVE,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",1276,False,False,2011,20,40,2,06,1155783,1938688,2006,2006-09-06 04:07:44,41.987558265,-87.702387107,"(41.987558265, -87.702387107)"
-6149991,HP239396,2008-03-23 02:31:00,017XX N CLARK ST,0460,BATTERY,SIMPLE,BAR OR TAVERN,2251,False,False,1814,18,43,7,08B,1174666,1912148,2008,2008-08-04 17:08:11,41.914329059,-87.633731746,"(41.914329059, -87.633731746)"
-2706200,HJ317376,2003-04-22 23:30:09,058XX W CHICAGO AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,1400,True,False,1511,NA,29,25,18,1137385,1904788,2003,ERROR,41.894885131,-87.77087487,"(41.894885131, -87.77087487)"
-3243981,HK261903,2004-03-25 07:00:00,041XX W BARRY AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,1844,False,False,2523,25,31,21,26,1148267,1920250,2004,ERROR,41.93711164,-87.730508643,"(41.93711164, -87.730508643)"
-4670809,HM272274,2006-04-04 16:30:00,001XX W 110TH PL,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,RESIDENCE PORCH/HALLWAY,2999,False,False,513,5,34,49,04B,1177196,1831716,2006,2006-09-04 03:54:02,41.693559196,-87.626865823,"(41.693559196, -87.626865823)"
-3551830,HK636854,2004-09-20 17:00:00,016XX W HOLLYWOOD AVE,0560,ASSAULT,SIMPLE,APARTMENT,2262,False,False,2012,20,40,77,08A,1164030,1937887,2004,ERROR,41.985189571,-87.672076994,"(41.985189571, -87.672076994)"
-5555385,HN360342,2007-05-20 06:00:00,071XX S SOUTH SHORE DR,0560,ASSAULT,SIMPLE,STREET,4204,False,True,334,3,7,43,08A,1194449,1858304,2007,2007-11-06 15:52:33,41.766113381,-87.562828234,"(41.766113381, -87.562828234)"
-8722256,HV398745,2012-04-24 09:00:00,080XX S DREXEL AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,1617,False,False,631,6,8,44,26,1183677,1851641,2012,ERROR,41.748087445,-87.602518345,"(41.748087445, -87.602518345)"
-2870583,HJ537839,2003-08-03 14:50:00,044XX N HAZEL ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,VEHICLE NON-COMMERCIAL,3347,False,True,2313,19,46,3,08B,1169420,1929662,2003,ERROR,41.96250405,-87.652493562,"(41.96250405, -87.652493562)"
-2202542,HH464935,2002-06-24 21:31:52,001XX W 87TH ST,1330,CRIMINAL TRESPASS,TO LAND,PARKING LOT/GARAGE(NON.RESID.),4936,True,False,622,NA,21,44,26,1176895,1847317,2002,ERROR,41.736377238,-87.627499596,"(41.736377238, -87.627499596)"
-9078445,HW222090,2013-04-06 15:50:00,003XX E 60TH ST,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,3449,False,False,232,2,20,40,03,1179110,1865332,2013,ERROR,41.785762231,-87.618836444,"(41.785762231, -87.618836444)"
-6538835,HP612336,2008-10-06 14:30:00,012XX N MONTICELLO AVE,1563,SEX OFFENSE,CRIMINAL SEXUAL ABUSE,"SCHOOL, PUBLIC, BUILDING",1976,True,False,2535,25,26,23,17,1151767,1908135,2008,2009-03-07 13:40:43,41.90379876,-87.717965025,"(41.90379876, -87.717965025)"
-1891896,G739646,2001-12-10 17:30:00,001XX W OAK ST,0460,BATTERY,SIMPLE,APARTMENT,3559,False,True,1824,NA,NA,NA,08B,1174581,1907163,2001,ERROR,41.900651882,-87.63419329,"(41.900651882, -87.63419329)"
-6361408,HP448520,2008-07-12 15:30:00,038XX W POLK ST,0560,ASSAULT,SIMPLE,APARTMENT,1593,False,False,1133,11,24,26,08A,1150950,1896132,2008,ERROR,41.870877306,-87.721280557,"(41.870877306, -87.721280557)"
-3347368,HK388596,2004-05-26 07:20:00,092XX S ABERDEEN ST,0460,BATTERY,SIMPLE,SIDEWALK,4671,False,False,2222,22,21,73,08B,1170580,1843176,2004,ERROR,41.725153538,-87.650755883,"(41.725153538, -87.650755883)"
-9948680,HY137572,2015-02-02 10:40:00,052XX W CONGRESS PKWY,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,4151,False,False,1522,15,29,25,05,1141716,1897144,2015,2015-09-02 12:45:35,41.873830036,-87.755157207,"(41.873830036, -87.755157207)"
-8157166,HT392395,2011-07-11 22:03:00,012XX N CLYBOURN AVE,0850,THEFT,ATTEMPT THEFT,SIDEWALK,2478,True,False,1821,18,27,8,06,1172815,1908509,2011,2011-12-07 09:18:42,41.904384688,-87.640639932,"(41.904384688, -87.640639932)"
-5615641,HN424136,2007-06-23 16:58:43,080XX S ST LOUIS AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,754,False,False,834,8,18,70,05,1154418,1850741,2007,2007-03-08 02:38:51,41.746249624,-87.709757369,"(41.746249624, -87.709757369)"
-6678841,HP753255,2008-12-27 17:05:00,052XX N SHERIDAN RD,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,1653,True,False,2023,20,48,77,06,1168731,1935088,2008,ERROR,41.977408151,-87.654868748,"(41.977408151, -87.654868748)"
-4652026,HM251430,2006-03-25 00:01:00,097XX S AVENUE L,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,1778,False,False,432,4,10,52,26,1201832,1841147,2006,2006-04-04 03:37:55,41.718848775,-87.53635002,"(41.718848775, -87.53635002)"
-7172268,HR582951,2009-10-11 12:15:00,003XX W NORTH AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,3748,False,False,1814,18,43,7,26,1173850,1911019,2009,ERROR,41.911249256,-87.636763258,"(41.911249256, -87.636763258)"
-6920364,HR325590,2009-05-17 03:50:00,053XX S NORDICA AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,1851,False,False,811,8,23,56,14,1130322,1868174,2009,ERROR,41.794534496,-87.797654615,"(41.794534496, -87.797654615)"
-6413617,HP474865,2008-07-25 22:40:00,044XX W MADISON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,4473,True,False,1113,11,28,26,18,1146975,1899609,2008,2008-07-08 12:18:35,41.880495455,-87.73578545,"(41.880495455, -87.73578545)"
-5099538,HM702282,2006-11-05 05:30:00,039XX N SHERIDAN RD,0870,THEFT,POCKET-PICKING,CTA TRAIN,1993,False,False,2324,19,44,6,06,1168850,1926543,2006,2006-08-11 05:36:51,41.953957812,-87.654680033,"(41.953957812, -87.654680033)"
-7256815,HR671119,2009-11-22 02:00:00,012XX N CLARK ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,ATM (AUTOMATIC TELLER MACHINE),2265,False,False,1821,18,42,8,11,1175274,1908508,2009,2009-04-12 14:38:56,41.904327102,-87.631607477,"(41.904327102, -87.631607477)"
-5593784,HN398599,2007-06-11 15:11:00,005XX E 71ST ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,1097,True,False,322,3,6,69,18,1181308,1858110,2007,ERROR,41.765893956,-87.61100002,"(41.765893956, -87.61100002)"
-6361346,HP448207,2008-07-11 08:30:00,051XX S HARPER AVE,0560,ASSAULT,SIMPLE,SIDEWALK,4863,False,True,2132,2,4,41,08A,1187200,1871163,2008,ERROR,41.801574625,-87.588989942,"(41.801574625, -87.588989942)"
-9900148,HX550849,2014-12-21 21:00:00,033XX S INDIANA AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,3486,False,False,211,2,3,35,14,1178111,1882842,2014,ERROR,41.833833929,-87.621967815,"(41.833833929, -87.621967815)"
-9176806,HW321737,2013-06-16 07:30:00,111XX S GREEN ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENTIAL YARD (FRONT/BACK),1833,False,False,2233,22,34,75,14,1172607,1830711,2013,ERROR,41.690903375,-87.643696617,"(41.690903375, -87.643696617)"
-9255503,HW400240,2013-08-09 09:00:00,016XX W WRIGHTWOOD AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),2272,False,False,1931,19,32,7,14,1164679,1917284,2013,ERROR,41.928640244,-87.670276344,"(41.928640244, -87.670276344)"
-8879865,HV553462,2012-11-08 14:00:00,075XX S CHAMPLAIN AVE,0880,THEFT,PURSE-SNATCHING,SIDEWALK,937,False,False,624,6,6,69,06,1181824,1855392,2012,2012-09-11 10:05:24,41.758423578,-87.609192588,"(41.758423578, -87.609192588)"
-8619862,HV293402,2012-05-19 09:00:00,051XX W HURON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,3108,False,True,1532,15,28,25,08B,1142167,1904099,2012,ERROR,41.892907066,-87.753328745,"(41.892907066, -87.753328745)"
-8939790,HV611318,2012-12-20 18:00:00,029XX W WASHINGTON BLVD,0610,BURGLARY,FORCIBLE ENTRY,"SCHOOL, PUBLIC, BUILDING",2123,False,False,1222,12,2,27,05,1156901,1900584,2012,2013-01-01 19:08:51,41.882975519,-87.699311498,"(41.882975519, -87.699311498)"
-4471061,HL762489,2005-11-29 18:24:56,025XX S DRAKE AVE,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,ALLEY,3995,True,False,1024,10,22,30,26,1153071,1886821,2005,ERROR,41.845285083,-87.71374025,"(41.845285083, -87.71374025)"
-6948596,HR354023,2009-05-08 09:00:00,002XX E GARFIELD BLVD,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,RESIDENCE,4044,False,False,232,2,3,40,11,1178909,1868644,2009,ERROR,41.794855256,-87.61947257,"(41.794855256, -87.61947257)"
-3024446,HJ731807,2003-11-01 10:19:57,082XX S COMMERCIAL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,2796,False,True,424,4,10,46,08B,1197665,1850765,2003,ERROR,41.745346184,-87.551291909,"(41.745346184, -87.551291909)"
-7096237,HR504828,2009-08-26 16:56:00,019XX W 33RD ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,4912,True,False,922,9,11,59,06,1163768,1882841,2009,ERROR,41.834145069,-87.674595443,"(41.834145069, -87.674595443)"
-5309441,HN169039,2007-02-09 18:50:00,061XX W 64TH PL,0560,ASSAULT,SIMPLE,APARTMENT,3080,False,False,812,8,13,64,08A,1136487,1861077,2007,ERROR,41.774951401,-87.775216121,"(41.774951401, -87.775216121)"
-3332497,HK367263,2004-05-16 04:42:21,003XX N STATE ST,0460,BATTERY,SIMPLE,STREET,3474,True,False,1831,18,42,8,08B,1176258,1902572,2004,ERROR,41.888016277,-87.628172231,"(41.888016277, -87.628172231)"
-5531805,HN344079,2007-05-15 14:45:00,080XX S COTTAGE GROVE AVE,033A,ROBBERY,ATTEMPT: ARMED-HANDGUN,SIDEWALK,4786,False,False,631,6,8,44,03,1182985,1852068,2007,2007-03-06 02:49:28,41.749275279,-87.605040789,"(41.749275279, -87.605040789)"
-2251499,HH522712,2002-07-19 21:00:00,054XX N LINCOLN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,811,False,False,2011,NA,40,4,14,1158470,1935921,2002,ERROR,41.979910751,-87.6925803,"(41.979910751, -87.6925803)"
-5009126,HM619632,2006-09-24 03:00:00,021XX N RICHMOND ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,1319,False,True,1414,14,35,22,08B,1156551,1914178,2006,ERROR,41.920285736,-87.700228327,"(41.920285736, -87.700228327)"
-5710364,HN518757,2007-08-01 17:00:00,084XX W BRYN MAWR AVE,0890,THEFT,FROM BUILDING,OTHER,1004,False,False,1614,16,41,76,06,1119008,1936135,2007,ERROR,41.98121629,-87.837704596,"(41.98121629, -87.837704596)"
-3722385,HK829243,2004-12-26 19:40:00,104XX S GREEN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,933,False,True,2233,22,34,73,08B,1172542,1835330,2004,ERROR,41.703580052,-87.643799205,"(41.703580052, -87.643799205)"
-3832461,HL203373,2005-02-26 12:35:31,002XX S HOYNE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,4153,False,False,1211,12,2,28,14,1162403,1898857,2005,ERROR,41.878123162,-87.679156235,"(41.878123162, -87.679156235)"
-8764513,HV439482,2012-08-20 20:08:00,038XX S ELLIS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE PORCH/HALLWAY,1953,True,True,212,2,4,36,08B,1182599,1879695,2012,ERROR,41.825095178,-87.605598354,"(41.825095178, -87.605598354)"
-4299664,HL609145,2005-09-13 08:30:00,014XX E 73RD ST,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,1634,False,False,324,3,5,43,26,1186949,1856916,2005,ERROR,41.762485657,-87.590361895,"(41.762485657, -87.590361895)"
-5945461,HN743993,2007-12-05 19:06:00,050XX W HURON ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,STREET,551,True,False,1531,15,28,25,26,1142830,1904220,2007,2007-09-12 01:04:39,41.89322678,-87.750890754,"(41.89322678, -87.750890754)"
-3283363,HK312676,2004-04-20 00:11:42,046XX N CLIFTON AVE,1220,DECEPTIVE PRACTICE,THEFT OF LOST/MISLAID PROP,STREET,2918,True,False,2311,19,46,3,11,1167636,1931034,2004,ERROR,41.966307576,-87.659012898,"(41.966307576, -87.659012898)"
-4021146,HL375918,2005-05-24 12:46:15,107XX S EBERHART AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,2789,False,False,513,5,9,49,14,1181446,1833717,2005,ERROR,41.698953504,-87.6112443,"(41.698953504, -87.6112443)"
-6928511,HR332778,2009-05-20 17:30:00,007XX W BARRY AVE,0810,THEFT,OVER $500,STREET,4362,False,False,2332,19,44,6,06,1170898,1920733,2009,ERROR,41.937970207,-87.647322338,"(41.937970207, -87.647322338)"
-9938186,HY126791,2015-01-24 00:10:00,026XX W EVERGREEN AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,3583,False,False,1423,14,26,24,11,1158479,1908916,2015,ERROR,41.905807118,-87.69328876,"(41.905807118, -87.69328876)"
-2981789,HJ644907,2003-09-21 22:04:00,062XX S MORGAN ST,2027,NARCOTICS,POSS: CRACK,STREET,4061,True,False,712,7,16,68,18,1170765,1863047,2003,ERROR,41.779678145,-87.649499695,"(41.779678145, -87.649499695)"
-2677516,HJ285368,2003-04-07 13:20:00,001XX W 72ND ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,1047,False,True,731,NA,6,69,26,1176827,1857294,2003,ERROR,41.763756849,-87.627448846,"(41.763756849, -87.627448846)"
-2878862,HJ547518,2003-08-07 12:00:00,048XX W BELLE PLAINE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,2191,False,False,1624,16,45,15,05,1143180,1926855,2003,ERROR,41.955332998,-87.749038868,"(41.955332998, -87.749038868)"
-6312467,HP396461,2008-06-13 07:20:00,018XX W LAWRENCE AVE,0460,BATTERY,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),4648,False,False,2032,20,47,4,08B,1163122,1931948,2008,ERROR,41.968911917,-87.675584255,"(41.968911917, -87.675584255)"
-2168387,HH420999,2002-06-02 19:00:00,005XX W WILSON DR,0610,BURGLARY,FORCIBLE ENTRY,OTHER,867,False,False,2313,NA,46,3,05,1171655,1930932,2002,ERROR,41.965939938,-87.644238851,"(41.965939938, -87.644238851)"
-4428519,HL721751,2005-11-07 17:35:28,003XX S CENTRAL AVE,0460,BATTERY,SIMPLE,STREET,2098,True,False,1522,15,29,25,08B,1139164,1897488,2005,ERROR,41.874820808,-87.764518699,"(41.874820808, -87.764518699)"
-6253386,HP340946,2008-05-17 00:30:00,021XX W DIVISION ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,1175,False,False,1424,14,32,24,05,1161971,1908008,2008,2008-02-06 10:11:07,41.903243283,-87.680486791,"(41.903243283, -87.680486791)"
-2704055,HJ328889,2003-04-28 10:00:00,115XX S YALE AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,STREET,1601,False,True,522,NA,34,53,26,1176713,1828501,2003,ERROR,41.684747613,-87.628730466,"(41.684747613, -87.628730466)"
-1882196,G720932,2001-12-01 13:10:00,020XX S STATE ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,800,True,False,2111,NA,NA,NA,18,1176680,1890478,2001,ERROR,41.854820078,-87.626987993,"(41.854820078, -87.626987993)"
-7507209,HS310705,2010-05-16 14:00:00,022XX S KOLIN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,3702,False,True,1013,10,22,29,08B,1147967,1888437,2010,ERROR,41.849819116,-87.732430029,"(41.849819116, -87.732430029)"
-5257400,HM683946,2006-10-26 19:50:02,002XX N KOSTNER AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,3908,True,False,1113,11,28,26,26,1146987,1900681,2006,ERROR,41.883436917,-87.735713976,"(41.883436917, -87.735713976)"
-4144821,HL474019,2005-07-10 03:55:00,0000X W 69TH ST,033A,ROBBERY,ATTEMPT: ARMED-HANDGUN,CTA GARAGE / OTHER PROPERTY,1874,False,False,731,7,6,69,03,1177312,1859236,2005,ERROR,41.769074977,-87.62561266,"(41.769074977, -87.62561266)"
-6107802,HP202858,2008-03-01 03:30:00,001XX E PERSHING RD,0890,THEFT,FROM BUILDING,HOTEL/MOTEL,2798,False,False,211,2,3,35,06,1177861,1879207,2008,2008-02-03 09:27:45,41.823864879,-87.622995394,"(41.823864879, -87.622995394)"
-9057825,HW202237,2013-03-22 08:00:00,039XX N LAMON AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,2777,False,True,1634,16,45,15,14,1143003,1926071,2013,ERROR,41.953184943,-87.74970919,"(41.953184943, -87.74970919)"
-10105129,HY293838,2015-06-09 11:30:00,034XX W 62ND ST,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE-GARAGE,3155,False,False,823,8,15,66,26,1154383,1863255,2015,ERROR,41.780590724,-87.709553096,"(41.780590724, -87.709553096)"
-8759952,HV434029,2012-08-16 21:00:00,028XX E 128TH ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,3407,False,False,433,4,10,55,07,1197309,1820733,2012,2012-04-09 11:30:20,41.662944388,-87.553592675,"(41.662944388, -87.553592675)"
-7959811,HT190199,2011-03-05 19:15:00,028XX S PULASKI RD,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,1473,False,False,1031,10,22,30,14,1150127,1885011,2011,2011-08-03 08:38:18,41.840375986,-87.724591567,"(41.840375986, -87.724591567)"
-3048313,HJ763595,2003-11-16 19:10:00,0000X N LOCKWOOD AVE,031A,ROBBERY,ARMED: HANDGUN,STREET,1950,False,False,1522,15,28,25,03,1141098,1899565,2003,ERROR,41.880484972,-87.757366589,"(41.880484972, -87.757366589)"
-5843787,HN653880,2007-10-17 06:15:00,005XX W DIVERSEY PKWY,0820,THEFT,$500 AND UNDER,SIDEWALK,154,False,False,2333,19,44,6,06,1172289,1918919,2007,2014-04-12 12:43:35,41.932961861,-87.642263918,"(41.932961861, -87.642263918)"
-2978407,HJ675858,2003-10-06 08:00:00,047XX N ARTESIAN AVE,0810,THEFT,OVER $500,STREET,3029,False,False,1911,19,47,4,06,1159226,1931185,2003,2014-04-12 12:43:35,41.966899389,-87.689930834,"(41.966899389, -87.689930834)"
-6332578,HP413557,2008-06-21 14:00:00,060XX S HERMITAGE AVE,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,RESIDENCE,524,False,True,714,7,15,67,20,1165681,1864379,2008,ERROR,41.783442756,-87.66810053,"(41.783442756, -87.66810053)"
-5107772,HM708459,2006-11-08 09:00:00,093XX S VINCENNES AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,DRIVEWAY - RESIDENTIAL,568,False,False,2222,22,21,73,14,1170848,1842818,2006,2006-11-11 07:40:50,41.724165299,-87.649784606,"(41.724165299, -87.649784606)"
-7053138,HR458882,2009-07-31 17:50:00,053XX W CHICAGO AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,3429,True,False,1524,15,37,25,18,1140842,1904779,2009,ERROR,41.894797544,-87.758178285,"(41.894797544, -87.758178285)"
-3752876,HK761921,2004-11-19 21:59:00,035XX W VAN BUREN ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,2319,True,False,1133,11,28,27,26,1152952,1897760,2004,ERROR,41.875305308,-87.713887327,"(41.875305308, -87.713887327)"
-8246665,HT470888,2011-08-28 17:00:00,043XX N CALIFORNIA AVE,0610,BURGLARY,FORCIBLE ENTRY,CAR WASH,1992,False,False,1724,17,33,16,05,1156889,1928818,2011,ERROR,41.960452042,-87.698588153,"(41.960452042, -87.698588153)"
-8588225,HV262806,2012-04-28 22:55:00,013XX W 15TH ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,2791,True,False,1231,12,2,28,26,1167750,1892902,2012,ERROR,41.861668647,-87.659694953,"(41.861668647, -87.659694953)"
-4546442,HM132623,2006-01-18 12:40:00,021XX N MELVINA AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,2714,False,False,2512,25,29,19,14,1134778,1913205,2006,ERROR,41.91802893,-87.780250515,"(41.91802893, -87.780250515)"
-4972684,HM583449,2006-09-04 17:00:00,050XX S ELIZABETH ST,0460,BATTERY,SIMPLE,STREET,1534,False,False,933,9,16,61,08B,1168804,1871336,2006,2006-03-10 05:10:58,41.802466697,-87.656449709,"(41.802466697, -87.656449709)"
-7723442,HS530831,2010-09-23 23:32:00,003XX W SCOTT ST,031A,ROBBERY,ARMED: HANDGUN,STREET,4018,True,False,1821,18,27,8,03,1173890,1908750,2010,2011-10-05 09:12:33,41.905022117,-87.636684012,"(41.905022117, -87.636684012)"
-2773492,HJ393599,2003-05-29 03:10:00,008XX N SPRINGFIELD AVE,2027,NARCOTICS,POSS: CRACK,STREET,844,True,False,1112,NA,27,23,18,1150284,1905083,2003,ERROR,41.895452828,-87.72349217,"(41.895452828, -87.72349217)"
-3692637,HK794797,2004-12-07 11:00:31,018XX W 34TH ST,0920,MOTOR VEHICLE THEFT,ATT: AUTOMOBILE,STREET,3810,False,False,922,9,11,59,07,1164352,1882114,2004,ERROR,41.832137795,-87.672473125,"(41.832137795, -87.672473125)"
-4219807,HL460633,2005-07-03 19:58:47,010XX N LECLAIRE AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,2203,True,False,1531,15,37,25,18,1142213,1906317,2005,ERROR,41.898992665,-87.753104713,"(41.898992665, -87.753104713)"
-2573876,HJ163682,2003-02-03 19:00:00,038XX N PLAINFIELD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,2418,False,False,1631,NA,36,17,14,1119989,1924864,2003,ERROR,41.950271676,-87.834338101,"(41.950271676, -87.834338101)"
-4071798,HL321033,2005-04-27 11:45:00,008XX N HUDSON AVE,2027,NARCOTICS,POSS: CRACK,STREET,4486,True,False,1823,18,27,8,18,1172997,1905793,2005,ERROR,41.896927801,-87.640052025,"(41.896927801, -87.640052025)"
-4818852,HM291602,2006-04-14 23:58:42,026XX W 23RD PL,2230,LIQUOR LAW VIOLATION,ILLEGAL CONSUMPTION BY MINOR,SIDEWALK,2628,False,False,1034,10,28,30,22,1158965,1888336,2006,ERROR,41.849323686,-87.69206834,"(41.849323686, -87.69206834)"
-7027138,HR426417,2009-07-13 04:45:00,060XX S WABASH AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,3907,False,False,311,3,20,40,05,1177713,1864904,2009,ERROR,41.784619498,-87.623971417,"(41.784619498, -87.623971417)"
-9581848,HX232365,2014-04-21 23:03:00,015XX W SUPERIOR ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,4430,False,False,1215,12,27,24,14,1165974,1905059,2014,ERROR,41.895066484,-87.66586726,"(41.895066484, -87.66586726)"
-9174144,HW318226,2013-06-11 00:01:00,014XX W ROSEMONT AVE,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,APARTMENT,4653,False,False,2433,24,40,77,06,1165397,1941952,2013,ERROR,41.996314969,-87.666932963,"(41.996314969, -87.666932963)"
-5992602,HP100090,2008-01-01 00:30:00,063XX S DR MARTIN LUTHER KING JR DR,051A,ASSAULT,AGGRAVATED: HANDGUN,SIDEWALK,3889,False,False,312,3,20,69,04A,1179974,1862865,2008,2008-06-01 06:49:00,41.778972797,-87.615744123,"(41.778972797, -87.615744123)"
-2254466,HH524868,2002-07-21 05:00:00,006XX N ST CLAIR ST,0460,BATTERY,SIMPLE,STREET,3497,False,False,1834,NA,42,8,08B,1177765,1904573,2002,ERROR,41.893473003,-87.622577223,"(41.893473003, -87.622577223)"
-7861681,HS675991,2010-12-20 07:00:00,016XX W WRIGHTWOOD AVE,0890,THEFT,FROM BUILDING,RESIDENCE-GARAGE,741,False,False,1931,19,32,7,06,1164728,1917284,2010,ERROR,41.928639204,-87.670096285,"(41.928639204, -87.670096285)"
-4305016,HL615231,2005-09-15 19:00:00,014XX W ESTES AVE,0820,THEFT,$500 AND UNDER,STREET,331,False,False,2423,24,49,1,06,1165114,1947454,2005,2014-04-12 12:43:35,42.011418612,-87.667816666,"(42.011418612, -87.667816666)"
-6978791,HR377687,2009-06-16 05:00:00,064XX S WINCHESTER AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,4805,True,False,726,7,15,67,26,1164424,1861895,2009,ERROR,41.776652928,-87.672779095,"(41.776652928, -87.672779095)"
-7951980,HT184140,2011-03-01 16:00:00,069XX S MAPLEWOOD AVE,0560,ASSAULT,SIMPLE,APARTMENT,4213,False,True,832,8,15,66,08A,1160614,1858473,2011,2011-03-03 11:09:39,41.767341932,-87.686840757,"(41.767341932, -87.686840757)"
-9319944,HW463891,2013-09-23 15:24:00,007XX N MENARD AVE,0650,BURGLARY,HOME INVASION,APARTMENT,3017,False,False,1511,15,29,25,05,1137630,1904607,2013,2013-09-10 17:10:49,41.894384033,-87.769979402,"(41.894384033, -87.769979402)"
-6757834,HR174805,2009-02-17 16:34:15,070XX S WENTWORTH AVE,1330,CRIMINAL TRESPASS,TO LAND,CURRENCY EXCHANGE,3968,True,False,731,7,6,69,26,1176225,1857985,2009,ERROR,41.765666567,-87.629634577,"(41.765666567, -87.629634577)"
-4825337,HM438385,2006-04-20 09:00:00,023XX S LAKE SHORE DR E,0890,THEFT,FROM BUILDING,OTHER,2059,False,False,133,1,2,33,06,1180538,1889157,2006,ERROR,41.851107212,-87.612868333,"(41.851107212, -87.612868333)"
-8933517,HV605679,2012-12-17 02:14:00,012XX S SPRINGFIELD AVE,0820,THEFT,$500 AND UNDER,STREET,31,False,False,1011,10,24,29,06,1150625,1893795,2012,ERROR,41.864470661,-87.722534804,"(41.864470661, -87.722534804)"
-3080102,HJ805073,2003-12-04 19:00:00,043XX N KEDVALE AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,2862,False,True,1722,17,39,16,26,1148110,1928515,2003,ERROR,41.959794498,-87.730872135,"(41.959794498, -87.730872135)"
-7734425,HS535056,2010-09-25 17:30:00,088XX S LOWE AVE,0810,THEFT,OVER $500,STREET,3573,False,False,2223,22,21,71,06,1173480,1846204,2010,2010-02-10 09:42:41,41.733399192,-87.640043801,"(41.733399192, -87.640043801)"
-9596026,HX246438,2014-05-02 23:30:00,005XX N KEDZIE AVE,033A,ROBBERY,ATTEMPT: ARMED-HANDGUN,STREET,4495,False,False,1121,11,27,23,03,1154906,1903472,2014,2014-07-05 00:40:24,41.890940685,-87.706559801,"(41.890940685, -87.706559801)"
-9870090,HX520069,2014-11-25 11:40:00,053XX N WESTERN AVE,0460,BATTERY,SIMPLE,NURSING HOME/RETIREMENT HOME,2515,False,False,2011,20,40,4,08B,1159335,1935518,2014,2014-02-12 12:51:55,41.978787101,-87.689410305,"(41.978787101, -87.689410305)"
-8508277,HV185019,2012-03-06 08:40:00,072XX S COLES AVE,1360,CRIMINAL TRESPASS,TO VEHICLE,STREET,3879,True,False,334,3,7,43,26,1194431,1857617,2012,2012-06-03 15:34:55,41.764228646,-87.562916763,"(41.764228646, -87.562916763)"
-2177946,HH431848,2002-06-03 16:00:00,041XX W 31ST ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,868,False,False,1031,NA,22,30,06,1149298,1883758,2002,ERROR,41.836953671,-87.727666121,"(41.836953671, -87.727666121)"
-1986260,HH132885,2002-01-17 20:40:00,030XX W MADISON ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,1187,True,False,1124,NA,NA,NA,16,1156378,1899829,2002,ERROR,41.880914312,-87.701252404,"(41.880914312, -87.701252404)"
-9234994,HW381728,2013-07-26 09:30:00,0000X W WACKER DR,0820,THEFT,$500 AND UNDER,SIDEWALK,318,False,False,111,1,42,32,06,1175775,1902091,2013,ERROR,41.886707265,-87.629960428,"(41.886707265, -87.629960428)"
-7988307,HT219886,2011-03-25 20:58:00,009XX N MOZART ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,797,True,True,1311,12,26,24,08B,1157172,1906205,2011,ERROR,41.898394561,-87.69816358,"(41.898394561, -87.69816358)"
-7278970,HR694241,2009-12-17 11:00:00,041XX N GREENVIEW AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,4882,False,False,1923,19,47,6,14,1165286,1927513,2009,ERROR,41.956696233,-87.66775397,"(41.956696233, -87.66775397)"
-8025601,HT256918,2011-04-19 13:00:00,076XX S KINGSTON AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,APARTMENT,2051,True,False,421,4,7,43,15,1194477,1854868,2011,ERROR,41.756684054,-87.562838411,"(41.756684054, -87.562838411)"
-2962454,HJ650469,2003-09-24 11:45:00,0000X W 79TH ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,OTHER,2463,True,False,623,6,6,44,11,1177482,1852588,2003,ERROR,41.750828271,-87.625190123,"(41.750828271, -87.625190123)"
-3532647,HK574001,2004-08-22 14:49:40,056XX S WINCHESTER AVE,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),SIDEWALK,1797,True,False,715,7,15,67,18,1164271,1867524,2004,ERROR,41.792102855,-87.673181553,"(41.792102855, -87.673181553)"
-3038829,HJ750788,2003-11-10 15:25:00,020XX S KOSTNER AVE,0560,ASSAULT,SIMPLE,OTHER,4836,True,False,1012,10,24,29,08A,1147406,1890054,2003,ERROR,41.854267127,-87.734447612,"(41.854267127, -87.734447612)"
-9806012,HX453692,2014-10-03 13:30:00,023XX N PULASKI RD,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),2254,False,False,2525,25,31,20,06,1149281,1915512,2014,2014-10-10 12:36:29,41.924090526,-87.72690517,"(41.924090526, -87.72690517)"
-3511264,HK589583,2004-08-29 14:30:00,022XX N CANNON DR,0820,THEFT,$500 AND UNDER,STREET,219,False,False,1814,18,43,7,06,1175058,1914939,2004,2014-04-12 12:43:35,41.921978896,-87.632207786,"(41.921978896, -87.632207786)"
-5053020,HM659802,2006-10-14 14:45:00,035XX S WESTERN BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,2470,False,False,913,9,11,59,08B,1161140,1881089,2006,ERROR,41.829392282,-87.684286808,"(41.829392282, -87.684286808)"
-8357212,HT590698,2011-11-15 15:45:00,040XX W WEST END AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,2682,True,False,1114,11,28,26,24,1149062,1900618,2011,ERROR,41.883224133,-87.728095989,"(41.883224133, -87.728095989)"
-2234104,HH504870,2002-07-12 13:00:00,016XX S SPRINGFIELD AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,SIDEWALK,557,False,False,1014,NA,24,29,04B,1150624,1891627,2002,ERROR,41.858521432,-87.722595091,"(41.858521432, -87.722595091)"
-5023389,HM632245,2006-09-29 19:00:00,012XX N LA SALLE DR,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,549,False,False,1821,18,43,8,14,1174869,1908585,2006,2006-03-10 05:10:58,41.904547475,-87.633092826,"(41.904547475, -87.633092826)"
-4167710,HL497000,2005-07-18 14:07:00,039XX N RAVENSWOOD AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,1045,False,False,1923,19,47,6,26,1163702,1926130,2005,ERROR,41.952934826,-87.673616315,"(41.952934826, -87.673616315)"
-2170727,HH421736,2002-06-05 21:00:00,100XX W OHARE ST,0460,BATTERY,SIMPLE,AIRPORT/AIRCRAFT,994,False,False,1651,NA,41,76,08B,1100635,1934208,2002,ERROR,41.976200173,-87.905312411,"(41.976200173, -87.905312411)"
-2294558,HH580615,2002-08-14 08:30:00,025XX N BOSWORTH AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,3119,False,False,1931,NA,32,7,07,1165613,1916698,2002,ERROR,41.927012356,-87.666860956,"(41.927012356, -87.666860956)"
-7991942,HT224010,2011-03-28 20:45:00,063XX S ARTESIAN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,1474,True,False,825,8,15,66,18,1161174,1862364,2011,ERROR,41.778007816,-87.684680539,"(41.778007816, -87.684680539)"
-3096983,HJ825511,2003-12-18 17:20:00,075XX N CLARK ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,706,True,False,2422,24,49,1,06,1162974,1949868,2003,ERROR,42.018088059,-87.675622371,"(42.018088059, -87.675622371)"
-2315170,HH603150,2002-08-25 01:40:00,093XX S PARNELL AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,3586,False,False,2223,NA,21,73,14,1174239,1842710,2002,ERROR,41.723794372,-87.637366715,"(41.723794372, -87.637366715)"
-4440071,HL737554,2005-11-15 19:05:00,032XX W 63RD ST,031A,ROBBERY,ARMED: HANDGUN,GROCERY FOOD STORE,4578,False,False,823,8,15,66,03,1155598,1862622,2005,ERROR,41.778829406,-87.705115599,"(41.778829406, -87.705115599)"
-7177547,HR586223,2009-10-09 10:00:00,081XX S PAULINA ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,2086,False,False,614,6,18,71,05,1166467,1850703,2009,2009-10-11 13:28:08,41.745897252,-87.665608013,"(41.745897252, -87.665608013)"
-3254737,HK280639,2004-03-01 00:00:00,067XX S MERRILL AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,4572,False,False,331,3,5,43,26,1191738,1860941,2004,ERROR,41.773415721,-87.57267934,"(41.773415721, -87.57267934)"
-3311197,HK345294,2004-05-05 21:35:00,008XX N CALIFORNIA AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,1400,True,True,1311,12,26,24,08B,1157536,1905231,2004,ERROR,41.895714416,-87.69685317,"(41.895714416, -87.69685317)"
-2302106,HH588911,2002-08-17 21:00:00,041XX S CAMPBELL AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,1887,False,False,914,NA,12,58,05,1160346,1876870,2002,ERROR,41.817831272,-87.687316372,"(41.817831272, -87.687316372)"
-6488303,HP564304,2008-09-10 18:17:00,001XX W BRAYTON ST,0560,ASSAULT,SIMPLE,RESIDENCE PORCH/HALLWAY,4667,False,False,523,5,9,53,08A,1177795,1821422,2008,ERROR,41.665297395,-87.62498261,"(41.665297395, -87.62498261)"
-5443072,HN275682,2006-02-01 08:00:00,006XX E 89TH ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,2116,False,False,632,6,6,44,06,1182025,1846146,2006,ERROR,41.733046889,-87.608741291,"(41.733046889, -87.608741291)"
-9210579,HW356745,2013-07-05 22:00:00,100XX S PROSPECT AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,594,False,False,2213,22,19,72,14,1167485,1837705,2013,ERROR,41.710207052,-87.662249162,"(41.710207052, -87.662249162)"
-9287060,HW431457,2013-08-30 19:00:00,081XX S EUCLID AVE,0890,THEFT,FROM BUILDING,APARTMENT,1161,False,False,414,4,8,46,06,1190659,1851058,2013,2013-01-09 07:31:32,41.746322062,-87.576953181,"(41.746322062, -87.576953181)"
-5794196,HN603823,2007-09-21 23:33:00,078XX S EAST END AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,1314,True,False,414,4,8,43,18,1188864,1853073,2007,ERROR,41.751894519,-87.583466017,"(41.751894519, -87.583466017)"
-4344629,HL643695,2005-09-29 21:00:00,063XX S PULASKI RD,051A,ASSAULT,AGGRAVATED: HANDGUN,STREET,2707,False,False,813,8,13,65,04A,1150755,1862153,2005,ERROR,41.777638112,-87.722882838,"(41.777638112, -87.722882838)"
-6191990,HP281294,2008-04-15 15:00:00,006XX W WAVELAND AVE,0810,THEFT,OVER $500,STREET,932,False,False,2323,19,46,6,06,1171539,1925299,2008,ERROR,41.950485378,-87.644831764,"(41.950485378, -87.644831764)"
-8304880,HT539173,2011-10-12 03:00:00,056XX W WASHINGTON BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,3476,False,True,1512,15,29,25,14,1138540,1900218,2011,ERROR,41.882323613,-87.766743629,"(41.882323613, -87.766743629)"
-1587243,G356113,2001-06-18 20:30:00,045XX N CLARK ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,4800,False,False,1922,NA,NA,NA,07,1165538,1930394,2001,ERROR,41.964596441,-87.66674516,"(41.964596441, -87.66674516)"
-9232964,HW379118,2013-07-25 19:00:00,060XX N FRANCISCO AVE,0460,BATTERY,SIMPLE,APARTMENT,4723,False,False,2413,24,50,2,08B,1155879,1940034,2013,ERROR,41.991249811,-87.701997517,"(41.991249811, -87.701997517)"
-5382099,HN230597,2007-03-15 20:30:00,016XX N PAULINA ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,562,False,False,1433,14,1,24,05,1164792,1911272,2007,2007-02-08 01:58:25,41.912140543,-87.670031984,"(41.912140543, -87.670031984)"
-8703573,HV379876,2012-07-12 19:30:00,040XX W LAKE ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA GARAGE / OTHER PROPERTY,4140,True,False,1114,11,28,26,11,1149648,1901481,2012,ERROR,41.885580938,-87.725921713,"(41.885580938, -87.725921713)"
-6026181,HP129984,2008-01-17 13:00:00,029XX S LOOMIS ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,1082,True,False,923,9,11,60,07,1168373,1885564,2008,ERROR,41.841519091,-87.657620002,"(41.841519091, -87.657620002)"
-3088718,HJ812750,2003-12-11 23:20:00,019XX N KARLOV AVE,0650,BURGLARY,HOME INVASION,RESIDENCE,2638,False,False,2534,25,30,20,05,1148701,1912536,2003,ERROR,41.915935339,-87.729113388,"(41.915935339, -87.729113388)"
-3998545,HL355767,2005-05-14 10:20:57,023XX E 75TH ST,0454,BATTERY,AGG PO HANDS NO/MIN INJURY,GAS STATION,1092,True,False,334,3,7,43,08B,1193470,1855738,2005,ERROR,41.759096092,-87.566500388,"(41.759096092, -87.566500388)"
-7613298,HS418212,2010-07-19 00:30:00,056XX S NEENAH AVE,0820,THEFT,$500 AND UNDER,STREET,101,False,False,811,8,23,56,06,1133641,1866435,2010,ERROR,41.789704965,-87.785524264,"(41.789704965, -87.785524264)"
-6980905,HR385311,2009-06-19 20:00:00,024XX N CICERO AVE,0610,BURGLARY,FORCIBLE ENTRY,OTHER,902,False,False,2521,25,31,19,05,1143947,1915964,2009,ERROR,41.9254327,-87.746493281,"(41.9254327, -87.746493281)"
-4628112,HM225198,2006-03-10 20:00:00,089XX S RIDGELAND AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,1600,False,False,413,4,8,48,14,1189473,1845824,2006,ERROR,41.731987986,-87.581466439,"(41.731987986, -87.581466439)"
-9426414,HW569938,2013-12-12 11:45:00,012XX N ASHLAND AVE,031A,ROBBERY,ARMED: HANDGUN,CTA BUS STOP,2851,False,False,1424,14,1,24,03,1165544,1908172,2013,ERROR,41.903617945,-87.667357779,"(41.903617945, -87.667357779)"
-5040428,HM649718,2006-10-09 02:00:00,005XX S CAMPBELL AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,4722,False,False,1135,11,2,28,07,1159775,1897648,2006,ERROR,41.874860113,-87.68883898,"(41.874860113, -87.68883898)"
-9176835,HW321707,2013-06-16 19:30:00,055XX W GRAND AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,POLICE FACILITY/VEH PARKING LOT,3141,True,False,2515,25,29,19,18,1138769,1913443,2013,ERROR,41.918610464,-87.765581339,"(41.918610464, -87.765581339)"
-9817998,HX467737,2014-10-14 15:30:00,020XX N MILWAUKEE AVE,0820,THEFT,$500 AND UNDER,OTHER,287,False,False,1431,14,1,22,06,1159346,1913460,2014,ERROR,41.918258409,-87.689978754,"(41.918258409, -87.689978754)"
-1398760,G114124,2001-02-22 23:55:00,082XX S JEFFERY BL,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),273,False,False,414,NA,NA,NA,06,1190990,1850969,2001,2014-04-12 12:43:35,41.746069847,-87.575743214,"(41.746069847, -87.575743214)"
-4410684,HL706234,2005-10-29 09:00:00,044XX N RAVENSWOOD AVE,0810,THEFT,OVER $500,STREET,3661,False,False,1922,19,47,3,06,1163617,1929327,2005,2014-04-12 12:43:35,41.961709338,-87.673838335,"(41.961709338, -87.673838335)"
-3516152,HK591240,2004-08-29 15:30:00,048XX N PAULINA ST,0820,THEFT,$500 AND UNDER,STREET,171,False,False,2032,20,47,3,06,1164401,1932012,2004,2014-04-12 12:43:35,41.969060495,-87.670879593,"(41.969060495, -87.670879593)"
-4894707,HM510203,2006-07-30 11:00:00,047XX N SAWYER AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,RESIDENTIAL YARD (FRONT/BACK),4021,False,False,1713,17,33,14,14,1153814,1931419,2006,2006-02-08 04:53:53,41.967651297,-87.709823827,"(41.967651297, -87.709823827)"
-9622145,HX271986,2014-05-22 13:00:00,017XX N HALSTED ST,0820,THEFT,$500 AND UNDER,STREET,139,False,False,1813,18,43,7,06,1170734,1911649,2014,ERROR,41.91304688,-87.648191824,"(41.91304688, -87.648191824)"
-4015702,HL308318,2005-04-21 01:56:40,042XX S PRAIRIE AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,2725,True,False,214,2,3,38,16,1178699,1877152,2005,ERROR,41.81820674,-87.619983684,"(41.81820674, -87.619983684)"
-3849018,HL221418,2005-03-07 20:10:00,113XX S EDBROOKE AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,1114,True,False,531,5,9,49,04B,1179176,1829999,2005,ERROR,41.68880271,-87.619668717,"(41.68880271, -87.619668717)"
-6545175,HP615080,2008-10-08 08:55:39,073XX S PHILLIPS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,1777,False,True,334,3,7,43,08B,1193960,1856732,2008,2008-10-10 05:55:47,41.761811703,-87.564672045,"(41.761811703, -87.564672045)"
-2948741,HJ636339,2003-09-17 19:10:00,011XX N MAYFIELD AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,2818,False,True,1511,15,29,25,26,1136769,1907269,2003,ERROR,41.901704366,-87.773077802,"(41.901704366, -87.773077802)"
-5845070,HN655038,2007-10-15 06:15:00,056XX W LAKE ST,0810,THEFT,OVER $500,STREET,1243,False,False,1512,15,29,25,06,1138390,1902170,2007,2014-04-12 12:43:35,41.88768287,-87.767247156,"(41.88768287, -87.767247156)"
-5008839,HM618424,2006-09-23 13:20:00,017XX N HUMBOLDT BLVD,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,4129,False,False,1421,14,35,23,03,1156056,1911677,2006,2006-01-10 07:25:21,41.913432802,-87.702114688,"(41.913432802, -87.702114688)"
-8587110,HV261318,2012-04-27 21:40:00,084XX S MANISTEE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,4048,False,False,423,4,7,46,08B,1195995,1849774,2012,ERROR,41.742668288,-87.557443682,"(41.742668288, -87.557443682)"
-7872477,HT102978,2011-01-03 10:30:00,034XX E 133RD ST,0810,THEFT,OVER $500,STREET,3405,False,False,433,4,10,55,06,1201126,1817424,2011,2011-04-01 07:20:15,41.653768523,-87.539736212,"(41.653768523, -87.539736212)"
-9916472,HY105958,2014-11-26 12:00:00,030XX E 92ND ST,1110,DECEPTIVE PRACTICE,BOGUS CHECK,BANK,4969,False,False,424,4,10,46,11,1198084,1844583,2014,2015-08-01 12:39:18,41.728371838,-87.549962839,"(41.728371838, -87.549962839)"
-9490762,HX144691,2014-02-07 22:00:00,055XX S ALBANY AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,3213,False,False,824,8,14,63,14,1156621,1867583,2014,2014-12-02 00:40:02,41.792422534,-87.701231458,"(41.792422534, -87.701231458)"
-5857483,HN656362,2007-10-18 18:45:00,005XX E 107TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,2928,False,True,512,5,9,49,08B,1181507,1834155,2007,ERROR,41.700154031,-87.611007498,"(41.700154031, -87.611007498)"
-2535237,HJ109917,2002-12-20 15:00:00,074XX S COLFAX AVE,1562,SEX OFFENSE,AGG CRIMINAL SEXUAL ABUSE,APARTMENT,4858,False,False,334,NA,7,43,17,1194702,1856124,2002,ERROR,41.76012508,-87.561972571,"(41.76012508, -87.561972571)"
-8804120,HV477870,2012-09-16 03:10:00,047XX S LOOMIS BLVD,0460,BATTERY,SIMPLE,RESIDENTIAL YARD (FRONT/BACK),3213,False,False,933,9,3,61,08B,1167835,1873371,2012,ERROR,41.808071849,-87.659944973,"(41.808071849, -87.659944973)"
-3273588,HK301044,2004-04-14 11:45:00,012XX S KOSTNER AVE,031A,ROBBERY,ARMED: HANDGUN,STREET,1578,False,False,1011,10,24,29,03,1147285,1893972,2004,ERROR,41.865020926,-87.734791482,"(41.865020926, -87.734791482)"
-7072295,HR477075,2009-08-08 19:00:00,001XX E 47TH ST,0870,THEFT,POCKET-PICKING,BAR OR TAVERN,3597,False,False,224,2,3,38,06,1178126,1873860,2009,2009-12-08 13:56:14,41.809186244,-87.622185553,"(41.809186244, -87.622185553)"
-9162055,HW306837,2013-04-09 11:00:00,068XX S CORNELL AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,3084,False,True,332,3,5,43,26,1188350,1859644,2013,2013-09-06 03:34:44,41.769938203,-87.585140129,"(41.769938203, -87.585140129)"
-2688193,HJ309724,2003-04-18 19:30:00,022XX N STOCKTON DR,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,2743,False,False,1814,NA,43,7,14,1174127,1915368,2003,ERROR,41.923176926,-87.635615683,"(41.923176926, -87.635615683)"
-7691340,HS497006,2010-09-03 13:07:00,009XX E MARQUETTE RD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,ALLEY,1215,False,True,321,3,5,42,08B,1183558,1861405,2010,2010-08-09 07:54:48,41.774883602,-87.602650487,"(41.774883602, -87.602650487)"
-3282771,HK213978,2004-03-01 16:53:11,007XX W 61ST ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,1555,True,False,711,7,16,68,18,1172314,1864376,2004,ERROR,41.783291129,-87.643781806,"(41.783291129, -87.643781806)"
-4751054,HM261185,2006-03-30 12:02:00,011XX N RIDGEWAY AVE,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,STREET,2426,True,False,1112,11,27,23,18,1151136,1907139,2006,ERROR,41.901078036,-87.720308999,"(41.901078036, -87.720308999)"
-5962440,HN757326,2007-12-12 22:20:00,055XX W ADAMS ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,1914,False,False,1522,15,29,25,07,1139406,1898740,2007,2008-06-01 01:05:00,41.878252056,-87.76359965,"(41.878252056, -87.76359965)"
-1834047,G670254,2001-11-06 18:00:00,013XX W ERIE ST,0560,ASSAULT,SIMPLE,STREET,3142,False,False,1324,NA,NA,NA,08A,1167244,1904514,2001,ERROR,41.89354376,-87.661218582,"(41.89354376, -87.661218582)"
-3301460,HK334439,2004-04-30 13:30:00,022XX N AUSTIN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,4380,False,True,2515,25,37,19,08B,1136060,1914635,2004,ERROR,41.921930233,-87.77550612,"(41.921930233, -87.77550612)"
-2669621,HJ286771,2003-04-06 01:00:00,030XX W 55TH ST,0460,BATTERY,SIMPLE,STREET,2322,False,False,824,NA,14,63,08B,1157258,1867977,2003,ERROR,41.793490848,-87.69888499,"(41.793490848, -87.69888499)"
-6369079,HP455430,2008-07-16 06:10:00,076XX S PHILLIPS AVE,0560,ASSAULT,SIMPLE,STREET,4088,False,False,421,4,7,43,08A,1193825,1854450,2008,ERROR,41.755553029,-87.565241501,"(41.755553029, -87.565241501)"
-6855131,HR261020,2009-04-10 00:00:00,014XX W ERIE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,3068,False,False,1324,12,27,24,14,1166896,1904503,2009,ERROR,41.893521048,-87.662496975,"(41.893521048, -87.662496975)"
-4274768,HL591848,2005-09-04 20:00:00,033XX W WRIGHTWOOD AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,2202,False,False,1412,14,35,22,07,1153390,1917123,2005,ERROR,41.928430509,-87.711764046,"(41.928430509, -87.711764046)"
-6197251,HP285608,2008-04-17 18:30:00,030XX W FILLMORE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),3447,True,False,1134,11,28,29,14,1155966,1895253,2008,ERROR,41.868365618,-87.702888661,"(41.868365618, -87.702888661)"
-8433392,HV111791,2012-01-10 01:40:00,067XX S ST LAWRENCE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,3287,False,True,321,3,20,42,08B,1181358,1860502,2012,ERROR,41.772456691,-87.610743096,"(41.772456691, -87.610743096)"
-7505865,HS308821,2010-05-04 08:00:00,080XX S SANGAMON ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,3502,False,True,621,6,21,71,26,1171334,1851481,2010,ERROR,41.747927164,-87.647751629,"(41.747927164, -87.647751629)"
-7557528,HS361664,2010-06-15 19:11:00,016XX S KARLOV AVE,2027,NARCOTICS,POSS: CRACK,RESIDENCE,863,True,False,1012,10,24,29,18,1149364,1891408,2010,ERROR,41.85794497,-87.727225818,"(41.85794497, -87.727225818)"
-6702067,HR108720,2009-01-06 14:45:00,004XX S KEELER AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, GROUNDS",4011,False,False,1132,11,24,26,08B,1148424,1897525,2009,ERROR,41.874748911,-87.730518558,"(41.874748911, -87.730518558)"
-6400565,HP436822,2008-07-06 21:08:22,064XX S CALIFORNIA AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,1031,True,False,825,8,15,66,26,1158854,1861997,2008,2008-03-08 06:46:11,41.777048422,-87.693195807,"(41.777048422, -87.693195807)"
-3736578,HL102041,2005-01-02 00:30:00,065XX S LOOMIS BLVD,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,1274,False,False,725,7,17,67,06,1168076,1861511,2005,2014-04-12 12:43:35,41.775521427,-87.659402042,"(41.775521427, -87.659402042)"
-9845718,HX495009,2014-11-04 15:30:00,013XX W 95TH ST,0810,THEFT,OVER $500,"SCHOOL, PUBLIC, BUILDING",2826,False,False,2213,22,21,73,06,1169214,1841722,2014,2014-11-11 12:41:33,41.72119316,-87.655801492,"(41.72119316, -87.655801492)"
-5253960,HN130561,2007-01-16 00:00:00,024XX N LINCOLN AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,2443,False,False,1933,19,43,7,26,1170249,1916386,2007,ERROR,41.926056068,-87.649834888,"(41.926056068, -87.649834888)"
-3927220,HL299821,2005-04-16 22:55:00,009XX W 32ND PL,0460,BATTERY,SIMPLE,APARTMENT,951,False,False,924,9,11,60,08B,1170602,1883473,2005,ERROR,41.835732786,-87.64950144,"(41.835732786, -87.64950144)"
-5904951,HN703395,2007-11-11 10:00:00,053XX S PULASKI RD,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,GROCERY FOOD STORE,2864,False,False,815,8,23,62,11,1150568,1868904,2007,ERROR,41.796167541,-87.723392818,"(41.796167541, -87.723392818)"
-7616487,HS420429,2010-07-20 13:40:00,071XX S JEFFERY BLVD,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,679,True,False,333,3,5,43,06,1190817,1858158,2010,ERROR,41.765801254,-87.576145268,"(41.765801254, -87.576145268)"
-2377718,HH685782,2002-10-01 16:00:00,008XX S WESTERN AVE,0890,THEFT,FROM BUILDING,COMMERCIAL / BUSINESS OFFICE,3836,False,False,1224,NA,25,28,06,1160551,1896364,2002,ERROR,41.871320673,-87.686025384,"(41.871320673, -87.686025384)"
-3401548,HK459731,2004-06-11 02:00:00,025XX N FRANCISCO AVE,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,BANK,3180,False,False,1414,14,35,22,11,1156577,1916550,2004,ERROR,41.92679416,-87.700068437,"(41.92679416, -87.700068437)"
-4828468,HM412792,2006-06-14 02:11:58,0000X E GARFIELD BLVD,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,3102,False,False,233,2,20,40,03,1177699,1868405,2006,2006-07-10 09:43:17,41.794226904,-87.62391683,"(41.794226904, -87.62391683)"
-2097067,HH314898,2002-04-18 08:40:00,024XX W LAWRENCE AV,0560,ASSAULT,SIMPLE,OTHER,4703,True,False,2031,NA,NA,NA,08A,1159019,1931842,2002,ERROR,41.968706493,-87.690673825,"(41.968706493, -87.690673825)"
-3603839,HK646357,2004-09-25 03:10:00,008XX N ASHLAND AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,2836,False,False,1322,12,1,24,14,1165526,1906012,2004,ERROR,41.897691137,-87.667485477,"(41.897691137, -87.667485477)"
-9512342,HX167043,2014-02-27 23:16:00,064XX S CARPENTER ST,0497,BATTERY,AGGRAVATED DOMESTIC BATTERY: OTHER DANG WEAPON,RESIDENCE,4856,False,False,724,7,16,68,04B,1170455,1862310,2014,2014-03-03 00:39:23,41.777662488,-87.650657643,"(41.777662488, -87.650657643)"
-4261619,HL578454,2005-08-29 09:15:00,134XX S VERNON AVE,0610,BURGLARY,FORCIBLE ENTRY,BAR OR TAVERN,1530,False,False,533,5,9,54,05,1181807,1816319,2005,ERROR,41.651202555,-87.610456918,"(41.651202555, -87.610456918)"
-6432144,HP512424,2008-08-14 09:50:00,069XX S MICHIGAN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,600,True,False,322,3,6,69,26,1178318,1859155,2008,ERROR,41.768829929,-87.621927626,"(41.768829929, -87.621927626)"
-2698942,HJ315490,2003-04-22 05:50:00,051XX W CHICAGO AVE,0340,ROBBERY,ATTEMPT: STRONGARM-NO WEAPON,STREET,1909,False,False,1531,NA,37,25,03,1141640,1904877,2003,ERROR,41.89505175,-87.755244987,"(41.89505175, -87.755244987)"
-9682603,HX333155,2014-07-06 03:15:00,037XX N PLAINFIELD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,4490,False,True,1631,16,36,17,08B,1120024,1923862,2014,ERROR,41.9475215,-87.83423089,"(41.9475215, -87.83423089)"
-6546232,HP616924,2008-10-09 08:25:00,099XX S PEORIA ST,051A,ASSAULT,AGGRAVATED: HANDGUN,RESIDENTIAL YARD (FRONT/BACK),1859,False,False,2232,22,34,73,04A,1172031,1838751,2008,ERROR,41.712979002,-87.645570329,"(41.712979002, -87.645570329)"
-1881357,G729957,2001-12-05 20:38:41,119XX S INDIANA AV,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,2478,False,False,532,NA,NA,NA,05,1179753,1826087,2001,ERROR,41.678054476,-87.617675326,"(41.678054476, -87.617675326)"
-6182379,HP269231,2008-04-08 20:30:00,004XX E RANDOLPH ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,RESIDENCE,739,True,False,124,1,42,32,16,1179262,1901369,2008,2008-12-04 06:24:35,41.884646917,-87.617177553,"(41.884646917, -87.617177553)"
-10144936,HY333549,2015-07-05 15:00:00,044XX N HARDING AVE,031A,ROBBERY,ARMED: HANDGUN,APARTMENT,1023,False,False,1723,17,39,14,03,1149292,1929511,2015,2015-12-07 12:42:46,41.962504708,-87.726500599,"(41.962504708, -87.726500599)"
-7819756,HS629676,2010-11-22 19:30:00,047XX S PRINCETON AVE,0810,THEFT,OVER $500,PARK PROPERTY,1456,False,False,935,9,3,37,06,1175039,1873724,2010,ERROR,41.808882585,-87.633512079,"(41.808882585, -87.633512079)"
-1863051,G706647,2001-11-24 19:41:00,019XX S SPRINGFIELD AV,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,ALLEY,1573,True,False,1014,NA,NA,NA,15,1150673,1889905,2001,ERROR,41.853795099,-87.722460197,"(41.853795099, -87.722460197)"
-5895492,HN672912,2007-10-27 10:56:31,0000X E 103RD PL,2027,NARCOTICS,POSS: CRACK,APARTMENT,2190,False,False,512,5,9,49,18,1178109,1836390,2007,2007-09-11 09:43:39,41.706364714,-87.623382077,"(41.706364714, -87.623382077)"
-9222506,HW369005,2013-07-19 01:00:00,062XX S EBERHART AVE,041A,BATTERY,AGGRAVATED: HANDGUN,SIDEWALK,2207,False,False,313,3,20,42,04B,1180607,1863944,2013,ERROR,41.781919159,-87.613390392,"(41.781919159, -87.613390392)"
-9470449,HX123527,2014-01-22 11:30:00,064XX N SHERIDAN RD,0810,THEFT,OVER $500,CHA PARKING LOT/GROUNDS,3913,False,False,2432,24,40,1,06,1167086,1942670,2014,ERROR,41.998248939,-87.660699164,"(41.998248939, -87.660699164)"
-4564532,HM151795,2006-01-29 12:30:00,005XX N STATE ST,0870,THEFT,POCKET-PICKING,CTA TRAIN,4085,False,False,1834,18,42,8,06,1176315,1903875,2006,2006-02-02 04:21:12,41.891590492,-87.627923574,"(41.891590492, -87.627923574)"
-7288999,HR705891,2009-12-26 01:00:00,060XX S WOLCOTT AVE,0560,ASSAULT,SIMPLE,RESIDENCE,1934,True,True,714,7,15,67,08A,1164770,1864356,2009,ERROR,41.783398933,-87.671441226,"(41.783398933, -87.671441226)"
-4264409,HL581667,2005-08-30 18:11:00,012XX N STATE PKWY,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,4438,True,False,1824,18,43,8,05,1176096,1908851,2005,ERROR,41.905249818,-87.628577732,"(41.905249818, -87.628577732)"
-4500033,HL801967,2005-12-20 22:30:00,027XX S KARLOV AVE,0810,THEFT,OVER $500,STREET,3640,False,False,1031,10,22,30,06,1149446,1885667,2005,2014-04-12 12:43:35,41.842189353,-87.727073599,"(41.842189353, -87.727073599)"
-4521721,HM109102,2006-01-05 22:30:00,072XX S KEDZIE AVE,0281,CRIM SEXUAL ASSAULT,NON-AGGRAVATED,APARTMENT,2123,False,False,831,8,18,66,02,1156259,1856141,2006,ERROR,41.761031279,-87.702866467,"(41.761031279, -87.702866467)"
-5579203,HN387749,2007-06-05 23:00:00,028XX N LAKE SHORE DR,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,1495,False,False,2333,19,44,6,26,1173893,1918915,2007,2007-10-07 01:59:11,41.932915255,-87.636369534,"(41.932915255, -87.636369534)"
-4112242,HL450369,2005-06-28 17:30:00,043XX S GREENWOOD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,3025,False,False,2123,2,4,39,14,1184277,1876379,2005,ERROR,41.815956678,-87.599546214,"(41.815956678, -87.599546214)"
-3506201,HK555583,2004-08-13 16:42:00,044XX W IOWA ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,4369,False,False,1111,11,37,23,08B,1146528,1905586,2004,ERROR,41.896905546,-87.737274355,"(41.896905546, -87.737274355)"
-2409260,HH725869,2002-10-20 01:00:00,007XX W ROSCOE ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESTAURANT,2063,False,False,2331,NA,44,6,26,1170419,1922748,2002,ERROR,41.943509953,-87.649023628,"(41.943509953, -87.649023628)"
-2203186,HH000417,2002-06-19 17:15:00,057XX S LOWE AVE,0313,ROBBERY,ARMED: OTHER DANGEROUS WEAPON,SIDEWALK,4447,False,False,711,NA,3,68,03,1172986,1866791,2002,ERROR,41.789903341,-87.641246766,"(41.789903341, -87.641246766)"
-5011028,HM467142,2006-07-10 17:24:47,039XX W ARMITAGE AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,4204,True,False,2535,25,30,20,16,1150000,1912964,2006,2006-07-10 09:43:17,41.917084599,-87.724329742,"(41.917084599, -87.724329742)"
-2248732,HH525417,2002-06-08 09:00:00,014XX N WELLS ST,1120,DECEPTIVE PRACTICE,FORGERY,RESIDENCE,4807,False,False,1821,NA,27,8,10,1174403,1909875,2002,ERROR,41.908097724,-87.634765969,"(41.908097724, -87.634765969)"
-5736007,HN543999,2007-08-22 14:00:45,072XX N CLARK ST,0820,THEFT,$500 AND UNDER,STREET,248,False,False,2423,24,49,1,06,1163247,1948255,2007,2014-04-12 12:43:35,42.013656195,-87.674663459,"(42.013656195, -87.674663459)"
-1601570,G372788,2001-06-26 17:53:24,089XX S COTTAGE GROVE,0460,BATTERY,SIMPLE,RESIDENCE,3120,False,False,633,NA,NA,NA,08B,1183085,1845890,2001,ERROR,41.732319842,-87.604865988,"(41.732319842, -87.604865988)"
-8530168,HV207435,2012-03-21 05:45:00,009XX E 130TH PL,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,4330,False,False,533,5,9,54,14,1184644,1819036,2012,ERROR,41.658592656,-87.599992356,"(41.658592656, -87.599992356)"
-4862832,HM474817,2006-07-10 20:00:00,055XX S SHORE DR,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESTAURANT,2392,False,False,2132,2,5,41,26,1189509,1868821,2006,ERROR,41.795092837,-87.580597321,"(41.795092837, -87.580597321)"
-4406900,HK742347,2004-11-10 11:59:00,078XX S CORNELL AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,4344,True,False,411,4,8,43,18,1188552,1853522,2004,ERROR,41.753134073,-87.584595019,"(41.753134073, -87.584595019)"
-3010706,HJ715145,2003-10-17 11:00:00,062XX N SACRAMENTO AVE,1120,DECEPTIVE PRACTICE,FORGERY,RESIDENCE,1790,False,False,2413,24,50,2,10,1155185,1941017,2003,ERROR,41.993961219,-87.704523692,"(41.993961219, -87.704523692)"
-5557879,HN367489,2007-05-27 01:15:00,020XX N STAVE ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,4000,True,False,1431,14,1,22,15,1158880,1913340,2007,2010-02-06 10:34:17,41.917938701,-87.691694171,"(41.917938701, -87.691694171)"
-3066701,HJ786859,2003-11-22 23:30:00,105XX S MICHIGAN AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,1288,False,False,512,5,9,49,26,1178884,1835024,2003,ERROR,41.702598655,-87.62058548,"(41.702598655, -87.62058548)"
-6668993,HP742526,2008-12-19 22:00:00,002XX W EVERGREEN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,2419,False,False,1821,NA,43,8,14,NA,NA,2008,1999-08-11 15:39:40,NA,NA,
-6353756,HP432354,2008-07-04 05:00:00,013XX W 97TH ST,0320,ROBBERY,STRONGARM - NO WEAPON,RESIDENCE PORCH/HALLWAY,2082,False,False,2213,22,21,73,03,1168978,1840625,2008,ERROR,41.71818792,-87.656697493,"(41.71818792, -87.656697493)"
-6669938,HP740347,2008-11-26 12:00:00,052XX W FOSTER AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,1990,False,False,1623,16,45,11,06,1140701,1934124,2008,ERROR,41.97532578,-87.757972837,"(41.97532578, -87.757972837)"
-4826288,HM332892,2006-05-05 20:37:41,042XX S COTTAGE GROVE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,1058,True,False,2123,2,4,36,18,1182360,1876778,2006,ERROR,41.817096269,-87.606565687,"(41.817096269, -87.606565687)"
-7886005,HT116106,2011-01-12 09:30:00,054XX S HERMITAGE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,4182,False,False,932,9,16,61,05,1165565,1868597,2011,ERROR,41.795019934,-87.668406224,"(41.795019934, -87.668406224)"
-6705302,HR108700,2005-12-31 09:00:00,029XX N KEDZIE AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,889,False,False,1411,NA,35,21,06,NA,NA,2005,ERROR,NA,NA,
-8964081,HW112182,2013-01-10 00:00:00,044XX S ARCHER AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,2466,False,False,821,8,14,58,14,1155228,1874993,2013,2013-11-01 07:24:01,41.81278459,-87.706141165,"(41.81278459, -87.706141165)"
-7699490,HS506358,2010-09-03 11:00:00,067XX S EAST END AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,776,False,False,332,3,5,43,26,1188736,1860587,2010,ERROR,41.77251665,-87.583695111,"(41.77251665, -87.583695111)"
-9180878,HW325720,2013-06-19 11:15:00,110XX S WESTERN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,4683,False,True,2212,22,19,75,08B,1162390,1831737,2013,ERROR,41.693937278,-87.681073558,"(41.693937278, -87.681073558)"
-2297694,HH568797,2002-08-09 14:50:00,050XX W WASHINGTON BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,3902,True,False,1533,NA,28,25,18,1142677,1899991,2002,ERROR,41.881624743,-87.751557974,"(41.881624743, -87.751557974)"
-7644446,HS448315,2010-08-06 05:23:00,008XX W ADDISON ST,0860,THEFT,RETAIL THEFT,GAS STATION,2267,False,False,2331,19,44,6,06,1170247,1924094,2010,2010-09-08 10:51:39,41.947207196,-87.64961635,"(41.947207196, -87.64961635)"
-1423522,G144180,2001-03-11 14:00:00,016XX W GREENLEAF AV,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,4093,False,True,2423,NA,NA,NA,26,1164186,1947061,2001,ERROR,42.010359958,-87.671242346,"(42.010359958, -87.671242346)"
-5125870,HM723860,2006-11-16 14:00:00,011XX W WILSON AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",4704,True,False,2311,19,46,3,08B,1167577,1930654,2006,ERROR,41.965266117,-87.659240822,"(41.965266117, -87.659240822)"
-2492829,HH829410,2002-12-10 12:59:05,041XX W 16TH ST,502R,OTHER OFFENSE,VEHICLE TITLE/REG OFFENSE,STREET,2870,True,False,1012,NA,24,29,26,1148968,1891764,2002,ERROR,41.85892954,-87.728670191,"(41.85892954, -87.728670191)"
-3807185,HL176495,2005-02-11 23:40:00,053XX W HIRSCH ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,3180,False,False,2532,25,37,25,07,1140742,1908843,2005,ERROR,41.905951477,-87.758445541,"(41.905951477, -87.758445541)"
-5654829,HN465341,2007-07-14 01:00:00,053XX S WABASH AVE,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,1188,False,False,232,2,3,40,07,1177577,1869771,2007,ERROR,41.7979781,-87.624322882,"(41.7979781, -87.624322882)"
-7897069,HT126881,2011-01-19 20:50:00,016XX N KIMBALL AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,RESIDENCE,3699,True,False,1422,14,26,23,18,1153494,1910674,2011,ERROR,41.910731836,-87.7115537,"(41.910731836, -87.7115537)"
-6053794,HP155338,2008-02-02 02:15:00,002XX N ASHLAND AVE,0560,ASSAULT,SIMPLE,TAVERN/LIQUOR STORE,4130,False,False,1333,12,27,28,08A,1165726,1901925,2008,2008-07-02 06:38:43,41.886471844,-87.666867499,"(41.886471844, -87.666867499)"
-5565328,HN356933,2007-05-21 21:30:57,070XX S JEFFERY BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,VEHICLE NON-COMMERCIAL,4147,True,False,331,3,5,43,18,1190805,1858575,2007,ERROR,41.766945825,-87.576175796,"(41.766945825, -87.576175796)"
-3248772,HK265125,2004-03-27 02:54:00,0000X W DIVISION ST,3710,INTERFERENCE WITH PUBLIC OFFICER,RESIST/OBSTRUCT/DISARM OFFICER,SIDEWALK,4310,True,False,1824,18,42,8,24,1175892,1908408,2004,2014-04-12 12:43:35,41.904038802,-87.629340436,"(41.904038802, -87.629340436)"
-1757877,G572420,2001-09-24 01:00:00,009XX N HARDING AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,797,False,False,1112,NA,NA,NA,07,1149924,1905981,2001,ERROR,41.897924046,-87.72479098,"(41.897924046, -87.72479098)"
-2774357,HJ418762,2003-06-09 22:26:00,017XX N TALMAN AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,826,False,False,1421,NA,1,24,03,1158398,1911205,2003,ERROR,41.912089977,-87.693523584,"(41.912089977, -87.693523584)"
-8926173,HV598195,2012-12-11 15:00:00,001XX N KEDZIE AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,4028,True,False,1331,12,27,27,26,1155069,1900663,2012,2012-11-12 15:22:54,41.883229246,-87.706036593,"(41.883229246, -87.706036593)"
-5741562,HN545599,2007-08-23 11:06:35,056XX W CORCORAN PL,0560,ASSAULT,SIMPLE,GROCERY FOOD STORE,2009,True,False,1512,15,29,25,08A,1138929,1901964,2007,ERROR,41.887107805,-87.765272755,"(41.887107805, -87.765272755)"
-7388091,HS188642,2010-03-02 16:00:00,059XX W AUGUSTA BLVD,0460,BATTERY,SIMPLE,SIDEWALK,4552,False,False,1511,15,29,25,08B,1136861,1906101,2010,2010-04-03 11:52:03,41.898497579,-87.772767903,"(41.898497579, -87.772767903)"
-4148394,HL472015,2005-07-09 03:00:00,004XX W 98TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,VEHICLE NON-COMMERCIAL,1031,False,True,2223,22,21,73,08B,1174873,1839956,2005,ERROR,41.716222926,-87.635126228,"(41.716222926, -87.635126228)"
-7111964,HR520191,2009-09-04 19:15:00,040XX W LAKE ST,2024,NARCOTICS,POSS: HEROIN(WHITE),CTA PLATFORM,1207,True,False,1114,11,28,26,18,1149614,1901481,2009,2009-04-09 20:50:05,41.885581599,-87.726046569,"(41.885581599, -87.726046569)"
-1498551,G234518,2001-04-24 20:15:00,005XX N COLUMBUS DR,0810,THEFT,OVER $500,STREET,1805,False,False,1834,NA,NA,NA,06,1178452,1903693,2001,2014-04-12 12:43:35,41.891042598,-87.620081013,"(41.891042598, -87.620081013)"
-1470135,G198684,2001-03-30 19:30:00,049XX W WEST END AV,0460,BATTERY,SIMPLE,APARTMENT,883,False,True,1532,NA,NA,NA,08B,1143503,1900491,2001,ERROR,41.882981399,-87.748512379,"(41.882981399, -87.748512379)"
-4380629,HL672144,2005-10-14 08:30:00,002XX W 87TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),2556,False,False,622,6,21,44,14,1176403,1847255,2005,ERROR,41.736218161,-87.629303966,"(41.736218161, -87.629303966)"
-3828054,HL198936,2005-02-23 21:00:00,049XX N MILWAUKEE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),1349,False,False,1623,16,45,11,14,1139081,1932636,2005,ERROR,41.971272283,-87.763966593,"(41.971272283, -87.763966593)"
-5649370,HN457869,2007-07-10 15:30:00,121XX S PRINCETON AVE,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,STREET,2266,False,False,523,5,34,53,04A,1176434,1824279,2007,ERROR,41.673168048,-87.629877991,"(41.673168048, -87.629877991)"
-1926664,G777431,2001-12-30 12:22:03,075XX S COTTAGE GROVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,2792,True,False,624,NA,NA,NA,18,1182916,1854849,2001,ERROR,41.756908237,-87.605207385,"(41.756908237, -87.605207385)"
-3349468,HK391905,2004-05-25 15:00:00,015XX E 93RD ST,0820,THEFT,$500 AND UNDER,SIDEWALK,483,False,False,413,4,8,48,06,1187948,1843656,2004,2014-04-12 12:43:35,41.726075187,-87.587121912,"(41.726075187, -87.587121912)"
-9289687,HW434804,2013-09-02 18:45:00,057XX S PRAIRIE AVE,2023,NARCOTICS,POSS: HEROIN(BRN/TAN),VACANT LOT/LAND,3421,True,False,232,2,20,40,18,1178985,1866850,2013,2013-03-09 11:05:41,41.789930619,-87.619248524,"(41.789930619, -87.619248524)"
-4657553,HM172738,2006-02-09 20:15:00,001XX E WACKER DR,1505,PROSTITUTION,CALL OPERATION,HOTEL/MOTEL,2254,True,False,124,1,42,32,16,1177757,1902582,2006,2006-01-04 03:33:39,41.888009783,-87.622667166,"(41.888009783, -87.622667166)"
-1848637,G686275,2001-10-17 17:00:00,031XX W 103 ST,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,BANK,2485,False,False,2211,NA,NA,NA,11,1157251,1836208,2001,ERROR,41.706311774,-87.699768468,"(41.706311774, -87.699768468)"
-1714907,G518024,2001-08-30 00:38:59,122XX S PEORIA ST,0326,ROBBERY,AGGRAVATED VEHICULAR HIJACKING,STREET,2852,False,False,524,NA,NA,NA,03,1172502,1823677,2001,ERROR,41.671603244,-87.644286984,"(41.671603244, -87.644286984)"
-8501862,HV177514,2012-02-19 16:00:00,027XX N MOBILE AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,3247,False,False,2512,25,29,19,26,1133987,1917369,2012,ERROR,41.9294694,-87.783058569,"(41.9294694, -87.783058569)"
-2788429,HJ437145,2003-06-17 08:00:00,084XX W GREGORY ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,3143,False,False,1614,NA,41,76,14,NA,NA,2003,ERROR,NA,NA,
-7308921,HS113429,2010-01-10 10:28:00,130XX S DR MARTIN LUTHER KING JR DR,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,4532,True,True,533,5,9,54,08B,1180948,1818658,2010,2010-11-01 05:08:49,41.657640861,-87.613528366,"(41.657640861, -87.613528366)"
-9411869,HW555535,2013-11-28 08:00:00,032XX W DOUGLAS BLVD,0890,THEFT,FROM BUILDING,COMMERCIAL / BUSINESS OFFICE,2178,False,False,1022,10,24,29,06,1154818,1893309,2013,2013-02-12 13:43:47,41.86305413,-87.707155285,"(41.86305413, -87.707155285)"
-6863000,HR267765,2009-04-14 15:32:00,016XX E 53RD ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,TAXICAB,1052,False,False,2132,2,4,41,11,1188194,1870489,2009,ERROR,41.799701444,-87.585366136,"(41.799701444, -87.585366136)"
-8079527,HT312157,2011-05-17 14:00:00,063XX N CLAREMONT AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,4304,False,False,2413,24,50,2,05,1159464,1941832,2011,ERROR,41.99611031,-87.688761143,"(41.99611031, -87.688761143)"
-5959995,HN755465,2007-11-06 08:00:00,076XX S PHILLIPS AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,BANK,3955,False,True,421,4,7,43,11,1193897,1854798,2007,2008-12-01 01:05:03,41.756506203,-87.564966253,"(41.756506203, -87.564966253)"
-4579723,HM166790,2006-02-06 17:30:00,084XX S INGLESIDE AVE,0460,BATTERY,SIMPLE,SIDEWALK,2807,False,False,632,6,8,44,08B,1183976,1849454,2006,ERROR,41.74207911,-87.601490881,"(41.74207911, -87.601490881)"
-5789960,HN600058,2007-09-20 10:00:00,071XX W DICKENS AVE,0820,THEFT,$500 AND UNDER,STREET,270,False,False,2512,25,36,25,06,1127875,1913121,2007,2014-04-12 12:43:35,41.917917786,-87.805614941,"(41.917917786, -87.805614941)"
-4775666,HM388940,2006-05-30 13:00:00,072XX S KIMBARK AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,2394,False,False,324,3,5,69,07,1186082,1857490,2006,2006-04-06 04:24:23,41.764081271,-87.593521441,"(41.764081271, -87.593521441)"
-7711055,HS518043,2010-09-16 12:05:00,026XX E 79TH ST,0460,BATTERY,SIMPLE,OTHER,3669,False,False,421,4,7,43,08B,1195505,1853147,2010,ERROR,41.751936174,-87.559127815,"(41.751936174, -87.559127815)"
-8137536,HT371329,2011-06-29 00:08:00,038XX W FLOURNOY ST,041A,BATTERY,AGGRAVATED: HANDGUN,RESIDENTIAL YARD (FRONT/BACK),4277,False,False,1133,11,24,26,04B,1150802,1896712,2011,ERROR,41.872471787,-87.721808751,"(41.872471787, -87.721808751)"
-7387445,HS189260,2010-01-24 20:37:00,016XX W 32ND ST,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,3922,False,False,922,9,11,59,26,1166016,1883413,2010,2010-05-03 08:02:26,41.835667093,-87.666330688,"(41.835667093, -87.666330688)"
-1834918,G668790,2001-11-05 19:00:00,015XX N SPRINGFIELD AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,3471,False,False,2535,NA,NA,NA,07,1150124,1910319,2001,ERROR,41.909824052,-87.723943209,"(41.909824052, -87.723943209)"
-6824043,HR227480,2009-03-21 10:35:00,124XX S EMERALD AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,896,False,False,523,5,34,53,14,1173548,1822149,2009,ERROR,41.667387143,-87.640503609,"(41.667387143, -87.640503609)"
-3334126,HK375058,2004-05-18 19:25:00,018XX W FARWELL AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,1815,False,False,2424,24,49,1,26,1162731,1945686,2004,ERROR,42.00661768,-87.676634619,"(42.00661768, -87.676634619)"
-4991479,HM599065,2006-09-13 14:20:00,004XX N STATE ST,0460,BATTERY,SIMPLE,RESTAURANT,4867,True,False,1831,18,42,8,08B,1176246,1903580,2006,ERROR,41.890782553,-87.628185879,"(41.890782553, -87.628185879)"
-4779523,HM394310,2006-06-02 15:30:00,042XX N CICERO AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,2942,False,False,1624,16,45,15,14,1143559,1928046,2006,2006-07-06 04:08:49,41.958594104,-87.747615647,"(41.958594104, -87.747615647)"
-7209980,HR625015,2009-11-04 00:05:00,012XX W DIVISION ST,2022,NARCOTICS,POSS: COCAINE,STREET,851,True,False,1323,12,27,24,18,1168223,1908112,2009,2009-04-11 01:46:02,41.903395808,-87.657518982,"(41.903395808, -87.657518982)"
-4855527,HM466819,2006-07-06 21:00:00,055XX N CLARK ST,0890,THEFT,FROM BUILDING,CHURCH/SYNAGOGUE/PLACE OF WORSHIP,3503,False,False,2012,20,40,77,06,1164942,1936997,2006,ERROR,41.982728013,-87.668748145,"(41.982728013, -87.668748145)"
-5216189,HN101090,2007-01-01 14:47:51,002XX E 136TH ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,4188,False,False,533,5,9,54,05,1180454,1815072,2007,ERROR,41.647811613,-87.615445268,"(41.647811613, -87.615445268)"
-6782563,HR197644,2009-03-03 19:00:00,076XX S CRANDON AVE,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",1821,False,False,414,4,7,43,06,1192816,1854954,2009,2009-04-03 06:03:14,41.756960706,-87.568922754,"(41.756960706, -87.568922754)"
-3363754,HK410972,2004-06-05 17:27:50,110XX S AVENUE M,0560,ASSAULT,SIMPLE,STREET,4448,True,False,433,4,10,52,08A,1201506,1832221,2004,ERROR,41.694363358,-87.537846019,"(41.694363358, -87.537846019)"
-9749727,HX399758,2014-08-23 16:50:00,101XX S VERNON AVE,0810,THEFT,OVER $500,RESIDENCE,4015,False,False,511,5,9,49,06,1181071,1837939,2014,ERROR,41.71054785,-87.612487964,"(41.71054785, -87.612487964)"
-3810647,HL177913,2005-02-12 19:10:00,072XX N SHERIDAN RD,1365,CRIMINAL TRESPASS,TO RESIDENCE,NURSING HOME/RETIREMENT HOME,4948,True,False,2423,24,49,1,26,1166257,1947965,2005,ERROR,42.012796343,-87.66359639,"(42.012796343, -87.66359639)"
-2167336,HH418150,2002-06-04 06:00:00,001XX W SUPERIOR ST,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,3092,False,False,1832,NA,42,8,06,1175214,1905375,2002,2014-04-12 12:43:35,41.895731339,-87.631921962,"(41.895731339, -87.631921962)"
-2336983,HH635814,2002-09-08 20:00:00,086XX S ELIZABETH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,1484,False,False,613,NA,21,71,14,1169547,1847226,2002,ERROR,41.736289738,-87.654422772,"(41.736289738, -87.654422772)"
-2283233,HH562586,2002-08-06 12:00:00,032XX W FLOURNOY ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,2392,False,True,1134,NA,24,27,26,1154671,1896885,2002,ERROR,41.87287,-87.707599224,"(41.87287, -87.707599224)"
-9145362,HW290799,2013-05-25 18:20:00,066XX S HALSTED ST,1330,CRIMINAL TRESPASS,TO LAND,SMALL RETAIL STORE,2918,False,False,723,7,6,68,26,1172149,1860963,2013,ERROR,41.773929092,-87.644486986,"(41.773929092, -87.644486986)"
-8134619,HT369020,2011-06-27 18:35:00,094XX S WESTERN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,1575,True,False,2221,22,19,72,18,1162082,1841641,2011,ERROR,41.721121942,-87.68192693,"(41.721121942, -87.68192693)"
-1662674,G451886,2001-07-31 08:30:00,077XX S CALUMET AV,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,4005,False,False,623,NA,NA,NA,05,1179859,1853565,2001,ERROR,41.753455253,-87.616449881,"(41.753455253, -87.616449881)"
-5926846,HN721506,2007-11-21 06:00:00,012XX W 98TH ST,0281,CRIM SEXUAL ASSAULT,NON-AGGRAVATED,RESIDENCE,3077,False,False,2213,22,21,73,02,1169700,1839736,2007,ERROR,41.715732768,-87.654078763,"(41.715732768, -87.654078763)"
-2621303,HJ224035,2003-03-05 10:00:00,019XX W GREENLEAF AVE,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,1523,False,False,2424,NA,49,1,07,1162189,1946919,2003,ERROR,42.010012453,-87.67859402,"(42.010012453, -87.67859402)"
-2334124,HH631588,2002-09-06 21:00:00,025XX N LINDER AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,3020,False,False,2515,NA,30,19,05,1139329,1916155,2002,ERROR,41.926042297,-87.763457601,"(41.926042297, -87.763457601)"
-8519471,HV196442,2012-03-12 21:40:00,100XX S CRANDON AVE,033A,ROBBERY,ATTEMPT: ARMED-HANDGUN,STREET,3149,False,False,431,4,7,51,03,1193441,1839086,2012,2012-11-04 16:56:10,41.713402235,-87.567149974,"(41.713402235, -87.567149974)"
-2508938,HH852379,2002-12-21 02:00:00,025XX N KEDZIE BLVD,0890,THEFT,FROM BUILDING,OTHER,3585,False,False,1414,NA,35,22,06,1154663,1916875,2002,ERROR,41.92772456,-87.707092848,"(41.92772456, -87.707092848)"
-4690771,HM291349,2006-04-14 20:50:00,012XX W 72ND ST,0460,BATTERY,SIMPLE,SIDEWALK,1089,False,False,734,7,17,67,08B,1169088,1857072,2006,ERROR,41.763318426,-87.655820347,"(41.763318426, -87.655820347)"
-3831802,HL200488,2005-02-24 21:20:56,124XX S EGGLESTON AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,RESIDENCE,3847,False,False,523,5,34,53,15,1175509,1822487,2005,2014-04-12 12:43:35,41.668271184,-87.633316824,"(41.668271184, -87.633316824)"
-4288666,HL600343,2005-09-09 00:45:00,033XX W OGDEN AVE,2850,PUBLIC PEACE VIOLATION,BOMB THREAT,POLICE FACILITY/VEH PARKING LOT,4337,False,False,1024,10,24,29,26,1154500,1890985,2005,ERROR,41.856683172,-87.708384737,"(41.856683172, -87.708384737)"
-6404121,HP476419,2008-07-26 18:15:00,068XX S CLAREMONT AVE,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,STREET,3421,False,True,832,8,17,66,04A,1161871,1859315,2008,2008-07-08 10:22:37,41.76962648,-87.682209929,"(41.76962648, -87.682209929)"
-7629586,HS432289,2010-07-26 14:00:00,011XX W 62ND ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,2636,False,True,712,7,16,68,08B,1169767,1863724,2010,ERROR,41.781557634,-87.653138853,"(41.781557634, -87.653138853)"
-1504741,G234331,2001-04-24 19:30:00,027XX N SAWYER AV,2027,NARCOTICS,POSS: CRACK,SIDEWALK,4441,True,False,1412,NA,NA,NA,18,1154131,1917989,2001,ERROR,41.930792109,-87.709017944,"(41.930792109, -87.709017944)"
-8748002,HV422718,2012-08-09 12:20:00,103XX S AVENUE L,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,3008,False,False,432,4,10,52,07,1201793,1836826,2012,ERROR,41.706992596,-87.536639296,"(41.706992596, -87.536639296)"
-3768741,HL139720,2005-01-23 13:30:00,035XX N ELSTON AVE,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,1452,False,False,1733,17,35,21,06,1154312,1923546,2005,2014-04-12 12:43:35,41.946037311,-87.708203891,"(41.946037311, -87.708203891)"
-4462522,HL759778,2005-11-28 11:36:00,074XX N GREENVIEW AVE,0460,BATTERY,SIMPLE,APARTMENT,3452,False,False,2422,24,49,1,08B,1164955,1949681,2005,ERROR,42.017532926,-87.668338035,"(42.017532926, -87.668338035)"
-6045601,HP146797,2008-01-26 15:00:00,006XX W WAYMAN ST,0610,BURGLARY,FORCIBLE ENTRY,CONSTRUCTION SITE,1006,False,False,1212,12,27,28,05,1171516,1902377,2008,2008-01-02 20:14:43,41.887586797,-87.645592029,"(41.887586797, -87.645592029)"
-5682032,HN490569,2007-07-23 00:01:00,004XX N DEARBORN ST,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,RESTAURANT,3345,False,False,1831,18,42,8,11,1175894,1903544,2007,ERROR,41.890691698,-87.629479667,"(41.890691698, -87.629479667)"
-8520242,HV196949,2012-03-14 12:15:00,015XX W 45TH ST,0312,ROBBERY,ARMED:KNIFE/CUTTING INSTRUMENT,STREET,4644,False,False,924,9,3,61,03,1167048,1874840,2012,ERROR,41.812119833,-87.662789453,"(41.812119833, -87.662789453)"
-8071070,HT292750,2011-05-12 17:18:00,006XX E GRAND AVE,0860,THEFT,RETAIL THEFT,OTHER,2071,True,False,1834,18,42,8,06,1180772,1904096,2011,ERROR,41.892095212,-87.611548469,"(41.892095212, -87.611548469)"
-9957363,HY146143,2014-12-27 21:00:00,013XX W CHICAGO AVE,0810,THEFT,OVER $500,RESIDENCE,904,False,False,1215,12,27,24,06,1167548,1905439,2014,ERROR,41.896075483,-87.660075443,"(41.896075483, -87.660075443)"
-10067136,HY256096,2015-05-11 16:00:00,087XX S BEVERLY AVE,0820,THEFT,$500 AND UNDER,STREET,466,False,False,2221,22,21,71,06,1164962,1846395,2015,ERROR,41.734107372,-87.671244102,"(41.734107372, -87.671244102)"
-8115927,HT350334,2011-06-16 12:00:00,058XX N KENMORE AVE,0560,ASSAULT,SIMPLE,RESIDENCE PORCH/HALLWAY,2492,False,False,2022,20,48,77,08A,1168225,1939040,2011,ERROR,41.988263519,-87.656614711,"(41.988263519, -87.656614711)"
-3295784,HK327947,2004-04-27 12:42:44,055XX W CHICAGO AVE,0860,THEFT,RETAIL THEFT,DRUG STORE,630,True,False,1524,15,37,25,06,1139149,1904830,2004,ERROR,41.894968468,-87.764395055,"(41.894968468, -87.764395055)"
-4118774,HL445725,2005-06-26 18:35:00,076XX S CICERO AVE,0560,ASSAULT,SIMPLE,OTHER,3553,True,False,833,8,13,65,08A,1145766,1853738,2005,ERROR,41.75464162,-87.741385158,"(41.75464162, -87.741385158)"
-4313295,HL623082,2005-09-19 22:30:00,078XX S CREGIER AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,2726,False,True,414,4,8,43,26,1189530,1853090,2005,ERROR,41.751925214,-87.581024925,"(41.751925214, -87.581024925)"
-5275302,HM771318,2006-12-12 14:30:00,016XX N VINE ST,0460,BATTERY,SIMPLE,CHA APARTMENT,3342,False,False,1813,18,43,7,08B,1171741,1910972,2006,2007-04-02 09:14:02,41.911167023,-87.644512341,"(41.911167023, -87.644512341)"
-9571206,HX221842,2014-04-12 21:00:00,036XX W ARMITAGE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,4457,True,True,2535,25,26,22,08B,1151558,1912999,2014,ERROR,41.91715014,-87.718604687,"(41.91715014, -87.718604687)"
-8225563,HT459910,2011-06-02 10:00:00,034XX N OVERHILL AVE,1120,DECEPTIVE PRACTICE,FORGERY,RESIDENCE,4580,False,True,1631,16,36,17,10,1124187,1921874,2011,ERROR,41.941998641,-87.818972224,"(41.941998641, -87.818972224)"
-3698614,HK800968,2004-12-06 07:28:00,039XX W 55TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,3905,False,False,822,8,23,62,14,1151189,1867799,2004,ERROR,41.793123142,-87.721144397,"(41.793123142, -87.721144397)"
-6279492,HP366498,2008-05-31 01:49:48,024XX E 77TH ST,0560,ASSAULT,SIMPLE,STREET,3375,False,False,421,4,7,43,08A,1193751,1854422,2008,2008-10-06 17:19:23,41.755478007,-87.565513603,"(41.755478007, -87.565513603)"
-3809022,HL179005,2005-02-12 23:00:00,079XX S SACRAMENTO AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,4081,False,False,835,8,18,70,14,1157711,1852042,2005,ERROR,41.749753656,-87.69765575,"(41.749753656, -87.69765575)"
-2448823,HH773171,2002-11-11 16:55:00,024XX W 46TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,4003,False,False,914,NA,12,58,08B,1160595,1874059,2002,ERROR,41.810112404,-87.686480579,"(41.810112404, -87.686480579)"
-8784290,HV458206,2012-09-02 15:00:00,010XX W BRYN MAWR AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,324,False,False,2022,20,48,77,06,1168220,1937410,2012,2012-03-09 10:10:19,41.98379087,-87.656680467,"(41.98379087, -87.656680467)"
-5066596,HM672873,2006-10-21 01:10:00,063XX S ELIZABETH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,ALLEY,1796,False,True,724,7,16,67,08B,1169122,1862553,2006,2006-04-11 05:54:38,41.778358245,-87.655537413,"(41.778358245, -87.655537413)"
-9049125,HW191204,2013-03-13 19:00:00,031XX N SHEFFIELD AVE,0820,THEFT,$500 AND UNDER,STREET,247,False,False,1933,19,44,6,06,1169094,1921197,2013,ERROR,41.939282865,-87.65393884,"(41.939282865, -87.65393884)"
-7764239,HS572350,2010-10-19 16:30:00,048XX N SPAULDING AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,3988,True,False,1713,17,39,14,18,1153546,1931968,2010,ERROR,41.969163134,-87.710794575,"(41.969163134, -87.710794575)"
-2410766,HH726508,2002-10-19 20:00:00,041XX W GRANVILLE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,3555,False,False,1711,NA,39,12,14,1147823,1940905,2002,ERROR,41.993799031,-87.731607315,"(41.993799031, -87.731607315)"
-6473955,HP548589,2008-09-02 10:38:27,002XX S LAVERGNE AVE,2017,NARCOTICS,MANU/DELIVER:CRACK,SIDEWALK,4717,True,False,1533,15,28,25,18,1143332,1898687,2008,2008-04-09 10:02:31,41.878034197,-87.749185395,"(41.878034197, -87.749185395)"
-3811299,HL180923,2005-02-14 14:30:00,001XX W CERMAK RD,0890,THEFT,FROM BUILDING,RESTAURANT,4526,False,False,2111,9,25,34,06,1175413,1889712,2005,ERROR,41.852746625,-87.631661358,"(41.852746625, -87.631661358)"
-8254428,HT488217,2011-09-09 08:20:00,012XX S KILDARE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,1355,True,True,1011,10,24,29,08B,1147879,1893831,2011,2011-10-09 08:30:21,41.864622619,-87.732614499,"(41.864622619, -87.732614499)"
-3052579,HJ766530,2003-11-18 11:15:00,010XX W NORTH AVE,0312,ROBBERY,ARMED:KNIFE/CUTTING INSTRUMENT,SMALL RETAIL STORE,1967,False,False,1811,18,32,7,03,1169395,1910873,2003,ERROR,41.910946738,-87.653133577,"(41.910946738, -87.653133577)"
-2802395,HJ456446,2003-06-26 17:00:00,014XX N MAPLEWOOD AVE,0810,THEFT,OVER $500,STREET,3051,False,False,1423,14,26,24,06,1159110,1909223,2003,2014-04-12 12:43:35,41.906636608,-87.690962425,"(41.906636608, -87.690962425)"
-8467589,HV143840,2012-02-03 12:45:00,064XX S ST LAWRENCE AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE-GARAGE,2128,False,False,312,3,20,42,26,1181306,1862283,2012,2012-05-02 07:39:28,41.777345128,-87.610878867,"(41.777345128, -87.610878867)"
-8672044,HV346931,2012-06-22 02:30:00,002XX N ASHLAND AVE,0820,THEFT,$500 AND UNDER,STREET,66,False,False,1333,12,27,28,06,1165729,1901839,2012,ERROR,41.88623579,-87.666858935,"(41.88623579, -87.666858935)"
-2087824,HH307538,2002-04-14 17:00:00,112XX S CORLISS AV,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),121,False,False,531,NA,NA,NA,06,1184357,1830749,2002,2014-04-12 12:43:35,41.690741459,-87.60067812,"(41.690741459, -87.60067812)"
-8209337,HT443374,2011-08-11 18:35:00,079XX S HALSTED ST,031A,ROBBERY,ARMED: HANDGUN,ATM (AUTOMATIC TELLER MACHINE),1050,False,False,621,6,17,71,03,1172299,1852411,2011,ERROR,41.750458051,-87.644188274,"(41.750458051, -87.644188274)"
-8256672,HT490030,2011-09-10 01:45:00,116XX S DOTY AVE W,0460,BATTERY,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),2252,False,False,532,5,9,54,08B,1183932,1827765,2011,2011-11-09 06:14:39,41.68256287,-87.602326758,"(41.68256287, -87.602326758)"
-5916746,HN714104,2007-11-18 02:00:00,026XX W CORTLAND ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,644,False,False,1421,14,1,22,14,1158683,1912488,2007,ERROR,41.91560479,-87.692441353,"(41.91560479, -87.692441353)"
-8191615,HT405749,2011-07-19 18:00:00,009XX W WINONA ST,2032,NARCOTICS,MANU/DELIVER: METHAMPHETAMINES,RESIDENCE,2116,True,False,2024,20,48,3,18,1169251,1934368,2011,2011-01-08 11:42:26,41.975421135,-87.652977507,"(41.975421135, -87.652977507)"
-2973180,HJ659852,2003-09-28 22:39:00,097XX S HARVARD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,3610,False,True,511,5,21,49,08B,1175644,1839981,2003,ERROR,41.716274337,-87.632301694,"(41.716274337, -87.632301694)"
-2653149,HJ262801,2003-03-26 22:50:00,083XX S ELLIS AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,721,False,False,632,NA,8,44,05,1184303,1849697,2003,ERROR,41.742738291,-87.600285187,"(41.742738291, -87.600285187)"
-2813880,HJ468442,2003-07-02 17:55:00,103XX S CHARLES ST,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,OTHER,1416,False,False,2212,22,19,72,07,1168443,1836435,2003,ERROR,41.70670143,-87.658777228,"(41.70670143, -87.658777228)"
-6853270,HR212470,2009-03-12 20:24:00,014XX W 63RD ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,3705,True,False,725,7,16,67,18,1167591,1862923,2009,ERROR,41.779406548,-87.661139539,"(41.779406548, -87.661139539)"
-4345470,HL643338,2005-09-29 12:30:00,054XX S WENTWORTH AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,3555,True,False,232,2,3,37,06,1175924,1869100,2005,ERROR,41.796174063,-87.6304048,"(41.796174063, -87.6304048)"
-3145808,HK138334,2004-01-20 14:45:00,001XX W 104TH ST,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,RESIDENCE,1034,False,True,512,5,34,49,20,1177238,1836035,2004,ERROR,41.705410205,-87.626582292,"(41.705410205, -87.626582292)"
-2564500,HJ117629,2003-01-10 06:30:00,046XX S SACRAMENTO AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,4870,True,False,912,NA,14,58,16,1157201,1873342,2003,ERROR,41.808214288,-87.698948812,"(41.808214288, -87.698948812)"
-2168460,HH416094,2002-05-31 17:00:00,063XX S ROCKWELL ST,0460,BATTERY,SIMPLE,STREET,1695,False,False,825,NA,15,66,08B,1160083,1862752,2002,ERROR,41.779095055,-87.688669541,"(41.779095055, -87.688669541)"
-3960086,HL326851,2005-04-30 08:50:00,017XX W HOWARD ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,1739,False,False,2422,24,49,1,06,1163125,1950306,2005,ERROR,42.019286753,-87.675054325,"(42.019286753, -87.675054325)"
-7233563,HR577284,2009-10-03 04:35:00,047XX S KILPATRICK AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,2783,False,False,815,8,23,56,14,1145784,1872768,2009,ERROR,41.80686277,-87.740838663,"(41.80686277, -87.740838663)"
-5041658,HM636747,2006-10-02 22:15:00,015XX N CALIFORNIA AVE,0560,ASSAULT,SIMPLE,STREET,4574,False,True,1423,14,26,24,08A,1157485,1910358,2006,ERROR,41.90978438,-87.696900782,"(41.90978438, -87.696900782)"
-1848119,G681456,2001-11-12 15:00:00,045XX S DAMEN AV,0820,THEFT,$500 AND UNDER,DEPARTMENT STORE,255,True,False,914,NA,NA,NA,06,1163704,1874123,2001,2014-04-12 12:43:35,41.810223247,-87.675075347999993,"(41.810223247, -87.675075348)"
-3152831,HK148739,2004-01-26 19:00:00,008XX W 52ND ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,4518,False,False,934,9,3,61,07,1171851,1870417,2004,ERROR,41.799878475,-87.64530206,"(41.799878475, -87.64530206)"
-3800712,HL168190,2005-02-06 18:00:00,026XX W 74TH ST,0460,BATTERY,SIMPLE,STREET,4990,False,False,835,8,18,66,08B,1159986,1855543,2005,ERROR,41.759314508,-87.689223078,"(41.759314508, -87.689223078)"
-4838698,HM450854,2006-07-02 20:22:36,066XX S DAMEN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,3325,False,True,726,7,15,67,08B,1164211,1860657,2006,2006-08-07 04:09:46,41.773260178,-87.673594762,"(41.773260178, -87.673594762)"
-4567800,HM152622,2006-01-30 01:05:00,062XX S PULASKI RD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,2362,True,True,823,8,13,65,08B,1150823,1862618,2006,2006-04-02 03:37:59,41.778912821,-87.72262144,"(41.778912821, -87.72262144)"
-4954048,HM567985,2006-08-28 19:00:00,039XX W NORTH AVE,0820,THEFT,$500 AND UNDER,RESTAURANT,63,False,False,2535,25,30,23,06,1149603,1910380,2006,2014-04-12 12:43:35,41.910001584,-87.725855574,"(41.910001584, -87.725855574)"
-6442605,HP523830,2008-08-19 19:15:00,022XX N STOCKTON DR,0810,THEFT,OVER $500,STREET,4144,False,False,1814,18,43,7,06,1174044,1915453,2008,ERROR,41.923412022,-87.635918111,"(41.923412022, -87.635918111)"
-8013905,HT245828,2011-04-12 01:00:00,117XX S ASHLAND AVE,0325,ROBBERY,VEHICULAR HIJACKING,STREET,3478,False,False,524,5,34,53,03,1167822,1827065,2011,ERROR,41.681001922,-87.66131902,"(41.681001922, -87.66131902)"
-2134668,HH365544,2002-05-11 19:52:00,012XX W 69TH ST,2111,NARCOTICS,SALE/DEL HYPODERMIC NEEDLE,STREET,3604,True,False,724,NA,17,67,26,1169493,1859073,2002,ERROR,41.768800662,-87.654278043,"(41.768800662, -87.654278043)"
-5610513,HN414573,2007-06-19 04:45:00,050XX W DICKENS AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,1912,False,False,2522,25,31,19,14,1142601,1913456,2007,ERROR,41.918575659,-87.75150172,"(41.918575659, -87.75150172)"
-8573841,HV248444,2012-04-19 00:45:00,048XX S MICHIGAN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,1936,False,False,224,2,3,38,14,1178018,1872850,2012,ERROR,41.806417165,-87.622612308,"(41.806417165, -87.622612308)"
-1561696,G322059,2001-06-04 02:17:00,051XX S WESTERN AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,OTHER,1450,True,False,915,NA,NA,NA,07,1161425,1870775,2001,ERROR,41.801083515,-87.683527305,"(41.801083515, -87.683527305)"
-3927547,HL299959,2005-04-17 00:05:00,106XX S PERRY AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,4821,False,False,512,5,34,49,14,1177525,1834390,2005,ERROR,41.700889625,-87.625580837,"(41.700889625, -87.625580837)"
-4652877,HM250481,2006-03-24 14:50:00,006XX N AVERS AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,3812,False,False,1122,11,27,23,04B,1150574,1903795,2006,ERROR,41.891912762,-87.722460741,"(41.891912762, -87.722460741)"
-5843579,HN653528,2007-10-15 01:00:00,021XX E 83RD ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,1948,False,False,414,4,8,46,14,1191722,1850396,2007,ERROR,41.744479768,-87.573079607,"(41.744479768, -87.573079607)"
-3203166,HK216335,2004-03-02 13:30:00,003XX N OAKLEY BLVD,0810,THEFT,OVER $500,STREET,4568,False,False,1332,12,27,28,06,1160987,1901959,2004,2014-04-12 12:43:35,41.886664822,-87.684269315,"(41.886664822, -87.684269315)"
-1606977,G378687,2001-06-29 10:15:00,0000X W RANDOLPH ST,0560,ASSAULT,SIMPLE,OTHER,3282,False,True,122,NA,NA,NA,08A,1176018,1901322,2001,ERROR,41.884591617,-87.629091254,"(41.884591617, -87.629091254)"
-4840076,HM452107,2006-07-03 13:00:00,040XX W 26TH ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA BUS,3532,True,False,1013,10,22,30,11,1150073,1886464,2006,2006-06-07 04:48:17,41.844364255,-87.724751946,"(41.844364255, -87.724751946)"
-2414638,HH721521,2002-10-18 01:45:00,057XX S NEWCASTLE AVE,0560,ASSAULT,SIMPLE,RESIDENCE,4205,True,True,811,NA,23,56,08A,1131678,1865389,2002,ERROR,41.786868643,-87.792746288,"(41.786868643, -87.792746288)"
-9984576,HY173853,2015-03-06 12:20:00,036XX S PAULINA ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,2794,True,True,912,9,11,59,08B,1165657,1880409,2015,ERROR,41.827431448,-87.667733383,"(41.827431448, -87.667733383)"
-4690605,HM294209,2006-04-15 23:30:00,035XX W DIVERSEY AVE,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,4065,False,False,1412,14,35,21,06,1152451,1918432,2006,2014-04-12 12:43:35,41.93204114,-87.715179885,"(41.93204114, -87.715179885)"
-9817517,HX467213,2014-10-14 12:15:00,023XX S MICHIGAN AVE,0560,ASSAULT,SIMPLE,APARTMENT,592,False,False,131,1,2,33,08A,1177532,1889067,2014,ERROR,41.850928923,-87.623903608,"(41.850928923, -87.623903608)"
-4924804,HM540163,2006-08-14 05:20:00,040XX W POTOMAC AVE,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,295,False,False,2534,25,27,23,06,1149398,1908322,2006,2014-04-12 12:43:35,41.90435821,-87.726662138,"(41.90435821, -87.726662138)"
-5819771,HN629390,2007-10-04 22:30:00,087XX S SAGINAW AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,2737,False,False,423,4,7,46,04B,1195348,1847736,2007,ERROR,41.737091832,-87.559881365,"(41.737091832, -87.559881365)"
-4431290,HL717454,2005-11-05 14:30:00,001XX W LAKE ST,0870,THEFT,POCKET-PICKING,CTA PLATFORM,1953,False,False,113,1,42,32,06,1175460,1901776,2005,ERROR,41.885849966,-87.631126643,"(41.885849966, -87.631126643)"
-3020488,HJ684755,2003-10-10 16:45:00,075XX S MORGAN ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,865,True,False,612,6,17,71,18,1170904,1855024,2003,ERROR,41.75765901,-87.649224054,"(41.75765901, -87.649224054)"
-7534781,HS338561,2010-06-01 17:30:00,015XX N CICERO AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,DEPARTMENT STORE,1334,False,True,2533,25,37,25,08B,1144129,1910138,2010,2010-06-06 10:23:35,41.909442134,-87.745971135,"(41.909442134, -87.745971135)"
-8552792,HV228661,2012-04-03 00:00:00,053XX W FOSTER AVE,1720,OFFENSE INVOLVING CHILDREN,CONTRIBUTE DELINQUENCY OF A CHILD,APARTMENT,3251,False,False,1623,16,45,11,20,1139799,1934106,2012,ERROR,41.975292963,-87.761290293,"(41.975292963, -87.761290293)"
-2489010,HH826679,2002-11-30 21:00:00,023XX S LAKE SHORE DR E,0890,THEFT,FROM BUILDING,OTHER,2775,False,False,133,NA,2,33,06,NA,NA,2002,ERROR,NA,NA,
-2690599,HJ311147,2003-04-19 21:46:00,059XX S ADA ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,4856,False,True,713,NA,16,67,08B,1168389,1865153,2003,ERROR,41.785508785,-87.658149785,"(41.785508785, -87.658149785)"
-3823642,HL190768,2005-02-19 15:30:00,028XX N MOZART ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,2877,False,False,1411,14,35,21,07,1156853,1918544,2005,ERROR,41.932260245,-87.699000048,"(41.932260245, -87.699000048)"
diff --git a/work-with-data/dataprep/data/crime-spring.csv b/work-with-data/dataprep/data/crime-spring.csv
deleted file mode 100644
index 3750a186..00000000
--- a/work-with-data/dataprep/data/crime-spring.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-ID,Case Number,Date,Block,IUCR,Primary Type,Description,Location Description,Arrest,Domestic,Beat,District,Ward,Community Area,FBI Code,X Coordinate,Y Coordinate,Year,Updated On,Latitude,Longitude,Location
-10498554,HZ239907,4/4/2016 23:56,007XX E 111TH ST,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,OTHER,FALSE,FALSE,531,5,9,50,11,1183356,1831503,2016,5/11/2016 15:48,41.69283384,-87.60431945,"(41.692833841, -87.60431945)"
-10516598,HZ258664,4/15/2016 17:00,082XX S MARSHFIELD AVE,890,THEFT,FROM BUILDING,RESIDENCE,FALSE,FALSE,614,6,21,71,6,1166776,1850053,2016,5/12/2016 15:48,41.74410697,-87.66449429,"(41.744106973, -87.664494285)"
-10519196,HZ261252,4/15/2016 10:00,104XX S SACRAMENTO AVE,1154,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT $300 AND UNDER,RESIDENCE,FALSE,FALSE,2211,22,19,74,11,,,2016,5/12/2016 15:50,,,
-10519591,HZ261534,4/15/2016 9:00,113XX S PRAIRIE AVE,1120,DECEPTIVE PRACTICE,FORGERY,RESIDENCE,FALSE,FALSE,531,5,9,49,10,,,2016,5/13/2016 15:51,,,
-10534446,HZ277630,4/15/2016 10:00,055XX N KEDZIE AVE,890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",FALSE,FALSE,1712,17,40,13,6,,,2016,5/25/2016 15:59,,,
-10535059,HZ278872,4/15/2016 4:30,004XX S KILBOURN AVE,810,THEFT,OVER $500,RESIDENCE,FALSE,FALSE,1131,11,24,26,6,,,2016,5/25/2016 15:59,,,
-10499802,HZ240778,4/15/2016 10:00,010XX N MILWAUKEE AVE,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,RESIDENCE,FALSE,FALSE,1213,12,27,24,11,,,2016,5/27/2016 15:45,,,
-10522293,HZ264802,4/15/2016 16:00,019XX W DIVISION ST,1110,DECEPTIVE PRACTICE,BOGUS CHECK,RESTAURANT,FALSE,FALSE,1424,14,1,24,11,1163094,1908003,2016,5/16/2016 15:48,41.90320604,-87.67636193,"(41.903206037, -87.676361925)"
-10523111,HZ265911,4/15/2016 8:00,061XX N SHERIDAN RD,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,RESIDENCE,FALSE,FALSE,2433,24,48,77,11,,,2016,5/16/2016 15:50,,,
-10525877,HZ268138,4/15/2016 15:00,023XX W EASTWOOD AVE,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,,FALSE,FALSE,1911,19,47,4,11,,,2016,5/18/2016 15:50,,,
diff --git a/work-with-data/dataprep/data/crime-winter.csv b/work-with-data/dataprep/data/crime-winter.csv
deleted file mode 100644
index 4c70d468..00000000
--- a/work-with-data/dataprep/data/crime-winter.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-ID,Case Number,Date,Block,IUCR,Primary Type,Description,Location Description,Arrest,Domestic,Beat,District,Ward,Community Area,FBI Code,X Coordinate,Y Coordinate,Year,Updated On,Latitude,Longitude,Location
-10378283,HZ114126,1/10/2016 11:00,033XX W IRVING PARK RD,610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,TRUE,FALSE,1724,17,33,16,5,1153593,1926401,2016,5/22/2016 15:51,41.95388599,-87.71077048,"(41.95388599, -87.710770479)"
-10382154,HZ118288,1/10/2016 21:00,055XX S FRANCISCO AVE,1754,OFFENSE INVOLVING CHILDREN,AGG SEX ASSLT OF CHILD FAM MBR,RESIDENCE,FALSE,TRUE,824,8,14,63,2,1157983,1867874,2016,6/1/2016 15:51,41.79319349,-87.69622926,"(41.793193489, -87.696229255)"
-10374287,HZ110730,1/10/2016 11:50,043XX W ARMITAGE AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,FALSE,TRUE,2522,25,30,20,26,1146917,1912931,2016,6/7/2016 15:55,41.91705356,-87.73565764,"(41.917053561, -87.735657637)"
-10374662,HZ110403,1/10/2016 1:30,073XX S CLAREMONT AVE,497,BATTERY,AGGRAVATED DOMESTIC BATTERY: OTHER DANG WEAPON,STREET,FALSE,TRUE,835,8,18,66,04B,1162007,1855951,2016,2/4/2016 15:44,41.76039236,-87.68180481,"(41.760392356, -87.681804812)"
-10374720,HZ110836,1/10/2016 7:30,079XX S RHODES AVE,890,THEFT,FROM BUILDING,OTHER,FALSE,FALSE,624,6,6,44,6,1181279,1852568,2016,2/4/2016 15:44,41.75068679,-87.61127681,"(41.75068679, -87.611276811)"
-10375178,HZ110832,1/10/2016 14:20,057XX S KEDZIE AVE,460,BATTERY,SIMPLE,RESTAURANT,FALSE,FALSE,824,8,14,63,08B,1156029,1866379,2016,2/4/2016 15:44,41.78913051,-87.7034346,"(41.78913051, -87.703434602)"
-10398695,HZ135279,1/10/2016 23:00,031XX S PARNELL AVE,620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,FALSE,FALSE,915,9,11,60,5,1173138,1884117,2016,2/4/2016 15:44,41.8374442,-87.64017699,"(41.837444199, -87.640176991)"
-10402270,HZ138745,1/10/2016 11:00,051XX S ELIZABETH ST,620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,FALSE,FALSE,934,9,16,61,5,,,2016,2/4/2016 6:53,,,
-10380619,HZ116583,1/10/2016 9:41,091XX S PAXTON AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,TRUE,TRUE,413,4,7,48,26,1192434,1844707,2016,2/2/2016 15:56,41.72885134,-87.57065553,"(41.728851343, -87.570655525)"
-10400131,HZ136171,1/10/2016 18:00,0000X W TERMINAL ST,810,THEFT,OVER $500,AIRPORT BUILDING NON-TERMINAL - SECURE AREA,FALSE,FALSE,1651,16,41,76,6,,,2016,2/2/2016 15:58,,,
diff --git a/work-with-data/dataprep/data/crime.dprep b/work-with-data/dataprep/data/crime.dprep
deleted file mode 100644
index 58a84196..00000000
--- a/work-with-data/dataprep/data/crime.dprep
+++ /dev/null
@@ -1,204 +0,0 @@
-{
-  "id": "75637565-60ad-4baa-87d3-396a7930cfe7",
-  "blocks": [
-    {
-      "id": "ba5a8061-129e-4618-953a-ce3e89c8f2cb",
-      "type": "Microsoft.DPrep.GetFilesBlock",
-      "arguments": {
-        "path": {
-          "target": 0,
-          "resourceDetails": [
-            {
-              "path": "./crime-spring.csv"
-            }
-          ]
-        }
-      },
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "1b345643-6b60-4ca1-99f9-2a64ae932a23",
-      "type": "Microsoft.DPrep.ParseDelimitedBlock",
-      "arguments": {
-        "columnHeadersMode": 1,
-        "fileEncoding": 0,
-        "handleQuotedLineBreaks": false,
-        "preview": false,
-        "separator": ",",
-        "skipRowsMode": 0
-      },
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "12cf73a2-1487-4915-bfa7-c86be7de08c0",
-      "type": "Microsoft.DPrep.SetColumnTypesBlock",
-      "arguments": {
-        "columnConversion": [
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "ID"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "IUCR"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Domestic"
-              }
-            },
-            "typeProperty": 1
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Beat"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "District"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Ward"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Community Area"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Year"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Longitude"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Arrest"
-              }
-            },
-            "typeProperty": 1
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "X Coordinate"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Updated On"
-              }
-            },
-            "typeArguments": {
-              "dateTimeFormats": [
-                "%m/%d/%Y %I:%M:%S %p"
-              ]
-            },
-            "typeProperty": 4
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Date"
-              }
-            },
-            "typeArguments": {
-              "dateTimeFormats": [
-                "%m/%d/%Y %I:%M:%S %p"
-              ]
-            },
-            "typeProperty": 4
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Y Coordinate"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Latitude"
-              }
-            },
-            "typeProperty": 3
-          }
-        ]
-      },
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "dfd62543-9285-412b-a930-0aeaaffde699",
-      "type": "Microsoft.DPrep.HandlePathColumnBlock",
-      "arguments": {
-        "pathColumnOperation": 0
-      },
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    }
-  ],
-  "inspectors": []
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/data/crime.parquet b/work-with-data/dataprep/data/crime.parquet
deleted file mode 100644
index dc06b731..00000000
Binary files a/work-with-data/dataprep/data/crime.parquet and /dev/null differ
diff --git a/work-with-data/dataprep/data/crime.txt b/work-with-data/dataprep/data/crime.txt
deleted file mode 100644
index d6d8b8d7..00000000
--- a/work-with-data/dataprep/data/crime.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-10140490 HY329907 7/5/2015  23:50                  050XX N NEWLAND AVE 820    THEFT                 
-10139776 HY329265 7/5/2015  23:30                  011XX W MORSE AVE 460    BATTERY
-10140270 HY329253 7/5/2015  23:20                  121XX S FRONT AVE 486    BATTERY
-10139885 HY329308 7/5/2015  23:19                  051XX W DIVISION ST 610    BURGLARY
-10140379 HY329556 7/5/2015  23:00                  012XX W LAKE ST 930    MOTOR VEHICLE THEFT
-10140868 HY330421 7/5/2015  22:54                  118XX S PEORIA ST 1320    CRIMINAL DAMAGE
-10139762 HY329232 7/5/2015  22:42                  026XX W 37TH PL 1020    ARSON
-10139722 HY329228 7/5/2015  22:30                  016XX S CENTRAL PARK AVE 1811    NARCOTICS
-10139774 HY329209 7/5/2015  22:15                  048XX N ASHLAND AVE 1310    CRIMINAL DAMAGE
-10139697 HY329177 7/5/2015  22:10                  058XX S ARTESIAN AVE 1320    CRIMINAL DAMAGE
diff --git a/work-with-data/dataprep/data/crime.xlsx b/work-with-data/dataprep/data/crime.xlsx
deleted file mode 100644
index 21f200b4..00000000
Binary files a/work-with-data/dataprep/data/crime.xlsx and /dev/null differ
diff --git a/work-with-data/dataprep/data/crime.zip b/work-with-data/dataprep/data/crime.zip
deleted file mode 100644
index 77235280..00000000
Binary files a/work-with-data/dataprep/data/crime.zip and /dev/null differ
diff --git a/work-with-data/dataprep/data/crime_duplicate_headers.csv b/work-with-data/dataprep/data/crime_duplicate_headers.csv
deleted file mode 100644
index 5f2efa26..00000000
--- a/work-with-data/dataprep/data/crime_duplicate_headers.csv
+++ /dev/null
@@ -1,12 +0,0 @@
-ID,Case Number,Date,Block,IUCR,Primary Type,Description,Location Description,Arrest,Domestic,Beat,District,Ward,Community Area,FBI Code,X Coordinate,Y Coordinate,Year,Updated On,Latitude,Longitude,Location
-ID,Case Number,Date,Block,IUCR,Primary Type,Description,Location Description,Arrest,Domestic,Beat,District,Ward,Community Area,FBI Code,X Coordinate,Y Coordinate,Year,Updated On,Latitude,Longitude,Location
-10498554,HZ239907,4/15/2016 23:56,007XX E 111TH ST,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,OTHER,FALSE,FALSE,531,5,9,50,11,1183356,1831503,2016,5/11/2016 15:48,41.69283384,-87.60431945,"(41.692833841, -87.60431945)"
-10516598,HZ258664,4/15/2016 17:00,082XX S MARSHFIELD AVE,890,THEFT,FROM BUILDING,RESIDENCE,FALSE,FALSE,614,6,21,71,6,1166776,1850053,2016,5/12/2016 15:48,41.74410697,-87.66449429,"(41.744106973, -87.664494285)"
-10519196,HZ261252,4/15/2016 10:00,104XX S SACRAMENTO AVE,1154,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT $300 AND UNDER,RESIDENCE,FALSE,FALSE,2211,22,19,74,11,,,2016,5/12/2016 15:50,,,
-10519591,HZ261534,4/15/2016 9:00,113XX S PRAIRIE AVE,1120,DECEPTIVE PRACTICE,FORGERY,RESIDENCE,FALSE,FALSE,531,5,9,49,10,,,2016,5/13/2016 15:51,,,
-10534446,HZ277630,4/15/2016 10:00,055XX N KEDZIE AVE,890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",FALSE,FALSE,1712,17,40,13,6,,,2016,5/25/2016 15:59,,,
-10535059,HZ278872,4/15/2016 4:30,004XX S KILBOURN AVE,810,THEFT,OVER $500,RESIDENCE,FALSE,FALSE,1131,11,24,26,6,,,2016,5/25/2016 15:59,,,
-10499802,HZ240778,4/15/2016 10:00,010XX N MILWAUKEE AVE,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,RESIDENCE,FALSE,FALSE,1213,12,27,24,11,,,2016,5/27/2016 15:45,,,
-10522293,HZ264802,4/15/2016 16:00,019XX W DIVISION ST,1110,DECEPTIVE PRACTICE,BOGUS CHECK,RESTAURANT,FALSE,FALSE,1424,14,1,24,11,1163094,1908003,2016,5/16/2016 15:48,41.90320604,-87.67636193,"(41.903206037, -87.676361925)"
-10523111,HZ265911,4/15/2016 8:00,061XX N SHERIDAN RD,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,RESIDENCE,FALSE,FALSE,2433,24,48,77,11,,,2016,5/16/2016 15:50,,,
-10525877,HZ268138,4/15/2016 15:00,023XX W EASTWOOD AVE,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,,FALSE,FALSE,1911,19,47,4,11,,,2016,5/18/2016 15:50,,,
diff --git a/work-with-data/dataprep/data/crime_fixed_width_file.txt b/work-with-data/dataprep/data/crime_fixed_width_file.txt
deleted file mode 100644
index d6d8b8d7..00000000
--- a/work-with-data/dataprep/data/crime_fixed_width_file.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-10140490 HY329907 7/5/2015  23:50                  050XX N NEWLAND AVE 820    THEFT                 
-10139776 HY329265 7/5/2015  23:30                  011XX W MORSE AVE 460    BATTERY
-10140270 HY329253 7/5/2015  23:20                  121XX S FRONT AVE 486    BATTERY
-10139885 HY329308 7/5/2015  23:19                  051XX W DIVISION ST 610    BURGLARY
-10140379 HY329556 7/5/2015  23:00                  012XX W LAKE ST 930    MOTOR VEHICLE THEFT
-10140868 HY330421 7/5/2015  22:54                  118XX S PEORIA ST 1320    CRIMINAL DAMAGE
-10139762 HY329232 7/5/2015  22:42                  026XX W 37TH PL 1020    ARSON
-10139722 HY329228 7/5/2015  22:30                  016XX S CENTRAL PARK AVE 1811    NARCOTICS
-10139774 HY329209 7/5/2015  22:15                  048XX N ASHLAND AVE 1310    CRIMINAL DAMAGE
-10139697 HY329177 7/5/2015  22:10                  058XX S ARTESIAN AVE 1320    CRIMINAL DAMAGE
diff --git a/work-with-data/dataprep/data/crime_multiple_separators.csv b/work-with-data/dataprep/data/crime_multiple_separators.csv
deleted file mode 100644
index 5fd79a5a..00000000
--- a/work-with-data/dataprep/data/crime_multiple_separators.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-ID |CaseNumber| |Completed|
-10140490 |HY329907| |Y|
-10139776 |HY329265| |Y|
-10140270 |HY329253| |N|
-10139885 |HY329308| |Y|
-10140379 |HY329556| |N|
-10140868 |HY330421| |N|
-10139762 |HY329232| |N|
-10139722 |HY329228| |Y|
-10139774 |HY329209| |N|
-10139697 |HY329177| |N|
\ No newline at end of file
diff --git a/work-with-data/dataprep/data/crime_partfiles/_SUCCESS b/work-with-data/dataprep/data/crime_partfiles/_SUCCESS
deleted file mode 100644
index e69de29b..00000000
diff --git a/work-with-data/dataprep/data/crime_partfiles/part-00000-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv b/work-with-data/dataprep/data/crime_partfiles/part-00000-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv
deleted file mode 100644
index 855bdd52..00000000
--- a/work-with-data/dataprep/data/crime_partfiles/part-00000-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv
+++ /dev/null
@@ -1,914 +0,0 @@
-10140382,HY329023,07/05/2015 07:02:00 PM,004XX N CENTRAL PARK BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,false,1123,,27,23,08B,,,2015,07/12/2015 12:42:46 PM,,,
-10139396,HY328581,07/04/2015 11:00:00 PM,004XX N MONTICELLO AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENTIAL YARD (FRONT/BACK),false,false,1122,011,27,23,14,1151917,1902866,2015,07/11/2015 12:39:38 PM,41.889337148,-87.717552947,"(41.889337148, -87.717552947)"
-10137054,HY325533,07/02/2015 11:37:00 PM,005XX N SPRINGFIELD AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,APARTMENT,true,false,1122,011,27,23,15,1150251,1903434,2015,07/09/2015 12:37:51 PM,41.890928444,-87.723656407,"(41.890928444, -87.723656407)"
-10137515,HY326210,07/02/2015 10:00:00 PM,017XX N CICERO AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,false,2533,025,37,25,08A,1144171,1911174,2015,07/09/2015 12:37:51 PM,41.912284239,-87.745790773,"(41.912284239, -87.745790773)"
-10136819,HY325292,07/02/2015 07:54:00 PM,016XX S DRAKE AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,APARTMENT,true,false,1021,010,24,29,15,1153024,1891215,2015,07/09/2015 12:37:51 PM,41.857343671,-87.713796395,"(41.857343671, -87.713796395)"
-10136842,HY325161,07/02/2015 06:50:00 PM,009XX E 131ST ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0533,005,9,54,06,1184854,1818532,2015,07/09/2015 12:37:51 PM,41.657204701,-87.599239629,"(41.657204701, -87.599239629)"
-10135642,HY324210,07/02/2015 02:10:00 AM,009XX E 130TH ST,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,CHA PARKING LOT/GROUNDS,false,false,0532,005,9,54,04B,1184757,1819322,2015,07/09/2015 12:37:51 PM,41.659374843,-87.599569957,"(41.659374843, -87.599569957)"
-10135444,HY323989,07/01/2015 08:40:00 PM,068XX S TALMAN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0831,008,15,66,18,1159936,1858880,2015,07/08/2015 12:38:00 PM,41.768472756,-87.689314761,"(41.768472756, -87.689314761)"
-10134135,HY322934,07/01/2015 04:00:00 AM,079XX S VINCENNES AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,false,0623,006,17,44,08B,1175024,1852356,2015,07/08/2015 12:38:00 PM,41.750246796,-87.634204288,"(41.750246796, -87.634204288)"
-10147117,HY336163,06/28/2015 07:00:00 AM,020XX N KIMBALL AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENTIAL YARD (FRONT/BACK),false,false,1413,014,26,22,14,1153332,1913617,2015,07/12/2015 12:40:52 PM,41.918810907,-87.712070517,"(41.918810907, -87.712070517)"
-10130196,HY318835,06/28/2015 12:20:00 AM,033XX N HALSTED ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1925,019,44,6,08B,1170364,1922754,2015,07/05/2015 12:38:04 PM,41.943527622,-87.649225604,"(41.943527622, -87.649225604)"
-10129636,HY318033,06/27/2015 12:00:00 PM,044XX N BROADWAY,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,1913,019,46,3,06,1168391,1929931,2015,07/04/2015 12:37:19 PM,41.963264568,-87.656268955,"(41.963264568, -87.656268955)"
-10129573,HY317986,06/26/2015 07:00:00 PM,010XX N WOOD ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1212,012,1,24,06,1164162,1907126,2015,07/03/2015 12:39:14 PM,41.900776974,-87.672463767,"(41.900776974, -87.672463767)"
-10128956,HY315657,06/25/2015 11:16:00 AM,001XX N WOLCOTT AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,OTHER,false,false,1223,012,27,28,26,1163750,1901322,2015,07/02/2015 12:42:40 PM,41.884859047,-87.674140851,"(41.884859047, -87.674140851)"
-10147340,HY336420,06/21/2015 01:30:00 PM,006XX W BARRY AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1934,019,44,6,14,1171641,1920753,2015,07/12/2015 12:42:46 PM,41.93800874,-87.644591096,"(41.93800874, -87.644591096)"
-10120242,HY308991,06/20/2015 12:00:00 PM,022XX E 103RD ST,1780,OFFENSE INVOLVING CHILDREN,OTHER OFFENSE,POLICE FACILITY/VEH PARKING LOT,false,false,0434,004,10,51,26,1193088,1837089,2015,06/27/2015 12:41:22 PM,41.707930876,-87.568507723,"(41.707930876, -87.568507723)"
-10122352,HY308873,06/20/2015 10:20:00 AM,055XX W GLADYS AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1522,,29,25,14,,,2015,06/27/2015 12:41:22 PM,,,
-10119796,HY308435,06/20/2015 01:28:00 AM,039XX S CALUMET AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0213,002,3,38,14,1179038,1878991,2015,06/27/2015 12:41:22 PM,41.823245374,-87.618684022,"(41.823245374, -87.618684022)"
-10119477,HY307724,06/19/2015 03:00:00 PM,052XX W LAKE ST,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,1523,015,28,25,03,1141587,1902060,2015,06/26/2015 12:42:22 PM,41.887322542,-87.755509318,"(41.887322542, -87.755509318)"
-10118190,HY306860,06/17/2015 07:48:00 PM,003XX S PULASKI RD,2250,LIQUOR LAW VIOLATION,LIQUOR LICENSE VIOLATION,TAVERN/LIQUOR STORE,true,false,1132,011,24,26,22,1149753,1897756,2015,06/24/2015 12:40:31 PM,41.875357085,-87.725632989,"(41.875357085, -87.725632989)"
-10117842,HY306398,06/17/2015 05:00:00 PM,030XX W GUNNISON ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1713,017,33,14,14,1154936,1932019,2015,06/24/2015 12:40:31 PM,41.969275252,-87.705682159,"(41.969275252, -87.705682159)"
-10116440,HY304973,06/16/2015 11:00:00 PM,016XX N HONORE ST,0810,THEFT,OVER $500,STREET,false,false,1434,014,32,24,06,1163721,1911003,2015,06/23/2015 12:51:44 PM,41.91142505,-87.673974124,"(41.91142505, -87.673974124)"
-10112616,HY301842,06/14/2015 09:00:00 PM,104XX S PROSPECT AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2212,022,19,72,14,1167593,1835635,2015,06/21/2015 12:38:51 PM,41.704524331,-87.661912753,"(41.704524331, -87.661912753)"
-10122748,HY311701,06/13/2015 03:30:00 PM,022XX S CENTRAL PARK AVE,0810,THEFT,OVER $500,APARTMENT,false,false,1024,010,22,30,06,1152766,1888660,2015,06/23/2015 12:51:44 PM,41.850337554,-87.714810965,"(41.850337554, -87.714810965)"
-10110096,HY298671,06/12/2015 06:00:00 PM,050XX S WINCHESTER AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0931,009,16,61,14,1164250,1871069,2015,06/19/2015 01:10:05 PM,41.801831215,-87.673158727,"(41.801831215, -87.673158727)"
-10113127,HY302162,06/12/2015 04:30:00 PM,031XX S ABERDEEN ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0913,009,11,60,05,1169448,1884115,2015,06/19/2015 01:10:05 PM,41.837519626,-87.653717198,"(41.837519626, -87.653717198)"
-10109843,HY298100,06/12/2015 01:05:00 PM,068XX S NORMAL BLVD,0820,THEFT,$500 AND UNDER,"SCHOOL, PUBLIC, BUILDING",true,false,0722,007,6,68,06,1174171,1859468,2015,06/19/2015 01:10:05 PM,41.769781958,-87.63711913,"(41.769781958, -87.63711913)"
-10109480,HY297992,06/12/2015 12:20:00 PM,068XX S PERRY AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,0722,007,6,69,08A,1176533,1859790,2015,06/19/2015 01:10:05 PM,41.770612768,-87.628451443,"(41.770612768, -87.628451443)"
-10133746,HY320661,06/12/2015 11:00:00 AM,069XX S CRANDON AVE,1154,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT $300 AND UNDER,APARTMENT,false,false,0331,003,5,43,11,1192560,1859518,2015,07/05/2015 12:36:37 PM,41.769490914,-87.569712448,"(41.769490914, -87.569712448)"
-10108325,HY296759,06/11/2015 09:00:00 AM,037XX W 26TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1031,010,22,30,14,1152090,1886433,2015,06/18/2015 12:41:41 PM,41.844239727,-87.717350648,"(41.844239727, -87.717350648)"
-10111213,HY300134,06/10/2015 01:00:00 PM,040XX W MELROSE ST,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,false,true,1731,017,31,16,26,1148889,1921264,2015,06/17/2015 12:40:49 PM,41.939882117,-87.728196374,"(41.939882117, -87.728196374)"
-10102842,HY291892,06/07/2015 08:00:00 PM,009XX S SPRINGFIELD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1133,011,24,26,14,1150571,1895527,2015,06/14/2015 12:39:55 PM,41.869224524,-87.722687811,"(41.869224524, -87.722687811)"
-10102538,HY291587,06/07/2015 02:40:00 PM,051XX S ROCKWELL ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0923,009,14,63,14,1159944,1870621,2015,06/14/2015 12:39:55 PM,41.800691514,-87.688962913,"(41.800691514, -87.688962913)"
-10102063,HY290911,06/06/2015 08:25:00 PM,054XX W FULTON ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,true,1523,015,28,25,26,1139936,1901441,2015,06/13/2015 12:39:41 PM,41.885654275,-87.761587509,"(41.885654275, -87.761587509)"
-10109910,HY298308,06/06/2015 03:00:00 PM,098XX S INDIANA AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0511,005,6,49,05,1179279,1839603,2015,06/20/2015 12:40:44 PM,41.715155065,-87.61900001,"(41.715155065, -87.61900001)"
-10101757,HY290389,06/06/2015 02:12:00 PM,029XX W FILLMORE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1135,011,28,29,08B,1157137,1895279,2015,06/13/2015 12:39:41 PM,41.868413285,-87.698588955,"(41.868413285, -87.698588955)"
-10103505,HY292428,06/05/2015 06:00:00 PM,048XX S PAULINA ST,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0931,009,20,61,06,1165781,1872809,2015,06/12/2015 12:42:30 PM,41.806573572,-87.667494505,"(41.806573572, -87.667494505)"
-10101148,HY289611,06/05/2015 05:30:00 PM,058XX S DORCHESTER AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0235,002,5,41,07,1186500,1866746,2015,06/12/2015 12:42:30 PM,41.789470647,-87.591696818,"(41.789470647, -87.591696818)"
-10099041,HY287215,06/04/2015 06:50:00 AM,048XX S ASHLAND AVE,031A,ROBBERY,ARMED: HANDGUN,BANK,false,false,0933,009,20,61,03,1166526,1872752,2015,07/07/2015 12:40:12 PM,41.806401287,-87.66476372,"(41.806401287, -87.66476372)"
-10097772,HY286029,06/02/2015 10:30:00 PM,053XX W BYRON ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1634,016,38,15,06,1140003,1925444,2015,06/09/2015 12:37:43 PM,41.951519927,-87.760752962,"(41.951519927, -87.760752962)"
-10096983,HY285435,06/02/2015 02:30:00 PM,023XX N MILWAUKEE AVE,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,1414,014,35,22,06,1156799,1915514,2015,06/09/2015 12:37:43 PM,41.923946796,-87.699280832,"(41.923946796, -87.699280832)"
-10095244,HY284182,06/01/2015 07:19:00 PM,057XX W MADISON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,JAIL / LOCK-UP FACILITY,true,false,1513,015,29,25,18,1138127,1899398,2015,06/08/2015 12:48:46 PM,41.880080898,-87.768280017,"(41.880080898, -87.768280017)"
-10093684,HY282559,05/31/2015 06:15:00 PM,071XX S VINCENNES AVE,0460,BATTERY,SIMPLE,GAS STATION,false,false,0731,007,6,69,08B,1176577,1857698,2015,06/07/2015 12:43:36 PM,41.764871097,-87.62835301,"(41.764871097, -87.62835301)"
-10092276,HY280537,05/29/2015 11:57:00 PM,068XX S CORNELL AVE,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,false,false,0332,003,5,43,04B,1188428,1859737,2015,06/05/2015 12:41:44 PM,41.77019154,-87.584851252,"(41.77019154, -87.584851252)"
-10093489,HY282318,05/29/2015 08:58:00 PM,016XX N LARAMIE AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,2532,025,37,25,04B,1141421,1910182,2015,06/05/2015 12:41:44 PM,41.90961333,-87.755918184,"(41.90961333, -87.755918184)"
-10091871,HY279905,05/29/2015 10:00:00 AM,050XX S SPAULDING AVE,0497,BATTERY,AGGRAVATED DOMESTIC BATTERY: OTHER DANG WEAPON,RESIDENCE,false,true,0821,008,14,63,04B,1155187,1871130,2015,06/05/2015 12:41:44 PM,41.802184814,-87.706394917,"(41.802184814, -87.706394917)"
-10088970,HY277782,05/28/2015 01:15:00 AM,015XX N LARAMIE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,2532,025,37,25,03,1141384,1909974,2015,06/04/2015 12:42:50 PM,41.909043238,-87.75605925,"(41.909043238, -87.75605925)"
-10088660,HY277409,05/27/2015 06:00:00 PM,054XX N CUMBERLAND AVE,0890,THEFT,FROM BUILDING,ATHLETIC CLUB,false,false,1614,016,41,76,06,1119277,1935253,2015,06/03/2015 12:42:25 PM,41.978791706,-87.836734078,"(41.978791706, -87.836734078)"
-10089078,HY277646,05/27/2015 05:52:00 PM,022XX W PERSHING RD,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,OTHER,false,true,0912,009,11,59,26,1162171,1878823,2015,06/03/2015 12:42:25 PM,41.823152685,-87.680567258,"(41.823152685, -87.680567258)"
-10085461,HY274661,05/25/2015 05:04:00 PM,074XX S ST LAWRENCE AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,RESIDENCE,false,false,0323,003,6,69,04B,1181559,1855960,2015,06/01/2015 12:47:00 PM,41.759988347,-87.610146282,"(41.759988347, -87.610146282)"
-10084832,HY273834,05/25/2015 12:49:00 AM,012XX W 73RD PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0734,007,17,67,08B,1168941,1855993,2015,06/01/2015 12:47:00 PM,41.760360685,-87.656390252,"(41.760360685, -87.656390252)"
-10083877,HY272579,05/23/2015 10:30:00 PM,048XX N KEYSTONE AVE,0560,ASSAULT,SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,1712,017,39,14,08A,1148471,1932134,2015,05/30/2015 12:39:53 PM,41.969718324,-87.72945115,"(41.969718324, -87.72945115)"
-10086359,HY274268,05/23/2015 04:00:00 AM,026XX S HAMLIN AVE,033A,ROBBERY,ATTEMPT: ARMED-HANDGUN,SIDEWALK,false,false,1031,,22,30,03,,,2015,05/30/2015 12:39:53 PM,,,
-10080813,HY269135,05/20/2015 05:25:00 PM,110XX S MICHIGAN AVE,0860,THEFT,RETAIL THEFT,DRUG STORE,false,false,0513,005,9,49,06,1178749,1831610,2015,05/27/2015 12:41:26 PM,41.693233223,-87.621183177,"(41.693233223, -87.621183177)"
-10078694,HY267487,05/20/2015 05:20:00 AM,001XX W 113TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,SIDEWALK,false,true,0522,005,34,49,14,1177168,1830059,2015,05/27/2015 12:41:26 PM,41.689012774,-87.627018081,"(41.689012774, -87.627018081)"
-10077527,HY266272,05/18/2015 09:00:00 PM,029XX W HARRISON ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1135,011,2,27,14,1156787,1897264,2015,05/25/2015 12:39:09 PM,41.873867422,-87.6998201,"(41.873867422, -87.6998201)"
-10074976,HY264023,05/17/2015 02:20:00 PM,011XX N MAYFIELD AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1511,015,29,25,18,1136783,1906846,2015,05/24/2015 12:39:16 PM,41.90054335,-87.773036525,"(41.90054335, -87.773036525)"
-10074408,HY263293,05/16/2015 11:00:00 PM,002XX W 70TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0731,007,6,69,08B,1176094,1858601,2015,05/23/2015 12:39:33 PM,41.767359881,-87.630096272,"(41.767359881, -87.630096272)"
-10070781,HY259424,05/13/2015 06:00:00 PM,054XX S WABASH AVE,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,0225,002,3,40,06,1177595,1869166,2015,05/21/2015 01:30:34 PM,41.796317515,-87.624275174,"(41.796317515, -87.624275174)"
-10069076,HY257420,05/12/2015 03:55:00 PM,0000X E 83RD ST,0460,BATTERY,SIMPLE,RESTAURANT,false,false,0632,006,6,44,08B,1177781,1849937,2015,05/21/2015 01:30:34 PM,41.743546853,-87.624174513,"(41.743546853, -87.624174513)"
-10068765,HY257238,05/11/2015 02:00:00 PM,074XX S SANGAMON ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0733,007,17,68,14,1171297,1855717,2015,05/21/2015 01:30:34 PM,41.759552106,-87.647763521,"(41.759552106, -87.647763521)"
-10066956,HY255874,05/10/2015 08:30:00 PM,088XX S LUELLA AVE,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,0412,004,8,48,06,1192674,1846539,2015,05/21/2015 01:30:34 PM,41.733872682,-87.569716826,"(41.733872682, -87.569716826)"
-10061067,HY249438,05/06/2015 02:30:00 PM,011XX N WESTERN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,STREET,false,true,1212,012,1,24,26,1160235,1907766,2015,05/21/2015 01:30:34 PM,41.902615292,-87.686870179,"(41.902615292, -87.686870179)"
-10057558,HY246574,05/04/2015 08:30:00 AM,002XX W 106TH ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0512,005,34,49,05,1176626,1834692,2015,05/11/2015 12:40:43 PM,41.701738583,-87.628863594,"(41.701738583, -87.628863594)"
-10057342,HY246477,05/03/2015 02:00:00 AM,002XX E 49TH ST,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0224,002,3,38,03,1178647,1872628,2015,05/10/2015 12:43:18 PM,41.805793681,-87.620312128,"(41.805793681, -87.620312128)"
-10055977,HY245326,05/02/2015 09:30:00 PM,059XX N NAVARRE AVE,1360,CRIMINAL TRESPASS,TO VEHICLE,STREET,false,false,1611,016,41,10,26,1131653,1939109,2015,05/09/2015 12:54:37 PM,41.989167023,-87.79113002,"(41.989167023, -87.79113002)"
-10047645,HY237074,04/26/2015 07:17:00 PM,022XX W 71ST ST,4625,OTHER OFFENSE,PAROLE VIOLATION,STREET,true,false,0832,008,17,66,26,1162647,1857583,2015,05/03/2015 12:41:02 PM,41.764857472,-87.679413712,"(41.764857472, -87.679413712)"
-10046013,HY234766,04/24/2015 07:00:00 PM,005XX N MICHIGAN AVE,0890,THEFT,FROM BUILDING,DEPARTMENT STORE,false,false,1834,018,42,8,06,1177300,1903904,2015,05/01/2015 12:39:29 PM,41.891647792,-87.624305286,"(41.891647792, -87.624305286)"
-10045777,HY234407,04/24/2015 12:00:00 PM,082XX S COTTAGE GROVE AVE,1122,DECEPTIVE PRACTICE,COUNTERFEIT CHECK,CURRENCY EXCHANGE,false,false,0631,006,6,44,10,1182961,1850200,2015,05/23/2015 12:37:53 PM,41.744149843,-87.605186658,"(41.744149843, -87.605186658)"
-10043099,HY232289,04/22/2015 10:02:00 PM,077XX S EBERHART AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0624,006,6,69,14,1180950,1854001,2015,04/29/2015 12:46:00 PM,41.754626668,-87.612438413,"(41.754626668, -87.612438413)"
-10044435,HY232550,04/22/2015 08:15:00 PM,093XX S STEWART AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0634,006,21,49,14,1175433,1842805,2015,04/29/2015 12:46:00 PM,41.724028492,-87.632990344,"(41.724028492, -87.632990344)"
-10042666,HY231827,04/22/2015 12:31:00 PM,004XX E 32ND ST,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,APARTMENT,false,false,0211,002,4,35,26,1179471,1883821,2015,04/29/2015 12:46:00 PM,41.836489362,-87.616947764,"(41.836489362, -87.616947764)"
-10042314,HY231616,04/22/2015 12:15:00 PM,068XX S MICHIGAN AVE,4625,OTHER OFFENSE,PAROLE VIOLATION,STREET,true,false,0322,003,20,69,26,1178301,1859745,2015,04/29/2015 12:46:00 PM,41.770449338,-87.621972052,"(41.770449338, -87.621972052)"
-10039516,HY229079,04/20/2015 11:30:00 AM,066XX S LAFLIN ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0725,007,17,67,14,1167437,1860810,2015,04/27/2015 12:45:29 PM,41.773611511,-87.661764615,"(41.773611511, -87.661764615)"
-10038323,HY228207,04/19/2015 03:39:00 PM,079XX S WOOD ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,APARTMENT,true,false,0611,006,21,71,15,1165767,1852106,2015,04/26/2015 12:39:05 PM,41.749762163,-87.668133219,"(41.749762163, -87.668133219)"
-10035528,HY224812,04/16/2015 09:10:00 PM,132XX S BURLEY AVE,0610,BURGLARY,FORCIBLE ENTRY,"SCHOOL, PUBLIC, BUILDING",false,false,0433,004,10,55,05,1199800,1817793,2015,04/23/2015 12:42:02 PM,41.654814497,-87.544575541,"(41.654814497, -87.544575541)"
-10037634,HY227208,04/16/2015 07:00:00 PM,079XX S YATES BLVD,0580,STALKING,SIMPLE,STREET,false,false,0414,004,7,46,08A,1193528,1852987,2015,04/23/2015 12:42:02 PM,41.751545711,-87.566377706,"(41.751545711, -87.566377706)"
-10035186,HY224450,04/16/2015 12:00:00 AM,043XX W 25TH PL,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,RESIDENCE,false,false,1013,010,22,30,20,1147698,1886734,2015,06/28/2015 12:40:29 PM,41.845151022,-87.733460968,"(41.845151022, -87.733460968)"
-10043019,HY232232,04/15/2015 01:45:00 PM,083XX S KERFOOT AVE,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,APARTMENT,false,false,0622,006,21,71,26,1173267,1849190,2015,04/24/2015 12:43:44 PM,41.74159788,-87.640736088,"(41.74159788, -87.640736088)"
-10036700,HY225094,04/15/2015 08:00:00 AM,058XX S BLACKSTONE AVE,1154,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT $300 AND UNDER,RESIDENCE,false,false,0235,002,5,41,11,1186947,1866698,2015,04/22/2015 12:47:10 PM,41.789328339,-87.590059355,"(41.789328339, -87.590059355)"
-10032646,HY222500,04/15/2015 03:24:00 AM,049XX W FULLERTON AVE,041A,BATTERY,AGGRAVATED: HANDGUN,SIDEWALK,false,false,2521,025,31,19,04B,1143266,1915551,2015,04/22/2015 12:47:10 PM,41.924312148,-87.749005973,"(41.924312148, -87.749005973)"
-10031809,HY221582,04/14/2015 12:00:00 PM,036XX W LEXINGTON ST,2093,NARCOTICS,FOUND SUSPECT NARCOTICS,ALLEY,true,false,1133,011,24,27,26,1152082,1896490,2015,04/21/2015 03:59:18 PM,41.871837474,-87.717115121,"(41.871837474, -87.717115121)"
-10027994,HY217421,04/10/2015 08:28:00 PM,003XX N LAMON AVE,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),STREET,true,false,1532,015,28,25,18,1143693,1901637,2015,04/17/2015 12:55:31 PM,41.886122605,-87.747785968,"(41.886122605, -87.747785968)"
-10028354,HY217751,04/10/2015 07:00:00 PM,022XX W 47TH ST,0610,BURGLARY,FORCIBLE ENTRY,COMMERCIAL / BUSINESS OFFICE,false,false,0931,009,12,61,05,1161901,1873418,2015,04/17/2015 12:55:31 PM,41.808326347,-87.681708152,"(41.808326347, -87.681708152)"
-10028175,HY217738,04/10/2015 04:00:00 PM,039XX S LAKE PARK AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0214,002,4,36,26,1183288,1879279,2015,04/17/2015 12:55:31 PM,41.823937602,-87.603083608,"(41.823937602, -87.603083608)"
-10026268,HY215497,04/08/2015 09:00:00 PM,086XX S SAGINAW AVE,0610,BURGLARY,FORCIBLE ENTRY,OTHER,false,false,0423,004,7,46,05,1195367,1848070,2015,04/15/2015 12:59:16 PM,41.738007887,-87.559800758,"(41.738007887, -87.559800758)"
-10026351,HY215662,04/08/2015 05:00:00 PM,048XX S WESTERN AVE,0820,THEFT,$500 AND UNDER,CTA TRAIN,false,false,0922,009,12,58,06,1161141,1872753,2015,04/15/2015 12:59:16 PM,41.806517283,-87.684514078,"(41.806517283, -87.684514078)"
-10025005,HY214424,04/08/2015 03:48:00 AM,013XX N CAMPBELL AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,APARTMENT,false,false,1423,014,26,24,11,1159529,1909108,2015,04/15/2015 12:59:16 PM,41.906312418,-87.689426439,"(41.906312418, -87.689426439)"
-10023943,HY213442,04/07/2015 12:49:00 PM,015XX S KEELER AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,true,false,1012,010,24,29,04B,1148671,1892315,2015,04/14/2015 12:54:50 PM,41.860447286,-87.729746171,"(41.860447286, -87.729746171)"
-10023029,HY212757,04/06/2015 06:50:00 PM,063XX S STEWART AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,OTHER,true,false,0722,007,20,68,18,1174672,1862929,2015,04/13/2015 12:58:14 PM,41.779268187,-87.635179701,"(41.779268187, -87.635179701)"
-10023241,HY213007,04/06/2015 04:00:00 PM,036XX W LE MOYNE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,2535,025,26,23,14,1151652,1909686,2015,04/13/2015 12:58:14 PM,41.908057114,-87.718346605,"(41.908057114, -87.718346605)"
-10022408,HY212071,04/05/2015 09:00:00 PM,037XX W WRIGHTWOOD AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2524,025,35,22,06,1151111,1916988,2015,04/12/2015 12:45:09 PM,41.928105088,-87.72014218,"(41.928105088, -87.72014218)"
-10021678,HY211343,04/05/2015 12:00:00 AM,049XX N AVERS AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,"SCHOOL, PUBLIC, GROUNDS",false,false,1712,017,39,14,14,1149775,1932807,2015,04/12/2015 12:45:09 PM,41.97153976,-87.724638691,"(41.97153976, -87.724638691)"
-10019065,HY208218,04/02/2015 07:45:00 PM,025XX E 95TH PL,1320,CRIMINAL DAMAGE,TO VEHICLE,DRIVEWAY - RESIDENTIAL,false,false,0431,004,7,51,14,1194456,1842139,2015,04/09/2015 12:47:19 PM,41.721755113,-87.563332757,"(41.721755113, -87.563332757)"
-10015678,HY204987,03/31/2015 01:17:00 PM,046XX N BROADWAY,1821,NARCOTICS,MANU/DEL:CANNABIS 10GM OR LESS,SIDEWALK,true,false,1914,019,46,3,18,1167959,1931001,2015,04/07/2015 12:49:51 PM,41.966210041,-87.657826246,"(41.966210041, -87.657826246)"
-10015737,HY205013,03/31/2015 09:00:00 AM,027XX E 89TH ST,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",true,false,0423,004,7,46,08A,1195945,1846528,2015,04/12/2015 12:43:21 PM,41.733762235,-87.557734065,"(41.733762235, -87.557734065)"
-10014530,HY204114,03/30/2015 05:35:00 PM,013XX N HUDSON AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,SIDEWALK,false,false,1821,018,27,8,04B,1173106,1909574,2015,04/06/2015 12:56:12 PM,41.907300651,-87.639539378,"(41.907300651, -87.639539378)"
-10012385,HY202038,03/28/2015 06:50:00 PM,036XX W GRAND AVE,0460,BATTERY,SIMPLE,GROCERY FOOD STORE,false,false,1112,011,27,23,08B,1152105,1907552,2015,04/04/2015 12:43:24 PM,41.902192292,-87.716738854,"(41.902192292, -87.716738854)"
-10020751,HY210143,03/27/2015 05:00:00 PM,105XX S SAWYER AVE,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,RESIDENCE,false,false,2211,022,19,74,11,1156598,1834160,2015,04/05/2015 12:44:22 PM,41.700704845,-87.702214708,"(41.700704845, -87.702214708)"
-10011486,HY200686,03/27/2015 04:30:00 AM,078XX S COLES AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0421,004,7,43,05,1197539,1853730,2015,04/03/2015 12:48:39 PM,41.753485501,-87.551654884,"(41.753485501, -87.551654884)"
-10008464,HY197715,03/25/2015 10:30:00 AM,049XX W CONCORD PL,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,RESIDENCE,false,false,2533,025,37,25,04A,1142971,1910472,2015,04/01/2015 12:59:33 PM,41.910380348,-87.750216831,"(41.910380348, -87.750216831)"
-10007608,HY197439,03/25/2015 02:02:00 AM,006XX S CICERO AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1533,015,24,25,18,1144445,1896879,2015,04/01/2015 12:59:33 PM,41.873051963,-87.745144168,"(41.873051963, -87.745144168)"
-10005857,HY195687,03/23/2015 02:45:00 PM,127XX S PEORIA ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0523,005,34,53,26,1172691,1820353,2015,03/30/2015 12:50:30 PM,41.662477478,-87.643692601,"(41.662477478, -87.643692601)"
-10005950,HY196073,03/23/2015 12:25:00 PM,016XX W NELSON ST,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,1931,019,32,6,06,1164709,1920349,2015,03/30/2015 12:50:30 PM,41.93705014,-87.670078974,"(41.93705014, -87.670078974)"
-10004311,HY194163,03/22/2015 12:05:00 PM,104XX S HOXIE AVE,1360,CRIMINAL TRESPASS,TO VEHICLE,STREET,false,false,0434,004,10,51,26,1195232,1835974,2015,03/29/2015 12:46:38 PM,41.704818682,-87.56069309,"(41.704818682, -87.56069309)"
-10010662,HY194007,03/22/2015 09:10:00 AM,0000X W TERMINAL ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,AIRPORT TERMINAL LOWER LEVEL - NON-SECURE AREA,true,false,1651,016,41,76,26,1100317,1935189,2015,03/29/2015 12:46:38 PM,41.978896531,-87.906463888,"(41.978896531, -87.906463888)"
-10003161,HY192560,03/20/2015 11:00:00 PM,003XX W 60TH PL,0496,BATTERY,AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST,RESIDENCE,true,true,0711,007,20,68,04B,1174990,1864872,2015,03/27/2015 12:43:43 PM,41.784592899,-87.633955949,"(41.784592899, -87.633955949)"
-10001206,HY190733,03/19/2015 03:15:00 PM,076XX S PERRY AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0623,006,6,69,14,1176677,1854255,2015,03/26/2015 12:42:14 PM,41.75542086,-87.628089942,"(41.75542086, -87.628089942)"
-9998224,HY188343,03/17/2015 04:28:00 PM,048XX W ROOSEVELT RD,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,CTA BUS,false,false,1533,015,24,25,08B,1144517,1894311,2015,03/24/2015 12:41:57 PM,41.866003693,-87.744944445,"(41.866003693, -87.744944445)"
-9995182,HY185943,03/15/2015 08:00:00 PM,040XX S INDIANA AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,false,0213,002,3,38,08A,1178229,1878171,2015,03/22/2015 12:40:27 PM,41.821013654,-87.621676823,"(41.821013654, -87.621676823)"
-9994150,HY184507,03/14/2015 01:00:00 PM,040XX W GEORGE ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2523,025,31,21,06,1148712,1919018,2015,03/21/2015 12:41:38 PM,41.933722327,-87.728905109,"(41.933722327, -87.728905109)"
-9993498,HY183509,03/13/2015 10:30:00 PM,009XX E 80TH ST,0920,MOTOR VEHICLE THEFT,ATT: AUTOMOBILE,STREET,false,false,0624,006,8,44,07,1184065,1852200,2015,03/20/2015 12:42:30 PM,41.749612347,-87.60107917,"(41.749612347, -87.60107917)"
-9993401,HY183397,03/13/2015 05:45:00 PM,0000X N STATE ST,0460,BATTERY,SIMPLE,STREET,false,false,0112,001,42,32,08B,1176403,1900554,2015,03/20/2015 12:42:30 PM,41.882475504,-87.627700686,"(41.882475504, -87.627700686)"
-9994352,HY184735,03/12/2015 06:30:00 PM,0000X E OHIO ST,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,1834,018,42,8,06,1176451,1904161,2015,03/19/2015 12:40:58 PM,41.892372222,-87.627415473,"(41.892372222, -87.627415473)"
-9991562,HY181458,03/12/2015 12:20:00 PM,029XX W ADDISON ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,false,false,1733,017,33,21,06,1155968,1923741,2015,03/19/2015 12:40:58 PM,41.946539101,-87.702111692,"(41.946539101, -87.702111692)"
-10004818,HY179615,03/10/2015 11:21:00 PM,079XX S COTTAGE GROVE AVE,0291,CRIM SEXUAL ASSAULT,ATTEMPT NON-AGGRAVATED,STREET,false,false,0624,,8,44,02,,,2015,03/23/2015 12:42:19 PM,,,
-9989176,HY179410,03/10/2015 07:24:00 PM,064XX W HIGGINS AVE,2890,PUBLIC PEACE VIOLATION,OTHER VIOLATION,STREET,false,false,1613,016,41,10,26,1132462,1934423,2015,03/17/2015 12:53:42 PM,41.976294114,-87.788264031,"(41.976294114, -87.788264031)"
-9985998,HY175947,03/08/2015 12:38:00 AM,036XX S MICHIGAN AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,APARTMENT,false,false,0212,002,3,35,04A,1177827,1881117,2015,03/15/2015 12:40:37 PM,41.829106844,-87.623062197,"(41.829106844, -87.623062197)"
-9985765,HY175547,03/07/2015 06:00:00 PM,016XX W SHERWIN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2423,024,49,1,08B,1164303,1948764,2015,03/14/2015 12:40:59 PM,42.015030538,-87.670763363,"(42.015030538, -87.670763363)"
-9991010,HY173529,03/06/2015 07:15:00 AM,0000X W CHECKPOINT 7 ST,5007,OTHER OFFENSE,OTHER WEAPONS VIOLATION,AIRPORT TERMINAL UPPER LEVEL - SECURE AREA,false,false,1653,016,41,76,26,1101708,1934266,2015,03/13/2015 03:58:05 PM,41.976344553,-87.901365347,"(41.976344553, -87.901365347)"
-9984641,HY173965,03/06/2015 12:15:00 AM,050XX N SHERIDAN RD,0810,THEFT,OVER $500,SIDEWALK,false,false,2024,020,46,3,06,1168685,1933669,2015,03/13/2015 03:58:05 PM,41.973515376,-87.655079227,"(41.973515376, -87.655079227)"
-9982348,HY172088,03/04/2015 08:00:00 PM,016XX W 47TH ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0931,009,20,61,08B,1166097,1873507,2015,03/11/2015 12:43:59 PM,41.80848224,-87.666315654,"(41.80848224, -87.666315654)"
-9980903,HY170838,03/03/2015 06:46:00 PM,035XX W CHICAGO AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,1121,011,27,23,18,1152272,1905061,2015,03/10/2015 12:50:41 PM,41.89535345,-87.716191254,"(41.89535345, -87.716191254)"
-9980703,HY170468,03/03/2015 01:30:00 PM,010XX N LAWNDALE AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),RESIDENCE PORCH/HALLWAY,true,false,1112,011,27,23,18,1151566,1906611,2015,03/10/2015 12:50:41 PM,41.89962071,-87.718743456,"(41.89962071, -87.718743456)"
-9975095,HY164313,02/25/2015 06:00:00 PM,035XX W PALMER ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,1413,014,26,22,05,1152200,1914429,2015,03/04/2015 12:46:43 PM,41.921061534,-87.716208154,"(41.921061534, -87.716208154)"
-9972872,HY162326,02/24/2015 01:30:00 PM,022XX W 19TH ST,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,SIDEWALK,false,true,1234,012,25,31,26,1161755,1890664,2015,03/03/2015 12:38:55 PM,41.855654326,-87.681763869,"(41.855654326, -87.681763869)"
-9970647,HY160602,02/22/2015 11:40:00 PM,0000X E GRAND AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,HOTEL/MOTEL,false,true,1834,018,42,8,08B,1176699,1903953,2015,03/01/2015 12:38:30 PM,41.891795857,-87.626510975,"(41.891795857, -87.626510975)"
-9979724,HY157398,02/20/2015 08:10:00 AM,072XX S RACINE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0734,,17,67,08B,,,2015,03/03/2015 12:38:55 PM,,,
-9968252,HY157292,02/19/2015 11:40:00 PM,052XX S CALIFORNIA AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0923,009,14,63,03,1158550,1869986,2015,02/26/2015 12:47:04 PM,41.798977552,-87.694092519,"(41.798977552, -87.694092519)"
-9966895,HY156125,02/18/2015 04:30:00 PM,0000X W 95TH ST,0560,ASSAULT,SIMPLE,CTA BUS STOP,false,false,0634,006,21,49,08A,1177744,1841988,2015,02/25/2015 12:47:36 PM,41.721734632,-87.624549931,"(41.721734632, -87.624549931)"
-9965540,HY155265,02/17/2015 11:45:00 PM,001XX N CENTRAL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1523,015,29,25,08B,1139065,1900502,2015,02/24/2015 12:49:00 PM,41.883093419,-87.764808886,"(41.883093419, -87.764808886)"
-9963921,HY153624,02/15/2015 09:12:00 PM,019XX W 103RD ST,0620,BURGLARY,UNLAWFUL ENTRY,RESTAURANT,false,false,2212,022,19,72,05,1165579,1836330,2015,02/22/2015 12:52:34 PM,41.706474378,-87.66926812,"(41.706474378, -87.66926812)"
-9962798,HY151920,02/14/2015 07:50:00 AM,059XX S CARPENTER ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,RESIDENCE,true,false,0712,007,16,68,18,1170291,1865347,2015,02/21/2015 12:48:00 PM,41.785999946,-87.651170542,"(41.785999946, -87.651170542)"
-9969490,HY159017,02/14/2015 12:00:00 AM,070XX S MERRILL AVE,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,APARTMENT,false,false,0331,003,5,43,20,1191721,1858717,2015,02/23/2015 12:43:57 PM,41.767313308,-87.572813747,"(41.767313308, -87.572813747)"
-9961582,HY150020,02/13/2015 10:22:00 AM,050XX W ADAMS ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,true,true,1533,015,28,25,08B,1142531,1898814,2015,02/20/2015 12:46:33 PM,41.878397624,-87.752123357,"(41.878397624, -87.752123357)"
-9960121,HY148185,02/11/2015 03:45:00 PM,013XX S ASHLAND AVE,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",true,false,1233,012,2,28,08B,1165893,1893995,2015,02/18/2015 01:01:53 PM,41.864707716,-87.666480488,"(41.864707716, -87.666480488)"
-9959747,HY147844,02/11/2015 11:00:00 AM,080XX S HALSTED ST,0334,ROBBERY,ATTEMPT: ARMED-KNIFE/CUT INSTR,SIDEWALK,false,false,0621,006,21,71,03,1172334,1851289,2015,02/18/2015 01:01:53 PM,41.747378368,-87.644092954,"(41.747378368, -87.644092954)"
-9960689,HY149104,02/11/2015 09:00:00 AM,044XX W CONGRESS PKWY,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1131,011,24,26,05,1146683,1897357,2015,02/18/2015 01:01:53 PM,41.874321274,-87.736915119,"(41.874321274, -87.736915119)"
-9957195,HY146047,02/09/2015 08:15:00 PM,038XX W MONROE ST,2170,NARCOTICS,POSSESSION OF DRUG EQUIPMENT,ALLEY,true,false,1122,011,28,26,18,1150474,1899339,2015,02/16/2015 12:49:01 PM,41.879686974,-87.722944403,"(41.879686974, -87.722944403)"
-9957141,HY145623,02/09/2015 06:30:00 AM,073XX S BLACKSTONE AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0324,003,5,43,05,1187445,1856254,2015,02/16/2015 12:49:01 PM,41.760657299,-87.588565015,"(41.760657299, -87.588565015)"
-9955125,HY143913,02/08/2015 03:15:00 AM,001XX S KENTON AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1113,011,28,25,16,1145635,1899142,2015,02/15/2015 12:43:39 PM,41.879239448,-87.740717694,"(41.879239448, -87.740717694)"
-9954997,HY143724,02/07/2015 10:30:00 PM,045XX W MONROE ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1113,011,28,26,16,1145901,1899233,2015,02/14/2015 12:46:15 PM,41.879484118,-87.739738665,"(41.879484118, -87.739738665)"
-9955338,HY144033,02/07/2015 07:30:00 PM,046XX N BROADWAY,0610,BURGLARY,FORCIBLE ENTRY,COMMERCIAL / BUSINESS OFFICE,false,false,1914,019,46,3,05,1167897,1931128,2015,02/14/2015 12:46:15 PM,41.966559874,-87.658050527,"(41.966559874, -87.658050527)"
-9954491,HY142587,02/06/2015 11:20:00 PM,092XX S JUSTINE ST,0460,BATTERY,SIMPLE,APARTMENT,false,false,2221,022,21,73,08B,1167666,1843431,2015,02/13/2015 12:43:08 PM,41.725916207,-87.661422654,"(41.725916207, -87.661422654)"
-9953753,HY141810,02/06/2015 01:15:00 PM,046XX W 59TH ST,1340,CRIMINAL DAMAGE,TO STATE SUP PROP,CTA STATION,false,false,0813,008,23,62,14,1146459,1865053,2015,02/13/2015 12:43:08 PM,41.785678796,-87.738558722,"(41.785678796, -87.738558722)"
-9953242,HY141398,02/06/2015 07:10:00 AM,025XX N PARKSIDE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,2515,025,30,19,14,1138314,1916618,2015,02/13/2015 12:43:08 PM,41.92733127,-87.767176057,"(41.92733127, -87.767176057)"
-9952676,HY140948,02/05/2015 05:02:00 PM,052XX N SHERIDAN RD,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,2023,020,48,77,06,1168740,1934786,2015,02/12/2015 12:43:51 PM,41.976579259,-87.654844448,"(41.976579259, -87.654844448)"
-9952089,HY140529,02/04/2015 03:15:00 PM,030XX S DR MARTIN LUTHER KING JR DR,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0133,001,2,35,08B,1179263,1885133,2015,02/11/2015 12:40:51 PM,41.840094342,-87.617670874,"(41.840094342, -87.617670874)"
-9951209,HY139612,02/03/2015 05:00:00 PM,046XX S SACRAMENTO AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0922,009,14,58,07,1157194,1873605,2015,02/10/2015 12:37:41 PM,41.808936137,-87.698967366,"(41.808936137, -87.698967366)"
-9960871,HY149190,02/02/2015 09:03:00 AM,035XX N LONG AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1634,016,38,15,26,1139776,1922962,2015,02/13/2015 12:43:08 PM,41.944713239,-87.761648277,"(41.944713239, -87.761648277)"
-9950021,HY138936,02/01/2015 03:00:00 PM,006XX E 84TH ST,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,0632,006,6,44,08A,1182016,1849383,2015,02/08/2015 12:39:37 PM,41.741929795,-87.60867441,"(41.741929795, -87.60867441)"
-9947532,HY136043,02/01/2015 03:30:00 AM,013XX N RITCHIE CT,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,1824,018,43,8,06,1176592,1909199,2015,02/08/2015 12:39:37 PM,41.906193549,-87.626745256,"(41.906193549, -87.626745256)"
-9949528,HY136813,01/31/2015 01:02:00 PM,068XX S CRANDON AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,0331,003,5,43,26,1192551,1859796,2015,02/07/2015 12:41:58 PM,41.770253986,-87.569736391,"(41.770253986, -87.569736391)"
-9946865,HY135120,01/31/2015 12:00:00 AM,055XX S LA SALLE ST,0820,THEFT,$500 AND UNDER,CTA TRAIN,false,false,0225,002,3,68,06,1176193,1868334,2015,02/07/2015 12:41:58 PM,41.794066043,-87.629441364,"(41.794066043, -87.629441364)"
-9945866,HY133776,01/30/2015 09:23:00 AM,076XX S COTTAGE GROVE AVE,4651,OTHER OFFENSE,SEX OFFENDER: FAIL REG NEW ADD,APARTMENT,true,false,0624,006,6,69,26,1182926,1854545,2015,04/22/2015 12:45:18 PM,41.756073797,-87.605180167,"(41.756073797, -87.605180167)"
-9945059,HY133480,01/29/2015 09:00:00 PM,049XX W NEWPORT AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,1634,016,38,15,26,1142659,1922440,2015,02/05/2015 12:49:48 PM,41.943227569,-87.751064454,"(41.943227569, -87.751064454)"
-9944712,HY132979,01/29/2015 02:30:00 PM,030XX W 26TH ST,0330,ROBBERY,AGGRAVATED,ALLEY,false,false,1033,010,12,30,03,1156719,1886639,2015,02/05/2015 12:49:48 PM,41.844712652,-87.700357361,"(41.844712652, -87.700357361)"
-9943063,HY131303,01/27/2015 11:00:00 PM,063XX N CALIFORNIA AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2413,024,50,2,06,1156547,1942203,2015,02/03/2015 12:50:43 PM,41.9971881,-87.699481376,"(41.9971881, -87.699481376)"
-9940929,HY129670,01/26/2015 05:27:00 PM,008XX E 40TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0214,002,4,36,08B,1182966,1878533,2015,02/02/2015 12:52:04 PM,41.82189803,-87.604288123,"(41.82189803, -87.604288123)"
-9939680,HY128781,01/25/2015 11:20:00 PM,023XX S BLUE ISLAND AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,1034,010,25,31,04B,1164548,1888723,2015,02/01/2015 12:44:26 PM,41.85026942,-87.671567136,"(41.85026942, -87.671567136)"
-9938486,HY127235,01/24/2015 01:00:00 PM,0000X E LAKE ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0111,001,42,32,06,1176901,1901784,2015,01/31/2015 12:47:54 PM,41.88583944,-87.625834807,"(41.88583944, -87.625834807)"
-9936457,HY125274,01/22/2015 11:00:00 PM,014XX W 115TH ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0524,005,34,53,08B,1168509,1828436,2015,01/29/2015 12:53:50 PM,41.684749447,-87.658764946,"(41.684749447, -87.658764946)"
-9927294,HY115893,01/15/2015 12:05:00 PM,0000X N KILBOURN AVE,4625,OTHER OFFENSE,PAROLE VIOLATION,STREET,true,false,1113,011,28,26,26,1146342,1899948,2015,01/22/2015 12:50:54 PM,41.881437781,-87.738101155,"(41.881437781, -87.738101155)"
-9923758,HY113006,01/13/2015 03:00:00 AM,077XX S EXCHANGE AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0421,004,7,43,04B,1196415,1854165,2015,01/20/2015 12:47:56 PM,41.754707122,-87.555759435,"(41.754707122, -87.555759435)"
-9922352,HY111851,01/11/2015 06:00:00 PM,039XX N WAYNE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1923,019,47,6,06,1166483,1926426,2015,01/18/2015 12:44:12 PM,41.953687864,-87.663384725,"(41.953687864, -87.663384725)"
-9921456,HY110645,01/10/2015 09:19:00 PM,035XX W 26TH ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,1032,010,22,30,06,1152863,1886453,2015,01/17/2015 12:47:56 PM,41.844279361,-87.714513323,"(41.844279361, -87.714513323)"
-9921209,HY110227,01/10/2015 02:33:00 PM,066XX S MICHIGAN AVE,0460,BATTERY,SIMPLE,SIDEWALK,true,false,0322,003,20,69,08B,1178131,1860693,2015,01/17/2015 12:47:56 PM,41.773054608,-87.622566481,"(41.773054608, -87.622566481)"
-9920010,HY108705,01/09/2015 09:00:00 AM,054XX S ELLIS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,false,0233,002,5,41,08B,1183904,1869756,2015,01/16/2015 12:48:48 PM,41.797791393,-87.60112141,"(41.797791393, -87.60112141)"
-9919121,HY107948,01/08/2015 01:49:00 PM,018XX W 21ST ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,1234,012,25,31,18,1164368,1890143,2015,01/15/2015 12:46:55 PM,41.854169841,-87.672187639,"(41.854169841, -87.672187639)"
-9917982,HY107286,01/07/2015 06:24:00 PM,045XX S ASHLAND AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,0924,009,3,61,06,1166395,1874589,2015,01/14/2015 12:49:41 PM,41.811445018,-87.665191803,"(41.811445018, -87.665191803)"
-9917893,HY107151,01/06/2015 08:20:00 PM,072XX S COLES AVE,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0334,003,7,43,03,1194421,1857630,2015,01/13/2015 12:50:18 PM,41.764264564,-87.562952988,"(41.764264564, -87.562952988)"
-9913396,HY102423,01/03/2015 05:05:00 AM,059XX S MORGAN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,0712,007,16,68,08B,1170624,1865338,2015,01/10/2015 12:39:37 PM,41.785967993,-87.649949865,"(41.785967993, -87.649949865)"
-9926946,HY102381,01/03/2015 12:15:00 AM,0000X W TERMINAL ST,0820,THEFT,$500 AND UNDER,AIRPORT TERMINAL LOWER LEVEL - NON-SECURE AREA,false,false,1653,016,41,76,06,1101811,1934379,2015,01/17/2015 12:47:56 PM,41.976653215,-87.900984463,"(41.976653215, -87.900984463)"
-9915311,HY105038,01/02/2015 05:00:00 PM,095XX S YALE AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,STREET,false,true,0511,005,21,49,26,1176335,1841584,2015,01/09/2015 12:40:58 PM,41.72065772,-87.629722924,"(41.72065772, -87.629722924)"
-9910855,HX560918,12/31/2014 03:30:00 PM,016XX S CLARK ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,0131,001,3,33,06,1175811,1892220,2014,01/07/2015 12:42:19 PM,41.859619819,-87.630125199,"(41.859619819, -87.630125199)"
-9911568,HY100634,12/31/2014 09:00:00 AM,019XX E 79TH ST,0820,THEFT,$500 AND UNDER,OTHER,false,false,0414,004,8,46,06,1190480,1852935,2014,01/07/2015 12:42:19 PM,41.751477033,-87.577548654,"(41.751477033, -87.577548654)"
-9905525,HX556019,12/26/2014 11:05:00 PM,033XX W 19TH ST,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,STREET,true,false,1024,010,24,29,18,1154543,1890475,2014,01/02/2015 12:40:27 PM,41.855282815,-87.708240531,"(41.855282815, -87.708240531)"
-9900039,HX550748,12/21/2014 07:00:00 PM,048XX N HARDING AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1712,017,39,14,14,1149138,1932092,2014,12/28/2014 12:47:37 PM,41.969590146,-87.726999648,"(41.969590146, -87.726999648)"
-9898956,HX549362,12/20/2014 01:20:00 PM,023XX W JACKSON BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA APARTMENT,false,true,1225,012,2,28,08B,1160776,1898600,2014,12/27/2014 12:41:59 PM,41.877451805,-87.685137328,"(41.877451805, -87.685137328)"
-9898429,HX548657,12/19/2014 08:27:00 PM,014XX W MADISON ST,2820,OTHER OFFENSE,TELEPHONE THREAT,OTHER,false,false,1224,012,2,28,26,1166919,1900092,2014,12/26/2014 12:47:30 PM,41.881416445,-87.662539142,"(41.881416445, -87.662539142)"
-9897672,HX547786,12/19/2014 07:30:00 AM,080XX S BURNHAM AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0422,004,7,46,08B,1196277,1852033,2014,12/26/2014 12:47:30 PM,41.748860177,-87.556335712,"(41.748860177, -87.556335712)"
-9898261,HX548370,12/19/2014 07:15:00 AM,013XX W ROSEDALE AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,2013,020,48,77,05,1166367,1939263,2014,12/26/2014 12:47:30 PM,41.988915519,-87.663442157,"(41.988915519, -87.663442157)"
-9896028,HX546312,12/17/2014 02:13:00 PM,008XX N WOLCOTT AVE,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,1212,012,1,24,06,1163616,1905871,2014,12/24/2014 12:48:14 PM,41.897344685,-87.674504657,"(41.897344685, -87.674504657)"
-9894751,HX544921,12/16/2014 07:40:00 PM,002XX W 63RD ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,true,false,0711,007,20,68,11,1175833,1863236,2014,12/23/2014 12:50:08 PM,41.780084684,-87.630914183,"(41.780084684, -87.630914183)"
-9891938,HX542544,12/14/2014 10:05:00 PM,069XX S MARSHFIELD AVE,031A,ROBBERY,ARMED: HANDGUN,ALLEY,false,false,0735,007,17,67,03,1166589,1858376,2014,12/21/2014 12:44:13 PM,41.766950424,-87.664942543,"(41.766950424, -87.664942543)"
-9894321,HX544462,12/14/2014 08:00:00 PM,048XX S DORCHESTER AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0222,002,4,39,07,1186425,1872929,2014,12/21/2014 12:44:13 PM,41.806439038,-87.591776239,"(41.806439038, -87.591776239)"
-9892945,HX543306,12/13/2014 12:01:00 AM,008XX N KEDVALE AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,VACANT LOT/LAND,false,false,1111,011,37,23,14,1148602,1905391,2014,12/20/2014 12:40:24 PM,41.896330653,-87.729661846,"(41.896330653, -87.729661846)"
-9886850,HX537621,12/10/2014 05:22:00 PM,003XX N MICHIGAN AVE,3300,PUBLIC PEACE VIOLATION,PUBLIC DEMONSTRATION,STREET,true,false,0114,001,42,32,26,1177284,1902460,2014,12/17/2014 12:53:10 PM,41.887685745,-87.624407859,"(41.887685745, -87.624407859)"
-9886725,HX537414,12/10/2014 05:00:00 PM,106XX S MICHIGAN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0512,005,9,49,26,1178778,1834510,2014,12/17/2014 12:53:10 PM,41.701190574,-87.62098919,"(41.701190574, -87.62098919)"
-9892333,HX542910,12/08/2014 01:00:00 PM,021XX E 87TH ST,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0412,004,8,45,08A,1191717,1847744,2014,12/16/2014 12:50:48 PM,41.737202561,-87.573183761,"(41.737202561, -87.573183761)"
-9898752,HX549109,12/08/2014 08:00:00 AM,029XX S ARCH ST,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,RESIDENCE,false,false,0913,009,11,60,11,1168235,1884726,2014,12/21/2014 12:42:21 PM,41.839222519,-87.658150589,"(41.839222519, -87.658150589)"
-9880507,HX531080,12/05/2014 11:00:00 AM,033XX N WESTERN AVE,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,false,false,1921,019,47,5,06,1159720,1922380,2014,12/12/2014 12:38:27 PM,41.942727784,-87.688358118,"(41.942727784, -87.688358118)"
-9880169,HX530851,12/05/2014 09:15:00 AM,045XX W GEORGE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,2521,025,31,20,08B,1145651,1918935,2014,12/12/2014 12:38:27 PM,41.933553234,-87.740156372,"(41.933553234, -87.740156372)"
-9880034,HX530796,12/05/2014 07:50:00 AM,053XX S SPAULDING AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,0822,008,14,63,07,1155333,1868802,2014,12/17/2014 12:51:14 PM,41.795793533,-87.705921787,"(41.795793533, -87.705921787)"
-9881039,HX530696,12/05/2014 04:00:00 AM,123XX S WALLACE ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,false,true,0523,005,34,53,04A,1174584,1823040,2014,12/12/2014 12:38:27 PM,41.669809275,-87.636685763,"(41.669809275, -87.636685763)"
-9876435,HX527370,12/01/2014 05:30:00 PM,024XX W ALTGELD ST,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,1431,014,35,22,06,1159423,1916577,2014,12/08/2014 12:48:35 PM,41.926810096,-87.689609883,"(41.926810096, -87.689609883)"
-9875501,HX526486,12/01/2014 02:30:00 PM,034XX W FLOURNOY ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,RESIDENCE PORCH/HALLWAY,true,false,1133,011,24,27,18,1153382,1896775,2014,12/08/2014 12:48:35 PM,41.872593836,-87.712334692,"(41.872593836, -87.712334692)"
-9875481,HX526344,12/01/2014 12:30:00 PM,089XX S BUFFALO AVE,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,0424,004,10,46,08B,1199555,1846379,2014,12/08/2014 12:48:35 PM,41.733263337,-87.544514098,"(41.733263337, -87.544514098)"
-9873984,HX524656,11/30/2014 12:10:00 AM,018XX W AUGUSTA BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1212,012,32,24,08B,1163731,1906655,2014,12/07/2014 12:49:14 PM,41.899493615,-87.67406015,"(41.899493615, -87.67406015)"
-9872781,HX523051,11/28/2014 02:39:00 PM,043XX W 26TH ST,1330,CRIMINAL TRESPASS,TO LAND,RESTAURANT,true,false,1013,010,22,30,26,1147940,1886407,2014,12/05/2014 12:46:44 PM,41.844249049,-87.732581242,"(41.844249049, -87.732581242)"
-9871926,HX522328,11/27/2014 03:30:00 PM,078XX S BURNHAM AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0421,004,7,43,14,1196037,1853716,2014,12/04/2014 12:50:41 PM,41.7534844,-87.557159507,"(41.7534844, -87.557159507)"
-9871360,HX521607,11/26/2014 04:45:00 PM,004XX N WABASH AVE,0820,THEFT,$500 AND UNDER,MEDICAL/DENTAL OFFICE,false,false,1834,018,42,8,06,1176706,1903104,2014,12/03/2014 12:41:59 PM,41.889465999,-87.626510952,"(41.889465999, -87.626510952)"
-9869821,HX520159,11/25/2014 08:00:00 AM,068XX S KILBOURN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,OTHER,false,true,0833,008,13,65,26,1147595,1858910,2014,12/02/2014 12:51:55 PM,41.768799751,-87.734550378,"(41.768799751, -87.734550378)"
-9867852,HX518460,11/23/2014 09:00:00 AM,049XX S ST LAWRENCE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,RESIDENTIAL YARD (FRONT/BACK),false,false,0223,002,4,38,14,1181132,1872374,2014,11/30/2014 12:40:35 PM,41.805039751,-87.611206014,"(41.805039751, -87.611206014)"
-9866997,HX517284,11/22/2014 09:45:00 PM,047XX S LAKE PARK AVE,0810,THEFT,OVER $500,STREET,false,false,0222,002,4,39,06,1186257,1874027,2014,11/29/2014 12:42:24 PM,41.809456003,-87.592357676,"(41.809456003, -87.592357676)"
-9871091,HX521295,11/22/2014 05:18:00 PM,027XX W 71ST ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,ATM (AUTOMATIC TELLER MACHINE),false,false,0831,008,18,66,11,1158968,1857400,2014,11/29/2014 12:42:24 PM,41.764431252,-87.692903372,"(41.764431252, -87.692903372)"
-9861563,HX511192,11/17/2014 08:44:00 PM,035XX W MONTROSE AVE,1582,OFFENSE INVOLVING CHILDREN,CHILD PORNOGRAPHY,RESIDENCE,false,false,1723,017,33,14,17,1152230,1929059,2014,11/24/2014 12:37:49 PM,41.961206786,-87.715710628,"(41.961206786, -87.715710628)"
-9860968,HX510651,11/17/2014 06:20:00 PM,092XX S MAY ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2222,022,21,73,08B,1170245,1843258,2014,11/24/2014 12:37:49 PM,41.725385841,-87.651980618,"(41.725385841, -87.651980618)"
-9858939,HX508481,11/15/2014 08:57:00 PM,048XX N WINTHROP AVE,1360,CRIMINAL TRESPASS,TO VEHICLE,STREET,true,false,2033,020,46,3,26,1167964,1932162,2014,11/22/2014 12:39:04 PM,41.969395755,-87.657774202,"(41.969395755, -87.657774202)"
-9899074,HX549382,11/12/2014 07:00:00 PM,034XX W BELMONT AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,1412,014,35,21,03,1153132,1921033,2014,12/21/2014 12:44:13 PM,41.939164986,-87.712608085,"(41.939164986, -87.712608085)"
-9855970,HX504637,11/12/2014 03:48:00 PM,085XX S COLFAX AVE,3710,INTERFERENCE WITH PUBLIC OFFICER,RESIST/OBSTRUCT/DISARM OFFICER,SIDEWALK,true,false,0423,004,7,46,24,1194946,1848557,2014,11/19/2014 12:39:51 PM,41.739354629,-87.561327161,"(41.739354629, -87.561327161)"
-9854870,HX503963,11/11/2014 04:30:00 PM,017XX E 74TH ST,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,0324,003,8,43,06,1189052,1856215,2014,11/18/2014 12:38:30 PM,41.760511949,-87.582676629,"(41.760511949, -87.582676629)"
-9853749,HX502584,11/10/2014 09:00:00 PM,099XX S LA SALLE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,0511,005,9,49,08B,1177069,1838933,2014,11/17/2014 12:40:31 PM,41.713366524,-87.627114115,"(41.713366524, -87.627114115)"
-9851182,HX499999,11/08/2014 04:45:00 PM,008XX N STATE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,CTA STATION,true,false,1832,018,42,8,18,1176184,1905758,2014,11/15/2014 12:38:17 PM,41.896760495,-87.62834785,"(41.896760495, -87.62834785)"
-9850584,HX499243,11/08/2014 01:20:00 AM,0000X W DIVISION ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,true,false,1824,018,42,8,24,1175834,1908327,2014,11/15/2014 12:38:17 PM,41.903817841,-87.629555923,"(41.903817841, -87.629555923)"
-9850838,HX499494,11/06/2014 03:00:00 PM,045XX N MALDEN ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1913,019,46,3,06,1166767,1930333,2014,11/13/2014 12:42:15 PM,41.964402729,-87.662228235,"(41.964402729, -87.662228235)"
-9914350,HY103574,11/05/2014 09:00:00 AM,018XX W HURON ST,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,OTHER,false,false,1221,012,1,24,11,1163988,1904752,2014,01/06/2015 12:40:42 PM,41.894266217,-87.673169976,"(41.894266217, -87.673169976)"
-9846021,HX495345,11/04/2014 10:20:00 PM,001XX E 71ST ST,0560,ASSAULT,SIMPLE,RESIDENCE,true,false,0323,003,6,69,08A,1178683,1857942,2014,11/11/2014 12:41:33 PM,41.765493035,-87.620626567,"(41.765493035, -87.620626567)"
-9840961,HX490074,10/31/2014 09:00:00 PM,069XX N CAMPBELL AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,2411,024,50,2,14,1158361,1946121,2014,11/07/2014 12:42:06 PM,42.007902189,-87.692700532,"(42.007902189, -87.692700532)"
-9843414,HX492917,10/31/2014 05:30:00 PM,002XX W 63RD ST,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,STREET,false,false,0711,007,20,68,11,1175833,1863236,2014,11/07/2014 12:42:06 PM,41.780084684,-87.630914183,"(41.780084684, -87.630914183)"
-9839306,HX488498,10/30/2014 02:30:00 PM,016XX W SHERWIN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2423,024,49,1,14,1164125,1948677,2014,11/06/2014 12:41:16 PM,42.014795587,-87.671420811,"(42.014795587, -87.671420811)"
-9838081,HX487366,10/29/2014 06:00:00 PM,054XX W DAKIN ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1633,016,38,15,05,1139528,1925704,2014,11/05/2014 12:45:59 PM,41.952242087,-87.76249271,"(41.952242087, -87.76249271)"
-9837898,HX486896,10/29/2014 12:30:00 AM,005XX W 87TH ST,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0622,006,21,71,03,1174455,1847316,2014,11/05/2014 12:45:59 PM,41.73642906,-87.636438922,"(41.73642906, -87.636438922)"
-9834815,HX484914,10/27/2014 11:57:00 PM,029XX W AUGUSTA BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1211,012,26,24,18,1156839,1906479,2014,11/03/2014 12:40:32 PM,41.8991532,-87.699379232,"(41.8991532, -87.699379232)"
-9832556,HX482550,10/26/2014 02:15:00 AM,057XX W MADISON ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,POLICE FACILITY/VEH PARKING LOT,true,false,1513,015,29,25,24,1138127,1899398,2014,11/02/2014 12:37:16 PM,41.880080898,-87.768280017,"(41.880080898, -87.768280017)"
-9832596,HX482655,10/25/2014 10:00:00 PM,015XX N NORTH PARK AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,1821,018,27,8,14,1173845,1910754,2014,11/01/2014 12:39:11 PM,41.910522194,-87.636789532,"(41.910522194, -87.636789532)"
-9829059,HX478613,10/22/2014 10:30:00 PM,042XX W VAN BUREN ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE PORCH/HALLWAY,false,false,1132,011,24,26,05,1148336,1897729,2014,10/29/2014 12:37:07 PM,41.875310407,-87.730836402,"(41.875310407, -87.730836402)"
-9829669,HX479074,10/21/2014 04:30:00 PM,105XX S MORGAN ST,0460,BATTERY,SIMPLE,PARK PROPERTY,false,false,2232,022,34,73,08B,1171485,1834726,2014,10/28/2014 12:36:38 PM,41.701945742,-87.64768733,"(41.701945742, -87.64768733)"
-9826811,HX476501,10/21/2014 03:00:00 PM,063XX S ARTESIAN AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,RESIDENCE,false,false,0825,008,15,66,04B,1161176,1862291,2014,10/28/2014 12:36:38 PM,41.777807452,-87.684675225,"(41.777807452, -87.684675225)"
-9824950,HX474998,10/20/2014 01:14:00 PM,062XX S PARK SHORE EAST CT,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,SIDEWALK,true,false,0314,003,5,42,24,1187522,1864274,2014,10/27/2014 12:36:31 PM,41.782663033,-87.58802809,"(41.782663033, -87.58802809)"
-9832261,HX482192,10/20/2014 10:00:00 AM,014XX W 47TH ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,APARTMENT,false,false,0933,009,20,61,11,1167243,1873534,2014,10/27/2014 12:36:31 PM,41.808531847,-87.662111611,"(41.808531847, -87.662111611)"
-9824158,HX474355,10/19/2014 08:50:00 PM,027XX E 95TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0431,004,10,51,18,1195951,1842451,2014,10/26/2014 12:37:49 PM,41.722574445,-87.557846645,"(41.722574445, -87.557846645)"
-9843649,HX493152,10/19/2014 02:56:00 PM,004XX N STATE ST,1206,DECEPTIVE PRACTICE,"THEFT BY LESSEE,MOTOR VEH",VEHICLE-COMMERCIAL,false,false,1834,018,42,8,11,1176341,1903042,2014,11/04/2014 12:43:25 PM,41.88930411,-87.627853241,"(41.88930411, -87.627853241)"
-9817656,HX467320,10/13/2014 05:15:00 PM,010XX W VAN BUREN ST,0810,THEFT,OVER $500,STREET,false,false,1232,012,2,28,06,1169285,1898385,2014,10/20/2014 12:41:20 PM,41.876681237,-87.653900961,"(41.876681237, -87.653900961)"
-9816325,HX466018,10/13/2014 01:55:00 PM,041XX S SACRAMENTO AVE,3100,PUBLIC PEACE VIOLATION,MOB ACTION,STREET,true,false,0921,009,14,58,24,1157079,1877024,2014,10/20/2014 12:41:20 PM,41.818320643,-87.699296642,"(41.818320643, -87.699296642)"
-9815967,HX465548,10/13/2014 06:25:00 AM,110XX S SANGAMON ST,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,2234,022,34,75,06,1172033,1831656,2014,10/20/2014 12:41:20 PM,41.693509196,-87.645770458,"(41.693509196, -87.645770458)"
-9815913,HX465489,10/13/2014 04:35:00 AM,049XX W SUPERIOR ST,0560,ASSAULT,SIMPLE,APARTMENT,false,false,1531,015,37,25,08A,1142973,1904469,2014,10/20/2014 12:41:20 PM,41.893907401,-87.750359347,"(41.893907401, -87.750359347)"
-9814725,HX464040,10/11/2014 02:00:00 PM,047XX S VINCENNES AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,true,true,0223,002,3,38,26,1180441,1873370,2014,11/10/2014 12:41:06 PM,41.807788763,-87.613709697,"(41.807788763, -87.613709697)"
-9814775,HX464088,10/11/2014 02:00:00 AM,014XX W WEBSTER AVE,0810,THEFT,OVER $500,STREET,false,false,1811,018,32,7,06,1165880,1914689,2014,10/18/2014 12:37:02 PM,41.921493844,-87.665937306,"(41.921493844, -87.665937306)"
-9814093,HX463139,10/11/2014 12:45:00 AM,031XX N WESTERN AVE,0337,ROBBERY,ATTEMPT: ARMED-OTHER DANG WEAP,GAS STATION,true,false,1931,019,1,5,03,1159894,1921024,2014,10/18/2014 12:37:02 PM,41.939003242,-87.687756119,"(41.939003242, -87.687756119)"
-9813903,HX462973,10/10/2014 08:40:00 PM,024XX S TRUMBULL AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1024,010,22,30,18,1153788,1887773,2014,10/17/2014 12:40:52 PM,41.84788326,-87.71108361,"(41.84788326, -87.71108361)"
-9813941,HX462999,10/10/2014 08:05:00 PM,043XX N CLARENDON AVE,0810,THEFT,OVER $500,APARTMENT,false,true,1915,019,46,3,06,1170166,1929352,2014,10/17/2014 12:40:52 PM,41.961637102,-87.649759923,"(41.961637102, -87.649759923)"
-9813848,HX462620,10/10/2014 04:00:00 PM,076XX S PRAIRIE AVE,0810,THEFT,OVER $500,VEHICLE-COMMERCIAL,false,false,0623,006,6,69,06,1179405,1854139,2014,10/17/2014 12:40:52 PM,41.755040742,-87.618096124,"(41.755040742, -87.618096124)"
-9813323,HX462198,10/10/2014 09:50:00 AM,034XX S PRAIRIE AVE,0560,ASSAULT,SIMPLE,OTHER,false,false,0211,002,2,35,08A,1178543,1881954,2014,10/17/2014 12:40:52 PM,41.831387362,-87.620409767,"(41.831387362, -87.620409767)"
-9820287,HX469854,10/10/2014 12:01:00 AM,051XX S MICHIGAN AVE,1154,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT $300 AND UNDER,RESIDENCE,false,false,0225,002,3,40,11,1177999,1870570,2014,10/17/2014 12:40:52 PM,41.800161069,-87.622751126,"(41.800161069, -87.622751126)"
-9812395,HX461105,10/09/2014 12:00:00 PM,002XX W 63RD ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,CTA PLATFORM,false,false,0711,007,20,68,04A,1175833,1863236,2014,10/16/2014 12:41:58 PM,41.780084684,-87.630914183,"(41.780084684, -87.630914183)"
-9817458,HX467003,10/09/2014 10:00:00 AM,017XX W PRYOR AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",true,false,2212,022,19,75,08B,1166552,1831113,2014,10/16/2014 12:41:58 PM,41.69213742,-87.66585308,"(41.69213742, -87.66585308)"
-9810084,HX459088,10/07/2014 06:35:00 PM,017XX S MICHIGAN AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0131,001,2,33,06,1177453,1891985,2014,10/14/2014 12:38:29 PM,41.858937901,-87.624105092,"(41.858937901, -87.624105092)"
-9817684,HX467316,10/06/2014 08:30:00 AM,026XX W NORTH AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,1421,014,1,24,06,1158234,1910572,2014,10/15/2014 12:40:17 PM,41.91035633,-87.694143404,"(41.91035633, -87.694143404)"
-9799224,HX448227,09/29/2014 01:15:00 PM,103XX S COTTAGE GROVE AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,STREET,false,false,0512,005,9,50,04A,1182666,1836790,2014,10/06/2014 12:37:42 PM,41.707358059,-87.60668233,"(41.707358059, -87.60668233)"
-9796745,HX445627,09/26/2014 07:00:00 PM,035XX N BELL AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1921,019,47,5,06,1160780,1923416,2014,10/03/2014 12:39:14 PM,41.945548677,-87.684433289,"(41.945548677, -87.684433289)"
-9797462,HX445594,09/26/2014 06:30:00 AM,041XX W CRYSTAL ST,0820,THEFT,$500 AND UNDER,ALLEY,false,false,2534,025,37,23,06,1148681,1907977,2014,10/03/2014 12:39:14 PM,41.903425379,-87.729304819,"(41.903425379, -87.729304819)"
-9794344,HX443056,09/25/2014 04:48:00 PM,020XX S HARDING AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1014,010,24,29,18,1150421,1889624,2014,10/02/2014 12:40:26 PM,41.853028916,-87.723392466,"(41.853028916, -87.723392466)"
-9793210,HX442170,09/24/2014 10:45:00 AM,040XX W CONGRESS PKWY,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,1132,011,24,26,03,1149557,1897346,2014,10/01/2014 12:43:13 PM,41.874235804,-87.726363276,"(41.874235804, -87.726363276)"
-9791274,HX439250,09/22/2014 09:00:00 PM,048XX W POTOMAC AVE,0313,ROBBERY,ARMED: OTHER DANGEROUS WEAPON,SIDEWALK,false,false,2533,025,37,25,03,1144065,1908256,2014,09/29/2014 12:37:44 PM,41.904278924,-87.746253564,"(41.904278924, -87.746253564)"
-9789624,HX439217,09/22/2014 08:00:00 PM,005XX N HALSTED ST,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1214,012,27,24,14,1170990,1903827,2014,09/29/2014 12:37:44 PM,41.891577242,-87.647481089,"(41.891577242, -87.647481089)"
-9790746,HX439202,09/22/2014 06:00:00 PM,057XX W LAWRENCE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1622,,45,15,14,,,2014,09/29/2014 12:37:44 PM,,,
-9788911,HX438418,09/22/2014 09:32:00 AM,097XX S COTTAGE GROVE AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,GOVERNMENT BUILDING/PROPERTY,true,false,0511,005,8,50,04A,1183369,1840641,2014,09/29/2014 12:37:44 PM,41.71790937,-87.603988507,"(41.71790937, -87.603988507)"
-9787064,HX436180,09/20/2014 02:08:00 PM,003XX E 63RD ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,0311,003,20,40,06,1179603,1863354,2014,09/27/2014 12:37:59 PM,41.780323149,-87.617089297,"(41.780323149, -87.617089297)"
-9786469,HX435495,09/19/2014 11:00:00 PM,023XX W FULLERTON AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1432,014,1,22,14,1160212,1915931,2014,09/26/2014 12:39:34 PM,41.925021137,-87.686728554,"(41.925021137, -87.686728554)"
-9785189,HX434311,09/18/2014 06:00:00 PM,033XX W LE MOYNE ST,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE-GARAGE,false,false,1422,014,26,23,14,1153836,1909729,2014,09/25/2014 12:42:39 PM,41.908131862,-87.710322526,"(41.908131862, -87.710322526)"
-9784201,HX433214,09/18/2014 11:00:00 AM,088XX S DOBSON AVE,0890,THEFT,FROM BUILDING,COMMERCIAL / BUSINESS OFFICE,false,false,0412,004,8,47,06,1184626,1846385,2014,09/25/2014 12:42:39 PM,41.733642248,-87.599205239,"(41.733642248, -87.599205239)"
-9787167,HX436428,09/17/2014 08:45:00 PM,0000X E LOWER WACKER DR,0460,BATTERY,SIMPLE,STREET,false,false,0111,001,42,32,08B,1176702,1902144,2014,09/24/2014 12:40:15 PM,41.886831799,-87.62655468,"(41.886831799, -87.62655468)"
-9781473,HX431358,09/17/2014 01:31:00 AM,029XX W WILCOX ST,051A,ASSAULT,AGGRAVATED: HANDGUN,RESIDENCE PORCH/HALLWAY,false,false,1124,011,2,27,04A,1156810,1899174,2014,09/24/2014 12:40:15 PM,41.879108186,-87.699683883,"(41.879108186, -87.699683883)"
-9779790,HX429994,09/15/2014 11:50:00 PM,017XX S STATE ST,2890,PUBLIC PEACE VIOLATION,OTHER VIOLATION,POLICE FACILITY/VEH PARKING LOT,true,false,0131,001,3,33,26,1176569,1891773,2014,09/22/2014 12:39:52 PM,41.858376151,-87.627356324,"(41.858376151, -87.627356324)"
-9779735,HX429906,09/15/2014 09:27:00 PM,006XX W GARFIELD BLVD,0320,ROBBERY,STRONGARM - NO WEAPON,CTA BUS,true,false,0935,009,3,61,03,1172959,1868459,2014,09/22/2014 12:39:52 PM,41.794481108,-87.641296534,"(41.794481108, -87.641296534)"
-9777994,HX428283,09/14/2014 04:30:00 PM,038XX W VAN BUREN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1133,011,24,26,08B,1150745,1897706,2014,09/21/2014 12:37:13 PM,41.875200548,-87.721992032,"(41.875200548, -87.721992032)"
-9778804,HX429064,09/14/2014 11:40:00 AM,060XX N TALMAN AVE,0890,THEFT,FROM BUILDING,RESIDENCE PORCH/HALLWAY,false,false,2413,024,50,2,06,1157604,1940249,2014,09/21/2014 12:37:13 PM,41.991804708,-87.695646609,"(41.991804708, -87.695646609)"
-9776421,HX426318,09/12/2014 11:06:00 PM,095XX S OGLESBY AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,OTHER,true,false,0431,004,7,51,18,1193714,1842377,2014,09/19/2014 12:39:17 PM,41.722426388,-87.566042754,"(41.722426388, -87.566042754)"
-9774786,HX424565,09/11/2014 04:30:00 PM,091XX S MARQUETTE AVE,041A,BATTERY,AGGRAVATED: HANDGUN,SIDEWALK,false,false,0423,004,7,48,04B,1195895,1844559,2014,09/18/2014 12:40:09 PM,41.728360367,-87.557982215,"(41.728360367, -87.557982215)"
-9782076,HX422991,09/09/2014 09:00:00 PM,027XX N CAMPBELL AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1411,014,1,22,26,1159181,1918143,2014,09/18/2014 12:40:09 PM,41.931112291,-87.690455982,"(41.931112291, -87.690455982)"
-9771220,HX421925,09/09/2014 02:47:00 PM,035XX W FLOURNOY ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1133,011,24,27,18,1152955,1896845,2014,09/16/2014 12:44:17 PM,41.87279439,-87.713900557,"(41.87279439, -87.713900557)"
-9771010,HX421841,09/09/2014 01:30:00 PM,051XX S HERMITAGE AVE,0554,ASSAULT,AGG PO HANDS NO/MIN INJURY,RESIDENCE PORCH/HALLWAY,true,false,0932,009,16,61,08A,1165508,1870630,2014,09/16/2014 12:44:17 PM,41.80059994,-87.668557591,"(41.80059994, -87.668557591)"
-9768137,HX418879,09/07/2014 07:40:00 AM,068XX S MICHIGAN AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,APARTMENT,false,true,0322,003,20,69,26,1178304,1859653,2014,09/14/2014 12:35:10 PM,41.770196812,-87.621963844,"(41.770196812, -87.621963844)"
-9766561,HX416651,09/05/2014 02:00:00 PM,073XX S UNION AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,true,false,0732,007,17,68,03,1172858,1856409,2014,09/12/2014 12:40:40 PM,41.761416758,-87.642022119,"(41.761416758, -87.642022119)"
-9767408,HX417854,09/05/2014 01:00:00 AM,001XX W GARFIELD BLVD,0820,THEFT,$500 AND UNDER,CTA TRAIN,false,false,0225,002,3,37,06,1176054,1868561,2014,09/12/2014 12:40:40 PM,41.794692076,-87.629944256,"(41.794692076, -87.629944256)"
-9765269,HX415148,09/04/2014 01:00:00 AM,013XX W 118TH ST,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,0524,005,34,53,06,1169461,1826552,2014,09/11/2014 12:39:33 PM,41.679558925,-87.655334203,"(41.679558925, -87.655334203)"
-9766288,HX416321,09/03/2014 01:12:00 PM,073XX S PHILLIPS AVE,1154,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT $300 AND UNDER,OTHER,false,false,0334,003,7,43,11,1193885,1856511,2014,09/10/2014 12:38:25 PM,41.761207101,-87.564954158,"(41.761207101, -87.564954158)"
-9759917,HX410132,08/31/2014 03:00:00 PM,013XX S CLINTON ST,1220,DECEPTIVE PRACTICE,THEFT OF LOST/MISLAID PROP,DEPARTMENT STORE,false,false,0124,001,2,28,11,1172883,1894339,2014,09/07/2014 12:35:09 PM,41.865499815,-87.640810193,"(41.865499815, -87.640810193)"
-9759486,HX409540,08/31/2014 06:30:00 AM,084XX S MACKINAW AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,0424,004,10,46,08B,1199894,1849752,2014,09/07/2014 12:35:09 PM,41.742510561,-87.543158753,"(41.742510561, -87.543158753)"
-9773776,HX409715,08/30/2014 09:00:00 AM,026XX W FRANCIS PL,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1431,014,1,22,14,1158394,1914043,2014,09/12/2014 12:40:40 PM,41.91987775,-87.693460491,"(41.91987775, -87.693460491)"
-9756933,HX406714,08/29/2014 02:35:00 AM,011XX W GRANVILLE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESTAURANT,false,false,2433,024,48,77,14,1167586,1941293,2014,09/05/2014 12:35:08 PM,41.994459632,-87.658899737,"(41.994459632, -87.658899737)"
-9755483,HX405270,08/27/2014 10:30:00 PM,031XX W LELAND AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1713,017,33,14,08B,1154402,1931093,2014,10/31/2014 03:20:56 PM,41.966744971,-87.70767055,"(41.966744971, -87.70767055)"
-9771082,HX421772,08/27/2014 09:00:00 PM,0000X W ELM ST,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,1824,018,42,8,03,1175926,1908130,2014,09/10/2014 12:38:25 PM,41.903275192,-87.629223928,"(41.903275192, -87.629223928)"
-9751837,HX401955,08/25/2014 02:00:00 PM,055XX S CALIFORNIA AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0824,008,16,63,08B,1158693,1867775,2014,09/01/2014 12:37:37 PM,41.792907351,-87.69362844,"(41.792907351, -87.69362844)"
-9751452,HX401715,08/24/2014 01:00:00 AM,064XX S RICHMOND ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,DRIVEWAY - RESIDENTIAL,false,false,0823,008,15,66,07,1157858,1861956,2014,08/31/2014 12:37:21 PM,41.776956199,-87.696848262,"(41.776956199, -87.696848262)"
-9744280,HX394241,08/19/2014 08:30:00 AM,016XX N PULASKI RD,0334,ROBBERY,ATTEMPT: ARMED-KNIFE/CUT INSTR,CTA BUS STOP,false,false,2535,025,30,23,03,1149495,1910935,2014,08/26/2014 12:39:33 PM,41.911526656,-87.726237895,"(41.911526656, -87.726237895)"
-9742984,HX393429,08/19/2014 03:48:00 AM,057XX W BERENICE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,RESIDENCE,false,false,1633,016,38,15,14,1137601,1924979,2014,08/26/2014 12:39:33 PM,41.950287617,-87.76959404,"(41.950287617, -87.76959404)"
-9758516,HX392623,08/18/2014 03:40:00 PM,0000X W TERMINAL ST,5007,OTHER OFFENSE,OTHER WEAPONS VIOLATION,AIRPORT TERMINAL UPPER LEVEL - SECURE AREA,false,false,1651,016,41,76,26,1100317,1935189,2014,10/31/2014 03:20:56 PM,41.978896531,-87.906463888,"(41.978896531, -87.906463888)"
-9741073,HX391490,08/17/2014 04:18:00 PM,036XX S RHODES AVE,0560,ASSAULT,SIMPLE,CHA HALLWAY/STAIRWELL/ELEVATOR,false,false,0212,002,4,35,08A,1180140,1880977,2014,08/24/2014 12:37:19 PM,41.828669884,-87.614580294,"(41.828669884, -87.614580294)"
-9740756,HX390940,08/17/2014 06:00:00 AM,063XX S ASHLAND AVE,0820,THEFT,$500 AND UNDER,CTA TRAIN,false,false,0725,007,16,67,06,1166712,1862870,2014,08/24/2014 12:37:19 PM,41.77927992,-87.664363567,"(41.77927992, -87.664363567)"
-9740047,HX390250,08/15/2014 07:00:00 PM,092XX S BLACKSTONE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,0413,004,8,48,26,1187771,1843919,2014,08/22/2014 12:36:05 PM,41.726801095,-87.587761923,"(41.726801095, -87.587761923)"
-9738616,HX388445,08/15/2014 10:59:00 AM,110XX S MICHIGAN AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0513,005,9,49,06,1178749,1831610,2014,08/22/2014 12:36:05 PM,41.693233223,-87.621183177,"(41.693233223, -87.621183177)"
-9736288,HX386331,08/13/2014 06:15:00 PM,102XX S MICHIGAN AVE,1330,CRIMINAL TRESPASS,TO LAND,OTHER,true,false,0511,005,9,49,26,1179016,1836736,2014,08/20/2014 12:42:23 PM,41.707293618,-87.62005021,"(41.707293618, -87.62005021)"
-9734857,HX384998,08/12/2014 07:15:00 PM,117XX S PEORIA ST,0820,THEFT,$500 AND UNDER,SIDEWALK,true,true,0524,005,34,53,06,1172476,1826876,2014,08/19/2014 12:37:52 PM,41.680382398,-87.644288515,"(41.680382398, -87.644288515)"
-9733500,HX383572,08/11/2014 06:00:00 PM,056XX N CENTRAL AVE,0460,BATTERY,SIMPLE,ALLEY,false,false,1622,016,45,11,08B,1137828,1937425,2014,08/18/2014 12:38:20 PM,41.984436467,-87.768457961,"(41.984436467, -87.768457961)"
-9732121,HX382303,08/10/2014 07:31:00 PM,100XX S WESTERN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2211,022,19,72,18,1162121,1837770,2014,08/17/2014 12:37:26 PM,41.710498462,-87.681891359,"(41.710498462, -87.681891359)"
-9731541,HX381526,08/09/2014 08:45:00 PM,087XX S LAFAYETTE AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0634,006,21,44,06,1177419,1847206,2014,08/16/2014 12:37:25 PM,41.736060832,-87.625583193,"(41.736060832, -87.625583193)"
-9731079,HX380994,08/09/2014 07:50:00 PM,059XX S LOOMIS BLVD,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0713,007,16,67,08B,1167971,1865431,2014,08/16/2014 12:37:25 PM,41.786280647,-87.65967438,"(41.786280647, -87.65967438)"
-9731005,HX380917,08/09/2014 04:00:00 PM,022XX N CICERO AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,2522,025,31,19,08A,1143994,1914282,2014,08/16/2014 12:37:25 PM,41.92081624,-87.746362884,"(41.92081624, -87.746362884)"
-9732914,HX382841,08/08/2014 09:00:00 AM,104XX S SANGAMON ST,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,RESIDENCE,false,false,2232,022,34,73,11,1171787,1835651,2014,08/15/2014 12:34:08 PM,41.70447748,-87.646554485,"(41.70447748, -87.646554485)"
-9728761,HX378475,08/07/2014 10:50:00 PM,050XX W WASHINGTON BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1533,015,28,25,18,1142638,1899990,2014,08/14/2014 12:38:24 PM,41.881622725,-87.751701207,"(41.881622725, -87.751701207)"
-9726019,HX375967,08/05/2014 04:00:00 PM,030XX W 24TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1033,010,12,30,14,1156374,1887862,2014,08/12/2014 12:35:59 PM,41.848075675,-87.70159045,"(41.848075675, -87.70159045)"
-9724865,HX374809,08/05/2014 12:00:00 AM,005XX S CLAREMONT AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1225,012,2,28,06,1160856,1897515,2014,08/12/2014 12:35:59 PM,41.874472807,-87.684873688,"(41.874472807, -87.684873688)"
-9724920,HX374891,08/04/2014 01:30:00 PM,002XX N KEDZIE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,1123,011,28,27,03,1154955,1901787,2014,08/11/2014 12:39:47 PM,41.886315899,-87.706425058,"(41.886315899, -87.706425058)"
-9722510,HX372885,08/03/2014 07:30:00 PM,075XX S JEFFERY BLVD,0820,THEFT,$500 AND UNDER,PARK PROPERTY,false,false,0414,004,8,43,06,1190802,1855567,2014,08/10/2014 12:34:42 PM,41.758691699,-87.576283827,"(41.758691699, -87.576283827)"
-9720689,HX370279,08/01/2014 11:53:00 PM,003XX W HILL ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1823,018,27,8,08B,1174098,1907721,2014,08/08/2014 12:34:16 PM,41.90219385,-87.635950699,"(41.90219385, -87.635950699)"
-9715033,HX365227,07/28/2014 05:30:00 PM,031XX N HALSTED ST,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1934,019,44,6,14,1170406,1921370,2014,08/04/2014 12:42:16 PM,41.939728951,-87.649111837,"(41.939728951, -87.649111837)"
-9712916,HX363368,07/28/2014 02:38:00 AM,046XX S FAIRFIELD AVE,2022,NARCOTICS,POSS: COCAINE,STREET,true,false,0922,009,12,58,18,1158781,1873576,2014,08/04/2014 12:42:16 PM,41.808824264,-87.693147323,"(41.808824264, -87.693147323)"
-9712145,HX362361,07/27/2014 10:12:00 AM,002XX E 32ND ST,1025,ARSON,AGGRAVATED,CHA APARTMENT,false,false,0211,002,3,35,09,1178601,1883799,2014,08/03/2014 12:37:30 PM,41.836448859,-87.620140758,"(41.836448859, -87.620140758)"
-9718124,HX360798,07/26/2014 03:00:00 AM,013XX W 79TH ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,APARTMENT,false,true,0612,,17,71,04A,,,2014,08/02/2014 12:35:35 PM,,,
-9711292,HX361045,07/25/2014 08:00:00 AM,058XX S MARYLAND AVE,1156,DECEPTIVE PRACTICE,ATTEMPT - FINANCIAL IDENTITY THEFT,OTHER,false,false,0235,002,5,41,11,1182938,1866306,2014,08/01/2014 12:38:57 PM,41.788346824,-87.604771066,"(41.788346824, -87.604771066)"
-9705965,HX356119,07/22/2014 06:40:00 PM,030XX N ROCKWELL ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1411,014,1,21,14,1158472,1920082,2014,07/29/2014 12:41:54 PM,41.936447598,-87.693008205,"(41.936447598, -87.693008205)"
-9705299,HX355475,07/22/2014 11:00:00 AM,047XX S LAKE PARK AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,0222,002,4,39,06,1186257,1874027,2014,07/29/2014 12:41:54 PM,41.809456003,-87.592357676,"(41.809456003, -87.592357676)"
-9703206,HX353776,07/21/2014 08:28:00 AM,016XX S KOMENSKY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1012,010,24,29,08B,1149694,1891491,2014,07/28/2014 12:40:00 PM,41.858166334,-87.72601235,"(41.858166334, -87.72601235)"
-9702075,HX352569,07/19/2014 08:00:00 PM,004XX W 24TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0914,009,25,34,14,1173230,1888308,2014,07/26/2014 12:40:58 PM,41.848942609,-87.639715206,"(41.848942609, -87.639715206)"
-9700756,HX350786,07/18/2014 09:13:00 PM,102XX S NORMAL AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2232,022,9,73,18,1174737,1836882,2014,07/25/2014 12:40:34 PM,41.707790457,-87.635715544,"(41.707790457, -87.635715544)"
-9700541,HX350526,07/18/2014 05:25:00 PM,044XX S LAPORTE AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,0814,008,23,56,18,1144029,1874968,2014,07/25/2014 12:40:34 PM,41.812932932,-87.747220515,"(41.812932932, -87.747220515)"
-9700483,HX350110,07/18/2014 12:20:00 PM,070XX S OGLESBY AVE,0650,BURGLARY,HOME INVASION,APARTMENT,true,false,0331,003,5,43,05,1193021,1858906,2014,10/31/2014 03:20:56 PM,41.767800297,-87.568042624,"(41.767800297, -87.568042624)"
-9700827,HX350780,07/17/2014 09:30:00 PM,0000X N WALLER AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1513,015,29,25,06,1138202,1899683,2014,07/24/2014 12:40:50 PM,41.88086162,-87.767997728,"(41.88086162, -87.767997728)"
-9699350,HX349080,07/17/2014 02:11:00 AM,009XX S CENTRAL AVE,0610,BURGLARY,FORCIBLE ENTRY,OTHER,true,false,1522,015,29,25,05,1139294,1895407,2014,10/31/2014 03:20:56 PM,41.8691079,-87.764092057,"(41.8691079, -87.764092057)"
-9697540,HX347063,07/16/2014 12:00:00 PM,104XX S CORLISS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0512,005,9,50,08B,1183456,1836137,2014,07/23/2014 12:42:44 PM,41.705547814,-87.603809628,"(41.705547814, -87.603809628)"
-9696774,HX347074,07/16/2014 09:10:00 AM,075XX N WESTERN AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,2411,024,50,2,06,1158976,1949813,2014,07/23/2014 12:42:44 PM,42.018020494,-87.690335774,"(42.018020494, -87.690335774)"
-9721392,HX371097,07/16/2014 08:30:00 AM,017XX E 72ND ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,true,0324,003,8,43,26,1189168,1857546,2014,10/31/2014 03:20:56 PM,41.764161549,-87.582208896,"(41.764161549, -87.582208896)"
-9694631,HX345022,07/14/2014 04:25:00 PM,044XX S DREXEL BLVD,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),ALLEY,true,false,0221,002,4,39,18,1182899,1875739,2014,07/21/2014 12:50:42 PM,41.814232649,-87.604620838,"(41.814232649, -87.604620838)"
-9692664,HX342799,07/12/2014 10:45:00 PM,123XX S UNION AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENTIAL YARD (FRONT/BACK),true,false,0523,005,34,53,26,1173849,1822967,2014,07/19/2014 12:41:41 PM,41.669625225,-87.639377922,"(41.669625225, -87.639377922)"
-9691906,HX341698,07/12/2014 02:55:00 AM,054XX S ABERDEEN ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,RESIDENTIAL YARD (FRONT/BACK),true,false,0934,009,16,61,15,1169943,1868877,2014,07/19/2014 12:41:41 PM,41.795694239,-87.652343958,"(41.795694239, -87.652343958)"
-9690050,HX339536,07/10/2014 04:20:00 PM,004XX W WINNECONNA PKWY,0820,THEFT,$500 AND UNDER,OTHER,false,false,0621,006,17,69,06,1174482,1853160,2014,07/17/2014 12:40:06 PM,41.75246514,-87.63616654,"(41.75246514, -87.63616654)"
-9689887,HX339616,07/10/2014 08:45:00 AM,002XX E RANDOLPH ST,0810,THEFT,OVER $500,SIDEWALK,false,false,0114,001,42,32,06,1177892,1901352,2014,07/17/2014 12:40:06 PM,41.884631532,-87.622208838,"(41.884631532, -87.622208838)"
-9688545,HX338746,07/10/2014 12:15:00 AM,071XX S YALE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,0731,007,6,69,08B,1175904,1857440,2014,07/17/2014 12:40:06 PM,41.764178225,-87.630827456,"(41.764178225, -87.630827456)"
-9700089,HX349974,07/08/2014 04:00:00 PM,032XX N SEMINARY AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,1924,019,44,6,05,1168412,1921843,2014,07/19/2014 12:41:41 PM,41.941070323,-87.656426615,"(41.941070323, -87.656426615)"
-9685806,HX336184,07/08/2014 09:30:00 AM,001XX W 79TH ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0623,006,17,69,06,1176831,1852649,2014,07/15/2014 12:40:16 PM,41.75101034,-87.627573854,"(41.75101034, -87.627573854)"
-9683505,HX334152,07/06/2014 09:35:00 PM,064XX S WOLCOTT AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0726,007,15,67,14,1164828,1862070,2014,07/13/2014 12:37:47 PM,41.777124626,-87.671293105,"(41.777124626, -87.671293105)"
-9683433,HX334071,07/06/2014 07:20:00 PM,024XX W PERSHING RD,0820,THEFT,$500 AND UNDER,RESIDENCE,false,false,0911,009,12,58,06,1160578,1878777,2014,07/13/2014 12:37:47 PM,41.823059518,-87.686412651,"(41.823059518, -87.686412651)"
-9683248,HX333952,07/06/2014 06:35:00 PM,064XX S LOOMIS BLVD,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0725,007,17,67,03,1168057,1862246,2014,07/13/2014 12:37:47 PM,41.777538767,-87.65945058,"(41.777538767, -87.65945058)"
-9681413,HX331657,07/04/2014 11:00:00 PM,003XX E ERIE ST,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,1834,018,42,8,04A,1178633,1904814,2014,07/11/2014 12:39:31 PM,41.894114543,-87.619382042,"(41.894114543, -87.619382042)"
-9736098,HX386130,07/03/2014 09:44:00 PM,043XX S ARTESIAN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0922,009,12,58,26,1160704,1875820,2014,08/14/2014 12:38:24 PM,41.814942552,-87.686032127,"(41.814942552, -87.686032127)"
-9678427,HX328336,07/02/2014 02:40:00 PM,051XX W OAKDALE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2521,025,31,19,08B,1141741,1919172,2014,07/09/2014 12:39:14 PM,41.934276921,-87.75451971,"(41.934276921, -87.75451971)"
-9679165,HX328128,07/02/2014 12:25:00 PM,122XX S WALLACE ST,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,RESIDENCE,false,false,0523,005,34,53,11,1174568,1823519,2014,07/09/2014 12:39:14 PM,41.671124084,-87.63673015,"(41.671124084, -87.63673015)"
-9676375,HX326244,06/30/2014 09:00:00 PM,054XX S CORNELL AVE,0810,THEFT,OVER $500,STREET,false,false,0234,002,5,41,06,1188135,1869647,2014,07/07/2014 12:43:15 PM,41.797392345,-87.585609361,"(41.797392345, -87.585609361)"
-9727785,HX377498,06/29/2014 02:38:00 PM,111XX S HALSTED ST,1582,OFFENSE INVOLVING CHILDREN,CHILD PORNOGRAPHY,APARTMENT,false,false,2233,022,34,49,17,1173009,1830995,2014,08/08/2014 12:34:16 PM,41.691673874,-87.642216524,"(41.691673874, -87.642216524)"
-9674893,HX323740,06/29/2014 12:45:00 PM,020XX E 79TH ST,0810,THEFT,OVER $500,SMALL RETAIL STORE,false,false,0414,,8,46,06,,,2014,07/06/2014 12:39:22 PM,,,
-9670786,HX320544,06/27/2014 06:20:00 AM,014XX W HURON ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,1215,012,27,24,04B,1166287,1904820,2014,07/04/2014 12:37:50 PM,41.894403963,-87.664724537,"(41.894403963, -87.664724537)"
-9671481,HX321007,06/27/2014 01:30:00 AM,009XX W WRIGHTWOOD AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1935,019,43,7,07,1169293,1917497,2014,07/04/2014 12:37:50 PM,41.929125564,-87.653315326,"(41.929125564, -87.653315326)"
-9670356,HX320302,06/26/2014 10:00:00 AM,028XX W WILCOX ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1124,011,2,27,05,1157348,1899266,2014,07/03/2014 12:45:31 PM,41.879349726,-87.697705934,"(41.879349726, -87.697705934)"
-9665087,HX315300,06/23/2014 10:59:00 AM,034XX W AUGUSTA BLVD,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1121,011,27,23,18,1153093,1906405,2014,06/30/2014 12:37:16 PM,41.899025271,-87.713140222,"(41.899025271, -87.713140222)"
-9663886,HX313975,06/21/2014 07:00:00 PM,018XX N TRIPP AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2534,025,30,20,07,1147795,1912188,2014,06/28/2014 12:36:34 PM,41.91499786,-87.732450961,"(41.91499786, -87.732450961)"
-9663074,HX312925,06/21/2014 12:30:00 PM,015XX N CLYBOURN AVE,0460,BATTERY,SIMPLE,CTA TRAIN,false,false,1822,018,27,8,08B,1170846,1910482,2014,06/28/2014 12:36:34 PM,41.909842113,-87.64781463,"(41.909842113, -87.64781463)"
-9663018,HX312850,06/21/2014 11:40:00 AM,115XX S YALE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0522,005,34,53,08B,1176635,1828469,2014,06/28/2014 12:36:34 PM,41.684661551,-87.629016959,"(41.684661551, -87.629016959)"
-9662643,HX312494,06/21/2014 04:36:00 AM,062XX W CUYLER AVE,1090,ARSON,ATTEMPT ARSON,RESIDENCE-GARAGE,false,false,1624,016,38,15,09,1133886,1926223,2014,06/28/2014 12:36:34 PM,41.953767525,-87.783220921,"(41.953767525, -87.783220921)"
-9662638,HX312474,06/21/2014 04:10:00 AM,074XX S GREEN ST,033A,ROBBERY,ATTEMPT: ARMED-HANDGUN,STREET,false,false,0733,007,17,68,03,1171884,1855553,2014,06/28/2014 12:36:34 PM,41.759089212,-87.645616987,"(41.759089212, -87.645616987)"
-9662569,HX312440,06/21/2014 01:20:00 AM,040XX W WILCOX ST,041A,BATTERY,AGGRAVATED: HANDGUN,RESIDENCE PORCH/HALLWAY,false,false,1115,011,28,26,04B,1149683,1899074,2014,06/28/2014 12:36:34 PM,41.878975185,-87.725855753,"(41.878975185, -87.725855753)"
-9661658,HX311366,06/20/2014 11:20:00 AM,069XX S ASHLAND AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0735,007,17,67,03,1166833,1858894,2014,06/27/2014 12:37:10 PM,41.768366678,-87.664033409,"(41.768366678, -87.664033409)"
-9658359,HX308295,06/18/2014 02:45:00 AM,091XX S BEVERLY AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2221,022,21,73,14,1166214,1844254,2014,06/25/2014 12:38:46 PM,41.728205623,-87.666718093,"(41.728205623, -87.666718093)"
-9657339,HX307593,06/17/2014 04:30:00 PM,053XX W QUINCY ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1522,015,29,25,14,1140920,1898446,2014,06/24/2014 12:40:13 PM,41.877417569,-87.758047739,"(41.877417569, -87.758047739)"
-9654848,HX305558,06/16/2014 09:12:00 AM,004XX W 105TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,true,false,2233,022,34,49,14,1175278,1835240,2014,06/23/2014 12:44:45 PM,41.703272527,-87.633783236,"(41.703272527, -87.633783236)"
-9654231,HX304988,06/15/2014 08:25:00 PM,027XX W CATALPA AVE,0460,BATTERY,SIMPLE,RESIDENCE,true,false,2011,020,40,4,08B,1157179,1936460,2014,06/22/2014 12:39:11 PM,41.981416194,-87.697313387,"(41.981416194, -87.697313387)"
-9654346,HX305220,06/15/2014 05:00:00 PM,056XX S MAPLEWOOD AVE,4388,OTHER OFFENSE,VIO BAIL BOND: DOM VIOLENCE,RESIDENCE,true,true,0824,008,16,63,26,1160379,1866998,2014,06/22/2014 12:39:11 PM,41.790740566,-87.687467443,"(41.790740566, -87.687467443)"
-9802938,HX452071,06/15/2014 12:00:00 PM,006XX S CENTRAL PARK AVE,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,APARTMENT,false,false,1133,011,24,27,11,1152525,1896827,2014,10/03/2014 12:39:14 PM,41.8727535,-87.715479771,"(41.8727535, -87.715479771)"
-9653006,HX303514,06/14/2014 05:24:00 PM,036XX E 106TH ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SMALL RETAIL STORE,true,false,0432,004,10,52,04B,1201982,1835237,2014,06/21/2014 12:36:33 PM,41.702627447,-87.536001073,"(41.702627447, -87.536001073)"
-9651540,HX301643,06/12/2014 07:30:00 PM,002XX W MONROE ST,0810,THEFT,OVER $500,OTHER,false,false,0122,001,2,32,06,1174707,1899918,2014,07/11/2014 12:37:03 PM,41.880768379,-87.633947393,"(41.880768379, -87.633947393)"
-9651273,HX301372,06/12/2014 05:30:00 PM,034XX W 31ST ST,0620,BURGLARY,UNLAWFUL ENTRY,RESTAURANT,false,false,1032,010,22,30,05,1154155,1883902,2014,06/19/2014 12:43:10 PM,41.837253462,-87.709839834,"(41.837253462, -87.709839834)"
-9678453,HX298334,06/10/2014 07:49:00 PM,008XX E 91ST ST,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0633,,8,47,03,,,2014,07/03/2014 12:45:31 PM,,,
-9645261,HX296258,06/09/2014 10:00:00 AM,028XX N AUSTIN AVE,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,2514,025,30,19,06,1135941,1918388,2014,06/16/2014 12:51:59 PM,41.932231013,-87.775853712,"(41.932231013, -87.775853712)"
-9644523,HX295659,06/08/2014 09:35:00 PM,002XX S TROY ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1124,011,28,27,18,1155379,1898669,2014,06/15/2014 12:40:25 PM,41.877751287,-87.704951861,"(41.877751287, -87.704951861)"
-9643705,HX294660,06/08/2014 03:20:00 AM,037XX S WOOD ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0912,009,11,59,14,1165002,1880015,2014,06/15/2014 12:40:25 PM,41.82636417,-87.670147645,"(41.82636417, -87.670147645)"
-9643858,HX294683,06/08/2014 01:00:00 AM,066XX N NORTHWEST HWY,0460,BATTERY,SIMPLE,STREET,false,false,1612,016,41,9,08B,1124915,1943895,2014,06/15/2014 12:40:25 PM,42.00241466,-87.81580751,"(42.00241466, -87.81580751)"
-9642121,HX292493,06/05/2014 08:00:00 PM,073XX S ASHLAND AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,ALLEY,false,false,0734,007,17,67,14,1166994,1856032,2014,06/12/2014 12:41:22 PM,41.760509529,-87.663524955,"(41.760509529, -87.663524955)"
-9641152,HX287518,06/03/2014 12:10:00 AM,024XX W 45TH ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,false,false,0922,009,12,58,24,1161087,1874736,2014,06/10/2014 12:45:15 PM,41.811959999,-87.684657237,"(41.811959999, -87.684657237)"
-9934688,HY123629,06/01/2014 12:00:00 AM,065XX S MINERVA AVE,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,RESIDENCE,false,false,0321,003,20,42,11,1185052,1861728,2014,01/26/2015 12:53:30 PM,41.775734979,-87.597163634,"(41.775734979, -87.597163634)"
-9633194,HX284351,05/31/2014 05:55:00 PM,076XX S CICERO AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,false,false,0833,008,13,65,06,1145766,1853739,2014,06/07/2014 12:40:43 PM,41.754644364,-87.741385133,"(41.754644364, -87.741385133)"
-9632434,HX283223,05/30/2014 09:50:00 PM,107XX S YATES AVE,0460,BATTERY,SIMPLE,SIDEWALK,true,false,0434,004,10,51,08B,1194304,1834510,2014,06/06/2014 12:40:22 PM,41.700824113,-87.564139118,"(41.700824113, -87.564139118)"
-9632364,HX283153,05/30/2014 08:30:00 PM,074XX S INDIANA AVE,0460,BATTERY,SIMPLE,STREET,false,false,0323,003,6,69,08B,1178934,1855397,2014,06/06/2014 12:40:22 PM,41.758503566,-87.619783957,"(41.758503566, -87.619783957)"
-9631862,HX282478,05/30/2014 12:21:00 PM,049XX W WEST END AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1532,015,28,25,18,1143435,1900569,2014,06/06/2014 12:40:22 PM,41.883196711,-87.74876013,"(41.883196711, -87.74876013)"
-9631861,HX282458,05/30/2014 11:00:00 AM,063XX S KOSTNER AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0813,008,13,65,14,1148169,1862253,2014,06/06/2014 12:40:22 PM,41.777962512,-87.732360716,"(41.777962512, -87.732360716)"
-9631036,HX281898,05/29/2014 10:40:00 PM,084XX S VERNON AVE,0560,ASSAULT,SIMPLE,RESIDENCE,true,true,0632,006,6,44,08A,1180874,1848896,2014,06/10/2014 12:41:59 PM,41.740619724,-87.612873573,"(41.740619724, -87.612873573)"
-9630714,HX281512,05/29/2014 10:20:00 AM,026XX S CALIFORNIA AVE,0810,THEFT,OVER $500,STREET,false,false,1033,010,12,30,06,1158082,1886563,2014,06/05/2014 12:38:31 PM,41.844476425,-87.695357404,"(41.844476425, -87.695357404)"
-9630844,HX281710,05/28/2014 10:00:00 PM,096XX S LA SALLE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0511,005,21,49,14,1177015,1841086,2014,06/04/2014 12:43:29 PM,41.719275863,-87.627247216,"(41.719275863, -87.627247216)"
-9629323,HX279878,05/28/2014 10:34:00 AM,017XX W ALBION AVE,0810,THEFT,OVER $500,APARTMENT,false,false,2432,024,40,1,06,1163702,1943935,2014,06/04/2014 12:43:29 PM,42.001792401,-87.673111851,"(42.001792401, -87.673111851)"
-9630401,HX281029,05/28/2014 12:48:00 AM,089XX S PARNELL AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,false,true,2223,022,21,71,26,1174237,1845760,2014,06/04/2014 12:43:29 PM,41.73216403,-87.637283701,"(41.73216403, -87.637283701)"
-9627038,HX277879,05/27/2014 04:14:00 AM,057XX W GRAND AVE,2022,NARCOTICS,POSS: COCAINE,STREET,true,false,2515,025,37,19,18,1137626,1913796,2014,06/03/2014 12:44:29 PM,41.919599824,-87.769772365,"(41.919599824, -87.769772365)"
-9669357,HX275028,05/24/2014 06:35:00 PM,030XX W VAN BUREN ST,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),PARK PROPERTY,true,false,1134,011,28,27,18,1156013,1897963,2014,10/31/2014 03:20:56 PM,41.87580119,-87.702643013,"(41.87580119, -87.702643013)"
-9624246,HX274031,05/23/2014 10:30:00 PM,041XX W CONGRESS PKWY,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,true,1132,011,24,26,14,1149055,1897334,2014,05/30/2014 12:40:32 PM,41.874212601,-87.728206718,"(41.874212601, -87.728206718)"
-9624539,HX274436,05/23/2014 07:00:00 PM,002XX S LAVERGNE AVE,0460,BATTERY,SIMPLE,STREET,false,false,1533,015,28,25,08B,1143330,1898753,2014,05/30/2014 12:40:32 PM,41.878215347,-87.74919109,"(41.878215347, -87.74919109)"
-9622445,HX272206,05/22/2014 03:05:00 PM,015XX W HOWARD ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,OTHER,true,false,2422,024,49,1,15,1164940,1950388,2014,05/29/2014 12:39:18 PM,42.019473264,-87.66837302,"(42.019473264, -87.66837302)"
-9667124,HX316825,05/22/2014 02:11:00 AM,0000X S STATE ST,0810,THEFT,OVER $500,DEPARTMENT STORE,true,false,0112,001,42,32,06,1176423,1900356,2014,07/15/2014 12:37:55 PM,41.881931729,-87.627633225,"(41.881931729, -87.627633225)"
-9616841,HX267003,05/18/2014 08:07:00 PM,030XX N NEWCASTLE AVE,4625,OTHER OFFENSE,PAROLE VIOLATION,PARK PROPERTY,true,false,2511,025,36,18,26,1130191,1919247,2014,05/25/2014 12:39:09 PM,41.934688863,-87.796964877,"(41.934688863, -87.796964877)"
-9615128,HX264609,05/16/2014 10:30:00 PM,072XX S LAWNDALE AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0833,008,13,65,08B,1152912,1856469,2014,05/23/2014 12:38:03 PM,41.761998025,-87.715124936,"(41.761998025, -87.715124936)"
-9615221,HX264704,05/16/2014 07:40:00 PM,007XX N CLARK ST,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,1832,018,42,8,06,1175363,1905385,2014,05/23/2014 12:38:03 PM,41.895755436,-87.631374423,"(41.895755436, -87.631374423)"
-9660802,HX310550,05/16/2014 06:00:00 PM,022XX N KARLOV AVE,0810,THEFT,OVER $500,STREET,false,false,2525,025,31,20,06,1148715,1914816,2014,06/20/2014 12:39:30 PM,41.922191603,-87.729002923,"(41.922191603, -87.729002923)"
-9664302,HX314663,05/13/2014 12:00:00 PM,108XX S EWING AVE,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,false,false,0432,004,10,52,26,1202234,1833580,2014,06/23/2014 12:44:45 PM,41.698074092,-87.535134571,"(41.698074092, -87.535134571)"
-9606666,HX256705,05/10/2014 08:20:00 PM,010XX N OAKLEY BLVD,0460,BATTERY,SIMPLE,APARTMENT,true,false,1212,012,1,24,08B,1160840,1907072,2014,06/14/2014 12:41:49 PM,41.900698369,-87.684667185,"(41.900698369, -87.684667185)"
-9605704,HX253729,05/08/2014 01:00:00 AM,071XX S SANGAMON ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0733,007,17,68,06,1171258,1857197,2014,05/15/2014 12:39:28 PM,41.763614265,-87.647863232,"(41.763614265, -87.647863232)"
-9602797,HX252929,05/08/2014 12:13:00 AM,009XX N LAVERGNE AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1531,015,37,25,26,1142815,1905711,2014,05/15/2014 12:39:28 PM,41.897318537,-87.750908671,"(41.897318537, -87.750908671)"
-9599431,HX250048,05/05/2014 08:00:00 PM,069XX S HALSTED ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0733,007,6,68,07,1172120,1859040,2014,05/07/2014 12:40:24 AM,41.768652788,-87.644649741,"(41.768652788, -87.644649741)"
-9600076,HX249518,05/04/2014 11:00:00 PM,012XX S MICHIGAN AVE,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,0131,001,2,33,06,1177378,1894927,2014,10/31/2014 03:20:56 PM,41.867012636,-87.6242912,"(41.867012636, -87.6242912)"
-9597140,HX247894,05/04/2014 12:00:00 AM,019XX W LUNT AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2424,024,49,1,07,1161952,1946467,2014,05/07/2014 12:40:24 AM,42.008777125,-87.679478726,"(42.008777125, -87.679478726)"
-9596923,HX247607,05/03/2014 10:30:00 PM,084XX S DANTE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0412,004,8,45,06,1187382,1849199,2014,05/06/2014 12:39:53 AM,41.741299201,-87.589019593,"(41.741299201, -87.589019593)"
-9597020,HX247812,05/03/2014 06:30:00 PM,037XX N SAWYER AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,RESIDENCE-GARAGE,false,false,1733,017,33,16,07,1154094,1924763,2014,05/07/2014 12:40:24 AM,41.949381205,-87.708972604,"(41.949381205, -87.708972604)"
-9593242,HX243752,05/01/2014 12:15:00 AM,0000X S KENTON AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1113,011,28,25,16,1145622,1899504,2014,05/04/2014 12:39:40 AM,41.880233065,-87.740756251,"(41.880233065, -87.740756251)"
-9592330,HX242881,04/30/2014 10:15:00 AM,008XX E 103RD ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",true,false,0512,005,9,50,08B,1183714,1836812,2014,05/03/2014 12:40:14 AM,41.707394098,-87.602843897,"(41.707394098, -87.602843897)"
-9591786,HX242257,04/29/2014 06:30:00 PM,052XX S RICHMOND ST,0820,THEFT,$500 AND UNDER,APARTMENT,false,false,0923,009,14,63,06,1157640,1869646,2014,05/02/2014 12:40:03 AM,41.798063068,-87.697438946,"(41.798063068, -87.697438946)"
-9591241,HX241733,04/29/2014 01:00:00 PM,086XX S CICERO AVE,0860,THEFT,RETAIL THEFT,DRUG STORE,false,false,0834,008,18,70,06,1145964,1846687,2014,05/01/2014 12:39:47 AM,41.735288641,-87.740837457,"(41.735288641, -87.740837457)"
-9590499,HX241039,04/28/2014 06:00:00 PM,073XX S CHAPPEL AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0333,003,5,43,05,1191193,1856740,2014,05/07/2014 12:40:24 AM,41.761901057,-87.574812957,"(41.761901057, -87.574812957)"
-9589211,HX239851,04/27/2014 08:00:00 PM,056XX W WEST END AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,RESIDENCE,false,true,1512,015,29,25,26,1138966,1900877,2014,04/30/2014 12:39:48 AM,41.884124266,-87.765163308,"(41.884124266, -87.765163308)"
-9589392,HX239092,04/26/2014 11:00:00 PM,036XX N SHEFFIELD AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,false,false,1923,,44,6,26,,,2014,04/30/2014 12:39:48 AM,,,
-21347,HX237678,04/26/2014 01:17:00 AM,054XX S WINCHESTER AVE,0110,HOMICIDE,FIRST DEGREE MURDER,APARTMENT,true,false,0932,009,16,61,01A,1164317,1868727,2014,02/27/2015 12:38:45 PM,41.795403068,-87.672978994,"(41.795403068, -87.672978994)"
-21343,HX233807,04/23/2014 06:18:00 AM,078XX S INGLESIDE AVE,0110,HOMICIDE,FIRST DEGREE MURDER,VESTIBULE,false,false,0624,006,8,69,01A,1183884,1853127,2014,04/23/2014 12:06:22 PM,41.752160356,-87.601713536,"(41.752160356, -87.601713536)"
-9583054,HX233226,04/22/2014 04:15:00 PM,005XX N MICHIGAN AVE,0890,THEFT,FROM BUILDING,COMMERCIAL / BUSINESS OFFICE,false,false,1834,018,42,8,06,1177300,1903904,2014,05/05/2014 12:38:28 AM,41.891647792,-87.624305286,"(41.891647792, -87.624305286)"
-9600374,HX233046,04/22/2014 01:59:00 PM,006XX S CALIFORNIA AVE,2018,NARCOTICS,MANU/DELIVER:SYNTHETIC DRUGS,VEHICLE NON-COMMERCIAL,true,false,1135,011,2,27,18,1157762,1897188,2014,06/17/2014 12:41:24 PM,41.873639068,-87.696242425,"(41.873639068, -87.696242425)"
-9579259,HX229835,04/19/2014 06:05:00 PM,083XX S STEWART AVE,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,false,false,0622,006,21,44,06,1175120,1849560,2014,04/22/2014 12:38:23 AM,41.742572081,-87.633935781,"(41.742572081, -87.633935781)"
-9646964,HX227294,04/17/2014 05:15:33 PM,040XX W WILCOX ST,2017,NARCOTICS,MANU/DELIVER:CRACK,APARTMENT,true,false,1115,011,28,26,18,1149354,1898987,2014,08/19/2014 12:37:52 PM,41.878742831,-87.727066047,"(41.878742831, -87.727066047)"
-9576335,HX226601,04/17/2014 01:00:00 AM,050XX W HURON ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1532,015,28,25,06,1142630,1904137,2014,04/20/2014 12:40:33 AM,41.893002742,-87.751627356,"(41.893002742, -87.751627356)"
-9578552,HX222468,04/13/2014 04:50:00 PM,0000X E MONROE ST,0870,THEFT,POCKET-PICKING,HOTEL/MOTEL,false,false,0112,,42,32,06,,,2014,04/21/2014 12:38:45 AM,,,
-9570986,HX221704,04/13/2014 02:40:00 AM,038XX N KENMORE AVE,0460,BATTERY,SIMPLE,STREET,true,false,1923,019,44,6,08B,1168625,1925785,2014,04/15/2014 12:40:31 AM,41.951882717,-87.655529192,"(41.951882717, -87.655529192)"
-9569616,HX220020,04/11/2014 07:00:00 PM,0000X E MADISON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0112,001,42,32,18,1176636,1900450,2014,04/16/2014 12:38:21 AM,41.882184862,-87.62684826,"(41.882184862, -87.62684826)"
-9570147,HX218847,04/10/2014 09:20:00 PM,001XX S KENTON AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,1113,011,28,25,16,1145635,1899142,2014,04/16/2014 12:38:21 AM,41.879239448,-87.740717694,"(41.879239448, -87.740717694)"
-9567953,HX218479,04/10/2014 04:00:00 PM,037XX W FULLERTON AVE,0810,THEFT,OVER $500,VEHICLE-COMMERCIAL,false,false,2524,025,35,22,06,1151278,1915742,2014,04/13/2014 12:40:06 AM,41.924682678,-87.719561286,"(41.924682678, -87.719561286)"
-9564306,HX215589,04/08/2014 01:30:00 PM,008XX E 79TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,GROCERY FOOD STORE,true,false,0624,006,8,44,18,1183306,1852770,2014,04/13/2014 12:40:06 AM,41.751194179,-87.603842721,"(41.751194179, -87.603842721)"
-9574602,HX225257,04/02/2014 10:00:00 PM,020XX W CHICAGO AVE,1220,DECEPTIVE PRACTICE,THEFT OF LOST/MISLAID PROP,PARKING LOT/GARAGE(NON.RESID.),false,false,1221,012,1,24,11,1162677,1905295,2014,05/02/2014 12:40:03 AM,41.895783831,-87.677969636,"(41.895783831, -87.677969636)"
-9555357,HX207194,04/01/2014 05:30:00 PM,013XX S CALIFORNIA BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1023,010,28,29,08B,1157936,1893781,2014,04/09/2014 12:42:18 AM,41.86428637,-87.695696497,"(41.86428637, -87.695696497)"
-9554659,HX206518,04/01/2014 08:30:00 AM,075XX N RIDGE BLVD,0460,BATTERY,SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,2411,024,49,2,08B,1160500,1950133,2014,04/27/2014 12:38:22 AM,42.018867029,-87.684718825,"(42.018867029, -87.684718825)"
-9553632,HX205472,03/31/2014 12:10:00 PM,077XX N EASTLAKE TER,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,LAKEFRONT/WATERFRONT/RIVERBANK,false,false,2422,024,49,1,04A,1165634,1951414,2014,04/12/2014 12:41:09 AM,42.022273797,-87.665789739,"(42.022273797, -87.665789739)"
-9550879,HX202382,03/28/2014 06:30:00 PM,037XX S ELLIS AVE,1020,ARSON,BY FIRE,VEHICLE NON-COMMERCIAL,false,false,0212,002,4,36,09,1182248,1880304,2014,05/11/2014 12:36:24 PM,41.826774468,-87.606867167,"(41.826774468, -87.606867167)"
-9550165,HX201779,03/28/2014 10:30:00 AM,050XX S WINCHESTER AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,0931,009,16,61,15,1164166,1871195,2014,03/31/2014 12:39:14 AM,41.802178744,-87.673463242,"(41.802178744, -87.673463242)"
-9549444,HX201405,03/27/2014 10:00:00 PM,0000X W ONTARIO ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,DRUG STORE,false,false,1832,018,42,8,11,1175596,1904429,2014,03/30/2014 12:39:29 AM,41.893126888,-87.630547435,"(41.893126888, -87.630547435)"
-9556693,HX208455,03/27/2014 08:00:00 PM,001XX N KILBOURN AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1113,011,28,26,26,1146325,1900456,2014,04/09/2014 12:42:18 AM,41.882832116,-87.738150643,"(41.882832116, -87.738150643)"
-9546928,HX199334,03/26/2014 12:00:00 PM,055XX S SHIELDS AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,RESIDENTIAL YARD (FRONT/BACK),true,false,0711,007,3,68,18,1174958,1868001,2014,03/30/2014 12:39:29 AM,41.793179917,-87.633979955,"(41.793179917, -87.633979955)"
-9546860,HX199339,03/26/2014 11:45:00 AM,035XX W 66TH PL,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0831,008,15,66,07,1153698,1860248,2014,03/28/2014 12:40:24 AM,41.772352662,-87.712144089,"(41.772352662, -87.712144089)"
-9546313,HX199061,03/26/2014 07:50:00 AM,027XX W 56TH ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0824,008,16,63,07,1159138,1867452,2014,03/28/2014 12:40:24 AM,41.792011897,-87.692005502,"(41.792011897, -87.692005502)"
-9545566,HX198242,03/25/2014 02:00:00 PM,132XX S BALTIMORE AVE,0460,BATTERY,SIMPLE,STREET,false,false,0433,004,10,55,08B,1199056,1817972,2014,04/03/2014 12:40:46 AM,41.655324343,-87.547291826,"(41.655324343, -87.547291826)"
-9847079,HX495791,03/24/2014 10:00:00 PM,014XX N ASTOR ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,1824,018,43,8,06,1176323,1909900,2014,12/24/2014 12:46:30 PM,41.9081232,-87.6277122,"(41.9081232, -87.6277122)"
-9542532,HX196008,03/23/2014 03:00:00 PM,089XX S ESSEX AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,0423,004,7,48,08A,1194342,1846245,2014,04/12/2014 12:41:09 AM,41.733025148,-87.563615828,"(41.733025148, -87.563615828)"
-9543954,HX197071,03/22/2014 05:00:00 PM,034XX W 53RD PL,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0822,008,14,63,05,1154278,1868889,2014,04/07/2014 12:40:59 AM,41.796053338,-87.709788246,"(41.796053338, -87.709788246)"
-9540644,HX193878,03/21/2014 06:30:00 PM,032XX W 13TH ST,4650,OTHER OFFENSE,SEX OFFENDER: FAIL TO REGISTER,SIDEWALK,true,false,1022,010,24,29,26,1155199,1893905,2014,03/29/2014 12:39:43 AM,41.864681984,-87.70574067,"(41.864681984, -87.70574067)"
-9539574,HX192950,03/21/2014 06:55:00 AM,093XX S BALTIMORE AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,SIDEWALK,false,false,0424,004,10,46,04A,1198571,1843332,2014,04/12/2014 12:41:09 AM,41.724926816,-87.548220696,"(41.724926816, -87.548220696)"
-9602243,HX252212,03/20/2014 12:00:00 AM,002XX N PINE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,false,false,1523,015,28,25,26,1139416,1901226,2014,05/08/2014 01:20:21 PM,41.885073779,-87.763502323,"(41.885073779, -87.763502323)"
-9537477,HX191040,03/19/2014 04:00:00 PM,001XX W 83RD ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0622,006,21,44,14,1176867,1849911,2014,03/22/2014 12:39:11 AM,41.743496127,-87.627524234,"(41.743496127, -87.627524234)"
-9537987,HX190836,03/19/2014 01:30:00 PM,062XX S STEWART AVE,0545,ASSAULT,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,0711,007,20,68,08A,1174731,1863728,2014,03/22/2014 12:39:11 AM,41.781459416,-87.634939611,"(41.781459416, -87.634939611)"
-9539073,HX192372,03/19/2014 12:36:00 PM,028XX W DEVON AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,ATM (AUTOMATIC TELLER MACHINE),false,false,2413,024,50,2,11,1156470,1942298,2014,03/29/2014 12:39:43 AM,41.997450348,-87.699762044,"(41.997450348, -87.699762044)"
-9537702,HX191355,03/19/2014 12:12:00 PM,053XX N OKETO AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1613,016,41,10,26,1125970,1934755,2014,03/27/2014 12:41:25 AM,41.977316027,-87.812130818,"(41.977316027, -87.812130818)"
-9536490,HX190346,03/19/2014 02:00:00 AM,072XX S CARPENTER ST,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,true,0733,007,17,68,14,1170525,1856823,2014,04/02/2014 12:41:31 AM,41.762603959,-87.65056071,"(41.762603959, -87.65056071)"
-9535418,HX189234,03/18/2014 09:00:00 AM,059XX N GLENWOOD AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,"SCHOOL, PUBLIC, BUILDING",true,false,2013,020,48,77,18,1165871,1939326,2014,03/23/2014 12:39:11 AM,41.989099025,-87.665264694,"(41.989099025, -87.665264694)"
-9535811,HX189169,03/18/2014 08:45:00 AM,057XX S MICHIGAN AVE,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0232,002,20,40,08A,1178101,1866823,2014,03/21/2014 12:39:35 AM,41.78987663,-87.622490691,"(41.78987663, -87.622490691)"
-9535770,HX189465,03/17/2014 05:00:00 PM,112XX S COTTAGE GROVE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,OTHER,false,false,0531,005,9,50,14,1181767,1830734,2014,03/21/2014 12:39:35 AM,41.690760356,-87.610160661,"(41.690760356, -87.610160661)"
-9532540,HX186429,03/15/2014 09:05:00 PM,071XX S ABERDEEN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,0733,007,17,68,08B,1170178,1857422,2014,03/28/2014 12:40:24 AM,41.764255242,-87.651815123,"(41.764255242, -87.651815123)"
-9531229,HX184578,03/14/2014 10:15:00 AM,051XX W CRYSTAL ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2533,025,37,25,05,1141869,1907798,2014,03/30/2014 12:39:29 AM,41.903063077,-87.754331516,"(41.903063077, -87.754331516)"
-9528024,HX182097,03/12/2014 09:30:00 AM,071XX S HALSTED ST,0497,BATTERY,AGGRAVATED DOMESTIC BATTERY: OTHER DANG WEAPON,RESIDENCE,false,true,0733,007,6,68,04B,1172169,1857259,2014,03/25/2014 12:39:08 AM,41.76376443,-87.644522411,"(41.76376443, -87.644522411)"
-9577540,HX227919,03/10/2014 11:00:00 PM,008XX N CALIFORNIA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1211,012,26,24,14,1157524,1905715,2014,04/24/2014 12:40:08 AM,41.897042799,-87.696884056,"(41.897042799, -87.696884056)"
-9528027,HX181023,03/10/2014 06:00:00 PM,002XX W MARQUETTE RD,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0722,007,6,68,08B,1175548,1860575,2014,03/17/2014 12:39:52 AM,41.772788992,-87.632038586,"(41.772788992, -87.632038586)"
-9524636,HX179370,03/10/2014 04:13:00 PM,051XX S WINCHESTER AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0932,009,16,61,08B,1164184,1870532,2014,03/28/2014 12:40:24 AM,41.800359012,-87.673415896,"(41.800359012, -87.673415896)"
-9522632,HX177750,03/09/2014 05:15:00 AM,007XX W 61ST ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0711,007,16,68,08B,1172424,1864460,2014,03/13/2014 12:40:30 AM,41.783519213,-87.643376039,"(41.783519213, -87.643376039)"
-9520000,HX175139,03/06/2014 10:27:00 PM,007XX N LAMON AVE,3731,INTERFERENCE WITH PUBLIC OFFICER,OBSTRUCTING IDENTIFICATION,SIDEWALK,true,false,1531,015,37,25,24,1143524,1904225,2014,03/09/2014 12:39:51 AM,41.893227545,-87.74834179,"(41.893227545, -87.74834179)"
-9519949,HX175085,03/06/2014 09:30:00 PM,086XX S KENWOOD AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0412,004,8,45,26,1186710,1847771,2014,03/21/2014 12:39:35 AM,41.737396545,-87.591526827,"(41.737396545, -87.591526827)"
-9519659,HX174630,03/06/2014 02:00:00 PM,065XX W DIVERSEY AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,2512,025,36,19,06,1132343,1917856,2014,03/09/2014 12:39:51 AM,41.930834579,-87.789088549,"(41.930834579, -87.789088549)"
-9518522,HX174018,03/05/2014 11:50:00 PM,0000X E 87TH ST,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,SIDEWALK,true,true,0632,006,6,44,04B,1177946,1847344,2014,03/08/2014 12:40:21 AM,41.736427612,-87.623648296,"(41.736427612, -87.623648296)"
-9516681,HX172093,03/04/2014 01:40:00 PM,052XX N MILWAUKEE AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,1623,016,45,11,06,1138091,1934091,2014,03/07/2014 12:40:58 AM,41.975282927,-87.767571657,"(41.975282927, -87.767571657)"
-9511742,HX166413,02/27/2014 01:26:00 PM,034XX W CHICAGO AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1121,011,27,23,18,1153532,1905166,2014,03/02/2014 12:39:31 AM,41.895616617,-87.711560762,"(41.895616617, -87.711560762)"
-9510829,HX165902,02/27/2014 12:21:00 AM,073XX S WOLCOTT AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,RESIDENCE,true,true,0735,007,17,67,04A,1164996,1856090,2014,05/16/2014 12:37:19 PM,41.760711147,-87.670846078,"(41.760711147, -87.670846078)"
-9508671,HX163921,02/25/2014 11:15:00 AM,001XX N STATE ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0111,001,42,32,06,1176392,1900920,2014,02/27/2014 12:42:14 AM,41.883480076,-87.627730028,"(41.883480076, -87.627730028)"
-9507959,HX163577,02/25/2014 01:11:00 AM,020XX W 63RD ST,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0726,007,15,67,06,1163901,1862818,2014,02/27/2014 12:42:14 AM,41.779196771,-87.674670479,"(41.779196771, -87.674670479)"
-9505515,HX160953,02/22/2014 04:15:00 PM,044XX W MONROE ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1113,011,28,26,18,1146819,1899334,2014,02/26/2014 12:40:16 AM,41.879743802,-87.736365296,"(41.879743802, -87.736365296)"
-9503518,HX158340,02/20/2014 01:30:00 PM,050XX S BLACKSTONE AVE,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",false,false,0222,002,4,39,06,1186813,1871938,2014,02/23/2014 12:41:45 AM,41.803710468,-87.590384628,"(41.803710468, -87.590384628)"
-9500504,HX155544,02/18/2014 07:00:00 AM,087XX S MERRILL AVE,0810,THEFT,OVER $500,STREET,false,false,0412,004,8,48,06,1191989,1847296,2014,02/21/2014 12:39:35 AM,41.735966611,-87.572201766,"(41.735966611, -87.572201766)"
-9499318,HX154389,02/17/2014 02:07:00 PM,007XX N TRUMBULL AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),OTHER,true,false,1121,011,27,23,18,1153204,1904794,2014,02/19/2014 12:39:37 AM,41.894602329,-87.712775319,"(41.894602329, -87.712775319)"
-9498714,HX153398,02/15/2014 05:00:00 PM,037XX N LAKE SHORE DR,0820,THEFT,$500 AND UNDER,STREET,false,false,1925,019,46,6,06,1171600,1925416,2014,02/19/2014 12:39:37 AM,41.950805085,-87.644604078,"(41.950805085, -87.644604078)"
-9508314,HX163806,02/15/2014 03:00:00 AM,006XX N CLARK ST,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,TAXICAB,false,false,1832,018,42,8,11,1175394,1904200,2014,03/09/2014 12:39:51 AM,41.892503037,-87.631296179,"(41.892503037, -87.631296179)"
-9499928,HX149804,02/11/2014 09:00:00 AM,035XX W BELDEN AVE,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,RESIDENCE,false,false,1413,014,26,22,05,1152338,1915099,2014,04/12/2014 12:41:09 AM,41.922897345,-87.715683376,"(41.922897345, -87.715683376)"
-9485217,HX137525,02/03/2014 11:10:00 AM,061XX N TALMAN AVE,0460,BATTERY,SIMPLE,OTHER,false,false,2413,024,50,2,08B,1157588,1940774,2014,02/07/2014 12:38:58 AM,41.993245657,-87.695691083,"(41.993245657, -87.695691083)"
-9484288,HX137883,02/03/2014 06:00:00 AM,008XX W WELLINGTON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1933,019,44,6,14,1170047,1920093,2014,02/06/2014 12:39:04 AM,41.936232663,-87.650468647,"(41.936232663, -87.650468647)"
-9482033,HX135312,02/01/2014 10:15:00 AM,037XX S WINCHESTER AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0912,009,11,59,14,1164016,1879590,2014,02/03/2014 12:39:04 AM,41.825218752,-87.673777053,"(41.825218752, -87.673777053)"
-9481922,HX135119,02/01/2014 01:45:00 AM,024XX N LINCOLN AVE,0460,BATTERY,SIMPLE,BAR OR TAVERN,false,false,1932,019,43,7,08B,1170293,1916346,2014,02/04/2014 12:39:03 AM,41.925945344,-87.649674382,"(41.925945344, -87.649674382)"
-9480375,HX133597,01/30/2014 08:45:00 PM,071XX S ARTESIAN AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,RESIDENCE,true,false,0832,008,18,66,15,1161245,1856944,2014,02/02/2014 12:39:28 AM,41.763133096,-87.684570132,"(41.763133096, -87.684570132)"
-9480165,HX133324,01/30/2014 04:20:00 PM,013XX W THORNDALE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,2013,020,48,77,03,1166205,1939678,2014,02/26/2014 12:40:16 AM,41.990057766,-87.664026082,"(41.990057766, -87.664026082)"
-9496144,HX129986,01/28/2014 02:00:00 AM,002XX S WELLS ST,0810,THEFT,OVER $500,SIDEWALK,false,false,0122,001,2,32,06,1174725,1899304,2014,02/16/2014 12:39:09 AM,41.879083123,-87.633899673,"(41.879083123, -87.633899673)"
-9475081,HX128795,01/26/2014 09:55:00 PM,055XX S JUSTINE ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,true,true,0713,007,16,67,14,1167002,1867662,2014,01/29/2014 12:39:43 AM,41.792423561,-87.663163463,"(41.792423561, -87.663163463)"
-9475067,HX128696,01/26/2014 07:00:00 PM,017XX E 70TH ST,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,OTHER,false,false,0332,003,5,43,04B,1189262,1858957,2014,04/28/2014 12:39:03 AM,41.768031199,-87.581819184,"(41.768031199, -87.581819184)"
-9470521,HX123587,01/20/2014 05:00:00 PM,086XX S HONORE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0614,006,18,71,14,1165484,1847286,2014,01/25/2014 12:39:51 AM,41.736541375,-87.669306547,"(41.736541375, -87.669306547)"
-9467720,HX120666,01/19/2014 09:55:00 PM,067XX S OAKLEY AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0832,008,17,66,07,1162192,1859765,2014,01/23/2014 12:40:19 AM,41.770854668,-87.68102077,"(41.770854668, -87.68102077)"
-9467146,HX120110,01/19/2014 10:46:00 AM,007XX N CHRISTIANA AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1121,011,27,23,26,1153876,1904532,2014,01/22/2014 12:39:52 AM,41.893870012,-87.710314231,"(41.893870012, -87.710314231)"
-9466913,HX119798,01/19/2014 12:14:00 AM,029XX W 63RD ST,3730,INTERFERENCE WITH PUBLIC OFFICER,OBSTRUCTING JUSTICE,OTHER,true,false,0823,008,15,66,24,1157682,1862760,2014,01/21/2014 12:39:39 AM,41.779166065,-87.697471684,"(41.779166065, -87.697471684)"
-9469352,HX119210,01/17/2014 10:30:00 PM,016XX S ALLPORT ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1233,012,25,31,08B,1168275,1891647,2014,03/13/2014 12:40:30 AM,41.858213501,-87.657804026,"(41.858213501, -87.657804026)"
-9466006,HX118511,01/17/2014 08:57:00 PM,044XX W GLADYS AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1131,011,24,26,05,1146809,1897945,2014,02/12/2014 12:40:02 AM,41.875932413,-87.736437486,"(41.875932413, -87.736437486)"
-9465524,HX117944,01/17/2014 11:55:00 AM,002XX S LA SALLE ST,0870,THEFT,POCKET-PICKING,CTA BUS,false,false,0122,001,42,32,06,1175123,1899399,2014,01/20/2014 12:40:05 AM,41.879334899,-87.632435454,"(41.879334899, -87.632435454)"
-9465089,HX117534,01/17/2014 07:23:00 AM,021XX E 71ST ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,0331,003,5,43,06,1191473,1858372,2014,01/20/2014 12:40:05 AM,41.766372615,-87.573733923,"(41.766372615, -87.573733923)"
-9465710,HX118038,01/16/2014 01:15:00 PM,009XX N ASHLAND AVE,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,1213,012,1,24,08A,1165513,1906501,2014,01/20/2014 12:40:05 AM,41.899033264,-87.667519286,"(41.899033264, -87.667519286)"
-9473883,HX116295,01/16/2014 06:33:48 AM,0000X W CHECKPOINT 5 ST,5007,OTHER OFFENSE,OTHER WEAPONS VIOLATION,AIRPORT TERMINAL UPPER LEVEL - SECURE AREA,false,false,1652,016,41,76,26,1100690,1934276,2014,01/29/2014 12:39:43 AM,41.97638602,-87.905108897,"(41.97638602, -87.905108897)"
-9463375,HX116168,01/15/2014 10:45:00 PM,064XX N WASHTENAW AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,2412,024,50,2,08B,1157108,1942956,2014,01/21/2014 12:39:39 AM,41.999242948,-87.697397101,"(41.999242948, -87.697397101)"
-9463073,HX115842,01/15/2014 04:58:00 PM,051XX W ROSCOE ST,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,false,false,1634,016,38,15,26,1141295,1922075,2014,01/23/2014 12:40:19 AM,41.94225129,-87.756086939,"(41.94225129, -87.756086939)"
-9562703,HX214341,01/14/2014 08:00:00 AM,082XX S MARSHFIELD AVE,0820,THEFT,$500 AND UNDER,APARTMENT,false,false,0614,006,21,71,06,1166811,1850232,2014,04/10/2014 12:40:25 AM,41.744597428,-87.664360942,"(41.744597428, -87.664360942)"
-9463358,HX116176,01/13/2014 10:30:00 PM,021XX N PULASKI RD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2525,025,30,22,08B,1149412,1913709,2014,01/19/2014 12:40:13 AM,41.919140386,-87.726470703,"(41.919140386, -87.726470703)"
-9458937,HX112171,01/12/2014 08:00:00 PM,015XX S SANGAMON ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1232,012,25,28,14,1170483,1892698,2014,01/15/2014 12:39:35 AM,41.861049572,-87.64966861,"(41.861049572, -87.64966861)"
-9458123,HX111210,01/11/2014 09:17:00 PM,069XX S STONY ISLAND AVE,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE PORCH/HALLWAY,true,false,0332,003,5,43,26,1188013,1859500,2014,01/20/2014 12:40:05 AM,41.769551093,-87.586379992,"(41.769551093, -87.586379992)"
-9455812,HX108637,01/09/2014 05:00:00 PM,024XX W CONGRESS PKWY,0810,THEFT,OVER $500,CTA BUS,false,false,1135,011,2,28,06,1160449,1897663,2014,01/22/2014 12:39:52 AM,41.874887361,-87.686363918,"(41.874887361, -87.686363918)"
-9461014,HX107512,01/08/2014 06:40:51 PM,070XX S CAMPBELL AVE,2017,NARCOTICS,MANU/DELIVER:CRACK,RESIDENCE,false,false,0832,008,18,66,18,1160881,1858057,2014,03/22/2014 12:39:11 AM,41.766194856,-87.685873561,"(41.766194856, -87.685873561)"
-9450517,HX103614,01/04/2014 04:35:00 PM,0000X E 8TH ST,0560,ASSAULT,SIMPLE,OTHER,true,false,0123,001,2,32,08A,1176501,1896707,2014,01/07/2014 12:39:59 AM,41.871916899,-87.627457011,"(41.871916899, -87.627457011)"
-9452542,HX105729,01/04/2014 02:00:00 PM,055XX W WAVELAND AVE,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,false,false,1633,016,38,15,26,1138414,1924083,2014,01/15/2014 12:39:35 AM,41.947814192,-87.766627259,"(41.947814192, -87.766627259)"
-9447548,HX101036,01/01/2014 06:00:00 PM,069XX S PRAIRIE AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0322,003,6,69,08B,1179274,1859254,2014,01/05/2014 12:39:48 AM,41.769079844,-87.61842041,"(41.769079844, -87.61842041)"
-9447499,HX100946,01/01/2014 02:30:00 PM,055XX W BELMONT AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2514,025,30,19,05,1139087,1920691,2014,01/30/2014 12:39:55 AM,41.938493968,-87.764236211,"(41.938493968, -87.764236211)"
-9446798,HW590618,12/31/2013 09:30:00 PM,045XX N MULLIGAN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,1622,016,38,15,14,1133162,1929412,2013,01/03/2014 11:11:01 AM,41.962531191,-87.785807568,"(41.962531191, -87.785807568)"
-9463485,HX116303,12/31/2013 11:49:00 AM,017XX W DIVERSEY PKWY,0810,THEFT,OVER $500,RESIDENCE,false,false,1931,019,32,7,06,1164115,1918593,2013,02/10/2014 10:57:46 AM,41.932244166,-87.672311764,"(41.932244166, -87.672311764)"
-9445506,HW589726,12/31/2013 03:30:00 AM,082XX S MARYLAND AVE,0810,THEFT,OVER $500,RESIDENCE,false,false,0631,006,8,44,06,1183387,1850211,2013,12/31/2013 06:37:38 AM,41.744170126,-87.603625425,"(41.744170126, -87.603625425)"
-9443677,HW588194,12/29/2013 02:00:00 PM,018XX S WENTWORTH AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0914,009,25,34,06,1175295,1891529,2013,12/30/2013 11:58:17 AM,41.857735248,-87.632039982,"(41.857735248, -87.632039982)"
-9441369,HW585335,12/27/2013 10:00:00 AM,069XX S VERNON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENTIAL YARD (FRONT/BACK),false,false,0322,003,6,69,14,1180491,1858955,2013,12/27/2013 12:11:46 PM,41.768231511,-87.613968674,"(41.768231511, -87.613968674)"
-9438988,HW583050,12/24/2013 04:15:00 PM,047XX S CALUMET AVE,031A,ROBBERY,ARMED: HANDGUN,OTHER,false,false,0224,002,3,38,03,1179280,1873373,2013,12/30/2013 10:06:52 PM,41.807823592,-87.617967803,"(41.807823592, -87.617967803)"
-9445303,HW589380,12/24/2013 11:00:00 AM,001XX E CHESTNUT ST,0460,BATTERY,SIMPLE,HOTEL/MOTEL,false,false,1833,018,42,8,08B,1176968,1906390,2013,01/12/2014 08:03:55 AM,41.898477019,-87.625449228,"(41.898477019, -87.625449228)"
-9438193,HW582421,12/22/2013 06:00:00 AM,004XX W BRIAR PL,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1934,019,44,6,05,1172707,1920990,2013,03/31/2014 09:32:37 AM,41.938635508,-87.640666327,"(41.938635508, -87.640666327)"
-9436007,HW580185,12/22/2013 04:20:00 AM,022XX N LINCOLN AVE,0460,BATTERY,SIMPLE,BAR OR TAVERN,false,false,1812,018,43,7,08B,1171535,1915285,2013,01/01/2014 08:26:11 AM,41.923006647,-87.645141966,"(41.923006647, -87.645141966)"
-9435371,HW579047,12/21/2013 06:35:00 AM,081XX S EAST END AVE,0880,THEFT,PURSE-SNATCHING,SIDEWALK,false,false,0414,004,8,45,06,1189056,1851233,2013,01/01/2014 05:42:14 PM,41.746840795,-87.58282125,"(41.746840795, -87.58282125)"
-9443033,HW587304,12/20/2013 03:00:00 PM,023XX W SCHOOL ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1921,019,32,5,05,1160178,1921820,2013,01/22/2014 12:00:33 PM,41.941181641,-87.686690277,"(41.941181641, -87.686690277)"
-9430964,HW574873,12/18/2013 02:45:00 AM,121XX S STEWART AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0523,005,34,53,08B,1175855,1824383,2013,12/27/2013 05:32:41 PM,41.673466392,-87.631994066,"(41.673466392, -87.631994066)"
-9430928,HW574834,12/18/2013 12:25:00 AM,044XX S DREXEL BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0221,002,4,39,08B,1182896,1875885,2013,01/15/2014 12:28:54 PM,41.814633354,-87.6046273,"(41.814633354, -87.6046273)"
-9427754,HW571866,12/15/2013 03:00:00 PM,037XX W LE MOYNE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2535,025,26,23,14,1151055,1909670,2013,12/16/2013 11:27:32 AM,41.908024933,-87.720540109,"(41.908024933, -87.720540109)"
-9422258,HW566091,12/09/2013 05:45:00 PM,024XX W THOMAS ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1212,012,1,24,06,1159993,1907286,2013,12/11/2013 10:38:33 AM,41.901303134,-87.687772355,"(41.901303134, -87.687772355)"
-9419862,HW563956,12/08/2013 08:15:00 PM,045XX S LOWE AVE,0650,BURGLARY,HOME INVASION,RESIDENCE,false,false,0925,009,11,61,05,1172767,1874641,2013,01/25/2014 02:12:32 PM,41.811449389,-87.641818209,"(41.811449389, -87.641818209)"
-9419440,HW563282,12/08/2013 12:40:00 PM,011XX N AVERS AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1112,011,27,23,18,1150467,1907365,2013,12/08/2013 01:55:00 PM,41.901711295,-87.722760401,"(41.901711295, -87.722760401)"
-9417989,HW561291,12/06/2013 05:15:00 PM,086XX S LOOMIS BLVD,0495,BATTERY,AGGRAVATED OF A SENIOR CITIZEN,RESIDENCE,false,true,0614,006,21,71,04B,1168464,1847493,2013,12/20/2013 12:35:23 PM,41.7370458,-87.658382843,"(41.7370458, -87.658382843)"
-9418209,HW560520,12/06/2013 03:45:00 AM,071XX W HIGGINS AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1613,016,41,10,08B,1127721,1936004,2013,12/08/2013 09:43:21 AM,41.980713995,-87.805663146,"(41.980713995, -87.805663146)"
-9417783,HW560249,12/05/2013 07:30:00 PM,026XX W CORTEZ ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1211,012,1,24,08B,1158569,1906844,2013,01/15/2014 12:25:13 PM,41.900119535,-87.69301497,"(41.900119535, -87.69301497)"
-9413386,HW556650,12/03/2013 09:20:00 AM,022XX N MILWAUKEE AVE,0870,THEFT,POCKET-PICKING,BANK,false,false,1431,014,1,22,06,1157424,1915108,2013,12/04/2013 08:36:57 AM,41.922819997,-87.696995399,"(41.922819997, -87.696995399)"
-9422734,HW555393,11/30/2013 05:00:00 PM,053XX N ELSTON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,GROCERY FOOD STORE,false,false,1621,016,45,11,14,1140926,1935350,2013,12/12/2013 10:14:16 AM,41.978685873,-87.757115096,"(41.978685873, -87.757115096)"
-9409073,HW552271,11/28/2013 04:30:00 PM,030XX W NORTH AVE,0820,THEFT,$500 AND UNDER,APARTMENT,false,false,1421,014,26,23,06,1155601,1910520,2013,12/02/2013 11:28:35 AM,41.910267073,-87.703817467,"(41.910267073, -87.703817467)"
-9408740,HW551993,11/27/2013 02:30:00 PM,004XX E MC FETRIDGE DR,0890,THEFT,FROM BUILDING,SPORTS ARENA/STADIUM,false,false,0132,001,2,33,06,1179306,1894170,2013,11/30/2013 07:00:12 AM,41.864891468,-87.617236533,"(41.864891468, -87.617236533)"
-9406044,HW549485,11/26/2013 09:15:00 PM,056XX N CLARK ST,1330,CRIMINAL TRESPASS,TO LAND,RESTAURANT,true,false,2012,020,40,77,26,1164868,1937443,2013,11/27/2013 11:00:28 AM,41.983953427,-87.669007577,"(41.983953427, -87.669007577)"
-9401846,HW544563,11/22/2013 09:39:00 PM,095XX S GREENWOOD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0511,005,8,50,14,1185261,1841750,2013,11/23/2013 06:00:53 AM,41.720908403,-87.597024187,"(41.720908403, -87.597024187)"
-9405301,HW548719,11/22/2013 01:30:00 PM,027XX E 89TH ST,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,0423,004,7,46,08B,1195945,1846528,2013,11/27/2013 08:46:04 AM,41.733762235,-87.557734065,"(41.733762235, -87.557734065)"
-9398511,HW541664,11/20/2013 08:30:00 PM,079XX S VINCENNES AVE,0460,BATTERY,SIMPLE,RESTAURANT,false,false,0621,006,17,44,08B,1174965,1852481,2013,11/21/2013 07:36:32 AM,41.750591127,-87.634416769,"(41.750591127, -87.634416769)"
-9397579,HW540801,11/20/2013 11:00:00 AM,069XX S STEWART AVE,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,true,false,0731,007,6,68,26,1174869,1858736,2013,11/21/2013 07:38:40 AM,41.767757733,-87.634582377,"(41.767757733, -87.634582377)"
-9396480,HW540058,11/19/2013 05:00:00 PM,040XX W LAKE ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,false,false,1114,011,28,26,11,1149614,1901482,2013,11/20/2013 10:49:30 AM,41.885584343,-87.726046543,"(41.885584343, -87.726046543)"
-9396178,HW539680,11/19/2013 01:53:00 PM,027XX W FLOURNOY ST,2017,NARCOTICS,MANU/DELIVER:CRACK,SIDEWALK,true,false,1135,011,2,27,18,1157878,1896874,2013,11/19/2013 02:51:35 PM,41.872775057,-87.695825091,"(41.872775057, -87.695825091)"
-9395154,HW538677,11/18/2013 09:30:00 AM,021XX N ST LOUIS AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1413,014,26,22,05,1152957,1913981,2013,12/30/2013 10:35:27 AM,41.919817201,-87.713438645,"(41.919817201, -87.713438645)"
-9388727,HW532069,11/13/2013 04:15:00 PM,031XX N CLARK ST,1310,CRIMINAL DAMAGE,TO PROPERTY,SMALL RETAIL STORE,false,false,1933,019,44,6,14,1170331,1920956,2013,11/14/2013 09:31:28 AM,41.93859456,-87.649399622,"(41.93859456, -87.649399622)"
-9388289,HW531527,11/12/2013 05:30:00 PM,008XX N MICHIGAN AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,1833,018,42,8,06,1177379,1906245,2013,11/28/2013 07:03:12 AM,41.898069817,-87.623944075,"(41.898069817, -87.623944075)"
-9387653,HW531130,11/12/2013 12:00:00 PM,016XX N CLARK ST,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,true,false,1814,018,43,7,07,1175174,1911062,2013,11/13/2013 10:58:10 AM,41.911337641,-87.631898083,"(41.911337641, -87.631898083)"
-9386279,HW530040,11/12/2013 12:02:00 AM,019XX W LAWRENCE AVE,0460,BATTERY,SIMPLE,SIDEWALK,true,false,1912,019,47,4,08B,1162205,1931845,2013,11/12/2013 07:07:42 AM,41.968648549,-87.678958932,"(41.968648549, -87.678958932)"
-9386177,HW529878,11/11/2013 07:31:00 PM,015XX E 55TH ST,0460,BATTERY,SIMPLE,GROCERY FOOD STORE,false,false,0234,002,4,41,08B,1187435,1868879,2013,11/14/2013 10:15:21 AM,41.795301576,-87.58820073,"(41.795301576, -87.58820073)"
-9385960,HW529741,11/11/2013 05:45:00 PM,001XX W MONROE ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,false,false,0122,001,42,32,06,1175444,1899856,2013,11/12/2013 08:01:46 AM,41.880581734,-87.63124308,"(41.880581734, -87.63124308)"
-9382909,HW525552,11/08/2013 11:30:00 AM,068XX S PERRY AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0722,007,6,69,05,1176620,1859576,2013,11/21/2013 02:50:51 PM,41.770023572,-87.628138968,"(41.770023572, -87.628138968)"
-9380477,HW523608,11/06/2013 08:00:00 PM,020XX W VAN BUREN ST,0460,BATTERY,SIMPLE,STREET,false,false,1225,012,2,28,08B,1163092,1898204,2013,11/07/2013 08:51:00 AM,41.876316834,-87.676644735,"(41.876316834, -87.676644735)"
-9378817,HW522179,11/05/2013 07:00:00 PM,064XX W IRVING PARK RD,0810,THEFT,OVER $500,GROCERY FOOD STORE,false,false,1632,016,38,17,06,1132665,1925939,2013,12/12/2013 12:32:58 PM,41.953009618,-87.787716178,"(41.953009618, -87.787716178)"
-9381262,HW524192,11/05/2013 01:15:00 PM,036XX N MILWAUKEE AVE,0810,THEFT,OVER $500,"SCHOOL, PUBLIC, BUILDING",false,false,1731,017,38,16,06,1146826,1923648,2013,11/10/2013 10:57:29 AM,41.94646373,-87.735717521,"(41.94646373, -87.735717521)"
-9378444,HW521658,11/04/2013 02:30:00 AM,040XX W NORTH AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,VEHICLE NON-COMMERCIAL,false,true,2534,025,30,23,08B,1149440,1910376,2013,11/07/2013 01:55:29 PM,41.909993773,-87.726454479,"(41.909993773, -87.726454479)"
-9375841,HW519378,11/03/2013 03:00:00 PM,033XX W 55TH ST,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0822,008,14,63,03,1155215,1867996,2013,12/22/2013 03:42:52 PM,41.793584117,-87.706376059,"(41.793584117, -87.706376059)"
-9375532,HW517470,11/02/2013 06:40:00 AM,073XX S DANTE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0324,003,5,43,14,1187106,1856500,2013,11/04/2013 11:58:09 AM,41.761340392,-87.589799654,"(41.761340392, -87.589799654)"
-9373859,HW516405,11/01/2013 01:40:00 PM,118XX S UNION AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0524,005,34,53,14,1173732,1826567,2013,11/03/2013 08:03:38 AM,41.679506796,-87.639700066,"(41.679506796, -87.639700066)"
-9373995,HW516673,11/01/2013 08:00:00 AM,082XX S HALSTED ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0622,006,21,71,05,1172449,1850113,2013,11/06/2013 12:32:16 AM,41.744148741,-87.643706097,"(41.744148741, -87.643706097)"
-9372591,HW515482,10/31/2013 08:30:00 PM,022XX N ORCHARD ST,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,1812,018,43,7,06,1171187,1915438,2013,11/01/2013 07:36:06 AM,41.923434143,-87.646416123,"(41.923434143, -87.646416123)"
-9369795,HW512980,10/30/2013 09:30:00 AM,091XX S STONY ISLAND AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0413,004,8,48,06,1188343,1844881,2013,10/31/2013 08:50:52 AM,41.72942731,-87.585636041,"(41.72942731, -87.585636041)"
-9369259,HW512671,10/30/2013 12:03:00 AM,011XX S PULASKI RD,2027,NARCOTICS,POSS: CRACK,STREET,true,false,1132,011,24,29,18,1149930,1894710,2013,10/30/2013 01:13:38 AM,41.86699507,-87.72506236,"(41.86699507, -87.72506236)"
-9374961,HW517969,10/29/2013 08:00:00 PM,017XX N CENTRAL PARK AVE,0890,THEFT,FROM BUILDING,GAS STATION,false,false,2535,025,26,23,06,1152074,1911121,2013,11/07/2013 12:45:33 PM,41.911986574,-87.716758491,"(41.911986574, -87.716758491)"
-9367633,HW511020,10/28/2013 03:00:00 PM,091XX S STEWART AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0634,006,21,49,26,1175435,1844197,2013,11/02/2013 10:54:45 AM,41.727848282,-87.632941532,"(41.727848282, -87.632941532)"
-9367453,HW510727,10/28/2013 11:20:00 AM,048XX W IRVING PARK RD,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,1634,016,45,15,06,1143518,1926115,2013,10/30/2013 11:17:00 AM,41.953296043,-87.747814885,"(41.953296043, -87.747814885)"
-9365490,HW508655,10/26/2013 07:30:00 PM,0000X E 112TH ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0531,005,9,49,06,1178300,1830750,2013,10/27/2013 08:44:45 AM,41.690883434,-87.622853021,"(41.690883434, -87.622853021)"
-9364471,HW507300,10/25/2013 07:00:00 PM,012XX N WELLS ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1821,018,43,8,14,1174531,1908411,2013,10/26/2013 07:48:48 AM,41.904077575,-87.634339591,"(41.904077575, -87.634339591)"
-9368192,HW510471,10/25/2013 05:00:00 PM,040XX W GRENSHAW ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1132,011,24,29,05,1149546,1894773,2013,11/03/2013 01:02:50 PM,41.867175407,-87.726470451,"(41.867175407, -87.726470451)"
-9362844,HW505807,10/24/2013 04:01:00 PM,010XX W 83RD ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0613,006,21,71,18,1170959,1849745,2013,10/24/2013 05:35:36 PM,41.743171538,-87.649176324,"(41.743171538, -87.649176324)"
-9362926,HW505629,10/24/2013 02:30:00 AM,002XX W 91ST ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0634,006,21,49,08B,1176260,1844567,2013,10/29/2013 10:56:45 AM,41.72884515,-87.629908349,"(41.72884515, -87.629908349)"
-9360142,HW503466,10/22/2013 06:24:00 PM,053XX W DIVISION ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,2532,025,37,25,15,1140246,1907512,2013,10/23/2013 07:13:58 AM,41.902308165,-87.760300215,"(41.902308165, -87.760300215)"
-9358780,HW502274,10/21/2013 06:45:00 PM,0000X E LAKE ST,0870,THEFT,POCKET-PICKING,SIDEWALK,false,false,0111,001,42,32,06,1176958,1901785,2013,10/27/2013 06:49:04 AM,41.885840894,-87.625625463,"(41.885840894, -87.625625463)"
-9357846,HW501395,10/20/2013 01:00:00 AM,027XX S HAMLIN AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1031,010,22,30,06,1151439,1885627,2013,10/21/2013 01:14:04 PM,41.84204075,-87.719760856,"(41.84204075, -87.719760856)"
-9356701,HW500367,10/18/2013 07:00:00 PM,015XX N HUDSON AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,RESIDENCE,false,false,1821,018,43,8,14,1173068,1910776,2013,10/21/2013 11:21:48 AM,41.910599843,-87.639643258,"(41.910599843, -87.639643258)"
-9355772,HW499307,10/18/2013 03:00:00 PM,054XX S MICHIGAN AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0231,002,3,40,06,1178124,1868891,2013,10/20/2013 10:27:44 AM,41.795550899,-87.622343646,"(41.795550899, -87.622343646)"
-9354975,HW498169,10/18/2013 03:00:00 PM,004XX E 42ND ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0214,002,3,38,08B,1180011,1877216,2013,10/22/2013 07:36:27 AM,41.818352371,-87.615168922,"(41.818352371, -87.615168922)"
-9351995,HW495731,10/16/2013 07:50:00 PM,067XX S UNION AVE,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,true,false,0723,007,6,68,26,1172857,1860296,2013,10/17/2013 04:28:08 AM,41.772083175,-87.641911265,"(41.772083175, -87.641911265)"
-9351560,HW495145,10/15/2013 06:09:00 PM,033XX W 26TH ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,1024,010,22,30,06,1154210,1886567,2013,11/05/2013 01:58:17 PM,41.844565451,-87.709566994,"(41.844565451, -87.709566994)"
-9350227,HW493843,10/15/2013 02:00:00 PM,004XX S STATE ST,1330,CRIMINAL TRESPASS,TO LAND,LIBRARY,true,false,0113,001,2,32,26,1176394,1898485,2013,10/16/2013 07:04:32 AM,41.876798252,-87.62779619,"(41.876798252, -87.62779619)"
-9350128,HW493773,10/15/2013 01:15:00 PM,060XX W NORTH AVE,0560,ASSAULT,SIMPLE,SMALL RETAIL STORE,false,false,2513,025,29,25,08A,1135975,1910053,2013,10/16/2013 08:07:07 AM,41.909358198,-87.775927859,"(41.909358198, -87.775927859)"
-9347997,HW491754,10/13/2013 05:15:00 PM,014XX S TRUMBULL AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,1021,010,24,29,04B,1153565,1892742,2013,10/14/2013 12:49:24 PM,41.861523204,-87.711770024,"(41.861523204, -87.711770024)"
-9347204,HW490756,10/12/2013 10:00:00 PM,025XX W MOFFAT ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1434,014,1,22,08B,1159251,1912170,2013,10/20/2013 10:03:29 AM,41.914720508,-87.690363324,"(41.914720508, -87.690363324)"
-9347060,HW490607,10/12/2013 06:38:00 PM,053XX W BELMONT AVE,0460,BATTERY,SIMPLE,BAR OR TAVERN,false,false,2514,025,30,19,08B,1139891,1920710,2013,10/13/2013 09:32:00 AM,41.938531422,-87.761280817,"(41.938531422, -87.761280817)"
-9346247,HW489698,10/12/2013 12:00:00 AM,033XX N HALSTED ST,0820,THEFT,$500 AND UNDER,BAR OR TAVERN,false,false,1925,019,44,6,06,1170364,1922754,2013,10/14/2013 09:23:06 AM,41.943527622,-87.649225604,"(41.943527622, -87.649225604)"
-9345820,HW489083,10/11/2013 03:30:00 PM,036XX N CENTRAL AVE,0820,THEFT,$500 AND UNDER,CONVENIENCE STORE,false,false,1633,016,38,15,06,1138423,1923411,2013,10/16/2013 02:31:08 PM,41.945969994,-87.766610509,"(41.945969994, -87.766610509)"
-9344416,HW488030,10/10/2013 08:25:00 PM,039XX S LAKE PARK AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0214,002,4,36,18,1183461,1878927,2013,10/10/2013 10:03:33 PM,41.822967653,-87.602459924,"(41.822967653, -87.602459924)"
-9360698,HW503849,10/10/2013 06:00:00 PM,020XX N KENMORE AVE,0810,THEFT,OVER $500,STREET,false,false,1811,018,43,7,06,1168932,1913720,2013,10/23/2013 11:11:05 AM,41.918769129,-87.654751715,"(41.918769129, -87.654751715)"
-9345919,HW489237,10/10/2013 03:00:00 PM,013XX S ST LOUIS AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1021,010,24,29,08B,1153223,1893084,2013,10/16/2013 10:12:48 AM,41.862468479,-87.71301638,"(41.862468479, -87.71301638)"
-9348839,HW492862,10/10/2013 10:00:00 AM,0000X W 114TH ST,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,OTHER,false,false,0522,005,34,49,06,1177998,1829418,2013,10/15/2013 12:19:52 PM,41.687235061,-87.623998815,"(41.687235061, -87.623998815)"
-9341149,HW485223,10/08/2013 05:30:00 PM,068XX S UNION AVE,0880,THEFT,PURSE-SNATCHING,SIDEWALK,false,false,0723,007,6,68,06,1172873,1859725,2013,10/09/2013 06:30:18 AM,41.770515931,-87.641869442,"(41.770515931, -87.641869442)"
-9340907,HW484734,10/08/2013 01:39:00 PM,001XX E 35TH ST,3731,INTERFERENCE WITH PUBLIC OFFICER,OBSTRUCTING IDENTIFICATION,STREET,true,false,0211,002,3,35,24,1178047,1881868,2013,10/09/2013 07:27:48 AM,41.831162653,-87.622232227,"(41.831162653, -87.622232227)"
-9340372,HW484367,10/07/2013 08:00:00 PM,011XX N CENTRAL PARK AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1112,011,27,23,07,1152130,1907216,2013,11/01/2013 10:35:50 AM,41.901269783,-87.716655897,"(41.901269783, -87.716655897)"
-9337231,HW481152,10/05/2013 05:23:00 PM,010XX W WILSON AVE,0460,BATTERY,SIMPLE,SIDEWALK,true,false,1914,019,46,3,08B,1168209,1930671,2013,10/06/2013 08:30:55 AM,41.965299098,-87.656916624,"(41.965299098, -87.656916624)"
-9335258,HW479058,10/03/2013 07:30:00 PM,010XX W VERNON PARK PL,0820,THEFT,$500 AND UNDER,RESIDENCE-GARAGE,false,false,1232,012,25,28,06,1169708,1897023,2013,10/08/2013 12:43:36 PM,41.872934609,-87.652387521,"(41.872934609, -87.652387521)"
-9334585,HW478184,10/03/2013 02:25:00 PM,066XX S GREENWOOD AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE PORCH/HALLWAY,true,false,0321,003,5,42,26,1184397,1861361,2013,10/04/2013 07:23:17 AM,41.774743259,-87.599576249,"(41.774743259, -87.599576249)"
-9332022,HW476133,10/02/2013 04:55:00 AM,054XX W HARRISON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,1522,015,29,25,08B,1140298,1896829,2013,10/03/2013 08:50:25 AM,41.872991728,-87.760371219,"(41.872991728, -87.760371219)"
-9331199,HW474837,10/01/2013 09:15:00 AM,0000X S STATE ST,0890,THEFT,FROM BUILDING,OTHER,false,false,0112,001,42,32,06,1176348,1900301,2013,10/02/2013 07:21:10 AM,41.881782498,-87.627910281,"(41.881782498, -87.627910281)"
-9329027,HW472960,09/29/2013 10:00:00 PM,035XX W HIRSCH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1422,014,26,23,14,1152377,1909122,2013,10/02/2013 09:47:51 AM,41.906495151,-87.715698231,"(41.906495151, -87.715698231)"
-9327302,HW470730,09/28/2013 08:30:00 AM,058XX W SCHOOL ST,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,1633,016,38,15,08A,1136934,1921306,2013,10/01/2013 08:34:04 AM,41.940220541,-87.772134309,"(41.940220541, -87.772134309)"
-9329276,HW473218,09/27/2013 11:00:00 PM,018XX W FULLERTON AVE,0820,THEFT,$500 AND UNDER,BAR OR TAVERN,false,false,1432,014,32,22,06,1163234,1916006,2013,10/01/2013 10:32:17 AM,41.925163873,-87.675622245,"(41.925163873, -87.675622245)"
-9333098,HW475335,09/25/2013 08:00:00 AM,051XX W DICKENS AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2522,025,37,19,14,1141835,1913519,2013,10/03/2013 07:28:36 AM,41.918762764,-87.754314544,"(41.918762764, -87.754314544)"
-9320262,HW464419,09/23/2013 10:22:00 PM,026XX E 77TH ST,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,0421,004,7,43,18,1195449,1854467,2013,09/23/2013 11:26:40 PM,41.755559736,-87.559289506,"(41.755559736, -87.559289506)"
-9322704,HW466341,09/23/2013 09:00:00 PM,013XX W LOYOLA AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2432,024,40,1,06,1166125,1943764,2013,09/26/2013 02:30:26 PM,42.001271558,-87.664202897,"(42.001271558, -87.664202897)"
-9318532,HW462977,09/22/2013 07:30:00 PM,011XX N AUSTIN BLVD,0312,ROBBERY,ARMED:KNIFE/CUTTING INSTRUMENT,SIDEWALK,false,false,1511,015,29,25,03,1136195,1907104,2013,09/27/2013 01:49:23 PM,41.901261852,-87.775190151,"(41.901261852, -87.775190151)"
-9318253,HW462564,09/22/2013 12:00:00 PM,083XX S WOOD ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0614,006,18,71,14,1165849,1849067,2013,09/23/2013 06:38:20 AM,41.741420967,-87.667918852,"(41.741420967, -87.667918852)"
-9318111,HW462324,09/22/2013 11:40:00 AM,054XX S EAST VIEW PARK,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0234,002,5,41,14,1189105,1869607,2013,09/22/2013 12:36:18 PM,41.797259374,-87.582053577,"(41.797259374, -87.582053577)"
-9316582,HW460471,09/20/2013 10:15:00 PM,027XX W 44TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0922,009,12,58,18,1158733,1875413,2013,06/14/2014 12:41:49 PM,41.813866207,-87.693273196,"(41.813866207, -87.693273196)"
-9311752,HW455833,09/17/2013 03:07:00 PM,022XX N RACINE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1811,018,32,7,06,1167986,1914852,2013,09/18/2013 06:57:03 AM,41.921895888,-87.658194649,"(41.921895888, -87.658194649)"
-9310874,HW455337,09/17/2013 10:00:00 AM,001XX N CLARK ST,0860,THEFT,RETAIL THEFT,DRUG STORE,true,false,0111,001,42,32,06,1175544,1901630,2013,09/17/2013 11:55:51 AM,41.885447447,-87.63082257,"(41.885447447, -87.63082257)"
-9308270,HW452815,09/15/2013 11:10:00 AM,015XX N CENTRAL AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,2532,025,37,25,08A,1138753,1909725,2013,09/18/2013 11:16:41 AM,41.908408135,-87.765730529,"(41.908408135, -87.765730529)"
-9308151,HW452734,09/15/2013 08:00:00 AM,044XX W GLADYS AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,RESIDENCE,false,false,1131,011,24,26,26,1146753,1897944,2013,09/17/2013 02:50:20 PM,41.875930737,-87.736643125,"(41.875930737, -87.736643125)"
-9307352,HW450765,09/13/2013 03:00:00 PM,062XX N RICHMOND ST,0560,ASSAULT,SIMPLE,APARTMENT,false,true,2413,024,50,2,08A,1155594,1941095,2013,09/27/2013 03:55:58 PM,41.994167003,-87.703017101,"(41.994167003, -87.703017101)"
-9305945,HW450171,09/13/2013 12:15:00 PM,006XX N HOMAN AVE,1330,CRIMINAL TRESPASS,TO LAND,ABANDONED BUILDING,true,false,1121,011,27,23,26,1153638,1904044,2013,09/18/2013 12:28:29 PM,41.892535632,-87.711201326,"(41.892535632, -87.711201326)"
-9314694,HW458575,09/12/2013 04:00:00 PM,080XX S HALSTED ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0621,006,21,71,07,1172408,1851475,2013,10/08/2013 10:06:51 AM,41.747887149,-87.643816335,"(41.747887149, -87.643816335)"
-9304727,HW448997,09/12/2013 03:10:00 PM,002XX E 75TH ST,0460,BATTERY,SIMPLE,BARBERSHOP,false,false,0623,006,6,69,08B,1179087,1855296,2013,09/17/2013 01:08:52 PM,41.758222927,-87.619226301,"(41.758222927, -87.619226301)"
-9304482,HW448781,09/12/2013 12:30:00 PM,012XX N LAWNDALE AVE,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,false,false,2535,025,26,23,26,1151287,1908142,2013,09/23/2013 01:42:04 PM,41.903827404,-87.719728008,"(41.903827404, -87.719728008)"
-9303427,HW448094,09/11/2013 03:30:00 PM,040XX W NELSON ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2523,025,31,21,07,1149128,1919939,2013,09/12/2013 11:21:18 AM,41.936241572,-87.727352391,"(41.936241572, -87.727352391)"
-9303778,HW448369,09/11/2013 11:00:00 AM,017XX W HOWARD ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2422,024,49,1,06,1163125,1950307,2013,09/12/2013 09:45:30 AM,42.019289497,-87.675054297,"(42.019289497, -87.675054297)"
-9301023,HW445780,09/10/2013 01:00:00 PM,061XX S COTTAGE GROVE AVE,1330,CRIMINAL TRESPASS,TO LAND,SIDEWALK,true,false,0313,003,20,42,26,1182662,1864400,2013,09/11/2013 11:15:39 AM,41.783123003,-87.605842173,"(41.783123003, -87.605842173)"
-9301033,HW445823,09/10/2013 10:15:00 AM,023XX N GREENVIEW AVE,0810,THEFT,OVER $500,STREET,false,false,1811,018,32,7,06,1165955,1915825,2013,09/10/2013 04:11:01 PM,41.924609492,-87.665629237,"(41.924609492, -87.665629237)"
-9297658,HW442865,09/08/2013 11:30:00 AM,050XX S BISHOP ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0933,009,16,61,18,1167489,1870990,2013,09/09/2013 04:08:50 PM,41.801545549,-87.661282298,"(41.801545549, -87.661282298)"
-9297353,HW442554,09/08/2013 04:10:00 AM,0000X N MASON AVE,0496,BATTERY,AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST,RESIDENCE,true,true,1513,015,29,25,04B,1136732,1899680,2013,09/09/2013 07:31:07 AM,41.880879833,-87.773395618,"(41.880879833, -87.773395618)"
-9296152,HW440966,09/06/2013 11:18:00 PM,028XX W 71ST ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",GAS STATION,false,false,0831,008,18,66,07,1158835,1857396,2013,09/09/2013 11:53:26 AM,41.764422991,-87.693390964,"(41.764422991, -87.693390964)"
-9298158,HW443520,09/03/2013 09:00:00 AM,087XX S DANTE AVE,0810,THEFT,OVER $500,RESIDENTIAL YARD (FRONT/BACK),false,false,0412,004,8,48,06,1187357,1847169,2013,09/09/2013 06:55:41 AM,41.735729267,-87.589175504,"(41.735729267, -87.589175504)"
-9289930,HW435033,09/02/2013 10:10:00 PM,059XX S MAPLEWOOD AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0824,008,16,66,05,1160359,1864856,2013,09/06/2013 02:02:19 PM,41.78486304,-87.687599767,"(41.78486304, -87.687599767)"
-9288722,HW433610,09/01/2013 09:15:00 PM,031XX W MADISON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1222,012,28,27,18,1155636,1899894,2013,09/01/2013 10:17:17 PM,41.881107642,-87.703975234,"(41.881107642, -87.703975234)"
-9289606,HW433509,09/01/2013 04:30:00 PM,069XX S CRANDON AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0331,003,5,43,05,1192486,1859296,2013,09/21/2013 07:34:06 PM,41.768883532,-87.569990916,"(41.768883532, -87.569990916)"
-9290660,HW435606,08/31/2013 11:55:00 PM,053XX S BLACKSTONE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0234,002,4,41,06,1186856,1870264,2013,09/03/2013 01:20:53 PM,41.799115871,-87.590280001,"(41.799115871, -87.590280001)"
-9287829,HW432489,08/31/2013 11:44:00 PM,022XX S ALBANY AVE,1710,OFFENSE INVOLVING CHILDREN,ENDANGERING LIFE/HEALTH CHILD,STREET,true,false,1033,010,24,30,26,1156084,1888859,2013,09/02/2013 02:27:18 PM,41.850817406,-87.702627894,"(41.850817406, -87.702627894)"
-9326423,HW469599,08/31/2013 09:30:00 AM,081XX S WOOD ST,0810,THEFT,OVER $500,STREET,false,false,0614,006,18,71,06,1165811,1850449,2013,09/28/2013 06:54:39 AM,41.745214183,-87.668018933,"(41.745214183, -87.668018933)"
-9291789,HW436861,08/30/2013 03:15:00 PM,042XX W THOMAS ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1111,011,37,23,14,1148112,1907042,2013,09/04/2013 09:38:08 AM,41.900870618,-87.731418997,"(41.900870618, -87.731418997)"
-9285193,HW429459,08/29/2013 08:06:00 PM,059XX S DR MARTIN LUTHER KING JR DR,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0232,002,20,40,08B,1179902,1865589,2013,09/01/2013 01:23:27 PM,41.786449366,-87.615924759,"(41.786449366, -87.615924759)"
-9281946,HW426728,08/27/2013 10:59:00 PM,063XX N MOZART ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,2413,024,50,2,08B,1156226,1941941,2013,08/28/2013 08:36:51 AM,41.996475675,-87.700669333,"(41.996475675, -87.700669333)"
-9280854,HW424692,08/26/2013 10:00:00 AM,078XX S YATES BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0414,004,7,43,14,1193523,1853283,2013,08/28/2013 07:40:53 AM,41.752358081,-87.566386359,"(41.752358081, -87.566386359)"
-9278699,HW423644,08/25/2013 01:00:00 AM,082XX W IRVING PARK RD,1310,CRIMINAL DAMAGE,TO PROPERTY,VACANT LOT/LAND,false,false,1631,016,36,17,14,1120290,1925502,2013,08/26/2013 09:23:11 AM,41.952017615,-87.833217939,"(41.952017615, -87.833217939)"
-9274671,HW419043,08/22/2013 03:15:00 PM,011XX S JEFFERSON ST,0860,THEFT,RETAIL THEFT,TAVERN/LIQUOR STORE,false,false,0124,001,2,28,06,1172454,1895218,2013,08/23/2013 07:32:33 AM,41.867921341,-87.642359085,"(41.867921341, -87.642359085)"
-9270198,HW415275,08/20/2013 12:40:00 AM,036XX N CLARK ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,PARKING LOT/GARAGE(NON.RESID.),true,false,1923,019,44,6,18,1168211,1924262,2013,08/20/2013 03:30:24 AM,41.947712517,-87.657095221,"(41.947712517, -87.657095221)"
-9268585,HW413851,08/18/2013 02:00:00 PM,106XX S CHAMPLAIN AVE,1790,OFFENSE INVOLVING CHILDREN,CHILD ABDUCTION,RESIDENCE,false,true,0512,005,9,50,20,1182539,1834278,2013,08/24/2013 06:57:52 AM,41.700467745,-87.607224962,"(41.700467745, -87.607224962)"
-9271584,HW416058,08/18/2013 05:00:00 AM,108XX S AVENUE H,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0432,004,10,52,08B,1202895,1833453,2013,08/31/2013 02:06:33 PM,41.697708757,-87.532718716,"(41.697708757, -87.532718716)"
-9275579,HW419823,08/17/2013 12:00:00 PM,028XX W TAYLOR ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,1135,011,28,27,26,1157337,1895616,2013,08/23/2013 11:52:43 AM,41.869333987,-87.697845551,"(41.869333987, -87.697845551)"
-9266861,HW411607,08/17/2013 07:00:00 AM,003XX N CENTRAL PARK AVE,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,true,false,1123,011,28,27,26,1152370,1901862,2013,09/03/2013 12:55:11 PM,41.886573136,-87.715915864,"(41.886573136, -87.715915864)"
-9274267,HW417282,08/14/2013 04:30:00 PM,059XX S LOWE AVE,0265,CRIM SEXUAL ASSAULT,AGGRAVATED: OTHER,OTHER,false,true,0711,007,20,68,02,1172948,1865675,2013,04/18/2014 09:34:42 PM,41.786841754,-87.641419032,"(41.786841754, -87.641419032)"
-9259779,HW405080,08/11/2013 07:19:00 PM,002XX E 131ST PL,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0533,005,9,54,05,1180405,1818201,2013,08/24/2013 04:49:01 PM,41.656399208,-87.615529218,"(41.656399208, -87.615529218)"
-9256443,HW401407,08/10/2013 08:30:00 AM,0000X E 111TH ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,OTHER,false,true,0531,005,9,49,04A,1178617,1831339,2013,08/27/2013 07:56:49 AM,41.692492554,-87.621674651,"(41.692492554, -87.621674651)"
-9256014,HW400932,08/09/2013 10:10:00 PM,014XX W 47TH ST,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,PARKING LOT/GARAGE(NON.RESID.),true,false,0924,009,3,61,04B,1167491,1873621,2013,08/10/2013 11:07:06 AM,41.808765266,-87.661199508,"(41.808765266, -87.661199508)"
-9255251,HW400104,08/09/2013 11:58:00 AM,002XX S LOTUS AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1522,015,29,25,26,1139956,1898540,2013,08/09/2013 01:09:47 PM,41.877693194,-87.761585036,"(41.877693194, -87.761585036)"
-9255880,HW400654,08/08/2013 11:00:00 AM,018XX E 72ND ST,0820,THEFT,$500 AND UNDER,APARTMENT,false,false,0324,003,5,43,06,1189563,1857635,2013,08/10/2013 07:00:16 AM,41.764396299,-87.580758295,"(41.764396299, -87.580758295)"
-9253099,HW398015,08/07/2013 09:20:00 PM,079XX S NORMAL AVE,0460,BATTERY,SIMPLE,ALLEY,false,false,0621,006,17,44,08B,1174311,1851915,2013,08/13/2013 09:45:58 AM,41.749052505,-87.636830113,"(41.749052505, -87.636830113)"
-9250995,HW396526,08/06/2013 09:05:00 PM,071XX S ARTESIAN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0832,008,18,66,18,1161231,1857440,2013,08/06/2013 10:36:15 PM,41.764494482,-87.684607734,"(41.764494482, -87.684607734)"
-9249286,HW395064,08/05/2013 08:13:00 PM,064XX S DR MARTIN LUTHER KING JR DR,0460,BATTERY,SIMPLE,STREET,false,false,0312,003,20,69,08B,1179983,1862579,2013,08/16/2013 07:36:58 AM,41.778187779,-87.615719878,"(41.778187779, -87.615719878)"
-9248845,HW394509,08/04/2013 03:00:00 PM,071XX S CONSTANCE AVE,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,APARTMENT,false,true,0324,003,5,43,26,1189567,1858033,2013,08/08/2013 11:41:47 AM,41.765488348,-87.58073087,"(41.765488348, -87.58073087)"
-9244376,HW389774,08/02/2013 03:50:00 AM,055XX S PARKSIDE AVE,0560,ASSAULT,SIMPLE,STREET,false,false,0811,008,23,56,08A,1139598,1867373,2013,08/05/2013 10:15:52 AM,41.792172764,-87.763658484,"(41.792172764, -87.763658484)"
-9309840,HW454411,07/31/2013 10:00:00 AM,132XX S RIVERDALE AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0533,005,9,54,06,1182090,1817252,2013,09/17/2013 07:29:24 AM,41.653756335,-87.609392811,"(41.653756335, -87.609392811)"
-9238374,HW384927,07/29/2013 09:45:00 PM,005XX E 47TH ST,041A,BATTERY,AGGRAVATED: HANDGUN,SIDEWALK,false,false,0223,002,3,38,04B,1180856,1873939,2013,08/28/2013 05:58:10 PM,41.8093406,-87.612170092,"(41.8093406, -87.612170092)"
-9238138,HW384399,07/29/2013 02:30:00 PM,005XX E 115TH ST,2017,NARCOTICS,MANU/DELIVER:CRACK,STREET,true,false,0532,005,9,54,18,1182061,1828766,2013,07/29/2013 08:16:36 PM,41.685353114,-87.609144885,"(41.685353114, -87.609144885)"
-9238988,HW385225,07/29/2013 08:15:00 AM,076XX S MORGAN ST,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,0621,006,17,71,06,1171011,1854036,2013,07/31/2013 07:05:36 AM,41.754945475,-87.648860721,"(41.754945475, -87.648860721)"
-9236482,HW383571,07/29/2013 02:45:00 AM,023XX S ALBANY AVE,2022,NARCOTICS,POSS: COCAINE,ALLEY,true,false,1033,010,24,30,18,1156105,1888191,2013,07/29/2013 04:14:27 AM,41.848983915,-87.702568828,"(41.848983915, -87.702568828)"
-9231548,HW377783,07/24/2013 10:23:00 PM,029XX W 71ST ST,0340,ROBBERY,ATTEMPT: STRONGARM-NO WEAPON,STREET,false,false,0831,008,18,66,03,1157634,1857360,2013,09/02/2013 02:41:57 PM,41.764348637,-87.69779395,"(41.764348637, -87.69779395)"
-9229928,HW376308,07/23/2013 11:17:00 PM,001XX N LECLAIRE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1532,015,28,25,14,1142303,1900934,2013,07/24/2013 01:19:42 PM,41.884219401,-87.752907889,"(41.884219401, -87.752907889)"
-9229636,HW375966,07/23/2013 08:25:00 AM,009XX N KINGSBURY ST,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1822,018,27,8,14,1171875,1906421,2013,07/24/2013 10:47:19 AM,41.898675875,-87.644154395,"(41.898675875, -87.644154395)"
-9225920,HW372833,07/21/2013 06:00:00 PM,027XX S WASHTENAW AVE,0460,BATTERY,SIMPLE,OTHER,false,false,1034,010,12,30,08B,1158754,1885829,2013,07/22/2013 01:39:46 PM,41.842448522,-87.69291135,"(41.842448522, -87.69291135)"
-9225609,HW372478,07/20/2013 11:00:00 PM,0000X E DIVISION ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENTIAL YARD (FRONT/BACK),false,false,1824,018,42,8,14,1176267,1908340,2013,07/22/2013 09:33:58 AM,41.903843754,-87.627965036,"(41.903843754, -87.627965036)"
-9224788,HW371354,07/20/2013 04:20:00 PM,037XX W 27TH ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,SIDEWALK,true,false,1031,010,22,30,24,1151841,1885757,2013,07/21/2013 01:38:28 PM,41.842389596,-87.718282208,"(41.842389596, -87.718282208)"
-9224725,HW371229,07/20/2013 02:53:00 PM,030XX W 63RD ST,0890,THEFT,FROM BUILDING,CLEANING STORE,false,false,0823,008,15,66,06,1157345,1862671,2013,07/21/2013 11:18:38 AM,41.778928668,-87.698709577,"(41.778928668, -87.698709577)"
-9223621,HW369682,07/19/2013 02:34:00 PM,012XX S INDEPENDENCE BLVD,0460,BATTERY,SIMPLE,STREET,false,false,1011,010,24,29,08B,1151252,1893995,2013,07/20/2013 10:03:14 AM,41.86500722,-87.720227835,"(41.86500722, -87.720227835)"
-9222372,HW368911,07/18/2013 09:30:00 PM,012XX W 119TH ST,1562,SEX OFFENSE,AGG CRIMINAL SEXUAL ABUSE,OTHER,false,false,0524,005,34,53,17,1169947,1825901,2013,10/15/2013 05:07:46 PM,41.677761963,-87.65357403,"(41.677761963, -87.65357403)"
-9221120,HW367655,07/18/2013 07:25:00 AM,017XX W MONTVALE AVE,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,2234,022,34,75,06,1166791,1829632,2013,07/20/2013 09:41:34 AM,41.688068218,-87.665020129,"(41.688068218, -87.665020129)"
-9241022,HW386903,07/17/2013 12:00:00 PM,097XX S JEFFERY AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,0431,004,7,51,06,1191256,1840695,2013,08/01/2013 07:47:00 AM,41.717870574,-87.575100186,"(41.717870574, -87.575100186)"
-9221658,HW366033,07/17/2013 04:40:00 AM,001XX N MASON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1512,015,29,25,14,1136785,1900556,2013,07/19/2013 09:23:35 AM,41.883282745,-87.773180007,"(41.883282745, -87.773180007)"
-9217203,HW363954,07/15/2013 05:50:00 PM,055XX S HALSTED ST,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0712,007,20,68,03,1171871,1868201,2013,07/24/2013 09:29:29 AM,41.793797093,-87.64529376,"(41.793797093, -87.64529376)"
-9216316,HW362123,07/14/2013 01:27:00 PM,061XX S VERNON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE PORCH/HALLWAY,false,true,0313,003,20,42,08B,1180348,1864290,2013,07/19/2013 10:47:41 AM,41.782874563,-87.614329336,"(41.782874563, -87.614329336)"
-9215223,HW361982,07/14/2013 11:35:00 AM,003XX S CICERO AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1533,015,24,25,18,1144396,1898214,2013,07/14/2013 12:21:10 PM,41.876716289,-87.745290492,"(41.876716289, -87.745290492)"
-9217885,HW363147,07/13/2013 04:30:00 PM,055XX S RACINE AVE,0313,ROBBERY,ARMED: OTHER DANGEROUS WEAPON,STREET,false,false,0713,007,16,67,03,1169222,1868128,2013,08/02/2013 12:27:23 PM,41.793654541,-87.655009565,"(41.793654541, -87.655009565)"
-9214023,HW360287,07/13/2013 05:25:00 AM,025XX S HOMAN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1024,010,22,30,14,1154074,1886680,2013,08/28/2013 07:23:18 AM,41.844878246,-87.710063086,"(41.844878246, -87.710063086)"
-9212997,HW358993,07/12/2013 10:57:00 AM,068XX S DAMEN AVE,031A,ROBBERY,ARMED: HANDGUN,STREET,false,false,0726,007,17,67,03,1164161,1859529,2013,08/16/2013 08:06:23 PM,41.770165845,-87.67380976,"(41.770165845, -87.67380976)"
-9216312,HW363056,07/12/2013 08:30:00 AM,0000X S WACKER DR,0890,THEFT,FROM BUILDING,OTHER,false,false,0122,001,2,32,06,1173977,1900284,2013,07/15/2013 12:49:42 PM,41.881788999,-87.636616958,"(41.881788999, -87.636616958)"
-9212351,HW358420,07/11/2013 07:31:00 PM,011XX W 66TH ST,1330,CRIMINAL TRESPASS,TO LAND,"SCHOOL, PUBLIC, GROUNDS",false,false,0724,007,17,68,26,1169672,1861069,2013,07/13/2013 07:45:55 AM,41.774274054,-87.65356409,"(41.774274054, -87.65356409)"
-9217432,HW357808,07/11/2013 02:45:00 PM,058XX S CALIFORNIA AVE,0460,BATTERY,SIMPLE,STREET,false,false,0824,008,16,63,08B,1158662,1866003,2013,07/22/2013 10:05:23 AM,41.788045372,-87.69379045,"(41.788045372, -87.69379045)"
-9211669,HW357693,07/11/2013 01:08:00 PM,002XX E 111TH ST,4650,OTHER OFFENSE,SEX OFFENDER: FAIL TO REGISTER,PARK PROPERTY,true,false,0531,005,9,49,26,1179618,1831366,2013,07/29/2013 09:58:40 AM,41.692543893,-87.618009026,"(41.692543893, -87.618009026)"
-9208756,HW354909,07/09/2013 01:52:00 PM,083XX S PHILLIPS AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,true,false,0423,004,7,46,05,1194003,1849875,2013,07/12/2013 01:00:09 AM,41.742994495,-87.564738956,"(41.742994495, -87.564738956)"
-9207821,HW353861,07/08/2013 06:00:00 PM,034XX W PETERSON AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,1711,017,50,13,06,1152420,1939708,2013,07/10/2013 10:09:45 AM,41.990424518,-87.714729357,"(41.990424518, -87.714729357)"
-9215757,HW353540,07/08/2013 02:06:00 PM,132XX S BUFFALO AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,0433,004,10,55,04B,1200052,1817538,2013,09/25/2013 06:44:57 AM,41.654108417,-87.543662034,"(41.654108417, -87.543662034)"
-9248873,HW394366,07/07/2013 11:00:00 AM,004XX W 107TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,2233,022,34,49,14,1175109,1833990,2013,08/06/2013 06:29:21 AM,41.699846109,-87.634439219,"(41.699846109, -87.634439219)"
-9204968,HW350693,07/06/2013 02:05:00 PM,012XX N LUIS MUNOZ MARIN DR E,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,PARK PROPERTY,true,false,1423,014,26,24,18,1157251,1908052,2013,07/06/2013 03:05:23 PM,41.903461284,-87.697823163,"(41.903461284, -87.697823163)"
-9206684,HW352801,07/05/2013 04:00:00 PM,033XX N AVONDALE AVE,0810,THEFT,OVER $500,CONSTRUCTION SITE,false,false,1732,017,35,21,06,1152705,1922495,2013,07/08/2013 11:54:47 AM,41.943185298,-87.714138621,"(41.943185298, -87.714138621)"
-9202921,HW348249,07/04/2013 10:00:00 PM,101XX S DR MARTIN LUTHER KING JR DR,0810,THEFT,OVER $500,RESIDENTIAL YARD (FRONT/BACK),false,false,0511,005,9,49,06,1180739,1837929,2013,07/05/2013 07:19:50 AM,41.710528029,-87.613704109,"(41.710528029, -87.613704109)"
-9204919,HW350585,07/04/2013 10:00:00 PM,028XX N MASON AVE,0810,THEFT,OVER $500,RESIDENCE-GARAGE,false,false,2514,025,30,19,06,1136275,1918287,2013,07/08/2013 12:20:59 PM,41.931947891,-87.774628703,"(41.931947891, -87.774628703)"
-9223105,HW369249,07/03/2013 10:00:00 AM,033XX W WARREN BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,ABANDONED BUILDING,false,false,1123,011,28,27,14,1153964,1900103,2013,08/05/2013 09:06:10 AM,41.881714642,-87.710109173,"(41.881714642, -87.710109173)"
-9200732,HW345900,07/03/2013 12:30:00 AM,033XX N SHEFFIELD AVE,0920,MOTOR VEHICLE THEFT,ATT: AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),false,false,1924,019,44,6,07,1168975,1922434,2013,07/03/2013 11:23:30 AM,41.942679834,-87.654340184,"(41.942679834, -87.654340184)"
-9198516,HW344014,07/01/2013 08:00:00 PM,034XX W 25TH ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,1024,010,22,30,26,1153466,1887213,2013,07/23/2013 09:03:54 AM,41.846352949,-87.712280232,"(41.846352949, -87.712280232)"
-9198766,HW344368,06/30/2013 08:00:00 PM,097XX S PEORIA ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2223,022,21,73,06,1172072,1840194,2013,07/02/2013 08:04:59 AM,41.716937907,-87.645377948,"(41.716937907, -87.645377948)"
-9196391,HW341658,06/30/2013 12:00:00 AM,038XX N KENNETH AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,RESIDENTIAL YARD (FRONT/BACK),false,false,1731,017,38,16,04B,1145743,1925481,2013,08/30/2013 04:51:17 PM,41.951514289,-87.739651662,"(41.951514289, -87.739651662)"
-9195089,HW340083,06/29/2013 01:35:00 AM,057XX S CARPENTER ST,0460,BATTERY,SIMPLE,STREET,false,true,0712,007,16,68,08B,1170260,1866528,2013,07/06/2013 04:20:37 PM,41.789241421,-87.651249851,"(41.789241421, -87.651249851)"
-9193441,HW338281,06/27/2013 08:20:00 PM,076XX N MARSHFIELD AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,2422,024,49,1,18,1164017,1950501,2013,06/27/2013 09:43:38 PM,42.019802964,-87.671766335,"(42.019802964, -87.671766335)"
-9193297,HW337998,06/27/2013 05:20:00 PM,077XX S ESSEX AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0421,004,7,43,08B,1194162,1854200,2013,07/06/2013 04:20:21 PM,41.754858746,-87.564014697,"(41.754858746, -87.564014697)"
-9191400,HW336273,06/26/2013 03:25:00 PM,0000X W WASHINGTON ST,1330,CRIMINAL TRESPASS,TO LAND,CHURCH/SYNAGOGUE/PLACE OF WORSHIP,true,false,0112,001,42,32,26,1175690,1900776,2013,06/27/2013 07:57:01 AM,41.883100742,-87.630312127,"(41.883100742, -87.630312127)"
-9188322,HW333653,06/24/2013 07:26:00 PM,013XX S AVERS AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),ALLEY,true,false,1011,010,24,29,18,1150974,1893170,2013,06/14/2014 12:41:49 PM,41.862748767,-87.721269978,"(41.862748767, -87.721269978)"
-9188191,HW333242,06/24/2013 03:13:00 PM,075XX S KINGSTON AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0421,004,7,43,14,1194551,1855130,2013,06/25/2013 07:03:15 AM,41.757401183,-87.562558617,"(41.757401183, -87.562558617)"
-9188305,HW333052,06/24/2013 04:42:00 AM,025XX E 79TH ST,0610,BURGLARY,FORCIBLE ENTRY,APPLIANCE STORE,false,false,0422,004,7,46,05,1194715,1853045,2013,08/02/2013 12:50:52 PM,41.751675749,-87.562026098,"(41.751675749, -87.562026098)"
-9185682,HW330763,06/22/2013 06:30:00 PM,062XX S DORCHESTER AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,0314,003,20,42,08A,1186574,1863800,2013,06/30/2013 07:02:03 AM,41.781384833,-87.591518683,"(41.781384833, -87.591518683)"
-9184955,HW329687,06/21/2013 08:30:00 PM,032XX S NORMAL AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,0915,009,11,60,08A,1173527,1883516,2013,06/25/2013 10:21:41 AM,41.835786381,-87.638767424,"(41.835786381, -87.638767424)"
-9184484,HW329145,06/21/2013 03:57:00 PM,017XX W HOWARD ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,2422,024,49,1,06,1163125,1950307,2013,06/24/2013 07:11:00 AM,42.019289497,-87.675054297,"(42.019289497, -87.675054297)"
-9189839,HW334835,06/20/2013 08:00:00 PM,036XX S UNION AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,APARTMENT,false,false,0915,009,11,60,04B,1172190,1880784,2013,07/23/2013 01:11:40 PM,41.82831909,-87.643753772,"(41.82831909, -87.643753772)"
-9179963,HW325198,06/18/2013 11:13:00 PM,051XX N LA CROSSE AVE,1821,NARCOTICS,MANU/DEL:CANNABIS 10GM OR LESS,ALLEY,true,false,1621,016,45,12,18,1143109,1934039,2013,06/19/2013 12:16:46 AM,41.975047812,-87.749119806,"(41.975047812, -87.749119806)"
-9179991,HW325187,06/18/2013 03:00:00 PM,050XX S MARSHFIELD AVE,0460,BATTERY,SIMPLE,SIDEWALK,true,false,0931,009,16,61,08B,1166147,1871453,2013,06/19/2013 09:29:19 AM,41.802844763,-87.666190742,"(41.802844763, -87.666190742)"
-9176155,HW320657,06/15/2013 09:30:00 PM,006XX E GRAND AVE,0917,MOTOR VEHICLE THEFT,"CYCLE, SCOOTER, BIKE W-VIN",OTHER,false,false,1834,018,42,8,07,1180773,1904096,2013,06/17/2013 09:39:20 AM,41.892095189,-87.611544796,"(41.892095189, -87.611544796)"
-9176135,HW320790,06/15/2013 09:30:00 AM,039XX S ELLIS AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0214,002,4,36,05,1183000,1878659,2013,07/13/2013 10:05:52 PM,41.822242991,-87.604159472,"(41.822242991, -87.604159472)"
-9175075,HW319332,06/15/2013 12:32:00 AM,121XX S HARVARD AVE,1512,PROSTITUTION,SOLICIT FOR PROSTITUTE,STREET,true,false,0523,005,34,53,16,1176182,1824444,2013,06/15/2013 02:21:40 AM,41.673626476,-87.630795402,"(41.673626476, -87.630795402)"
-9628844,HX273082,06/14/2013 09:33:00 PM,043XX W DIVERSEY AVE,1122,DECEPTIVE PRACTICE,COUNTERFEIT CHECK,CURRENCY EXCHANGE,true,false,2524,,31,20,10,,,2013,05/29/2014 12:39:18 PM,,,
-9181321,HW326307,06/14/2013 06:00:00 PM,114XX S SPAULDING AVE,1725,OFFENSE INVOLVING CHILDREN,CONTRIBUTE CRIM DELINQUENCY JUVENILE,"SCHOOL, PUBLIC, GROUNDS",false,false,2211,022,19,74,26,1156373,1828648,2013,06/29/2013 06:42:39 PM,41.685583396,-87.703186236,"(41.685583396, -87.703186236)"
-9174786,HW318851,06/14/2013 06:00:00 PM,078XX S KINGSTON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0421,004,7,43,08B,1194592,1853385,2013,07/01/2013 04:30:11 PM,41.752611761,-87.562465663,"(41.752611761, -87.562465663)"
-9171134,HW316051,06/12/2013 08:30:00 PM,109XX S MICHIGAN AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,0513,005,9,49,18,1178806,1832645,2013,06/12/2013 10:13:06 PM,41.696072117,-87.620943149,"(41.696072117, -87.620943149)"
-9171140,HW316046,06/12/2013 07:20:00 PM,073XX S RICHMOND ST,3730,INTERFERENCE WITH PUBLIC OFFICER,OBSTRUCTING JUSTICE,APARTMENT,true,false,0835,008,18,66,24,1157945,1855601,2013,06/13/2013 10:52:15 AM,41.759515361,-87.696701762,"(41.759515361, -87.696701762)"
-9171032,HW315703,06/12/2013 04:30:00 PM,069XX S SANGAMON ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0733,007,17,68,06,1171217,1858668,2013,06/13/2013 07:08:10 AM,41.767651767,-87.647970546,"(41.767651767, -87.647970546)"
-9170962,HW315686,06/11/2013 10:00:00 PM,069XX N OTTAWA AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1611,016,41,9,06,1124454,1945564,2013,06/13/2013 10:07:18 AM,42.007002188,-87.817466529,"(42.007002188, -87.817466529)"
-9167610,HW313022,06/10/2013 06:00:00 AM,067XX N NEWGARD AVE,0810,THEFT,OVER $500,RESIDENCE,false,false,2432,024,40,1,06,1165416,1944842,2013,06/11/2013 03:39:55 PM,42.004244788,-87.666780311,"(42.004244788, -87.666780311)"
-9163290,HW308086,06/06/2013 02:00:00 PM,036XX W GRENSHAW ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,1133,011,24,29,26,1152089,1894753,2013,06/10/2013 02:47:20 PM,41.867070814,-87.7171352,"(41.867070814, -87.7171352)"
-9162152,HW306814,06/06/2013 11:50:00 AM,005XX W HARRISON ST,0560,ASSAULT,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),false,false,0124,001,2,28,08A,1172629,1897533,2013,06/12/2013 10:00:18 AM,41.874269995,-87.64164815,"(41.874269995, -87.64164815)"
-9162380,HW307138,06/06/2013 07:00:00 AM,050XX S DR MARTIN LUTHER KING JR DR,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0223,002,3,38,14,1179753,1871765,2013,06/07/2013 11:15:20 AM,41.803400285,-87.616282185,"(41.803400285, -87.616282185)"
-9160245,HW305229,06/04/2013 10:58:00 AM,001XX W ROOSEVELT RD,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0123,001,2,32,06,1175197,1895070,2013,06/05/2013 01:18:16 PM,41.867454191,-87.632293564,"(41.867454191, -87.632293564)"
-9158235,HW303611,06/03/2013 05:37:00 PM,008XX W NORTH AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,SMALL RETAIL STORE,false,false,1822,018,43,8,11,1170686,1910833,2013,06/06/2013 09:09:48 AM,41.910808784,-87.648392103,"(41.910808784, -87.648392103)"
-9156124,HW301954,06/03/2013 05:34:00 AM,064XX S LECLAIRE AVE,0460,BATTERY,SIMPLE,RESIDENCE,true,false,0813,008,13,64,08B,1143611,1861564,2013,06/14/2014 12:41:49 PM,41.776157974,-87.749087922,"(41.776157974, -87.749087922)"
-9160865,HW304695,06/02/2013 03:50:00 PM,063XX S HALSTED ST,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,RESIDENCE,false,true,0723,007,20,68,20,1172012,1863016,2013,06/23/2013 02:13:39 PM,41.779565774,-87.644928954,"(41.779565774, -87.644928954)"
-9154075,HW299357,06/01/2013 01:55:00 AM,014XX N WESTERN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1423,014,1,24,08B,1160104,1909656,2013,06/05/2013 02:08:47 PM,41.90780431,-87.687299081,"(41.90780431, -87.687299081)"
-9156958,HW299827,05/31/2013 07:00:00 AM,072XX S MICHIGAN AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0323,003,6,69,05,1178460,1856858,2013,06/29/2013 09:45:53 AM,41.762523488,-87.621476808,"(41.762523488, -87.621476808)"
-9150070,HW295819,05/29/2013 06:05:00 PM,033XX W 61ST PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,0823,008,15,66,08B,1155382,1863614,2013,05/30/2013 09:23:57 AM,41.781555929,-87.705880943,"(41.781555929, -87.705880943)"
-9150142,HW295826,05/29/2013 05:50:00 PM,025XX N LONG AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,true,false,2515,025,31,19,24,1139985,1916603,2013,05/30/2013 07:38:43 AM,41.927259668,-87.761036097,"(41.927259668, -87.761036097)"
-9149287,HW295100,05/28/2013 04:10:00 PM,083XX S ASHLAND AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0614,006,18,71,14,1167080,1849613,2013,11/08/2013 08:05:05 AM,41.742893061,-87.663392947,"(41.742893061, -87.663392947)"
-9146439,HW292102,05/26/2013 08:00:00 PM,013XX W TAYLOR ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1231,012,2,28,05,1167641,1895666,2013,06/05/2013 03:27:41 PM,41.869255639,-87.660015494,"(41.869255639, -87.660015494)"
-9146120,HW291662,05/26/2013 02:25:00 PM,011XX S STATE ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,CTA TRAIN,true,false,0123,001,2,32,24,1176558,1895307,2013,06/14/2014 12:41:49 PM,41.868073926,-87.627290019,"(41.868073926, -87.627290019)"
-9154481,HW299716,05/25/2013 02:00:00 PM,084XX S BRANDON AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0424,004,10,46,05,1198983,1849794,2013,06/13/2013 06:52:12 AM,41.742648719,-87.546495178,"(41.742648719, -87.546495178)"
-9144290,HW289353,05/24/2013 04:45:00 PM,048XX S LAMON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0814,008,23,56,08B,1144554,1871928,2013,05/27/2013 02:55:59 PM,41.804580858,-87.745371057,"(41.804580858, -87.745371057)"
-9143805,HW288615,05/24/2013 09:00:00 AM,065XX S MOZART ST,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0831,008,15,66,03,1158553,1860816,2013,07/29/2013 09:50:13 AM,41.77381373,-87.694331445,"(41.77381373, -87.694331445)"
-9142866,HW287719,05/23/2013 03:00:00 PM,068XX S PERRY AVE,0820,THEFT,$500 AND UNDER,APARTMENT,false,true,0722,007,6,69,06,1176536,1859701,2013,05/15/2014 12:36:49 PM,41.770368475,-87.62844312,"(41.770368475, -87.62844312)"
-9141631,HW286473,05/22/2013 03:30:00 PM,063XX S DR MARTIN LUTHER KING JR DR,1310,CRIMINAL DAMAGE,TO PROPERTY,CHA PARKING LOT/GROUNDS,false,false,0312,003,20,69,14,1179976,1862780,2013,06/18/2013 05:06:54 PM,41.778739503,-87.615739391,"(41.778739503, -87.615739391)"
-9140376,HW285731,05/22/2013 09:55:00 AM,064XX N RIDGE BLVD,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,true,2412,024,50,2,26,1162639,1942586,2013,05/23/2013 02:49:19 PM,41.998113133,-87.677060522,"(41.998113133, -87.677060522)"
-9140699,HW285570,05/21/2013 10:00:00 PM,027XX W ROOSEVELT RD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1135,011,28,29,08B,1157985,1894634,2013,05/24/2013 02:10:56 PM,41.866626091,-87.695493353,"(41.866626091, -87.695493353)"
-9150666,HW296406,05/21/2013 07:59:00 PM,008XX S WOOD ST,0890,THEFT,FROM BUILDING,HOSPITAL BUILDING/GROUNDS,false,false,1231,012,2,28,06,1164480,1896066,2013,06/09/2013 09:45:02 AM,41.870420725,-87.671608993,"(41.870420725, -87.671608993)"
-9139399,HW284567,05/21/2013 02:30:00 PM,062XX N TROY ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2413,024,50,2,26,1154260,1941488,2013,05/30/2013 01:08:21 PM,41.995272253,-87.707913591,"(41.995272253, -87.707913591)"
-9200265,HW345507,05/21/2013 12:01:00 AM,061XX S TROY ST,0850,THEFT,ATTEMPT THEFT,RESIDENCE,false,false,0823,008,15,66,06,1156485,1863563,2013,07/03/2013 07:51:42 AM,41.78139382,-87.701838422,"(41.78139382, -87.701838422)"
-9136040,HW281044,05/19/2013 04:30:00 AM,042XX W DIVISION ST,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,false,false,2534,025,37,23,04B,1148092,1907707,2013,05/22/2013 12:52:57 PM,41.902695832,-87.731475322,"(41.902695832, -87.731475322)"
-9138856,HW284161,05/19/2013 02:00:00 AM,010XX W BELMONT AVE,0890,THEFT,FROM BUILDING,BAR OR TAVERN,false,false,1924,019,44,6,06,1168911,1921462,2013,05/23/2013 03:31:36 PM,41.940014015,-87.6546037,"(41.940014015, -87.6546037)"
-9136464,HW281646,05/18/2013 02:00:00 PM,105XX S OGLESBY AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0434,004,10,51,14,1193818,1835718,2013,05/20/2013 07:59:39 AM,41.704150888,-87.565879225,"(41.704150888, -87.565879225)"
-9137395,HW282668,05/18/2013 12:30:00 PM,040XX E 106TH ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0432,004,10,52,07,1204462,1835244,2013,07/28/2013 07:05:06 PM,41.70258329,-87.526919961,"(41.70258329, -87.526919961)"
-9138860,HW284195,05/18/2013 11:38:00 AM,036XX N BROADWAY,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,BANK,false,false,1925,019,46,6,11,1170904,1924204,2013,05/22/2013 08:05:07 AM,41.947494633,-87.647198177,"(41.947494633, -87.647198177)"
-9159962,HW279894,05/18/2013 10:50:00 AM,079XX S HALSTED ST,1821,NARCOTICS,MANU/DEL:CANNABIS 10GM OR LESS,STREET,true,false,0621,006,17,71,18,1172303,1852301,2013,07/11/2013 02:15:23 PM,41.750156108,-87.644176845,"(41.750156108, -87.644176845)"
-9134618,HW278767,05/17/2013 12:45:00 PM,030XX N MANGO AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,2514,025,30,19,08B,1137481,1919428,2013,05/19/2013 06:52:55 AM,41.935057267,-87.770169215,"(41.935057267, -87.770169215)"
-9134160,HW278200,05/17/2013 02:30:00 AM,027XX W POLK ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1135,011,2,27,06,1158078,1896294,2013,05/20/2013 09:49:28 AM,41.871179401,-87.695106631,"(41.871179401, -87.695106631)"
-9213479,HW359690,05/17/2013 12:01:00 AM,018XX W ROSCOE ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,1922,019,32,5,06,1163583,1922647,2013,07/15/2013 11:50:47 AM,41.943379809,-87.674152237,"(41.943379809, -87.674152237)"
-9129516,HW274386,05/14/2013 06:30:00 PM,022XX W 18TH ST,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,OTHER,false,false,1234,012,25,31,26,1161699,1891330,2013,05/20/2013 10:29:27 AM,41.857483062,-87.681950871,"(41.857483062, -87.681950871)"
-9129370,HW272824,05/13/2013 04:58:00 PM,075XX S SANGAMON ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0621,006,17,71,26,1171244,1854744,2013,06/11/2013 08:01:06 AM,41.756883228,-87.647986174,"(41.756883228, -87.647986174)"
-9128054,HW272670,05/13/2013 04:20:00 PM,003XX S KEDZIE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,CTA BUS,false,false,1134,011,28,27,14,1155052,1898458,2013,05/14/2013 07:17:19 AM,41.877178845,-87.706158189,"(41.877178845, -87.706158189)"
-9136086,HW272284,05/13/2013 11:00:00 AM,047XX S UNION AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,"SCHOOL, PUBLIC, BUILDING",false,false,0935,009,11,61,26,1172475,1873205,2013,05/19/2013 10:59:42 AM,41.807515304,-87.642931557,"(41.807515304, -87.642931557)"
-9127449,HW272097,05/12/2013 03:30:00 PM,123XX S LA SALLE ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENTIAL YARD (FRONT/BACK),false,false,0523,005,9,53,14,1177537,1823178,2013,05/14/2013 06:39:38 AM,41.670121952,-87.625874034,"(41.670121952, -87.625874034)"
-9131698,HW276693,05/10/2013 12:00:00 PM,022XX E 68TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0331,003,5,43,14,1192169,1860273,2013,05/16/2013 10:38:03 AM,41.771572208,-87.571121124,"(41.771572208, -87.571121124)"
-9123045,HW267491,05/09/2013 04:24:00 PM,007XX W 79TH ST,051A,ASSAULT,AGGRAVATED: HANDGUN,STREET,false,false,0621,006,17,71,04A,1172637,1852527,2013,06/23/2013 09:50:30 PM,41.750768934,-87.642946278,"(41.750768934, -87.642946278)"
-9120662,HW265424,05/08/2013 11:40:00 AM,013XX S ASHLAND AVE,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",true,false,1233,012,2,28,08B,1165893,1893995,2013,05/08/2013 02:18:35 PM,41.864707716,-87.666480488,"(41.864707716, -87.666480488)"
-9116986,HW262048,05/06/2013 12:15:00 AM,068XX S LANGLEY AVE,0261,CRIM SEXUAL ASSAULT,AGGRAVATED: HANDGUN,ALLEY,false,false,0321,003,6,42,02,1182024,1860012,2013,06/29/2013 04:35:59 PM,41.771096702,-87.608316896,"(41.771096702, -87.608316896)"
-9116423,HW261455,05/05/2013 11:05:00 AM,009XX N MICHIGAN AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,1833,018,42,8,06,1177285,1906688,2013,05/06/2013 12:35:12 PM,41.899287561,-87.624275876,"(41.899287561, -87.624275876)"
-9115988,HW260851,05/05/2013 02:14:00 AM,070XX S PARNELL AVE,0560,ASSAULT,SIMPLE,RESIDENCE,true,true,0732,007,6,68,08A,1173791,1858424,2013,05/05/2013 06:20:42 AM,41.766925532,-87.638542952,"(41.766925532, -87.638542952)"
-9115437,HW260187,05/04/2013 03:40:00 PM,009XX W 65TH ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,0723,007,17,68,18,1171290,1861695,2013,05/04/2013 04:30:52 PM,41.775956625,-87.647614506,"(41.775956625, -87.647614506)"
-9114984,HW259600,05/04/2013 04:45:00 AM,042XX W CONGRESS PKWY,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,1132,011,24,26,03,1148404,1897400,2013,05/08/2013 03:38:38 PM,41.874406282,-87.730595213,"(41.874406282, -87.730595213)"
-9115014,HW259633,05/04/2013 01:00:00 AM,060XX S MAY ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0712,007,16,68,08B,1169725,1864741,2013,05/21/2013 02:21:15 PM,41.784349315,-87.653263346,"(41.784349315, -87.653263346)"
-9112219,HW256823,05/01/2013 10:00:00 PM,071XX S RIDGELAND AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0324,003,5,43,05,1189062,1858051,2013,05/11/2013 09:43:03 AM,41.765549852,-87.582581251,"(41.765549852, -87.582581251)"
-9110002,HW254930,04/30/2013 10:55:00 PM,050XX W IRVING PARK RD,0560,ASSAULT,SIMPLE,RESTAURANT,true,false,1624,016,45,15,08A,1142279,1926169,2013,05/01/2013 07:12:52 AM,41.953467362,-87.752368259,"(41.953467362, -87.752368259)"
-9109890,HW254690,04/30/2013 07:26:00 PM,024XX W 72ND ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,true,false,0832,008,18,66,15,1161389,1856805,2013,05/01/2013 02:49:07 PM,41.762748679,-87.684046188,"(41.762748679, -87.684046188)"
-9108423,HW252877,04/29/2013 03:00:00 PM,001XX N DEARBORN ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,0111,001,42,32,06,1175967,1901030,2013,04/30/2013 10:03:02 AM,41.883791501,-87.629287328,"(41.883791501, -87.629287328)"
-9107683,HW252437,04/27/2013 09:00:00 PM,032XX N LAKE SHORE DR SB,0486,BATTERY,DOMESTIC BATTERY SIMPLE,VEHICLE NON-COMMERCIAL,false,true,1925,019,44,6,08B,1173234,1921685,2013,05/06/2013 01:48:49 PM,41.940530912,-87.638708797,"(41.940530912, -87.638708797)"
-9105741,HW250287,04/27/2013 02:35:00 PM,092XX S MICHIGAN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0634,006,6,49,08B,1178744,1843430,2013,05/13/2013 07:43:13 PM,41.725669014,-87.620843411,"(41.725669014, -87.620843411)"
-9105987,HW250437,04/27/2013 01:00:00 PM,066XX S ROCKWELL ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0831,008,15,66,05,1160150,1860287,2013,07/21/2013 01:00:33 PM,41.772329368,-87.688491674,"(41.772329368, -87.688491674)"
-9102244,HW246692,04/25/2013 01:15:00 AM,005XX S STATE ST,0460,BATTERY,SIMPLE,COLLEGE/UNIVERSITY RESIDENCE HALL,false,false,0123,001,2,32,08B,1176487,1897892,2013,07/24/2013 08:50:59 AM,41.875168926,-87.627472632,"(41.875168926, -87.627472632)"
-9102820,HW246613,04/24/2013 11:45:00 PM,091XX S STONY ISLAND AVE,0560,ASSAULT,SIMPLE,GAS STATION,false,false,0413,004,8,48,08A,1188557,1844370,2013,05/05/2013 03:13:45 PM,41.728019969,-87.58486839,"(41.728019969, -87.58486839)"
-9101969,HW246211,04/24/2013 05:50:00 PM,051XX S MORGAN ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0934,009,20,61,08B,1170555,1870817,2013,04/25/2013 01:21:22 PM,41.801004486,-87.650043184,"(41.801004486, -87.650043184)"
-9101721,HW245973,04/24/2013 03:30:00 PM,064XX S DR MARTIN LUTHER KING JR DR,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0312,003,20,69,08B,1179988,1862397,2013,05/06/2013 02:15:52 PM,41.777688239,-87.615707116,"(41.777688239, -87.615707116)"
-9099852,HW244419,04/23/2013 11:45:00 AM,070XX S DAMEN AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,0735,007,17,67,06,1164286,1857782,2013,04/24/2013 06:38:15 AM,41.765369206,-87.673400694,"(41.765369206, -87.673400694)"
-9097276,HW242018,04/21/2013 06:00:00 PM,045XX W NORTH AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,2533,025,37,23,06,1145935,1910208,2013,04/22/2013 06:26:20 AM,41.909600093,-87.739334828,"(41.909600093, -87.739334828)"
-9124498,HW242997,04/21/2013 01:49:00 PM,031XX S HALSTED ST,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE,false,false,0913,009,11,60,14,1171440,1884079,2013,05/11/2013 08:12:44 AM,41.837377356,-87.64640877,"(41.837377356, -87.64640877)"
-9096188,HW240498,04/20/2013 12:10:00 PM,019XX E 71ST ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0332,003,5,43,18,1190377,1858351,2013,04/20/2013 01:54:55 PM,41.766341479,-87.577751774,"(41.766341479, -87.577751774)"
-9096034,HW240298,04/20/2013 09:45:00 AM,061XX S CAMPBELL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0825,008,15,66,08B,1160804,1863629,2013,04/24/2013 06:55:44 AM,41.781486802,-87.686002068,"(41.781486802, -87.686002068)"
-9094519,HW238821,04/19/2013 07:00:00 AM,008XX E 82ND ST,1365,CRIMINAL TRESPASS,TO RESIDENCE,APARTMENT,true,false,0631,006,8,44,26,1183188,1850845,2013,04/20/2013 05:37:53 AM,41.745914519,-87.604334893,"(41.745914519, -87.604334893)"
-9095079,HW238865,04/18/2013 07:15:00 PM,040XX W MONROE ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1115,011,28,26,06,1149638,1899319,2013,04/21/2013 07:41:23 AM,41.879648367,-87.72601462,"(41.879648367, -87.72601462)"
-9093809,HW238106,04/18/2013 03:50:00 PM,004XX S CENTRAL PARK AVE,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,STREET,false,true,1133,011,28,27,04A,1152411,1897881,2013,05/01/2013 02:27:46 PM,41.875648042,-87.715870482,"(41.875648042, -87.715870482)"
-9092071,HW236897,04/17/2013 02:45:00 PM,038XX N CALIFORNIA AVE,2850,PUBLIC PEACE VIOLATION,BOMB THREAT,PARK PROPERTY,false,false,1733,017,33,16,26,1157066,1925582,2013,05/16/2013 02:26:15 PM,41.951568663,-87.698025611,"(41.951568663, -87.698025611)"
-9089544,HW234630,04/15/2013 09:40:00 PM,035XX N RACINE AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE PORCH/HALLWAY,true,false,1924,019,44,6,26,1167694,1923791,2013,04/16/2013 06:46:55 AM,41.94643125,-87.659009199,"(41.94643125, -87.659009199)"
-9089553,HW234605,04/15/2013 09:15:00 PM,003XX S CICERO AVE,1330,CRIMINAL TRESPASS,TO LAND,GAS STATION,true,false,1533,015,24,25,26,1144396,1898214,2013,04/16/2013 06:41:58 AM,41.876716289,-87.745290492,"(41.876716289, -87.745290492)"
-9093500,HW237885,04/15/2013 12:00:00 AM,073XX S DORCHESTER AVE,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,APARTMENT,false,false,0324,003,5,43,11,1186858,1856368,2013,04/19/2013 09:37:46 AM,41.760984051,-87.590712758,"(41.760984051, -87.590712758)"
-9087954,HW232859,04/14/2013 01:20:00 PM,047XX S ASHLAND AVE,0810,THEFT,OVER $500,STREET,false,false,0931,009,20,61,06,1166424,1873487,2013,04/15/2013 11:38:43 AM,41.808420388,-87.665116861,"(41.808420388, -87.665116861)"
-9086602,HW231156,04/12/2013 06:30:00 PM,001XX S SANGAMON ST,0810,THEFT,OVER $500,STREET,false,false,1232,012,2,28,06,1170081,1899476,2013,04/14/2013 01:55:56 PM,41.879657691,-87.650946469,"(41.879657691, -87.650946469)"
-9087178,HW231905,04/12/2013 05:00:00 PM,035XX N KOSTNER AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1731,017,30,16,26,1146463,1923263,2013,04/17/2013 11:06:35 AM,41.945414194,-87.737061654,"(41.945414194, -87.737061654)"
-9085913,HW230261,04/12/2013 04:16:00 PM,103XX S MICHIGAN AVE,2170,NARCOTICS,POSSESSION OF DRUG EQUIPMENT,CTA BUS STOP,true,false,0512,005,9,49,18,1178930,1836678,2013,04/12/2013 05:31:34 PM,41.707136412,-87.620366899,"(41.707136412, -87.620366899)"
-9084425,HW228700,04/11/2013 02:00:00 PM,033XX W 25TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1024,010,22,30,18,1154461,1887233,2013,04/11/2013 04:36:22 PM,41.846388028,-87.708628079,"(41.846388028, -87.708628079)"
-9083647,HW227107,04/09/2013 06:00:00 PM,0000X E 75TH ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,DRUG STORE,false,false,0623,006,6,69,11,1177736,1855240,2013,04/12/2013 07:40:30 AM,41.758099925,-87.624179245,"(41.758099925, -87.624179245)"
-9080749,HW225436,04/09/2013 07:00:00 AM,049XX S KEDVALE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,RESIDENCE,true,false,0815,008,14,57,07,1149569,1871599,2013,04/17/2013 02:31:32 PM,41.803582419,-87.726986553,"(41.803582419, -87.726986553)"
-9089099,HW233021,04/08/2013 08:30:00 PM,026XX N MELVINA AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,2512,025,29,19,08A,1134574,1917167,2013,04/25/2013 10:19:11 AM,41.928904731,-87.780906258,"(41.928904731, -87.780906258)"
-9080856,HW225580,04/08/2013 07:00:00 PM,072XX S EAST END AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0324,003,8,43,08B,1188748,1857345,2013,04/12/2013 12:15:57 PM,41.763620042,-87.583754694,"(41.763620042, -87.583754694)"
-9080119,HW224505,04/08/2013 01:45:00 PM,132XX S INDIANA AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0533,005,9,54,07,1179955,1817599,2013,06/07/2013 01:54:36 PM,41.654757495,-87.617194112,"(41.654757495, -87.617194112)"
-9079746,HW224647,04/08/2013 08:00:00 AM,083XX S RHODES AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENTIAL YARD (FRONT/BACK),false,false,0632,006,6,44,14,1181219,1849481,2013,04/09/2013 07:02:44 AM,41.742217098,-87.611591562,"(41.742217098, -87.611591562)"
-9079183,HW223969,04/08/2013 07:20:00 AM,050XX S CORNELL AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,GAS STATION,false,false,0222,002,4,39,14,1187850,1871631,2013,04/08/2013 12:41:19 PM,41.802843379,-87.586591273,"(41.802843379, -87.586591273)"
-9078158,HW223047,04/07/2013 12:30:00 AM,0000X W DIVISION ST,0890,THEFT,FROM BUILDING,BAR OR TAVERN,false,false,1824,018,42,8,06,1175957,1908331,2013,04/07/2013 02:24:22 PM,41.903826047,-87.629103999,"(41.903826047, -87.629103999)"
-9077116,HW221868,04/06/2013 12:05:00 PM,032XX W NORTH AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1422,014,26,23,05,1154215,1910485,2013,05/05/2013 11:37:00 AM,41.910198831,-87.70891005,"(41.910198831, -87.70891005)"
-9076982,HW221682,04/05/2013 07:00:00 PM,057XX S KENWOOD AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0235,002,5,41,14,1186123,1867281,2013,04/06/2013 12:05:08 PM,41.790947645,-87.593062248,"(41.790947645, -87.593062248)"
-9076211,HW220435,04/05/2013 11:15:00 AM,024XX E 79TH ST,0810,THEFT,OVER $500,RESTAURANT,false,false,0422,004,7,46,06,1193846,1853022,2013,04/16/2013 01:20:46 AM,41.751633969,-87.565211265,"(41.751633969, -87.565211265)"
-9081081,HW225549,04/05/2013 11:00:00 AM,038XX W LEXINGTON ST,0810,THEFT,OVER $500,APARTMENT,false,false,1133,011,24,26,06,1150627,1896456,2013,04/10/2013 08:34:07 AM,41.871772713,-87.722457947,"(41.871772713, -87.722457947)"
-9074727,HW219477,04/04/2013 02:45:00 PM,010XX N LARAMIE AVE,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,"SCHOOL, PUBLIC, BUILDING",false,false,1524,015,37,25,20,1141465,1906395,2013,04/21/2013 12:34:53 PM,41.899220555,-87.755850198,"(41.899220555, -87.755850198)"
-9074623,HW219388,04/03/2013 04:30:00 PM,005XX W SUPERIOR ST,1320,CRIMINAL DAMAGE,TO VEHICLE,RESIDENCE-GARAGE,false,false,1831,018,42,8,14,1172377,1905224,2013,04/04/2013 06:27:22 PM,41.895380157,-87.642346001,"(41.895380157, -87.642346001)"
-9071659,HW216846,04/02/2013 10:00:00 AM,027XX S KOSTNER AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE-GARAGE,false,false,1031,010,22,30,14,1147538,1885623,2013,04/03/2013 07:40:25 AM,41.842105356,-87.734076604,"(41.842105356, -87.734076604)"
-9070533,HW215888,04/02/2013 08:05:00 AM,021XX W CORTEZ ST,0810,THEFT,OVER $500,STREET,true,false,1212,012,32,24,06,1161650,1907002,2013,05/13/2013 09:38:40 AM,41.900489439,-87.681693959,"(41.900489439, -87.681693959)"
-9090581,HW235594,04/02/2013 07:00:00 AM,009XX N LEAMINGTON AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1531,015,37,25,05,1141890,1905919,2013,05/14/2013 11:14:36 AM,41.897906495,-87.754300962,"(41.897906495, -87.754300962)"
-9070013,HW215174,04/01/2013 03:50:00 PM,063XX S MORGAN ST,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,0724,007,16,68,06,1170688,1862978,2013,04/02/2013 06:39:25 AM,41.779490481,-87.649783997,"(41.779490481, -87.649783997)"
-9071172,HW216235,03/31/2013 10:00:00 AM,043XX S WENTWORTH AVE,1121,DECEPTIVE PRACTICE,COUNTERFEITING DOCUMENT,STREET,false,false,0925,009,3,37,10,1175609,1876459,2013,04/26/2013 10:39:54 AM,41.81637492,-87.631339504,"(41.81637492, -87.631339504)"
-9068088,HW213295,03/31/2013 12:19:00 AM,025XX N SPRINGFIELD AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2524,025,30,22,18,1149920,1916406,2013,03/31/2013 02:49:30 AM,41.926531317,-87.724533886,"(41.926531317, -87.724533886)"
-9066571,HW211257,03/29/2013 02:00:00 PM,062XX N CLAREMONT AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2413,024,50,2,06,1159557,1941410,2013,03/31/2013 09:11:26 AM,41.994950405,-87.688430729,"(41.994950405, -87.688430729)"
-9061084,HW206073,03/25/2013 01:00:00 PM,003XX E 47TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0215,002,3,38,18,1179017,1873966,2013,03/25/2013 02:03:45 PM,41.809456838,-87.618914329,"(41.809456838, -87.618914329)"
-9077264,HW222126,03/25/2013 09:00:00 AM,088XX S LUELLA AVE,0810,THEFT,OVER $500,RESIDENCE,false,true,0412,004,8,48,06,1192749,1846725,2013,04/24/2013 06:37:23 PM,41.734381257,-87.56943602,"(41.734381257, -87.56943602)"
-9083816,HW228168,03/25/2013 01:00:00 AM,0000X W DIVISION ST,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,OTHER,false,false,1824,018,42,8,11,1175751,1908405,2013,04/12/2013 12:17:46 PM,41.904033744,-87.629858449,"(41.904033744, -87.629858449)"
-9060406,HW205350,03/24/2013 06:47:00 PM,069XX N GREENVIEW AVE,0820,THEFT,$500 AND UNDER,STREET,true,false,2431,024,49,1,06,1165164,1945801,2013,03/26/2013 08:31:31 AM,42.006881683,-87.667679987,"(42.006881683, -87.667679987)"
-9059825,HW204857,03/24/2013 11:00:00 AM,024XX W GLADYS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1125,011,2,28,08B,1160283,1898322,2013,03/28/2013 02:41:23 PM,41.87669915,-87.686955179,"(41.87669915, -87.686955179)"
-9059326,HW204214,03/23/2013 10:00:00 AM,103XX S AVENUE G,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,true,0432,004,10,52,05,1203112,1836868,2013,03/25/2013 06:51:56 PM,41.707074259,-87.531807828,"(41.707074259, -87.531807828)"
-9071447,HW216570,03/22/2013 03:10:00 PM,033XX W 71ST ST,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0831,008,18,66,08A,1155297,1857375,2013,04/03/2013 11:33:22 AM,41.764436863,-87.70635933,"(41.764436863, -87.70635933)"
-9054964,HW199729,03/20/2013 11:31:00 AM,057XX S ADA ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0713,007,16,67,18,1168276,1866364,2013,03/20/2013 01:31:09 PM,41.788834348,-87.658529247,"(41.788834348, -87.658529247)"
-9054429,HW196935,03/18/2013 09:29:00 AM,048XX S PAULINA ST,1570,SEX OFFENSE,PUBLIC INDECENCY,RESIDENCE,false,false,0931,009,20,61,17,1165781,1872809,2013,04/02/2013 02:20:52 AM,41.806573572,-87.667494505,"(41.806573572, -87.667494505)"
-9051424,HW196816,03/18/2013 08:50:00 AM,026XX N HOYNE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA APARTMENT,false,true,1931,019,1,7,08B,1162397,1917830,2013,03/21/2013 11:14:38 AM,41.930186621,-87.678646594,"(41.930186621, -87.678646594)"
-9049988,HW195184,03/16/2013 08:05:00 PM,011XX W ADDISON ST,0460,BATTERY,SIMPLE,SIDEWALK,true,false,1924,019,44,6,08B,1168041,1924023,2013,03/17/2013 11:23:09 AM,41.94706037,-87.657727021,"(41.94706037, -87.657727021)"
-9048547,HW193160,03/15/2013 12:13:00 PM,001XX W VAN BUREN ST,0460,BATTERY,SIMPLE,SIDEWALK,true,false,0122,001,2,32,08B,1175483,1898467,2013,03/16/2013 09:11:20 AM,41.876769359,-87.631141605,"(41.876769359, -87.631141605)"
-9048620,HW193267,03/15/2013 12:00:00 PM,006XX W 35TH ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0915,009,11,60,26,1172360,1881726,2013,03/23/2013 02:22:03 PM,41.830900277,-87.643102292,"(41.830900277, -87.643102292)"
-9047753,HW192045,03/14/2013 02:48:00 PM,029XX E 78TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0421,004,7,43,14,1196937,1853930,2013,03/15/2013 06:06:48 AM,41.754049303,-87.553854304,"(41.754049303, -87.553854304)"
-9046903,HW191731,03/14/2013 12:00:00 PM,029XX N CALIFORNIA AVE,0810,THEFT,OVER $500,OTHER,false,false,1411,014,1,21,06,1157241,1919449,2013,03/15/2013 01:49:09 PM,41.934735743,-87.697549531,"(41.934735743, -87.697549531)"
-9044518,HW190109,03/13/2013 08:15:00 AM,008XX N STATE ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,true,false,1832,018,42,8,11,1176184,1905758,2013,03/13/2013 10:46:18 AM,41.896760495,-87.62834785,"(41.896760495, -87.62834785)"
-9043531,HW189099,03/12/2013 01:00:00 PM,075XX S MAY ST,1330,CRIMINAL TRESPASS,TO LAND,CONVENIENCE STORE,true,false,0612,006,17,71,26,1169927,1854496,2013,03/13/2013 06:04:50 AM,41.756231373,-87.652819939,"(41.756231373, -87.652819939)"
-9042054,HW188077,03/11/2013 02:00:00 PM,016XX W 95TH ST,0820,THEFT,$500 AND UNDER,RESTAURANT,false,false,2221,022,19,72,06,1166876,1841743,2013,03/12/2013 06:46:06 AM,41.721300961,-87.664364532,"(41.721300961, -87.664364532)"
-9039993,HW186374,03/09/2013 08:45:00 PM,015XX W MORSE AVE,0810,THEFT,OVER $500,STREET,false,false,2431,024,49,1,06,1164714,1946111,2013,03/11/2013 07:28:47 AM,42.007741918,-87.669326759,"(42.007741918, -87.669326759)"
-9049578,HW194495,03/09/2013 10:00:00 AM,069XX S CLYDE AVE,0610,BURGLARY,FORCIBLE ENTRY,VACANT LOT/LAND,false,false,0331,003,5,43,05,1191378,1859098,2013,03/26/2013 04:26:09 PM,41.768367118,-87.574058636,"(41.768367118, -87.574058636)"
-9038460,HW184610,03/08/2013 08:59:00 PM,096XX S HALSTED ST,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,STREET,true,false,2223,022,21,73,18,1172638,1840858,2013,03/08/2013 10:30:11 PM,41.718747589,-87.643285453,"(41.718747589, -87.643285453)"
-9038211,HW184346,03/08/2013 05:40:00 PM,019XX N MILWAUKEE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1434,014,1,22,14,1160282,1912690,2013,03/10/2013 09:19:08 AM,41.916126154,-87.686561159,"(41.916126154, -87.686561159)"
-9037585,HW183828,03/08/2013 11:25:00 AM,065XX S FAIRFIELD AVE,0454,BATTERY,AGG PO HANDS NO/MIN INJURY,STREET,true,false,0831,008,15,66,08B,1159125,1861348,2013,03/09/2013 03:12:31 PM,41.775261933,-87.692220057,"(41.775261933, -87.692220057)"
-9052012,HW197372,03/08/2013 09:00:00 AM,038XX W 82ND PL,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,STREET,false,false,0834,008,18,70,06,1152355,1849562,2013,03/19/2013 11:09:20 AM,41.743055023,-87.717347677,"(41.743055023, -87.717347677)"
-9034777,HW181955,03/06/2013 09:30:00 PM,049XX W NORTH AVE,0820,THEFT,$500 AND UNDER,ATHLETIC CLUB,false,false,2533,025,37,25,06,1143341,1910146,2013,03/07/2013 07:09:42 AM,41.909478858,-87.748865737,"(41.909478858, -87.748865737)"
-9034845,HW182127,03/06/2013 03:00:00 PM,039XX S WESTERN AVE,0810,THEFT,OVER $500,STREET,false,false,0921,009,12,58,06,1160989,1878147,2013,03/07/2013 09:58:33 AM,41.821322222,-87.68492229,"(41.821322222, -87.68492229)"
-9032370,HW180288,03/05/2013 04:07:00 PM,036XX W CONGRESS PKWY,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1133,011,28,27,18,1152343,1897501,2013,03/05/2013 06:04:36 PM,41.874606623,-87.716130188,"(41.874606623, -87.716130188)"
-9032230,HW180137,03/05/2013 01:50:00 PM,021XX E 83RD ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,0412,004,8,46,08B,1192261,1850330,2013,03/16/2013 09:27:22 AM,41.744285573,-87.571106823,"(41.744285573, -87.571106823)"
-9031719,HW179778,03/04/2013 03:30:00 PM,046XX S WASHTENAW AVE,0890,THEFT,FROM BUILDING,ABANDONED BUILDING,false,false,0922,009,12,58,06,1159193,1873606,2013,03/05/2013 11:19:53 AM,41.808898157,-87.691635362,"(41.808898157, -87.691635362)"
-9029384,HW176994,03/02/2013 09:00:00 PM,019XX W MADISON ST,0460,BATTERY,SIMPLE,OTHER,false,false,1223,012,27,28,08B,1163705,1900003,2013,03/03/2013 07:14:30 PM,41.88124055,-87.674343291,"(41.88124055, -87.674343291)"
-9027267,HW174500,03/01/2013 12:30:00 AM,021XX N CLARK ST,0460,BATTERY,SIMPLE,BAR OR TAVERN,false,false,1814,018,43,7,08B,1173584,1914628,2013,03/07/2013 07:45:30 AM,41.921158441,-87.637632895,"(41.921158441, -87.637632895)"
-9028017,HW175099,02/28/2013 03:00:00 PM,011XX W DIVISION ST,0560,ASSAULT,SIMPLE,WAREHOUSE,true,false,1822,018,32,8,08A,1168614,1908122,2013,03/10/2013 07:38:53 AM,41.903414787,-87.656082473,"(41.903414787, -87.656082473)"
-9034124,HW172770,02/27/2013 04:16:35 PM,012XX W 73RD PL,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,RESIDENCE,true,false,0734,007,17,67,18,1169065,1855997,2013,04/10/2013 11:47:01 AM,41.760368983,-87.655935674,"(41.760368983, -87.655935674)"
-9023328,HW170363,02/25/2013 08:03:00 PM,019XX E 71ST ST,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0333,003,5,43,03,1190310,1858206,2013,03/01/2013 08:33:01 PM,41.765945202,-87.578002016,"(41.765945202, -87.578002016)"
-9022699,HW170319,02/25/2013 06:30:00 PM,003XX S WESTERN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1225,012,2,28,14,1160508,1898174,2013,02/26/2013 08:25:44 AM,41.876288371,-87.686133147,"(41.876288371, -87.686133147)"
-9022889,HW170516,02/25/2013 04:00:00 PM,080XX S CHAPPEL AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0414,004,8,46,05,1191304,1851864,2013,04/05/2013 09:55:57 AM,41.748518214,-87.574563752,"(41.748518214, -87.574563752)"
-9026296,HW173462,02/25/2013 12:00:00 PM,019XX W NORTH AVE,0890,THEFT,FROM BUILDING,ATHLETIC CLUB,false,false,1434,014,32,24,06,1163045,1910702,2013,02/28/2013 12:24:02 PM,41.910613318,-87.676465994,"(41.910613318, -87.676465994)"
-9020820,HW168391,02/24/2013 12:30:00 AM,054XX W EDDY ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1633,016,38,15,14,1139666,1923034,2013,02/25/2013 06:32:32 AM,41.944912826,-87.762050833,"(41.944912826, -87.762050833)"
-9013249,HW161197,02/18/2013 02:00:00 PM,049XX W KAMERLING AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2533,025,37,25,07,1142893,1908481,2013,03/11/2013 01:05:25 PM,41.904918279,-87.75055307,"(41.904918279, -87.75055307)"
-9011044,HW158167,02/15/2013 07:10:00 PM,053XX S KIMBARK AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0234,002,4,41,03,1185604,1870253,2013,03/04/2013 10:43:42 PM,41.799115293,-87.594871663,"(41.799115293, -87.594871663)"
-9058660,HW202916,02/15/2013 03:00:00 PM,030XX W PALMER BLVD,0820,THEFT,$500 AND UNDER,RESIDENCE,false,false,1414,014,35,22,06,1155762,1914653,2013,04/28/2013 08:14:51 AM,41.921605121,-87.703114443,"(41.921605121, -87.703114443)"
-9010117,HW156490,02/14/2013 02:37:00 PM,085XX S COTTAGE GROVE AVE,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,SIDEWALK,false,false,0632,006,6,44,11,1183020,1848266,2013,02/15/2013 02:40:42 PM,41.738841364,-87.605030451,"(41.738841364, -87.605030451)"
-9008368,HW155749,02/13/2013 09:40:00 PM,049XX W WALTON ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,true,false,1531,015,37,25,15,1142993,1905814,2013,02/14/2013 07:35:20 AM,41.897597864,-87.750252322,"(41.897597864, -87.750252322)"
-9006217,HW153678,02/12/2013 08:00:00 AM,076XX W ADDISON ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1631,016,36,17,06,1124228,1923095,2013,02/13/2013 07:36:41 AM,41.945348542,-87.818794589,"(41.945348542, -87.818794589)"
-9007607,HW153414,02/12/2013 07:44:00 AM,068XX S ST LAWRENCE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0321,003,20,42,14,1181376,1859881,2013,02/18/2013 08:44:34 AM,41.770752191,-87.610696241,"(41.770752191, -87.610696241)"
-9004775,HW152149,02/11/2013 07:50:00 AM,069XX S VERNON AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0322,003,6,69,07,1180413,1858861,2013,02/11/2013 10:36:06 AM,41.767975356,-87.614257459,"(41.767975356, -87.614257459)"
-9000744,HW147488,02/06/2013 05:00:00 PM,058XX W WASHINGTON BLVD,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,1513,015,29,25,26,1137493,1900114,2013,02/23/2013 08:14:36 PM,41.882057128,-87.770590787,"(41.882057128, -87.770590787)"
-8998290,HW145657,02/05/2013 10:25:00 PM,080XX S INGLESIDE AVE,2017,NARCOTICS,MANU/DELIVER:CRACK,APARTMENT,true,false,0631,006,8,44,18,1183997,1851981,2013,02/06/2013 12:22:58 AM,41.749012976,-87.601335174,"(41.749012976, -87.601335174)"
-8996938,HW144451,02/04/2013 08:25:00 PM,031XX S PULASKI RD,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,1031,010,22,30,08A,1150163,1883671,2013,03/21/2013 05:24:35 PM,41.836698151,-87.724494316,"(41.836698151, -87.724494316)"
-8996688,HW144024,02/04/2013 05:15:00 PM,121XX S PRINCETON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0523,005,34,53,14,1176435,1824280,2013,02/05/2013 06:35:29 AM,41.67317077,-87.629874301,"(41.67317077, -87.629874301)"
-8996793,HW144123,02/04/2013 07:00:00 AM,079XX S INGLESIDE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0624,006,8,44,03,1183894,1852756,2013,02/15/2013 12:58:07 AM,41.751142061,-87.601688452,"(41.751142061, -87.601688452)"
-8995157,HW142539,02/03/2013 10:34:00 AM,038XX W JACKSON BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1122,011,28,26,18,1150946,1898455,2013,02/03/2013 12:12:29 PM,41.877251956,-87.721234423,"(41.877251956, -87.721234423)"
-8993669,HW140577,02/01/2013 02:56:00 PM,010XX E 47TH ST,0890,THEFT,FROM BUILDING,"SCHOOL, PRIVATE, BUILDING",true,false,0222,002,4,39,06,1184100,1874102,2013,02/02/2013 07:29:10 AM,41.809712569,-87.600266727,"(41.809712569, -87.600266727)"
-8992364,HW139341,01/31/2013 04:05:00 PM,0000X W 95TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,CTA GARAGE / OTHER PROPERTY,true,false,0634,006,21,49,18,1177744,1841988,2013,01/31/2013 06:26:31 PM,41.721734632,-87.624549931,"(41.721734632, -87.624549931)"
-8992227,HW139175,01/31/2013 12:00:00 PM,079XX S ASHLAND AVE,1330,CRIMINAL TRESPASS,TO LAND,PARKING LOT/GARAGE(NON.RESID.),true,false,0611,006,21,71,26,1167010,1852247,2013,02/01/2013 08:10:13 AM,41.750122626,-87.663574309,"(41.750122626, -87.663574309)"
-8990994,HW138279,01/30/2013 08:20:00 PM,052XX S BISHOP ST,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,0934,009,16,61,18,1167598,1869903,2013,01/30/2013 10:00:57 PM,41.798560357,-87.660913739,"(41.798560357, -87.660913739)"
-8990552,HW137681,01/30/2013 01:35:00 PM,095XX S JEFFERY AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,0431,004,7,51,06,1191138,1842112,2013,01/31/2013 05:05:00 AM,41.721761818,-87.575486681,"(41.721761818, -87.575486681)"
-8990681,HW137895,01/30/2013 02:00:00 AM,032XX S HALSTED ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0913,009,11,60,14,1171460,1883306,2013,01/31/2013 07:08:33 AM,41.835255736,-87.646358067,"(41.835255736, -87.646358067)"
-8989530,HW136892,01/29/2013 09:30:00 PM,015XX S HOMAN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1021,010,24,29,08B,1153913,1892140,2013,01/30/2013 03:37:15 PM,41.859864328,-87.710508603,"(41.859864328, -87.710508603)"
-8990672,HW137671,01/29/2013 08:00:00 PM,004XX W 129TH PL,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0523,005,9,53,07,1175628,1818856,2013,02/03/2013 11:31:00 AM,41.65830447,-87.632989292,"(41.65830447, -87.632989292)"
-8986960,HW134560,01/28/2013 11:15:00 AM,044XX W VAN BUREN ST,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,1131,011,24,26,18,1146763,1897692,2013,01/28/2013 12:10:26 PM,41.875239029,-87.736612841,"(41.875239029, -87.736612841)"
-8985714,HW133357,01/27/2013 06:00:00 AM,029XX W SHAKESPEARE AVE,0560,ASSAULT,SIMPLE,RESIDENCE PORCH/HALLWAY,false,true,1414,014,35,22,08A,1156469,1914257,2013,01/30/2013 03:25:18 PM,41.920504179,-87.700527469,"(41.920504179, -87.700527469)"
-8985371,HW132935,01/26/2013 08:57:00 PM,051XX S DREXEL AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0233,002,4,41,18,1183086,1870792,2013,01/26/2013 10:58:23 PM,41.800653342,-87.604088881,"(41.800653342, -87.604088881)"
-8984770,HW132070,01/26/2013 09:00:00 AM,060XX S CARPENTER ST,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,APARTMENT,false,true,0712,007,16,68,26,1170384,1864906,2013,01/28/2013 04:53:39 PM,41.784787766,-87.650842393,"(41.784787766, -87.650842393)"
-8984117,HW131241,01/25/2013 04:34:00 PM,027XX W DEVON AVE,0460,BATTERY,SIMPLE,GROCERY FOOD STORE,false,false,2412,024,50,2,08B,1156696,1942384,2013,01/28/2013 11:34:11 AM,41.997681744,-87.698928329,"(41.997681744, -87.698928329)"
-8978349,HW125975,01/21/2013 02:15:00 PM,046XX W ROSCOE ST,0810,THEFT,OVER $500,STREET,false,false,1731,017,30,15,06,1144678,1922239,2013,01/22/2013 06:21:53 AM,41.942638133,-87.743648613,"(41.942638133, -87.743648613)"
-8981092,HW128360,01/20/2013 07:00:00 PM,011XX N DEARBORN ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1824,018,42,8,06,1175689,1907957,2013,01/24/2013 01:26:08 PM,41.902805806,-87.630099679,"(41.902805806, -87.630099679)"
-8975632,HW122697,01/18/2013 06:40:00 PM,058XX S LAFLIN ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,0713,007,16,67,15,1167373,1866083,2013,01/20/2013 06:11:06 PM,41.788082651,-87.661848281,"(41.788082651, -87.661848281)"
-8970992,HW118789,01/15/2013 07:15:00 PM,0000X W 79TH ST,0340,ROBBERY,ATTEMPT: STRONGARM-NO WEAPON,SIDEWALK,false,false,0623,006,6,69,03,1177642,1852676,2013,02/17/2013 06:09:30 PM,41.751066138,-87.624601153,"(41.751066138, -87.624601153)"
-8970568,HW118275,01/15/2013 12:01:00 AM,071XX S MAY ST,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,0733,007,17,68,08A,1169850,1857322,2013,02/02/2013 06:25:16 AM,41.763987954,-87.653020222,"(41.763987954, -87.653020222)"
-8970587,HW118183,01/15/2013 12:00:00 AM,043XX W AINSLIE ST,0890,THEFT,FROM BUILDING,CHURCH/SYNAGOGUE/PLACE OF WORSHIP,false,false,1712,017,39,14,06,1146191,1932245,2013,01/16/2013 09:43:24 AM,41.97006671,-87.73783201,"(41.97006671, -87.73783201)"
-8969173,HW117366,01/14/2013 06:34:00 PM,005XX E 50TH PL,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE PORCH/HALLWAY,true,false,0223,002,3,38,26,1180678,1871611,2013,01/15/2013 11:04:53 AM,41.80295647,-87.612894532,"(41.80295647, -87.612894532)"
-8963545,HW111688,01/10/2013 01:00:00 AM,008XX E 87TH ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,0632,006,8,44,04B,1183116,1847509,2013,01/19/2013 09:20:22 AM,41.736761842,-87.604702211,"(41.736761842, -87.604702211)"
-8987839,HW134962,01/09/2013 05:00:00 PM,071XX S STONY ISLAND AVE,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,BANK,false,false,0324,003,5,43,11,1188000,1858165,2013,01/29/2013 01:30:49 PM,41.765888048,-87.586470135,"(41.765888048, -87.586470135)"
-8962609,HW110972,01/08/2013 11:30:00 PM,016XX E 69TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0332,003,5,43,14,1188296,1859599,2013,01/10/2013 09:22:16 AM,41.769816008,-87.585339501,"(41.769816008, -87.585339501)"
-8956430,HW104433,01/04/2013 01:35:00 PM,030XX N CLYBOURN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1931,019,1,5,14,1160603,1920381,2013,01/06/2013 08:36:54 AM,41.937224129,-87.685168228,"(41.937224129, -87.685168228)"
-8956589,HW105718,01/03/2013 11:00:00 PM,025XX N NEVA AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2512,025,36,18,07,1128039,1915946,2013,01/15/2013 10:12:35 AM,41.92566718,-87.804948431,"(41.92566718, -87.804948431)"
-8950257,HV622625,12/30/2012 08:30:00 PM,080XX S KENWOOD AVE,0810,THEFT,OVER $500,STREET,false,false,0411,004,8,45,06,1186572,1851692,2012,01/01/2013 07:26:21 AM,41.74815943,-87.591908643,"(41.74815943, -87.591908643)"
-8948936,HV621249,12/29/2012 08:50:00 PM,0000X S HOYNE AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,true,false,1223,012,2,28,24,1162382,1899663,2012,01/01/2013 02:50:39 PM,41.880335335,-87.679210786,"(41.880335335, -87.679210786)"
-8943965,HV616299,12/25/2012 04:00:00 PM,055XX W WASHINGTON BLVD,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1522,015,29,25,08B,1139122,1900154,2012,12/27/2012 12:37:24 PM,41.882137425,-87.764608045,"(41.882137425, -87.764608045)"
-8954009,HV616983,12/25/2012 12:04:00 PM,009XX N HOMAN AVE,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),VEHICLE NON-COMMERCIAL,true,false,1121,011,27,23,18,1153499,1905991,2012,01/10/2013 08:24:15 AM,41.897881152,-87.711660008,"(41.897881152, -87.711660008)"
-8944826,HV617170,12/24/2012 05:00:00 PM,001XX E CHESTNUT ST,0460,BATTERY,SIMPLE,RESTAURANT,false,false,1833,018,42,8,08B,1176968,1906390,2012,12/30/2012 09:07:05 AM,41.898477019,-87.625449228,"(41.898477019, -87.625449228)"
-8942797,HV614869,12/24/2012 01:15:00 AM,055XX N CLARK ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,2012,020,40,77,06,1164970,1936797,2012,12/24/2012 07:34:57 AM,41.982178611,-87.668650876,"(41.982178611, -87.668650876)"
-8942616,HV614636,12/23/2012 03:00:00 PM,012XX S FAIRFIELD AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1023,010,28,29,05,1158191,1894112,2012,12/26/2012 02:04:24 PM,41.865189467,-87.694751358,"(41.865189467, -87.694751358)"
-8941478,HV613170,12/22/2012 03:25:00 PM,031XX W 15TH ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1022,010,24,29,18,1155375,1892717,2012,12/22/2012 04:15:20 PM,41.861418451,-87.705126484,"(41.861418451, -87.705126484)"
-8941877,HV613667,12/22/2012 01:30:00 PM,078XX S ELLIS AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0624,006,8,69,05,1184215,1853079,2012,01/16/2013 03:02:59 PM,41.752020909,-87.60050208,"(41.752020909, -87.60050208)"
-8942103,HV614034,12/22/2012 12:00:00 PM,060XX W NELSON ST,5000,OTHER OFFENSE,OTHER CRIME AGAINST PERSON,RESIDENCE,false,false,2511,025,29,19,26,1135501,1919686,2012,01/03/2013 09:41:19 AM,41.935800708,-87.777439753,"(41.935800708, -87.777439753)"
-8977239,HV615005,12/21/2012 09:30:00 PM,038XX S ELLIS AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0212,002,4,36,14,1182574,1879753,2012,01/20/2013 01:50:56 PM,41.825254915,-87.605688269,"(41.825254915, -87.605688269)"
-8939087,HV610608,12/20/2012 02:00:00 PM,026XX W WASHINGTON BLVD,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,1222,012,2,27,03,1158585,1900620,2012,01/10/2013 01:04:53 PM,41.88304,-87.693126773,"(41.88304, -87.693126773)"
-8938768,HV610334,12/20/2012 12:50:00 PM,009XX E 132ND ST,0560,ASSAULT,SIMPLE,CHA PARKING LOT/GROUNDS,false,false,0533,005,9,54,08A,1184927,1818054,2012,12/21/2012 09:05:01 AM,41.655891292,-87.598987414,"(41.655891292, -87.598987414)"
-8939390,HV610836,12/20/2012 11:30:00 AM,009XX N ORLEANS ST,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,1823,018,27,8,06,1173710,1906560,2012,12/21/2012 07:48:39 AM,41.899016651,-87.63741046,"(41.899016651, -87.63741046)"
-8938409,HV610040,12/20/2012 08:00:00 AM,064XX S ARTESIAN AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,SIDEWALK,true,false,0825,008,15,66,04B,1161113,1861643,2012,01/08/2013 08:45:32 PM,41.776030554,-87.684924098,"(41.776030554, -87.684924098)"
-8934500,HV606305,12/17/2012 02:15:00 PM,063XX W ROSCOE ST,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,RESIDENCE,false,false,1633,016,36,17,05,1133418,1921895,2012,12/26/2012 02:25:15 PM,41.941899258,-87.785043149,"(41.941899258, -87.785043149)"
-8932488,HV604420,12/16/2012 12:00:00 AM,038XX N BROADWAY,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1923,019,46,6,08B,1170189,1925866,2012,12/18/2012 03:17:59 PM,41.952070898,-87.649777594,"(41.952070898, -87.649777594)"
-8932415,HV604268,12/15/2012 08:00:00 PM,004XX W 59TH ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0711,007,20,68,18,1174197,1865844,2012,12/16/2012 12:03:37 AM,41.787277835,-87.636834528,"(41.787277835, -87.636834528)"
-8931281,HV602682,12/14/2012 06:50:00 PM,027XX W 68TH ST,0560,ASSAULT,SIMPLE,HOSPITAL BUILDING/GROUNDS,true,false,0831,008,15,66,08A,1159516,1859408,2012,12/15/2012 10:55:44 AM,41.769930285,-87.690839819,"(41.769930285, -87.690839819)"
-8936102,HV607615,12/14/2012 08:00:00 AM,010XX W MADISON ST,0560,ASSAULT,SIMPLE,CTA BUS,false,false,1224,012,27,28,08A,1169746,1900256,2012,12/19/2012 12:53:51 PM,41.881805369,-87.6521538,"(41.881805369, -87.6521538)"
-8929871,HV601276,12/13/2012 07:00:00 PM,007XX N DRAKE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,true,false,1121,011,27,23,08B,1152626,1904577,2012,12/14/2012 08:53:22 AM,41.894018312,-87.714903904,"(41.894018312, -87.714903904)"
-8926381,HV598340,12/11/2012 02:38:00 PM,023XX S HOMAN AVE,1710,OFFENSE INVOLVING CHILDREN,ENDANGERING LIFE/HEALTH CHILD,APARTMENT,false,false,1024,010,22,30,26,1154025,1888288,2012,12/22/2012 03:32:31 PM,41.849291765,-87.710200092,"(41.849291765, -87.710200092)"
-8925652,HV597834,12/10/2012 11:00:00 PM,013XX S KOMENSKY AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,1011,010,24,29,07,1149559,1893412,2012,01/16/2013 10:56:16 AM,41.863440409,-87.726458047,"(41.863440409, -87.726458047)"
-8944565,HV597396,12/10/2012 09:00:00 PM,056XX S MAY ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0712,007,16,68,26,1169580,1867154,2012,12/27/2012 03:15:12 PM,41.790974014,-87.653725041,"(41.790974014, -87.653725041)"
-8924953,HV597314,12/10/2012 07:25:00 PM,039XX N SHERIDAN RD,2022,NARCOTICS,POSS: COCAINE,SIDEWALK,true,false,1923,019,46,6,18,1168934,1926424,2012,12/10/2012 09:02:05 PM,41.953629446,-87.654374706,"(41.953629446, -87.654374706)"
-8924197,HV596201,12/10/2012 12:20:00 AM,064XX N WESTERN AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,false,false,2412,024,50,2,04B,1159114,1942452,2012,12/17/2012 11:14:04 PM,41.997818835,-87.6900315,"(41.997818835, -87.6900315)"
-8913011,HV586561,12/03/2012 05:20:00 AM,016XX S HOMAN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1021,010,24,29,14,1154008,1891667,2012,12/07/2012 10:38:38 AM,41.858564471,-87.710172484,"(41.858564471, -87.710172484)"
-8908591,HV581713,11/29/2012 03:30:00 PM,118XX S WESTERN AVE,1330,CRIMINAL TRESPASS,TO LAND,DRUG STORE,true,false,2212,022,19,75,26,1162569,1825927,2012,12/02/2012 10:35:22 AM,41.677989869,-87.680579369,"(41.677989869, -87.680579369)"
-8907239,HV580907,11/28/2012 09:25:00 PM,040XX W 21ST PL,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1012,010,24,29,18,1149805,1889414,2012,10/31/2014 03:20:56 PM,41.852464635,-87.725658863,"(41.852464635, -87.725658863)"
-8907796,HV581139,11/28/2012 11:30:00 AM,022XX S ST LOUIS AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1024,010,22,30,05,1153350,1888675,2012,12/02/2012 02:00:54 PM,41.850367153,-87.71266717,"(41.850367153, -87.71266717)"
-8905818,HV579789,11/28/2012 09:30:00 AM,011XX S STATE ST,1330,CRIMINAL TRESPASS,TO LAND,PARKING LOT/GARAGE(NON.RESID.),true,false,0123,001,2,32,26,1176547,1895698,2012,11/28/2012 10:23:47 AM,41.869147103,-87.627318596,"(41.869147103, -87.627318596)"
-8906677,HV580211,11/26/2012 11:00:00 PM,100XX S YATES AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0431,004,7,51,26,1194191,1838551,2012,11/29/2012 05:02:42 PM,41.711915796,-87.564420757,"(41.711915796, -87.564420757)"
-8902768,HV576942,11/26/2012 08:00:00 AM,016XX S CALIFORNIA BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1023,010,28,29,08B,1158001,1891592,2012,11/29/2012 11:02:10 PM,41.858278201,-87.695517581,"(41.858278201, -87.695517581)"
-20712,HV575223,11/25/2012 08:47:00 PM,055XX S EMERALD AVE,0110,HOMICIDE,FIRST DEGREE MURDER,HALLWAY,false,false,0711,007,3,68,01A,1172212,1867877,2012,05/11/2015 12:38:40 PM,41.792900506,-87.644052865,"(41.792900506, -87.644052865)"
-8901791,HV576185,11/25/2012 02:45:00 PM,019XX W FARGO AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,2424,024,49,1,08B,1162178,1949372,2012,11/26/2012 11:50:43 AM,42.016743771,-87.678565457,"(42.016743771, -87.678565457)"
-8901115,HV575538,11/25/2012 12:20:00 AM,051XX W CHICAGO AVE,0460,BATTERY,SIMPLE,SIDEWALK,true,false,1531,015,28,25,08B,1142191,1904807,2012,11/27/2012 09:21:49 AM,41.894849457,-87.75322302,"(41.894849457, -87.75322302)"
-8997924,HW144989,11/21/2012 02:00:00 PM,068XX S MAY ST,1120,DECEPTIVE PRACTICE,FORGERY,RESIDENCE,false,false,0724,007,17,68,10,1169876,1859257,2012,02/06/2013 10:56:56 AM,41.769297274,-87.652868818,"(41.769297274, -87.652868818)"
-8897548,HV571030,11/20/2012 04:00:00 PM,047XX N KILBOURN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1722,017,45,16,14,1145182,1931135,2012,11/26/2012 08:34:41 AM,41.96703997,-87.741570378,"(41.96703997, -87.741570378)"
diff --git a/work-with-data/dataprep/data/crime_partfiles/part-00001-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv b/work-with-data/dataprep/data/crime_partfiles/part-00001-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv
deleted file mode 100644
index 28f57ae8..00000000
--- a/work-with-data/dataprep/data/crime_partfiles/part-00001-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv
+++ /dev/null
@@ -1,921 +0,0 @@
-8892616,HV566862,11/18/2012 03:10:00 AM,008XX N ORLEANS ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1823,018,27,8,14,1173794,1906444,2012,11/20/2012 10:24:11 AM,41.898696471,-87.637105392,"(41.898696471, -87.637105392)"
-8891107,HV564871,11/16/2012 04:42:00 PM,060XX S PAULINA ST,0560,ASSAULT,SIMPLE,STREET,false,false,0714,007,15,67,08A,1166013,1864342,2012,11/22/2012 12:36:55 PM,41.783334168,-87.666884354,"(41.783334168, -87.666884354)"
-8889934,HV563994,11/16/2012 01:30:00 AM,067XX S RIDGELAND AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,0332,003,5,43,08B,1189067,1860619,2012,10/31/2014 03:20:56 PM,41.772596537,-87.582480754,"(41.772596537, -87.582480754)"
-8889296,HV563198,11/15/2012 01:40:00 PM,027XX W WASHINGTON BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1331,012,2,27,18,1158294,1900533,2012,11/15/2012 03:34:31 PM,41.882807216,-87.694197721,"(41.882807216, -87.694197721)"
-8987498,HW134941,11/15/2012 12:00:00 AM,036XX W 114TH PL,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,2211,022,19,74,26,1154166,1828373,2012,02/07/2013 07:46:23 AM,41.684872758,-87.711272932,"(41.684872758, -87.711272932)"
-8895356,HV569442,11/14/2012 07:00:00 PM,010XX W 81ST ST,0266,CRIM SEXUAL ASSAULT,PREDATORY,APARTMENT,false,true,0612,006,21,71,02,1170431,1851137,2012,12/01/2012 12:56:41 PM,41.747002878,-87.651070501,"(41.747002878, -87.651070501)"
-8886202,HV560657,11/12/2012 07:30:00 PM,016XX N WASHTENAW AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,1421,014,1,24,26,1158070,1911058,2012,12/20/2012 11:51:43 AM,41.911693305,-87.694732585,"(41.911693305, -87.694732585)"
-8883343,HV556942,11/10/2012 09:00:00 PM,094XX S LOOMIS ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2222,022,21,73,08B,1168702,1841826,2012,11/21/2012 02:24:45 PM,41.721489594,-87.657673857,"(41.721489594, -87.657673857)"
-8882678,HV556791,11/10/2012 08:15:00 PM,049XX S LAFLIN ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0933,009,20,61,18,1167129,1872007,2012,11/10/2012 09:13:53 PM,41.80434403,-87.662573448,"(41.80434403, -87.662573448)"
-8881920,HV555670,11/10/2012 03:09:00 AM,058XX W LAKE ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,1512,015,29,25,14,1137413,1902282,2012,11/12/2012 10:01:38 AM,41.888007843,-87.770832369,"(41.888007843, -87.770832369)"
-8882507,HV556465,11/07/2012 02:30:00 PM,014XX N HUMBOLDT DR,0820,THEFT,$500 AND UNDER,PARK PROPERTY,false,false,1423,014,26,24,06,1156241,1909195,2012,11/11/2012 11:33:11 AM,41.906618247,-87.701502203,"(41.906618247, -87.701502203)"
-8876670,HV550232,11/06/2012 05:45:00 AM,021XX W JACKSON BLVD,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1211,012,2,28,14,1162323,1898638,2012,11/06/2012 01:09:20 PM,41.877523879,-87.679456102,"(41.877523879, -87.679456102)"
-8876036,HV549818,11/05/2012 11:38:00 PM,004XX E 49TH ST,0326,ROBBERY,AGGRAVATED VEHICULAR HIJACKING,STREET,false,false,0223,002,4,38,03,1180177,1872592,2012,11/29/2012 05:40:16 PM,41.805659927,-87.614701828,"(41.805659927, -87.614701828)"
-8874194,HV548057,11/04/2012 04:00:00 PM,082XX S MARSHFIELD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0614,006,21,71,08B,1166818,1849952,2012,11/17/2012 01:22:07 PM,41.743828919,-87.664343269,"(41.743828919, -87.664343269)"
-8872064,HV545303,11/02/2012 02:30:00 PM,015XX S ST LOUIS AVE,031A,ROBBERY,ARMED: HANDGUN,STREET,false,false,1021,010,24,29,03,1153247,1892246,2012,11/05/2012 05:53:29 PM,41.860168436,-87.712950507,"(41.860168436, -87.712950507)"
-8871002,HV544671,11/02/2012 03:57:00 AM,022XX N MAJOR AVE,2022,NARCOTICS,POSS: COCAINE,STREET,true,false,2515,025,37,19,18,1137985,1914146,2012,11/02/2012 05:29:16 AM,41.920553782,-87.768444856,"(41.920553782, -87.768444856)"
-8870894,HV544583,11/02/2012 12:05:00 AM,051XX W CHICAGO AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1531,015,37,25,18,1141942,1904883,2012,11/02/2012 01:00:31 AM,41.895062626,-87.754135657,"(41.895062626, -87.754135657)"
-8870226,HV543679,11/01/2012 12:20:00 PM,049XX S LEAMINGTON AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,true,false,0814,008,23,56,05,1142820,1871433,2012,11/11/2012 03:52:23 PM,41.803254873,-87.751742981,"(41.803254873, -87.751742981)"
-8866638,HV540483,10/30/2012 12:00:00 AM,005XX E 88TH PL,0890,THEFT,FROM BUILDING,VACANT LOT/LAND,false,false,0632,006,6,44,06,1181529,1846385,2012,10/31/2012 09:48:26 AM,41.733714178,-87.610551004,"(41.733714178, -87.610551004)"
-8866240,HV540055,10/29/2012 08:00:00 PM,029XX N CENTRAL AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,2514,025,31,19,06,1138562,1919170,2012,11/20/2012 04:59:59 AM,41.934329733,-87.766202716,"(41.934329733, -87.766202716)"
-8863886,HV537827,10/28/2012 03:38:00 AM,047XX S WESTERN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0922,009,12,58,14,1161120,1873431,2012,10/30/2012 11:11:06 AM,41.808378235,-87.684572328,"(41.808378235, -87.684572328)"
-8863850,HV537713,10/27/2012 09:30:00 PM,002XX N ADA ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1212,012,27,28,06,1167420,1901949,2012,10/28/2012 02:39:14 PM,41.886501431,-87.66064605,"(41.886501431, -87.66064605)"
-8862944,HV536619,10/27/2012 07:00:00 AM,005XX N PINE AVE,0810,THEFT,OVER $500,STREET,false,false,1523,015,37,25,06,1139400,1903001,2012,10/28/2012 07:55:04 AM,41.889944895,-87.763517796,"(41.889944895, -87.763517796)"
-8862980,HV536621,10/27/2012 06:40:00 AM,029XX S POPLAR AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,0913,009,11,60,08B,1170520,1885472,2012,11/04/2012 02:25:07 PM,41.841220013,-87.649743948,"(41.841220013, -87.649743948)"
-8854574,HV528223,10/21/2012 01:16:00 PM,054XX W CRYSTAL ST,0460,BATTERY,SIMPLE,STREET,false,false,2532,025,37,25,08B,1139913,1907756,2012,10/23/2012 02:06:06 PM,41.902983827,-87.761517419,"(41.902983827, -87.761517419)"
-8853044,HV526400,10/20/2012 02:39:00 AM,050XX S PULASKI RD,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,SMALL RETAIL STORE,true,false,0815,008,14,57,14,1150519,1870611,2012,10/20/2012 07:54:05 AM,41.800852758,-87.723528099,"(41.800852758, -87.723528099)"
-8851467,HV524786,10/18/2012 09:00:00 PM,062XX N KENMORE AVE,2093,NARCOTICS,FOUND SUSPECT NARCOTICS,RESIDENCE PORCH/HALLWAY,true,false,2433,024,48,77,26,1168137,1941934,2012,10/19/2012 03:44:51 AM,41.996206622,-87.656854296,"(41.996206622, -87.656854296)"
-8857204,HV530966,10/18/2012 03:30:00 PM,020XX W BIRCHWOOD AVE,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,RESIDENCE,false,false,2424,024,49,1,11,1161122,1949761,2012,11/05/2012 05:01:37 PM,42.017833296,-87.682440366,"(42.017833296, -87.682440366)"
-8854312,HV523305,10/18/2012 05:45:00 AM,0000X W TERMINAL ST,5007,OTHER OFFENSE,OTHER WEAPONS VIOLATION,AIRPORT TERMINAL UPPER LEVEL - SECURE AREA,false,false,1653,016,41,76,26,1101811,1934379,2012,10/23/2012 09:45:21 AM,41.976653215,-87.900984463,"(41.976653215, -87.900984463)"
-8849703,HV522771,10/17/2012 05:30:00 PM,019XX S CARPENTER ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,1233,012,25,31,04B,1169512,1890706,2012,11/25/2012 01:04:08 AM,41.855604522,-87.653290842,"(41.855604522, -87.653290842)"
-8850913,HV523621,10/17/2012 01:25:00 AM,080XX S KARLOV AVE,5004,SEX OFFENSE,ATT CRIM SEXUAL ABUSE,RESIDENCE,false,false,0834,008,13,70,17,1150437,1850878,2012,11/26/2012 10:47:51 AM,41.746703818,-87.724341277,"(41.746703818, -87.724341277)"
-8849423,HV522180,10/16/2012 04:20:00 PM,011XX S ALBANY AVE,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,1134,011,24,29,03,1155829,1894856,2012,11/08/2012 08:59:13 AM,41.867278968,-87.703402314,"(41.867278968, -87.703402314)"
-8847526,HV520690,10/16/2012 12:00:00 PM,001XX N LONG AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1523,015,28,25,08B,1140341,1900867,2012,10/20/2012 01:00:03 PM,41.884071733,-87.760114326,"(41.884071733, -87.760114326)"
-8847568,HV520541,10/15/2012 03:40:00 PM,039XX S DR MARTIN LUTHER KING JR DR,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0213,002,3,38,08B,1179457,1879187,2012,10/20/2012 11:57:50 AM,41.82377364,-87.617140893,"(41.82377364, -87.617140893)"
-8855590,HV529120,10/15/2012 09:00:00 AM,132XX S RHODES AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENTIAL YARD (FRONT/BACK),false,false,0533,005,9,54,26,1182015,1817681,2012,10/23/2012 09:00:25 AM,41.654935305,-87.609654054,"(41.654935305, -87.609654054)"
-8847777,HV520951,10/14/2012 05:00:00 PM,021XX W GLADYS AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,1211,012,2,28,05,1161991,1898370,2012,11/07/2012 06:05:21 PM,41.876795398,-87.6806826,"(41.876795398, -87.6806826)"
-8840086,HV513018,10/10/2012 09:29:00 PM,044XX W WASHINGTON BLVD,0610,BURGLARY,FORCIBLE ENTRY,VACANT LOT/LAND,true,false,1113,011,28,26,05,1146556,1900081,2012,11/20/2012 03:35:42 PM,41.881798673,-87.737311957,"(41.881798673, -87.737311957)"
-8839833,HV512787,10/10/2012 04:00:00 PM,035XX W 62ND PL,0560,ASSAULT,SIMPLE,APARTMENT,false,false,0823,008,15,66,08A,1153640,1862983,2012,10/11/2012 10:04:43 AM,41.779859073,-87.712284299,"(41.779859073, -87.712284299)"
-8837875,HV510858,10/09/2012 01:35:00 PM,071XX S EAST END AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,false,false,0324,003,5,43,18,1188807,1858045,2012,10/10/2012 12:29:41 PM,41.765539491,-87.583516085,"(41.765539491, -87.583516085)"
-8837856,HV510806,10/09/2012 10:00:00 AM,061XX N DAMEN AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,2413,024,40,2,05,1161837,1940962,2012,10/27/2012 02:24:30 PM,41.993673655,-87.680056379,"(41.993673655, -87.680056379)"
-8838396,HV511339,10/08/2012 07:00:00 PM,015XX W JARVIS AVE,0320,ROBBERY,STRONGARM - NO WEAPON,CTA TRAIN,false,false,2423,024,49,1,03,1164762,1949091,2012,10/29/2012 09:21:52 AM,42.015918069,-87.669065079,"(42.015918069, -87.669065079)"
-8836180,HV509278,10/08/2012 11:40:00 AM,047XX S EVANS AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0223,002,4,38,05,1182096,1873636,2012,01/04/2013 12:55:29 PM,41.808480501,-87.607631429,"(41.808480501, -87.607631429)"
-8835822,HV508892,10/07/2012 06:00:00 PM,020XX W CRYSTAL ST,0820,THEFT,$500 AND UNDER,OTHER,false,false,1424,014,1,24,06,1162685,1908290,2012,10/08/2012 08:31:13 AM,41.904002172,-87.677856209,"(41.904002172, -87.677856209)"
-8835690,HV508681,10/07/2012 03:30:00 PM,064XX N CALIFORNIA AVE,0560,ASSAULT,SIMPLE,OTHER,false,true,2412,024,50,2,08A,1156448,1942784,2012,10/10/2012 02:52:50 PM,41.998784398,-87.699829747,"(41.998784398, -87.699829747)"
-8834781,HV507576,10/06/2012 10:40:00 PM,049XX W THOMAS ST,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,STREET,true,false,1531,015,37,25,18,1143047,1906903,2012,10/07/2012 12:16:29 AM,41.900585197,-87.750026788,"(41.900585197, -87.750026788)"
-8832916,HV505266,10/04/2012 11:00:00 PM,046XX N KEDVALE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1722,017,39,14,07,1147948,1930214,2012,10/05/2012 01:00:02 PM,41.964459805,-87.731423849,"(41.964459805, -87.731423849)"
-8830934,HV503596,10/04/2012 04:40:00 AM,027XX S SPAULDING AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,1032,010,22,30,08B,1154760,1885842,2012,10/08/2012 12:35:15 PM,41.842564985,-87.707567953,"(41.842564985, -87.707567953)"
-8829061,HV501890,10/02/2012 10:13:00 PM,023XX E 79TH ST,051A,ASSAULT,AGGRAVATED: HANDGUN,ALLEY,false,false,0414,004,7,46,04A,1193012,1853001,2012,10/22/2012 03:02:22 PM,41.751596734,-87.568268108,"(41.751596734, -87.568268108)"
-8828651,HV501364,10/02/2012 03:00:00 PM,010XX W LAWRENCE AVE,0460,BATTERY,SIMPLE,NURSING HOME/RETIREMENT HOME,false,false,2024,020,46,3,08B,1168520,1932090,2012,10/03/2012 10:54:43 AM,41.969186136,-87.655731893,"(41.969186136, -87.655731893)"
-8827127,HV499976,10/01/2012 06:30:00 AM,035XX S BELL AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0912,009,11,59,06,1161890,1881424,2012,10/02/2012 12:22:54 PM,41.830295978,-87.681525747,"(41.830295978, -87.681525747)"
-8826682,HV499662,09/29/2012 10:30:00 PM,0000X E 118TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0532,005,9,53,14,1178653,1826778,2012,10/02/2012 07:32:03 AM,41.679975685,-87.621680793,"(41.679975685, -87.621680793)"
-8823617,HV496477,09/29/2012 03:48:00 AM,037XX N HERMITAGE AVE,0460,BATTERY,SIMPLE,STREET,false,false,1922,019,47,6,08B,1164057,1924647,2012,10/07/2012 09:07:18 AM,41.948857896,-87.672353361,"(41.948857896, -87.672353361)"
-8821568,HV494637,09/27/2012 08:20:00 PM,025XX W GLENLAKE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,2413,024,50,2,18,1158076,1940449,2012,09/27/2012 09:34:51 PM,41.992343859,-87.693904957,"(41.992343859, -87.693904957)"
-8851056,HV523925,09/25/2012 09:00:00 AM,053XX N LINCOLN AVE,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,APARTMENT,false,false,2011,020,40,4,11,1158509,1935343,2012,10/24/2012 02:36:04 PM,41.978323891,-87.692452777,"(41.978323891, -87.692452777)"
-8816430,HV489840,09/21/2012 02:00:00 PM,087XX W HIGGINS RD,0890,THEFT,FROM BUILDING,COMMERCIAL / BUSINESS OFFICE,false,false,1614,016,41,76,06,1116990,1938423,2012,09/25/2012 03:07:36 PM,41.98752663,-87.845078401,"(41.98752663, -87.845078401)"
-8811375,HV484526,09/20/2012 06:55:00 PM,004XX N LARAMIE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1523,015,28,25,18,1141584,1902490,2012,09/20/2012 07:33:05 PM,41.88850257,-87.755509702,"(41.88850257, -87.755509702)"
-8808747,HV482121,09/19/2012 08:25:00 AM,064XX S DR MARTIN LUTHER KING JR DR,1310,CRIMINAL DAMAGE,TO PROPERTY,SMALL RETAIL STORE,false,false,0312,003,20,42,14,1180067,1862427,2012,10/02/2012 01:17:30 PM,41.777768752,-87.615416585,"(41.777768752, -87.615416585)"
-8808318,HV481854,09/18/2012 10:00:00 PM,083XX S PULASKI RD,0460,BATTERY,SIMPLE,BAR OR TAVERN,false,false,0834,008,18,70,08B,1151154,1848626,2012,09/23/2012 08:11:11 AM,41.740509982,-87.721772621,"(41.740509982, -87.721772621)"
-8809115,HV482202,09/18/2012 05:00:00 PM,016XX N RICHMOND ST,1563,SEX OFFENSE,CRIMINAL SEXUAL ABUSE,APARTMENT,false,false,1421,014,35,24,17,1156597,1911117,2012,10/25/2012 02:24:19 PM,41.911885173,-87.700142349,"(41.911885173, -87.700142349)"
-8807141,HV480759,09/17/2012 04:00:00 PM,002XX W 106TH ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0512,005,34,49,05,1176425,1834607,2012,12/01/2013 10:44:44 PM,41.70150984,-87.629602134,"(41.70150984, -87.629602134)"
-8805576,HV479445,09/17/2012 10:25:00 AM,071XX S ARTESIAN AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),PARKING LOT/GARAGE(NON.RESID.),true,false,0832,008,18,66,18,1161312,1857393,2012,09/17/2012 11:37:12 AM,41.764363831,-87.684312146,"(41.764363831, -87.684312146)"
-8805646,HV479394,09/16/2012 03:30:00 PM,025XX W 60TH ST,0890,THEFT,FROM BUILDING,RESIDENCE-GARAGE,false,false,0824,008,16,66,06,1160416,1864831,2012,09/18/2012 08:35:47 AM,41.784793262,-87.687391469,"(41.784793262, -87.687391469)"
-8811483,HV484469,09/15/2012 06:30:00 PM,022XX W WALTON ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,1312,012,32,24,06,1161445,1906253,2012,09/21/2012 08:35:03 AM,41.898438394,-87.682467806,"(41.898438394, -87.682467806)"
-8800941,HV474090,09/13/2012 01:05:00 PM,064XX W IRVING PARK RD,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,1632,016,38,17,06,1132426,1925932,2012,09/14/2012 10:19:48 AM,41.952994581,-87.788594938,"(41.952994581, -87.788594938)"
-8800909,HV473073,09/12/2012 08:00:00 AM,024XX W RICE ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1312,012,1,24,05,1160110,1905629,2012,09/28/2012 10:42:19 AM,41.896753774,-87.687388428,"(41.896753774, -87.687388428)"
-8796621,HV470735,09/11/2012 12:00:00 AM,042XX W DIVISION ST,0890,THEFT,FROM BUILDING,VACANT LOT/LAND,false,false,1111,011,37,23,06,1147817,1907620,2012,09/11/2012 10:07:06 AM,41.902462385,-87.732487693,"(41.902462385, -87.732487693)"
-8796622,HV470753,09/10/2012 10:00:00 PM,043XX N KENNETH AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1722,017,38,16,07,1145640,1928625,2012,11/15/2012 09:58:28 AM,41.96014364,-87.739950267,"(41.96014364, -87.739950267)"
-8796998,HV470983,09/10/2012 09:00:00 PM,027XX W FULTON ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1331,012,27,27,06,1157934,1901933,2012,09/11/2012 01:45:22 PM,41.886656296,-87.695481445,"(41.886656296, -87.695481445)"
-8794045,HV468321,09/09/2012 01:30:00 PM,085XX S COTTAGE GROVE AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0632,006,6,44,06,1183016,1848390,2012,09/10/2012 08:41:47 AM,41.739181727,-87.605041262,"(41.739181727, -87.605041262)"
-8793816,HV467932,09/09/2012 02:00:00 AM,010XX W ROSCOE ST,0890,THEFT,FROM BUILDING,APARTMENT,false,false,1924,019,44,6,06,1168691,1922790,2012,09/19/2012 11:45:07 AM,41.943662882,-87.655373663,"(41.943662882, -87.655373663)"
-8792473,HV466281,09/08/2012 01:10:00 AM,008XX W ALDINE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,1924,019,44,6,03,1169835,1922153,2012,09/11/2012 01:11:53 PM,41.941890022,-87.65118752,"(41.941890022, -87.65118752)"
-8795642,HV469689,09/07/2012 02:00:00 PM,030XX N CLYBOURN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,1931,019,1,5,26,1160787,1920205,2012,09/26/2012 02:37:56 PM,41.936737354,-87.684496894,"(41.936737354, -87.684496894)"
-8790993,HV464929,09/06/2012 05:00:00 PM,017XX W IRVING PARK RD,0810,THEFT,OVER $500,STREET,false,false,1922,019,47,6,06,1163931,1926567,2012,09/10/2012 10:34:32 AM,41.954129135,-87.672762117,"(41.954129135, -87.672762117)"
-8786460,HV460471,09/04/2012 10:05:00 AM,040XX W LAWRENCE AVE,0860,THEFT,RETAIL THEFT,DRUG STORE,false,false,1712,017,39,14,06,1148728,1931636,2012,09/04/2012 12:29:10 PM,41.968346803,-87.728519074,"(41.968346803, -87.728519074)"
-8799927,HV470945,09/04/2012 06:45:00 AM,056XX S KIMBARK AVE,0810,THEFT,OVER $500,SIDEWALK,false,false,0235,002,5,41,06,1185668,1867788,2012,09/13/2012 10:14:30 AM,41.792349633,-87.594714634,"(41.792349633, -87.594714634)"
-8788412,HV462310,09/04/2012 12:00:00 AM,020XX W JARVIS AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,COMMERCIAL / BUSINESS OFFICE,false,false,2424,024,49,1,26,1161166,1948739,2012,09/26/2012 02:36:48 PM,42.015027986,-87.682307059,"(42.015027986, -87.682307059)"
-8785107,HV459106,09/03/2012 05:22:00 AM,037XX N CLARK ST,0610,BURGLARY,FORCIBLE ENTRY,RESTAURANT,false,false,1923,019,44,6,05,1167840,1925021,2012,01/14/2013 08:58:07 AM,41.949803269,-87.658436951,"(41.949803269, -87.658436951)"
-8784696,HV458645,09/02/2012 10:35:00 PM,064XX N CLARK ST,3710,INTERFERENCE WITH PUBLIC OFFICER,RESIST/OBSTRUCT/DISARM OFFICER,POLICE FACILITY/VEH PARKING LOT,true,false,2432,024,40,1,24,1164194,1943199,2012,09/03/2012 12:10:39 PM,41.999762374,-87.671322786,"(41.999762374, -87.671322786)"
-8785938,HV458640,09/02/2012 09:55:00 PM,134XX S HOUSTON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0433,004,10,55,08B,1198807,1816227,2012,09/28/2012 10:05:10 AM,41.650542087,-87.54826106,"(41.650542087, -87.54826106)"
-8781836,HV455146,08/31/2012 12:40:00 PM,048XX W HURON ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1532,015,28,25,26,1143764,1904157,2012,08/31/2012 01:03:25 PM,41.893036452,-87.747462052,"(41.893036452, -87.747462052)"
-8781876,HV454874,08/31/2012 09:47:00 AM,016XX W 38TH PL,0650,BURGLARY,HOME INVASION,RESIDENCE,true,true,0912,009,11,59,05,1166017,1879238,2012,09/01/2012 10:51:15 PM,41.824210439,-87.666445942,"(41.824210439, -87.666445942)"
-8992059,HW139075,08/30/2012 12:00:00 PM,071XX S CONSTANCE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0324,003,5,43,14,1189652,1857840,2012,02/01/2013 07:04:02 AM,41.764956699,-87.580425515,"(41.764956699, -87.580425515)"
-8779225,HV453091,08/30/2012 12:14:00 AM,0000X E 23RD ST,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,0131,001,2,33,06,1176902,1889156,2012,09/18/2012 12:18:34 AM,41.851187404,-87.626213124,"(41.851187404, -87.626213124)"
-8777107,HV450875,08/28/2012 12:00:00 PM,053XX S PRINCETON AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,APARTMENT,false,false,0935,009,3,37,11,1175157,1869390,2012,08/29/2012 02:22:40 PM,41.796987029,-87.633208764,"(41.796987029, -87.633208764)"
-8774797,HV449269,08/27/2012 02:00:00 PM,056XX W MADISON ST,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,STREET,true,true,1513,015,29,25,26,1138744,1899496,2012,08/28/2012 08:54:30 AM,41.880338653,-87.766012053,"(41.880338653, -87.766012053)"
-8774320,HV448970,08/27/2012 09:57:00 AM,0000X E BELLEVUE PL,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,false,false,1824,018,42,8,26,1176755,1907564,2012,09/25/2012 05:38:39 PM,41.90170335,-87.626196011,"(41.90170335, -87.626196011)"
-8777461,HV449286,08/27/2012 12:00:00 AM,072XX S YALE AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,0731,007,17,69,06,1175830,1857192,2012,08/29/2012 08:20:12 AM,41.763499343,-87.631106103,"(41.763499343, -87.631106103)"
-8773252,HV448055,08/26/2012 03:00:00 PM,051XX N KENMORE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,2024,020,48,3,14,1168360,1934506,2012,08/27/2012 08:12:27 AM,41.975819182,-87.656249997,"(41.975819182, -87.656249997)"
-8773414,HV448263,08/26/2012 01:00:00 PM,018XX S CALIFORNIA AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1023,010,28,29,05,1158023,1890945,2012,08/31/2012 11:52:43 PM,41.856502315,-87.695454471,"(41.856502315, -87.695454471)"
-8772501,HV446998,08/25/2012 09:45:00 PM,061XX S ELLIS AVE,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0313,003,20,42,03,1183909,1864404,2012,08/30/2012 08:25:25 PM,41.783104937,-87.601270196,"(41.783104937, -87.601270196)"
-8771532,HV445766,08/25/2012 02:00:00 AM,011XX N PULASKI RD,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,1112,011,27,23,04B,1149555,1907197,2012,08/27/2012 02:44:36 PM,41.901268049,-87.726114681,"(41.901268049, -87.726114681)"
-8770828,HV444908,08/24/2012 01:50:00 PM,043XX N ELSTON AVE,1330,CRIMINAL TRESPASS,TO LAND,OTHER,false,false,1722,017,39,16,26,1148579,1928738,2012,08/26/2012 07:43:41 AM,41.960397365,-87.729142083,"(41.960397365, -87.729142083)"
-8770260,HV444434,08/24/2012 08:00:00 AM,039XX W BELMONT AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1732,017,31,21,06,1149943,1921037,2012,08/24/2012 10:58:00 AM,41.939238728,-87.724328494,"(41.939238728, -87.724328494)"
-8812544,HV485356,08/24/2012 12:01:00 AM,054XX S KOSTNER AVE,1206,DECEPTIVE PRACTICE,"THEFT BY LESSEE,MOTOR VEH",OTHER,false,false,0815,008,23,62,11,1147909,1868367,2012,09/25/2012 08:31:14 AM,41.794745288,-87.733157437,"(41.794745288, -87.733157437)"
-8767493,HV441686,08/22/2012 12:15:00 PM,113XX S NORMAL AVE,0454,BATTERY,AGG PO HANDS NO/MIN INJURY,STREET,true,false,2233,022,34,49,08B,1174945,1829912,2012,09/07/2012 04:53:03 PM,41.688659113,-87.635160749,"(41.688659113, -87.635160749)"
-8767607,HV441927,08/22/2012 09:00:00 AM,021XX W BRADLEY PL,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,1921,019,47,5,05,1161574,1924845,2012,09/26/2012 05:29:47 PM,41.949453401,-87.681474889,"(41.949453401, -87.681474889)"
-8766314,HV441118,08/21/2012 11:22:00 PM,044XX W THOMAS ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1111,011,37,23,14,1146410,1906994,2012,08/23/2012 07:17:41 AM,41.900771499,-87.73767185,"(41.900771499, -87.73767185)"
-8760182,HV434134,08/17/2012 12:00:00 AM,056XX W THOMAS ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,1511,015,29,25,14,1138721,1906735,2012,08/20/2012 11:50:48 AM,41.900203799,-87.765920735,"(41.900203799, -87.765920735)"
-8773822,HV433038,08/16/2012 12:45:00 PM,003XX W MARQUETTE RD,2024,NARCOTICS,POSS: HEROIN(WHITE),VEHICLE NON-COMMERCIAL,true,false,0722,007,6,68,18,1174897,1860475,2012,06/27/2013 08:46:27 AM,41.772529123,-87.634427947,"(41.772529123, -87.634427947)"
-8757825,HV432610,08/16/2012 06:52:00 AM,059XX S EGGLESTON AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,APARTMENT,false,true,0711,007,20,68,26,1174355,1865321,2012,09/06/2012 07:01:31 AM,41.785839153,-87.636270761,"(41.785839153, -87.636270761)"
-8848748,HV521983,08/15/2012 09:00:00 AM,103XX S AVENUE G,1140,DECEPTIVE PRACTICE,EMBEZZLEMENT,APARTMENT,false,false,0432,004,10,52,12,1203115,1836651,2012,11/25/2012 04:43:08 PM,41.706478717,-87.531804239,"(41.706478717, -87.531804239)"
-8756448,HV431403,08/15/2012 02:00:00 AM,042XX S ARTESIAN AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,0921,009,12,58,06,1160768,1876410,2012,08/15/2012 01:22:48 PM,41.81656026,-87.685781056,"(41.81656026, -87.685781056)"
-8755869,HV430952,08/15/2012 01:45:00 AM,032XX S LITUANICA AVE,0560,ASSAULT,SIMPLE,SIDEWALK,true,true,0913,009,11,60,08A,1170872,1883541,2012,08/15/2012 06:50:43 PM,41.835913482,-87.648508735,"(41.835913482, -87.648508735)"
-8755544,HV430602,08/14/2012 07:36:00 PM,007XX W 66TH PL,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,OTHER,true,false,0723,007,6,68,15,1172438,1860735,2012,08/15/2012 08:28:57 AM,41.773297076,-87.643434278,"(41.773297076, -87.643434278)"
-8755254,HV430199,08/14/2012 05:15:00 PM,059XX N LEONARD AVE,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,1622,016,45,11,06,1136596,1939256,2012,08/15/2012 08:43:27 AM,41.989483089,-87.772945108,"(41.989483089, -87.772945108)"
-8756409,HV431232,08/11/2012 12:00:00 PM,045XX W SCHUBERT AVE,0820,THEFT,$500 AND UNDER,ALLEY,false,false,2521,025,31,20,06,1145466,1917528,2012,08/15/2012 12:51:00 PM,41.929695804,-87.740871975,"(41.929695804, -87.740871975)"
-8750026,HV425009,08/10/2012 09:15:00 PM,030XX W FLOURNOY ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1134,011,28,27,08B,1156190,1896917,2012,08/19/2012 09:49:19 AM,41.872927289,-87.702021379,"(41.872927289, -87.702021379)"
-8783123,HV456645,08/10/2012 10:00:00 AM,007XX N PULASKI RD,0810,THEFT,OVER $500,OTHER,false,false,1112,011,27,23,06,1149633,1904538,2012,09/02/2012 11:17:47 AM,41.893969964,-87.725897327,"(41.893969964, -87.725897327)"
-8750836,HV424995,08/08/2012 07:00:00 PM,021XX W 19TH ST,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,STREET,false,true,1223,012,25,31,04A,1162476,1890763,2012,09/10/2012 01:17:16 PM,41.855910947,-87.679114684,"(41.855910947, -87.679114684)"
-8742851,HV418566,08/06/2012 05:40:00 PM,010XX W PRATT BLVD,2230,LIQUOR LAW VIOLATION,ILLEGAL CONSUMPTION BY MINOR,PARK PROPERTY,true,false,2432,024,49,1,22,1167682,1945305,2012,08/06/2012 06:59:15 PM,42.005466555,-87.658430283,"(42.005466555, -87.658430283)"
-8742679,HV418323,08/06/2012 03:10:00 PM,093XX S EMERALD AVE,1661,GAMBLING,GAME/DICE,SIDEWALK,true,false,2223,022,21,73,19,1172905,1843130,2012,08/06/2012 04:11:04 PM,41.724976402,-87.642240722,"(41.724976402, -87.642240722)"
-8741361,HV417467,08/06/2012 12:05:00 AM,023XX W DIVISION ST,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,1424,014,1,24,18,1160513,1907962,2012,08/06/2012 01:02:55 AM,41.903147379,-87.685843603,"(41.903147379, -87.685843603)"
-8741373,HV417429,08/05/2012 11:55:00 PM,113XX S AVENUE H,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0433,004,10,52,08B,1202924,1830187,2012,08/09/2012 08:28:00 AM,41.68874583,-87.532723684,"(41.68874583, -87.532723684)"
-8741412,HV417455,08/05/2012 09:50:00 PM,003XX S MAPLEWOOD AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1125,011,2,28,07,1159391,1898542,2012,08/21/2012 06:58:06 AM,41.877321238,-87.690224275,"(41.877321238, -87.690224275)"
-8740881,HV416907,08/05/2012 03:15:00 PM,116XX S MARSHFIELD AVE,0560,ASSAULT,SIMPLE,DEPARTMENT STORE,false,false,2234,022,34,75,08A,1167443,1827362,2012,08/06/2012 06:45:25 AM,41.681825045,-87.662697896,"(41.681825045, -87.662697896)"
-8740391,HV414546,08/03/2012 11:30:00 PM,009XX N SPRINGFIELD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,false,1112,011,27,23,08B,1150173,1906094,2012,08/07/2012 10:26:08 AM,41.89822928,-87.723873472,"(41.89822928, -87.723873472)"
-8748176,HV421582,08/03/2012 10:30:00 PM,035XX N CENTRAL AVE,0880,THEFT,PURSE-SNATCHING,SIDEWALK,false,false,1633,016,38,15,06,1138439,1922891,2012,08/10/2012 09:05:52 AM,41.944542772,-87.766564336,"(41.944542772, -87.766564336)"
-8737442,HV413001,08/03/2012 12:10:00 AM,018XX S ALLPORT ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1222,012,25,31,06,1168294,1890966,2012,08/04/2012 09:54:55 AM,41.856344369,-87.657753953,"(41.856344369, -87.657753953)"
-8737391,HV412889,08/02/2012 10:35:00 PM,028XX W 40TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0921,009,14,58,08B,1157924,1877960,2012,08/08/2012 10:42:46 PM,41.820871993,-87.696171417,"(41.820871993, -87.696171417)"
-8738145,HV413512,08/02/2012 05:15:00 PM,005XX E 88TH PL,0460,BATTERY,SIMPLE,STREET,false,true,0632,006,6,44,08B,1181629,1846467,2012,08/07/2012 09:29:17 PM,41.73393689,-87.610182132,"(41.73393689, -87.610182132)"
-8782125,HV413616,08/02/2012 02:00:00 PM,057XX S HONORE ST,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,RESIDENCE,false,false,0715,007,15,67,06,1165046,1866373,2012,09/02/2012 12:43:44 PM,41.788928002,-87.670372294,"(41.788928002, -87.670372294)"
-8736740,HV412067,08/02/2012 01:26:00 PM,050XX W WEST END AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,APARTMENT,true,false,1532,015,28,25,04A,1142804,1900554,2012,08/03/2012 11:01:53 AM,41.883167321,-87.751077602,"(41.883167321, -87.751077602)"
-8737017,HV412444,08/02/2012 09:15:00 AM,008XX W EVERGREEN AVE,0917,MOTOR VEHICLE THEFT,"CYCLE, SCOOTER, BIKE W-VIN",STREET,false,false,1822,018,32,8,07,1170643,1909177,2012,08/03/2012 08:01:18 AM,41.90626557,-87.648598632,"(41.90626557, -87.648598632)"
-8733721,HV409698,07/31/2012 10:10:00 PM,012XX N SPRINGFIELD AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2535,025,27,23,18,1150202,1907790,2012,07/31/2012 11:02:35 PM,41.902882709,-87.723722692,"(41.902882709, -87.723722692)"
-8733322,HV409181,07/31/2012 04:14:00 PM,010XX N WALLER AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,SIDEWALK,true,false,1511,015,29,25,24,1138210,1906173,2012,08/01/2012 12:36:09 PM,41.898670861,-87.767811292,"(41.898670861, -87.767811292)"
-8739644,HV414909,07/31/2012 12:00:00 AM,032XX N Broadway,0320,ROBBERY,STRONGARM - NO WEAPON,,false,false,1925,019,44,6,03,1171635,1921871,2012,08/16/2012 11:00:18 PM,41.941076707,-87.644580134,"(41.941076707, -87.644580134)"
-8732079,HV408258,07/30/2012 11:49:00 PM,015XX N LAWLER AVE,0312,ROBBERY,ARMED:KNIFE/CUTTING INSTRUMENT,RESIDENCE-GARAGE,false,false,2533,025,37,25,03,1142471,1909537,2012,09/02/2012 06:05:02 PM,41.907823917,-87.752076939,"(41.907823917, -87.752076939)"
-8731318,HV407370,07/30/2012 02:05:00 PM,001XX W WASHINGTON ST,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,COMMERCIAL / BUSINESS OFFICE,false,false,0122,001,42,32,04A,1175431,1900774,2012,08/08/2012 01:06:09 PM,41.883101072,-87.631263239,"(41.883101072, -87.631263239)"
-8730762,HV407014,07/30/2012 10:00:00 AM,035XX S UNION AVE,1780,OFFENSE INVOLVING CHILDREN,OTHER OFFENSE,RESIDENCE,false,true,0915,009,11,60,26,1172260,1881133,2012,09/02/2012 09:27:45 PM,41.829275236,-87.643486667,"(41.829275236, -87.643486667)"
-8728444,HV404456,07/28/2012 02:18:00 PM,012XX W 59TH ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0713,007,16,67,08B,1169141,1865617,2012,07/29/2012 07:37:19 AM,41.786765818,-87.655379194,"(41.786765818, -87.655379194)"
-8727499,HV403318,07/27/2012 07:17:00 PM,005XX W 60TH PL,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,true,false,0711,007,20,68,15,1173969,1864831,2012,07/28/2012 09:08:15 AM,41.784503118,-87.637700551,"(41.784503118, -87.637700551)"
-8727517,HV403153,07/27/2012 05:15:00 PM,053XX W WASHINGTON BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,1523,015,28,25,08B,1140609,1900271,2012,08/26/2012 09:28:35 AM,41.882431317,-87.759144831,"(41.882431317, -87.759144831)"
-8727504,HV403340,07/27/2012 05:00:00 PM,045XX N CLARENDON AVE,1780,OFFENSE INVOLVING CHILDREN,OTHER OFFENSE,PARK PROPERTY,false,true,1914,019,46,3,26,1170064,1930106,2012,07/30/2012 02:23:59 PM,41.963708333,-87.650112825,"(41.963708333, -87.650112825)"
-8725667,HV401356,07/26/2012 09:00:00 AM,089XX S BLACKSTONE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0413,004,8,48,05,1187712,1846231,2012,12/20/2013 12:10:36 PM,41.733146868,-87.587904699,"(41.733146868, -87.587904699)"
-8724334,HV400669,07/26/2012 01:40:00 AM,027XX S SACRAMENTO AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1033,010,12,30,08B,1156767,1885652,2012,07/29/2012 09:24:48 AM,41.842003239,-87.700207909,"(41.842003239, -87.700207909)"
-8725086,HV401201,07/25/2012 10:03:00 PM,018XX E 81ST ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0414,004,8,46,14,1190038,1851599,2012,07/27/2012 08:19:59 AM,41.747821572,-87.579211254,"(41.747821572, -87.579211254)"
-8724066,HV400462,07/25/2012 09:35:00 PM,023XX W 69TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0832,008,17,66,18,1161806,1858887,2012,07/26/2012 01:03:35 PM,41.768453337,-87.682460064,"(41.768453337, -87.682460064)"
-8724186,HV400441,07/25/2012 08:00:00 PM,028XX N RUTHERFORD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,2511,025,36,18,08B,1130886,1918314,2012,08/12/2012 09:49:20 AM,41.93211664,-87.794432245,"(41.93211664, -87.794432245)"
-8720457,HV397159,07/23/2012 08:10:00 PM,035XX W WALNUT ST,041A,BATTERY,AGGRAVATED: HANDGUN,SIDEWALK,false,false,1123,011,28,27,04B,1152747,1901428,2012,08/13/2012 10:33:09 PM,41.885374744,-87.714542909,"(41.885374744, -87.714542909)"
-8717976,HV394811,07/22/2012 08:05:00 AM,024XX W THOMAS ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,false,1312,012,1,24,08B,1159937,1907285,2012,07/25/2012 07:08:20 PM,41.901301547,-87.687978076,"(41.901301547, -87.687978076)"
-8717082,HV393225,07/19/2012 12:00:00 AM,035XX N PINE GROVE AVE,0820,THEFT,$500 AND UNDER,,false,false,1925,019,46,6,06,1171392,1924182,2012,07/26/2012 11:54:47 AM,41.94742353,-87.645405076,"(41.94742353, -87.645405076)"
-8712738,HV389345,07/18/2012 04:00:00 PM,023XX W LOGAN BLVD,0890,THEFT,FROM BUILDING,ATHLETIC CLUB,false,false,1432,014,1,22,06,1160445,1917855,2012,07/20/2012 01:28:39 PM,41.930295899,-87.685819017,"(41.930295899, -87.685819017)"
-8709803,HV386612,07/17/2012 12:47:00 AM,093XX S PAXTON AVE,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,false,false,0413,004,7,48,04B,1192440,1843186,2012,08/20/2012 06:22:00 AM,41.724677428,-87.57068291,"(41.724677428, -87.57068291)"
-8709484,HV386265,07/16/2012 07:45:00 PM,015XX W ROOSEVELT RD,1330,CRIMINAL TRESPASS,TO LAND,GAS STATION,true,false,1231,012,2,28,26,1166067,1894741,2012,08/01/2012 07:28:48 AM,41.866751093,-87.665820442,"(41.866751093, -87.665820442)"
-8707046,HV383595,07/15/2012 04:30:00 AM,056XX S SAWYER AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0822,008,14,63,08B,1155710,1867274,2012,07/18/2012 05:52:48 PM,41.791592926,-87.704580279,"(41.791592926, -87.704580279)"
-8708857,HV385356,07/14/2012 05:30:00 AM,056XX W BYRON ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1633,016,38,15,07,1137691,1925310,2012,08/08/2012 09:57:35 AM,41.951194289,-87.769255195,"(41.951194289, -87.769255195)"
-8778704,HV452317,07/13/2012 01:00:00 PM,037XX W DICKENS AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2525,025,26,22,07,1151070,1913655,2012,12/21/2012 12:06:05 PM,41.918959848,-87.720380388,"(41.918959848, -87.720380388)"
-8701643,HV377699,07/11/2012 02:50:00 PM,008XX N MICHIGAN AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,false,false,1833,018,42,8,06,1177368,1906565,2012,07/12/2012 07:11:47 AM,41.898948161,-87.623974758,"(41.898948161, -87.623974758)"
-8702044,HV378360,07/11/2012 12:30:00 AM,013XX N MENARD AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,2531,025,29,25,05,1137405,1908099,2012,08/06/2012 07:36:21 PM,41.903970566,-87.770721666,"(41.903970566, -87.770721666)"
-8720834,HV396427,07/10/2012 09:00:00 AM,076XX S YATES BLVD,1110,DECEPTIVE PRACTICE,BOGUS CHECK,APARTMENT,false,false,0421,004,7,43,11,1193572,1854574,2012,07/25/2012 12:11:16 PM,41.755899489,-87.566164613,"(41.755899489, -87.566164613)"
-8698791,HV375217,07/10/2012 12:30:00 AM,085XX S BURLEY AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,STREET,false,false,0424,004,10,46,04A,1199252,1849034,2012,07/10/2012 07:35:11 AM,41.74055647,-87.545535073,"(41.74055647, -87.545535073)"
-8698255,HV374681,07/09/2012 03:30:00 AM,065XX S CALIFORNIA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0831,008,15,66,14,1158793,1861339,2012,07/10/2012 07:45:41 AM,41.775244021,-87.693437382,"(41.775244021, -87.693437382)"
-8697075,HV373643,07/09/2012 01:30:00 AM,070XX S SOUTH SHORE DR,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0331,003,5,43,07,1193400,1858993,2012,07/12/2012 11:39:05 AM,41.768029769,-87.566650603,"(41.768029769, -87.566650603)"
-8696105,HV372538,07/08/2012 12:20:00 PM,074XX S SOUTH CHICAGO AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,VEHICLE NON-COMMERCIAL,true,false,0324,003,5,69,18,1184924,1856156,2012,07/08/2012 01:30:34 PM,41.7604479,-87.597807541,"(41.7604479, -87.597807541)"
-8696062,HV372440,07/08/2012 11:31:00 AM,050XX W ROSCOE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1634,016,38,15,18,1142021,1922092,2012,07/08/2012 12:44:02 PM,41.942284493,-87.753418108,"(41.942284493, -87.753418108)"
-8695927,HV372296,07/08/2012 08:30:00 AM,065XX N SEELEY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2412,024,50,2,08B,1161402,1943394,2012,07/15/2012 09:21:13 AM,42.000356235,-87.681588394,"(42.000356235, -87.681588394)"
-8692580,HV368196,07/05/2012 03:30:00 PM,112XX S MICHIGAN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,ALLEY,true,false,0531,005,9,49,08B,1178759,1830704,2012,07/06/2012 07:49:47 AM,41.690746802,-87.621173989,"(41.690746802, -87.621173989)"
-8756598,HV424349,07/05/2012 03:00:00 PM,046XX W NORTH AVE,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,RESIDENCE,false,false,2533,025,37,25,11,1144835,1910260,2012,08/19/2012 12:46:12 PM,41.909763619,-87.743374487,"(41.909763619, -87.743374487)"
-8692966,HV368692,07/05/2012 02:00:00 AM,069XX S THROOP ST,0460,BATTERY,SIMPLE,STREET,false,false,0734,007,17,67,08B,1168810,1858982,2012,07/09/2012 04:28:21 PM,41.768565716,-87.656784208,"(41.768565716, -87.656784208)"
-8690397,HV365857,07/04/2012 04:00:00 AM,042XX W MADISON ST,0496,BATTERY,AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST,APARTMENT,false,true,1115,011,28,26,04B,1147966,1899712,2012,07/14/2012 08:39:11 AM,41.880759107,-87.732143909,"(41.880759107, -87.732143909)"
-8690252,HV365573,07/03/2012 10:56:00 PM,009XX N ORLEANS ST,1320,CRIMINAL DAMAGE,TO VEHICLE,ALLEY,false,false,1823,018,27,8,14,1173778,1906880,2012,07/04/2012 08:35:58 AM,41.899893234,-87.637151163,"(41.899893234, -87.637151163)"
-8694640,HV369348,07/02/2012 07:00:00 PM,022XX W GRAND AVE,0890,THEFT,FROM BUILDING,SMALL RETAIL STORE,false,false,1313,012,26,24,06,1161138,1903474,2012,07/07/2012 10:52:03 AM,41.890818976,-87.683672683,"(41.890818976, -87.683672683)"
-8685708,HV361109,07/01/2012 03:00:00 AM,034XX N ELSTON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1733,017,33,21,08B,1155285,1922663,2012,07/03/2012 08:16:41 AM,41.943594769,-87.704651262,"(41.943594769, -87.704651262)"
-8688193,HV363852,06/29/2012 04:00:00 PM,009XX N OAKLEY BLVD,0820,THEFT,$500 AND UNDER,STREET,false,false,1312,012,32,24,06,1160933,1906575,2012,07/03/2012 08:52:24 AM,41.899332631,-87.6843394,"(41.899332631, -87.6843394)"
-8683576,HV358559,06/29/2012 12:10:00 PM,014XX S KEDVALE AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),ABANDONED BUILDING,true,false,1011,010,24,29,18,1148922,1892420,2012,06/29/2012 01:12:21 PM,41.860730573,-87.728822089,"(41.860730573, -87.728822089)"
-8681095,HV356197,06/26/2012 08:00:00 AM,024XX N SHEFFIELD AVE,0810,THEFT,OVER $500,STREET,false,false,1932,019,43,7,06,1169183,1916147,2012,06/28/2012 08:54:35 AM,41.925423491,-87.653758857,"(41.925423491, -87.653758857)"
-8678121,HV353460,06/25/2012 06:00:00 PM,016XX W 105TH ST,0810,THEFT,OVER $500,STREET,false,false,2212,022,19,72,06,1166912,1835085,2012,06/27/2012 07:20:46 AM,41.703029584,-87.664422123,"(41.703029584, -87.664422123)"
-8678078,HV352301,06/25/2012 01:28:00 PM,024XX W 47TH ST,0460,BATTERY,SIMPLE,CURRENCY EXCHANGE,false,false,0922,009,12,58,08B,1161126,1873491,2012,07/11/2012 03:42:47 PM,41.808542758,-87.68454866,"(41.808542758, -87.68454866)"
-8680482,HV351831,06/22/2012 05:00:00 PM,044XX N CENTRAL AVE,0810,THEFT,OVER $500,STREET,false,false,1623,016,45,15,06,1138248,1928952,2012,06/28/2012 09:30:00 AM,41.961178206,-87.767119197,"(41.961178206, -87.767119197)"
-8691881,HV346450,06/21/2012 06:12:00 PM,037XX W CHICAGO AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,APARTMENT,true,false,1112,011,27,23,18,1151260,1905041,2012,07/11/2012 11:18:18 AM,41.895318483,-87.719908633,"(41.895318483, -87.719908633)"
-8670091,HV345276,06/20/2012 09:00:00 PM,110XX S WESTERN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,COMMERCIAL / BUSINESS OFFICE,false,false,2212,022,19,75,14,1162314,1831613,2012,06/21/2012 06:43:03 AM,41.693598579,-87.681355251,"(41.693598579, -87.681355251)"
-8669818,HV344880,06/20/2012 08:15:00 PM,083XX S STEWART AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0622,006,21,44,06,1175115,1849742,2012,06/21/2012 06:10:36 AM,41.743071624,-87.633948682,"(41.743071624, -87.633948682)"
-8669280,HV344028,06/20/2012 12:00:00 PM,106XX S EDBROOKE AVE,0810,THEFT,OVER $500,STREET,false,false,0512,005,9,49,06,1179099,1834590,2012,06/21/2012 06:51:48 AM,41.701402813,-87.619811378,"(41.701402813, -87.619811378)"
-8672653,HV343627,06/19/2012 03:30:00 PM,065XX S CHAMPLAIN AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0321,003,20,42,06,1181737,1861593,2012,06/23/2012 06:58:55 AM,41.775441755,-87.609320135,"(41.775441755, -87.609320135)"
-8663205,HV338481,06/16/2012 10:50:00 PM,058XX N MEDINA AVE,0560,ASSAULT,SIMPLE,STREET,false,false,1622,016,45,10,08A,1134505,1938775,2012,06/18/2012 09:02:58 AM,41.988200472,-87.780647694,"(41.988200472, -87.780647694)"
-8662645,HV337633,06/16/2012 11:19:00 AM,075XX S CARPENTER ST,3760,INTERFERENCE WITH PUBLIC OFFICER,OBSTRUCTING SERVICE,RESIDENCE,true,false,0612,006,17,71,24,1170660,1854784,2012,06/17/2012 10:18:33 AM,41.75700574,-87.650125267,"(41.75700574, -87.650125267)"
-8660118,HV335090,06/14/2012 07:42:00 PM,063XX W FULLERTON AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,PARK PROPERTY,true,false,2512,025,29,19,24,1133847,1915308,2012,06/15/2012 10:24:41 AM,41.923816239,-87.783621592,"(41.923816239, -87.783621592)"
-8658543,HV333813,06/14/2012 01:30:00 AM,024XX N CLYBOURN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,true,true,1931,019,32,7,08B,1164918,1915987,2012,03/10/2013 04:03:40 PM,41.925076125,-87.669435003,"(41.925076125, -87.669435003)"
-9019372,HW166261,06/12/2012 09:00:00 AM,013XX S SACRAMENTO DR,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",false,false,1022,010,24,29,06,1156216,1893730,2012,03/04/2013 02:28:31 PM,41.864181304,-87.702011974,"(41.864181304, -87.702011974)"
-8654237,HV329565,06/11/2012 04:00:00 PM,043XX W WEST END AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1114,011,28,26,08B,1147147,1900574,2012,06/16/2012 03:16:51 PM,41.883140238,-87.735129177,"(41.883140238, -87.735129177)"
-8652150,HV327782,06/10/2012 01:00:00 PM,073XX S DAMEN AVE,0820,THEFT,$500 AND UNDER,STREET,false,true,0735,007,17,67,06,1164337,1855926,2012,06/15/2012 09:03:09 AM,41.760275009,-87.673265964,"(41.760275009, -87.673265964)"
-8651233,HV326643,06/09/2012 01:15:00 PM,010XX W WINONA ST,0890,THEFT,FROM BUILDING,RESIDENTIAL YARD (FRONT/BACK),false,false,2024,020,46,3,06,1168482,1934266,2012,06/11/2012 11:09:01 AM,41.975157968,-87.65580834,"(41.975157968, -87.65580834)"
-8652000,HV327502,06/08/2012 04:00:00 PM,011XX N LA SALLE DR,0820,THEFT,$500 AND UNDER,STREET,false,false,1824,018,43,8,06,1174888,1908039,2012,06/10/2012 12:33:03 PM,41.903048799,-87.633039407,"(41.903048799, -87.633039407)"
-8652102,HV326699,06/08/2012 11:00:00 AM,036XX S RHODES AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0212,002,4,35,14,1180140,1880967,2012,06/10/2012 02:13:13 PM,41.828642443,-87.614580601,"(41.828642443, -87.614580601)"
-8658159,HV333474,06/06/2012 09:00:00 PM,013XX N HOMAN AVE,0890,THEFT,FROM BUILDING,RESIDENCE PORCH/HALLWAY,false,false,1422,014,26,23,06,1153421,1908458,2012,06/14/2012 10:53:47 AM,41.904652383,-87.711880857,"(41.904652383, -87.711880857)"
-8643881,HV319179,06/05/2012 01:45:00 AM,010XX N CENTRAL AVE,0820,THEFT,$500 AND UNDER,RESIDENCE,false,true,1511,015,29,25,06,1138784,1906365,2012,06/05/2012 08:29:13 AM,41.899187331,-87.765698323,"(41.899187331, -87.765698323)"
-8639719,HV314336,06/01/2012 07:50:00 PM,076XX S INGLESIDE AVE,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,PARK PROPERTY,true,false,0624,006,8,69,18,1183923,1854644,2012,06/01/2012 09:20:31 PM,41.756322249,-87.601523334,"(41.756322249, -87.601523334)"
-8639648,HV314276,06/01/2012 07:00:00 PM,062XX S KILBOURN AVE,1780,OFFENSE INVOLVING CHILDREN,OTHER OFFENSE,APARTMENT,false,false,0813,008,13,65,26,1147480,1863020,2012,06/18/2012 02:52:40 PM,41.780080472,-87.734867079,"(41.780080472, -87.734867079)"
-8641603,HV314163,06/01/2012 06:10:00 PM,060XX N SHERIDAN RD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,2433,024,48,77,08B,1168569,1940512,2012,06/06/2012 12:23:53 PM,41.992295252,-87.65530659,"(41.992295252, -87.65530659)"
-8641357,HV316429,06/01/2012 02:30:00 PM,0000X E 112TH PL,0890,THEFT,FROM BUILDING,BARBERSHOP,false,false,0531,005,9,49,06,1178586,1830347,2012,06/04/2012 03:20:05 PM,41.689771066,-87.621818146,"(41.689771066, -87.621818146)"
-9047741,HW192464,06/01/2012 06:00:00 AM,057XX S DORCHESTER AVE,0281,CRIM SEXUAL ASSAULT,NON-AGGRAVATED,RESIDENCE,false,true,0235,002,5,41,02,1186482,1867417,2012,04/13/2013 10:05:51 AM,41.79131235,-87.591741595,"(41.79131235, -87.591741595)"
-8635872,HV310025,05/29/2012 10:30:00 PM,045XX N ARTESIAN AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1911,019,47,4,06,1159258,1930157,2012,05/30/2012 11:41:47 AM,41.964077845,-87.689841562,"(41.964077845, -87.689841562)"
-8635205,HV309669,05/29/2012 06:30:00 PM,082XX S TRIPP AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,0834,008,13,70,14,1149102,1849618,2012,05/30/2012 09:06:27 AM,41.743271978,-87.729265531,"(41.743271978, -87.729265531)"
-8631342,HV305139,05/27/2012 12:00:00 AM,040XX W HIRSCH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,2534,025,27,23,14,1148824,1908965,2012,05/28/2012 10:10:51 AM,41.906133787,-87.728753967,"(41.906133787, -87.728753967)"
-8631615,HV305515,05/26/2012 07:30:00 PM,059XX W MIDWAY PARK,0820,THEFT,$500 AND UNDER,STREET,false,false,1512,015,29,25,06,1136332,1902581,2012,05/29/2012 08:54:59 AM,41.888847715,-87.774795081,"(41.888847715, -87.774795081)"
-8631158,HV304541,05/26/2012 02:30:00 PM,002XX W GARFIELD BLVD,0820,THEFT,$500 AND UNDER,CTA PLATFORM,false,false,0935,009,3,37,06,1175655,1868540,2012,05/27/2012 12:10:37 PM,41.794643399,-87.631408003,"(41.794643399, -87.631408003)"
-8642794,HV315554,05/25/2012 05:00:00 PM,029XX N ROCKWELL ST,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,RESIDENCE,false,false,1411,014,1,21,06,1158575,1919329,2012,06/05/2012 10:43:35 AM,41.934379203,-87.692650349,"(41.934379203, -87.692650349)"
-8629536,HV302800,05/25/2012 12:50:00 PM,0000X W CTA 69TH ST LN,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA GARAGE / OTHER PROPERTY,true,false,0322,003,6,69,11,1177342,1859092,2012,05/26/2012 09:50:16 AM,41.768679147,-87.625507039,"(41.768679147, -87.625507039)"
-8628525,HV301826,05/24/2012 07:03:00 PM,066XX N WESTERN AVE,2021,NARCOTICS,POSS: BARBITUATES,SIDEWALK,true,false,2412,024,50,2,18,1159144,1944294,2012,05/24/2012 08:44:38 PM,42.002872728,-87.689870218,"(42.002872728, -87.689870218)"
-8628713,HV301852,05/24/2012 06:50:00 PM,008XX N MICHIGAN AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,1833,018,42,8,06,1177298,1906201,2012,05/25/2012 12:56:59 PM,41.897950916,-87.624242912,"(41.897950916, -87.624242912)"
-8630841,HV303343,05/22/2012 10:00:00 PM,008XX E 87TH PL,0820,THEFT,$500 AND UNDER,ALLEY,false,true,0632,006,8,44,06,1183770,1847194,2012,10/31/2014 03:20:56 PM,41.735882228,-87.602315991,"(41.735882228, -87.602315991)"
-8669705,HV344216,05/22/2012 11:10:00 AM,023XX W GRAND AVE,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,OTHER,false,false,1313,012,26,24,11,1160573,1903457,2012,07/26/2012 11:36:56 AM,41.890784046,-87.685748114,"(41.890784046, -87.685748114)"
-8623741,HV297509,05/19/2012 08:00:00 AM,007XX W FULTON MARKET,0890,THEFT,FROM BUILDING,APARTMENT,false,false,1212,012,27,28,06,1171022,1902125,2012,05/22/2012 03:21:57 PM,41.886906142,-87.647413534,"(41.886906142, -87.647413534)"
-8619940,HV293456,05/18/2012 09:00:00 PM,032XX W POTOMAC AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1422,014,26,23,06,1154489,1908506,2012,05/22/2012 09:42:40 AM,41.904762801,-87.707956481,"(41.904762801, -87.707956481)"
-8620799,HV292454,05/18/2012 05:00:00 PM,0000X E 87TH ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0632,006,6,44,06,1178576,1847300,2012,05/20/2012 10:59:32 AM,41.736292592,-87.621341537,"(41.736292592, -87.621341537)"
-8617765,HV291031,05/17/2012 07:00:00 AM,029XX N MONITOR AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,2514,025,30,19,05,1136842,1918713,2012,06/15/2012 08:26:11 PM,41.933106724,-87.772534787,"(41.933106724, -87.772534787)"
-8615169,HV288861,05/16/2012 11:05:00 AM,008XX W SUNNYSIDE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1914,019,46,3,18,1169688,1930041,2012,05/16/2012 11:39:38 AM,41.96353819,-87.651497149,"(41.96353819, -87.651497149)"
-8611059,HV285278,05/14/2012 01:35:00 AM,043XX W ADAMS ST,0497,BATTERY,AGGRAVATED DOMESTIC BATTERY: OTHER DANG WEAPON,RESIDENCE,false,true,1115,011,28,26,04B,1147331,1898695,2012,05/16/2012 04:26:04 PM,41.877980523,-87.734501656,"(41.877980523, -87.734501656)"
-8609345,HV283125,05/11/2012 03:00:00 PM,058XX N WHIPPLE ST,0820,THEFT,$500 AND UNDER,RESIDENCE,false,false,2011,020,40,2,06,1155018,1938471,2012,05/14/2012 10:56:33 AM,41.986978237,-87.70520668,"(41.986978237, -87.70520668)"
-8608773,HV282295,05/11/2012 07:30:00 AM,046XX S EVANS AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0221,002,4,38,14,1182076,1874427,2012,05/12/2012 07:59:35 AM,41.810651531,-87.607680287,"(41.810651531, -87.607680287)"
-8609380,HV283172,05/10/2012 12:00:00 PM,0000X N CENTRAL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1513,015,29,25,08B,1138998,1900113,2012,05/14/2012 02:12:45 PM,41.88202717,-87.765064376,"(41.88202717, -87.765064376)"
-8606119,HV279814,05/10/2012 08:00:00 AM,025XX W DIVISION ST,031A,ROBBERY,ARMED: HANDGUN,VEHICLE NON-COMMERCIAL,false,false,1312,012,26,24,03,1159152,1907850,2012,06/17/2012 10:12:42 AM,41.902868122,-87.690845917,"(41.902868122, -87.690845917)"
-8603802,HV277767,05/08/2012 09:20:00 PM,001XX E 71ST ST,1330,CRIMINAL TRESPASS,TO LAND,SIDEWALK,true,false,0322,003,6,69,26,1178758,1858024,2012,05/09/2012 09:16:06 AM,41.765716346,-87.620349179,"(41.765716346, -87.620349179)"
-8602636,HV276524,05/08/2012 08:20:00 AM,076XX S RHODES AVE,0820,THEFT,$500 AND UNDER,APARTMENT,true,true,0624,006,6,69,06,1181197,1854108,2012,05/09/2012 08:19:43 AM,41.754914605,-87.611529951,"(41.754914605, -87.611529951)"
-8602715,HV276546,05/07/2012 10:00:00 PM,025XX E 71ST ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0334,003,7,43,14,1194985,1858337,2012,05/08/2012 12:00:40 PM,41.766190743,-87.560862559,"(41.766190743, -87.560862559)"
-8646153,HV321150,05/05/2012 12:05:00 PM,042XX W 63RD ST,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,RESIDENCE,false,false,0813,008,13,65,11,1149239,1862449,2012,06/12/2012 02:28:31 PM,41.778479781,-87.728432954,"(41.778479781, -87.728432954)"
-8597902,HV271639,05/04/2012 06:37:00 PM,065XX S JUSTINE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0725,007,17,67,18,1167084,1861589,2012,05/04/2012 07:51:58 PM,41.775756744,-87.66303638,"(41.775756744, -87.66303638)"
-8596376,HV270390,05/03/2012 03:48:00 PM,031XX N PINE GROVE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1934,019,44,6,06,1172314,1921284,2012,05/04/2012 10:31:23 AM,41.93945096,-87.64210197,"(41.93945096, -87.64210197)"
-8594229,HV268270,05/02/2012 05:30:00 PM,057XX S THROOP ST,0460,BATTERY,SIMPLE,APARTMENT,false,false,0713,007,16,67,08B,1168598,1866710,2012,05/05/2012 01:31:43 PM,41.789776871,-87.657338611,"(41.789776871, -87.657338611)"
-8593951,HV267902,05/02/2012 01:30:00 PM,017XX N KEELER AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2534,025,30,20,08B,1148147,1911543,2012,05/03/2012 08:01:32 AM,41.913221143,-87.73117438,"(41.913221143, -87.73117438)"
-8585706,HV259905,04/26/2012 11:20:00 PM,048XX N SAWYER AVE,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,1713,017,39,14,06,1153800,1931903,2012,04/27/2012 10:17:13 AM,41.968979702,-87.709862353,"(41.968979702, -87.709862353)"
-8585468,HV259642,04/26/2012 07:28:00 PM,019XX W 70TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0735,007,17,67,18,1164526,1858206,2012,04/26/2012 08:33:22 PM,41.766527664,-87.672509077,"(41.766527664, -87.672509077)"
-8584053,HV258407,04/24/2012 04:00:00 PM,056XX S SANGAMON ST,0820,THEFT,$500 AND UNDER,RESIDENCE,false,true,0712,007,16,68,06,1170895,1867580,2012,04/27/2012 03:44:34 PM,41.79211438,-87.648890808,"(41.79211438, -87.648890808)"
-8582091,HV256096,04/23/2012 11:00:00 AM,068XX S DORCHESTER AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,false,false,0321,003,5,43,11,1186674,1860096,2012,05/03/2012 03:09:42 PM,41.771218375,-87.591269244,"(41.771218375, -87.591269244)"
-8579874,HV254421,04/23/2012 02:00:00 AM,075XX S NORMAL AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,false,true,0621,006,17,69,26,1174329,1854786,2012,05/23/2012 03:52:24 PM,41.756930484,-87.636678968,"(41.756930484, -87.636678968)"
-8579192,HV253936,04/23/2012 12:40:00 AM,005XX E 63RD ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0312,003,20,42,18,1180970,1863318,2012,04/23/2012 02:15:15 AM,41.780193007,-87.612078805,"(41.780193007, -87.612078805)"
-8578730,HV253411,04/22/2012 03:40:00 PM,057XX S CARPENTER ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,0712,007,16,68,14,1170335,1866716,2012,04/23/2012 02:35:52 PM,41.789755681,-87.650969379,"(41.789755681, -87.650969379)"
-8578418,HV252952,04/22/2012 09:34:00 AM,0000X S LOTUS AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1522,015,29,25,18,1139926,1899441,2012,04/22/2012 10:45:44 AM,41.880166203,-87.761673154,"(41.880166203, -87.761673154)"
-8575475,HV249684,04/19/2012 07:50:00 PM,075XX N ASHLAND AVE,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,false,false,2422,024,49,1,04B,1164177,1950187,2012,12/09/2013 09:10:58 PM,42.018937949,-87.67118649,"(42.018937949, -87.67118649)"
-8575924,HV250191,04/19/2012 06:30:00 PM,083XX S COTTAGE GROVE AVE,031A,ROBBERY,ARMED: HANDGUN,GROCERY FOOD STORE,false,false,0632,006,8,44,03,1183053,1849808,2012,06/20/2012 08:27:03 PM,41.743072016,-87.604861723,"(41.743072016, -87.604861723)"
-8573885,HV248462,04/19/2012 01:26:00 AM,016XX E 93RD ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0413,004,8,48,14,1188816,1843678,2012,04/30/2012 06:54:46 AM,41.726114865,-87.583941708,"(41.726114865, -87.583941708)"
-8573133,HV247525,04/18/2012 12:26:00 PM,056XX N CENTRAL AVE,0495,BATTERY,AGGRAVATED OF A SENIOR CITIZEN,RESTAURANT,false,false,1623,016,45,11,04B,1137909,1937492,2012,05/05/2012 07:27:24 PM,41.984618854,-87.768158423,"(41.984618854, -87.768158423)"
-8572337,HV247001,04/18/2012 02:32:00 AM,084XX S PEORIA ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0613,006,21,71,08B,1171745,1848776,2012,04/18/2012 05:45:44 AM,41.740495287,-87.646324732,"(41.740495287, -87.646324732)"
-8572107,HV246743,04/17/2012 07:31:00 PM,057XX S WINCHESTER AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0715,007,15,67,14,1164292,1866788,2012,04/18/2012 06:45:01 AM,41.790082736,-87.673125275,"(41.790082736, -87.673125275)"
-8572879,HV247242,04/17/2012 02:00:00 PM,029XX W POLK ST,0820,THEFT,$500 AND UNDER,"SCHOOL, PUBLIC, BUILDING",false,false,1135,011,28,27,06,1156777,1896187,2012,04/19/2012 10:15:35 AM,41.870912227,-87.699885997,"(41.870912227, -87.699885997)"
-8575778,HV250136,04/17/2012 01:00:00 AM,033XX S LOWE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0915,009,11,60,06,1172465,1882984,2012,04/20/2012 11:30:59 AM,41.834350025,-87.642679933,"(41.834350025, -87.642679933)"
-8569855,HV244629,04/16/2012 12:30:00 PM,085XX S INGLESIDE AVE,0545,ASSAULT,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,0632,006,8,44,08A,1184079,1848753,2012,04/17/2012 08:42:41 AM,41.740153086,-87.601135348,"(41.740153086, -87.601135348)"
-8567357,HV242221,04/14/2012 07:15:00 PM,033XX W PIERCE AVE,0820,THEFT,$500 AND UNDER,STREET,false,true,1422,014,26,23,06,1153567,1910054,2012,04/16/2012 09:49:28 AM,41.909029048,-87.711302041,"(41.909029048, -87.711302041)"
-8761437,HV436101,04/12/2012 06:45:00 PM,016XX W 105TH PL,051B,ASSAULT,AGGRAVATED: OTHER FIREARM,DRIVEWAY - RESIDENTIAL,false,false,2212,022,19,72,04A,1167209,1834765,2012,09/24/2012 02:18:02 PM,41.702145116,-87.663343692,"(41.702145116, -87.663343692)"
-8562941,HV237772,04/11/2012 04:54:00 PM,028XX N DRAKE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1412,014,35,21,14,1152188,1918680,2012,04/12/2012 10:00:20 AM,41.932726871,-87.716139818,"(41.932726871, -87.716139818)"
-8562145,HV237215,04/11/2012 10:49:00 AM,033XX N ASHLAND AVE,0460,BATTERY,SIMPLE,DEPARTMENT STORE,true,false,1922,019,44,6,08B,1165028,1921997,2012,04/12/2012 08:20:19 AM,41.94156556,-87.668859659,"(41.94156556, -87.668859659)"
-8563848,HV238662,04/10/2012 06:00:00 PM,019XX W WILSON AVE,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,1912,019,47,4,06,1162436,1930597,2012,04/13/2012 08:29:00 AM,41.965219137,-87.678144646,"(41.965219137, -87.678144646)"
-8561022,HV236200,04/10/2012 11:59:00 AM,035XX S DR MARTIN LUTHER KING JR DR,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,OTHER,false,false,0212,002,4,35,26,1179528,1881716,2012,04/17/2012 01:49:29 PM,41.830711784,-87.61680304,"(41.830711784, -87.61680304)"
-8565863,HV240430,04/09/2012 08:00:00 PM,027XX W 38TH ST,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,0911,009,12,58,06,1158609,1879296,2012,04/14/2012 10:05:18 AM,41.824524173,-87.693622,"(41.824524173, -87.693622)"
-8556826,HV232670,04/07/2012 09:00:00 PM,026XX W GLADYS AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,1125,011,28,27,26,1158977,1898374,2012,05/10/2012 12:32:13 PM,41.876868732,-87.691748981,"(41.876868732, -87.691748981)"
-8556774,HV232597,04/07/2012 08:43:00 PM,021XX S DRAKE AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENTIAL YARD (FRONT/BACK),true,false,1024,010,24,29,26,1153112,1889677,2012,04/08/2012 11:58:23 AM,41.853121477,-87.713514134,"(41.853121477, -87.713514134)"
-8556355,HV232065,04/07/2012 09:00:00 AM,028XX E 84TH ST,0820,THEFT,$500 AND UNDER,RESIDENCE PORCH/HALLWAY,false,false,0423,004,10,46,06,1196817,1849869,2012,04/08/2012 07:44:12 AM,41.742908598,-87.554428774,"(41.742908598, -87.554428774)"
-8742646,HV407063,04/06/2012 12:00:00 PM,045XX S MICHIGAN AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,0215,002,3,38,06,1177885,1874735,2012,08/07/2012 10:56:05 AM,41.811592789,-87.623042964,"(41.811592789, -87.623042964)"
-8555602,HV231037,04/06/2012 07:15:00 AM,001XX E WACKER DR,0820,THEFT,$500 AND UNDER,HOTEL/MOTEL,false,false,0114,001,42,32,06,1177785,1902579,2012,04/07/2012 11:57:14 AM,41.888000915,-87.622564433,"(41.888000915, -87.622564433)"
-8590527,HV230287,04/05/2012 08:00:00 PM,006XX W 129TH PL,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0523,005,9,53,07,1174290,1818880,2012,05/25/2012 01:24:06 PM,41.658400059,-87.63788462,"(41.658400059, -87.63788462)"
-8554015,HV229644,04/05/2012 06:30:00 PM,024XX N NEW ENGLAND AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2512,025,36,18,18,1129969,1915661,2012,04/05/2012 07:11:45 PM,41.92485225,-87.79786305,"(41.92485225, -87.79786305)"
-8554151,HV229608,04/05/2012 06:00:00 PM,063XX S RHODES AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0312,003,20,42,14,1180958,1862832,2012,04/06/2012 12:46:46 PM,41.778859653,-87.612137741,"(41.778859653, -87.612137741)"
-8552329,HV228218,04/04/2012 05:00:00 PM,082XX S EVANS AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0631,006,6,44,26,1182711,1850180,2012,04/05/2012 03:39:23 PM,41.744100762,-87.606103294,"(41.744100762, -87.606103294)"
-8553267,HV228883,04/04/2012 02:00:00 PM,065XX S WASHTENAW AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENTIAL YARD (FRONT/BACK),false,false,0831,008,15,66,26,1159467,1860962,2012,04/07/2012 09:23:44 PM,41.77419569,-87.690976888,"(41.77419569, -87.690976888)"
-8551852,HV227702,04/04/2012 11:57:00 AM,080XX S ELLIS AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0631,006,8,44,18,1184243,1852130,2012,04/04/2012 01:04:16 PM,41.749416101,-87.600429097,"(41.749416101, -87.600429097)"
-8550902,HV226653,04/03/2012 10:30:00 AM,069XX S PAXTON AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0331,003,5,43,05,1192125,1859119,2012,04/10/2012 03:25:17 PM,41.768406615,-87.571319889,"(41.768406615, -87.571319889)"
-8549744,HV225861,04/03/2012 03:15:00 AM,016XX W 18TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1222,012,25,31,08B,1165476,1891501,2012,04/08/2012 10:29:45 AM,41.857872835,-87.668082232,"(41.857872835, -87.668082232)"
-8549475,HV225488,04/01/2012 08:00:00 PM,017XX W WABANSIA AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,1433,014,32,24,06,1164312,1911322,2012,04/04/2012 10:13:43 AM,41.912287919,-87.671793947,"(41.912287919, -87.671793947)"
-8546265,HV221938,03/31/2012 02:51:00 AM,063XX S DR MARTIN LUTHER KING JR DR,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,true,0312,003,20,69,14,1179978,1862707,2012,04/02/2012 08:58:12 AM,41.778539138,-87.615734292,"(41.778539138, -87.615734292)"
-8551096,HV226673,03/31/2012 01:00:00 AM,011XX W 51ST ST,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0933,009,16,61,03,1169631,1871020,2012,04/08/2012 01:05:40 PM,41.801581646,-87.653425916,"(41.801581646, -87.653425916)"
-8545511,HV221010,03/30/2012 01:00:00 PM,008XX E 133RD PL,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",true,false,0533,005,9,54,08B,1184160,1817039,2012,03/31/2012 08:18:14 AM,41.653123877,-87.601825426,"(41.653123877, -87.601825426)"
-8544255,HV220018,03/29/2012 04:00:00 PM,042XX W BYRON ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1731,017,38,16,07,1146924,1925535,2012,04/02/2012 09:40:55 AM,41.951639939,-87.735308905,"(41.951639939, -87.735308905)"
-8559788,HV219312,03/29/2012 10:05:00 AM,003XX S SACRAMENTO BLVD,2017,NARCOTICS,MANU/DELIVER:CRACK,VEHICLE NON-COMMERCIAL,true,false,1124,011,28,27,18,1156396,1898085,2012,04/18/2012 02:21:07 PM,41.876128239,-87.701233473,"(41.876128239, -87.701233473)"
-8541124,HV217186,03/27/2012 07:10:00 PM,019XX W VAN BUREN ST,0460,BATTERY,SIMPLE,COLLEGE/UNIVERSITY GROUNDS,true,false,1211,012,2,28,08B,1163738,1898218,2012,03/28/2012 11:24:48 AM,41.876341664,-87.674272449,"(41.876341664, -87.674272449)"
-8583035,HV257505,03/27/2012 12:01:00 AM,043XX N KENNETH AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,1722,017,38,16,06,1145727,1928410,2012,06/04/2012 12:33:00 PM,41.95955201,-87.739635887,"(41.95955201, -87.739635887)"
-8536015,HV212727,03/24/2012 02:40:00 PM,035XX W FLOURNOY ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1133,011,24,27,18,1152892,1896763,2012,03/24/2012 03:35:33 PM,41.87257062,-87.714134032,"(41.87257062, -87.714134032)"
-8549938,HV212650,03/24/2012 12:45:54 PM,020XX N SAWYER AVE,2022,NARCOTICS,POSS: COCAINE,APARTMENT,true,false,1413,014,35,22,18,1154213,1913732,2012,04/06/2012 02:22:40 PM,41.919108918,-87.708830542,"(41.919108918, -87.708830542)"
-8532515,HV209192,03/22/2012 12:00:00 AM,034XX N CENTRAL AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,OTHER,false,false,1633,016,38,15,14,1138372,1922501,2012,03/22/2012 01:26:15 PM,41.943473788,-87.766820077,"(41.943473788, -87.766820077)"
-8531688,HV208081,03/21/2012 02:45:00 PM,010XX N FRANCISCO AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,HOSPITAL BUILDING/GROUNDS,false,true,1311,012,26,24,08B,1156820,1906978,2012,03/31/2012 10:26:55 AM,41.900522885,-87.699435477,"(41.900522885, -87.699435477)"
-8530733,HV207701,03/21/2012 06:30:00 AM,052XX S DREXEL AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0233,002,5,41,07,1183092,1870494,2012,03/23/2012 08:00:46 AM,41.799835467,-87.604076149,"(41.799835467, -87.604076149)"
-8528962,HV206095,03/20/2012 11:05:00 AM,066XX S MARYLAND AVE,1661,GAMBLING,GAME/DICE,RESIDENTIAL YARD (FRONT/BACK),true,false,0321,003,5,42,19,1183071,1861360,2012,03/20/2012 11:06:58 AM,41.774771458,-87.604437136,"(41.774771458, -87.604437136)"
-8526127,HV204026,03/18/2012 11:50:00 PM,071XX S DR MARTIN LUTHER KING JR DR,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0323,003,6,69,08B,1180190,1857608,2012,04/03/2012 05:22:08 PM,41.764542106,-87.6151132,"(41.764542106, -87.6151132)"
-8527704,HV204791,03/18/2012 10:30:00 PM,003XX E OHIO ST,0810,THEFT,OVER $500,STREET,false,false,1834,018,42,8,06,1178885,1904235,2012,03/20/2012 07:22:59 AM,41.892519983,-87.618474255,"(41.892519983, -87.618474255)"
-8524723,HV202348,03/17/2012 07:45:00 PM,020XX W 69TH ST,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,0735,007,17,67,18,1164184,1858860,2012,03/17/2012 07:31:07 PM,41.768329534,-87.673744258,"(41.768329534, -87.673744258)"
-8536309,HV205338,03/17/2012 02:00:00 PM,035XX W 116TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,2211,022,19,74,08B,1154906,1827486,2012,04/12/2012 10:48:09 AM,41.682423961,-87.708587502,"(41.682423961, -87.708587502)"
-8522814,HV198568,03/15/2012 10:30:00 AM,059XX N CALIFORNIA AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,false,2011,020,40,2,08B,1156635,1939501,2012,03/22/2012 02:34:09 PM,41.989771908,-87.699231258,"(41.989771908, -87.699231258)"
-8520709,HV197625,03/14/2012 07:05:00 PM,049XX W KAMERLING AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,2533,025,37,25,08B,1143259,1908489,2012,03/15/2012 06:44:48 AM,41.9049334,-87.749208425,"(41.9049334, -87.749208425)"
-8520352,HV197126,03/14/2012 12:37:00 PM,065XX S LAFLIN ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,RESIDENCE,true,false,0725,007,17,67,18,1167429,1861062,2012,03/14/2012 02:28:27 PM,41.774303202,-87.661786728,"(41.774303202, -87.661786728)"
-8520059,HV196809,03/14/2012 07:45:00 AM,050XX S ARTESIAN AVE,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0923,009,14,63,08A,1160846,1870986,2012,05/22/2014 12:38:42 PM,41.801674518,-87.68564488,"(41.801674518, -87.68564488)"
-8519188,HV195689,03/13/2012 12:55:00 PM,112XX S WESTERN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CURRENCY EXCHANGE,false,true,2212,022,19,75,08B,1162357,1830213,2012,03/21/2012 09:26:28 AM,41.689755837,-87.681236621,"(41.689755837, -87.681236621)"
-8517659,HV194867,03/12/2012 10:00:00 PM,079XX S HERMITAGE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0611,006,21,71,08B,1166015,1852212,2012,03/21/2012 10:21:37 AM,41.750047777,-87.667221432,"(41.750047777, -87.667221432)"
-8515199,HV192319,03/11/2012 02:00:00 AM,021XX E 96TH PL,0496,BATTERY,AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST,RESIDENCE,false,true,0431,004,7,51,04B,1191865,1841431,2012,04/15/2012 10:23:26 AM,41.719875493,-87.572845884,"(41.719875493, -87.572845884)"
-8514805,HV191863,03/10/2012 05:15:00 PM,0000X W HURON ST,0870,THEFT,POCKET-PICKING,GROCERY FOOD STORE,true,false,1832,018,42,8,06,1175745,1905098,2012,03/11/2012 11:51:55 AM,41.894959308,-87.629980079,"(41.894959308, -87.629980079)"
-8514372,HV191085,03/10/2012 07:25:00 AM,033XX N PULASKI RD,0860,THEFT,RETAIL THEFT,DRUG STORE,false,false,1732,017,30,21,06,1149182,1921804,2012,03/11/2012 07:17:17 AM,41.941358241,-87.727105464,"(41.941358241, -87.727105464)"
-8511951,HV188768,03/08/2012 12:30:00 PM,007XX W 31ST ST,0560,ASSAULT,SIMPLE,RESTAURANT,true,false,0915,009,11,60,08A,1171606,1884286,2012,03/16/2012 11:08:39 AM,41.837941738,-87.645793568,"(41.837941738, -87.645793568)"
-8506953,HV184090,03/05/2012 12:31:00 PM,061XX N RICHMOND ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2413,024,50,2,06,1155613,1940497,2012,03/06/2012 08:18:13 AM,41.992525681,-87.702963403,"(41.992525681, -87.702963403)"
-8506533,HV183688,03/05/2012 08:53:00 AM,083XX S RACINE AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,false,0613,006,21,71,06,1169812,1849596,2012,03/08/2012 07:52:52 AM,41.742787608,-87.653383299,"(41.742787608, -87.653383299)"
-8504949,HV181647,03/02/2012 07:20:00 AM,026XX E 95TH ST,0820,THEFT,$500 AND UNDER,OTHER,false,false,0423,004,7,48,06,1195615,1842521,2012,03/04/2012 08:52:25 AM,41.722774831,-87.55907504,"(41.722774831, -87.55907504)"
-8503005,HV179775,03/02/2012 01:35:00 AM,007XX E 79TH ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0624,006,6,44,08B,1182683,1852751,2012,03/03/2012 07:46:51 AM,41.751156513,-87.606126271,"(41.751156513, -87.606126271)"
-8496611,HV173990,02/26/2012 09:59:00 PM,059XX S DAMEN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0714,007,15,67,18,1163997,1865492,2012,02/26/2012 10:36:20 PM,41.786532558,-87.67424339,"(41.786532558, -87.67424339)"
-8494866,HV171615,02/24/2012 07:50:00 PM,064XX S DREXEL AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,VACANT LOT/LAND,false,false,0312,003,20,42,15,1183457,1862632,2012,02/25/2012 10:30:36 AM,41.778252957,-87.602982534,"(41.778252957, -87.602982534)"
-8494724,HV171572,02/24/2012 07:00:00 AM,014XX W GARFIELD BLVD,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0713,007,16,67,05,1167773,1868102,2012,03/12/2012 11:07:08 AM,41.793614445,-87.660323686,"(41.793614445, -87.660323686)"
-8490022,HV167358,02/21/2012 06:00:00 PM,026XX N LARAMIE AVE,0810,THEFT,OVER $500,APPLIANCE STORE,false,false,2521,025,31,19,06,1141296,1917403,2012,02/25/2012 07:21:38 PM,41.929430838,-87.756198864,"(41.929430838, -87.756198864)"
-8494235,HV171059,02/21/2012 01:00:00 AM,001XX N HOYNE AVE,0820,THEFT,$500 AND UNDER,APARTMENT,false,false,1332,012,2,28,06,1162349,1900905,2012,02/27/2012 08:10:51 AM,41.88374418,-87.679297203,"(41.88374418, -87.679297203)"
-8488754,HV166207,02/20/2012 09:30:00 PM,007XX N RIDGEWAY AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1112,011,27,23,18,1151216,1904615,2012,02/20/2012 10:56:18 PM,41.894150359,-87.720081418,"(41.894150359, -87.720081418)"
-8489063,HV166425,02/20/2012 10:00:00 AM,079XX S CAMPBELL AVE,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,false,false,0835,008,18,70,26,1161055,1851768,2012,02/25/2012 02:07:43 PM,41.748933309,-87.685409398,"(41.748933309, -87.685409398)"
-8487903,HV165075,02/19/2012 10:13:00 PM,004XX N DRAKE AVE,0496,BATTERY,AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST,STREET,false,true,1123,011,27,23,04B,1152677,1902816,2012,02/25/2012 08:05:47 PM,41.889184943,-87.714763222,"(41.889184943, -87.714763222)"
-8486123,HV162736,02/17/2012 11:30:00 PM,037XX W 59TH ST,051A,ASSAULT,AGGRAVATED: HANDGUN,SIDEWALK,false,false,0822,008,14,62,04A,1152206,1865259,2012,02/25/2012 09:59:53 PM,41.786133066,-87.717481838,"(41.786133066, -87.717481838)"
-8484304,HV160943,02/16/2012 06:05:00 PM,040XX W MADISON ST,2028,NARCOTICS,POSS: SYNTHETIC DRUGS,SIDEWALK,true,false,1115,011,28,26,18,1149363,1899744,2012,02/16/2012 08:49:03 PM,41.88081995,-87.727013364,"(41.88081995, -87.727013364)"
-8483084,HV159815,02/15/2012 10:35:00 PM,020XX N LA CROSSE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,2522,025,31,19,18,1143736,1913429,2012,02/16/2012 12:05:36 AM,41.918480362,-87.747332259,"(41.918480362, -87.747332259)"
-8481524,HV158303,02/14/2012 09:15:00 PM,067XX S PEORIA ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0723,007,17,68,18,1171508,1860156,2012,02/14/2012 09:53:43 PM,41.771728652,-87.646860372,"(41.771728652, -87.646860372)"
-8479851,HV156306,02/13/2012 11:55:00 AM,019XX S JOURDAN CT,0820,THEFT,$500 AND UNDER,STREET,false,false,1233,012,25,31,06,1171139,1890766,2012,02/14/2012 09:33:41 AM,41.855733652,-87.647317253,"(41.855733652, -87.647317253)"
-9500616,HX155728,02/13/2012 08:00:00 AM,049XX S KOLIN AVE,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,RESIDENCE,false,false,0815,008,23,57,06,1148161,1871327,2012,02/20/2014 10:21:36 AM,41.802863151,-87.732157431,"(41.802863151, -87.732157431)"
-8479884,HV156556,02/13/2012 03:30:00 AM,002XX E 121ST PL,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0532,005,9,53,07,1179994,1824407,2012,02/22/2012 11:29:11 PM,41.673438809,-87.616844321,"(41.673438809, -87.616844321)"
-8489388,HV155104,02/12/2012 09:41:00 AM,041XX N PLAINFIELD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1614,016,36,17,14,1120043,1926843,2012,02/22/2012 11:34:08 AM,41.955701439,-87.83409722,"(41.955701439, -87.83409722)"
-8487598,HV164636,02/11/2012 01:00:00 PM,041XX W 31ST ST,0820,THEFT,$500 AND UNDER,TAVERN/LIQUOR STORE,true,false,1031,010,22,30,06,1149226,1883676,2012,02/20/2012 07:52:18 AM,41.836730045,-87.727932442,"(41.836730045, -87.727932442)"
-8477616,HV153534,02/10/2012 09:30:00 PM,044XX S DREXEL BLVD,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,2123,002,4,39,05,1182898,1875775,2012,02/19/2012 02:30:49 PM,41.814331459,-87.604623386,"(41.814331459, -87.604623386)"
-8475730,HV151081,02/09/2012 09:05:00 AM,065XX N CALIFORNIA AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,true,false,2412,024,50,2,03,1156518,1943202,2012,02/16/2012 02:51:50 PM,41.999929986,-87.699560856,"(41.999929986, -87.699560856)"
-8474038,HV150006,02/08/2012 09:30:00 AM,034XX W DOUGLAS BLVD,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,1021,010,24,29,08B,1153435,1893278,2012,02/09/2012 11:08:27 AM,41.86299663,-87.712232998,"(41.86299663, -87.712232998)"
-8472882,HV149004,02/07/2012 02:50:00 PM,020XX N ORCHARD ST,0265,CRIM SEXUAL ASSAULT,AGGRAVATED: OTHER,"SCHOOL, PUBLIC, BUILDING",true,false,1812,018,43,7,02,1171322,1913569,2012,04/24/2012 09:28:35 AM,41.918302548,-87.645975132,"(41.918302548, -87.645975132)"
-8529428,HV182027,02/07/2012 09:00:00 AM,082XX S STATE ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0631,006,6,44,06,1177762,1850454,2012,03/21/2012 06:14:49 AM,41.744965993,-87.62422852,"(41.744965993, -87.62422852)"
-8470222,HV146964,02/05/2012 11:25:00 PM,002XX W GARFIELD BLVD,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0711,007,3,68,03,1175663,1868333,2012,03/01/2012 01:54:07 PM,41.794075191,-87.631384866,"(41.794075191, -87.631384866)"
-8468776,HV145036,02/04/2012 10:30:00 AM,038XX S LAKE PARK AVE,0890,THEFT,FROM BUILDING,OTHER,false,false,2122,002,4,36,06,1182900,1879908,2012,02/05/2012 11:24:47 AM,41.825672663,-87.604487453,"(41.825672663, -87.604487453)"
-8468702,HV145109,02/03/2012 07:45:00 PM,070XX S HALSTED ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0733,007,6,68,04B,1172138,1858410,2012,02/11/2012 04:04:11 PM,41.766923596,-87.644602254,"(41.766923596, -87.644602254)"
-8466595,HV143266,02/02/2012 11:38:00 PM,034XX W FULLERTON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,TAVERN/LIQUOR STORE,false,true,1413,014,26,22,08B,1152937,1915701,2012,02/05/2012 07:28:46 AM,41.924537423,-87.713466447,"(41.924537423, -87.713466447)"
-8466493,HV143132,02/02/2012 08:53:00 PM,001XX N STATE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SMALL RETAIL STORE,true,false,0122,001,42,32,18,1176311,1900931,2012,02/02/2012 11:11:00 PM,41.883512088,-87.62802713,"(41.883512088, -87.62802713)"
-8463335,HV140420,01/31/2012 10:17:00 PM,084XX S DORCHESTER AVE,1477,WEAPONS VIOLATION,RECKLESS FIREARM DISCHARGE,SIDEWALK,false,false,0412,004,8,45,15,1187045,1849391,2012,02/01/2012 07:13:30 AM,41.74183406,-87.590248249,"(41.74183406, -87.590248249)"
-8463106,HV140223,01/31/2012 08:22:00 PM,071XX S ASHLAND AVE,1330,CRIMINAL TRESPASS,TO LAND,GROCERY FOOD STORE,true,false,0735,007,17,67,26,1166868,1857616,2012,02/01/2012 10:40:20 AM,41.764858929,-87.663941573,"(41.764858929, -87.663941573)"
-8463109,HV140111,01/31/2012 06:40:00 PM,029XX E 90TH ST,0460,BATTERY,SIMPLE,STREET,true,false,0423,004,10,46,08B,1196970,1845889,2012,02/01/2012 08:12:11 AM,41.731983359,-87.554000243,"(41.731983359, -87.554000243)"
-8957708,HW101855,01/30/2012 08:00:00 PM,017XX W 21ST ST,1320,CRIMINAL DAMAGE,TO VEHICLE,ALLEY,false,false,1234,012,25,31,14,1164832,1890155,2012,01/07/2013 10:54:58 AM,41.854192953,-87.670484239,"(41.854192953, -87.670484239)"
-8459933,HV137434,01/29/2012 09:45:00 PM,016XX W 21ST ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,1222,012,25,31,18,1165565,1890093,2012,01/29/2012 11:06:24 PM,41.854007261,-87.667795608,"(41.854007261, -87.667795608)"
-8460854,HV138073,01/28/2012 07:30:00 PM,020XX W ADAMS ST,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1211,012,2,28,14,1162594,1899036,2012,01/31/2012 06:45:57 AM,41.878610356,-87.678449913,"(41.878610356, -87.678449913)"
-8458787,HV136073,01/28/2012 05:57:00 PM,024XX W CONGRESS PKWY,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,1135,011,2,28,18,1159836,1897650,2012,01/28/2012 08:08:24 PM,41.874864345,-87.688614959,"(41.874864345, -87.688614959)"
-8460278,HV137680,01/28/2012 09:00:00 AM,026XX W WASHINGTON BLVD,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1331,012,2,28,07,1158917,1900628,2012,02/26/2012 02:17:31 PM,41.88305515,-87.69190743,"(41.88305515, -87.69190743)"
-8459371,HV134472,01/27/2012 11:45:00 AM,048XX W NEWPORT AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1634,016,38,15,05,1143295,1922535,2012,03/30/2012 09:27:06 PM,41.943476379,-87.748724425,"(41.943476379, -87.748724425)"
-8457294,HV134030,01/27/2012 10:20:00 AM,081XX S EXCHANGE AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,SIDEWALK,true,true,0422,004,7,46,26,1197278,1851719,2012,01/28/2012 07:23:59 AM,41.747973674,-87.552678197,"(41.747973674, -87.552678197)"
-8456599,HV133666,01/27/2012 12:01:00 AM,0000X E HARRISON ST,0460,BATTERY,SIMPLE,STREET,false,false,0132,001,2,32,08B,1176786,1897574,2012,01/27/2012 07:35:45 AM,41.874289563,-87.626384454,"(41.874289563, -87.626384454)"
-8455684,HV132760,01/26/2012 11:50:00 AM,102XX S PERRY AVE,0460,BATTERY,SIMPLE,STREET,true,false,0511,005,9,49,08B,1177365,1837006,2012,01/27/2012 08:55:29 AM,41.708071904,-87.62608803,"(41.708071904, -87.62608803)"
-8454559,HV132002,01/25/2012 07:00:00 PM,004XX E 103RD ST,1330,CRIMINAL TRESPASS,TO LAND,CONVENIENCE STORE,true,false,0512,005,9,49,26,1181002,1836726,2012,01/26/2012 06:00:49 AM,41.707220804,-87.612777825,"(41.707220804, -87.612777825)"
-8454153,HV131562,01/25/2012 02:15:00 PM,061XX S LAWNDALE AVE,031A,ROBBERY,ARMED: HANDGUN,STREET,false,false,0823,008,13,65,03,1152796,1863892,2012,02/23/2012 02:12:07 PM,41.782370194,-87.715354604,"(41.782370194, -87.715354604)"
-8451039,HV128899,01/23/2012 02:30:00 PM,008XX N ASHLAND AVE,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,RESIDENCE,false,false,1323,012,1,24,05,1165618,1905599,2012,01/25/2012 09:45:29 AM,41.896555875,-87.667159351,"(41.896555875, -87.667159351)"
-8458576,HX135858A,01/20/2012 02:30:00 PM,047XX S ARCHER AVE,0820,THEFT,$500 AND UNDER,RESIDENCE,false,false,0821,008,14,57,06,1152379,1872621,2012,01/29/2012 10:17:33 AM,41.806332073,-87.716653887,"(41.806332073, -87.716653887)"
-8531167,HV208003,01/19/2012 12:00:00 AM,013XX N WALLER AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,RESIDENCE-GARAGE,true,false,2531,025,29,25,07,1138143,1908201,2012,07/17/2012 05:04:26 PM,41.904237152,-87.768008297,"(41.904237152, -87.768008297)"
-8488522,HV123348,01/18/2012 09:08:00 PM,068XX S DR MARTIN LUTHER KING JR DR,0460,BATTERY,SIMPLE,STREET,false,false,0322,003,20,69,08B,1180050,1860023,2012,02/24/2012 07:00:38 AM,41.771172323,-87.615552464,"(41.771172323, -87.615552464)"
-8447846,HV122065,01/17/2012 08:00:00 PM,054XX W HURON ST,031A,ROBBERY,ARMED: HANDGUN,STREET,false,false,1524,015,37,25,03,1139420,1904052,2012,02/17/2012 06:22:25 PM,41.892828605,-87.763418709,"(41.892828605, -87.763418709)"
-8443730,HV121908,01/17/2012 05:45:00 PM,061XX S RHODES AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0313,003,20,42,08B,1180924,1864158,2012,01/20/2012 08:03:00 AM,41.782499106,-87.61222162,"(41.782499106, -87.61222162)"
-8442491,HV120854,01/17/2012 06:30:00 AM,027XX W FULLERTON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1431,014,1,22,08B,1157387,1915793,2012,01/19/2012 09:25:01 PM,41.924700442,-87.697112676,"(41.924700442, -87.697112676)"
-8441712,HV119790,01/16/2012 10:28:00 AM,005XX N CENTRAL PARK AVE,0880,THEFT,PURSE-SNATCHING,SIDEWALK,false,false,1121,011,27,23,06,1152329,1903277,2012,01/17/2012 07:04:52 AM,41.89045685,-87.716029046,"(41.89045685, -87.716029046)"
-8440259,HV118161,01/14/2012 08:25:00 PM,038XX W MONTROSE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1723,017,39,14,18,1150249,1929012,2012,01/14/2012 09:24:46 PM,41.961116765,-87.722995125,"(41.961116765, -87.722995125)"
-8439675,HV117305,01/14/2012 03:40:00 AM,011XX S MASON AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1513,015,29,25,26,1136939,1894186,2012,01/15/2012 12:10:59 PM,41.865799833,-87.772767267,"(41.865799833, -87.772767267)"
-8438678,HV114955,01/12/2012 07:45:00 AM,052XX S HYDE PARK BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2132,002,4,41,08B,1188493,1870554,2012,01/31/2012 07:59:49 AM,41.799872664,-87.584267562,"(41.799872664, -87.584267562)"
-8430595,HV109026,01/07/2012 07:30:00 PM,037XX W ROOSEVELT RD,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1011,010,24,29,08B,1151286,1894405,2012,01/09/2012 01:25:36 AM,41.866131641,-87.720092268,"(41.866131641, -87.720092268)"
-8430353,HV108810,01/07/2012 01:43:00 PM,012XX S LAKE SHORE DR E,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,OTHER,false,false,0133,001,2,33,11,1178826,1895067,2012,01/19/2012 03:59:08 PM,41.867363862,-87.618971193,"(41.867363862, -87.618971193)"
-8434845,HV112810,01/07/2012 06:00:00 AM,012XX S WASHTENAW AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,1023,010,28,29,26,1158521,1894318,2012,01/14/2012 03:03:36 PM,41.86574801,-87.69353428,"(41.86574801, -87.69353428)"
-8428075,HV106341,01/05/2012 09:00:00 AM,085XX S WABASH AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0632,006,6,44,03,1178154,1848625,2012,01/15/2012 11:20:13 PM,41.739938124,-87.622847514,"(41.739938124, -87.622847514)"
-8426117,HV104761,01/04/2012 04:45:00 PM,023XX N LAVERGNE AVE,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,2522,025,31,19,06,1142635,1915045,2012,01/05/2012 11:59:50 AM,41.922935408,-87.751337177,"(41.922935408, -87.751337177)"
-8425973,HV104560,01/04/2012 03:45:00 PM,011XX S CANAL ST,0460,BATTERY,SIMPLE,RESTAURANT,true,false,0131,001,2,28,08B,1173357,1895045,2012,01/05/2012 07:37:50 AM,41.867426623,-87.639049172,"(41.867426623, -87.639049172)"
-8422568,HV101447,01/02/2012 05:50:00 AM,070XX S STONY ISLAND AVE,0560,ASSAULT,SIMPLE,GAS STATION,false,false,0332,003,5,43,08A,1188171,1858862,2012,01/03/2012 07:18:35 AM,41.7677966,-87.585821169,"(41.7677966, -87.585821169)"
-8422160,HV101026,01/01/2012 05:30:00 PM,061XX S WESTERN AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0825,008,15,66,06,1161462,1863973,2012,01/01/2012 06:57:13 PM,41.782417166,-87.683580145,"(41.782417166, -87.683580145)"
-8421756,HV100392,01/01/2012 05:30:00 AM,052XX N OAKVIEW AVE,1365,CRIMINAL TRESPASS,TO RESIDENCE,OTHER,true,false,1614,016,41,76,26,1117355,1933533,2012,01/02/2012 09:16:47 AM,41.974102179,-87.84383866,"(41.974102179, -87.84383866)"
-8422024,HV100714,12/31/2011 05:00:00 PM,064XX W WARNER AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,OTHER,false,false,1632,016,38,17,14,1132534,1927100,2011,01/02/2012 09:11:02 AM,41.956197812,-87.788170602,"(41.956197812, -87.788170602)"
-8422057,HV100880,12/31/2011 03:00:00 PM,002XX N CENTRAL AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1512,015,29,25,05,1138963,1901113,2011,02/15/2012 10:27:54 AM,41.884771935,-87.765168587,"(41.884771935, -87.765168587)"
-8422790,HV101621,12/31/2011 06:00:00 AM,095XX S BENNETT AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0431,004,7,51,26,1190482,1841848,2011,01/05/2012 11:25:05 AM,41.721053197,-87.577897944,"(41.721053197, -87.577897944)"
-8419926,HT653035,12/30/2011 12:30:00 PM,069XX S DAMEN AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0735,007,17,67,03,1164177,1858867,2011,01/27/2012 03:22:05 PM,41.76834889,-87.673769719,"(41.76834889, -87.673769719)"
-8416674,HT650089,12/28/2011 10:50:00 AM,008XX N PARKSIDE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1511,015,29,25,14,1138490,1905282,2011,12/29/2011 09:26:59 AM,41.896220778,-87.766804461,"(41.896220778, -87.766804461)"
-8415475,HT648915,12/27/2011 12:10:00 PM,069XX S PEORIA ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA APARTMENT,false,true,0733,007,17,68,08B,1171549,1858604,2011,12/31/2011 01:57:40 PM,41.767468878,-87.646755494,"(41.767468878, -87.646755494)"
-8414546,HT648298,12/26/2011 09:00:00 PM,006XX N MC CLURG CT,0870,THEFT,POCKET-PICKING,SIDEWALK,false,false,1834,018,42,8,06,1179123,1904546,2011,12/27/2011 08:10:56 AM,41.893367937,-87.617590662,"(41.893367937, -87.617590662)"
-8413638,HT647149,12/25/2011 05:30:00 PM,069XX S CAMPBELL AVE,0330,ROBBERY,AGGRAVATED,SIDEWALK,false,false,0832,008,15,66,03,1160874,1858299,2011,01/19/2012 09:31:04 AM,41.766859084,-87.685892542,"(41.766859084, -87.685892542)"
-8419643,HT652664,12/23/2011 07:00:00 AM,016XX S RACINE AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,1233,012,25,31,05,1168675,1892082,2011,12/30/2011 10:49:14 AM,41.859398532,-87.656323192,"(41.859398532, -87.656323192)"
-8410987,HT643546,12/22/2011 02:15:00 PM,117XX S INDIANA AVE,1020,ARSON,BY FIRE,VEHICLE NON-COMMERCIAL,false,false,0532,005,9,53,09,1179645,1827037,2011,01/22/2012 05:29:27 PM,41.680663877,-87.618041772,"(41.680663877, -87.618041772)"
-8411701,HT643154,12/22/2011 06:30:00 AM,044XX S WALLACE ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0935,009,11,61,07,1173078,1875455,2011,12/24/2011 08:11:13 AM,41.813676212,-87.640653418,"(41.813676212, -87.640653418)"
-8409075,HT642050,12/20/2011 10:00:00 AM,112XX S STATE ST,0460,BATTERY,SIMPLE,STREET,false,false,0522,005,34,49,08B,1178213,1830477,2011,12/25/2011 11:06:23 AM,41.690136251,-87.623179772,"(41.690136251, -87.623179772)"
-8404259,HT637601,12/18/2011 03:05:00 PM,001XX N KENTON AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,false,false,1113,011,28,25,04B,1145734,1900510,2011,01/15/2012 01:01:09 PM,41.882991525,-87.74031947,"(41.882991525, -87.74031947)"
-8403560,HT636678,12/17/2011 07:39:00 PM,006XX N LOTUS AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,ALLEY,false,true,1524,015,37,25,04A,1139757,1903921,2011,01/13/2012 03:46:34 PM,41.892462976,-87.762184224,"(41.892462976, -87.762184224)"
-8406970,HT635908,12/17/2011 03:20:00 AM,049XX W PARKER AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,2521,025,31,19,08B,1142721,1917788,2011,12/21/2011 01:25:36 PM,41.930460876,-87.750952726,"(41.930460876, -87.750952726)"
-8402666,HT635396,12/16/2011 07:30:00 PM,004XX S WESTERN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,CTA PLATFORM,true,false,1135,011,2,28,18,1160438,1897806,2011,12/16/2011 10:13:22 PM,41.875279993,-87.686400348,"(41.875279993, -87.686400348)"
-8403882,HT635508,12/16/2011 06:00:00 PM,0000X E MADISON ST,0820,THEFT,$500 AND UNDER,RESTAURANT,false,false,0123,001,42,32,06,1176961,1900378,2011,12/19/2011 10:33:28 AM,41.881979942,-87.625657054,"(41.881979942, -87.625657054)"
-8402368,HT634847,12/16/2011 12:45:00 PM,045XX W DIVERSEY AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2521,025,31,20,06,1145738,1918272,2011,12/19/2011 01:04:00 PM,41.931732248,-87.739853515,"(41.931732248, -87.739853515)"
-8421362,HT654960,12/14/2011 11:30:00 PM,012XX W ARDMORE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,true,false,2013,020,48,77,26,1166557,1939013,2011,01/12/2012 03:55:28 AM,41.988225433,-87.662750515,"(41.988225433, -87.662750515)"
-8396755,HT629506,12/12/2011 10:10:00 PM,043XX W 63RD ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,0813,008,13,65,08B,1148529,1862513,2011,12/16/2011 01:50:03 PM,41.778669083,-87.73103425,"(41.778669083, -87.73103425)"
-8396600,HT629124,12/12/2011 05:30:00 PM,003XX E 95TH ST,0860,THEFT,RETAIL THEFT,DRUG STORE,true,false,0511,005,6,49,06,1180430,1842024,2011,12/13/2011 06:48:01 AM,41.721772315,-87.614710546,"(41.721772315, -87.614710546)"
-8395819,HT628288,12/12/2011 08:30:00 AM,060XX S ROCKWELL ST,0331,ROBBERY,ATTEMPT: AGGRAVATED,SIDEWALK,false,false,0825,008,15,66,03,1160029,1864712,2011,01/31/2012 01:34:50 PM,41.784474679,-87.688813652,"(41.784474679, -87.688813652)"
-8395112,HT628161,12/11/2011 09:00:00 PM,007XX N WASHTENAW AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1313,012,26,24,06,1158289,1905186,2011,12/12/2011 09:51:11 AM,41.89557557,-87.694088795,"(41.89557557, -87.694088795)"
-8405083,HT638429,12/11/2011 08:00:00 AM,029XX N PINE GROVE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2333,019,44,6,07,1172442,1919822,2011,12/19/2011 10:47:40 AM,41.935436343,-87.64167489,"(41.935436343, -87.64167489)"
-8390958,HT623419,12/07/2011 05:20:00 PM,026XX W HARRISON ST,5011,OTHER OFFENSE,LICENSE VIOLATION,ATHLETIC CLUB,false,false,1135,011,28,27,26,1158695,1897222,2011,12/09/2011 07:41:05 AM,41.873713313,-87.692815967,"(41.873713313, -87.692815967)"
-8408265,HT640273,12/07/2011 08:00:00 AM,023XX S CHRISTIANA AVE,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",false,false,1024,010,22,30,06,1154443,1888049,2011,01/19/2012 11:04:57 AM,41.848627587,-87.708672355,"(41.848627587, -87.708672355)"
-8389405,HT622125,12/07/2011 07:00:00 AM,102XX S WALLACE ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENTIAL YARD (FRONT/BACK),false,false,2232,022,9,73,14,1174147,1837091,2011,12/08/2011 05:52:33 AM,41.708377079,-87.637869958,"(41.708377079, -87.637869958)"
-8392701,HT625095,12/07/2011 06:30:00 AM,037XX W HAYFORD ST,0820,THEFT,$500 AND UNDER,RESIDENCE,false,false,0833,008,18,70,06,1152292,1854222,2011,12/26/2011 01:17:13 PM,41.75584409,-87.717456298,"(41.75584409, -87.717456298)"
-8401174,HT633412,12/07/2011 12:01:00 AM,038XX S COTTAGE GROVE AVE,1110,DECEPTIVE PRACTICE,BOGUS CHECK,OTHER,false,false,0212,002,4,36,11,1182131,1879460,2011,12/26/2011 12:57:01 PM,41.824461186,-87.60732258,"(41.824461186, -87.60732258)"
-8397310,HT621077,12/06/2011 09:25:00 PM,062XX S ASHLAND AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0714,007,16,67,16,1166696,1863537,2011,12/15/2011 11:54:29 AM,41.781110593,-87.664403203,"(41.781110593, -87.664403203)"
-8386968,HT620111,12/06/2011 10:50:00 AM,044XX S MICHIGAN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,true,false,0221,002,3,38,14,1177855,1875741,2011,10/31/2014 03:20:56 PM,41.814354021,-87.623122506,"(41.814354021, -87.623122506)"
-8383828,HT617227,12/03/2011 05:30:00 PM,075XX N SHERIDAN RD,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2422,024,49,1,14,1165674,1950059,2011,12/05/2011 08:37:22 AM,42.018554805,-87.665681435,"(42.018554805, -87.665681435)"
-8380523,HT613625,12/01/2011 08:40:00 PM,062XX S LANGLEY AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0313,003,20,42,14,1182012,1863585,2011,12/02/2011 06:39:48 AM,41.780901634,-87.608250454,"(41.780901634, -87.608250454)"
-8495145,HV166428,12/01/2011 08:11:00 PM,031XX W ROOSEVELT RD,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,1134,011,24,29,06,1155229,1894574,2011,02/27/2012 08:07:09 AM,41.866517191,-87.705612582,"(41.866517191, -87.705612582)"
-8388022,HT620849,12/01/2011 07:30:00 PM,009XX W MADISON ST,0560,ASSAULT,SIMPLE,OTHER,false,false,1212,012,27,28,08A,1170276,1900272,2011,12/07/2011 08:46:24 AM,41.881837714,-87.650207197,"(41.881837714, -87.650207197)"
-8378964,HT612034,11/30/2011 04:46:00 PM,034XX W NORTH AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,PAWN SHOP,false,false,1422,014,26,23,04B,1153271,1910383,2011,02/18/2012 12:45:28 PM,41.909937739,-87.712380665,"(41.909937739, -87.712380665)"
-8378729,HT611796,11/30/2011 03:35:00 PM,088XX S ABERDEEN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2222,022,21,71,08B,1170567,1846339,2011,12/06/2011 09:42:51 AM,41.733833545,-87.650711618,"(41.733833545, -87.650711618)"
-8378960,HT612265,11/30/2011 02:30:00 PM,003XX N CLINTON ST,1200,DECEPTIVE PRACTICE,STOLEN PROP: BUY/RECEIVE/POS.,STREET,true,false,1212,012,42,28,13,1172708,1902440,2011,12/07/2011 11:19:06 AM,41.887733374,-87.641212806,"(41.887733374, -87.641212806)"
-8377396,HT610895,11/29/2011 07:25:00 PM,086XX S SANGAMON ST,0313,ROBBERY,ARMED: OTHER DANGEROUS WEAPON,SIDEWALK,false,false,0613,006,21,71,03,1171442,1847772,2011,12/07/2011 09:33:39 PM,41.737746806,-87.647464208,"(41.737746806, -87.647464208)"
-8392519,HT624749,11/29/2011 04:00:00 PM,012XX N PARKSIDE AVE,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,RESIDENCE,false,false,2531,025,29,25,06,1138401,1907952,2011,12/14/2011 11:26:45 AM,41.903549197,-87.767066618,"(41.903549197, -87.767066618)"
-8374581,HT608499,11/28/2011 11:55:00 AM,014XX N CENTRAL PARK AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,true,true,2535,025,26,23,08B,1152054,1909529,2011,11/29/2011 07:18:03 AM,41.907618373,-87.716874,"(41.907618373, -87.716874)"
-8373692,HT607965,11/27/2011 11:45:00 PM,020XX W 69TH PL,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0735,007,17,67,18,1163628,1858517,2011,11/28/2011 01:20:22 AM,41.767399976,-87.675791884,"(41.767399976, -87.675791884)"
-8373656,HT607934,11/27/2011 10:30:00 PM,046XX W MADISON ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1113,011,28,25,16,1145668,1899660,2011,11/28/2011 12:34:36 AM,41.880660276,-87.740583386,"(41.880660276, -87.740583386)"
-8430488,HV108970,11/27/2011 07:00:00 PM,068XX S PAXTON AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0331,003,5,43,26,1192102,1859866,2011,01/08/2012 06:40:17 AM,41.770456999,-87.571379937,"(41.770456999, -87.571379937)"
-8375140,HT609044,11/27/2011 07:00:00 PM,024XX W WILSON AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1911,019,47,4,26,1159506,1930485,2011,01/02/2012 02:49:46 PM,41.964972779,-87.688920669,"(41.964972779, -87.688920669)"
-8373299,HT607398,11/27/2011 12:27:00 PM,081XX S RACINE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0613,006,21,71,14,1169699,1850736,2011,11/28/2011 05:49:07 AM,41.745918375,-87.653764347,"(41.745918375, -87.653764347)"
-8372764,HT606779,11/27/2011 01:50:00 AM,031XX N RICHMOND ST,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,1411,014,33,21,26,1156205,1920677,2011,11/28/2011 12:11:59 PM,41.938126482,-87.701323587,"(41.938126482, -87.701323587)"
-8371281,HT604522,11/25/2011 01:29:00 PM,069XX S CONSTANCE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0332,003,5,43,05,1189699,1859287,2011,01/07/2012 09:39:49 PM,41.768926257,-87.580206811,"(41.768926257, -87.580206811)"
-8370493,HT603806,11/24/2011 08:05:00 PM,012XX W 108TH PL,0460,BATTERY,SIMPLE,RESIDENCE,true,true,2234,022,34,75,08B,1170131,1832874,2011,11/25/2011 06:45:56 AM,41.696893035,-87.65269887,"(41.696893035, -87.65269887)"
-8370127,HT595061,11/24/2011 08:00:00 AM,087XX S LOOMIS ST,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,2222,022,21,71,06,1168565,1846740,2011,11/25/2011 06:24:59 AM,41.734977285,-87.658034456,"(41.734977285, -87.658034456)"
-8370187,HT603438,11/23/2011 08:00:00 PM,014XX W 110TH PL,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,2234,022,34,75,05,1168584,1831509,2011,11/21/2013 09:14:36 PM,41.693180659,-87.658402252,"(41.693180659, -87.658402252)"
-8369611,HT602644,11/23/2011 05:51:00 PM,009XX W MARQUETTE RD,1710,OFFENSE INVOLVING CHILDREN,ENDANGERING LIFE/HEALTH CHILD,APARTMENT,true,false,0723,007,17,68,26,1171078,1860364,2011,01/25/2012 04:32:21 PM,41.772308837,-87.648430537,"(41.772308837, -87.648430537)"
-8367494,HT600595,11/22/2011 12:00:00 PM,069XX S MICHIGAN AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,0322,003,6,69,07,1178316,1859228,2011,12/10/2011 11:04:31 PM,41.769030295,-87.621932743,"(41.769030295, -87.621932743)"
-8366499,HT599937,11/21/2011 11:28:00 PM,055XX S LAFLIN ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,SIDEWALK,true,false,0713,007,16,67,24,1167247,1867822,2011,11/22/2011 09:32:25 AM,41.792857376,-87.662260503,"(41.792857376, -87.662260503)"
-8369107,HT600366,11/21/2011 07:00:00 PM,078XX S ASHLAND AVE,0610,BURGLARY,FORCIBLE ENTRY,SMALL RETAIL STORE,false,false,0611,006,17,71,05,1167002,1852471,2011,12/13/2011 09:36:39 AM,41.750737484,-87.663597235,"(41.750737484, -87.663597235)"
-8367602,HT600407,11/20/2011 02:00:00 PM,038XX N SOUTHPORT AVE,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,1923,019,44,6,06,1166235,1925928,2011,11/23/2011 10:36:39 AM,41.952326647,-87.664310686,"(41.952326647, -87.664310686)"
-8363914,HT597358,11/20/2011 04:40:00 AM,011XX S KOSTNER AVE,0460,BATTERY,SIMPLE,STREET,true,false,1131,011,24,29,08B,1147265,1894629,2011,11/21/2011 09:40:59 AM,41.866824197,-87.734848089,"(41.866824197, -87.734848089)"
-8361209,HT594244,11/17/2011 10:05:00 PM,006XX W HARRISON ST,0870,THEFT,POCKET-PICKING,STREET,false,false,0131,001,2,28,06,1172057,1897599,2011,11/18/2011 08:49:48 AM,41.874463733,-87.643746311,"(41.874463733, -87.643746311)"
-8359867,HT592988,11/16/2011 05:30:00 PM,004XX W MARQUETTE RD,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0722,007,6,68,14,1174515,1860464,2011,11/17/2011 10:20:59 AM,41.772507448,-87.635828573,"(41.772507448, -87.635828573)"
-8358821,HT592050,11/16/2011 07:00:00 AM,123XX S INDIANA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,0532,005,9,53,14,1179899,1823385,2011,11/17/2011 07:46:09 AM,41.670636454,-87.617223112,"(41.670636454, -87.617223112)"
-8357833,HT591424,11/16/2011 12:20:00 AM,0000X W HUBBARD ST,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,1831,018,42,8,06,1175757,1903263,2011,01/11/2012 05:10:47 PM,41.889923702,-87.629991251,"(41.889923702, -87.629991251)"
-8353104,HT587002,11/13/2011 03:10:00 AM,063XX S EBERHART AVE,0460,BATTERY,SIMPLE,RESIDENCE PORCH/HALLWAY,true,false,0312,003,20,42,08B,1180637,1862751,2011,11/14/2011 05:57:59 AM,41.778644763,-87.613317028,"(41.778644763, -87.613317028)"
-8352603,HT586220,11/11/2011 09:20:00 PM,049XX S KEDVALE AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0815,008,14,57,05,1149573,1871433,2011,11/13/2011 12:21:00 PM,41.803126813,-87.726976178,"(41.803126813, -87.726976178)"
-8348023,HT581496,11/09/2011 10:15:00 AM,044XX N MONTICELLO AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,1723,017,33,14,06,1151223,1929077,2011,11/10/2011 10:46:37 AM,41.961276037,-87.719412448,"(41.961276037, -87.719412448)"
-8349072,HT582089,11/09/2011 05:45:00 AM,012XX N SPAULDING AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1422,014,26,23,05,1154089,1908335,2011,12/04/2011 11:34:11 PM,41.904301554,-87.709430374,"(41.904301554, -87.709430374)"
-8350135,HT580415,11/07/2011 07:00:00 PM,022XX N FREMONT ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1812,018,43,7,06,1169954,1915239,2011,11/11/2011 07:09:59 AM,41.922915091,-87.650952402,"(41.922915091, -87.650952402)"
-8342615,HT576457,11/05/2011 05:29:00 PM,104XX S AVENUE G,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,APARTMENT,true,false,0432,004,10,52,18,1203119,1836163,2011,11/05/2011 06:45:35 PM,41.705139504,-87.531806224,"(41.705139504, -87.531806224)"
-8341657,HT575266,11/04/2011 12:00:00 PM,054XX S HYDE PARK BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,2132,002,5,41,14,1188621,1869351,2011,11/05/2011 09:41:28 AM,41.796568486,-87.583836625,"(41.796568486, -87.583836625)"
-8339839,HT573503,11/03/2011 06:40:00 PM,087XX S MICHIGAN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0632,006,6,44,18,1178632,1847274,2011,11/03/2011 07:23:14 PM,41.736219973,-87.621137162,"(41.736219973, -87.621137162)"
-8339484,HT573069,11/03/2011 02:30:00 PM,011XX S CANAL ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0131,001,2,28,14,1173340,1895546,2011,11/04/2011 07:53:05 AM,41.86880178,-87.639096707,"(41.86880178, -87.639096707)"
-8338280,HT571928,11/02/2011 03:45:00 PM,035XX W POLK ST,0560,ASSAULT,SIMPLE,APARTMENT,false,false,1133,011,24,27,08A,1152984,1896183,2011,11/04/2011 08:54:27 AM,41.870977216,-87.713811626,"(41.870977216, -87.713811626)"
-8337809,HT571444,11/02/2011 12:50:00 PM,099XX S DR MARTIN LUTHER KING JR DR,0486,BATTERY,DOMESTIC BATTERY SIMPLE,GOVERNMENT BUILDING/PROPERTY,true,true,0511,005,6,49,08B,1180691,1839393,2011,11/03/2011 09:41:49 AM,41.714546535,-87.613835091,"(41.714546535, -87.613835091)"
-8336008,HT569844,11/01/2011 01:55:00 PM,061XX S INDIANA AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0311,003,20,40,18,1178687,1864551,2011,11/01/2011 01:33:28 PM,41.783628725,-87.620411093,"(41.783628725, -87.620411093)"
-8337296,HT569778,10/31/2011 11:00:00 PM,067XX S ARTESIAN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0832,008,15,66,08B,1161157,1860092,2011,11/27/2011 10:11:30 AM,41.771773482,-87.684805673,"(41.771773482, -87.684805673)"
-8336429,HT570286,10/31/2011 11:00:00 AM,070XX S CONSTANCE AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,false,false,0332,003,5,43,11,1189801,1858523,2011,11/10/2011 11:24:34 AM,41.766827327,-87.579857467,"(41.766827327, -87.579857467)"
-8334381,HT568383,10/31/2011 07:00:00 AM,008XX N LARAMIE AVE,0820,THEFT,$500 AND UNDER,VEHICLE-COMMERCIAL,false,false,1531,015,37,25,06,1141573,1905222,2011,11/01/2011 10:59:49 AM,41.89599971,-87.75548253,"(41.89599971, -87.75548253)"
-8347175,HT580534,10/29/2011 04:00:00 PM,049XX W ADAMS ST,1790,OFFENSE INVOLVING CHILDREN,CHILD ABDUCTION,RESIDENCE,true,true,1533,015,28,25,20,1143689,1898922,2011,11/30/2011 09:23:40 PM,41.878672391,-87.747868674,"(41.878672391, -87.747868674)"
-8331568,HT565489,10/29/2011 03:30:00 PM,011XX E 46TH ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,false,false,2123,002,4,39,11,1184440,1874774,2011,12/11/2011 10:07:07 AM,41.811548622,-87.598998616,"(41.811548622, -87.598998616)"
-8330822,HT564505,10/28/2011 06:00:00 PM,074XX S WESTERN AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,0835,008,18,66,06,1161622,1855356,2011,10/30/2011 11:23:58 AM,41.758767582,-87.683232336,"(41.758767582, -87.683232336)"
-8330414,HT563849,10/28/2011 11:00:00 AM,056XX W WASHINGTON BLVD,0820,THEFT,$500 AND UNDER,RESIDENCE PORCH/HALLWAY,false,false,1513,015,29,25,06,1138772,1900144,2011,10/29/2011 01:05:28 PM,41.882116342,-87.765893506,"(41.882116342, -87.765893506)"
-8329221,HT562907,10/27/2011 05:00:00 PM,0000X S WABASH AVE,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",false,false,0123,001,42,32,06,1176860,1900085,2011,10/28/2011 07:59:05 AM,41.881178218,-87.626036787,"(41.881178218, -87.626036787)"
-8339637,HT573245,10/27/2011 04:00:00 PM,027XX W AUGUSTA BLVD,1320,CRIMINAL DAMAGE,TO VEHICLE,"SCHOOL, PUBLIC, GROUNDS",false,false,1311,012,1,24,14,1158045,1906579,2011,11/04/2011 06:28:19 AM,41.899403068,-87.694946883,"(41.899403068, -87.694946883)"
-8327425,HT561420,10/26/2011 11:20:00 PM,047XX W OHIO ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1111,011,28,25,08B,1144429,1903601,2011,10/27/2011 10:22:42 AM,41.891498237,-87.745033731,"(41.891498237, -87.745033731)"
-8329344,HT563113,10/26/2011 08:35:00 PM,047XX W LAKE ST,0320,ROBBERY,STRONGARM - NO WEAPON,CTA TRAIN,false,false,1113,011,28,25,03,1144387,1901853,2011,11/21/2011 06:51:54 PM,41.886702312,-87.745231977,"(41.886702312, -87.745231977)"
-8326862,HT560713,10/26/2011 03:05:00 PM,054XX W WEST END AVE,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,1523,015,28,25,04A,1139770,1900895,2011,10/30/2011 12:11:40 PM,41.884159015,-87.762210447,"(41.884159015, -87.762210447)"
-8337455,HT560593,10/26/2011 01:32:00 PM,0000X W TERMINAL ST,0460,BATTERY,SIMPLE,AIRPORT TERMINAL UPPER LEVEL - NON-SECURE AREA,false,false,1655,016,41,76,08B,1104700,1933700,2011,11/12/2011 11:55:23 AM,41.974749442,-87.890372931,"(41.974749442, -87.890372931)"
-8325038,HT559357,10/25/2011 02:26:00 PM,021XX S CHINA PL,0820,THEFT,$500 AND UNDER,TAVERN/LIQUOR STORE,false,false,2111,009,25,34,06,1174721,1890179,2011,11/27/2011 11:32:30 AM,41.854043596,-87.634187235,"(41.854043596, -87.634187235)"
-8325360,HT559539,10/25/2011 12:15:00 PM,053XX S WABASH AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0232,002,3,40,05,1177582,1869587,2011,11/17/2011 10:24:13 AM,41.797473073,-87.624310112,"(41.797473073, -87.624310112)"
-8323027,HT557441,10/24/2011 02:55:00 PM,050XX W DIVISION ST,1822,NARCOTICS,MANU/DEL:CANNABIS OVER 10 GMS,SIDEWALK,true,false,1531,015,37,25,18,1142407,1907478,2011,10/24/2011 04:16:38 PM,41.902174979,-87.752363271,"(41.902174979, -87.752363271)"
-8335164,HT569233,10/24/2011 09:00:00 AM,027XX W 37TH PL,0560,ASSAULT,SIMPLE,APARTMENT,false,false,0913,009,12,58,08A,1158785,1879714,2011,11/05/2011 11:43:39 AM,41.825667617,-87.692964877,"(41.825667617, -87.692964877)"
-8333790,HT567771,10/24/2011 08:00:00 AM,103XX S AVENUE M,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0432,004,10,52,07,1201458,1837201,2011,11/04/2011 12:24:08 PM,41.708030126,-87.537853346,"(41.708030126, -87.537853346)"
-8321636,HT556270,10/23/2011 07:50:00 PM,033XX S MORGAN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0924,009,11,60,08B,1170149,1882713,2011,11/07/2011 12:53:30 PM,41.83365716,-87.651185788,"(41.83365716, -87.651185788)"
-8321333,HT556046,10/23/2011 03:20:00 PM,019XX W 63RD ST,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,0714,007,15,67,14,1164222,1862905,2011,10/24/2011 01:52:56 PM,41.779428758,-87.673491209,"(41.779428758, -87.673491209)"
-8321339,HT555949,10/23/2011 02:00:00 PM,005XX W FULLERTON PKWY,0820,THEFT,$500 AND UNDER,RESIDENCE-GARAGE,false,false,1933,019,43,7,06,1172403,1916263,2011,10/24/2011 07:18:22 AM,41.925671163,-87.64192369,"(41.925671163, -87.64192369)"
-8320599,HT554752,10/22/2011 05:23:00 PM,068XX S CHAMPLAIN AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,true,false,0321,003,6,42,04B,1181777,1860004,2011,11/02/2011 06:56:36 PM,41.77108046,-87.609222547,"(41.77108046, -87.609222547)"
-8349213,HT582646,10/22/2011 02:00:00 PM,040XX N TROY ST,1710,OFFENSE INVOLVING CHILDREN,ENDANGERING LIFE/HEALTH CHILD,RESIDENCE,false,true,1724,017,33,16,26,1154704,1926765,2011,11/14/2011 12:19:47 PM,41.954862611,-87.706676513,"(41.954862611, -87.706676513)"
-8320100,HT554315,10/22/2011 01:05:00 PM,087XX S UNION AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,2223,022,21,71,14,1173128,1846903,2011,10/24/2011 08:02:56 AM,41.735325116,-87.641312757,"(41.735325116, -87.641312757)"
-8332800,HT567119,10/22/2011 12:00:00 PM,061XX S ARCHER AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,0811,008,23,56,06,1137719,1868155,2011,10/31/2011 10:02:40 AM,41.79435267,-87.770529848,"(41.79435267, -87.770529848)"
-8338422,HT572051,10/19/2011 06:00:00 PM,003XX W 79TH ST,1110,DECEPTIVE PRACTICE,BOGUS CHECK,CURRENCY EXCHANGE,false,false,0623,006,17,44,11,1175105,1852514,2011,12/08/2011 01:33:59 PM,41.750678561,-87.633902763,"(41.750678561, -87.633902763)"
-8314552,HT548916,10/18/2011 09:23:00 PM,009XX W BELMONT AVE,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,true,false,1924,019,44,6,11,1169268,1921470,2011,10/19/2011 08:42:08 AM,41.940028203,-87.653291387,"(41.940028203, -87.653291387)"
-8314434,HT548711,10/18/2011 12:12:00 PM,063XX N RIDGE AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,OTHER,false,true,2413,024,50,2,26,1162959,1941688,2011,10/20/2011 03:04:28 PM,41.995642259,-87.675908715,"(41.995642259, -87.675908715)"
-8315041,HT548270,10/18/2011 10:00:00 AM,002XX S WABASH AVE,0890,THEFT,FROM BUILDING,COLLEGE/UNIVERSITY GROUNDS,true,false,0113,001,42,32,06,1176807,1899299,2011,06/09/2012 07:47:28 PM,41.879022588,-87.626255176,"(41.879022588, -87.626255176)"
-8311911,HT546386,10/17/2011 10:30:00 AM,011XX N WESTERN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,"SCHOOL, PUBLIC, BUILDING",true,false,1312,012,1,24,18,1160235,1907765,2011,10/17/2011 12:11:17 PM,41.902612548,-87.686870207,"(41.902612548, -87.686870207)"
-8311034,HT545597,10/16/2011 05:00:00 PM,001XX W 112TH PL,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0522,005,34,49,08B,1177096,1830309,2011,10/19/2011 11:39:11 AM,41.689700431,-87.627274168,"(41.689700431, -87.627274168)"
-8327576,HT544942,10/16/2011 04:31:00 AM,069XX W ARCHER AVE,0610,BURGLARY,FORCIBLE ENTRY,RESTAURANT,false,false,0811,008,23,56,05,1130829,1867321,2011,11/08/2011 01:50:07 PM,41.792185021,-87.795814969,"(41.792185021, -87.795814969)"
-8307866,HT542001,10/14/2011 08:30:00 AM,003XX N LARAMIE AVE,033A,ROBBERY,ATTEMPT: ARMED-HANDGUN,SIDEWALK,false,false,1523,015,28,25,03,1141601,1901917,2011,11/20/2011 11:31:41 AM,41.886929874,-87.755461441,"(41.886929874, -87.755461441)"
-8308402,HT541921,10/13/2011 08:30:00 PM,087XX S HOUSTON AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0424,004,10,46,07,1198002,1847455,2011,10/16/2011 01:31:53 AM,41.736254882,-87.550167487,"(41.736254882, -87.550167487)"
-8307276,HT541443,10/13/2011 10:04:00 AM,026XX S CALIFORNIA AVE,3960,INTIMIDATION,INTIMIDATION,GOVERNMENT BUILDING/PROPERTY,false,false,1033,010,12,30,26,1158098,1886049,2011,11/28/2011 11:25:25 AM,41.843065624,-87.695312699,"(41.843065624, -87.695312699)"
-8308062,HT541956,10/12/2011 11:00:00 AM,033XX W 16TH ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1021,010,24,29,08B,1153998,1891796,2011,10/15/2011 05:58:09 AM,41.85891866,-87.710205754,"(41.85891866, -87.710205754)"
-8333674,HT537661,10/11/2011 12:50:00 PM,129XX S GREEN ST,4650,OTHER OFFENSE,SEX OFFENDER: FAIL TO REGISTER,RESIDENCE,false,false,0523,005,34,53,26,1173069,1819055,2011,11/09/2011 08:38:51 AM,41.658907236,-87.642347405,"(41.658907236, -87.642347405)"
-8305878,HT538654,10/11/2011 10:00:00 AM,001XX N CENTRAL AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1523,015,29,25,05,1139061,1900593,2011,01/22/2012 10:39:02 PM,41.883343207,-87.764821361,"(41.883343207, -87.764821361)"
-8301012,HT535325,10/09/2011 08:30:00 PM,055XX S ASHLAND AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0715,007,15,67,03,1166574,1868056,2011,11/16/2011 04:04:52 PM,41.793513889,-87.664721643,"(41.793513889, -87.664721643)"
-8300413,HT534494,10/09/2011 08:50:00 AM,081XX S KIMBARK AVE,0460,BATTERY,SIMPLE,STREET,false,true,0411,004,8,45,08B,1186252,1851238,2011,10/12/2011 11:04:35 AM,41.746921169,-87.593095525,"(41.746921169, -87.593095525)"
-8300857,HT533972,10/08/2011 07:00:00 PM,049XX N LINCOLN AVE,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,2031,020,47,4,06,1159081,1932778,2011,10/12/2011 03:34:47 PM,41.971273647,-87.690420026,"(41.971273647, -87.690420026)"
-8297301,HT531053,10/06/2011 11:55:00 PM,031XX W MARQUETTE RD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0831,008,15,66,08B,1156728,1860073,2011,10/09/2011 10:33:19 AM,41.771811843,-87.701041599,"(41.771811843, -87.701041599)"
-8297088,HT530620,10/06/2011 05:48:00 PM,018XX W TOUHY AVE,0820,THEFT,$500 AND UNDER,STREET,true,false,2424,024,49,1,06,1162463,1947818,2011,10/07/2011 10:38:00 AM,42.012473575,-87.677560543,"(42.012473575, -87.677560543)"
-8292850,HT526873,10/04/2011 11:00:00 AM,025XX N NARRAGANSETT AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,2512,025,36,19,06,1133270,1916413,2011,10/05/2011 10:31:23 AM,41.926858621,-87.785715823,"(41.926858621, -87.785715823)"
-8293620,HT527662,10/03/2011 11:00:00 PM,015XX N ASHLAND AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,false,false,1433,014,1,24,26,1165485,1910303,2011,10/07/2011 01:07:08 PM,41.909466809,-87.66751374,"(41.909466809, -87.66751374)"
-8290441,HT524040,10/02/2011 08:00:00 AM,058XX N CENTRAL PARK AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1711,017,39,13,14,1151365,1938621,2011,10/04/2011 09:52:32 AM,41.987462576,-87.718638597,"(41.987462576, -87.718638597)"
-8288251,HT522361,10/01/2011 01:30:00 AM,133XX S BRANDON AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,ALLEY,true,false,0433,004,10,55,14,1199480,1816887,2011,10/01/2011 06:48:20 AM,41.652336358,-87.54577669,"(41.652336358, -87.54577669)"
-8288297,HT520630,09/29/2011 11:50:00 PM,087XX S STATE ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",GAS STATION,false,false,0632,006,6,44,07,1177847,1847232,2011,10/22/2011 11:22:30 PM,41.73612251,-87.624014378,"(41.73612251, -87.624014378)"
-8285890,HT519652,09/29/2011 12:05:00 PM,0000X W 87TH ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,0634,006,21,44,06,1176990,1847235,2011,09/30/2011 06:15:37 AM,41.736150082,-87.627154015,"(41.736150082, -87.627154015)"
-8285428,HT519286,09/28/2011 07:00:00 PM,033XX S RHODES AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2122,002,4,35,14,1180108,1883107,2011,09/29/2011 12:44:12 PM,41.834515493,-87.614632318,"(41.834515493, -87.614632318)"
-8286045,HT518445,09/28/2011 03:30:00 PM,034XX W ROOSEVELT RD,1330,CRIMINAL TRESPASS,TO LAND,DRUG STORE,false,false,1021,010,24,29,26,1153821,1894461,2011,09/30/2011 10:54:17 AM,41.866235242,-87.710784531,"(41.866235242, -87.710784531)"
-8304239,HT517637,09/28/2011 05:30:00 AM,016XX N WOOD ST,1512,PROSTITUTION,SOLICIT FOR PROSTITUTE,STREET,true,false,1433,014,32,24,16,1164141,1910758,2011,10/18/2011 02:40:36 PM,41.910743883,-87.672438119,"(41.910743883, -87.672438119)"
-8282789,HT517511,09/27/2011 11:43:00 PM,054XX S PAULINA ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0932,009,16,61,18,1165980,1868459,2011,09/28/2011 12:52:35 AM,41.794632427,-87.666888339,"(41.794632427, -87.666888339)"
-8282632,HT517278,09/27/2011 08:09:00 PM,119XX S LA SALLE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0522,005,9,53,18,1177361,1825974,2011,09/27/2011 09:51:42 PM,41.677798574,-87.626434225,"(41.677798574, -87.626434225)"
-8278192,HT512765,09/24/2011 09:30:00 PM,080XX S WENTWORTH AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0623,006,17,44,08B,1176418,1851864,2011,09/26/2011 07:34:56 AM,41.748865492,-87.629110826,"(41.748865492, -87.629110826)"
-8278095,HT512535,09/24/2011 06:03:00 PM,066XX S PULASKI RD,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,0833,008,13,65,06,1150808,1860166,2011,09/25/2011 01:09:52 PM,41.772184433,-87.722740256,"(41.772184433, -87.722740256)"
-8277882,HT512185,09/24/2011 01:51:00 PM,038XX W MADISON ST,041A,BATTERY,AGGRAVATED: HANDGUN,VEHICLE NON-COMMERCIAL,true,false,1122,011,28,26,04B,1150474,1899690,2011,02/06/2014 12:24:29 PM,41.880650157,-87.722935236,"(41.880650157, -87.722935236)"
-8280827,HT515204,09/23/2011 04:30:00 PM,017XX W HARRISON ST,0810,THEFT,OVER $500,HOSPITAL BUILDING/GROUNDS,false,false,1211,012,2,28,06,1164590,1897457,2011,09/30/2011 09:50:31 AM,41.87423542,-87.671165752,"(41.87423542, -87.671165752)"
-8278902,HT510406,09/23/2011 11:03:00 AM,020XX E 95TH ST,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,PARKING LOT/GARAGE(NON.RESID.),false,true,0413,004,7,51,20,1191226,1842411,2011,10/05/2011 02:30:31 PM,41.722580178,-87.575154712,"(41.722580178, -87.575154712)"
-8277925,HT512400,09/23/2011 12:00:00 AM,052XX W HUTCHINSON ST,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,OTHER,false,false,1624,016,38,15,11,1140859,1927799,2011,10/11/2011 10:25:14 AM,41.957966526,-87.757548117,"(41.957966526, -87.757548117)"
-8275568,HT509875,09/22/2011 11:15:00 PM,081XX S KIMBARK AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,true,0411,004,8,45,14,1186331,1851277,2011,09/28/2011 02:25:35 PM,41.747026324,-87.592804822,"(41.747026324, -87.592804822)"
-8275471,HT509676,09/22/2011 09:10:00 PM,071XX S CALUMET AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,true,0323,003,6,69,14,1179752,1857860,2011,09/29/2011 12:18:15 PM,41.765243645,-87.616710877,"(41.765243645, -87.616710877)"
-8273433,HT507606,09/21/2011 04:50:00 PM,009XX N DAMEN AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,true,1312,012,32,24,08A,1162865,1906059,2011,09/22/2011 01:13:25 PM,41.897876363,-87.677257697,"(41.897876363, -87.677257697)"
-8267089,HT500753,09/17/2011 06:20:00 AM,006XX W 43RD PL,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0935,009,11,61,14,1172823,1876099,2011,09/18/2011 01:51:09 PM,41.815449047,-87.641569755,"(41.815449047, -87.641569755)"
-8274146,HT507313,09/17/2011 03:00:00 AM,061XX N DAMEN AVE,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,RESIDENCE,false,false,2413,024,40,2,11,1161848,1940511,2011,10/01/2011 09:10:35 PM,41.992435866,-87.680028573,"(41.992435866, -87.680028573)"
-8267016,HT500510,09/17/2011 12:20:00 AM,068XX S CHAMPLAIN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0321,003,6,42,08B,1181778,1859986,2011,10/06/2011 11:49:40 AM,41.771031043,-87.609219437,"(41.771031043, -87.609219437)"
-8264747,HT498083,09/15/2011 12:30:00 PM,001XX W 87TH ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,0622,006,21,44,06,1176857,1847317,2011,09/16/2011 06:00:16 AM,41.736378094,-87.627638815,"(41.736378094, -87.627638815)"
-8358677,HT497801,09/15/2011 09:05:00 AM,011XX N MONTICELLO AVE,2050,NARCOTICS,CRIMINAL DRUG CONSPIRACY,STREET,true,false,1112,011,27,23,18,1151799,1907151,2011,12/15/2011 12:23:16 PM,41.901097939,-87.717873412,"(41.901097939, -87.717873412)"
-8263219,HT496865,09/14/2011 04:30:00 PM,057XX W AUGUSTA BLVD,0460,BATTERY,SIMPLE,GROCERY FOOD STORE,false,false,1511,015,29,25,08B,1137625,1906042,2011,09/18/2011 11:30:52 AM,41.898321943,-87.769963158,"(41.898321943, -87.769963158)"
-8263191,HT496856,09/14/2011 11:00:00 AM,057XX S LAKE SHORE DR,0820,THEFT,$500 AND UNDER,STREET,false,false,0331,003,5,41,06,1189657,1867446,2011,09/15/2011 06:28:15 AM,41.791316184,-87.580098791,"(41.791316184, -87.580098791)"
-8263063,HT496362,09/13/2011 08:00:00 PM,013XX W WILSON AVE,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,SMALL RETAIL STORE,false,false,2311,019,46,3,05,1166447,1930624,2011,09/26/2011 11:23:33 AM,41.965208115,-87.663396414,"(41.965208115, -87.663396414)"
-8259402,HT493213,09/12/2011 01:15:00 PM,074XX S NORMAL AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,0732,007,17,68,06,1174209,1855638,2011,09/26/2011 10:28:36 AM,41.759271141,-87.637093475,"(41.759271141, -87.637093475)"
-8261535,HT495224,09/11/2011 10:00:00 AM,071XX S GREENWOOD AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,true,0324,003,5,69,06,1184754,1857612,2011,09/19/2011 10:43:54 AM,41.76444729,-87.598384996,"(41.76444729, -87.598384996)"
-8255344,HT489151,09/09/2011 02:00:00 PM,082XX S COMMERCIAL AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0424,004,7,46,06,1197578,1851035,2011,09/10/2011 08:19:41 AM,41.746089253,-87.551601697,"(41.746089253, -87.551601697)"
-8245338,HT478898,09/03/2011 12:36:00 AM,002XX W SCOTT ST,1365,CRIMINAL TRESPASS,TO RESIDENCE,APARTMENT,false,false,1821,018,43,8,26,1174008,1908674,2011,09/23/2011 05:52:31 PM,41.904810938,-87.636252834,"(41.904810938, -87.636252834)"
-8244822,HT478166,09/02/2011 04:40:00 PM,085XX S COTTAGE GROVE AVE,0454,BATTERY,AGG PO HANDS NO/MIN INJURY,SMALL RETAIL STORE,true,false,0632,006,6,44,08B,1183008,1848629,2011,09/03/2011 09:24:55 AM,41.739837755,-87.605063162,"(41.739837755, -87.605063162)"
-8244840,HT478154,09/02/2011 04:24:00 PM,002XX N LAVERGNE AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1532,015,28,25,18,1142961,1901186,2011,09/02/2011 05:51:29 PM,41.884898681,-87.75048532,"(41.884898681, -87.75048532)"
-8244891,HT478071,09/02/2011 03:05:00 PM,028XX W DEVON AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,2413,024,50,2,06,1156226,1942289,2011,09/06/2011 10:03:26 AM,41.997430602,-87.700659875,"(41.997430602, -87.700659875)"
-8240713,HT474275,08/31/2011 09:30:00 AM,083XX S DREXEL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0632,006,8,44,08B,1183709,1850088,2011,09/04/2011 09:20:01 AM,41.743825102,-87.602449424,"(41.743825102, -87.602449424)"
-8243063,HT475018,08/30/2011 12:00:00 PM,041XX N PULASKI RD,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,RESIDENCE,false,false,1722,017,39,16,11,1148936,1927489,2011,09/06/2011 12:37:09 PM,41.956963101,-87.727862011,"(41.956963101, -87.727862011)"
-8238855,HT472827,08/29/2011 01:00:00 PM,005XX W BELMONT AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,2332,019,44,6,06,1172311,1921553,2011,08/30/2011 11:44:51 AM,41.940189172,-87.642105024,"(41.940189172, -87.642105024)"
-8235343,HT469765,08/28/2011 10:10:00 AM,022XX N KARLOV AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2525,025,31,20,14,1148720,1914613,2011,08/28/2011 12:29:07 PM,41.921634456,-87.728989808,"(41.921634456, -87.728989808)"
-8235217,HT469651,08/27/2011 10:00:00 PM,034XX W ROOSEVELT RD,0620,BURGLARY,UNLAWFUL ENTRY,NURSING HOME/RETIREMENT HOME,false,false,1133,011,24,29,05,1153452,1894533,2011,08/29/2011 11:02:03 AM,41.866440153,-87.712137257,"(41.866440153, -87.712137257)"
-8234595,HT468660,08/27/2011 01:30:00 PM,044XX W FILLMORE ST,0820,THEFT,$500 AND UNDER,ABANDONED BUILDING,false,false,1131,011,24,29,06,1147145,1895040,2011,08/29/2011 08:37:18 AM,41.867954326,-87.735278116,"(41.867954326, -87.735278116)"
-8236231,HT468461,08/27/2011 12:45:00 PM,055XX S WOOD ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0715,007,15,67,08B,1165255,1867852,2011,09/02/2011 02:27:55 PM,41.792982137,-87.669564091,"(41.792982137, -87.669564091)"
-8234133,HT468129,08/27/2011 06:30:00 AM,066XX N DAMEN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,STREET,false,false,2412,024,50,2,14,1161713,1944069,2011,08/29/2011 10:08:23 AM,42.002201953,-87.680425356,"(42.002201953, -87.680425356)"
-8296309,HT530002,08/26/2011 06:00:00 PM,028XX N LAWNDALE AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,false,true,2523,025,35,21,26,1151194,1918550,2011,10/26/2011 11:31:24 AM,41.932389718,-87.71979611,"(41.932389718, -87.71979611)"
-8231079,HT464653,08/25/2011 06:00:00 AM,015XX S KOMENSKY AVE,0460,BATTERY,SIMPLE,ALLEY,false,false,1012,010,24,29,08B,1149595,1892154,2011,08/26/2011 09:35:21 AM,41.859987607,-87.726358541,"(41.859987607, -87.726358541)"
-8229064,HT463066,08/23/2011 09:00:00 PM,017XX W HIGHLAND AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,2433,024,40,77,06,1163271,1942068,2011,08/25/2011 08:27:49 AM,41.996678411,-87.67475027,"(41.996678411, -87.67475027)"
-8225807,HT460161,08/22/2011 12:15:00 PM,016XX N PULASKI RD,2029,NARCOTICS,POSS: HEROIN(BLACK TAR),STREET,true,false,2535,025,30,23,18,1149507,1910532,2011,08/22/2011 01:02:26 PM,41.910420552,-87.72620429,"(41.910420552, -87.72620429)"
-8226220,HT459529,08/21/2011 06:42:00 PM,072XX N DAMEN AVE,0460,BATTERY,SIMPLE,STREET,false,false,2424,024,49,1,08B,1161757,1948135,2011,09/21/2011 02:47:06 PM,42.013358242,-87.680149357,"(42.013358242, -87.680149357)"
-8226639,HT461066,08/20/2011 07:00:00 PM,020XX W JACKSON BLVD,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1211,012,2,28,07,1162603,1898579,2011,08/23/2011 10:20:50 AM,41.87735612,-87.678429671,"(41.87735612, -87.678429671)"
-8276946,HT511084,08/20/2011 05:01:00 PM,006XX N CLARK ST,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,DRUG STORE,false,false,1832,018,42,8,06,1175463,1904612,2011,10/06/2011 08:20:13 PM,41.893632038,-87.631030388,"(41.893632038, -87.631030388)"
-8230825,HT457039,08/20/2011 08:40:00 AM,046XX S EVANS AVE,2027,NARCOTICS,POSS: CRACK,RESIDENCE,true,false,0222,002,4,38,18,1182076,1874427,2011,09/16/2011 03:24:59 PM,41.810651531,-87.607680287,"(41.810651531, -87.607680287)"
-8222903,HT456666,08/20/2011 12:30:00 AM,002XX N SANGAMON ST,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,1212,012,27,28,06,1170091,1901809,2011,08/22/2011 09:29:38 AM,41.886059387,-87.650841624,"(41.886059387, -87.650841624)"
-8222396,HT456058,08/19/2011 08:30:00 AM,031XX W VAN BUREN ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1124,011,28,27,07,1155245,1898026,2011,08/29/2011 03:44:53 PM,41.87598952,-87.705461148,"(41.87598952, -87.705461148)"
-8219649,HT453592,08/18/2011 07:37:00 AM,052XX N RIVERS EDGE TER,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1712,017,39,13,26,1146906,1934790,2011,08/23/2011 07:13:25 PM,41.977036703,-87.735137552,"(41.977036703, -87.735137552)"
-8219867,HT453521,08/18/2011 07:15:00 AM,079XX S WOLCOTT AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0611,006,18,71,14,1165116,1851741,2011,08/19/2011 07:05:11 AM,41.748774337,-87.670529069,"(41.748774337, -87.670529069)"
-8219036,HT452972,08/17/2011 07:15:00 PM,010XX W 87TH ST,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,2222,022,21,71,06,1170803,1847084,2011,08/23/2011 12:29:27 PM,41.73587279,-87.649825354,"(41.73587279, -87.649825354)"
-8215722,HT450080,08/15/2011 11:29:00 PM,006XX N STATE ST,1330,CRIMINAL TRESPASS,TO LAND,HOTEL/MOTEL,true,false,1832,018,42,8,26,1176208,1904830,2011,08/16/2011 10:51:13 AM,41.894213475,-87.628287711,"(41.894213475, -87.628287711)"
-8215432,HT449746,08/15/2011 07:10:00 PM,071XX S SEELEY AVE,0460,BATTERY,SIMPLE,STREET,false,false,0735,007,17,67,08B,1163882,1857503,2011,08/16/2011 10:42:46 AM,41.764612087,-87.674889314,"(41.764612087, -87.674889314)"
-8214614,HT449010,08/15/2011 07:00:00 AM,097XX S YATES AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,0431,004,7,51,26,1194064,1841029,2011,08/24/2011 03:03:22 PM,41.71871878,-87.564804852,"(41.71871878, -87.564804852)"
-8217280,HT451466,08/14/2011 10:45:00 PM,002XX W 103RD ST,0460,BATTERY,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),true,false,0511,005,9,49,08B,1176620,1836684,2011,08/17/2011 04:43:14 AM,41.707205049,-87.628825892,"(41.707205049, -87.628825892)"
-8210360,HT444123,08/12/2011 08:00:00 AM,007XX E 132ND ST,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,0533,005,9,54,08A,1183855,1817980,2011,08/18/2011 12:39:12 PM,41.655713219,-87.602912216,"(41.655713219, -87.602912216)"
-8210759,HT443403,08/11/2011 07:15:00 PM,022XX N MILWAUKEE AVE,0340,ROBBERY,ATTEMPT: STRONGARM-NO WEAPON,BANK,false,false,1431,014,1,22,03,1158029,1914620,2011,08/29/2011 07:23:30 PM,41.921468547,-87.694785777,"(41.921468547, -87.694785777)"
-8212765,HT442225,08/11/2011 01:35:00 AM,010XX N WELLS ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1824,018,42,8,06,1174571,1907312,2011,08/15/2011 10:55:39 AM,41.901060969,-87.634225561,"(41.901060969, -87.634225561)"
-8207408,HT441365,08/10/2011 03:17:00 PM,066XX S CARPENTER ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ABANDONED BUILDING,true,false,0724,007,17,68,18,1170417,1860744,2011,08/10/2011 04:10:36 PM,41.773366023,-87.650842512,"(41.773366023, -87.650842512)"
-8208847,HT442568,08/10/2011 11:30:00 AM,014XX W GREENLEAF AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,2423,024,49,1,05,1165494,1947023,2011,08/21/2011 04:31:48 PM,42.010227825,-87.666430844,"(42.010227825, -87.666430844)"
-8206491,HT440691,08/10/2011 03:49:00 AM,033XX W ADAMS ST,0820,THEFT,$500 AND UNDER,ALLEY,false,false,1124,011,28,27,06,1154205,1898837,2011,08/10/2011 08:33:18 AM,41.878235802,-87.709258027,"(41.878235802, -87.709258027)"
-8206386,HT440538,08/09/2011 11:15:00 PM,047XX N SACRAMENTO AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1713,017,33,14,08B,1155472,1931597,2011,08/11/2011 02:38:06 PM,41.968106468,-87.703722683,"(41.968106468, -87.703722683)"
-8207044,HT440807,08/09/2011 09:30:00 PM,054XX W ARDMORE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,1621,016,45,11,05,1139021,1938007,2011,08/29/2011 10:26:35 AM,41.986011847,-87.764055968,"(41.986011847, -87.764055968)"
-8204306,HT438794,08/08/2011 08:59:00 PM,026XX W 51ST ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,true,false,0911,009,14,63,15,1159525,1870787,2011,08/09/2011 11:02:18 AM,41.801155649,-87.69049498,"(41.801155649, -87.69049498)"
-8201944,HT436528,08/07/2011 12:20:00 PM,010XX W ARGYLE ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,2024,020,48,3,08B,1168164,1933540,2011,08/22/2011 01:34:01 PM,41.973172699,-87.656998805,"(41.973172699, -87.656998805)"
-8200452,HT434575,08/06/2011 06:00:00 AM,092XX S CALUMET AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,0633,006,6,49,26,1180254,1843498,2011,08/10/2011 02:20:13 PM,41.725821184,-87.615310167,"(41.725821184, -87.615310167)"
-8217967,HT433059,08/05/2011 10:00:00 AM,018XX W CULLERTON ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1223,012,25,31,14,1164486,1890479,2011,08/17/2011 11:17:29 AM,41.855089362,-87.671745031,"(41.855089362, -87.671745031)"
-8205599,HT439277,08/05/2011 09:30:00 AM,003XX N LATROBE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1523,015,28,25,26,1141361,1901595,2011,08/11/2011 10:45:12 AM,41.886050698,-87.756350749,"(41.886050698, -87.756350749)"
-8197719,HT431583,08/04/2011 01:30:00 PM,072XX S WESTERN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,DAY CARE CENTER,false,false,0832,008,18,66,26,1161663,1856787,2011,08/05/2011 12:28:02 PM,41.762693604,-87.683042426,"(41.762693604, -87.683042426)"
-8197857,HT431658,08/04/2011 02:00:00 AM,046XX S WINCHESTER AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0914,009,20,61,07,1164091,1873992,2011,08/05/2011 08:04:20 AM,41.809855625,-87.673659566,"(41.809855625, -87.673659566)"
-8195375,HT428065,08/02/2011 01:38:00 PM,037XX S DR MARTIN LUTHER KING JR DR,1320,CRIMINAL DAMAGE,TO VEHICLE,SIDEWALK,false,false,0211,002,3,35,14,1179426,1880431,2011,08/20/2011 12:10:32 PM,41.827187981,-87.61721658,"(41.827187981, -87.61721658)"
-8193764,HT427887,08/02/2011 12:00:00 AM,016XX S DRAKE AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1021,010,24,29,05,1153019,1891362,2011,08/06/2011 04:05:35 PM,41.857747155,-87.713810855,"(41.857747155, -87.713810855)"
-8530853,HV207767,08/01/2011 09:00:00 AM,044XX W WASHINGTON BLVD,1549,PROSTITUTION,OTHER PROSTITUTION OFFENSE,APARTMENT,true,true,1113,011,28,26,16,1147010,1900171,2011,03/21/2012 12:12:36 PM,41.882036979,-87.73564256,"(41.882036979, -87.73564256)"
-8191144,HT425728,08/01/2011 04:15:00 AM,062XX S LANGLEY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,false,0313,003,20,42,08B,1181928,1863748,2011,08/03/2011 12:11:08 PM,41.781350864,-87.608553375,"(41.781350864, -87.608553375)"
-8190386,HT424643,07/31/2011 11:00:00 AM,091XX S WENTWORTH AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,0634,006,21,49,15,1176595,1844252,2011,08/01/2011 10:57:43 AM,41.72797323,-87.628690619,"(41.72797323, -87.628690619)"
-8190221,HT424554,07/31/2011 10:00:00 AM,008XX E 89TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,0632,006,8,44,08B,1183580,1846189,2011,08/01/2011 06:27:45 AM,41.733128821,-87.603043316,"(41.733128821, -87.603043316)"
-8192494,HT426858,07/30/2011 08:00:00 PM,030XX N KEARSARGE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2523,025,31,21,07,1147652,1919612,2011,08/10/2011 09:55:47 AM,41.935372756,-87.732785307,"(41.935372756, -87.732785307)"
-8189477,HT423589,07/30/2011 04:45:00 PM,074XX S STEWART AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,0732,007,17,69,18,1174880,1855347,2011,07/30/2011 07:24:06 PM,41.758457674,-87.634642957,"(41.758457674, -87.634642957)"
-8189023,HT422989,07/30/2011 10:00:00 AM,019XX S LEAVITT ST,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,1223,012,25,31,26,1161970,1890476,2011,08/11/2011 04:50:12 PM,41.855133955,-87.680979958,"(41.855133955, -87.680979958)"
-8189200,HT422892,07/29/2011 06:00:00 PM,0000X W 105TH ST,0281,CRIM SEXUAL ASSAULT,NON-AGGRAVATED,RESIDENCE-GARAGE,false,false,0512,005,34,49,02,1177854,1835386,2011,03/20/2013 07:31:14 AM,41.703615364,-87.624346139,"(41.703615364, -87.624346139)"
-8189402,HT423489,07/28/2011 05:30:00 PM,061XX W FULLERTON AVE,0890,THEFT,FROM BUILDING,PARK PROPERTY,false,false,2512,025,29,19,06,1135302,1915346,2011,07/31/2011 09:16:58 AM,41.923894796,-87.778274361,"(41.923894796, -87.778274361)"
-8187995,HT420516,07/28/2011 04:30:00 AM,009XX W BELMONT AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1924,019,44,6,08B,1169603,1921481,2011,08/01/2011 08:32:38 AM,41.940051089,-87.652059842,"(41.940051089, -87.652059842)"
-8184808,HT419107,07/27/2011 11:45:00 PM,062XX S MENARD AVE,0460,BATTERY,SIMPLE,RESIDENTIAL YARD (FRONT/BACK),true,false,0812,008,13,64,08B,1138766,1862788,2011,08/19/2011 10:34:46 AM,41.779605803,-87.766820133,"(41.779605803, -87.766820133)"
-8184922,HT419157,07/27/2011 09:30:00 PM,030XX W 108TH ST,0460,BATTERY,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),false,false,2211,022,19,74,08B,1157969,1832892,2011,07/31/2011 10:17:26 AM,41.697197541,-87.697228851,"(41.697197541, -87.697228851)"
-8186340,HT418500,07/27/2011 04:30:00 PM,011XX N LARAMIE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,1531,015,37,25,03,1141530,1906972,2011,08/21/2011 01:47:29 PM,41.90080271,-87.755597179,"(41.90080271, -87.755597179)"
-8188544,HT421964,07/27/2011 09:52:00 AM,0000X N GREEN ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1212,012,27,28,05,1170765,1900517,2011,08/02/2011 04:03:47 PM,41.882499316,-87.648404431,"(41.882499316, -87.648404431)"
-8183688,HT417968,07/26/2011 07:30:00 PM,001XX W DIVISION ST,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,1821,018,43,8,03,1174774,1908377,2011,08/02/2011 08:07:29 AM,41.90397884,-87.633448018,"(41.90397884, -87.633448018)"
-8180921,HT415883,07/26/2011 01:00:00 AM,119XX S WALLACE ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0524,005,34,53,14,1174423,1825505,2011,07/26/2011 06:41:47 AM,41.676577205,-87.637202121,"(41.676577205, -87.637202121)"
-8178328,HT413548,07/24/2011 03:00:00 PM,070XX S UNION AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0732,007,6,68,14,1172890,1858307,2011,08/13/2011 05:11:59 PM,41.766624395,-87.641848915,"(41.766624395, -87.641848915)"
-8227273,HT450789,07/22/2011 02:00:00 PM,102XX S INDIANA AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0511,005,9,49,26,1179355,1836894,2011,08/25/2011 12:15:31 PM,41.707719479,-87.618803997,"(41.707719479, -87.618803997)"
-8174634,HT409211,07/21/2011 10:45:00 PM,057XX W MADISON ST,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,1513,015,29,25,18,1138024,1899475,2011,07/22/2011 12:35:37 AM,41.880294056,-87.768656366,"(41.880294056, -87.768656366)"
-8193549,HT408163,07/21/2011 08:45:00 AM,019XX W 21ST PL,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1223,012,25,31,05,1163691,1889792,2011,08/04/2011 03:57:04 PM,41.85322094,-87.674682376,"(41.85322094, -87.674682376)"
-8187883,HT406450,07/20/2011 10:58:00 AM,033XX W CHICAGO AVE,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),SIDEWALK,true,false,1121,011,27,23,18,1153771,1905090,2011,07/29/2011 02:39:32 PM,41.89540331,-87.710684991,"(41.89540331, -87.710684991)"
-8170901,HT405572,07/19/2011 06:05:00 PM,046XX W NORTH AVE,0460,BATTERY,SIMPLE,DEPARTMENT STORE,false,false,2533,025,37,25,08B,1144852,1910260,2011,07/24/2011 02:19:14 PM,41.909763298,-87.743312036,"(41.909763298, -87.743312036)"
-8170221,HT404935,07/19/2011 12:29:00 PM,044XX S MAPLEWOOD AVE,2890,PUBLIC PEACE VIOLATION,OTHER VIOLATION,SIDEWALK,true,false,0914,009,12,58,26,1160059,1875341,2011,10/31/2014 03:20:56 PM,41.813641425,-87.688411277,"(41.813641425, -87.688411277)"
-8363574,HT596276,07/19/2011 07:00:00 AM,031XX S AVERS AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1031,010,22,30,07,1151160,1883701,2011,11/23/2011 10:37:15 AM,41.836761024,-87.720835112,"(41.836761024, -87.720835112)"
-8165791,HT400725,07/16/2011 07:50:00 PM,030XX W 63RD ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,0823,008,15,66,18,1157431,1862752,2011,07/16/2011 08:24:16 PM,41.779149201,-87.698392099,"(41.779149201, -87.698392099)"
-8165792,HT400633,07/16/2011 06:45:00 PM,047XX S MICHIGAN AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,true,0231,002,3,38,08A,1177917,1873553,2011,07/19/2011 09:39:55 AM,41.808348552,-87.622961427,"(41.808348552, -87.622961427)"
-8166011,HT400672,07/16/2011 11:50:00 AM,020XX N MILWAUKEE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1431,014,1,22,06,1159347,1913561,2011,07/28/2011 08:34:22 AM,41.91853554,-87.689972296,"(41.91853554, -87.689972296)"
-8161211,HT396025,07/14/2011 12:30:00 AM,005XX N LARAMIE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,true,true,1523,015,28,25,08B,1141551,1903428,2011,07/27/2011 02:38:05 PM,41.891077166,-87.755607699,"(41.891077166, -87.755607699)"
-8160782,HT395545,07/13/2011 06:30:00 PM,012XX S ST LOUIS AVE,0820,THEFT,$500 AND UNDER,APARTMENT,false,false,1021,010,24,29,06,1153202,1893781,2011,07/14/2011 10:31:58 AM,41.864381542,-87.713074983,"(41.864381542, -87.713074983)"
-8160569,HT395162,07/13/2011 02:49:00 PM,054XX S UNION AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,OTHER,true,false,0934,009,3,61,15,1172518,1868771,2011,07/14/2011 11:10:42 AM,41.795347,-87.642904475,"(41.795347, -87.642904475)"
-8156930,HT391935,07/11/2011 05:29:00 PM,010XX W DAKIN ST,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,2324,019,44,6,18,1168677,1926334,2011,07/11/2011 07:43:54 PM,41.953388066,-87.655322076,"(41.953388066, -87.655322076)"
-8165081,HT391063,07/11/2011 07:40:00 AM,035XX S RHODES AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,true,0212,002,4,35,08A,1180121,1881754,2011,07/18/2011 11:54:51 AM,41.830802465,-87.614626154,"(41.830802465, -87.614626154)"
-8155163,HT390302,07/10/2011 07:00:00 PM,042XX S SACRAMENTO AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,SIDEWALK,true,false,0912,009,14,58,14,1157021,1876376,2011,10/31/2014 03:20:56 PM,41.816543623,-87.69952694,"(41.816543623, -87.69952694)"
-8154687,HT389442,07/10/2011 05:55:00 AM,077XX S CICERO AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,HOTEL/MOTEL,false,false,0834,008,13,70,08B,1145779,1853068,2011,07/13/2011 07:26:41 AM,41.752802774,-87.74135441,"(41.752802774, -87.74135441)"
-8159018,HT392216,07/08/2011 05:30:00 PM,0000X E 101ST ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0511,005,9,49,05,1178315,1837975,2011,07/13/2011 07:11:55 PM,41.710709508,-87.622579811,"(41.710709508, -87.622579811)"
-8151753,HT386133,07/07/2011 08:30:00 PM,109XX S CENTRAL PARK AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2211,022,19,74,14,1154333,1832025,2011,07/09/2011 08:03:16 AM,41.694891246,-87.71056493,"(41.694891246, -87.71056493)"
-8151069,HT385515,07/07/2011 07:58:00 PM,015XX S ALBANY AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1022,010,24,29,18,1155929,1892066,2011,07/07/2011 08:26:35 PM,41.859620894,-87.703110387,"(41.859620894, -87.703110387)"
-8209114,HT443088,07/07/2011 05:00:00 PM,089XX S LOWE AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,2223,022,21,71,26,1173491,1845837,2011,08/18/2011 12:38:35 PM,41.732391851,-87.640014333,"(41.732391851, -87.640014333)"
-8149592,HT384333,07/07/2011 12:30:00 AM,079XX S MICHIGAN AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,0623,006,6,44,07,1178486,1852465,2011,07/11/2011 08:48:49 AM,41.75046801,-87.621514736,"(41.75046801, -87.621514736)"
-8149591,HT384339,07/06/2011 04:35:00 PM,002XX N PARKSIDE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,"SCHOOL, PUBLIC, BUILDING",false,false,1512,015,29,25,14,1138585,1901355,2011,07/08/2011 10:54:14 AM,41.885442873,-87.766550811,"(41.885442873, -87.766550811)"
-8146886,HT381904,07/05/2011 06:00:00 PM,066XX S COTTAGE GROVE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,0321,003,20,42,14,1182658,1861358,2011,07/06/2011 08:12:12 AM,41.774775566,-87.605951177,"(41.774775566, -87.605951177)"
-8146600,HT381005,07/05/2011 05:00:00 AM,061XX S INGLESIDE AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,0313,003,20,42,07,1183458,1864701,2011,07/11/2011 07:52:46 AM,41.783930456,-87.602914438,"(41.783930456, -87.602914438)"
-8146167,HT380958,07/03/2011 07:00:00 AM,054XX W POTOMAC AVE,0820,THEFT,$500 AND UNDER,RESIDENCE,false,true,2532,025,37,25,06,1139969,1908086,2011,07/06/2011 08:27:16 AM,41.903888361,-87.761303634,"(41.903888361, -87.761303634)"
-8157643,HT384810,07/01/2011 05:00:00 PM,057XX S GREEN ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0712,007,16,68,06,1171657,1866840,2011,07/13/2011 11:26:29 AM,41.790067056,-87.646118375,"(41.790067056, -87.646118375)"
-8142827,HT376635,07/01/2011 12:00:00 PM,045XX N SHERIDAN RD,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,2313,019,46,3,06,1168753,1930348,2011,08/01/2011 09:05:19 AM,41.964400973,-87.654925876,"(41.964400973, -87.654925876)"
-8385246,HT618573,07/01/2011 07:00:00 AM,061XX N WASHTENAW AVE,0820,THEFT,$500 AND UNDER,RESIDENCE-GARAGE,false,false,2413,024,50,2,06,1157174,1940855,2011,12/06/2011 08:36:29 AM,41.993476374,-87.697211722,"(41.993476374, -87.697211722)"
-8143944,HT378458,06/29/2011 06:30:00 PM,098XX S AVENUE G,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,PARK PROPERTY,false,false,0432,004,10,52,11,1203077,1840713,2011,07/26/2011 01:29:57 PM,41.71762613,-87.531804924,"(41.71762613, -87.531804924)"
-8138704,HT371707,06/29/2011 12:00:00 PM,001XX W 51ST ST,0560,ASSAULT,SIMPLE,GOVERNMENT BUILDING/PROPERTY,false,false,0232,002,3,37,08A,1175993,1871129,2011,07/20/2011 12:34:48 PM,41.801740289,-87.630090902,"(41.801740289, -87.630090902)"
-8136851,HT370897,06/28/2011 09:55:00 PM,015XX S AVERS AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,SIDEWALK,false,true,1014,010,24,29,04A,1151011,1891889,2011,07/03/2011 03:47:40 PM,41.859232828,-87.721167683,"(41.859232828, -87.721167683)"
-8136722,HT370650,06/28/2011 06:35:00 PM,046XX W IRVING PARK RD,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,1722,017,45,15,06,1144771,1926228,2011,06/29/2011 09:29:23 AM,41.953582541,-87.743205844,"(41.953582541, -87.743205844)"
-8136185,HT368689,06/27/2011 04:08:00 PM,048XX N DAMEN AVE,4230,KIDNAPPING,UNLAWFUL RESTRAINT,SIDEWALK,false,false,2032,020,47,4,26,1162095,1932149,2011,08/06/2011 05:24:47 PM,41.969485043,-87.679354865,"(41.969485043, -87.679354865)"
-8134734,HT369216,06/27/2011 08:00:00 AM,034XX S WENTWORTH AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0924,009,11,34,06,1175462,1882360,2011,06/28/2011 11:48:57 AM,41.832571068,-87.631701956,"(41.832571068, -87.631701956)"
-8131470,HT365897,06/25/2011 10:30:00 PM,075XX S RACINE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0612,006,17,71,18,1169664,1854897,2011,06/25/2011 11:32:43 PM,41.757337475,-87.65377217,"(41.757337475, -87.65377217)"
-8144866,HT379541,06/25/2011 08:00:00 AM,078XX S MARYLAND AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,0624,006,6,69,26,1183229,1852892,2011,07/07/2011 12:52:47 PM,41.751530751,-87.604121095,"(41.751530751, -87.604121095)"
-8130428,HT364331,06/24/2011 10:25:00 PM,085XX S BUFFALO AVE,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0424,004,10,46,03,1199585,1848878,2011,06/27/2011 07:46:58 AM,41.740120023,-87.544320265,"(41.740120023, -87.544320265)"
-8127082,HT361635,06/23/2011 04:00:00 AM,020XX W 75TH PL,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,0611,006,18,71,06,1163930,1854651,2011,06/24/2011 09:40:37 AM,41.756784788,-87.674793405,"(41.756784788, -87.674793405)"
-8126602,HT361373,06/23/2011 02:40:00 AM,009XX W WEED ST,0460,BATTERY,SIMPLE,BAR OR TAVERN,false,false,1822,018,32,8,08B,1169901,1910422,2011,06/25/2011 04:25:50 PM,41.909698142,-87.651287898,"(41.909698142, -87.651287898)"
-8126564,HT361296,06/23/2011 12:45:00 AM,085XX S KINGSTON AVE,0560,ASSAULT,SIMPLE,SIDEWALK,true,false,0423,004,7,46,08A,1194605,1848987,2011,06/24/2011 04:45:30 AM,41.740542973,-87.562562391,"(41.740542973, -87.562562391)"
-8132557,HT362379,06/21/2011 03:00:00 PM,027XX S TRUMBULL AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1032,010,22,30,05,1153774,1885396,2011,07/21/2011 10:57:43 AM,41.841360763,-87.711198186,"(41.841360763, -87.711198186)"
-8122324,HT357645,06/20/2011 08:40:00 PM,037XX W GRENSHAW ST,2024,NARCOTICS,POSS: HEROIN(WHITE),RESIDENTIAL YARD (FRONT/BACK),true,false,1133,011,24,29,18,1151769,1894825,2011,06/20/2011 09:41:16 PM,41.867274687,-87.718308077,"(41.867274687, -87.718308077)"
-8122368,HT357660,06/20/2011 04:00:00 PM,057XX N SHERIDAN RD,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,false,false,2022,020,48,77,26,1168551,1938391,2011,08/09/2011 12:50:20 PM,41.986475574,-87.655434554,"(41.986475574, -87.655434554)"
-8126295,HT355454,06/19/2011 03:30:00 PM,075XX S EGGLESTON AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,false,0621,006,17,69,06,1174560,1854874,2011,07/21/2011 10:56:56 AM,41.757166831,-87.635829781,"(41.757166831, -87.635829781)"
-8121120,HT354934,06/19/2011 06:15:00 AM,114XX S STEWART AVE,0496,BATTERY,AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST,RESIDENCE,false,false,0522,005,34,49,04B,1175713,1829073,2011,06/21/2011 07:04:15 AM,41.686339654,-87.632374142,"(41.686339654, -87.632374142)"
-8119682,HT354890,06/19/2011 01:30:00 AM,043XX W 13TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1011,010,24,29,08B,1147889,1893731,2011,06/21/2011 01:58:14 PM,41.864348015,-87.732580357,"(41.864348015, -87.732580357)"
-8119257,HT354389,06/18/2011 08:00:00 PM,027XX W DIVISION ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,ALLEY,false,false,1423,014,26,24,07,1157852,1907904,2011,09/26/2011 11:23:13 AM,41.90304292,-87.695619594,"(41.90304292, -87.695619594)"
-8119202,HT352757,06/17/2011 05:30:00 PM,032XX N KEATING AVE,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,RESIDENCE,false,true,1731,017,30,15,04A,1144290,1921381,2011,06/23/2011 01:25:44 PM,41.940291018,-87.745096351,"(41.940291018, -87.745096351)"
-8118068,HT352745,06/17/2011 09:00:00 AM,054XX W CORTEZ ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1524,015,37,25,14,1139582,1906420,2011,07/20/2011 03:57:47 PM,41.899323731,-87.762765902,"(41.899323731, -87.762765902)"
-8116429,HT351311,06/17/2011 12:30:00 AM,032XX E 92ND ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,true,false,0424,004,10,46,15,1198960,1844524,2011,06/17/2011 05:50:22 AM,41.728188015,-87.546755921,"(41.728188015, -87.546755921)"
-8115962,HT350424,06/16/2011 03:35:00 PM,059XX W BERTEAU AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1624,016,38,15,08B,1135801,1927348,2011,06/19/2011 10:52:22 AM,41.95682069,-87.776154166,"(41.95682069, -87.776154166)"
-8108772,HT344000,06/12/2011 07:40:00 PM,070XX S EAST END AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,SIDEWALK,false,false,0332,003,5,43,04A,1189030,1858767,2011,06/13/2011 07:18:44 AM,41.767515382,-87.582675636,"(41.767515382, -87.582675636)"
-8113951,HT342638,06/11/2011 08:00:00 AM,026XX W CHICAGO AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1311,012,26,24,05,1158425,1905263,2011,08/25/2011 12:44:07 PM,41.895784083,-87.693587189,"(41.895784083, -87.693587189)"
-8106672,HT341354,06/11/2011 12:02:00 AM,105XX S WABASH AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0512,005,9,49,18,1178397,1835167,2011,06/11/2011 01:08:55 AM,41.703002113,-87.622364414,"(41.703002113, -87.622364414)"
-8106500,HT341052,06/10/2011 07:38:00 PM,115XX S STATE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0522,005,34,53,18,1178283,1828197,2011,06/10/2011 09:58:39 PM,41.683878012,-87.622992329,"(41.683878012, -87.622992329)"
-8105095,HT339692,06/10/2011 02:26:00 AM,056XX S ABERDEEN ST,031A,ROBBERY,ARMED: HANDGUN,STREET,false,false,0712,007,16,68,03,1169908,1867254,2011,06/29/2011 06:15:58 PM,41.791241304,-87.652519438,"(41.791241304, -87.652519438)"
-8107149,HT340083,06/08/2011 12:00:00 PM,033XX W POLK ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,OTHER,false,false,1134,011,24,27,06,1154355,1896215,2011,06/30/2011 01:11:27 PM,41.871037764,-87.708777311,"(41.871037764, -87.708777311)"
-8101366,HT335950,06/07/2011 07:55:00 PM,097XX S EWING AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,CTA BUS,false,false,0432,004,10,52,04B,1202080,1841338,2011,06/26/2011 03:30:40 PM,41.71936659,-87.535435219,"(41.71936659, -87.535435219)"
-8180527,HT415429,06/06/2011 03:00:00 PM,062XX W GEORGE ST,0495,BATTERY,AGGRAVATED OF A SENIOR CITIZEN,APARTMENT,true,false,2511,025,29,19,04B,1134017,1918568,2011,09/15/2011 07:08:37 PM,41.932759065,-87.782920041,"(41.932759065, -87.782920041)"
-8099062,HT333537,06/06/2011 02:00:00 PM,042XX N DRAKE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,1723,017,33,16,08B,1151926,1927842,2011,06/07/2011 08:11:58 AM,41.957873262,-87.716860491,"(41.957873262, -87.716860491)"
-8135801,HT333347,06/06/2011 12:50:00 PM,040XX W MONTROSE AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,1722,017,39,16,06,1148903,1928899,2011,07/05/2011 12:45:39 AM,41.960832886,-87.727946709,"(41.960832886, -87.727946709)"
-8097650,HT332252,06/05/2011 06:00:00 PM,058XX S PRAIRIE AVE,0460,BATTERY,SIMPLE,STREET,false,false,0233,002,20,40,08B,1178993,1866552,2011,06/28/2011 11:13:22 AM,41.789112696,-87.619228266,"(41.789112696, -87.619228266)"
-8095793,HT330030,06/04/2011 02:30:00 AM,003XX N RACINE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1212,012,27,28,07,1168415,1902057,2011,07/11/2011 02:25:52 PM,41.886776329,-87.656989056,"(41.886776329, -87.656989056)"
-8101971,HT325148,06/02/2011 11:30:00 AM,111XX S MICHIGAN AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0531,005,9,49,08B,1178833,1831020,2011,06/09/2011 11:25:43 AM,41.691612272,-87.620893504,"(41.691612272, -87.620893504)"
-8206731,HT440861,06/01/2011 12:01:00 AM,049XX S HONORE ST,1753,OFFENSE INVOLVING CHILDREN,SEX ASSLT OF CHILD BY FAM MBR,RESIDENCE,false,false,0931,009,16,61,02,1164815,1871713,2011,08/17/2011 09:19:04 PM,41.803586501,-87.671068448,"(41.803586501, -87.671068448)"
-8088990,HT322535,05/31/2011 04:00:00 AM,038XX W CERMAK RD,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,RESIDENCE,false,false,1014,010,24,29,07,1151224,1889149,2011,06/14/2011 03:12:04 PM,41.851709768,-87.72045761,"(41.851709768, -87.72045761)"
-8088302,HT321931,05/30/2011 08:06:00 PM,060XX S WABASH AVE,041A,BATTERY,AGGRAVATED: HANDGUN,SIDEWALK,false,false,0311,003,20,40,04B,1177800,1864651,2011,06/06/2011 06:24:30 PM,41.78392327,-87.623660098,"(41.78392327, -87.623660098)"
-8087428,HT320970,05/30/2011 03:43:00 AM,045XX W MADISON ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,SIDEWALK,true,false,1113,011,28,26,16,1145861,1899664,2011,05/30/2011 05:06:24 AM,41.880667592,-87.739874597,"(41.880667592, -87.739874597)"
-8086759,HT320019,05/29/2011 02:44:00 AM,010XX W 19TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1233,012,25,31,14,1169808,1891051,2011,05/30/2011 09:04:50 AM,41.856544793,-87.652194341,"(41.856544793, -87.652194341)"
-8092320,HT318642,05/28/2011 11:00:00 AM,002XX W 111TH PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,0522,005,34,49,08B,1176405,1830955,2011,06/15/2011 02:52:48 PM,41.691488666,-87.629784598,"(41.691488666, -87.629784598)"
-8085062,HT317756,05/27/2011 07:04:00 PM,022XX S WOOD ST,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,false,false,1034,010,25,31,04B,1164675,1889392,2011,12/27/2011 06:38:34 PM,41.852102533,-87.671082089,"(41.852102533, -87.671082089)"
-8083695,HT316594,05/27/2011 02:51:00 AM,014XX W DIVISION ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1433,014,1,24,06,1166376,1908131,2011,05/27/2011 10:07:02 AM,41.903487673,-87.664302838,"(41.903487673, -87.664302838)"
-8083624,HT316507,05/27/2011 01:10:00 AM,021XX N NARRAGANSETT AVE,2022,NARCOTICS,POSS: COCAINE,ALLEY,true,false,2512,025,29,19,18,1133442,1913499,2011,05/27/2011 02:30:56 AM,41.918859241,-87.785152237,"(41.918859241, -87.785152237)"
-8090670,HT316327,05/26/2011 08:43:00 PM,084XX S SAGINAW AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,true,false,0423,004,7,46,15,1195254,1849703,2011,06/02/2011 09:03:14 AM,41.74249176,-87.560161001,"(41.74249176, -87.560161001)"
-8081767,HT314640,05/25/2011 07:00:00 PM,003XX E 136TH ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0533,005,9,54,04B,1180825,1815075,2011,06/15/2011 09:54:12 AM,41.64781136,-87.614087833,"(41.64781136, -87.614087833)"
-8081248,HT313977,05/25/2011 02:15:00 PM,032XX W 38TH ST,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,0913,009,12,58,18,1155408,1879182,2011,05/25/2011 03:59:46 PM,41.824276145,-87.705368622,"(41.824276145, -87.705368622)"
-8081740,HT314660,05/25/2011 03:30:00 AM,057XX W 64TH ST,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,0812,008,13,64,06,1139228,1861580,2011,05/26/2011 09:09:55 AM,41.77628247,-87.76515561,"(41.77628247, -87.76515561)"
-8079676,HT312823,05/24/2011 02:45:00 PM,013XX N SEDGWICK ST,1330,CRIMINAL TRESPASS,TO LAND,RESIDENTIAL YARD (FRONT/BACK),true,false,1821,018,27,8,26,1173372,1909048,2011,05/25/2011 07:50:00 AM,41.905851372,-87.638577902,"(41.905851372, -87.638577902)"
-8088873,HT311973,05/24/2011 12:19:03 PM,010XX N WALLER AVE,0935,MOTOR VEHICLE THEFT,"THEFT/RECOVERY: TRUCK,BUS,MHOME",STREET,true,false,1511,015,29,25,07,1138112,1906692,2011,10/31/2014 03:20:56 PM,41.900096833,-87.768158688,"(41.900096833, -87.768158688)"
-8082081,HT311743,05/24/2011 10:00:00 AM,001XX N CICERO AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,1113,011,28,25,07,1144394,1900624,2011,06/07/2011 01:33:16 PM,41.883329658,-87.7452372,"(41.883329658, -87.7452372)"
-8092370,HT311326,05/23/2011 11:12:02 PM,133XX S RIVERDALE AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0533,005,9,54,04B,1182391,1816800,2011,07/13/2011 02:43:54 PM,41.652509035,-87.60830538,"(41.652509035, -87.60830538)"
-8079152,HT312051,05/23/2011 03:30:00 PM,021XX N HAMLIN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2525,025,26,22,08B,1150647,1914242,2011,05/26/2011 10:00:20 AM,41.920578915,-87.72191917,"(41.920578915, -87.72191917)"
-8080674,HT313552,05/23/2011 02:30:00 PM,088XX S BISHOP ST,3960,INTIMIDATION,INTIMIDATION,RESIDENCE,false,false,2222,022,21,71,26,1168258,1845911,2011,06/12/2011 09:56:53 AM,41.732708993,-87.659182965,"(41.732708993, -87.659182965)"
-8107086,HT341841,05/23/2011 12:01:00 AM,002XX E 121ST PL,1242,DECEPTIVE PRACTICE,COMPUTER FRAUD,RESIDENCE,false,false,0532,005,9,53,11,1180276,1824494,2011,06/15/2011 12:14:44 PM,41.673671111,-87.615809532,"(41.673671111, -87.615809532)"
-8075346,HT307996,05/22/2011 02:19:00 AM,032XX S WOOD ST,0560,ASSAULT,SIMPLE,STREET,true,false,0922,009,11,59,08A,1164922,1883054,2011,05/22/2011 10:39:12 AM,41.834705201,-87.670355102,"(41.834705201, -87.670355102)"
-8077939,HT310612,05/22/2011 01:00:00 AM,005XX N CLINTON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,1323,012,42,24,08B,1172360,1903547,2011,05/28/2011 09:20:43 AM,41.890778745,-87.642458031,"(41.890778745, -87.642458031)"
-8075172,HT307572,05/21/2011 08:20:00 PM,056XX W CHICAGO AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1511,015,29,25,08B,1138760,1904743,2011,05/26/2011 09:59:55 AM,41.894736799,-87.765825883,"(41.894736799, -87.765825883)"
-8076092,HT308970,05/21/2011 04:00:00 PM,023XX W SHAKESPEARE AVE,0917,MOTOR VEHICLE THEFT,"CYCLE, SCOOTER, BIKE W-VIN",STREET,true,false,1432,014,32,22,07,1160484,1914296,2011,06/28/2011 08:45:09 AM,41.920528951,-87.685774469,"(41.920528951, -87.685774469)"
-8073584,HT305337,05/20/2011 01:50:00 PM,006XX N SPAULDING AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE PORCH/HALLWAY,false,true,1121,011,27,23,14,1154296,1904274,2011,05/23/2011 07:01:09 AM,41.893153656,-87.708778599,"(41.893153656, -87.708778599)"
-8081361,HT314162,05/20/2011 12:00:00 PM,072XX S COLES AVE,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,RESIDENCE,false,false,0334,003,7,43,06,1194315,1857776,2011,06/30/2011 02:45:35 PM,41.764667803,-87.563336706,"(41.764667803, -87.563336706)"
-8147950,HT305083,05/20/2011 09:00:00 AM,057XX S UNIVERSITY AVE,0890,THEFT,FROM BUILDING,OTHER,false,false,2133,002,5,41,06,1184717,1867322,2011,07/06/2011 03:13:20 PM,41.791093259,-87.598216374,"(41.791093259, -87.598216374)"
-8070677,HT302709,05/19/2011 12:15:00 AM,059XX S MOZART ST,1460,WEAPONS VIOLATION,POSS FIREARM/AMMO:NO FOID CARD,RESIDENCE,true,false,0824,008,16,66,15,1158353,1865164,2011,05/19/2011 10:33:16 AM,41.785749341,-87.69494628,"(41.785749341, -87.69494628)"
-8070824,HT302836,05/18/2011 10:00:00 PM,001XX S LOTUS AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,OTHER,false,false,1522,015,29,25,07,1139859,1899043,2011,05/20/2011 08:54:06 AM,41.879075264,-87.761928904,"(41.879075264, -87.761928904)"
-8163062,HT301117,05/17/2011 11:30:00 PM,027XX W 61ST ST,0460,BATTERY,SIMPLE,APARTMENT,false,true,0825,008,15,66,08B,1159187,1864129,2011,07/15/2011 10:00:10 AM,41.782892123,-87.691916728,"(41.782892123, -87.691916728)"
-8067364,HT299543,05/17/2011 01:05:00 AM,012XX S KARLOV AVE,0810,THEFT,OVER $500,APARTMENT,false,true,1011,010,24,29,06,1149212,1893846,2011,06/01/2011 09:11:01 AM,41.864638081,-87.72772063,"(41.864638081, -87.72772063)"
-8069345,HT299726,05/16/2011 03:00:00 PM,118XX S LAFAYETTE AVE,0610,BURGLARY,FORCIBLE ENTRY,VACANT LOT/LAND,false,false,0522,005,34,53,05,1178088,1826234,2011,06/21/2011 09:56:33 AM,41.678495655,-87.623765339,"(41.678495655, -87.623765339)"
-8066706,HT298657,05/16/2011 02:30:00 PM,065XX S EBERHART AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,false,false,0321,003,20,42,14,1180736,1861867,2011,05/17/2011 05:29:29 AM,41.776216705,-87.612981238,"(41.776216705, -87.612981238)"
-8065194,HT297018,05/15/2011 01:00:00 PM,061XX S COTTAGE GROVE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0313,003,20,42,08B,1182585,1864268,2011,05/30/2011 08:26:48 AM,41.78276257,-87.606128569,"(41.78276257, -87.606128569)"
-8064874,HT296937,05/15/2011 06:45:00 AM,011XX W ADAMS ST,0810,THEFT,OVER $500,STREET,false,false,1213,012,2,28,06,1168741,1899293,2011,05/16/2011 08:58:44 AM,41.879184657,-87.655872031,"(41.879184657, -87.655872031)"
-8063245,HT294646,05/13/2011 07:35:00 PM,050XX S DR MARTIN LUTHER KING JR DR,0460,BATTERY,SIMPLE,APARTMENT,true,false,0223,002,3,38,08B,1179812,1871457,2011,05/14/2011 08:33:51 AM,41.802553755,-87.616075232,"(41.802553755, -87.616075232)"
-8057556,HT287630,05/09/2011 06:20:00 PM,069XX S THROOP ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,true,false,0734,007,17,67,04B,1168826,1858417,2011,05/26/2011 12:38:28 PM,41.767014939,-87.656741852,"(41.767014939, -87.656741852)"
-8053020,HT284956,05/07/2011 07:00:00 PM,0000X W KINZIE ST,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,1831,018,42,8,06,1175932,1902973,2011,05/09/2011 10:41:22 AM,41.889123988,-87.629357318,"(41.889123988, -87.629357318)"
-8052803,HT284725,05/06/2011 09:00:00 PM,018XX W CULLERTON ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1223,012,25,31,14,1164192,1890472,2011,05/09/2011 08:27:56 AM,41.855076365,-87.672824339,"(41.855076365, -87.672824339)"
-8051732,HT283254,05/06/2011 07:32:00 PM,005XX W 80TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0621,006,17,44,18,1174174,1851901,2011,05/06/2011 10:10:07 PM,41.74901713,-87.637332547,"(41.74901713, -87.637332547)"
-8051046,HT282557,05/06/2011 01:00:00 PM,068XX N NORTHWEST HWY,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,false,false,1611,016,41,9,26,1123582,1945116,2011,05/07/2011 09:27:56 AM,42.005787206,-87.820684712,"(42.005787206, -87.820684712)"
-8055273,HT287130,05/05/2011 01:55:00 PM,0000X E BALBO AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),false,false,0132,001,2,32,07,1176491,1897095,2011,05/27/2011 09:16:29 AM,41.87298182,-87.62748201,"(41.87298182, -87.62748201)"
-8049083,HT280638,05/05/2011 10:20:00 AM,039XX W 79TH ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,"SCHOOL, PUBLIC, BUILDING",true,false,0834,008,18,70,24,1151306,1851847,2011,05/06/2011 08:54:15 AM,41.749346,-87.72113174,"(41.749346, -87.72113174)"
-8048380,HT280239,05/05/2011 01:43:00 AM,060XX N WASHTENAW AVE,2028,NARCOTICS,POSS: SYNTHETIC DRUGS,OTHER,true,false,2413,024,50,2,18,1157274,1940225,2011,05/05/2011 03:30:45 AM,41.991745588,-87.696861105,"(41.991745588, -87.696861105)"
-8049180,HT280829,05/04/2011 08:00:00 PM,019XX N CLARK ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1814,018,43,7,06,1174173,1913200,2011,05/06/2011 06:46:31 AM,41.917226815,-87.635511486,"(41.917226815, -87.635511486)"
-8046984,HT278433,05/03/2011 09:03:00 PM,065XX S WESTERN AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,STREET,true,false,0832,008,15,66,04A,1161451,1861421,2011,05/06/2011 10:36:46 AM,41.775414355,-87.683691158,"(41.775414355, -87.683691158)"
-8048512,HT277738,05/03/2011 02:00:00 PM,042XX S MICHIGAN AVE,0460,BATTERY,SIMPLE,STREET,false,false,0214,002,3,38,08B,1177824,1876874,2011,05/10/2011 11:30:03 AM,41.817463773,-87.623201871,"(41.817463773, -87.623201871)"
-8040960,HT271788,04/29/2011 03:15:00 PM,043XX S PRINCETON AVE,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,0935,009,3,37,08B,1174965,1876291,2011,04/30/2011 09:11:47 AM,41.815928326,-87.633706843,"(41.815928326, -87.633706843)"
-8039299,HT270861,04/28/2011 10:30:00 PM,031XX W 63RD ST,0560,ASSAULT,SIMPLE,RESTAURANT,true,false,0823,008,15,66,08A,1156355,1862643,2011,04/29/2011 08:52:56 AM,41.778871823,-87.702339786,"(41.778871823, -87.702339786)"
-8037612,HT269226,04/27/2011 08:15:00 PM,054XX W CHICAGO AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1524,015,37,25,06,1140130,1904767,2011,04/28/2011 09:17:15 AM,41.894777682,-87.760793595,"(41.894777682, -87.760793595)"
-8037783,HT269379,04/27/2011 02:00:00 PM,051XX S LAWLER AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0814,008,23,56,05,1143609,1869852,2011,04/28/2011 01:59:28 PM,41.798901669,-87.74888872,"(41.798901669, -87.74888872)"
-8044507,HT268468,04/27/2011 01:30:00 PM,056XX S WINCHESTER AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0715,007,15,67,06,1164287,1866965,2011,05/03/2011 11:02:21 AM,41.790568552,-87.673138624,"(41.790568552, -87.673138624)"
-8041659,HT269132,04/27/2011 07:40:00 AM,024XX W MC LEAN AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1431,014,1,22,05,1159967,1913623,2011,05/19/2011 10:31:08 PM,41.918692887,-87.687692647,"(41.918692887, -87.687692647)"
-8194024,HT427928,04/26/2011 05:00:00 PM,030XX N LAVERGNE AVE,0275,CRIM SEXUAL ASSAULT,ATTEMPT AGG: OTHER,ALLEY,false,false,2521,025,31,19,02,1142460,1920037,2011,10/19/2011 10:17:05 AM,41.936637215,-87.751855799,"(41.936637215, -87.751855799)"
-8033959,HT265692,04/25/2011 04:54:00 PM,005XX W ROOSEVELT RD,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,0131,001,2,28,06,1172714,1894928,2011,04/26/2011 07:55:00 AM,41.867119813,-87.641413167,"(41.867119813, -87.641413167)"
-8033784,HT265359,04/25/2011 01:50:00 PM,008XX N STATE ST,1570,SEX OFFENSE,PUBLIC INDECENCY,CTA TRAIN,true,false,1832,018,42,8,17,1176183,1905757,2011,10/31/2014 03:20:56 PM,41.896757773,-87.628351553,"(41.896757773, -87.628351553)"
-8032360,HT261807,04/22/2011 09:30:00 PM,028XX N LINCOLN AVE,0460,BATTERY,SIMPLE,STREET,false,false,1932,019,32,6,08B,1167704,1918895,2011,04/25/2011 06:50:21 AM,41.932996181,-87.659113982,"(41.932996181, -87.659113982)"
-8028964,HT260052,04/21/2011 04:10:00 PM,022XX W 66TH ST,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE PORCH/HALLWAY,true,false,0832,008,15,66,26,1162242,1860800,2011,04/22/2011 10:56:02 AM,41.773693812,-87.680808702,"(41.773693812, -87.680808702)"
-8030877,HT262231,04/21/2011 03:30:00 PM,038XX S VINCENNES AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0212,002,4,35,14,1180684,1879440,2011,04/23/2011 09:39:30 AM,41.824439735,-87.612631725,"(41.824439735, -87.612631725)"
-8024592,HT256281,04/19/2011 01:45:00 AM,015XX N LONG AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,2532,025,37,25,18,1140102,1909933,2011,04/19/2011 02:31:51 AM,41.908954309,-87.7607698,"(41.908954309, -87.7607698)"
-8021780,HT253275,04/16/2011 06:00:00 PM,073XX S JEFFERY BLVD,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0333,003,5,43,05,1190779,1856472,2011,04/21/2011 11:09:18 AM,41.76117565,-87.576338932,"(41.76117565, -87.576338932)"
-8018212,HT249385,04/13/2011 06:00:00 PM,021XX N HARLEM AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,2512,025,36,25,06,1127782,1913502,2011,04/15/2011 09:52:39 AM,41.91896487,-87.805948027,"(41.91896487, -87.805948027)"
-8035165,HT265312,04/12/2011 03:00:00 PM,039XX W LEXINGTON ST,0479,BATTERY,AGG: HANDS/FIST/FEET SERIOUS INJURY,STREET,false,false,1132,011,24,26,04B,1150434,1896452,2011,06/21/2011 01:47:04 PM,41.871765503,-87.723166638,"(41.871765503, -87.723166638)"
-8011901,HT244026,04/10/2011 10:30:00 PM,011XX S LAFLIN ST,0460,BATTERY,SIMPLE,STREET,false,false,1231,012,2,28,08B,1166585,1895258,2011,06/19/2011 01:42:51 PM,41.868158716,-87.66390401,"(41.868158716, -87.66390401)"
-8011880,HT243977,04/10/2011 07:40:00 PM,096XX S BALTIMORE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0431,004,10,51,14,1198529,1841623,2011,04/11/2011 06:24:10 AM,41.720238229,-87.548431615,"(41.720238229, -87.548431615)"
-8009866,HT241212,04/09/2011 02:00:00 AM,071XX S SEELEY AVE,0560,ASSAULT,SIMPLE,SIDEWALK,true,false,0735,007,17,67,08A,1163969,1857249,2011,04/09/2011 08:30:34 AM,41.763913248,-87.674577564,"(41.763913248, -87.674577564)"
-8011130,HT241500,04/09/2011 01:30:00 AM,072XX S ADA ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0734,007,17,67,07,1168644,1856966,2011,04/13/2011 10:06:12 AM,41.763037133,-87.657450747,"(41.763037133, -87.657450747)"
-8009960,HT240398,04/08/2011 02:30:00 PM,057XX W 58TH ST,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,RESIDENCE,false,false,0811,008,23,56,05,1139271,1865511,2011,04/17/2011 11:29:34 AM,41.787069053,-87.764902707,"(41.787069053, -87.764902707)"
-8008910,HT239861,04/08/2011 10:30:00 AM,105XX S VERNON AVE,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,RESIDENCE,false,false,0512,005,9,49,05,1181155,1834986,2011,04/10/2011 01:57:35 PM,41.7024425,-87.612270896,"(41.7024425, -87.612270896)"
-8007588,HT238851,04/07/2011 03:00:00 PM,032XX S HAMLIN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,1031,010,22,30,14,1151512,1882941,2011,04/13/2011 05:33:13 PM,41.834668591,-87.7195634,"(41.834668591, -87.7195634)"
-8012912,HT244601,04/07/2011 07:00:00 AM,057XX W OHIO ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,1511,015,29,25,05,1137977,1903438,2011,04/13/2011 11:29:52 AM,41.89116989,-87.768733207,"(41.89116989, -87.768733207)"
-8028763,HT234802,04/04/2011 11:25:00 PM,043XX W 36TH ST,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,WAREHOUSE,true,false,1031,010,22,30,18,1147660,1880121,2011,05/02/2011 12:26:38 PM,41.827004777,-87.733769823,"(41.827004777, -87.733769823)"
-8002383,HT232921,04/03/2011 08:37:00 PM,049XX S LAMON AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,STREET,false,false,0814,008,23,56,04A,1144495,1871095,2011,04/05/2011 08:56:51 PM,41.802296082,-87.745608336,"(41.802296082, -87.745608336)"
-8000348,HT232127,04/03/2011 11:50:00 AM,066XX S HALSTED ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0723,007,6,68,18,1172162,1860466,2011,04/03/2011 12:29:31 PM,41.772564979,-87.644453924,"(41.772564979, -87.644453924)"
-7997139,HT228483,03/31/2011 08:39:00 PM,068XX S DR MARTIN LUTHER KING JR DR,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0322,003,20,69,08B,1180144,1859515,2011,04/03/2011 08:47:38 AM,41.769776166,-87.615223444,"(41.769776166, -87.615223444)"
-7996704,HT227856,03/31/2011 01:51:00 PM,015XX E 92ND ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0413,004,8,48,18,1188087,1844326,2011,03/31/2011 03:28:02 PM,41.72791043,-87.586591461,"(41.72791043, -87.586591461)"
-7991936,HT223872,03/27/2011 06:00:00 PM,024XX N NORMANDY AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2512,025,36,18,07,1131383,1915493,2011,03/31/2011 10:46:16 AM,41.92436689,-87.792671171,"(41.92436689, -87.792671171)"
-7990336,HT222510,03/27/2011 02:00:00 PM,123XX S PARNELL AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0523,005,34,53,26,1174833,1823007,2011,04/01/2011 08:06:00 AM,41.669713191,-87.635775433,"(41.669713191, -87.635775433)"
-7989970,HT221773,03/27/2011 10:53:00 AM,008XX N LARAMIE AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,RESIDENCE,false,false,1531,015,37,25,04B,1141574,1905185,2011,03/28/2011 10:10:00 AM,41.895898159,-87.755479772,"(41.895898159, -87.755479772)"
-7989616,HT221506,03/27/2011 03:27:00 AM,002XX N LARAMIE AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,1532,015,28,25,15,1141707,1901211,2011,03/27/2011 09:33:20 AM,41.884990564,-87.755089646,"(41.884990564, -87.755089646)"
-7996330,HT222014,03/26/2011 04:00:00 PM,004XX W 107TH ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,2233,022,34,49,07,1174988,1833987,2011,08/10/2011 01:19:19 AM,41.699840571,-87.634882359,"(41.699840571, -87.634882359)"
-7988307,HT219886,03/25/2011 08:58:00 PM,009XX N MOZART ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,1311,012,26,24,08B,1157172,1906205,2011,03/27/2011 08:14:29 AM,41.898394561,-87.69816358,"(41.898394561, -87.69816358)"
-7986363,HT218186,03/24/2011 09:00:00 AM,071XX S SOUTH CHICAGO AVE,1345,CRIMINAL DAMAGE,TO CITY OF CHICAGO PROPERTY,"SCHOOL, PRIVATE, BUILDING",false,false,0324,003,5,69,14,1183073,1857902,2011,03/25/2011 06:28:39 AM,41.765282327,-87.60453722,"(41.765282327, -87.60453722)"
-7984843,HT216548,03/23/2011 02:00:00 PM,082XX S SOUTH SHORE DR,0460,BATTERY,SIMPLE,"SCHOOL, PRIVATE, BUILDING",false,false,0424,004,7,46,08B,1198623,1850862,2011,03/25/2011 06:31:02 AM,41.745588413,-87.54777847,"(41.745588413, -87.54777847)"
-7984755,HT216480,03/22/2011 09:00:00 PM,107XX S WENTWORTH AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0513,005,34,49,06,1176812,1833692,2011,03/24/2011 08:16:59 AM,41.698990262,-87.628212501,"(41.698990262, -87.628212501)"
-7981143,HT213342,03/21/2011 06:00:00 AM,053XX S CAMPBELL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,0911,009,14,63,08B,1160654,1868993,2011,03/22/2011 10:07:01 AM,41.796209436,-87.68640403,"(41.796209436, -87.68640403)"
-7979839,HT212520,03/21/2011 12:01:00 AM,088XX S HERMITAGE AVE,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,RESIDENCE,false,false,2221,022,21,71,05,1166268,1845880,2011,04/16/2011 09:33:32 AM,41.732666466,-87.666474133,"(41.732666466, -87.666474133)"
-7979639,HT212268,03/20/2011 07:40:00 PM,057XX S PAULINA ST,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,OTHER,true,false,0715,007,15,67,18,1165945,1866816,2011,03/20/2011 09:19:28 PM,41.790124579,-87.667063376,"(41.790124579, -87.667063376)"
-7978871,HT211307,03/20/2011 12:30:00 AM,001XX W 104TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0512,005,34,49,18,1177134,1835952,2011,03/20/2011 02:34:09 AM,41.705184783,-87.62696562,"(41.705184783, -87.62696562)"
-7979051,HT211569,03/19/2011 11:15:00 PM,005XX W 28TH ST,0920,MOTOR VEHICLE THEFT,ATT: AUTOMOBILE,STREET,false,false,0923,009,11,60,07,1172853,1886308,2011,03/20/2011 09:38:40 AM,41.843462794,-87.641157982,"(41.843462794, -87.641157982)"
-7978077,HT210270,03/19/2011 10:15:00 AM,025XX E 79TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,0422,004,7,46,18,1194674,1853044,2011,03/19/2011 10:58:28 AM,41.751674014,-87.562176373,"(41.751674014, -87.562176373)"
-7974107,HT206294,03/16/2011 12:00:00 AM,054XX W ALTGELD ST,1242,DECEPTIVE PRACTICE,COMPUTER FRAUD,RESIDENCE,false,false,2515,025,30,19,11,1139480,1916043,2011,04/06/2011 10:14:27 AM,41.925732202,-87.762905479,"(41.925732202, -87.762905479)"
-7972698,HT204873,03/15/2011 10:30:00 PM,035XX W MONROE ST,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,1123,011,28,27,26,1152855,1899346,2011,03/17/2011 12:11:57 PM,41.879659382,-87.714201468,"(41.879659382, -87.714201468)"
-7972441,HT204774,03/15/2011 08:30:00 PM,026XX N MILWAUKEE AVE,1120,DECEPTIVE PRACTICE,FORGERY,SIDEWALK,true,false,1412,014,35,22,10,1154173,1917635,2011,04/02/2011 10:48:53 AM,41.929819866,-87.708873076,"(41.929819866, -87.708873076)"
-7972272,HT204499,03/15/2011 05:10:00 PM,035XX W DOUGLAS BLVD,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,1021,010,24,29,08B,1153191,1893272,2011,03/26/2011 10:22:16 AM,41.862985006,-87.713128863,"(41.862985006, -87.713128863)"
-7975197,HT207140,03/15/2011 07:00:00 AM,135XX S FOREST AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,VACANT LOT/LAND,false,false,0533,005,9,54,14,1180643,1815411,2011,03/18/2011 06:54:20 AM,41.648737563,-87.614743453,"(41.648737563, -87.614743453)"
-7970782,HT203434,03/14/2011 10:50:00 PM,045XX W MADISON ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,ALLEY,false,false,1113,011,28,26,07,1146143,1899670,2011,03/28/2011 02:04:59 PM,41.880678701,-87.738838954,"(41.880678701, -87.738838954)"
-7970488,HT203018,03/14/2011 05:15:00 PM,090XX S GREENWOOD AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0413,004,8,47,08B,1185089,1845150,2011,03/15/2011 08:45:28 AM,41.730242423,-87.597547733,"(41.730242423, -87.597547733)"
-7972214,HT204265,03/14/2011 11:30:00 AM,026XX N ASHLAND AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1931,019,32,7,05,1165169,1917781,2011,04/09/2011 12:57:57 PM,41.929993628,-87.668461602,"(41.929993628, -87.668461602)"
-7969107,HT201805,03/13/2011 07:00:00 PM,045XX N LONG AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1623,016,45,15,05,1139552,1929832,2011,04/01/2011 07:45:39 PM,41.96356926,-87.762303366,"(41.96356926, -87.762303366)"
-7968881,HT201551,03/13/2011 04:45:00 PM,043XX W MAYPOLE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,RESIDENCE PORCH/HALLWAY,true,false,1114,011,28,26,18,1147254,1901057,2011,03/13/2011 05:47:04 PM,41.884463597,-87.734723892,"(41.884463597, -87.734723892)"
-7968243,HT200761,03/13/2011 12:50:00 AM,005XX N MICHIGAN AVE,0850,THEFT,ATTEMPT THEFT,DEPARTMENT STORE,false,false,1834,018,42,8,06,1177299,1903904,2011,03/14/2011 08:08:00 AM,41.891647815,-87.624308958,"(41.891647815, -87.624308958)"
-7968247,HT200713,03/12/2011 11:30:00 PM,053XX S MASSASOIT AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,true,false,0811,008,23,56,14,1138975,1868677,2011,03/13/2011 09:29:18 AM,41.795762471,-87.765911411,"(41.795762471, -87.765911411)"
-7968097,HT200522,03/12/2011 08:30:00 PM,043XX W CULLERTON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,1012,010,24,29,08B,1147910,1890030,2011,03/15/2011 10:02:32 AM,41.854191606,-87.732598328,"(41.854191606, -87.732598328)"
-7972401,HT204702,03/12/2011 05:00:00 PM,078XX S PHILLIPS AVE,0890,THEFT,FROM BUILDING,RESIDENCE-GARAGE,false,false,0421,004,7,43,06,1193852,1853292,2011,03/16/2011 06:17:36 AM,41.752374723,-87.565180445,"(41.752374723, -87.565180445)"
-7971191,HT203690,03/12/2011 03:00:00 PM,040XX S STATE ST,0560,ASSAULT,SIMPLE,STREET,false,false,0214,002,3,38,08A,1176906,1878155,2011,03/19/2011 07:30:03 AM,41.820999716,-87.626530678,"(41.820999716, -87.626530678)"
-7968676,HT200373,03/12/2011 03:00:00 AM,029XX W FLOURNOY ST,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,1135,011,2,27,08A,1157018,1896935,2011,03/18/2011 01:01:24 PM,41.872959931,-87.698980903,"(41.872959931, -87.698980903)"
-7966754,HT198767,03/11/2011 03:30:00 PM,023XX S KEDZIE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1024,010,24,30,14,1155356,1888198,2011,03/14/2011 08:34:43 AM,41.849018188,-87.705317557,"(41.849018188, -87.705317557)"
-7966981,HT199043,03/11/2011 01:00:00 PM,029XX N LONG AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2514,025,31,19,05,1139902,1919016,2011,03/13/2011 09:22:38 AM,41.933882712,-87.761281933,"(41.933882712, -87.761281933)"
-7966283,HT198133,03/11/2011 11:15:00 AM,018XX S KARLOV AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1012,010,24,29,08B,1149392,1890763,2011,03/13/2011 12:38:33 PM,41.856174469,-87.727139755,"(41.856174469, -87.727139755)"
-7970079,HT202475,03/10/2011 07:00:00 PM,111XX S WHIPPLE ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,2211,022,19,74,06,1157957,1830550,2011,03/23/2011 10:22:00 AM,41.690770902,-87.697336105,"(41.690770902, -87.697336105)"
-7962100,HT194402,03/08/2011 07:00:00 PM,038XX W MONTROSE AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE,true,false,1723,017,39,16,14,1149767,1928920,2011,03/09/2011 08:24:10 AM,41.960873718,-87.724769628,"(41.960873718, -87.724769628)"
-7961340,HT193633,03/08/2011 11:44:00 AM,011XX W 76TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0612,006,17,71,18,1169831,1854438,2011,03/08/2011 01:50:34 PM,41.756074297,-87.653173439,"(41.756074297, -87.653173439)"
-7960365,HT193182,03/08/2011 12:50:00 AM,073XX S MAY ST,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,0733,007,17,68,06,1169963,1856107,2011,03/08/2011 10:18:44 AM,41.760651385,-87.652641291,"(41.760651385, -87.652641291)"
-7961261,HT192166,03/06/2011 11:00:00 PM,018XX W ADDISON ST,0890,THEFT,FROM BUILDING,RESIDENCE-GARAGE,false,false,1923,019,47,5,06,1163466,1923894,2011,03/10/2011 03:19:38 PM,41.946804111,-87.674547044,"(41.946804111, -87.674547044)"
-7963633,HT191219,03/06/2011 02:00:00 PM,029XX S PARNELL AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0923,009,11,60,06,1173150,1885169,2011,03/10/2011 10:05:46 AM,41.84033071,-87.640101803,"(41.84033071, -87.640101803)"
-7958416,HT190773,03/05/2011 10:45:00 PM,008XX S CLARK ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0131,001,2,32,14,1175605,1896660,2011,03/07/2011 07:33:38 AM,41.8718081,-87.630747973,"(41.8718081, -87.630747973)"
-7957821,HT189709,03/05/2011 01:09:00 PM,058XX N CALIFORNIA AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,2011,020,40,2,18,1156658,1938780,2011,03/05/2011 01:59:17 PM,41.987792984,-87.699166297,"(41.987792984, -87.699166297)"
-7959182,HT189048,03/04/2011 10:20:00 PM,029XX S MICHIGAN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,true,2112,001,2,35,14,1177771,1885549,2011,03/18/2011 07:34:57 AM,41.84126986,-87.623133214,"(41.84126986, -87.623133214)"
-7955091,HT186699,03/03/2011 12:01:00 AM,045XX N ELSTON AVE,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,OTHER,false,true,1722,017,39,14,26,1147140,1930154,2011,03/07/2011 11:53:41 AM,41.964310698,-87.73439621,"(41.964310698, -87.73439621)"
-7953853,HT185945,03/02/2011 08:41:00 PM,076XX S KINGSTON AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,0421,004,7,43,18,1194481,1854723,2011,03/02/2011 10:09:46 PM,41.756286065,-87.562828511,"(41.756286065, -87.562828511)"
-7953864,HT185885,03/02/2011 06:45:00 PM,066XX N SHERIDAN RD,0460,BATTERY,SIMPLE,APARTMENT,false,false,2432,024,49,1,08B,1167085,1944378,2011,03/03/2011 06:10:56 AM,42.002935744,-87.66065348,"(42.002935744, -87.66065348)"
-7953408,HT185312,03/02/2011 01:20:00 PM,011XX W WILSON AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,2311,019,46,3,18,1167811,1930740,2011,03/02/2011 02:51:04 PM,41.965497049,-87.658377972,"(41.965497049, -87.658377972)"
-7951937,HT184105,03/01/2011 03:22:00 PM,069XX S HALSTED ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,ALLEY,false,false,0733,007,6,68,15,1172135,1858508,2011,03/02/2011 10:08:48 AM,41.767192586,-87.644610374,"(41.767192586, -87.644610374)"
-7953891,HT185966,02/28/2011 07:00:00 PM,022XX W FILLMORE ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1224,012,25,28,06,1161423,1895151,2011,03/03/2011 07:56:12 AM,41.867973998,-87.682857688,"(41.867973998, -87.682857688)"
-7950240,HT182392,02/28/2011 09:00:00 AM,075XX S PULASKI RD,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,0833,008,13,65,06,1150961,1854579,2011,03/01/2011 09:30:54 AM,41.756849802,-87.722324877,"(41.756849802, -87.722324877)"
-7949916,HT181973,02/27/2011 10:30:00 PM,071XX S MERRILL AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0333,003,5,43,05,1191831,1858042,2011,03/18/2011 06:11:39 AM,41.765458386,-87.572432445,"(41.765458386, -87.572432445)"
-7949359,HT181785,02/27/2011 08:00:00 PM,015XX W JONQUIL TER,0810,THEFT,OVER $500,APARTMENT,false,false,2422,024,49,1,06,1164447,1951034,2011,03/25/2011 03:45:47 PM,42.021256393,-87.670168781,"(42.021256393, -87.670168781)"
-7951658,HT183763,02/26/2011 10:45:00 PM,065XX S ROCKWELL ST,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,RESIDENCE,true,true,0831,008,15,66,20,1160129,1861073,2011,06/10/2011 09:28:31 AM,41.774486696,-87.688547052,"(41.774486696, -87.688547052)"
-7947337,HT179321,02/26/2011 12:01:00 AM,060XX N NAVARRE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,1611,016,41,10,05,1130635,1939960,2011,03/02/2011 10:45:46 AM,41.991519869,-87.794854763,"(41.991519869, -87.794854763)"
-7947024,HT178573,02/25/2011 04:55:00 PM,088XX S EXCHANGE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0423,004,10,46,03,1197341,1846865,2011,03/18/2011 08:46:49 AM,41.734652356,-87.552608715,"(41.734652356, -87.552608715)"
-7946481,HT178167,02/24/2011 03:00:00 PM,016XX N HARDING AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,2535,025,30,23,26,1149759,1910538,2011,02/27/2011 08:31:40 PM,41.910432118,-87.725278374,"(41.910432118, -87.725278374)"
-7943351,HT176060,02/23/2011 08:09:00 PM,048XX W HURON ST,2027,NARCOTICS,POSS: CRACK,RESIDENCE,true,false,1531,015,28,25,18,1143759,1904237,2011,02/23/2011 10:18:13 PM,41.893256075,-87.747478409,"(41.893256075, -87.747478409)"
-7943321,HT175846,02/23/2011 06:08:00 PM,017XX N PULASKI RD,0313,ROBBERY,ARMED: OTHER DANGEROUS WEAPON,RESTAURANT,false,false,2535,025,30,23,03,1149490,1911068,2011,04/11/2011 05:12:40 PM,41.911891717,-87.726252805,"(41.911891717, -87.726252805)"
-7951248,HT182070,02/22/2011 11:23:00 AM,036XX N LEAVITT ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1912,019,47,5,26,1160999,1924433,2011,05/08/2011 04:57:02 PM,41.948334831,-87.683599989,"(41.948334831, -87.683599989)"
-7941160,HT174389,02/22/2011 09:00:00 AM,026XX W 47TH ST,0820,THEFT,$500 AND UNDER,OTHER,false,false,0911,009,14,58,06,1159402,1873362,2011,02/23/2011 10:19:19 AM,41.808224305,-87.690875478,"(41.808224305, -87.690875478)"
-7939198,HT172361,02/21/2011 01:00:00 PM,008XX W BUENA AVE,0810,THEFT,OVER $500,OTHER,false,false,2322,019,46,3,06,1169423,1928146,2011,02/22/2011 06:40:18 AM,41.95834403,-87.652526816,"(41.95834403, -87.652526816)"
-7938556,HT171312,02/18/2011 06:00:00 PM,016XX E 83RD ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0412,004,8,45,05,1188856,1850240,2011,03/07/2011 10:10:04 AM,41.744120693,-87.583585793,"(41.744120693, -87.583585793)"
-7953792,HT185194,02/18/2011 03:00:00 PM,080XX S MANISTEE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0422,004,7,46,05,1195860,1852314,2011,03/03/2011 02:35:40 PM,41.749641586,-87.557854442,"(41.749641586, -87.557854442)"
-7933872,HT166179,02/17/2011 11:20:00 AM,021XX E 71ST ST,1330,CRIMINAL TRESPASS,TO LAND,PARKING LOT/GARAGE(NON.RESID.),true,false,0333,003,5,43,26,1191476,1858232,2011,02/18/2011 06:06:23 AM,41.765988371,-87.573727459,"(41.765988371, -87.573727459)"
-7933726,HT166134,02/17/2011 09:00:00 AM,030XX N AUSTIN AVE,0560,ASSAULT,SIMPLE,RESIDENTIAL YARD (FRONT/BACK),false,false,2514,025,30,19,08A,1135902,1919565,2011,02/20/2011 07:20:50 AM,41.935461526,-87.775968914,"(41.935461526, -87.775968914)"
-7931002,HT164081,02/15/2011 10:00:00 PM,052XX N CLARK ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,SIDEWALK,true,false,2013,020,48,77,24,1165088,1934651,2011,10/31/2014 03:20:56 PM,41.976287402,-87.668278182,"(41.976287402, -87.668278182)"
-7931029,HT163950,02/15/2011 08:40:00 PM,032XX S HARDING AVE,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,false,false,1031,010,22,30,26,1150593,1883005,2011,02/16/2011 09:19:25 AM,41.834862185,-87.722933828,"(41.834862185, -87.722933828)"
-7932868,HT165689,02/15/2011 08:00:00 PM,065XX S ALBANY AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0831,008,15,66,06,1156879,1861300,2011,03/01/2011 12:35:34 PM,41.775175866,-87.700454977,"(41.775175866, -87.700454977)"
-7928356,HT161330,02/14/2011 12:00:00 AM,015XX N ARTESIAN AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1423,014,1,24,06,1159838,1910041,2011,02/15/2011 08:14:25 AM,41.908866273,-87.688265589,"(41.908866273, -87.688265589)"
-7927377,HT160373,02/13/2011 02:57:00 AM,032XX N MILWAUKEE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,SMALL RETAIL STORE,false,false,1732,017,30,21,14,1149616,1921404,2011,02/25/2011 10:16:01 AM,41.940252175,-87.725520754,"(41.940252175, -87.725520754)"
-7941518,HT159580,02/12/2011 08:00:00 PM,011XX N CLEAVER ST,0810,THEFT,OVER $500,STREET,false,false,1323,012,27,24,06,1166550,1907910,2011,03/25/2011 07:15:20 PM,41.902877509,-87.663670038,"(41.902877509, -87.663670038)"
-7926418,HT158705,02/12/2011 12:15:00 PM,061XX N WINTHROP AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,2433,024,48,77,03,1167705,1940958,2011,03/04/2011 09:23:21 PM,41.993537812,-87.658471716,"(41.993537812, -87.658471716)"
-8107097,HT341923,02/11/2011 09:00:00 AM,069XX S PAXTON AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0331,003,5,43,26,1192127,1859048,2011,06/14/2011 11:36:43 AM,41.768211737,-87.571314864,"(41.768211737, -87.571314864)"
-7924781,HT156332,02/10/2011 09:32:00 PM,119XX S PERRY AVE,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,0522,005,9,53,18,1177683,1825981,2011,02/10/2011 10:32:11 PM,41.677810527,-87.625255393,"(41.677810527, -87.625255393)"
-7920353,HT151588,02/07/2011 06:35:00 PM,035XX W CERMAK RD,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,1024,010,22,30,14,1153237,1889114,2011,02/28/2011 11:19:37 AM,41.851574062,-87.713070265,"(41.851574062, -87.713070265)"
-8083519,HT316125,02/07/2011 12:00:00 PM,055XX N KENMORE AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,2023,020,48,77,26,1168285,1937084,2011,05/30/2011 12:17:20 PM,41.982894908,-87.656450884,"(41.982894908, -87.656450884)"
-7919067,HT150472,02/06/2011 11:37:00 PM,003XX W 79TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0623,006,17,44,18,1175543,1852527,2011,02/07/2011 02:06:40 AM,41.750704453,-87.632297342,"(41.750704453, -87.632297342)"
-7918739,HT149945,02/06/2011 11:00:00 AM,004XX W 95TH ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2223,022,21,73,06,1175279,1841955,2011,02/07/2011 05:49:43 AM,41.721699413,-87.633579745,"(41.721699413, -87.633579745)"
-7918270,HT149263,02/05/2011 09:50:00 PM,040XX N LEAVITT ST,1345,CRIMINAL DAMAGE,TO CITY OF CHICAGO PROPERTY,"SCHOOL, PUBLIC, GROUNDS",true,false,1912,019,47,5,14,1160924,1927058,2011,02/07/2011 07:07:04 AM,41.955539534,-87.683802545,"(41.955539534, -87.683802545)"
-7919483,HT148775,02/05/2011 01:50:00 PM,038XX S ARCHER AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0913,009,12,58,08B,1160172,1879108,2011,02/07/2011 02:29:08 PM,41.8239762,-87.68789299,"(41.8239762, -87.68789299)"
-7918532,HT148664,02/05/2011 12:35:00 PM,039XX S WESTERN AVE,4651,OTHER OFFENSE,SEX OFFENDER: FAIL REG NEW ADD,APARTMENT,false,false,0921,009,12,58,26,1160987,1878203,2011,02/09/2014 04:23:26 PM,41.821475934,-87.684928077,"(41.821475934, -87.684928077)"
-7916503,HT146874,02/03/2011 10:00:00 PM,048XX N KENTUCKY AVE,0820,THEFT,$500 AND UNDER,DRIVEWAY - RESIDENTIAL,false,false,1712,017,39,14,06,1144701,1931749,2011,02/05/2011 09:45:27 AM,41.968733936,-87.743323425,"(41.968733936, -87.743323425)"
-7915300,HT144992,02/01/2011 04:26:37 PM,026XX W 68TH ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0831,008,15,66,07,1159623,1859410,2011,02/06/2011 08:52:26 AM,41.769933579,-87.690447546,"(41.769933579, -87.690447546)"
-7913579,HT143749,01/30/2011 10:00:00 PM,085XX S BUFFALO AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0424,004,10,46,14,1199585,1848859,2011,02/01/2011 06:18:28 AM,41.740067886,-87.544320904,"(41.740067886, -87.544320904)"
-7924637,HT141204,01/30/2011 11:58:00 AM,075XX S WABASH AVE,0460,BATTERY,SIMPLE,APARTMENT,false,false,0623,006,6,69,08B,1178069,1854742,2011,02/13/2011 03:32:22 PM,41.756725817,-87.622973913,"(41.756725817, -87.622973913)"
-7912041,HT142080,01/30/2011 10:25:00 AM,001XX W 127TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,GAS STATION,true,false,0523,005,9,53,18,1177406,1820765,2011,01/30/2011 12:20:02 PM,41.663503247,-87.626425918,"(41.663503247, -87.626425918)"
-7908857,HT138673,01/27/2011 07:28:00 PM,037XX W 80TH ST,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,false,false,0834,008,18,70,04B,1152550,1851234,2011,02/05/2011 03:47:14 PM,41.747639444,-87.716589271,"(41.747639444, -87.716589271)"
-7906069,HT136068,01/26/2011 12:30:00 AM,011XX E 45TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2123,002,4,39,14,1184931,1875369,2011,01/26/2011 10:44:04 AM,41.813169817,-87.597178983,"(41.813169817, -87.597178983)"
-7905357,HT135505,01/25/2011 08:15:00 PM,044XX W FULLERTON AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,GAS STATION,true,false,2522,025,31,20,18,1146617,1915552,2011,01/25/2011 08:58:41 PM,41.924251571,-87.73669285,"(41.924251571, -87.73669285)"
-7904377,HT134470,01/25/2011 06:30:00 AM,018XX N RICHMOND ST,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",true,false,1421,014,35,22,08A,1156561,1912252,2011,01/26/2011 09:07:32 AM,41.915000435,-87.700243824,"(41.915000435, -87.700243824)"
-7906831,HT133905,01/22/2011 03:00:00 PM,017XX W ROSCOE ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1924,019,32,6,05,1164038,1922658,2011,02/16/2011 02:57:36 PM,41.94340038,-87.672479572,"(41.94340038, -87.672479572)"
-7905952,HT130234,01/22/2011 10:00:00 AM,0000X W CHECKPOINT 7 ST,5007,OTHER OFFENSE,OTHER WEAPONS VIOLATION,AIRPORT TERMINAL UPPER LEVEL - SECURE AREA,false,false,1653,016,41,76,26,1101708,1934266,2011,01/28/2011 10:13:55 AM,41.976344553,-87.901365347,"(41.976344553, -87.901365347)"
-7900305,HT129785,01/21/2011 09:41:00 PM,013XX N MASON AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,2531,025,29,25,18,1136412,1908160,2011,01/21/2011 10:21:18 PM,41.904155771,-87.774367803,"(41.904155771, -87.774367803)"
-7900766,HT128660,01/20/2011 12:15:00 PM,112XX S WALLACE ST,0545,ASSAULT,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,2233,022,34,49,08A,1174273,1830362,2011,01/23/2011 07:14:17 AM,41.689908902,-87.637607596,"(41.689908902, -87.637607596)"
-7897339,HT127151,01/19/2011 10:00:00 PM,035XX W DOUGLAS BLVD,1710,OFFENSE INVOLVING CHILDREN,ENDANGERING LIFE/HEALTH CHILD,RESIDENCE,false,true,1021,010,24,29,26,1153209,1893027,2011,02/10/2011 09:22:36 AM,41.862312342,-87.713069284,"(41.862312342, -87.713069284)"
-7897234,HT126930,01/19/2011 08:00:00 PM,024XX E 73RD ST,0560,ASSAULT,SIMPLE,APARTMENT,false,false,0334,003,7,43,08A,1193733,1857068,2011,02/01/2011 02:51:17 PM,41.762739275,-87.565493016,"(41.762739275, -87.565493016)"
-7907298,HT126752,01/19/2011 07:30:00 PM,095XX S WINCHESTER AVE,1360,CRIMINAL TRESPASS,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,2213,022,19,72,26,1165181,1841317,2011,01/27/2011 05:34:40 AM,41.720167926,-87.670585018,"(41.720167926, -87.670585018)"
-7895435,HT125514,01/19/2011 12:05:00 AM,037XX S CALIFORNIA AVE,0560,ASSAULT,SIMPLE,APARTMENT,true,false,0913,009,12,58,08A,1158364,1879360,2011,01/19/2011 10:16:31 AM,41.824704799,-87.694519089,"(41.824704799, -87.694519089)"
-7900828,HT130283,01/17/2011 09:00:00 PM,060XX S MENARD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,0812,008,13,64,14,1138814,1863667,2011,01/24/2011 09:20:29 AM,41.782017069,-87.766622917,"(41.782017069, -87.766622917)"
-7892814,HT122806,01/17/2011 12:00:00 AM,090XX S EUCLID AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0413,004,8,48,14,1190712,1845486,2011,01/18/2011 06:31:09 AM,41.731030691,-87.576938428,"(41.731030691, -87.576938428)"
-7894113,HT124071,01/16/2011 10:50:00 AM,019XX N LA CROSSE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2533,025,31,19,07,1143778,1912771,2011,01/22/2011 09:10:59 AM,41.916673954,-87.747194469,"(41.916673954, -87.747194469)"
-7892238,HT121481,01/15/2011 11:45:00 PM,016XX N TRIPP AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2534,025,30,23,08B,1147768,1910396,2011,01/19/2011 10:17:16 AM,41.910080954,-87.732596269,"(41.910080954, -87.732596269)"
-7893464,HT121316,01/15/2011 08:20:00 PM,079XX S EXCHANGE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,0422,004,7,46,14,1197169,1852958,2011,01/20/2011 06:58:20 AM,41.751376294,-87.553036426,"(41.751376294, -87.553036426)"
-7891298,HT120801,01/15/2011 12:50:00 PM,080XX S ELLIS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,false,0631,006,8,44,08B,1184255,1851710,2011,01/16/2011 05:52:39 AM,41.748263297,-87.600398234,"(41.748263297, -87.600398234)"
-7885926,HT116119,01/12/2011 10:30:00 AM,058XX S PEORIA ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,RESIDENCE,true,false,0712,007,16,68,18,1171349,1865958,2011,01/12/2011 11:19:41 AM,41.787653499,-87.647273529,"(41.787653499, -87.647273529)"
-7890186,HT119543,01/10/2011 01:00:00 PM,037XX S DAMEN AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,false,0922,009,11,59,06,1163680,1879692,2011,01/16/2011 10:22:41 AM,41.825505723,-87.675006881,"(41.825505723, -87.675006881)"
-7881488,HT112581,01/09/2011 02:00:00 PM,062XX S NATOMA AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0812,008,23,64,05,1133190,1862469,2011,01/12/2011 08:52:06 AM,41.778829394,-87.78727034,"(41.778829394, -87.78727034)"
-7898133,HT117793,01/07/2011 01:59:00 PM,002XX W JACKSON BLVD,0870,THEFT,POCKET-PICKING,RESTAURANT,false,false,0112,001,2,32,06,1174733,1898992,2011,01/24/2011 06:26:11 PM,41.878226797,-87.633879635,"(41.878226797, -87.633879635)"
-7874436,HT105007,01/04/2011 02:20:00 PM,081XX S MAY ST,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,0613,006,21,71,08B,1170110,1850747,2011,01/05/2011 06:46:20 AM,41.745939643,-87.652258041,"(41.745939643, -87.652258041)"
-7873961,HT104666,01/03/2011 10:00:00 AM,032XX W MONROE ST,0820,THEFT,$500 AND UNDER,"SCHOOL, PUBLIC, BUILDING",true,false,1124,011,28,,06,1154528,1899388,2011,01/05/2011 10:22:29 AM,41.879741351,-87.708057298,"(41.879741351, -87.708057298)"
-7870056,HT100716,01/01/2011 11:25:00 AM,078XX S ASHLAND AVE,0560,ASSAULT,SIMPLE,SMALL RETAIL STORE,true,false,0611,006,17,71,08A,1166991,1852927,2011,01/02/2011 07:57:47 AM,41.751989047,-87.663624538,"(41.751989047, -87.663624538)"
-8027624,HT258669,01/01/2011 09:00:00 AM,031XX N LECLAIRE AVE,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,RESIDENCE,false,false,2521,025,30,19,06,1141787,1920163,2011,06/18/2011 08:45:25 AM,41.936995471,-87.754326062,"(41.936995471, -87.754326062)"
-8129452,HT363355,01/01/2011 08:00:00 AM,011XX N LOCKWOOD AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,1524,015,37,25,06,1140848,1907381,2011,06/29/2011 10:01:21 AM,41.901937629,-87.758092172,"(41.901937629, -87.758092172)"
-7867271,HS681358,12/29/2010 05:41:04 PM,068XX S PRAIRIE AVE,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,false,false,0322,003,20,69,26,1179188,1859446,2010,12/31/2010 06:20:15 AM,41.769608673,-87.618729795,"(41.769608673, -87.618729795)"
-7865317,HS679558,12/28/2010 01:28:00 PM,027XX W ADDISON ST,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,BRIDGE,true,false,1912,019,47,5,26,1157699,1923851,2010,12/28/2010 06:40:32 PM,41.946805791,-87.695746045,"(41.946805791, -87.695746045)"
-7862059,HS676648,12/25/2010 10:00:00 PM,038XX W ARGYLE ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1712,017,39,14,07,1149779,1932907,2010,12/31/2010 11:56:37 AM,41.971814088,-87.724621369,"(41.971814088, -87.724621369)"
-7861627,HS676024,12/25/2010 11:00:00 AM,050XX W WABANSIA AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2533,025,37,25,07,1142539,1910795,2010,01/06/2011 09:21:01 AM,41.911274743,-87.751795808,"(41.911274743, -87.751795808)"
-7868299,HS681212,12/23/2010 03:00:00 PM,012XX W 31ST ST,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,0924,009,11,60,08A,1168702,1884210,2010,12/31/2010 09:07:19 AM,41.83779648,-87.656451843,"(41.83779648, -87.656451843)"
-7857300,HS671175,12/21/2010 10:00:00 AM,068XX S PERRY AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,0722,007,6,69,06,1176624,1859414,2010,12/24/2010 11:29:59 AM,41.769578936,-87.628129175,"(41.769578936, -87.628129175)"
-7854474,HS668561,12/20/2010 12:30:00 AM,039XX S WESTERN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,0914,009,12,58,14,1160960,1878738,2010,12/20/2010 11:11:37 AM,41.822944596,-87.685012317,"(41.822944596, -87.685012317)"
-7853532,HS667173,12/18/2010 08:45:00 PM,015XX N LATROBE AVE,1121,DECEPTIVE PRACTICE,COUNTERFEITING DOCUMENT,APARTMENT,false,false,2532,025,37,25,10,1141122,1909812,2010,12/21/2010 02:15:06 PM,41.908603526,-87.757025729,"(41.908603526, -87.757025729)"
-7852399,HS665543,12/17/2010 05:00:00 PM,0000X W WACKER DR,0890,THEFT,FROM BUILDING,BAR OR TAVERN,false,false,0122,001,42,32,06,1175970,1902093,2010,12/20/2010 07:55:08 AM,41.886708365,-87.629244285,"(41.886708365, -87.629244285)"
-7852508,HS665313,12/17/2010 12:00:00 PM,107XX S COTTAGE GROVE AVE,0810,THEFT,OVER $500,STREET,false,false,0513,005,9,50,06,1182283,1833916,2010,12/18/2010 07:04:06 AM,41.699480288,-87.608173488,"(41.699480288, -87.608173488)"
-7852570,HS665721,12/16/2010 07:30:00 PM,030XX S QUINN ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0923,009,11,60,05,1170767,1884704,2010,12/31/2010 09:59:51 AM,41.839107155,-87.648860008,"(41.839107155, -87.648860008)"
-7849405,HS662290,12/15/2010 09:50:00 AM,026XX W 36TH ST,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0913,009,12,58,05,1159204,1880648,2010,12/23/2010 06:23:52 PM,41.828222044,-87.691402056,"(41.828222044, -87.691402056)"
-7846554,HS659630,12/14/2010 12:43:00 AM,055XX S WOOD ST,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,0715,007,15,67,14,1165251,1868005,2010,12/14/2010 02:39:32 PM,41.793402072,-87.669574427,"(41.793402072, -87.669574427)"
-7845069,HS658108,12/13/2010 05:15:00 AM,0000X S DEARBORN ST,0820,THEFT,$500 AND UNDER,CTA PLATFORM,false,false,0112,001,42,32,06,1175909,1900111,2010,12/13/2010 10:47:41 AM,41.881271018,-87.629527982,"(41.881271018, -87.629527982)"
-8082273,HT315077,12/12/2010 09:21:00 PM,037XX N RICHMOND ST,2860,PUBLIC PEACE VIOLATION,FALSE POLICE REPORT,RESIDENCE,true,false,1733,017,33,16,24,1156011,1924755,2010,07/28/2011 08:46:13 AM,41.949320715,-87.70192618,"(41.949320715, -87.70192618)"
-7854329,HS668352,12/12/2010 09:00:00 PM,016XX W JULIAN ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1433,014,1,24,14,1165352,1909936,2010,12/20/2010 10:25:03 AM,41.908462569,-87.66801278,"(41.908462569, -87.66801278)"
-7842420,HS653618,12/09/2010 10:00:00 PM,015XX W 77TH ST,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,APARTMENT,false,true,0612,006,17,71,26,1167160,1853621,2010,12/22/2010 11:10:59 AM,41.753889867,-87.662985413,"(41.753889867, -87.662985413)"
-7839663,HS651580,12/07/2010 05:00:00 PM,024XX S LEAVITT ST,0810,THEFT,OVER $500,STREET,false,false,1034,010,25,31,06,1162121,1887975,2010,12/09/2010 10:02:21 AM,41.848267809,-87.680495511,"(41.848267809, -87.680495511)"
-7834911,HS646182,12/04/2010 03:00:00 PM,124XX S HARVARD AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,0523,005,34,53,07,1176162,1822631,2010,01/18/2011 07:06:10 PM,41.668651763,-87.630922692,"(41.668651763, -87.630922692)"
-7833814,HS644806,12/03/2010 09:04:00 PM,035XX N OKETO AVE,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,1631,016,36,17,03,1126174,1922812,2010,12/06/2010 05:43:33 PM,41.944539677,-87.811647992,"(41.944539677, -87.811647992)"
-7872035,HT102893,12/03/2010 12:01:00 AM,024XX W GRANVILLE AVE,5000,OTHER OFFENSE,OTHER CRIME AGAINST PERSON,RESIDENCE,false,false,2413,024,50,2,26,1158938,1941048,2010,01/04/2011 07:17:46 AM,41.993969833,-87.690717702,"(41.993969833, -87.690717702)"
-7831961,HS642754,12/02/2010 03:00:00 PM,035XX N CLAREMONT AVE,0820,THEFT,$500 AND UNDER,ALLEY,false,false,1913,019,47,5,06,1160112,1923637,2010,12/03/2010 09:08:02 AM,41.946168965,-87.686882488,"(41.946168965, -87.686882488)"
-7831435,HS642493,12/02/2010 10:30:00 AM,055XX S WASHTENAW AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0824,008,16,63,05,1159361,1867632,2010,12/21/2010 10:33:42 AM,41.792501273,-87.691182863,"(41.792501273, -87.691182863)"
-7830558,HS641975,12/01/2010 07:30:00 PM,013XX N LAWNDALE AVE,0820,THEFT,$500 AND UNDER,RESIDENCE,false,false,2535,025,26,23,06,1151494,1908833,2010,12/02/2010 08:57:00 AM,41.905719509,-87.718949463,"(41.905719509, -87.718949463)"
-7833599,HS641579,12/01/2010 05:00:00 PM,057XX S COTTAGE GROVE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2133,002,5,41,07,1182594,1867250,2010,12/04/2010 07:36:53 AM,41.790945235,-87.606003087,"(41.790945235, -87.606003087)"
-7829693,HS640744,11/30/2010 04:31:00 PM,070XX N TONTY AVE,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,false,false,1621,016,41,12,26,1136849,1946197,2010,01/13/2011 03:07:32 PM,42.00852518,-87.771847104,"(42.00852518, -87.771847104)"
-7823720,HS634374,11/27/2010 12:40:00 AM,045XX W JACKSON BLVD,0820,THEFT,$500 AND UNDER,STREET,false,false,1131,011,24,26,06,1146390,1898267,2010,12/28/2010 02:35:43 PM,41.876824005,-87.737967719,"(41.876824005, -87.737967719)"
-7821678,HS630225,11/23/2010 06:00:00 AM,002XX S LAVERGNE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1533,015,28,25,14,1143329,1898775,2010,11/25/2010 09:05:39 AM,41.878275736,-87.749194212,"(41.878275736, -87.749194212)"
-8313661,HS628691,11/22/2010 09:48:00 PM,053XX S PAULINA ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,OTHER,false,false,0932,009,16,61,07,1165872,1869490,2010,12/04/2011 11:52:39 AM,41.797463912,-87.667255079,"(41.797463912, -87.667255079)"
-7855696,HS669242,11/20/2010 12:00:00 PM,108XX S AVENUE H,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,0432,004,10,52,06,1202893,1833543,2010,01/06/2011 10:32:23 PM,41.697955776,-87.532722976,"(41.697955776, -87.532722976)"
-7839122,HS622794,11/19/2010 09:30:00 AM,033XX W FILLMORE ST,2093,NARCOTICS,FOUND SUSPECT NARCOTICS,POLICE FACILITY/VEH PARKING LOT,true,false,1134,011,24,29,26,1154096,1895210,2010,12/28/2010 01:48:53 PM,41.868285103,-87.709755003,"(41.868285103, -87.709755003)"
-7813524,HS622967,11/18/2010 02:30:00 PM,036XX W FIFTH AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,false,1133,011,28,27,08A,1151962,1897752,2010,12/06/2010 12:47:21 PM,41.875302904,-87.717522452,"(41.875302904, -87.717522452)"
-7828192,HS621548,11/18/2010 11:38:00 AM,045XX W CONGRESS PKWY,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),VEHICLE NON-COMMERCIAL,true,false,1131,011,24,26,18,1146126,1897343,2010,12/03/2010 03:00:27 PM,41.874293459,-87.73896055,"(41.874293459, -87.73896055)"
-7810398,HS620433,11/17/2010 04:25:00 PM,003XX N LOCKWOOD AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1523,015,28,25,18,1140935,1901958,2010,11/17/2010 05:11:18 PM,41.887054664,-87.757906199,"(41.887054664, -87.757906199)"
-7811594,HS621527,11/17/2010 02:30:00 PM,076XX N SHERIDAN RD,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2422,024,49,1,26,1165513,1950757,2010,12/14/2010 12:43:12 PM,42.02047357,-87.666253868,"(42.02047357, -87.666253868)"
-7808983,HS619435,11/17/2010 12:57:00 AM,006XX N LEAMINGTON AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1532,015,28,25,18,1141949,1903873,2010,11/17/2010 03:03:55 AM,41.892290936,-87.754134988,"(41.892290936, -87.754134988)"
-7803831,HS613974,11/13/2010 03:35:00 PM,002XX E HURON ST,0460,BATTERY,SIMPLE,HOSPITAL BUILDING/GROUNDS,true,false,1834,018,42,8,08B,1178235,1905097,2010,10/31/2014 03:20:56 PM,41.894900187,-87.62083512,"(41.894900187, -87.62083512)"
-7811379,HS613400,11/13/2010 10:15:00 AM,0000X W CHECKPOINT 7 ST,5007,OTHER OFFENSE,OTHER WEAPONS VIOLATION,AIRPORT TERMINAL UPPER LEVEL - SECURE AREA,false,false,1653,016,41,76,26,1101708,1934266,2010,11/22/2010 10:18:16 AM,41.976344553,-87.901365347,"(41.976344553, -87.901365347)"
-7803598,HS613525,11/13/2010 12:30:00 AM,074XX N RIDGE BLVD,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2411,024,49,2,14,1160595,1949116,2010,11/15/2010 10:24:53 AM,42.016074381,-87.684397612,"(42.016074381, -87.684397612)"
-7803095,HS612603,11/12/2010 07:15:00 PM,009XX N RIDGEWAY AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,1112,011,27,23,04B,1151173,1905992,2010,12/29/2010 12:20:27 PM,41.897929829,-87.720203203,"(41.897929829, -87.720203203)"
-7801102,HS610865,11/11/2010 06:15:00 PM,005XX W 85TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,OTHER,true,false,0622,006,21,71,18,1173899,1848514,2010,11/11/2010 09:13:11 PM,41.739728866,-87.638440474,"(41.739728866, -87.638440474)"
-7802497,HS611729,11/11/2010 05:00:00 PM,062XX S KENWOOD AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,0314,003,20,42,26,1186185,1863926,2010,11/17/2010 04:48:41 PM,41.781739788,-87.592940847,"(41.781739788, -87.592940847)"
-7900336,HS609652,11/10/2010 11:20:00 PM,041XX S PRAIRIE AVE,0291,CRIM SEXUAL ASSAULT,ATTEMPT NON-AGGRAVATED,ALLEY,false,false,0214,002,3,38,02,1178767,1877557,2010,02/16/2011 06:46:05 PM,41.819316544,-87.619721901,"(41.819316544, -87.619721901)"
-8582916,HV257272,11/10/2010 09:00:00 AM,013XX W WASHBURNE AVE,0842,THEFT,AGG: FINANCIAL ID THEFT,APARTMENT,false,false,1231,012,2,28,06,1167755,1894557,2010,04/27/2012 01:40:01 PM,41.866209998,-87.659628924,"(41.866209998, -87.659628924)"
-7807117,HS617845,11/10/2010 06:30:00 AM,011XX N HERMITAGE AVE,0890,THEFT,FROM BUILDING,RESIDENCE PORCH/HALLWAY,false,false,1322,012,1,24,06,1164553,1907838,2010,11/16/2010 09:38:06 AM,41.902722479,-87.671007411,"(41.902722479, -87.671007411)"
-7833073,HS634848,11/09/2010 01:00:00 PM,038XX N CICERO AVE,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,false,false,1634,016,45,15,07,1143644,1925050,2010,12/21/2010 06:59:34 AM,41.950371227,-87.74737846,"(41.950371227, -87.74737846)"
-7797721,HS606075,11/09/2010 02:45:00 AM,0000X E CHICAGO AVE,0810,THEFT,OVER $500,RESTAURANT,false,false,1833,018,42,8,06,1176344,1905771,2010,11/10/2010 07:43:33 AM,41.896792558,-87.62775981,"(41.896792558, -87.62775981)"
-7795635,HS604804,11/08/2010 11:00:00 AM,108XX S AVENUE H,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,false,false,0432,004,10,52,26,1202891,1833798,2010,11/09/2010 06:25:28 AM,41.698655569,-87.53272162,"(41.698655569, -87.53272162)"
-7796332,HS605277,11/08/2010 10:00:00 AM,003XX W MARQUETTE RD,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,APARTMENT,false,false,0722,007,6,68,06,1175433,1860491,2010,12/03/2010 02:11:19 PM,41.772561059,-87.632462653,"(41.772561059, -87.632462653)"
-7798840,HS605201,11/08/2010 08:50:00 AM,0000X W PARKING LOT C ST,0810,THEFT,OVER $500,AIRPORT PARKING LOT,false,false,1654,016,41,76,06,1099358,1935446,2010,11/15/2010 09:23:47 AM,41.979614836,-87.909986119,"(41.979614836, -87.909986119)"
-7805463,HS604803,11/06/2010 10:00:00 PM,080XX S LANGLEY AVE,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,RESIDENCE,false,true,0631,006,6,44,20,1182326,1851993,2010,12/06/2010 07:37:28 PM,41.749084752,-87.607457919,"(41.749084752, -87.607457919)"
-7814366,HS599788,11/05/2010 02:01:00 AM,064XX S DR MARTIN LUTHER KING JR DR,0460,BATTERY,SIMPLE,STREET,false,false,0312,,20,69,08B,,,2010,11/23/2010 01:20:06 PM,,,
-7789631,HS598339,11/04/2010 10:00:00 AM,050XX W DIVISION ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,2533,025,37,25,26,1142444,1907559,2010,11/04/2010 11:53:37 AM,41.902396565,-87.752225347,"(41.902396565, -87.752225347)"
-7788644,HS597310,11/03/2010 04:00:00 PM,057XX W BELMONT AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,SMALL RETAIL STORE,false,false,2514,025,30,19,14,1137551,1920654,2010,12/08/2010 12:07:54 PM,41.938420278,-87.769882345,"(41.938420278, -87.769882345)"
-7788991,HS596292,11/03/2010 01:30:00 AM,058XX S MICHIGAN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,true,0233,002,20,40,14,1178201,1866105,2010,11/09/2010 03:24:29 PM,41.787904099,-87.6221458,"(41.787904099, -87.6221458)"
-7788221,HS596964,11/02/2010 10:30:00 PM,042XX N BROADWAY,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2322,019,46,3,14,1169192,1928464,2010,11/04/2010 07:11:18 AM,41.959221666,-87.653366775,"(41.959221666, -87.653366775)"
-7788591,HS597340,11/02/2010 09:30:00 PM,061XX S MASON AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0812,008,13,64,07,1137826,1863335,2010,11/04/2010 08:27:40 AM,41.781123812,-87.77025323,"(41.781123812, -87.77025323)"
-7787164,HS596299,11/02/2010 04:00:00 PM,013XX W 79TH ST,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,0612,006,17,71,08A,1168740,1852418,2010,11/06/2010 09:48:25 AM,41.750554744,-87.657229892,"(41.750554744, -87.657229892)"
-7784394,HS593301,11/01/2010 11:05:00 AM,045XX N CENTRAL PARK AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1723,017,33,14,18,1151521,1930245,2010,11/01/2010 12:12:54 PM,41.964475244,-87.718286009,"(41.964475244, -87.718286009)"
-7783560,HS592386,10/31/2010 05:30:00 PM,094XX S ELIZABETH ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,RESIDENCE,false,false,2222,022,21,73,04B,1169696,1841935,2010,11/24/2010 10:56:57 AM,41.721767241,-87.654029866,"(41.721767241, -87.654029866)"
-7782620,HS591236,10/30/2010 11:00:00 PM,030XX W IRVING PARK RD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1733,017,33,16,08B,1155394,1926395,2010,11/20/2010 04:38:23 PM,41.953833434,-87.704149932,"(41.953833434, -87.704149932)"
-7782654,HS591233,10/30/2010 10:44:00 PM,048XX N SAWYER AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1713,017,39,14,08B,1153876,1932048,2010,11/04/2010 12:12:42 PM,41.969376072,-87.70957902,"(41.969376072, -87.70957902)"
-7782267,HS590420,10/30/2010 12:01:00 AM,034XX N RACINE AVE,0870,THEFT,POCKET-PICKING,APARTMENT,false,false,1924,019,44,6,06,1167710,1923249,2010,11/01/2010 06:49:30 AM,41.944943633,-87.658966062,"(41.944943633, -87.658966062)"
-7780703,HS588432,10/29/2010 08:20:00 AM,078XX S HALSTED ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0621,006,17,71,26,1172283,1852998,2010,11/01/2010 06:06:30 AM,41.752069206,-87.644229677,"(41.752069206, -87.644229677)"
-7780045,HS587912,10/28/2010 08:55:00 PM,006XX N ST LOUIS AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1121,011,27,23,18,1152971,1904151,2010,10/29/2010 12:03:52 AM,41.892842495,-87.713648122,"(41.892842495, -87.713648122)"
-7786244,HS595130,10/28/2010 12:00:00 AM,025XX W 57TH ST,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE,false,false,0824,008,16,63,14,1160146,1866818,2010,11/02/2010 04:23:01 PM,41.790251422,-87.688326754,"(41.790251422, -87.688326754)"
-7776048,HS584218,10/26/2010 05:53:00 PM,044XX W 59TH ST,1710,OFFENSE INVOLVING CHILDREN,ENDANGERING LIFE/HEALTH CHILD,APARTMENT,false,true,0813,008,13,62,26,1147787,1865155,2010,12/02/2010 06:52:25 AM,41.785933389,-87.733686968,"(41.785933389, -87.733686968)"
-7775250,HS583310,10/26/2010 10:00:00 AM,034XX W WILSON AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,"SCHOOL, PUBLIC, BUILDING",true,false,1723,017,33,14,04B,1152451,1930390,2010,11/04/2010 10:45:51 AM,41.964854764,-87.714862788,"(41.964854764, -87.714862788)"
-7775877,HS584123,10/25/2010 09:00:00 PM,036XX W OGDEN AVE,0610,BURGLARY,FORCIBLE ENTRY,CHURCH/SYNAGOGUE/PLACE OF WORSHIP,false,false,1014,010,24,29,05,1152535,1890176,2010,02/03/2011 04:38:15 PM,41.854502202,-87.715618756,"(41.854502202, -87.715618756)"
-7772009,HS580340,10/24/2010 02:55:00 PM,086XX S LAFLIN ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,RESIDENTIAL YARD (FRONT/BACK),true,false,0614,006,21,71,15,1167801,1847475,2010,10/25/2010 06:57:10 AM,41.737010648,-87.660812394,"(41.737010648, -87.660812394)"
-7771506,HS579696,10/24/2010 02:40:00 AM,001XX W ERIE ST,0460,BATTERY,SIMPLE,STREET,false,false,1832,018,42,8,08B,1174976,1904785,2010,10/25/2010 09:52:34 AM,41.894117684,-87.632813766,"(41.894117684, -87.632813766)"
-7777646,HS585285,10/23/2010 10:00:00 PM,0000X W ONTARIO ST,1310,CRIMINAL DAMAGE,TO PROPERTY,COMMERCIAL / BUSINESS OFFICE,false,false,1832,018,42,8,14,1176174,1904444,2010,10/28/2010 06:57:33 AM,41.893155038,-87.628424228,"(41.893155038, -87.628424228)"
-7773542,HS581424,10/22/2010 12:00:00 PM,006XX S STATE ST,0820,THEFT,$500 AND UNDER,"SCHOOL, PUBLIC, BUILDING",false,false,0132,001,2,32,06,1176412,1897619,2010,01/04/2011 01:46:36 PM,41.87442149,-87.627756239,"(41.87442149, -87.627756239)"
-7769524,HS576820,10/22/2010 09:30:00 AM,013XX W 83RD ST,0890,THEFT,FROM BUILDING,APARTMENT,false,false,0613,006,21,71,06,1168719,1849761,2010,10/23/2010 09:16:23 AM,41.743264021,-87.657383333,"(41.743264021, -87.657383333)"
-7784284,HS576050,10/21/2010 07:27:56 PM,008XX E 89TH ST,2027,NARCOTICS,POSS: CRACK,APARTMENT,true,false,0632,006,8,44,18,1183562,1846188,2010,11/10/2010 03:04:15 PM,41.733126496,-87.603109289,"(41.733126496, -87.603109289)"
-7767952,HS575714,10/21/2010 05:00:00 PM,107XX S WABASH AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0513,005,9,49,18,1178519,1833748,2010,10/21/2010 05:39:20 PM,41.699105414,-87.621960601,"(41.699105414, -87.621960601)"
-7766176,HS573956,10/20/2010 04:44:00 PM,021XX N CALIFORNIA AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1431,014,1,22,18,1157398,1914002,2010,10/20/2010 05:40:22 PM,41.919785577,-87.697121082,"(41.919785577, -87.697121082)"
-7766055,HS573714,10/19/2010 10:00:00 PM,058XX S PRAIRIE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0233,002,20,40,07,1179004,1866093,2010,10/21/2010 08:56:07 AM,41.787852905,-87.619201912,"(41.787852905, -87.619201912)"
-7761672,HS570049,10/18/2010 10:00:00 AM,006XX W 63RD ST,0820,THEFT,$500 AND UNDER,GOVERNMENT BUILDING/PROPERTY,false,false,0723,007,20,68,06,1172817,1863065,2010,11/07/2010 08:15:20 AM,41.779682513,-87.641976291,"(41.779682513, -87.641976291)"
-7763702,HS571572,10/18/2010 03:13:00 AM,026XX W PRATT BLVD,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,false,true,2412,024,50,2,26,1157480,1944987,2010,10/27/2010 05:22:18 PM,42.004808498,-87.695972995,"(42.004808498, -87.695972995)"
-7761063,HS569758,10/17/2010 09:00:00 PM,070XX N WASHTENAW AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2411,024,50,2,07,1157128,1946776,2010,10/22/2010 08:28:39 AM,42.009724761,-87.697219117,"(42.009724761, -87.697219117)"
-7760097,HS568612,10/16/2010 10:00:00 PM,036XX N OLEANDER AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENTIAL YARD (FRONT/BACK),false,false,1631,016,36,17,14,1125054,1923483,2010,10/18/2010 10:56:30 AM,41.946399614,-87.815749841,"(41.946399614, -87.815749841)"
-7758332,HS565929,10/15/2010 12:53:00 PM,072XX N ROGERS AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2424,024,49,1,26,1161400,1948081,2010,11/08/2010 08:03:36 PM,42.013217532,-87.681464463,"(42.013217532, -87.681464463)"
-7758838,HS567045,10/15/2010 06:00:00 AM,045XX W WASHINGTON BLVD,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,1113,011,28,26,26,1146006,1900148,2010,10/23/2010 09:29:35 PM,41.881992993,-87.739329863,"(41.881992993, -87.739329863)"
-7765099,HS571976,10/14/2010 10:30:00 AM,044XX S DREXEL BLVD,0820,THEFT,$500 AND UNDER,"SCHOOL, PUBLIC, BUILDING",false,false,2123,002,4,39,06,1183113,1875561,2010,10/20/2010 09:43:21 AM,41.813739222,-87.603841416,"(41.813739222, -87.603841416)"
-7754712,HS562910,10/13/2010 01:20:00 PM,122XX S WALLACE ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0523,005,34,53,26,1174485,1823607,2010,10/31/2014 03:20:56 PM,41.671367411,-87.637031323,"(41.671367411, -87.637031323)"
-7753010,HS561735,10/13/2010 12:09:00 AM,109XX S EDBROOKE AVE,502R,OTHER OFFENSE,VEHICLE TITLE/REG OFFENSE,STREET,false,false,0513,005,9,49,26,1179226,1832471,2010,10/13/2010 07:08:50 AM,41.695585094,-87.619410664,"(41.695585094, -87.619410664)"
-7751081,HS559915,10/11/2010 11:45:00 PM,014XX S RIDGEWAY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1011,010,24,29,08B,1151573,1892682,2010,10/15/2010 12:20:08 PM,41.861397899,-87.719083917,"(41.861397899, -87.719083917)"
-7747811,HS555914,10/09/2010 01:05:00 PM,050XX S JUSTINE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENTIAL YARD (FRONT/BACK),false,true,0931,009,16,61,08B,1166899,1871231,2010,10/15/2010 01:16:56 PM,41.802219519,-87.663439163,"(41.802219519, -87.663439163)"
-7747472,HS555477,10/09/2010 12:30:00 AM,004XX N DRAKE AVE,0820,THEFT,$500 AND UNDER,CHA PARKING LOT/GROUNDS,false,false,1123,011,27,23,06,1152594,1902893,2010,10/11/2010 09:21:46 AM,41.88939788,-87.715065995,"(41.88939788, -87.715065995)"
-7751690,HS560313,10/08/2010 05:00:00 PM,021XX N LONG AVE,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",false,false,2515,025,37,19,06,1139990,1913950,2010,10/13/2010 10:25:49 AM,41.919979458,-87.761082782,"(41.919979458, -87.761082782)"
-7746719,HS554338,10/08/2010 02:44:00 PM,007XX W 111TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,2233,022,34,49,08B,1173508,1831295,2010,10/09/2010 06:55:42 AM,41.69248612,-87.640380793,"(41.69248612, -87.640380793)"
-7747112,HS554119,10/08/2010 12:30:00 PM,034XX W MADISON ST,0880,THEFT,PURSE-SNATCHING,STREET,false,false,1123,011,28,27,06,1153650,1899849,2010,10/11/2010 11:46:39 AM,41.881023891,-87.71126894,"(41.881023891, -87.71126894)"
-7745611,HS553532,10/08/2010 03:33:00 AM,017XX W 79TH ST,0610,BURGLARY,FORCIBLE ENTRY,TAVERN/LIQUOR STORE,false,false,0611,006,21,71,05,1166250,1852273,2010,10/11/2010 09:17:47 AM,41.750210174,-87.666358554,"(41.750210174, -87.666358554)"
-7747676,HS555521,10/08/2010 01:00:00 AM,049XX N SAWYER AVE,0810,THEFT,OVER $500,STREET,false,false,1713,017,39,14,06,1153858,1932693,2010,10/10/2010 11:59:23 AM,41.971146351,-87.70962794,"(41.971146351, -87.70962794)"
-7743726,HS550721,10/06/2010 07:50:00 AM,012XX S CENTRAL PARK AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1021,010,24,29,05,1152619,1893738,2010,10/26/2010 07:26:41 PM,41.864275083,-87.715216315,"(41.864275083, -87.715216315)"
-7741621,HS549410,10/04/2010 08:00:00 PM,027XX W FLOURNOY ST,0460,BATTERY,SIMPLE,STREET,false,false,1135,011,2,27,08B,1158408,1896965,2010,10/24/2010 09:57:32 AM,41.873013952,-87.693876723,"(41.873013952, -87.693876723)"
-7740322,HS548471,10/04/2010 07:30:00 PM,0000X W HURON ST,0460,BATTERY,SIMPLE,GROCERY FOOD STORE,false,false,1832,018,42,8,08B,1175730,1905097,2010,10/05/2010 07:04:07 AM,41.894956902,-87.630035199,"(41.894956902, -87.630035199)"
-7737766,HS545801,10/02/2010 08:00:00 PM,064XX W MC LEAN AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE-GARAGE,false,false,2512,025,36,19,14,1133224,1912742,2010,10/04/2010 09:50:34 AM,41.916785761,-87.785970958,"(41.916785761, -87.785970958)"
-7736649,HS544216,10/02/2010 07:35:00 AM,017XX N MAJOR AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,2531,025,29,25,26,1138091,1910761,2010,10/07/2010 01:30:47 PM,41.911263036,-87.768137348,"(41.911263036, -87.768137348)"
-7738468,HS544243,10/02/2010 05:30:00 AM,039XX N BROADWAY,0610,BURGLARY,FORCIBLE ENTRY,RESTAURANT,false,false,2324,019,46,6,05,1170128,1926552,2010,10/08/2010 01:54:35 PM,41.953954641,-87.649981722,"(41.953954641, -87.649981722)"
-7736403,HS543805,10/01/2010 10:20:00 PM,055XX W IRVING PARK RD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,false,1633,016,38,15,08B,1138680,1926007,2010,10/02/2010 10:39:36 AM,41.953089005,-87.765602643,"(41.953089005, -87.765602643)"
-7736131,HS543586,10/01/2010 07:08:00 PM,040XX W LEXINGTON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1132,011,24,26,18,1149673,1896434,2010,10/01/2010 08:20:53 PM,41.87173092,-87.725961067,"(41.87173092, -87.725961067)"
-7735036,HS542388,09/30/2010 07:00:00 PM,092XX S COTTAGE GROVE AVE,0620,BURGLARY,UNLAWFUL ENTRY,SMALL RETAIL STORE,false,false,0633,006,9,44,05,1183132,1844091,2010,10/06/2010 05:13:55 PM,41.727382087,-87.604749594,"(41.727382087, -87.604749594)"
-7739909,HS547901,09/30/2010 12:15:00 PM,050XX S LAKE SHORE DR W,1790,OFFENSE INVOLVING CHILDREN,CHILD ABDUCTION,RESIDENCE,false,false,2132,002,4,39,20,1188596,1871952,2010,11/08/2010 01:09:59 PM,41.803706407,-87.583845135,"(41.803706407, -87.583845135)"
-7733158,HS540767,09/30/2010 01:36:00 AM,054XX W MADISON ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1522,015,28,25,26,1140309,1899534,2010,09/30/2010 02:15:35 AM,41.880414398,-87.760264522,"(41.880414398, -87.760264522)"
-7734477,HS541715,09/29/2010 11:15:00 PM,001XX W LAKE ST,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0113,001,42,32,03,1175460,1901776,2010,12/18/2010 08:36:19 AM,41.885849966,-87.631126643,"(41.885849966, -87.631126643)"
diff --git a/work-with-data/dataprep/data/crime_partfiles/part-00002-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv b/work-with-data/dataprep/data/crime_partfiles/part-00002-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv
deleted file mode 100644
index 6b346eec..00000000
--- a/work-with-data/dataprep/data/crime_partfiles/part-00002-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv
+++ /dev/null
@@ -1,930 +0,0 @@
-7726719,HS534541,09/26/2010 12:30:00 AM,003XX N PINE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1523,015,28,25,07,1139478,1901774,2010,09/29/2010 09:16:34 AM,41.886576431,-87.763261277,"(41.886576431, -87.763261277)"
-7725902,HS533637,09/25/2010 04:50:00 PM,004XX E 75TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0323,003,6,69,18,1180426,1855420,2010,09/25/2010 05:35:25 PM,41.758532591,-87.614315237,"(41.758532591, -87.614315237)"
-7725179,HS532650,09/25/2010 01:00:00 AM,008XX W GARFIELD BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,GAS STATION,false,false,0712,007,20,68,14,1171877,1868213,2010,09/25/2010 10:23:11 AM,41.793829891,-87.645271406,"(41.793829891, -87.645271406)"
-7724003,HS531307,09/24/2010 10:00:00 AM,0000X E 71ST ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0322,003,6,69,18,1178054,1857999,2010,09/24/2010 10:47:22 AM,41.765663728,-87.622930311,"(41.765663728, -87.622930311)"
-7723768,HS531110,09/23/2010 06:00:00 PM,020XX W ROSCOE ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,1913,019,32,5,05,1161928,1922607,2010,09/27/2010 03:48:57 PM,41.94330481,-87.68023632,"(41.94330481, -87.68023632)"
-7722705,HS529862,09/23/2010 12:50:00 PM,052XX W MONTANA ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,APARTMENT,true,false,2515,025,31,19,15,1141114,1915844,2010,09/24/2010 10:49:18 AM,41.92515614,-87.756906183,"(41.92515614, -87.756906183)"
-7729447,HS529754,09/23/2010 12:35:38 PM,0000X W 79TH ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA BUS,true,false,0623,006,6,44,11,1177484,1852588,2010,09/29/2010 05:55:45 AM,41.750828226,-87.625182794,"(41.750828226, -87.625182794)"
-7719075,HS526532,09/21/2010 01:30:00 PM,068XX S WABASH AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0322,003,20,69,05,1177867,1859601,2010,10/20/2010 08:32:08 PM,41.770064026,-87.623567268,"(41.770064026, -87.623567268)"
-7719103,HS526637,09/21/2010 07:00:00 AM,052XX S KILBOURN AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0815,008,23,57,07,1147285,1869677,2010,09/22/2010 08:41:44 AM,41.798352065,-87.735412258,"(41.798352065, -87.735412258)"
-7717155,HS524746,09/20/2010 02:45:00 PM,012XX W ERIE ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",true,false,1324,012,27,24,08B,1167774,1904530,2010,09/22/2010 08:17:41 AM,41.893576256,-87.659271624,"(41.893576256, -87.659271624)"
-7715556,HS523527,09/19/2010 07:20:00 PM,047XX W HARRISON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1131,011,24,25,18,1144735,1896886,2010,09/19/2010 08:19:41 PM,41.873065713,-87.744079251,"(41.873065713, -87.744079251)"
-7717289,HS523467,09/19/2010 04:56:00 PM,064XX N WESTERN AVE,0810,THEFT,OVER $500,STREET,false,false,2412,024,50,2,06,1159192,1942487,2010,10/29/2010 10:01:55 PM,41.997913268,-87.689743598,"(41.997913268, -87.689743598)"
-7716220,HS521071,09/18/2010 03:30:00 AM,026XX W FULLERTON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,SMALL RETAIL STORE,false,false,1431,014,1,22,14,1158218,1915809,2010,09/22/2010 09:16:23 AM,41.924727387,-87.694058773,"(41.924727387, -87.694058773)"
-7713193,HS520113,09/17/2010 11:45:00 AM,020XX W CHASE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,2424,024,49,1,14,1161041,1948412,2010,09/20/2010 06:42:29 AM,42.014133298,-87.682776158,"(42.014133298, -87.682776158)"
-7711525,HS518656,09/16/2010 05:15:00 PM,001XX W LOWER WACKER DR,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0113,001,42,32,14,1175183,1902085,2010,09/25/2010 09:47:15 PM,41.886704095,-87.632134562,"(41.886704095, -87.632134562)"
-7711064,HS517957,09/16/2010 10:30:00 AM,080XX S EXCHANGE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0422,004,7,46,14,1197184,1852293,2010,09/17/2010 08:09:23 AM,41.749551112,-87.553003561,"(41.749551112, -87.553003561)"
-7736533,HS544005,09/15/2010 08:00:00 PM,070XX S CLYDE AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0331,003,5,43,05,1191387,1858761,2010,10/05/2010 08:09:20 PM,41.767442145,-87.574036552,"(41.767442145, -87.574036552)"
-7707018,HS514400,09/14/2010 02:00:00 AM,050XX S WASHINGTON PARK CT,0810,THEFT,OVER $500,STREET,false,false,0223,002,3,38,06,1180071,1871413,2010,09/14/2010 10:59:55 AM,41.80242708,-87.615126724,"(41.80242708, -87.615126724)"
-7712294,HS511402,09/12/2010 11:00:00 AM,066XX S SEELEY AVE,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,false,false,0726,007,15,67,07,1163802,1860519,2010,09/29/2010 08:19:34 AM,41.772890087,-87.675097936,"(41.772890087, -87.675097936)"
-7704308,HS510748,09/11/2010 09:30:00 PM,005XX S LARAMIE AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,true,1522,015,29,25,08A,1141767,1897107,2010,09/14/2010 12:19:48 PM,41.873727561,-87.754970872,"(41.873727561, -87.754970872)"
-7703621,HS510137,09/11/2010 01:40:53 PM,057XX W MIDWAY PARK,1310,CRIMINAL DAMAGE,TO PROPERTY,STREET,false,false,1512,015,29,25,14,1137747,1902607,2010,09/14/2010 10:39:26 AM,41.88889367,-87.769597949,"(41.88889367, -87.769597949)"
-7702838,HS509722,09/11/2010 08:02:00 AM,048XX S MICHIGAN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0231,002,3,38,26,1177931,1873073,2010,09/14/2010 11:53:40 AM,41.807031071,-87.622924631,"(41.807031071, -87.622924631)"
-7701899,HS508746,09/10/2010 03:30:00 PM,002XX N JEFFERSON ST,0460,BATTERY,SIMPLE,STREET,false,false,1212,012,42,28,08B,1172322,1901910,2010,10/10/2010 11:31:35 AM,41.886287555,-87.642645975,"(41.886287555, -87.642645975)"
-7700231,HS507234,09/09/2010 04:00:00 PM,055XX S SPRINGFIELD AVE,0560,ASSAULT,SIMPLE,STREET,false,false,0822,008,23,62,08A,1151267,1867806,2010,09/11/2010 10:10:23 AM,41.793140827,-87.720858193,"(41.793140827, -87.720858193)"
-7698892,HS502571,09/07/2010 04:30:00 AM,105XX S EBERHART AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0512,005,9,49,08B,1181478,1835380,2010,09/13/2010 06:07:53 AM,41.703516261,-87.611076066,"(41.703516261, -87.611076066)"
-7695823,HS502925,09/05/2010 08:10:00 PM,015XX N CENTRAL AVE,0820,THEFT,$500 AND UNDER,RESIDENCE,false,false,2532,025,37,25,06,1138750,1909815,2010,09/08/2010 10:53:53 AM,41.90865516,-87.765739362,"(41.90865516, -87.765739362)"
-7693577,HS500654,09/05/2010 06:50:00 PM,084XX S DAMEN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,0614,006,18,71,14,1164529,1848772,2010,09/06/2010 07:43:52 AM,41.74063935,-87.672763577,"(41.74063935, -87.672763577)"
-7694030,HS498832,09/04/2010 02:02:08 PM,019XX E 79TH ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,SIDEWALK,false,false,0414,004,8,43,04A,1190708,1853021,2010,09/30/2010 12:11:18 PM,41.751707526,-87.576710386,"(41.751707526, -87.576710386)"
-7692391,HS499079,09/04/2010 10:00:00 AM,028XX W 19TH ST,0810,THEFT,OVER $500,STREET,false,false,1022,010,12,29,06,1157175,1890618,2010,09/06/2010 10:09:00 AM,41.855622237,-87.698575963,"(41.855622237, -87.698575963)"
-7692043,HS498379,09/04/2010 08:40:00 AM,029XX S ASHLAND AVE,0820,THEFT,$500 AND UNDER,CTA TRAIN,false,false,0922,009,11,31,06,1166107,1884938,2010,09/05/2010 11:44:01 AM,41.839849905,-87.665953302,"(41.839849905, -87.665953302)"
-7695722,HS502985,09/03/2010 12:00:00 AM,051XX N LOWELL AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1712,017,39,14,06,1146412,1933752,2010,09/08/2010 09:56:49 AM,41.974197809,-87.736980802,"(41.974197809, -87.736980802)"
-7692100,HS498406,09/01/2010 03:00:00 PM,048XX W ERIE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1532,015,28,25,14,1143844,1903833,2010,09/05/2010 10:51:57 AM,41.892145859,-87.747176368,"(41.892145859, -87.747176368)"
-7686516,HS492686,08/31/2010 10:00:00 PM,029XX N SAWYER AVE,3760,INTERFERENCE WITH PUBLIC OFFICER,OBSTRUCTING SERVICE,SIDEWALK,true,false,1412,014,35,21,24,1154243,1919419,2010,09/01/2010 09:27:14 AM,41.934713901,-87.708568072,"(41.934713901, -87.708568072)"
-7685505,HS491446,08/30/2010 05:30:00 PM,022XX N MENARD AVE,0810,THEFT,OVER $500,STREET,false,false,2515,025,37,19,06,1137322,1914128,2010,09/01/2010 10:49:26 AM,41.920516346,-87.770881323,"(41.920516346, -87.770881323)"
-7683754,HS489812,08/30/2010 12:20:00 PM,013XX N HARDING AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2535,025,30,23,18,1149755,1908923,2010,08/30/2010 01:03:49 PM,41.906000478,-87.725335122,"(41.906000478, -87.725335122)"
-7684503,HS490673,08/30/2010 06:15:00 AM,0000X W 94TH ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0634,006,21,49,07,1177542,1842602,2010,09/25/2010 02:43:20 PM,41.72342409,-87.62527132,"(41.72342409, -87.62527132)"
-7683529,HS489383,08/29/2010 09:45:00 PM,004XX W 102ND PL,031A,ROBBERY,ARMED: HANDGUN,RESIDENCE PORCH/HALLWAY,false,false,2232,022,9,73,03,1174961,1836901,2010,09/04/2010 04:30:54 PM,41.707837613,-87.634894687,"(41.707837613, -87.634894687)"
-7686383,HS488944,08/29/2010 09:00:00 PM,057XX N CAMPBELL AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2011,020,40,2,26,1158664,1938037,2010,09/11/2010 02:11:04 PM,41.985713164,-87.691808557,"(41.985713164, -87.691808557)"
-7683525,HS488528,08/29/2010 04:15:00 PM,036XX W WRIGHTWOOD AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,2524,025,35,22,05,1151888,1917087,2010,09/08/2010 09:32:01 PM,41.928361468,-87.717284357,"(41.928361468, -87.717284357)"
-7681672,HS487468,08/29/2010 12:50:00 AM,038XX W 109TH ST,0460,BATTERY,SIMPLE,SIDEWALK,true,false,2211,022,19,74,08B,1152358,1832051,2010,08/29/2010 06:49:12 AM,41.695001554,-87.717795418,"(41.695001554, -87.717795418)"
-7681502,HS487169,08/28/2010 07:35:00 PM,015XX W CHICAGO AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,ALLEY,false,false,1324,012,27,24,04B,1166172,1905399,2010,09/05/2010 10:31:31 AM,41.895995239,-87.665130335,"(41.895995239, -87.665130335)"
-7680946,HS486178,08/28/2010 04:40:00 AM,020XX W VAN BUREN ST,1320,CRIMINAL DAMAGE,TO VEHICLE,CHA PARKING LOT/GROUNDS,false,false,1211,012,2,28,14,1162744,1898198,2010,08/30/2010 10:04:37 AM,41.876307669,-87.677922641,"(41.876307669, -87.677922641)"
-7683897,HS489912,08/27/2010 06:00:00 PM,018XX S WABASH AVE,0320,ROBBERY,STRONGARM - NO WEAPON,RESIDENCE,false,false,0132,001,3,33,03,1177007,1891539,2010,09/30/2010 02:54:51 PM,41.857724146,-87.625755682,"(41.857724146, -87.625755682)"
-7680815,HS483701,08/26/2010 10:00:00 PM,001XX S CLINTON ST,0330,ROBBERY,AGGRAVATED,SIDEWALK,false,false,0111,001,2,28,03,1172795,1899529,2010,09/12/2010 02:38:10 PM,41.87974348,-87.640979563,"(41.87974348, -87.640979563)"
-7685128,HS479861,08/24/2010 10:00:00 PM,071XX N HARLEM AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,GROCERY FOOD STORE,false,false,1611,016,41,9,14,1127346,1947084,2010,09/01/2010 09:28:16 AM,42.01112489,-87.806791733,"(42.01112489, -87.806791733)"
-7736119,HS543485,08/24/2010 09:00:00 AM,069XX S ASHLAND AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,0734,007,17,67,06,1166916,1858768,2010,10/02/2010 12:06:56 PM,41.768019145,-87.663732769,"(41.768019145, -87.663732769)"
-7674590,HS479809,08/23/2010 06:00:00 PM,058XX S TRUMBULL AVE,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,APARTMENT,false,true,0822,008,14,63,20,1154347,1865401,2010,08/31/2010 10:15:31 AM,41.786480385,-87.70962801,"(41.786480385, -87.70962801)"
-7672594,HS477742,08/23/2010 08:00:00 AM,006XX N ASHLAND AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,false,false,1324,012,1,24,14,1165573,1904386,2010,08/24/2010 08:35:28 AM,41.893228273,-87.667359209,"(41.893228273, -87.667359209)"
-7670244,HS475423,08/22/2010 10:50:00 AM,082XX S WESTERN AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,0835,008,18,70,18,1161775,1849634,2010,08/22/2010 02:30:55 PM,41.743062378,-87.682830148,"(41.743062378, -87.682830148)"
-7669682,HS474881,08/22/2010 12:15:00 AM,005XX W DIVISION ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1821,018,27,8,18,1172320,1908305,2010,08/22/2010 02:25:04 AM,41.903835855,-87.642464221,"(41.903835855, -87.642464221)"
-7682766,HS469510,08/18/2010 07:15:00 PM,004XX N MICHIGAN AVE,0890,THEFT,FROM BUILDING,OTHER,false,false,1834,018,42,8,06,1177312,1903554,2010,08/30/2010 10:44:08 AM,41.890687102,-87.624271838,"(41.890687102, -87.624271838)"
-7662064,HS466719,08/17/2010 08:11:00 AM,031XX W 42ND PL,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,false,false,0912,009,14,58,26,1155946,1876242,2010,08/23/2010 09:52:24 AM,41.8161976,-87.703473925,"(41.8161976, -87.703473925)"
-7659407,HS464752,08/16/2010 02:01:00 AM,017XX N LAWNDALE AVE,0810,THEFT,OVER $500,STREET,true,false,2535,025,26,22,06,1151398,1911454,2010,08/16/2010 10:33:19 AM,41.912913665,-87.719233176,"(41.912913665, -87.719233176)"
-7659467,HS464734,08/16/2010 01:30:00 AM,082XX S HERMITAGE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,true,0614,006,18,71,14,1166077,1849864,2010,08/18/2010 05:02:27 PM,41.74360321,-87.66706086,"(41.74360321, -87.66706086)"
-7659323,HS464701,08/16/2010 12:20:00 AM,131XX S LANGLEY AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,CHA PARKING LOT/GROUNDS,true,false,0533,005,9,54,15,1183283,1818451,2010,08/16/2010 08:17:17 AM,41.657018996,-87.604990629,"(41.657018996, -87.604990629)"
-7664073,HS468729,08/14/2010 10:30:00 PM,050XX W SCHOOL ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1634,016,38,15,14,1142035,1921507,2010,09/20/2010 10:38:20 AM,41.940678937,-87.753381198,"(41.940678937, -87.753381198)"
-7657722,HS462625,08/14/2010 03:07:00 PM,012XX W 64TH ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0724,007,16,67,05,1169262,1862384,2010,08/29/2010 05:12:45 PM,41.777891459,-87.655029053,"(41.777891459, -87.655029053)"
-7657560,HS462223,08/14/2010 09:42:00 AM,044XX N SHERIDAN RD,0890,THEFT,FROM BUILDING,APARTMENT,false,false,2313,019,46,3,06,1168844,1929649,2010,08/16/2010 07:08:56 AM,41.962480916,-87.654611654,"(41.962480916, -87.654611654)"
-7655260,HS458866,08/12/2010 01:45:28 PM,080XX S ESCANABA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,true,0422,004,7,46,14,1196859,1852012,2010,08/18/2010 05:02:57 PM,41.748788109,-87.554203792,"(41.748788109, -87.554203792)"
-7654302,HS458427,08/11/2010 05:00:00 PM,009XX N FRANCISCO AVE,0810,THEFT,OVER $500,STREET,false,false,1311,012,26,24,06,1156916,1906299,2010,08/13/2010 09:49:36 AM,41.898657702,-87.6991013,"(41.898657702, -87.6991013)"
-7651643,HS456526,08/10/2010 11:00:00 AM,061XX S WOLCOTT AVE,0820,THEFT,$500 AND UNDER,"SCHOOL, PUBLIC, BUILDING",false,false,0714,007,15,67,06,1164703,1863863,2010,08/11/2010 10:20:48 AM,41.782047492,-87.67170078,"(41.782047492, -87.67170078)"
-7647875,HS453086,08/09/2010 04:05:00 AM,035XX W LAWRENCE AVE,0325,ROBBERY,VEHICULAR HIJACKING,PARKING LOT/GARAGE(NON.RESID.),false,false,1712,017,39,14,03,1151928,1931706,2010,08/25/2010 10:25:25 PM,41.968476297,-87.716750916,"(41.968476297, -87.716750916)"
-7647525,HS452565,08/08/2010 06:28:00 PM,059XX S TALMAN AVE,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,RESIDENCE,false,false,0824,008,16,66,05,1159694,1864836,2010,08/10/2010 02:41:18 PM,41.784821836,-87.690038503,"(41.784821836, -87.690038503)"
-7648923,HS452577,08/08/2010 04:30:00 PM,036XX W 81ST PL,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0834,008,18,70,05,1153406,1850340,2010,08/29/2010 10:45:42 AM,41.745169277,-87.71347619,"(41.745169277, -87.71347619)"
-7641667,HS446099,08/04/2010 08:30:00 PM,115XX S HALSTED ST,2820,OTHER OFFENSE,TELEPHONE THREAT,VEHICLE NON-COMMERCIAL,false,false,0524,005,34,53,26,1173006,1828552,2010,08/09/2010 06:35:44 AM,41.684969963,-87.642299255,"(41.684969963, -87.642299255)"
-7641240,HS445506,08/04/2010 03:10:00 PM,041XX W WEST END AVE,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),PARK PROPERTY,true,false,1114,011,28,26,18,1148965,1900696,2010,08/04/2010 05:07:28 PM,41.88344005,-87.728450163,"(41.88344005, -87.728450163)"
-7650024,HS444455,08/03/2010 10:31:10 PM,039XX W ROOSEVELT RD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1132,011,24,29,18,1150531,1894466,2010,08/17/2010 11:32:10 AM,41.866313798,-87.722862363,"(41.866313798, -87.722862363)"
-7640670,HS443391,08/03/2010 11:50:00 AM,036XX W MONTROSE AVE,0460,BATTERY,SIMPLE,STREET,true,false,1723,017,33,14,08B,1151562,1929043,2010,08/05/2010 08:38:01 AM,41.961176067,-87.718166991,"(41.961176067, -87.718166991)"
-7650317,HS453536,08/02/2010 09:00:00 AM,009XX W MONTROSE AVE,0820,THEFT,$500 AND UNDER,ATHLETIC CLUB,false,false,2322,019,46,3,06,1169329,1929366,2010,08/11/2010 03:01:58 PM,41.9616938,-87.652836774,"(41.9616938, -87.652836774)"
-7640369,HS441351,08/02/2010 06:45:10 AM,111XX S CORLISS AVE,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,GAS STATION,false,false,0531,005,9,50,04A,1184332,1831515,2010,08/16/2010 08:39:27 AM,41.692844047,-87.600745795,"(41.692844047, -87.600745795)"
-7637873,HS442273,08/01/2010 06:00:00 PM,080XX S INDIANA AVE,0920,MOTOR VEHICLE THEFT,ATT: AUTOMOBILE,STREET,false,false,0623,006,6,44,07,1179024,1851870,2010,08/03/2010 07:13:51 AM,41.748823031,-87.619561356,"(41.748823031, -87.619561356)"
-9285624,HW429954,08/01/2010 12:00:00 AM,048XX S JUSTINE ST,1752,OFFENSE INVOLVING CHILDREN,AGG CRIM SEX ABUSE FAM MEMBER,RESIDENCE,false,false,0933,009,20,61,20,1166865,1872523,2010,09/09/2013 08:30:14 PM,41.805765642,-87.66352693,"(41.805765642, -87.66352693)"
-7638019,HS442404,07/31/2010 09:00:00 AM,021XX W LE MOYNE ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,1424,014,32,24,06,1161826,1909850,2010,08/04/2010 10:29:46 AM,41.908300897,-87.68096795,"(41.908300897, -87.68096795)"
-7634283,HS438223,07/31/2010 12:30:00 AM,005XX W STRATFORD PL,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,2331,019,44,6,26,1172069,1923549,2010,08/06/2010 01:16:47 PM,41.945671619,-87.64293536,"(41.945671619, -87.64293536)"
-7632052,HS435926,07/29/2010 04:50:00 PM,017XX W HOWARD ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,PARKING LOT/GARAGE(NON.RESID.),true,false,2422,024,49,1,18,1162855,1950303,2010,07/29/2010 06:13:18 PM,42.019284215,-87.676047975,"(42.019284215, -87.676047975)"
-7628870,HS433344,07/28/2010 03:09:00 AM,045XX S DREXEL BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2123,002,4,39,08B,1182922,1874802,2010,07/28/2010 11:51:25 AM,41.811660915,-87.604565619,"(41.811660915, -87.604565619)"
-7630739,HS433324,07/28/2010 02:50:00 AM,079XX S JUSTINE ST,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,APARTMENT,false,false,0612,006,21,71,04B,1167425,1852037,2010,10/04/2010 06:59:14 PM,41.749537482,-87.662059566,"(41.749537482, -87.662059566)"
-7628708,HS433094,07/27/2010 10:00:00 PM,036XX W 82ND ST,0560,ASSAULT,SIMPLE,APARTMENT,true,true,0834,008,18,70,08A,1153483,1850009,2010,07/28/2010 02:29:16 PM,41.744259435,-87.713202787,"(41.744259435, -87.713202787)"
-7628739,HS433026,07/27/2010 09:51:00 PM,050XX W MAYPOLE AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,1532,015,28,25,08A,1142832,1900955,2010,07/29/2010 11:43:51 AM,41.884267193,-87.75096479,"(41.884267193, -87.75096479)"
-7629750,HS433878,07/27/2010 09:00:00 PM,012XX W WEBSTER AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,1811,018,32,7,06,1167502,1914717,2010,08/01/2010 09:57:41 PM,41.921535883,-87.659976885,"(41.921535883, -87.659976885)"
-7640117,HS444540,07/27/2010 07:46:00 PM,005XX N CLARK ST,2820,OTHER OFFENSE,TELEPHONE THREAT,OTHER,false,false,1831,018,42,8,26,1175410,1903616,2010,08/27/2010 11:30:26 AM,41.890900151,-87.631254968,"(41.890900151, -87.631254968)"
-7637847,HS439341,07/27/2010 02:00:00 PM,032XX W CULLOM AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1724,017,33,16,05,1154084,1928358,2010,08/31/2010 10:14:11 AM,41.959246325,-87.708913078,"(41.959246325, -87.708913078)"
-7625231,HS429695,07/25/2010 11:00:00 PM,001XX E WACKER DR,0810,THEFT,OVER $500,HOTEL/MOTEL,false,false,0124,001,42,32,06,1177757,1902582,2010,07/26/2010 11:48:31 AM,41.888009783,-87.622667166,"(41.888009783, -87.622667166)"
-7625450,HS429921,07/25/2010 01:00:00 PM,002XX E 121ST PL,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0532,005,9,53,06,1180006,1824407,2010,08/13/2010 09:55:28 AM,41.673438536,-87.6168004,"(41.673438536, -87.6168004)"
-7624282,HS428494,07/25/2010 07:00:00 AM,005XX W MADISON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0111,001,42,28,08B,1172495,1900252,2010,07/26/2010 07:36:17 AM,41.881734075,-87.642059728,"(41.881734075, -87.642059728)"
-7622829,HS426267,07/23/2010 08:00:00 PM,028XX S KOSTNER AVE,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,false,false,1031,010,22,30,26,1147487,1884543,2010,07/26/2010 07:47:10 AM,41.839142667,-87.734291403,"(41.839142667, -87.734291403)"
-7622660,HS425998,07/23/2010 05:00:00 PM,053XX S NEVA AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0811,008,23,56,26,1129543,1868428,2010,07/30/2010 12:03:46 PM,41.795244805,-87.80050549,"(41.795244805, -87.80050549)"
-7623018,HS425060,07/23/2010 05:30:00 AM,098XX S GENOA AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,2223,022,21,73,05,1170450,1839659,2010,07/28/2010 07:32:28 AM,41.715505199,-87.651334132,"(41.715505199, -87.651334132)"
-7622570,HS425572,07/22/2010 10:00:00 AM,011XX N LEAMINGTON AVE,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,OTHER,false,false,1531,015,37,25,11,1141781,1907125,2010,08/13/2010 09:34:24 AM,41.901217917,-87.754671436,"(41.901217917, -87.754671436)"
-7627713,HS431812,07/21/2010 10:30:00 AM,013XX S OAKLEY AVE,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,1224,012,25,28,06,1161208,1893900,2010,02/27/2011 10:48:34 AM,41.864545602,-87.683681748,"(41.864545602, -87.683681748)"
-7617160,HS421323,07/20/2010 05:00:00 PM,0000X N PEORIA ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,1212,012,27,28,07,1170370,1900404,2010,07/23/2010 09:00:15 AM,41.882197877,-87.649858173,"(41.882197877, -87.649858173)"
-7614733,HS419593,07/19/2010 10:20:00 PM,0000X N LATROBE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1522,015,28,25,18,1141345,1899760,2010,07/19/2010 11:52:42 PM,41.881015524,-87.756454804,"(41.881015524, -87.756454804)"
-7727207,HS535027,07/19/2010 12:00:00 AM,132XX S VERNON AVE,1110,DECEPTIVE PRACTICE,BOGUS CHECK,RESIDENCE,false,false,0533,005,9,54,11,1181504,1817086,2010,10/10/2010 12:12:51 PM,41.65331429,-87.611542037,"(41.65331429, -87.611542037)"
-7612803,HS417519,07/18/2010 04:15:00 PM,022XX W WARREN BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1332,012,2,28,14,1161456,1900272,2010,07/19/2010 12:26:42 PM,41.882025801,-87.682594004,"(41.882025801, -87.682594004)"
-7610455,HS414734,07/16/2010 11:11:00 PM,023XX N MARMORA AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,SIDEWALK,true,false,2515,025,37,19,24,1136639,1914745,2010,07/17/2010 10:25:39 AM,41.92222173,-87.773376038,"(41.92222173, -87.773376038)"
-7608583,HS413018,07/16/2010 02:20:00 AM,037XX W 59TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,true,false,0822,008,13,65,14,1152412,1865184,2010,07/16/2010 11:26:05 AM,41.785923204,-87.716728504,"(41.785923204, -87.716728504)"
-7608148,HS412302,07/12/2010 09:00:00 PM,007XX N AUSTIN BLVD,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,1511,015,29,25,26,1136273,1904508,2010,07/18/2010 10:54:24 AM,41.894136706,-87.774965704,"(41.894136706, -87.774965704)"
-7604863,HS409619,07/12/2010 05:00:00 PM,076XX S OGLESBY AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,RESIDENTIAL YARD (FRONT/BACK),false,false,0414,004,7,43,07,1193160,1854507,2010,07/15/2010 11:25:06 PM,41.755725707,-87.567676655,"(41.755725707, -87.567676655)"
-7602199,HS407057,07/12/2010 11:30:00 AM,047XX S ASHLAND AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),false,false,0931,009,20,61,07,1166424,1873486,2010,07/13/2010 07:35:34 AM,41.808417643,-87.66511689,"(41.808417643, -87.66511689)"
-7600598,HS405607,07/11/2010 03:29:00 PM,001XX E 107TH ST,1661,GAMBLING,GAME/DICE,ALLEY,true,false,0512,005,9,49,19,1179320,1834096,2010,07/11/2010 06:02:42 PM,41.700042182,-87.619017156,"(41.700042182, -87.619017156)"
-7607014,HS400320,07/08/2010 10:35:00 AM,025XX S ARCHER AVE,5011,OTHER OFFENSE,LICENSE VIOLATION,OTHER,false,false,0923,009,11,60,26,1171212,1887498,2010,09/15/2010 01:44:27 PM,41.84676439,-87.647145161,"(41.84676439, -87.647145161)"
-7594920,HS399515,07/08/2010 12:45:00 AM,056XX S PRINCETON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,0711,007,3,68,14,1175291,1867432,2010,07/11/2010 04:31:15 PM,41.791611082,-87.632775887,"(41.791611082, -87.632775887)"
-7592697,HS397211,07/06/2010 05:30:00 PM,002XX S DEARBORN ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,CTA GARAGE / OTHER PROPERTY,true,false,0123,001,42,32,18,1176007,1899265,2010,07/06/2010 06:50:25 PM,41.878947339,-87.629193623,"(41.878947339, -87.629193623)"
-7592545,HS396766,07/06/2010 12:30:00 PM,117XX S HALSTED ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,ALLEY,true,false,0524,005,34,53,24,1173069,1826670,2010,07/09/2010 10:09:31 AM,41.679804066,-87.642123903,"(41.679804066, -87.642123903)"
-7592075,HS396515,07/06/2010 11:20:00 AM,012XX S RUBLE ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,true,false,0131,001,2,28,26,1172029,1894734,2010,07/06/2010 02:00:38 PM,41.866602586,-87.643933595,"(41.866602586, -87.643933595)"
-7612040,HS415071,07/05/2010 09:00:00 AM,002XX W 108TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0513,005,34,49,14,1176341,1833356,2010,07/19/2010 07:01:10 AM,41.698078798,-87.629947124,"(41.698078798, -87.629947124)"
-7591078,HS394282,07/04/2010 11:40:00 PM,032XX W PIERCE AVE,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,APARTMENT,false,false,1422,014,26,23,05,1154490,1910076,2010,07/28/2010 10:20:33 PM,41.909071001,-87.707910759,"(41.909071001, -87.707910759)"
-7589464,HS393863,07/04/2010 06:18:00 PM,072XX S SANGAMON ST,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,0733,007,17,68,08A,1171272,1856660,2010,07/05/2010 01:21:26 PM,41.762140363,-87.647827604,"(41.762140363, -87.647827604)"
-7589786,HS393074,07/04/2010 02:00:00 AM,039XX S WENTWORTH AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0925,009,3,37,08B,1175545,1878753,2010,07/07/2010 11:11:44 AM,41.822671294,-87.631505536,"(41.822671294, -87.631505536)"
-7590103,HS390871,07/02/2010 04:45:00 PM,037XX W CHICAGO AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1112,011,27,23,08B,1151126,1905117,2010,07/26/2010 10:42:30 AM,41.895529663,-87.720398791,"(41.895529663, -87.720398791)"
-7584689,HS388244,07/01/2010 09:14:00 AM,021XX N KILDARE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2522,025,30,20,08B,1147408,1913910,2010,07/05/2010 11:22:22 AM,41.919730628,-87.733828542,"(41.919730628, -87.733828542)"
-7581011,HS384955,06/29/2010 10:45:00 AM,027XX N CLYBOURN AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,1931,019,32,7,06,1162877,1918139,2010,06/30/2010 07:06:20 AM,41.931024465,-87.676874021,"(41.931024465, -87.676874021)"
-7579364,HS383427,06/27/2010 10:30:00 PM,013XX S AVERS AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1011,010,24,29,05,1150959,1893702,2010,07/05/2010 10:16:02 AM,41.864208931,-87.721311119,"(41.864208931, -87.721311119)"
-7577382,HS381697,06/26/2010 10:00:00 PM,019XX N KARLOV AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE-GARAGE,false,false,2534,025,30,20,14,1148696,1912704,2010,06/28/2010 01:42:16 PM,41.916396444,-87.729127409,"(41.916396444, -87.729127409)"
-7580847,HS380514,06/26/2010 07:59:23 PM,075XX N RIDGE BLVD,3730,INTERFERENCE WITH PUBLIC OFFICER,OBSTRUCTING JUSTICE,STREET,false,false,2411,024,49,2,24,1160499,1950133,2010,07/01/2010 07:47:50 AM,42.01886705,-87.684722505,"(42.01886705, -87.684722505)"
-7660687,HS465960,06/26/2010 05:15:00 PM,031XX W HOMER ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1421,014,35,22,08B,1154997,1912831,2010,09/11/2010 04:19:58 PM,41.916620796,-87.705974237,"(41.916620796, -87.705974237)"
-7577124,HS380084,06/26/2010 02:11:23 PM,007XX N LOCKWOOD AVE,0497,BATTERY,AGGRAVATED DOMESTIC BATTERY: OTHER DANG WEAPON,APARTMENT,false,true,1524,015,28,25,04B,1140937,1904225,2010,07/13/2010 12:01:01 PM,41.893275553,-87.757843019,"(41.893275553, -87.757843019)"
-7620766,HS417291,06/26/2010 09:00:00 AM,033XX N KARLOV AVE,0890,THEFT,FROM BUILDING,RESIDENCE,true,false,1731,017,31,16,06,1148433,1921801,2010,07/25/2010 10:48:43 AM,41.941364511,-87.729858435,"(41.941364511, -87.729858435)"
-7575754,HS379449,06/26/2010 05:12:00 AM,017XX N MOZART ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,RESIDENCE-GARAGE,true,false,1421,014,1,24,15,1157186,1911663,2010,06/27/2010 01:03:18 PM,41.913371489,-87.697963672,"(41.913371489, -87.697963672)"
-7575714,HS378813,06/25/2010 07:00:00 PM,049XX S INDIANA AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0224,002,3,38,08B,1178389,1872513,2010,07/13/2010 12:42:22 PM,41.805483981,-87.62126186,"(41.805483981, -87.62126186)"
-7574438,HS377945,06/25/2010 10:47:00 AM,068XX S LAFLIN ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,SIDEWALK,true,false,0725,007,17,67,24,1167469,1859538,2010,06/26/2010 11:09:26 AM,41.770120293,-87.661683725,"(41.770120293, -87.661683725)"
-7574378,HS377662,06/25/2010 07:10:00 AM,050XX N SHERIDAN RD,4650,OTHER OFFENSE,SEX OFFENDER: FAIL TO REGISTER,APARTMENT,false,false,2024,020,46,3,26,1168671,1934150,2010,08/12/2010 04:23:23 PM,41.974835557,-87.655116705,"(41.974835557, -87.655116705)"
-7572164,HS375995,06/23/2010 01:30:00 PM,043XX S COTTAGE GROVE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,0222,002,4,38,14,1182286,1876479,2010,06/24/2010 01:17:14 PM,41.816277508,-87.606846409,"(41.816277508, -87.606846409)"
-7568177,HS371046,06/21/2010 11:53:06 AM,074XX S WESTERN AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,OTHER,false,false,0835,008,18,66,04B,1161700,1855394,2010,06/28/2010 06:16:41 PM,41.758870241,-87.682945416,"(41.758870241, -87.682945416)"
-7563971,HS368386,06/19/2010 05:37:00 PM,118XX S MICHIGAN AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,true,false,0532,005,9,53,04A,1178933,1826256,2010,06/22/2010 11:05:10 AM,41.678536888,-87.620671676,"(41.678536888, -87.620671676)"
-7586029,HS389521,06/19/2010 09:00:00 AM,048XX S PRAIRIE AVE,0810,THEFT,OVER $500,RESIDENCE,false,false,0224,002,3,38,06,1178903,1872807,2010,07/04/2010 12:30:06 PM,41.806279041,-87.619367772,"(41.806279041, -87.619367772)"
-7563837,HS368255,06/18/2010 07:30:00 PM,063XX N CICERO AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,false,false,1621,016,39,12,14,1143225,1941995,2010,06/20/2010 12:28:54 PM,41.996877476,-87.74849345,"(41.996877476, -87.74849345)"
-7561094,HS365140,06/17/2010 07:23:00 PM,008XX N MOHAWK ST,1330,CRIMINAL TRESPASS,TO LAND,CHA PARKING LOT/GROUNDS,true,false,1823,018,27,8,26,1172641,1906146,2010,06/18/2010 07:58:42 AM,41.89790434,-87.64134909,"(41.89790434, -87.64134909)"
-7559652,HS363844,06/17/2010 01:15:00 AM,016XX E 67TH ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0332,003,5,43,04B,1188492,1860849,2010,07/03/2010 02:20:06 PM,41.773241432,-87.584581171,"(41.773241432, -87.584581171)"
-7562815,HS364649,06/16/2010 03:45:00 PM,066XX S GREENWOOD AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE PORCH/HALLWAY,false,false,0321,003,5,42,14,1184316,1861395,2010,06/19/2010 05:51:17 AM,41.774838455,-87.599872116,"(41.774838455, -87.599872116)"
-7559166,HS362923,06/16/2010 01:15:00 PM,051XX W FULLERTON AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,2521,025,31,19,14,1141814,1915515,2010,06/29/2010 11:30:21 AM,41.924240388,-87.754342187,"(41.924240388, -87.754342187)"
-7557055,HS360926,06/15/2010 01:00:00 PM,003XX E 61ST ST,5001,OTHER OFFENSE,OTHER CRIME INVOLVING PROPERTY,SIDEWALK,true,false,0311,003,20,40,26,1179720,1864686,2010,06/16/2010 07:50:44 AM,41.783975612,-87.616619657,"(41.783975612, -87.616619657)"
-7555587,HS359899,06/14/2010 08:04:00 PM,066XX S COTTAGE GROVE AVE,1330,CRIMINAL TRESPASS,TO LAND,RESTAURANT,true,false,0321,003,20,42,26,1182670,1860827,2010,06/15/2010 08:50:05 AM,41.773318173,-87.605923651,"(41.773318173, -87.605923651)"
-7629961,HS433857,06/14/2010 04:30:00 PM,017XX N MONITOR AVE,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,APARTMENT,false,false,2531,025,29,25,11,1137195,1911151,2010,07/30/2010 11:09:57 AM,41.912349397,-87.77141962,"(41.912349397, -87.77141962)"
-7555315,HS359370,06/14/2010 03:17:00 PM,073XX S MORGAN ST,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,0733,007,17,68,06,1170869,1856352,2010,06/15/2010 12:00:21 PM,41.761303976,-87.649313625,"(41.761303976, -87.649313625)"
-7556305,HS360434,06/14/2010 03:00:00 PM,072XX S YATES BLVD,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0334,003,7,43,26,1193501,1857472,2010,06/22/2010 11:07:43 AM,41.763853561,-87.566330113,"(41.763853561, -87.566330113)"
-7556722,HS359104,06/14/2010 08:00:00 AM,006XX N ST CLAIR ST,0810,THEFT,OVER $500,STREET,false,false,1834,018,42,8,06,1177753,1905042,2010,06/16/2010 11:17:28 AM,41.894760235,-87.622607026,"(41.894760235, -87.622607026)"
-7609373,HS356771,06/12/2010 07:45:00 PM,043XX W GLADYS AVE,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),STREET,true,false,1131,011,28,26,18,1147400,1898039,2010,07/16/2010 01:28:17 PM,41.876179061,-87.734265115,"(41.876179061, -87.734265115)"
-7555695,HS359701,06/10/2010 01:51:00 PM,023XX S TROY ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,GROCERY FOOD STORE,false,false,1033,010,24,30,06,1155681,1888479,2010,06/28/2010 08:40:48 AM,41.849782756,-87.704117212,"(41.849782756, -87.704117212)"
-7548121,HS351889,06/10/2010 01:49:00 AM,016XX S HAMLIN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1014,010,24,29,18,1151358,1891694,2010,06/10/2010 02:57:00 AM,41.858690929,-87.71989906,"(41.858690929, -87.71989906)"
-7546967,HS350244,06/09/2010 07:50:00 AM,041XX W CONGRESS PKWY,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1132,011,24,26,08B,1148832,1897408,2010,06/11/2010 11:30:32 AM,41.874419977,-87.729023567,"(41.874419977, -87.729023567)"
-7542317,HS346461,06/06/2010 11:56:00 PM,0000X W ONTARIO ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1832,018,42,8,16,1175595,1904429,2010,06/07/2010 12:27:22 AM,41.89312691,-87.630551108,"(41.89312691, -87.630551108)"
-7541653,HS345456,06/06/2010 09:45:00 AM,062XX N LOUISE AVE,1570,SEX OFFENSE,PUBLIC INDECENCY,RESIDENTIAL YARD (FRONT/BACK),false,false,1621,016,41,12,17,1138251,1941319,2010,08/16/2010 02:30:33 PM,41.995114244,-87.766807457,"(41.995114244, -87.766807457)"
-7972582,HS345251,06/06/2010 05:10:00 AM,016XX W 49TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0931,009,20,61,14,1165869,1872255,2010,03/16/2011 11:13:32 AM,41.805051461,-87.667187499,"(41.805051461, -87.667187499)"
-7541285,HS344992,06/05/2010 10:00:00 PM,025XX S PULASKI RD,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,false,false,1013,010,22,30,26,1150069,1886967,2010,06/10/2010 10:17:15 AM,41.845744629,-87.724753544,"(41.845744629, -87.724753544)"
-7541791,HS345412,06/05/2010 01:00:00 PM,006XX N LOREL AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1524,015,37,25,14,1140600,1903811,2010,06/07/2010 09:02:04 AM,41.892145682,-87.759090891,"(41.892145682, -87.759090891)"
-7540017,HS343399,06/04/2010 03:30:00 PM,074XX S MAPLEWOOD AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0835,008,18,66,05,1160704,1855249,2010,06/06/2010 01:30:25 PM,41.758492945,-87.686599716,"(41.758492945, -87.686599716)"
-7542581,HS345578,06/04/2010 12:20:00 PM,036XX W 70TH ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0833,008,13,65,05,1153315,1857986,2010,06/10/2010 06:43:46 AM,41.766152959,-87.713607818,"(41.766152959, -87.713607818)"
-7546376,HS341649,06/03/2010 10:45:00 PM,023XX N LINCOLN PARK WEST,0810,THEFT,OVER $500,SIDEWALK,false,false,1814,018,43,7,06,1173834,1915813,2010,06/23/2010 08:01:45 AM,41.924404562,-87.636678973,"(41.924404562, -87.636678973)"
-7539396,HS342549,06/03/2010 10:34:00 PM,068XX S JEFFERY BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0332,003,5,43,08B,1190692,1859698,2010,06/08/2010 11:52:38 AM,41.770030155,-87.576553762,"(41.770030155, -87.576553762)"
-7539578,HS341409,06/03/2010 05:00:00 PM,014XX N DAYTON ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1822,018,32,8,07,1170372,1909761,2010,06/05/2010 02:33:20 PM,41.907874031,-87.649577011,"(41.907874031, -87.649577011)"
-7542067,HS346045,06/03/2010 12:00:00 PM,037XX W 32ND ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1031,010,22,30,07,1152180,1883174,2010,06/07/2010 10:15:16 AM,41.835294849,-87.717106169,"(41.835294849, -87.717106169)"
-7535788,HS338939,06/02/2010 12:00:00 AM,025XX W BALMORAL AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2011,020,40,4,07,1158628,1935737,2010,07/08/2010 04:54:51 PM,41.979402601,-87.692004303,"(41.979402601, -87.692004303)"
-7533273,HS336943,06/01/2010 11:00:00 AM,0000X W 69TH ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,false,false,0731,007,6,69,11,1177312,1859236,2010,06/01/2010 02:59:51 PM,41.769074977,-87.62561266,"(41.769074977, -87.62561266)"
-7532330,HS336344,05/31/2010 10:15:00 PM,040XX W POTOMAC AVE,0460,BATTERY,SIMPLE,OTHER,true,false,2534,025,27,23,08B,1148953,1908310,2010,06/01/2010 09:03:15 AM,41.904333905,-87.728297068,"(41.904333905, -87.728297068)"
-7531016,HS334708,05/30/2010 02:00:00 PM,013XX W FULLERTON AVE,0890,THEFT,FROM BUILDING,ATHLETIC CLUB,false,false,1933,019,32,7,06,1167103,1916106,2010,06/09/2010 10:19:46 PM,41.925355965,-87.661402916,"(41.925355965, -87.661402916)"
-7531017,HS334419,05/30/2010 12:40:00 PM,008XX N MICHIGAN AVE,0820,THEFT,$500 AND UNDER,DEPARTMENT STORE,false,false,1833,018,42,8,06,1177376,1906134,2010,05/31/2010 08:34:35 AM,41.897765295,-87.623958464,"(41.897765295, -87.623958464)"
-7530652,HS334282,05/30/2010 02:00:00 AM,041XX W LELAND AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1722,017,39,14,07,1147771,1931132,2010,06/23/2010 09:58:39 AM,41.966982274,-87.732050948,"(41.966982274, -87.732050948)"
-7530103,HS333627,05/29/2010 10:00:00 PM,060XX S WABASH AVE,0890,THEFT,FROM BUILDING,RESIDENCE PORCH/HALLWAY,false,false,0311,003,20,40,06,1177715,1864849,2010,06/07/2010 04:12:44 PM,41.784468527,-87.623965748,"(41.784468527, -87.623965748)"
-7529895,HS333086,05/29/2010 05:05:00 PM,008XX N LARAMIE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1531,015,37,25,08B,1141574,1905185,2010,06/06/2010 06:43:49 PM,41.895898159,-87.755479772,"(41.895898159, -87.755479772)"
-7530485,HS332564,05/28/2010 11:00:00 PM,108XX S PARNELL AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2233,022,34,49,07,1174608,1832695,2010,05/31/2010 09:03:56 AM,41.696303577,-87.636312042,"(41.696303577, -87.636312042)"
-7529092,HS329110,05/27/2010 06:10:00 AM,002XX E 35TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESTAURANT,false,false,0211,002,3,35,14,1178249,1881793,2010,05/29/2010 10:45:26 AM,41.830952256,-87.621493362,"(41.830952256, -87.621493362)"
-7526426,HS327526,05/26/2010 01:45:00 PM,044XX S EVANS AVE,0545,ASSAULT,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,0222,002,3,38,08A,1181853,1875803,2010,05/28/2010 09:43:12 AM,41.814432546,-87.608455646,"(41.814432546, -87.608455646)"
-7523509,HS326891,05/26/2010 06:25:00 AM,008XX S STATE ST,0890,THEFT,FROM BUILDING,ATHLETIC CLUB,false,false,0132,001,2,32,06,1176526,1896553,2010,06/01/2010 03:42:56 PM,41.871493749,-87.627369876,"(41.871493749, -87.627369876)"
-7523042,HS325928,05/25/2010 08:00:00 AM,089XX S HARPER AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0413,004,8,48,05,1187970,1845966,2010,06/01/2010 10:46:49 AM,41.732413545,-87.586967954,"(41.732413545, -87.586967954)"
-7521051,HS324564,05/24/2010 07:55:00 PM,033XX N MONTICELLO AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1732,017,35,21,08B,1151502,1922150,2010,06/09/2010 05:20:30 PM,41.942262352,-87.718569369,"(41.942262352, -87.718569369)"
-7523548,HS325330,05/24/2010 06:00:00 PM,032XX N LINDER AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1633,016,38,15,06,1139169,1920992,2010,05/28/2010 10:04:35 AM,41.939318449,-87.76392749,"(41.939318449, -87.76392749)"
-7519607,HS322690,05/23/2010 07:00:00 PM,024XX N KILBOURN AVE,2220,LIQUOR LAW VIOLATION,ILLEGAL POSSESSION BY MINOR,SIDEWALK,true,false,2524,025,31,20,22,1146027,1915660,2010,05/24/2010 07:38:09 AM,41.924559178,-87.738858024,"(41.924559178, -87.738858024)"
-7517907,HS321105,05/22/2010 06:00:00 PM,088XX S GREENWOOD AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0412,004,8,47,06,1185127,1846746,2010,05/23/2010 05:31:55 AM,41.73462113,-87.597358535,"(41.73462113, -87.597358535)"
-7517657,HS320668,05/21/2010 02:00:00 AM,061XX N SEELEY AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,2413,024,40,2,14,1161512,1940577,2010,05/24/2010 07:24:34 AM,41.992623999,-87.681262643,"(41.992623999, -87.681262643)"
-7515162,HS316640,05/20/2010 04:00:00 AM,023XX W LOGAN BLVD,0810,THEFT,OVER $500,ATHLETIC CLUB,false,false,1432,014,1,22,06,1160430,1917838,2010,05/21/2010 10:19:25 AM,41.93024956,-87.68587461,"(41.93024956, -87.68587461)"
-7514442,HS317294,05/19/2010 10:16:00 PM,065XX S WOLCOTT AVE,4510,OTHER OFFENSE,PROBATION VIOLATION,RESIDENCE,true,false,0726,007,15,67,26,1164772,1861241,2010,05/21/2010 09:12:19 AM,41.774850924,-87.671521789,"(41.774850924, -87.671521789)"
-7512776,HS315850,05/19/2010 03:20:00 PM,014XX E 70TH ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0332,003,5,43,08B,1186787,1858903,2010,05/22/2010 11:07:24 AM,41.767942002,-87.590892781,"(41.767942002, -87.590892781)"
-7510988,HS314106,05/18/2010 04:04:00 PM,047XX N SHERIDAN RD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2312,019,46,3,18,1168817,1931530,2010,05/18/2010 08:02:48 PM,41.967643028,-87.654656143,"(41.967643028, -87.654656143)"
-7539698,HS342974,05/18/2010 03:30:00 PM,087XX S WALLACE ST,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",false,false,2223,022,21,71,06,1173792,1846913,2010,06/05/2010 06:08:09 AM,41.735337881,-87.638879837,"(41.735337881, -87.638879837)"
-7510863,HS314255,05/18/2010 02:30:00 PM,019XX S INDIANA AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0133,001,2,33,06,1177914,1891016,2010,05/19/2010 06:49:21 AM,41.85626844,-87.622442383,"(41.85626844, -87.622442383)"
-7510240,HS313440,05/18/2010 09:59:00 AM,026XX E 78TH ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,true,false,0421,004,7,43,05,1195485,1853726,2010,05/21/2010 07:13:36 AM,41.753525488,-87.559182013,"(41.753525488, -87.559182013)"
-7510658,HS313234,05/17/2010 08:00:00 PM,022XX S LAWNDALE AVE,0610,BURGLARY,FORCIBLE ENTRY,"SCHOOL, PUBLIC, BUILDING",false,false,1013,010,22,30,05,1152095,1888880,2010,05/25/2010 09:08:40 AM,41.850954497,-87.717267878,"(41.850954497, -87.717267878)"
-7509060,HS312724,05/17/2010 07:30:00 PM,043XX W NORTH AVE,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,GROCERY FOOD STORE,true,false,2534,025,30,23,04A,1147394,1910245,2010,05/18/2010 10:04:09 AM,41.909673774,-87.733974084,"(41.909673774, -87.733974084)"
-7508733,HS312100,05/17/2010 01:40:00 PM,009XX W FULLERTON AVE,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,OTHER COMMERCIAL TRANSPORTATION,false,false,1933,019,43,7,11,1169331,1916173,2010,05/18/2010 06:40:53 AM,41.925491616,-87.653214279,"(41.925491616, -87.653214279)"
-7540000,HS343187,05/17/2010 08:00:00 AM,012XX N SPRINGFIELD AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2535,025,27,23,26,1150111,1908120,2010,06/06/2010 10:18:22 AM,41.903790035,-87.724048345,"(41.903790035, -87.724048345)"
-7508266,HS311779,05/17/2010 12:01:00 AM,046XX N ASHLAND AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1922,019,47,3,14,1164782,1930832,2010,05/17/2010 11:29:55 AM,41.965814436,-87.669512282,"(41.965814436, -87.669512282)"
-7508992,HS310786,05/16/2010 12:00:00 PM,033XX S BELL AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,0913,009,12,59,05,1161944,1882290,2010,05/26/2010 01:18:25 PM,41.832671253,-87.681303502,"(41.832671253, -87.681303502)"
-7535023,HS310452,05/16/2010 11:25:00 AM,039XX W MADISON ST,2024,NARCOTICS,POSS: HEROIN(WHITE),PARKING LOT/GARAGE(NON.RESID.),true,false,1122,011,28,26,18,1150322,1899686,2010,06/03/2010 04:36:49 PM,41.880642145,-87.723493476,"(41.880642145, -87.723493476)"
-7512267,HS315360,05/16/2010 02:00:00 AM,013XX W FLETCHER ST,0890,THEFT,FROM BUILDING,APARTMENT,false,false,1932,019,32,6,06,1167036,1920997,2010,05/20/2010 07:20:48 AM,41.938778563,-87.661508279,"(41.938778563, -87.661508279)"
-7506332,HS309723,05/15/2010 07:30:00 PM,011XX W 59TH ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0712,007,16,68,06,1169585,1865708,2010,05/17/2010 09:44:58 AM,41.787005914,-87.653748618,"(41.787005914, -87.653748618)"
-7505299,HS308531,05/15/2010 12:30:00 AM,019XX W 47TH ST,0810,THEFT,OVER $500,BAR OR TAVERN,false,false,0914,009,20,61,06,1164178,1873546,2010,05/15/2010 10:30:18 AM,41.808629915,-87.673353025,"(41.808629915, -87.673353025)"
-7505977,HS307837,05/14/2010 06:00:00 PM,008XX W 115TH ST,4255,KIDNAPPING,UNLAWFUL INTERFERE/VISITATION,DRUG STORE,false,false,0524,005,34,53,26,1172688,1828543,2010,05/19/2010 11:10:36 AM,41.684952259,-87.643463633,"(41.684952259, -87.643463633)"
-7504657,HS307637,05/14/2010 04:45:00 PM,028XX W MONROE ST,2024,NARCOTICS,POSS: HEROIN(WHITE),VACANT LOT/LAND,true,false,1124,011,2,27,18,1157359,1899517,2010,05/14/2010 05:55:59 PM,41.880038271,-87.697658717,"(41.880038271, -87.697658717)"
-7504613,HS307446,05/14/2010 12:30:00 PM,011XX N WESTERN AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,1312,012,1,24,08B,1160235,1907765,2010,05/18/2010 11:53:26 AM,41.902612548,-87.686870207,"(41.902612548, -87.686870207)"
-7505778,HS308883,05/14/2010 02:00:00 AM,014XX W THOME AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2433,024,40,77,26,1165408,1941597,2010,05/29/2010 06:01:20 PM,41.995340606,-87.666902662,"(41.995340606, -87.666902662)"
-7503276,HS306525,05/13/2010 11:38:00 PM,064XX S KEDZIE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0823,008,15,66,18,1156114,1861977,2010,05/14/2010 01:09:11 AM,41.77704907,-87.70324121,"(41.77704907, -87.70324121)"
-7501539,HS304804,05/12/2010 10:36:00 PM,010XX N ASHLAND AVE,141A,WEAPONS VIOLATION,UNLAWFUL USE HANDGUN,STREET,false,false,1323,012,27,24,15,1165575,1907112,2010,05/13/2010 10:23:34 AM,41.900708571,-87.66727414,"(41.900708571, -87.66727414)"
-7516976,HS318755,05/12/2010 10:30:00 PM,012XX W 57TH ST,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0713,007,16,67,04A,1168746,1866933,2010,06/06/2010 01:46:52 PM,41.790385615,-87.656789505,"(41.790385615, -87.656789505)"
-7510823,HS314147,05/12/2010 07:30:00 PM,047XX W FULTON ST,0890,THEFT,FROM BUILDING,RESIDENTIAL YARD (FRONT/BACK),false,false,1113,011,28,25,06,1144507,1901326,2010,05/19/2010 11:54:35 AM,41.885253904,-87.744804573,"(41.885253904, -87.744804573)"
-7501299,HS304567,05/12/2010 07:28:00 PM,092XX S THROOP ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,2222,022,21,73,14,1169238,1843692,2010,05/13/2010 07:52:27 AM,41.726598614,-87.655656781,"(41.726598614, -87.655656781)"
-7497510,HS300936,05/10/2010 08:00:00 AM,078XX S BURNHAM AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0421,004,7,43,05,1196117,1853717,2010,05/22/2010 07:41:02 AM,41.753485163,-87.55686631,"(41.753485163, -87.55686631)"
-7496758,HS300159,05/10/2010 07:00:00 AM,059XX S HERMITAGE AVE,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,true,true,0714,007,15,67,06,1165741,1865118,2010,05/12/2010 01:40:21 PM,41.785469391,-87.66785958,"(41.785469391, -87.66785958)"
-7535571,HS339108,05/07/2010 01:00:00 PM,013XX S KARLOV AVE,0810,THEFT,OVER $500,VACANT LOT/LAND,false,false,1011,010,24,29,06,1149305,1893459,2010,06/03/2010 10:26:17 AM,41.863574306,-87.727389252,"(41.863574306, -87.727389252)"
-7491406,HS294446,05/06/2010 04:15:00 PM,018XX W BELLE PLAINE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,1923,019,47,5,18,1163141,1927212,2010,05/06/2010 05:31:59 PM,41.955915719,-87.675648056,"(41.955915719, -87.675648056)"
-7496487,HS293257,05/05/2010 05:40:00 PM,013XX W 76TH ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0612,006,17,71,06,1168610,1854408,2010,05/12/2010 05:25:59 AM,41.756018376,-87.657649003,"(41.756018376, -87.657649003)"
-7483406,HS286482,05/02/2010 12:45:00 AM,009XX W ARMITAGE AVE,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,1813,018,43,7,06,1169344,1913441,2010,05/03/2010 08:56:04 AM,41.917994583,-87.653246125,"(41.917994583, -87.653246125)"
-7482591,HS285188,05/01/2010 01:00:00 AM,006XX W DIVERSEY PKWY,0810,THEFT,OVER $500,STREET,false,false,2333,019,43,7,06,1171612,1918815,2010,05/03/2010 07:26:13 AM,41.932691428,-87.644754883,"(41.932691428, -87.644754883)"
-7481765,HS284129,04/30/2010 04:00:00 PM,013XX S LAWNDALE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1011,010,24,29,18,1151970,1893189,2010,04/30/2010 04:40:00 PM,41.862781362,-87.717613248,"(41.862781362, -87.717613248)"
-8215139,HS282326,04/29/2010 03:54:00 PM,010XX S RACINE AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,1231,012,2,28,08A,1168498,1895423,2010,08/16/2011 09:36:40 AM,41.868570338,-87.65687627,"(41.868570338, -87.65687627)"
-7479792,HS281783,04/29/2010 12:01:00 AM,033XX W MADISON ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,false,false,1123,011,28,27,26,1154239,1899861,2010,05/12/2010 07:42:54 AM,41.881045085,-87.709105839,"(41.881045085, -87.709105839)"
-7479752,HS281857,04/28/2010 11:30:00 PM,0000X W GRAND AVE,0330,ROBBERY,AGGRAVATED,CTA PLATFORM,false,false,1831,018,42,8,03,1176234,1903861,2010,05/04/2010 03:22:40 PM,41.891553903,-87.628221468,"(41.891553903, -87.628221468)"
-7478426,HS280653,04/28/2010 03:50:00 PM,038XX W ARMITAGE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2525,025,26,22,14,1150614,1913056,2010,04/29/2010 08:36:21 AM,41.917325069,-87.72207148,"(41.917325069, -87.72207148)"
-7476943,HS279262,04/27/2010 07:08:00 PM,004XX W 57TH PL,2025,NARCOTICS,POSS: HALLUCINOGENS,STREET,true,false,0711,007,20,68,18,1174248,1866768,2010,04/27/2010 08:47:14 PM,41.789812255,-87.63662008,"(41.789812255, -87.63662008)"
-7528694,HS331299,04/25/2010 09:00:00 PM,035XX N NEVA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,RESIDENTIAL YARD (FRONT/BACK),false,false,1632,016,36,17,14,1127821,1922754,2010,05/29/2010 09:52:19 AM,41.944352852,-87.805595496,"(41.944352852, -87.805595496)"
-7472392,HS274237,04/24/2010 06:00:00 PM,002XX W 110TH ST,0560,ASSAULT,SIMPLE,RESIDENCE,true,false,0513,005,34,49,08A,1176505,1832031,2010,04/25/2010 06:50:01 AM,41.694439127,-87.629386294,"(41.694439127, -87.629386294)"
-7479570,HS281733,04/23/2010 09:00:00 AM,010XX W LAKE ST,1122,DECEPTIVE PRACTICE,COUNTERFEIT CHECK,RESIDENCE,false,false,1212,012,27,28,10,1169560,1901587,2010,06/01/2010 01:35:41 PM,41.885461776,-87.652798027,"(41.885461776, -87.652798027)"
-7469161,HS270647,04/22/2010 04:43:00 PM,0000X S CICERO AVE,2026,NARCOTICS,POSS: PCP,ALLEY,true,false,1113,011,28,25,18,1144431,1899557,2010,04/22/2010 06:32:45 PM,41.880400986,-87.745128187,"(41.880400986, -87.745128187)"
-7467269,HS269044,04/21/2010 04:35:00 PM,031XX N CLARK ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,2332,019,44,6,06,1170330,1920955,2010,04/22/2010 07:16:31 AM,41.938591838,-87.649403327,"(41.938591838, -87.649403327)"
-7469140,HS270602,04/21/2010 02:40:00 PM,029XX W ADDISON ST,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,ATM (AUTOMATIC TELLER MACHINE),false,false,1733,017,33,21,11,1156130,1923742,2010,05/14/2010 01:09:47 PM,41.94653857,-87.701516201,"(41.94653857, -87.701516201)"
-7478404,HS279289,04/21/2010 01:30:00 PM,081XX S COTTAGE GROVE AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0631,006,6,44,08B,1182926,1851399,2010,04/30/2010 05:17:36 PM,41.747440842,-87.605277729,"(41.747440842, -87.605277729)"
-7465621,HS267579,04/20/2010 05:30:00 PM,093XX S UNIVERSITY AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0413,004,8,47,08B,1185463,1843479,2010,04/25/2010 06:12:28 AM,41.725648232,-87.596230089,"(41.725648232, -87.596230089)"
-7473992,HS275009,04/19/2010 04:00:00 PM,026XX E 79TH ST,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,0422,004,7,46,06,1195258,1853060,2010,04/27/2010 06:59:10 AM,41.751703535,-87.560035804,"(41.751703535, -87.560035804)"
-7581783,HS385836,04/19/2010 10:00:00 AM,003XX W 29TH ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,2113,009,11,34,06,1174565,1885509,2010,07/11/2010 12:05:17 PM,41.84123224,-87.634899213,"(41.84123224, -87.634899213)"
-7464549,HS266680,04/16/2010 01:00:00 PM,012XX W 47TH ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0933,009,3,61,07,1168729,1873572,2010,04/20/2010 01:38:04 PM,41.808604147,-87.656660204,"(41.808604147, -87.656660204)"
-7456855,HS258018,04/14/2010 09:54:00 PM,028XX W POLK ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1135,011,2,27,18,1157283,1896197,2010,04/14/2010 11:06:04 PM,41.870929407,-87.698028014,"(41.870929407, -87.698028014)"
-7455912,HS256820,04/14/2010 09:40:00 AM,039XX N LAWNDALE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,"SCHOOL, PUBLIC, BUILDING",true,false,1732,017,39,16,18,1150988,1925736,2010,04/14/2010 12:01:28 PM,41.95211271,-87.720364302,"(41.95211271, -87.720364302)"
-7455169,HS256509,04/14/2010 02:35:00 AM,050XX W DIVISION ST,1821,NARCOTICS,MANU/DEL:CANNABIS 10GM OR LESS,GAS STATION,true,false,1531,015,37,25,18,1142407,1907478,2010,04/14/2010 03:44:56 AM,41.902174979,-87.752363271,"(41.902174979, -87.752363271)"
-7455463,HS256635,04/13/2010 09:30:00 PM,043XX S SPAULDING AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0821,008,14,58,14,1155031,1875789,2010,04/14/2010 11:31:29 AM,41.814972864,-87.706842484,"(41.814972864, -87.706842484)"
-7454911,HS256073,04/13/2010 06:00:00 PM,080XX S RACINE AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0612,006,21,71,08B,1169673,1851673,2010,04/17/2010 10:26:14 AM,41.748490194,-87.653832503,"(41.748490194, -87.653832503)"
-7453190,HS254753,04/13/2010 12:39:00 AM,077XX S CRANDON AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0414,004,7,43,18,1192832,1854293,2010,04/13/2010 01:50:14 AM,41.755146478,-87.568885643,"(41.755146478, -87.568885643)"
-7454603,HS255580,04/12/2010 05:45:00 PM,017XX N NATCHEZ AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2513,025,36,25,14,1132540,1911176,2010,04/14/2010 10:30:59 AM,41.912500413,-87.788520559,"(41.912500413, -87.788520559)"
-7446860,HS247899,04/08/2010 05:00:00 PM,013XX W 82ND ST,2024,NARCOTICS,POSS: HEROIN(WHITE),ALLEY,true,false,0613,006,21,71,18,1168909,1850431,2010,04/08/2010 06:21:57 PM,41.745098497,-87.656667858,"(41.745098497, -87.656667858)"
-7444848,HS246512,04/07/2010 08:30:00 AM,055XX S INDIANA AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,true,false,0233,002,20,40,05,1178504,1868233,2010,04/21/2010 08:48:17 PM,41.793736652,-87.620970179,"(41.793736652, -87.620970179)"
-7439156,HS241486,04/04/2010 05:30:00 PM,074XX S BLACKSTONE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0324,003,5,43,03,1187457,1855798,2010,04/17/2010 06:32:56 PM,41.759405708,-87.588535505,"(41.759405708, -87.588535505)"
-7438865,HS241005,04/04/2010 11:50:00 AM,015XX N LUNA AVE,041A,BATTERY,AGGRAVATED: HANDGUN,VEHICLE NON-COMMERCIAL,false,false,2532,025,37,25,04B,1139019,1909490,2010,05/16/2010 08:35:13 AM,41.907758433,-87.764759084,"(41.907758433, -87.764759084)"
-7446616,HS239965,04/03/2010 03:00:00 AM,014XX N SEDGWICK ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,1821,018,27,8,04B,1173336,1910187,2010,04/28/2010 03:39:46 PM,41.908977646,-87.638676259,"(41.908977646, -87.638676259)"
-7436398,HS238020,04/02/2010 11:37:00 AM,016XX W GREENLEAF AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,true,2423,024,49,1,26,1164208,1946982,2010,04/07/2010 01:43:15 PM,42.010142714,-87.671163649,"(42.010142714, -87.671163649)"
-7436578,HS237923,04/02/2010 10:45:00 AM,014XX W SHAKESPEARE AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,1811,018,32,7,06,1166199,1914371,2010,04/05/2010 06:33:42 PM,41.920614415,-87.664774332,"(41.920614415, -87.664774332)"
-7438562,HS236397,04/01/2010 01:45:00 PM,016XX W JONQUIL TER,0460,BATTERY,SIMPLE,RESTAURANT,false,false,2422,024,49,1,08B,1163925,1951041,2010,04/09/2010 06:07:23 PM,42.021286684,-87.672089533,"(42.021286684, -87.672089533)"
-7949817,HT182132,04/01/2010 12:01:00 AM,023XX W 95TH ST,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,BANK,false,false,2221,022,19,72,06,1162676,1841631,2010,03/12/2011 11:41:06 AM,41.721082146,-87.679751496,"(41.721082146, -87.679751496)"
-7432387,HS234280,03/31/2010 09:54:00 AM,033XX W CHICAGO AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1121,011,27,23,18,1153638,1905087,2010,03/31/2010 10:47:22 AM,41.895397725,-87.71117355,"(41.895397725, -87.71117355)"
-7431895,HS234022,03/31/2010 12:18:00 AM,051XX S WINCHESTER AVE,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,TAXICAB,false,false,0915,009,16,61,11,1164177,1870787,2010,04/06/2010 09:39:54 AM,41.801058911,-87.673434388,"(41.801058911, -87.673434388)"
-7431482,HS233578,03/30/2010 06:45:00 PM,015XX S CENTRAL PARK AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1014,010,24,29,26,1152594,1891871,2010,04/02/2010 10:35:27 AM,41.859152315,-87.715357423,"(41.859152315, -87.715357423)"
-7429702,HS232320,03/29/2010 11:00:00 PM,048XX W BELLE PLAINE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1624,016,45,15,14,1143464,1926782,2010,03/30/2010 09:50:12 AM,41.955127361,-87.747996648,"(41.955127361, -87.747996648)"
-7427949,HS230872,03/29/2010 05:02:00 AM,049XX W POTOMAC AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,2533,025,37,25,08B,1143431,1908162,2010,03/29/2010 08:38:55 AM,41.90403286,-87.748584797,"(41.90403286, -87.748584797)"
-7427635,HS230488,03/28/2010 07:00:00 PM,053XX S WINCHESTER AVE,0460,BATTERY,SIMPLE,STREET,false,false,0915,009,16,61,08B,1164297,1869375,2010,03/30/2010 08:11:45 AM,41.797181681,-87.673034082,"(41.797181681, -87.673034082)"
-7429416,HS231615,03/27/2010 09:00:00 PM,013XX W BIRCHWOOD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2422,024,49,1,08B,1165831,1949896,2010,03/30/2010 03:54:57 PM,42.018104169,-87.665108386,"(42.018104169, -87.665108386)"
-7425586,HS227818,03/27/2010 01:08:00 AM,015XX N DAYTON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1822,018,43,8,18,1170366,1910031,2010,03/27/2010 03:26:57 AM,41.908615057,-87.649591146,"(41.908615057, -87.649591146)"
-7426290,HS228833,03/26/2010 07:00:00 PM,030XX W 25TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1033,010,12,30,14,1156618,1887209,2010,03/29/2010 10:22:43 AM,41.84627884,-87.700712609,"(41.84627884, -87.700712609)"
-7427567,HS230295,03/26/2010 03:00:00 PM,022XX S WESTERN AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,1034,010,25,31,06,1160669,1889318,2010,03/29/2010 11:51:02 AM,41.851983308,-87.685787272,"(41.851983308, -87.685787272)"
-7424633,HS226282,03/26/2010 07:45:00 AM,001XX N WABASH AVE,1330,CRIMINAL TRESPASS,TO LAND,OTHER,true,false,0122,001,42,32,26,1176838,1901009,2010,03/29/2010 11:26:01 AM,41.883714223,-87.626089608,"(41.883714223, -87.626089608)"
-7425731,HS226499,03/26/2010 05:11:00 AM,014XX E HYDE PARK BLVD,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2124,002,4,39,26,1186646,1871510,2010,04/06/2010 09:26:10 AM,41.802539965,-87.591010648,"(41.802539965, -87.591010648)"
-7613968,HS327414,03/26/2010 02:00:00 AM,036XX S ASHLAND AVE,0890,THEFT,FROM BUILDING,OTHER,false,false,0922,009,11,59,06,1166310,1880305,2010,08/25/2010 08:42:20 AM,41.827132153,-87.665340586,"(41.827132153, -87.665340586)"
-7423447,HS225547,03/25/2010 02:55:00 PM,030XX W 60TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0824,008,16,66,14,1156759,1864724,2010,03/26/2010 11:59:02 AM,41.784574246,-87.700802556,"(41.784574246, -87.700802556)"
-7421970,HS224476,03/24/2010 11:45:00 PM,049XX N KEDZIE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,1713,017,33,14,08B,1154194,1932571,2010,03/25/2010 08:42:16 AM,41.970804856,-87.708395699,"(41.970804856, -87.708395699)"
-7421272,HS223558,03/24/2010 01:00:00 AM,054XX W SCHUBERT AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,2514,025,30,19,05,1139629,1917378,2010,03/25/2010 10:58:10 AM,41.929392863,-87.762325303,"(41.929392863, -87.762325303)"
-7419947,HS222603,03/23/2010 09:19:00 PM,007XX N SPRINGFIELD AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),ALLEY,true,false,1112,011,27,23,18,1150210,1904877,2010,03/23/2010 10:10:05 PM,41.894888986,-87.723769332,"(41.894888986, -87.723769332)"
-7418092,HS221037,03/22/2010 10:00:00 PM,024XX S WHIPPLE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1033,010,12,30,08B,1156379,1887472,2010,03/25/2010 09:17:00 PM,41.84700537,-87.701582629,"(41.84700537, -87.701582629)"
-7417520,HS220257,03/22/2010 02:40:00 PM,004XX W 57TH ST,2017,NARCOTICS,MANU/DELIVER:CRACK,VACANT LOT/LAND,true,false,0711,007,3,68,18,1174358,1867176,2010,03/22/2010 03:25:44 PM,41.790929403,-87.636204614,"(41.790929403, -87.636204614)"
-7417643,HS220213,03/22/2010 01:45:00 PM,010XX N LARAMIE AVE,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, GROUNDS",false,false,1524,015,37,25,08B,1141464,1906394,2010,03/23/2010 09:31:24 AM,41.89921783,-87.755853896,"(41.89921783, -87.755853896)"
-7417864,HS220760,03/19/2010 06:00:00 PM,033XX S MAY ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0924,009,11,60,14,1169163,1882399,2010,03/23/2010 09:44:53 AM,41.832816943,-87.654812723,"(41.832816943, -87.654812723)"
-7414371,HS215066,03/18/2010 07:30:00 PM,048XX N BROADWAY,0610,BURGLARY,FORCIBLE ENTRY,SMALL RETAIL STORE,false,false,2033,020,48,3,05,1167386,1932844,2010,04/01/2010 02:56:07 PM,41.971279672,-87.659879788,"(41.971279672, -87.659879788)"
-7411897,HS214108,03/17/2010 05:00:00 PM,016XX S DRAKE AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,1021,010,24,29,04B,1152929,1891746,2010,03/30/2010 11:06:04 AM,41.858802677,-87.714131046,"(41.858802677, -87.714131046)"
-7413752,HS212563,03/17/2010 11:00:00 AM,021XX N MERRIMAC AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,2512,025,29,19,05,1134429,1913821,2010,09/08/2010 09:33:15 PM,41.919725477,-87.78151822,"(41.919725477, -87.78151822)"
-7419050,HS211322,03/16/2010 09:45:00 PM,004XX N SPRINGFIELD AVE,3730,INTERFERENCE WITH PUBLIC OFFICER,OBSTRUCTING JUSTICE,VEHICLE NON-COMMERCIAL,true,false,1122,011,27,23,24,1150352,1902604,2010,03/24/2010 01:05:07 PM,41.888648867,-87.723307152,"(41.888648867, -87.723307152)"
-7407904,HS209979,03/15/2010 04:00:00 PM,028XX W 38TH PL,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0913,009,12,58,14,1158046,1879023,2010,03/16/2010 01:57:26 PM,41.823786512,-87.695694919,"(41.823786512, -87.695694919)"
-7407391,HS208019,03/14/2010 11:50:00 AM,095XX S CALUMET AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0511,005,6,49,08B,1180304,1841682,2010,03/24/2010 02:20:08 PM,41.720836709,-87.615182509,"(41.720836709, -87.615182509)"
-7404613,HS207058,03/13/2010 06:30:00 PM,057XX N EAST CIRCLE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1612,016,41,10,14,1129048,1938078,2010,03/15/2010 09:10:30 AM,41.986382717,-87.800735363,"(41.986382717, -87.800735363)"
-7402091,HS203592,03/12/2010 07:00:00 AM,082XX S STATE ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0631,006,6,44,05,1177761,1850513,2010,03/14/2010 06:42:19 PM,41.745127919,-87.624230403,"(41.745127919, -87.624230403)"
-7403434,HS203453,03/12/2010 01:25:00 AM,028XX N LINCOLN AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,OTHER COMMERCIAL TRANSPORTATION,true,false,1931,019,32,6,14,1167173,1919285,2010,03/15/2010 06:41:02 AM,41.934077803,-87.661054114,"(41.934077803, -87.661054114)"
-7401116,HS202021,03/11/2010 08:30:00 AM,067XX N RAVENSWOOD AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,true,2432,024,49,1,08A,1163175,1944635,2010,03/12/2010 02:30:52 PM,42.003724353,-87.675030812,"(42.003724353, -87.675030812)"
-7421014,HS201912,03/11/2010 06:35:00 AM,073XX S HALSTED ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0732,007,17,68,16,1172277,1856209,2010,03/25/2010 12:29:52 PM,41.760880727,-87.644157402,"(41.760880727, -87.644157402)"
-7397956,HS200072,03/09/2010 10:00:00 PM,077XX S HONORE ST,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,RESIDENCE,false,false,0611,006,18,71,20,1165326,1853221,2010,03/25/2010 01:04:15 PM,41.752831227,-87.66971772,"(41.752831227, -87.66971772)"
-7397971,HS198712,03/09/2010 09:00:00 AM,044XX S PRINCETON AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,OTHER,false,false,0935,009,3,37,07,1174991,1875402,2010,03/11/2010 09:12:08 AM,41.813488247,-87.633638023,"(41.813488247, -87.633638023)"
-7393809,HS196598,03/08/2010 01:59:00 AM,056XX S JUSTINE ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0713,007,16,67,18,1167014,1867176,2010,03/08/2010 04:01:42 AM,41.791089662,-87.663133354,"(41.791089662, -87.663133354)"
-7396590,HS195897,03/07/2010 12:30:00 PM,038XX N SAYRE AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,1632,016,38,17,08A,1129016,1924692,2010,03/10/2010 03:28:42 AM,41.949650674,-87.801158873,"(41.949650674, -87.801158873)"
-7393906,HS196488,03/06/2010 07:30:00 PM,081XX S PEORIA ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0613,006,21,71,05,1171769,1850664,2010,03/09/2010 09:35:43 PM,41.745675686,-87.646181561,"(41.745675686, -87.646181561)"
-7394482,HS196831,03/06/2010 06:00:00 PM,114XX S FOREST AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0531,005,9,49,26,1180325,1829248,2010,03/10/2010 11:09:04 AM,41.686715661,-87.615485216,"(41.686715661, -87.615485216)"
-7391672,HS193803,03/06/2010 12:05:00 AM,042XX N BROADWAY,1365,CRIMINAL TRESPASS,TO RESIDENCE,APARTMENT,false,false,2322,019,46,3,26,1169149,1928554,2010,03/08/2010 08:01:31 AM,41.959469565,-87.653522235,"(41.959469565, -87.653522235)"
-7391824,HS193655,03/05/2010 03:15:00 PM,049XX N NEENAH AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1613,016,41,10,05,1131854,1932301,2010,03/19/2010 02:40:16 PM,41.970481733,-87.790549377,"(41.970481733, -87.790549377)"
-7432206,HS190568,03/04/2010 01:30:00 AM,112XX S INDIANA AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,APARTMENT,false,true,0531,005,9,49,04A,1179536,1830363,2010,04/09/2010 06:45:41 AM,41.689793387,-87.61833972,"(41.689793387, -87.61833972)"
-7384418,HS186534,03/01/2010 03:30:00 PM,010XX N MONITOR AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,1511,015,29,25,06,1137202,1906596,2010,03/16/2010 06:04:39 PM,41.8998498,-87.771503505,"(41.8998498, -87.771503505)"
-7384351,HS186398,03/01/2010 12:30:00 PM,022XX W CERMAK RD,0460,BATTERY,SIMPLE,STREET,true,false,1034,010,25,31,08B,1161638,1889319,2010,03/02/2010 09:03:44 AM,41.851965946,-87.68223075,"(41.851965946, -87.68223075)"
-7384144,HS186174,02/28/2010 06:00:00 PM,016XX N OAKLEY AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1434,014,1,24,06,1160804,1911192,2010,03/01/2010 12:46:39 PM,41.912004714,-87.684684982,"(41.912004714, -87.684684982)"
-7383227,HS184962,02/27/2010 07:00:00 PM,062XX N HERMITAGE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2433,024,40,77,14,1163598,1941414,2010,03/01/2010 06:34:15 AM,41.994876909,-87.673565914,"(41.994876909, -87.673565914)"
-7382052,HS183318,02/27/2010 09:45:00 AM,017XX N SEDGWICK ST,0460,BATTERY,SIMPLE,STREET,false,false,1813,018,43,7,08B,1173280,1911989,2010,03/30/2010 03:22:16 PM,41.913923666,-87.638828374,"(41.913923666, -87.638828374)"
-7377970,HS179806,02/24/2010 08:43:00 PM,028XX W LAWRENCE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,VEHICLE NON-COMMERCIAL,true,false,2031,020,40,4,18,1156713,1931800,2010,02/24/2010 10:33:51 PM,41.968638395,-87.699154072,"(41.968638395, -87.699154072)"
-7465111,HS180764,02/24/2010 02:30:00 PM,064XX S HOYNE AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0726,007,15,67,06,1163507,1862018,2010,04/21/2010 10:58:37 AM,41.777009736,-87.676137342,"(41.777009736, -87.676137342)"
-7375942,HS178045,02/23/2010 06:02:00 PM,013XX S CANAL ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,0131,001,2,28,06,1173296,1893975,2010,02/24/2010 09:17:45 AM,41.864491821,-87.639304867,"(41.864491821, -87.639304867)"
-7375690,HS177803,02/23/2010 03:30:00 PM,013XX S INDEPENDENCE BLVD,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1011,010,24,29,26,1151506,1893589,2010,02/23/2010 04:37:24 PM,41.863888128,-87.71930605,"(41.863888128, -87.71930605)"
-7378264,HS178692,02/23/2010 08:00:00 AM,046XX N ASHLAND AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1922,019,47,3,05,1164782,1930832,2010,02/26/2010 10:02:58 PM,41.965814436,-87.669512282,"(41.965814436, -87.669512282)"
-7376068,HS178238,02/20/2010 10:30:00 PM,004XX E 48TH PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0223,002,3,38,08B,1179904,1872995,2010,03/03/2010 11:38:49 AM,41.806772052,-87.615690735,"(41.806772052, -87.615690735)"
-7371672,HS174273,02/20/2010 09:00:00 PM,101XX S AVENUE M,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0432,004,10,52,14,1201451,1838194,2010,02/21/2010 07:09:24 AM,41.710755177,-87.537845387,"(41.710755177, -87.537845387)"
-7371390,HS173751,02/20/2010 05:27:00 PM,031XX W LEXINGTON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1134,011,24,27,08B,1155676,1896493,2010,02/24/2010 09:44:46 AM,41.871774148,-87.703919937,"(41.871774148, -87.703919937)"
-7394552,HS172420,02/19/2010 06:00:00 PM,001XX N ASHLAND AVE,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,1333,012,27,28,03,1165683,1901222,2010,03/22/2010 02:41:21 PM,41.884543674,-87.667045449,"(41.884543674, -87.667045449)"
-7369616,HS171753,02/19/2010 10:17:00 AM,018XX E 71ST ST,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE PORCH/HALLWAY,true,false,0333,003,5,43,26,1190032,1858201,2010,02/20/2010 06:10:56 AM,41.765938176,-87.579021126,"(41.765938176, -87.579021126)"
-7368399,HS170710,02/18/2010 02:35:00 PM,022XX S SPRINGFIELD AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1013,010,22,30,18,1150781,1888655,2010,02/18/2010 05:13:32 PM,41.850362836,-87.722096452,"(41.850362836, -87.722096452)"
-7366801,HS169482,02/17/2010 07:51:00 PM,084XX S PULASKI RD,0860,THEFT,RETAIL THEFT,DRUG STORE,false,false,0834,008,18,70,06,1151171,1848065,2010,02/18/2010 09:48:57 AM,41.738970167,-87.721724942,"(41.738970167, -87.721724942)"
-7451206,HS168334,02/17/2010 01:46:00 AM,088XX S EAST END AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0412,004,8,48,05,1189143,1846394,2010,04/19/2010 06:52:56 AM,41.733560028,-87.582657134,"(41.733560028, -87.582657134)"
-7391772,HS168307,02/17/2010 12:30:00 AM,071XX S YATES BLVD,0313,ROBBERY,ARMED: OTHER DANGEROUS WEAPON,APARTMENT,false,false,0334,003,7,43,03,1193491,1857974,2010,09/28/2010 06:47:37 PM,41.765231332,-87.566350359,"(41.765231332, -87.566350359)"
-7366529,HS169109,02/16/2010 06:00:00 PM,110XX S GREEN BAY AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0433,004,10,52,26,1200512,1832505,2010,02/21/2010 12:40:29 PM,41.695167814,-87.541475676,"(41.695167814, -87.541475676)"
-7363469,HS165336,02/14/2010 09:15:00 PM,010XX N LATROBE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1524,015,37,25,14,1141202,1906667,2010,02/16/2010 08:07:45 AM,41.899971809,-87.75680949,"(41.899971809, -87.75680949)"
-7362255,HS164555,02/14/2010 05:50:00 AM,040XX S WESTERN AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,RESTAURANT,false,false,0914,009,12,58,04A,1161006,1877522,2010,01/27/2012 09:38:53 PM,41.819606796,-87.684877229,"(41.819606796, -87.684877229)"
-7361698,HS163558,02/13/2010 11:00:00 AM,003XX N CICERO AVE,5011,OTHER OFFENSE,LICENSE VIOLATION,OTHER,false,false,1113,011,28,25,26,1144364,1901512,2010,02/15/2010 08:57:58 AM,41.885767,-87.74532502,"(41.885767, -87.74532502)"
-7372229,HS174973,02/12/2010 09:00:00 AM,081XX S SPAULDING AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,OTHER,false,false,0834,008,18,70,06,1155765,1850174,2010,03/30/2010 12:03:29 PM,41.744666789,-87.704836784,"(41.744666789, -87.704836784)"
-7360321,HS161754,02/12/2010 08:50:00 AM,024XX N ASHLAND AVE,0810,THEFT,OVER $500,STREET,false,false,1931,019,32,7,06,1165287,1916645,2010,02/12/2010 09:48:41 AM,41.926873868,-87.668060373,"(41.926873868, -87.668060373)"
-7359652,HS160830,02/11/2010 02:00:00 PM,0000X S LA SALLE ST,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,0112,001,42,32,06,1175107,1899908,2010,02/16/2010 03:24:35 PM,41.880731984,-87.632478941,"(41.880731984, -87.632478941)"
-7360035,HS161046,02/11/2010 02:00:00 PM,0000X E OHIO ST,0870,THEFT,POCKET-PICKING,OTHER,false,false,1834,018,42,8,06,1176733,1904171,2010,03/02/2010 01:34:54 PM,41.892393291,-87.626379514,"(41.892393291, -87.626379514)"
-7376618,HS159970,02/10/2010 08:20:00 PM,057XX S CALUMET AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0234,002,20,40,16,1179429,1866772,2010,02/25/2010 02:10:56 PM,41.789706448,-87.617622898,"(41.789706448, -87.617622898)"
-7357237,HS158739,02/10/2010 12:35:00 AM,071XX S ST LAWRENCE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,0323,003,6,69,08B,1181438,1857530,2010,02/13/2010 03:48:01 AM,41.76429938,-87.610541394,"(41.76429938, -87.610541394)"
-7353844,HS155898,02/08/2010 12:05:00 AM,059XX S FRANCISCO AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,true,0824,008,16,66,14,1158031,1864788,2010,02/21/2010 11:04:01 AM,41.784724098,-87.69613711,"(41.784724098, -87.69613711)"
-7358677,HS154493,02/06/2010 08:05:00 PM,066XX S MARQUETTE RD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,RESIDENCE PORCH/HALLWAY,true,false,0321,003,20,42,18,1180759,1860912,2010,02/11/2010 10:07:02 AM,41.773595562,-87.61292625,"(41.773595562, -87.61292625)"
-7357835,HS159017,02/06/2010 12:00:00 PM,026XX S WABASH AVE,0820,THEFT,$500 AND UNDER,TAXICAB,false,false,2113,001,2,35,06,1177205,1886936,2010,02/11/2010 10:43:24 AM,41.845088714,-87.625168248,"(41.845088714, -87.625168248)"
-7352165,HS151831,02/04/2010 11:45:00 PM,011XX W ERIE ST,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,APARTMENT,true,false,1323,012,27,24,18,1168510,1904469,2010,02/09/2010 06:07:36 AM,41.893392969,-87.656570333,"(41.893392969, -87.656570333)"
-7350253,HS151720,02/04/2010 11:04:00 PM,012XX W MARQUETTE RD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0724,007,17,67,18,1169195,1860392,2010,02/05/2010 12:27:38 AM,41.772426615,-87.65533225,"(41.772426615, -87.65533225)"
-7390624,HS166161,02/04/2010 03:59:00 PM,110XX S MICHIGAN AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,ATM (AUTOMATIC TELLER MACHINE),false,false,0513,005,9,49,06,1178748,1831610,2010,04/08/2010 06:44:05 AM,41.693233246,-87.621186839,"(41.693233246, -87.621186839)"
-7347094,HS148822,02/02/2010 11:40:00 PM,003XX E 119TH ST,1330,CRIMINAL TRESPASS,TO LAND,RESIDENTIAL YARD (FRONT/BACK),true,false,0532,005,9,53,26,1180078,1826153,2010,02/05/2010 06:01:00 AM,41.678228176,-87.616483711,"(41.678228176, -87.616483711)"
-7399677,HS148770,02/02/2010 09:43:00 PM,004XX W 77TH ST,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,0621,006,17,69,18,1174394,1853903,2010,03/10/2010 10:45:59 PM,41.75450598,-87.636466967,"(41.75450598, -87.636466967)"
-7357193,HS147475,02/02/2010 02:45:00 AM,073XX S SOUTH SHORE DR,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,false,false,0334,003,7,43,07,1195247,1857378,2010,02/12/2010 09:54:50 AM,41.763552718,-87.55993386,"(41.763552718, -87.55993386)"
-7362463,HS145825,01/31/2010 09:57:41 PM,0000X N HOMAN BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1123,011,28,27,18,1153749,1900226,2010,02/14/2010 12:59:54 PM,41.882056448,-87.710895376,"(41.882056448, -87.710895376)"
-7343109,HS145826,01/31/2010 09:30:00 PM,003XX E 137TH ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0533,005,9,54,26,1180665,1814313,2010,02/03/2010 01:39:59 PM,41.645723971,-87.614696448,"(41.645723971, -87.614696448)"
-7343716,HS146152,01/31/2010 09:30:00 PM,052XX N OLCOTT AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,1613,016,41,10,05,1125305,1934102,2010,05/13/2010 10:27:46 AM,41.975535202,-87.814590928,"(41.975535202, -87.814590928)"
-7342341,HS144247,01/29/2010 11:30:00 PM,029XX N MILDRED AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,1932,019,44,6,05,1169728,1919551,2010,02/25/2010 01:22:10 PM,41.934752358,-87.651656834,"(41.934752358, -87.651656834)"
-7341887,HS144219,01/29/2010 05:00:00 PM,017XX W THOME AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2433,024,40,77,07,1163787,1941719,2010,04/02/2010 12:00:35 PM,41.99570984,-87.672862031,"(41.99570984, -87.672862031)"
-7340658,HS142405,01/29/2010 11:30:00 AM,020XX N CICERO AVE,0810,THEFT,OVER $500,STREET,false,false,2522,025,31,19,06,1144101,1913128,2010,01/30/2010 09:16:31 AM,41.917647535,-87.745998777,"(41.917647535, -87.745998777)"
-7347748,HS149189,01/29/2010 09:00:00 AM,013XX N PULASKI RD,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,APARTMENT,false,true,2534,025,27,23,11,1149438,1908435,2010,02/06/2010 09:35:25 AM,41.904667517,-87.72651227,"(41.904667517, -87.72651227)"
-7340213,HS142068,01/27/2010 09:00:00 PM,019XX N KARLOV AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE,false,false,2534,025,30,20,14,1148695,1912759,2010,01/30/2010 09:29:34 AM,41.916547389,-87.72912966,"(41.916547389, -87.72912966)"
-7337469,HS140164,01/27/2010 08:35:00 PM,031XX W CHICAGO AVE,1330,CRIMINAL TRESPASS,TO LAND,GAS STATION,true,false,1313,012,27,23,26,1154990,1905114,2010,01/28/2010 10:01:05 AM,41.895444805,-87.706207234,"(41.895444805, -87.706207234)"
-7352083,HS139704,01/27/2010 04:42:10 PM,004XX S LARAMIE AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,false,false,1522,015,29,25,04A,1141762,1897305,2010,02/10/2010 11:33:18 AM,41.874270991,-87.754984332,"(41.874270991, -87.754984332)"
-7358920,HS139006,01/27/2010 09:24:36 AM,052XX W MADISON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,PARKING LOT/GARAGE(NON.RESID.),true,false,1522,015,29,25,18,1141148,1899474,2010,03/10/2010 01:57:33 PM,41.880234335,-87.757185235,"(41.880234335, -87.757185235)"
-7347880,HS149447,01/26/2010 01:00:00 PM,103XX S ELIZABETH ST,1340,CRIMINAL DAMAGE,TO STATE SUP PROP,"SCHOOL, PUBLIC, BUILDING",false,false,2232,022,21,73,14,1169808,1836140,2010,02/04/2010 07:13:58 AM,41.705862453,-87.653787153,"(41.705862453, -87.653787153)"
-7351301,HS137842,01/26/2010 12:00:00 PM,072XX S VERNON AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0323,003,6,69,26,1180461,1857072,2010,02/09/2010 03:58:25 PM,41.763065052,-87.614136347,"(41.763065052, -87.614136347)"
-7333997,HS137511,01/26/2010 08:30:00 AM,022XX W WARREN BLVD,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1332,012,2,28,05,1161417,1900351,2010,02/18/2010 10:27:38 PM,41.882243396,-87.682735012,"(41.882243396, -87.682735012)"
-7360901,HS162511,01/25/2010 09:00:00 AM,005XX N KINGSBURY ST,1120,DECEPTIVE PRACTICE,FORGERY,RESIDENCE,false,false,1831,018,42,8,10,1173074,1903709,2010,03/05/2010 04:35:34 PM,41.891207477,-87.639831086,"(41.891207477, -87.639831086)"
-7333223,HS136736,01/24/2010 12:00:00 PM,019XX E 74TH ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,false,false,0333,003,5,43,11,1190666,1856333,2010,02/17/2010 11:20:00 AM,41.760796951,-87.576757559,"(41.760796951, -87.576757559)"
-7330923,HS135062,01/24/2010 11:45:00 AM,022XX W 79TH ST,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),true,false,0835,008,18,70,06,1162695,1852184,2010,01/25/2010 11:39:09 AM,41.750040829,-87.679388203,"(41.750040829, -87.679388203)"
-7331778,HS135016,01/24/2010 11:09:00 AM,040XX W LAKE ST,0880,THEFT,PURSE-SNATCHING,STREET,false,true,1114,011,28,26,06,1149614,1901481,2010,10/31/2014 03:20:56 PM,41.885581599,-87.726046569,"(41.885581599, -87.726046569)"
-7330875,HS134845,01/24/2010 07:30:00 AM,011XX E 54TH ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,2131,002,4,41,05,1184965,1869874,2010,03/02/2010 10:00:11 AM,41.798090328,-87.597226898,"(41.798090328, -87.597226898)"
-7331528,HS135100,01/23/2010 04:00:00 PM,002XX N OGDEN AVE,0810,THEFT,OVER $500,WAREHOUSE,false,false,1333,012,27,28,06,1166838,1901577,2010,01/25/2010 09:13:58 AM,41.885493135,-87.662793963,"(41.885493135, -87.662793963)"
-7330119,HS132766,01/22/2010 08:00:00 PM,022XX S WESTERN AVE,0460,BATTERY,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1034,010,25,31,08B,1160669,1889301,2010,02/18/2010 12:06:44 PM,41.851936659,-87.685787742,"(41.851936659, -87.685787742)"
-7328897,HS132376,01/22/2010 03:06:00 PM,038XX W CERMAK RD,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1013,010,22,30,26,1151217,1889069,2010,02/04/2010 03:24:59 PM,41.851490376,-87.720485398,"(41.851490376, -87.720485398)"
-7329004,HS132588,01/22/2010 01:15:00 PM,064XX S KEATING AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0813,008,13,64,05,1145785,1861315,2010,01/24/2010 07:24:52 AM,41.775433869,-87.74112435,"(41.775433869, -87.74112435)"
-7321425,HS126462,01/18/2010 06:50:00 PM,003XX W OAK ST,2027,NARCOTICS,POSS: CRACK,CHA PARKING LOT/GROUNDS,true,false,1823,018,27,8,18,1173426,1907052,2010,01/18/2010 10:26:24 PM,41.900373043,-87.638438935,"(41.900373043, -87.638438935)"
-7321140,HS126282,01/18/2010 03:30:00 PM,015XX E 72ND PL,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,0324,003,5,43,08A,1187637,1857181,2010,01/22/2010 09:34:25 AM,41.763196506,-87.587831893,"(41.763196506, -87.587831893)"
-7320468,HS125376,01/18/2010 12:10:00 AM,061XX S KIMBARK AVE,1477,WEAPONS VIOLATION,RECKLESS FIREARM DISCHARGE,STREET,true,false,0314,003,20,42,15,1185661,1864341,2010,01/18/2010 10:46:12 AM,41.782890948,-87.594848866,"(41.782890948, -87.594848866)"
-7320557,HS124798,01/17/2010 03:37:23 PM,017XX W HOWARD ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,2422,024,49,1,06,1163413,1950307,2010,01/20/2010 09:55:54 AM,42.019283414,-87.673994494,"(42.019283414, -87.673994494)"
-7327362,HS122443,01/15/2010 09:30:00 PM,027XX W SCHUBERT AVE,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,1411,014,35,22,06,1157868,1917874,2010,01/22/2010 09:03:59 AM,41.93040105,-87.695288367,"(41.93040105, -87.695288367)"
-7318457,HS120439,01/14/2010 06:00:00 PM,014XX S KOLIN AVE,0460,BATTERY,SIMPLE,SIDEWALK,true,false,1011,010,24,29,08B,1147574,1892840,2010,01/19/2010 10:23:44 AM,41.861909044,-87.73375957,"(41.861909044, -87.73375957)"
-7316030,HS120172,01/14/2010 01:30:00 PM,060XX S PAULINA ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0714,007,15,67,05,1166001,1864753,2010,01/17/2010 09:50:00 AM,41.784462259,-87.666916671,"(41.784462259, -87.666916671)"
-7316103,HS119858,01/14/2010 12:50:00 PM,013XX N HOYNE AVE,0340,ROBBERY,ATTEMPT: STRONGARM-NO WEAPON,SIDEWALK,false,false,1424,014,32,24,03,1162124,1908670,2010,01/18/2010 08:51:51 AM,41.905056665,-87.679906268,"(41.905056665, -87.679906268)"
-7316444,HS120474,01/12/2010 05:00:00 PM,082XX S KEDZIE AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,0835,008,18,70,06,1156527,1849529,2010,02/02/2010 01:29:03 PM,41.742881497,-87.702062025,"(41.742881497, -87.702062025)"
-7311502,HS115043,01/11/2010 12:30:00 PM,025XX W ADDISON ST,0460,BATTERY,SIMPLE,SIDEWALK,true,false,1912,019,47,5,08B,1158533,1923865,2010,02/06/2010 01:50:23 PM,41.946827143,-87.692680115,"(41.946827143, -87.692680115)"
-7311214,HS115011,01/11/2010 08:00:00 AM,007XX E 51ST ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,true,false,0223,002,4,38,05,1181851,1871389,2010,06/22/2011 10:51:02 AM,41.802320223,-87.608599517,"(41.802320223, -87.608599517)"
-7309015,HS113681,01/10/2010 02:10:00 PM,045XX N PULASKI RD,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,true,false,1723,017,39,14,26,1148942,1930126,2010,10/31/2014 03:20:56 PM,41.964199107,-87.727771443,"(41.964199107, -87.727771443)"
-7311182,HS113208,01/10/2010 03:46:46 AM,004XX W DIVERSEY PKWY,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,2333,019,43,7,08B,1172528,1918847,2010,01/14/2010 07:56:20 AM,41.932759,-87.641387757,"(41.932759, -87.641387757)"
-7308572,HS113232,01/10/2010 03:00:00 AM,008XX W JACKSON BLVD,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,1213,012,27,28,06,1171061,1898899,2010,01/11/2010 09:50:22 AM,41.878052925,-87.647365009,"(41.878052925, -87.647365009)"
-7309469,HS113088,01/09/2010 10:30:00 PM,091XX S LA SALLE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE-GARAGE,false,true,0634,006,21,49,08B,1176854,1844075,2010,01/22/2010 09:32:33 AM,41.727481697,-87.627747174,"(41.727481697, -87.627747174)"
-7308143,HS112539,01/09/2010 04:00:00 PM,051XX S PRAIRIE AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,0232,002,3,40,18,1178874,1870944,2010,01/09/2010 05:04:57 PM,41.801167465,-87.619530878,"(41.801167465, -87.619530878)"
-7307188,HS111171,01/08/2010 04:40:00 PM,002XX S CANAL ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,true,false,0111,001,2,28,26,1173137,1899230,2010,10/31/2014 03:20:56 PM,41.878915426,-87.63973267,"(41.878915426, -87.63973267)"
-7305722,HS109286,01/06/2010 10:00:00 PM,058XX S WABASH AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0233,002,20,40,05,1177751,1866366,2010,01/09/2010 07:18:38 AM,41.788630511,-87.623787859,"(41.788630511, -87.623787859)"
-7302491,HS105940,01/05/2010 12:45:00 PM,007XX N TRUMBULL AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1121,011,27,23,26,1153205,1904739,2010,01/16/2010 02:43:54 PM,41.894451384,-87.712773107,"(41.894451384, -87.712773107)"
-7301051,HS104252,01/04/2010 11:30:00 AM,066XX N CLARK ST,0460,BATTERY,SIMPLE,OTHER,false,false,2432,024,49,1,08B,1163848,1944483,2010,01/05/2010 07:01:20 AM,42.003293036,-87.672559179,"(42.003293036, -87.672559179)"
-7298812,HS102601,01/02/2010 10:50:00 PM,047XX S COTTAGE GROVE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0223,002,4,38,18,1182346,1873893,2010,01/03/2010 01:03:22 AM,41.809179933,-87.606706526,"(41.809179933, -87.606706526)"
-7298518,HS102219,01/02/2010 05:25:00 PM,012XX N LARRABEE ST,1330,CRIMINAL TRESPASS,TO LAND,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,1822,018,27,8,26,1172064,1908588,2010,01/04/2010 09:59:02 AM,41.904618077,-87.643396202,"(41.904618077, -87.643396202)"
-7297748,HS101239,01/01/2010 08:10:00 PM,066XX S HALSTED ST,1121,DECEPTIVE PRACTICE,COUNTERFEITING DOCUMENT,GAS STATION,false,false,0723,007,6,68,10,1172162,1860466,2010,01/03/2010 11:42:06 AM,41.772564979,-87.644453924,"(41.772564979, -87.644453924)"
-7297647,HS101187,01/01/2010 06:00:00 PM,035XX W 78TH PL,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0835,008,18,70,26,1154205,1852361,2010,01/05/2010 07:08:37 AM,41.750699407,-87.710494888,"(41.750699407, -87.710494888)"
-7293190,HR710205,12/29/2009 02:00:00 PM,020XX W HOWARD ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2424,024,49,1,06,1161082,1950315,2009,12/30/2009 07:22:06 AM,42.019354319,-87.682572059,"(42.019354319, -87.682572059)"
-7290788,HR706967,12/26/2009 10:00:00 PM,019XX W THOMAS ST,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,1322,012,1,24,03,1163024,1907378,2009,01/08/2010 03:35:45 PM,41.901492461,-87.676636623,"(41.901492461, -87.676636623)"
-7289677,HR706823,12/26/2009 07:41:00 PM,001XX E 51ST ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0232,002,3,40,18,1178310,1871209,2009,12/26/2009 08:46:11 PM,41.801907483,-87.621591201,"(41.801907483, -87.621591201)"
-7297129,HR706696,12/26/2009 05:00:00 PM,043XX W CORTEZ ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1111,011,37,23,08B,1147080,1906678,2009,01/06/2010 09:38:54 AM,41.89989157,-87.735218964,"(41.89989157, -87.735218964)"
-7291011,HR708309,12/24/2009 07:30:00 PM,012XX N LARRABEE ST,0498,BATTERY,AGGRAVATED DOMESTIC BATTERY: HANDS/FIST/FEET SERIOUS INJURY,CHA HALLWAY/STAIRWELL/ELEVATOR,false,true,1822,018,27,8,04B,1172064,1908588,2009,12/30/2009 01:19:12 PM,41.904618077,-87.643396202,"(41.904618077, -87.643396202)"
-7304298,HS107712,12/24/2009 09:00:00 AM,019XX W VAN BUREN ST,0820,THEFT,$500 AND UNDER,"SCHOOL, PRIVATE, BUILDING",false,false,1211,012,2,28,06,1163527,1898211,2009,01/07/2010 10:04:22 AM,41.876326899,-87.675047366,"(41.876326899, -87.675047366)"
-7287904,HR704272,12/23/2009 06:00:00 PM,025XX W LEXINGTON ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),false,false,1135,011,2,28,07,1159349,1896653,2009,02/25/2010 01:13:00 PM,41.872138505,-87.690430439,"(41.872138505, -87.690430439)"
-7279564,HR693646,12/17/2009 12:47:09 PM,065XX S RICHMOND ST,2850,PUBLIC PEACE VIOLATION,BOMB THREAT,"SCHOOL, PUBLIC, BUILDING",false,false,0831,008,15,66,26,1157805,1860868,2009,01/13/2010 02:46:30 PM,41.773971644,-87.697072069,"(41.773971644, -87.697072069)"
-7278379,HR693267,12/16/2009 09:00:00 PM,007XX E 76TH ST,0810,THEFT,OVER $500,STREET,false,false,0624,006,6,69,06,1182804,1854751,2009,12/18/2009 07:14:08 AM,41.756641915,-87.60562088,"(41.756641915, -87.60562088)"
-7276805,HR691909,12/16/2009 09:30:00 AM,063XX S TALMAN AVE,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,0825,008,15,66,06,1159841,1862405,2009,12/17/2009 12:18:19 PM,41.778147814,-87.689566265,"(41.778147814, -87.689566265)"
-7276345,HR691563,12/16/2009 01:00:00 AM,041XX S WALLACE ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0935,009,11,61,18,1173025,1877358,2009,12/16/2009 07:52:05 AM,41.818899398,-87.640791567,"(41.818899398, -87.640791567)"
-7276714,HR691684,12/16/2009 12:00:00 AM,046XX S LAVERGNE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0814,008,23,56,14,1143770,1873237,2009,12/16/2009 12:07:09 PM,41.808187642,-87.74821378,"(41.808187642, -87.74821378)"
-7276086,HR691334,12/15/2009 07:10:00 PM,035XX W 65TH ST,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0823,008,15,66,08B,1153960,1861325,2009,12/19/2009 12:59:14 PM,41.775302919,-87.7111551,"(41.775302919, -87.7111551)"
-7275158,HR690323,12/14/2009 08:00:00 PM,008XX W GARFIELD BLVD,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),false,false,0934,009,20,61,07,1171801,1868423,2009,12/15/2009 11:45:32 AM,41.794407822,-87.645543932,"(41.794407822, -87.645543932)"
-7274203,HR689545,12/14/2009 11:30:00 AM,013XX N MAYFIELD AVE,031A,ROBBERY,ARMED: HANDGUN,STREET,false,false,2531,025,29,25,03,1136744,1908114,2009,12/17/2009 04:29:54 PM,41.904023598,-87.773149364,"(41.904023598, -87.773149364)"
-7273615,HR688761,12/14/2009 10:15:00 AM,096XX S MICHIGAN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,"SCHOOL, PUBLIC, BUILDING",true,false,0511,005,6,49,18,1178815,1840807,2009,12/14/2009 12:34:38 PM,41.718469551,-87.620662877,"(41.718469551, -87.620662877)"
-7272925,HR688266,12/14/2009 12:05:00 AM,010XX N WASHTENAW AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1311,012,1,24,18,1158239,1907110,2009,12/14/2009 12:56:38 AM,41.900856214,-87.694219793,"(41.900856214, -87.694219793)"
-7275122,HR688365,12/13/2009 10:30:00 PM,001XX N LEAVITT ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA APARTMENT,false,true,1332,012,2,28,08B,1161677,1901147,2009,12/30/2009 12:51:48 PM,41.884422276,-87.681758099,"(41.884422276, -87.681758099)"
-7272880,HR688147,12/13/2009 09:35:00 PM,046XX N BROADWAY,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,RESIDENCE PORCH/HALLWAY,false,false,2312,019,46,3,04A,1167920,1931080,2009,12/28/2009 02:10:44 PM,41.966427663,-87.657967351,"(41.966427663, -87.657967351)"
-7276737,HR686159,12/12/2009 01:26:00 PM,064XX W IRVING PARK RD,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,1632,016,38,17,06,1132450,1925932,2009,12/22/2009 10:49:47 AM,41.952994163,-87.788506711,"(41.952994163, -87.788506711)"
-7270852,HR685236,12/11/2009 10:00:00 PM,012XX W 109TH PL,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,2234,022,34,75,05,1169804,1832202,2009,01/14/2010 07:21:59 AM,41.695056044,-87.653915563,"(41.695056044, -87.653915563)"
-7270488,HR684673,12/11/2009 03:30:00 PM,061XX S KARLOV AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0813,008,13,65,26,1150133,1863459,2009,12/14/2009 11:40:00 AM,41.781234078,-87.725129264,"(41.781234078, -87.725129264)"
-7269798,HR683891,12/11/2009 07:10:00 AM,091XX S JEFFERY AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",PARKING LOT/GARAGE(NON.RESID.),false,false,0413,004,8,48,07,1191148,1844668,2009,01/22/2010 11:55:40 PM,41.728775495,-87.575367619,"(41.728775495, -87.575367619)"
-7269177,HR683412,12/10/2009 09:00:00 AM,008XX W SUPERIOR ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1323,012,27,24,14,1170326,1905298,2009,12/11/2009 09:11:35 AM,41.895628298,-87.649876604,"(41.895628298, -87.649876604)"
-7279960,HR695225,12/09/2009 09:00:00 AM,021XX N LINCOLN PARK WEST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,1814,018,43,7,06,1173868,1914703,2009,12/21/2009 02:47:51 PM,41.921357917,-87.636587183,"(41.921357917, -87.636587183)"
-7347642,HR679717,12/08/2009 01:15:00 PM,010XX N KEDVALE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1111,011,37,23,18,1148563,1906746,2009,02/06/2010 10:10:35 AM,41.900049668,-87.729770076,"(41.900049668, -87.729770076)"
-7266045,HR680349,12/08/2009 12:00:00 PM,032XX W ROOSEVELT RD,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,1134,011,24,29,06,1154754,1894563,2009,12/28/2009 01:34:38 PM,41.866496523,-87.707356659,"(41.866496523, -87.707356659)"
-7258829,HR672843,12/03/2009 11:30:00 AM,052XX N BERNARD ST,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,false,false,1712,017,39,13,26,1152479,1934770,2009,12/04/2009 10:51:53 AM,41.976873201,-87.71464354,"(41.976873201, -87.71464354)"
-7253044,HR667594,11/30/2009 03:40:00 PM,031XX N OSCEOLA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,true,2511,025,36,17,14,1125904,1920301,2009,12/02/2009 10:48:38 AM,41.937653689,-87.812696477,"(41.937653689, -87.812696477)"
-7282751,HR697208,11/30/2009 02:00:00 PM,069XX S LAFAYETTE AVE,0880,THEFT,PURSE-SNATCHING,SIDEWALK,false,false,0731,007,6,69,06,1176984,1859255,2009,12/24/2009 06:08:03 PM,41.769134515,-87.626814372,"(41.769134515, -87.626814372)"
-7253306,HR667494,11/30/2009 02:00:00 PM,095XX S VINCENNES AVE,0460,BATTERY,SIMPLE,STREET,false,false,2213,022,21,73,08B,1170553,1841685,2009,12/02/2009 11:41:01 AM,41.721062604,-87.65089808,"(41.721062604, -87.65089808)"
-7250105,HR665438,11/29/2009 01:39:00 AM,063XX S AUSTIN AVE,2022,NARCOTICS,POSS: COCAINE,PARKING LOT/GARAGE(NON.RESID.),true,false,0812,008,13,64,18,1137456,1862112,2009,11/29/2009 03:10:42 AM,41.777774319,-87.771639045,"(41.777774319, -87.771639045)"
-7248468,HR662974,11/27/2009 11:00:00 AM,033XX S WESTERN BLVD,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0913,009,12,59,04B,1160921,1882724,2009,12/22/2009 09:24:39 AM,41.833883444,-87.685045045,"(41.833883444, -87.685045045)"
-7248383,HR661591,11/26/2009 12:20:00 AM,053XX W FERDINAND ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,1523,015,37,25,08B,1140880,1902676,2009,11/30/2009 11:45:48 AM,41.889025957,-87.758090501,"(41.889025957, -87.758090501)"
-7246805,HR660868,11/25/2009 04:20:00 PM,007XX N MICHIGAN AVE,0860,THEFT,RETAIL THEFT,DRUG STORE,true,false,1833,018,42,8,06,1177332,1905641,2009,10/31/2014 03:20:56 PM,41.896413478,-87.624135037,"(41.896413478, -87.624135037)"
-7245073,HR659418,11/24/2009 07:25:00 PM,048XX W MONROE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1533,015,28,25,18,1143984,1899179,2009,11/24/2009 07:57:14 PM,41.879372103,-87.746779031,"(41.879372103, -87.746779031)"
-7244983,HR659140,11/24/2009 04:50:00 PM,007XX E 65TH ST,031A,ROBBERY,ARMED: HANDGUN,STREET,false,false,0321,003,20,42,03,1182285,1862036,2009,09/09/2011 06:12:43 AM,41.776644711,-87.60729754,"(41.776644711, -87.60729754)"
-7246093,HR660208,11/24/2009 04:00:00 AM,016XX N DRAKE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1422,014,26,23,07,1152604,1910820,2009,01/21/2010 10:59:38 AM,41.911150131,-87.714819388,"(41.911150131, -87.714819388)"
-7244176,HR656967,11/23/2009 11:50:00 AM,116XX S MORGAN ST,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,RESIDENCE,false,false,0524,005,34,53,04A,1171706,1827675,2009,02/08/2010 12:03:09 PM,41.682591849,-87.647083787,"(41.682591849, -87.647083787)"
-7242812,HR657319,11/23/2009 08:00:00 AM,001XX N MENARD AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1512,015,29,25,07,1137655,1900323,2009,06/30/2010 11:40:57 AM,41.882627735,-87.769990877,"(41.882627735, -87.769990877)"
-7236938,HR651848,11/19/2009 11:35:00 PM,015XX W 95TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2221,022,21,73,18,1167545,1841760,2009,11/20/2009 01:08:55 AM,41.72133332,-87.661913626,"(41.72133332, -87.661913626)"
-7235911,HR650603,11/19/2009 11:20:00 AM,001XX W LAKE ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA GARAGE / OTHER PROPERTY,true,false,0113,001,42,32,11,1175354,1901695,2009,11/20/2009 08:17:25 AM,41.885630077,-87.631518326,"(41.885630077, -87.631518326)"
-7246326,HR660344,11/18/2009 06:30:00 AM,020XX W JARVIS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2424,024,49,1,08B,1161586,1948817,2009,11/30/2009 02:49:55 PM,42.015233244,-87.680759426,"(42.015233244, -87.680759426)"
-7233030,HR648350,11/17/2009 11:00:00 PM,070XX S STATE ST,1330,CRIMINAL TRESPASS,TO LAND,GAS STATION,true,false,0322,003,6,69,26,1177555,1857928,2009,11/18/2009 05:47:10 AM,41.765480191,-87.624761441,"(41.765480191, -87.624761441)"
-7234541,HR647610,11/17/2009 05:30:00 PM,065XX W DIVERSEY AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,2511,025,36,19,08A,1132340,1917935,2009,11/20/2009 11:05:19 AM,41.931051417,-87.789097731,"(41.931051417, -87.789097731)"
-7232691,HR647899,11/17/2009 05:30:00 PM,013XX N PAULINA ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,1433,014,1,24,06,1164854,1909126,2009,11/18/2009 09:35:28 AM,41.906250459,-87.669865196,"(41.906250459, -87.669865196)"
-7234444,HR649318,11/16/2009 10:00:00 PM,015XX N FAIRFIELD AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1423,014,26,24,06,1157759,1910309,2009,11/19/2009 08:33:45 AM,41.909644337,-87.695895555,"(41.909644337, -87.695895555)"
-7252597,HR667486,11/15/2009 06:30:00 PM,061XX N BROADWAY,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,2433,024,48,77,03,1167222,1941117,2009,12/08/2009 02:10:04 PM,41.993984543,-87.660243774,"(41.993984543, -87.660243774)"
-7227574,HR642987,11/14/2009 01:48:00 PM,078XX S STATE ST,0331,ROBBERY,ATTEMPT: AGGRAVATED,SIDEWALK,true,false,0623,006,6,69,03,1177695,1852672,2009,11/15/2009 11:32:22 AM,41.751053963,-87.624407057,"(41.751053963, -87.624407057)"
-7228436,HR642327,11/14/2009 03:17:54 AM,063XX N GREENVIEW AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,false,2433,024,40,77,08B,1165109,1942513,2009,11/17/2009 04:46:33 PM,41.997860512,-87.667976348,"(41.997860512, -87.667976348)"
-7234329,HR642030,11/13/2009 10:20:00 PM,058XX W AUGUSTA BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1511,015,29,25,18,1137475,1906118,2009,12/02/2009 09:44:19 AM,41.898533198,-87.770512275,"(41.898533198, -87.770512275)"
-7235945,HR641994,11/13/2009 10:00:00 PM,010XX N KEDVALE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,STREET,false,false,1111,011,37,23,14,1148477,1906945,2009,11/20/2009 09:34:04 AM,41.900597404,-87.730080819,"(41.900597404, -87.730080819)"
-7224548,HR639798,11/12/2009 03:45:00 PM,003XX W 112TH ST,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,RESIDENTIAL YARD (FRONT/BACK),true,true,0522,005,34,49,04A,1175920,1830692,2009,11/24/2009 01:17:09 AM,41.690777811,-87.631568073,"(41.690777811, -87.631568073)"
-7222959,HR638785,11/12/2009 01:55:00 AM,027XX W DIVISION ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1423,014,26,24,18,1158002,1907908,2009,11/12/2009 03:15:57 AM,41.903050835,-87.695068504,"(41.903050835, -87.695068504)"
-7222224,HR637680,11/11/2009 11:10:00 AM,082XX S MARYLAND AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0631,006,8,44,18,1183372,1850743,2009,11/11/2009 01:01:26 PM,41.74563034,-87.603663856,"(41.74563034, -87.603663856)"
-7219131,HR634785,11/09/2009 04:00:00 PM,012XX N ASHLAND AVE,0870,THEFT,POCKET-PICKING,CTA BUS,false,false,1424,014,1,24,06,1165466,1908095,2009,11/10/2009 08:56:38 AM,41.903408313,-87.667646484,"(41.903408313, -87.667646484)"
-7225274,HR633615,11/09/2009 01:50:00 AM,058XX N MAGNOLIA AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2013,020,48,77,08B,1166932,1939230,2009,11/17/2009 07:33:57 AM,41.988812821,-87.661364976,"(41.988812821, -87.661364976)"
-19164,HR633105,11/08/2009 09:30:00 PM,007XX E 43RD ST,0110,HOMICIDE,FIRST DEGREE MURDER,AUTO,true,false,0222,002,4,38,01A,1181752,1876617,2009,10/31/2014 03:20:56 PM,41.816668562,-87.608800946,"(41.816668562, -87.608800946)"
-7219186,HR633349,11/08/2009 06:30:00 PM,034XX W MONROE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1123,011,28,27,14,1153664,1899365,2009,11/27/2009 10:04:57 PM,41.879695466,-87.711230415,"(41.879695466, -87.711230415)"
-7214857,HR629626,11/06/2009 02:00:00 PM,068XX S EMERALD AVE,2027,NARCOTICS,POSS: CRACK,ABANDONED BUILDING,true,false,0723,007,6,68,18,1172557,1859405,2009,11/06/2009 03:04:04 PM,41.769644778,-87.643037185,"(41.769644778, -87.643037185)"
-7220507,HR635975,11/05/2009 04:00:00 PM,014XX N CLARK ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1821,018,42,8,14,1175236,1909710,2009,11/11/2009 06:38:41 AM,41.9076263,-87.631710946,"(41.9076263, -87.631710946)"
-7213198,HR627951,11/05/2009 03:20:00 PM,0000X W CTA 69TH ST LN,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CTA PLATFORM,true,false,0731,007,6,69,26,1177341,1859091,2009,10/31/2014 03:20:56 PM,41.768676426,-87.625510735,"(41.768676426, -87.625510735)"
-7218871,HR627019,11/05/2009 01:30:00 AM,113XX S LANGLEY AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,0531,005,9,50,26,1182975,1829890,2009,11/14/2009 11:37:56 AM,41.688416391,-87.605764228,"(41.688416391, -87.605764228)"
-7212332,HR627274,11/05/2009 12:00:00 AM,024XX N MONITOR AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE-GARAGE,false,false,2515,025,30,19,14,1136931,1915933,2009,11/06/2009 10:15:30 AM,41.925476499,-87.772274566,"(41.925476499, -87.772274566)"
-7324274,HR626577,11/04/2009 06:00:00 PM,014XX N CICERO AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,2533,025,37,25,16,1144078,1908944,2009,01/25/2010 10:28:37 AM,41.906166627,-87.746188513,"(41.906166627, -87.746188513)"
-7211642,HR626177,11/03/2009 10:00:00 PM,006XX N LONG AVE,0620,BURGLARY,UNLAWFUL ENTRY,ABANDONED BUILDING,false,false,1524,015,37,25,05,1140165,1903818,2009,11/13/2009 04:22:27 PM,41.892172869,-87.760688311,"(41.892172869, -87.760688311)"
-7209092,HR623719,11/03/2009 10:48:00 AM,077XX S DR MARTIN LUTHER KING JR DR,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,0624,006,6,69,18,1180284,1853851,2009,11/03/2009 01:43:46 PM,41.75423034,-87.614883672,"(41.75423034, -87.614883672)"
-7205926,HR620751,11/01/2009 05:20:00 PM,026XX N ELSTON AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,1432,014,1,22,06,1161014,1917576,2009,11/02/2009 09:22:44 AM,41.929518494,-87.683735847,"(41.929518494, -87.683735847)"
-7205988,HR620794,11/01/2009 03:00:00 PM,071XX S TALMAN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,0831,008,18,66,14,1159902,1857180,2009,11/05/2009 07:58:25 AM,41.763808405,-87.689486033,"(41.763808405, -87.689486033)"
-7205283,HR619296,10/31/2009 08:45:00 PM,054XX N CLARK ST,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,2013,020,48,77,06,1165115,1935982,2009,11/03/2009 08:29:55 AM,41.979939136,-87.668140883,"(41.979939136, -87.668140883)"
-7285542,HR701248,10/31/2009 09:00:00 AM,006XX E BOWEN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0213,002,4,38,26,1181407,1877664,2009,12/26/2009 10:35:33 AM,41.819549586,-87.610034146,"(41.819549586, -87.610034146)"
-7200148,HR613568,10/28/2009 07:50:00 PM,046XX W NORTH AVE,0820,THEFT,$500 AND UNDER,SMALL RETAIL STORE,true,false,2533,025,37,25,06,1144852,1910260,2009,10/29/2009 09:31:15 AM,41.909763298,-87.743312036,"(41.909763298, -87.743312036)"
-7210962,HR625878,10/28/2009 10:00:00 AM,016XX S RACINE AVE,0820,THEFT,$500 AND UNDER,RESIDENCE PORCH/HALLWAY,false,false,1233,012,25,31,06,1168675,1892082,2009,11/05/2009 10:25:17 AM,41.859398532,-87.656323192,"(41.859398532, -87.656323192)"
-7259886,HR611788,10/27/2009 09:37:00 PM,034XX W FULTON BLVD,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1123,011,28,27,18,1153451,1901745,2009,01/25/2010 10:53:02 AM,41.886230665,-87.711949248,"(41.886230665, -87.711949248)"
-7197519,HR610684,10/27/2009 11:00:00 AM,010XX W GRAND AVE,0810,THEFT,OVER $500,STREET,false,false,1323,012,27,24,06,1169120,1903602,2009,10/28/2009 09:22:37 AM,41.891000638,-87.654355231,"(41.891000638, -87.654355231)"
-7196546,HR609899,10/26/2009 09:02:00 PM,007XX S CICERO AVE,1544,SEX OFFENSE,SEXUAL EXPLOITATION OF A CHILD,CTA TRAIN,false,false,1533,015,24,25,17,1144467,1896304,2009,12/01/2009 07:43:23 PM,41.871473677,-87.745077862,"(41.871473677, -87.745077862)"
-7196382,HR609824,10/26/2009 08:00:00 PM,007XX N LONG AVE,2027,NARCOTICS,POSS: CRACK,GAS STATION,true,false,1524,015,37,25,18,1140134,1904700,2009,10/26/2009 08:41:55 PM,41.894593752,-87.760780546,"(41.894593752, -87.760780546)"
-7196339,HR609763,10/26/2009 07:30:00 PM,059XX W MADISON ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1513,015,29,25,26,1136963,1899445,2009,10/26/2009 07:56:27 PM,41.880230823,-87.772553027,"(41.880230823, -87.772553027)"
-7193989,HR606615,10/25/2009 01:30:00 AM,051XX S CARPENTER ST,0460,BATTERY,SIMPLE,APARTMENT,false,false,0934,009,16,61,08B,1170141,1870871,2009,11/02/2009 08:06:15 AM,41.801161689,-87.651559891,"(41.801161689, -87.651559891)"
-7194113,HR606808,10/25/2009 01:00:00 AM,002XX W ONTARIO ST,0890,THEFT,FROM BUILDING,BAR OR TAVERN,false,false,1831,018,42,8,06,1174358,1904476,2009,10/26/2009 12:30:39 PM,41.893283594,-87.635092697,"(41.893283594, -87.635092697)"
-7201895,HR615093,10/24/2009 11:00:00 PM,061XX S KIMBARK AVE,0890,THEFT,FROM BUILDING,OTHER,false,false,0314,003,20,42,06,1185651,1864726,2009,10/30/2009 05:05:00 AM,41.783947656,-87.594873406,"(41.783947656, -87.594873406)"
-7193953,HR604913,10/23/2009 10:30:00 PM,079XX S HERMITAGE AVE,0340,ROBBERY,ATTEMPT: STRONGARM-NO WEAPON,SIDEWALK,false,false,0611,006,21,71,03,1166094,1852224,2009,11/04/2009 12:06:58 AM,41.750079028,-87.6669316,"(41.750079028, -87.6669316)"
-7283601,HR699833,10/23/2009 10:54:00 AM,102XX S WESTERN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2213,022,19,72,26,1162236,1836592,2009,12/24/2009 10:02:11 AM,41.707263441,-87.681502861,"(41.707263441, -87.681502861)"
-7191569,HR601876,10/22/2009 01:00:00 PM,071XX S EAST END AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0324,003,5,43,14,1188812,1857852,2009,10/28/2009 09:07:04 AM,41.765009763,-87.583503925,"(41.765009763, -87.583503925)"
-7201159,HR601630,10/22/2009 11:12:05 AM,073XX S WESTERN AVE,0460,BATTERY,SIMPLE,OTHER,false,false,0835,008,18,66,08B,1161609,1855755,2009,10/31/2009 09:17:27 AM,41.759862766,-87.68326893,"(41.759862766, -87.68326893)"
-7190045,HR601391,10/22/2009 08:35:00 AM,003XX W OAK ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,CHA PARKING LOT/GROUNDS,true,false,1823,018,27,8,18,1173426,1907052,2009,10/22/2009 12:16:42 PM,41.900373043,-87.638438935,"(41.900373043, -87.638438935)"
-7189089,HR600955,10/21/2009 09:50:00 PM,003XX E NORTH WATER ST,1505,PROSTITUTION,CALL OPERATION,HOTEL/MOTEL,true,false,1834,018,42,8,16,1178389,1903114,2009,10/21/2009 10:34:36 PM,41.889455231,-87.620330047,"(41.889455231, -87.620330047)"
-7187528,HR599244,10/20/2009 10:00:00 PM,075XX S PAULINA ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,true,0611,006,17,71,14,1166365,1854485,2009,10/23/2009 07:02:52 AM,41.756277765,-87.665874257,"(41.756277765, -87.665874257)"
-7187593,HR597985,10/20/2009 11:30:00 AM,069XX N CLARK ST,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,2424,024,49,1,06,1163469,1946226,2009,11/10/2009 03:57:39 PM,42.008083886,-87.673904109,"(42.008083886, -87.673904109)"
-7186149,HR597603,10/20/2009 09:45:00 AM,051XX W BELMONT AVE,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,PARKING LOT/GARAGE(NON.RESID.),false,false,1634,016,30,15,04A,1141278,1920822,2009,10/21/2009 09:15:19 AM,41.93881325,-87.75618043,"(41.93881325, -87.75618043)"
-7185743,HR597510,10/19/2009 08:30:00 PM,019XX W ELLEN ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1424,014,1,24,14,1163500,1908877,2009,10/21/2009 09:12:16 AM,41.905595815,-87.674845963,"(41.905595815, -87.674845963)"
-7185273,HR595501,10/19/2009 12:53:00 AM,015XX S WABASH AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),true,false,0132,001,3,33,06,1176976,1892782,2009,10/20/2009 07:39:01 AM,41.861135721,-87.625831873,"(41.861135721, -87.625831873)"
-7192134,HR603739,10/16/2009 09:00:00 AM,031XX S BENSON ST,0810,THEFT,OVER $500,STREET,false,false,0924,009,11,60,06,1167464,1884185,2009,10/25/2009 01:09:33 PM,41.837754559,-87.660995333,"(41.837754559, -87.660995333)"
-7178726,HR588961,10/15/2009 11:50:00 AM,067XX S WOOD ST,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",false,false,0725,007,15,67,06,1165549,1859990,2009,10/15/2009 06:32:34 PM,41.771401571,-87.668708855,"(41.771401571, -87.668708855)"
-7178871,HR588427,10/14/2009 07:30:00 PM,033XX W 61ST PL,0810,THEFT,OVER $500,STREET,false,false,0823,008,15,66,06,1155014,1863683,2009,10/15/2009 06:28:12 PM,41.781752636,-87.707228288,"(41.781752636, -87.707228288)"
-7175880,HR586575,10/14/2009 12:00:00 AM,046XX W DIVERSEY AVE,502R,OTHER OFFENSE,VEHICLE TITLE/REG OFFENSE,PARKING LOT/GARAGE(NON.RESID.),true,false,2521,025,31,19,26,1145125,1918176,2009,10/31/2014 03:20:56 PM,41.931480437,-87.74210865,"(41.931480437, -87.74210865)"
-7174357,HR585006,10/13/2009 08:00:00 AM,0000X S SPAULDING AVE,1565,SEX OFFENSE,INDECENT SOLICITATION/CHILD,STREET,false,false,1124,011,28,27,17,1154451,1899551,2009,11/22/2009 08:30:10 AM,41.880190179,-87.708335674,"(41.880190179, -87.708335674)"
-7175650,HR584651,10/12/2009 09:40:00 PM,096XX S YALE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0511,005,21,49,05,1176277,1840919,2009,11/12/2009 10:55:54 PM,41.71883417,-87.629955264,"(41.71883417, -87.629955264)"
-7173650,HR584046,10/12/2009 02:46:00 PM,053XX S PULASKI RD,0850,THEFT,ATTEMPT THEFT,PARKING LOT/GARAGE(NON.RESID.),false,false,0815,008,23,62,06,1150568,1868904,2009,10/31/2014 03:20:56 PM,41.796167541,-87.723392818,"(41.796167541, -87.723392818)"
-7550800,HR583027,10/11/2009 08:58:56 PM,025XX S HARDING AVE,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,false,false,1013,010,22,30,26,1150496,1886573,2009,06/25/2010 11:15:30 AM,41.844655129,-87.723196751,"(41.844655129, -87.723196751)"
-7171202,HR581414,10/10/2009 05:45:00 PM,017XX E 69TH ST,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,true,false,0332,,5,43,26,,,2009,10/11/2009 07:10:33 AM,,,
-7171086,HR581273,10/10/2009 04:00:00 PM,065XX S LOWE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0723,,20,68,14,,,2009,10/11/2009 12:36:18 PM,,,
-7168252,HR577957,10/08/2009 03:00:00 PM,076XX S CICERO AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,0833,008,13,65,14,1145766,1853738,2009,10/09/2009 10:31:20 AM,41.75464162,-87.741385158,"(41.75464162, -87.741385158)"
-7168132,HR577486,10/08/2009 09:00:00 AM,024XX S MILLARD AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,ALLEY,false,false,1013,010,22,30,14,1152380,1887690,2009,10/09/2009 07:42:10 AM,41.847683377,-87.716253245,"(41.847683377, -87.716253245)"
-7305375,HR576128,10/07/2009 04:30:00 PM,041XX W WILCOX ST,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1115,011,28,26,18,1148667,1898970,2009,01/26/2010 09:18:04 AM,41.878709471,-87.729589037,"(41.878709471, -87.729589037)"
-7165480,HR574742,10/06/2009 08:00:00 AM,064XX S FRANCISCO AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0823,008,15,66,05,1158197,1861671,2009,10/11/2009 08:29:57 AM,41.776167226,-87.695613235,"(41.776167226, -87.695613235)"
-7161498,HR571224,10/04/2009 07:30:00 PM,040XX E 134TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0433,004,10,55,08B,1204506,1816685,2009,10/07/2009 06:39:20 AM,41.651654577,-87.527394495,"(41.651654577, -87.527394495)"
-7161300,HR571038,10/04/2009 05:10:00 PM,017XX N KIMBALL AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,1422,014,26,22,03,1153379,1911733,2009,11/02/2009 10:40:24 AM,41.913640113,-87.711947986,"(41.913640113, -87.711947986)"
-7163817,HR571761,10/04/2009 05:00:00 PM,072XX S EVANS AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,0323,003,6,69,07,1182442,1856970,2009,10/22/2009 09:43:35 AM,41.762739474,-87.606878855,"(41.762739474, -87.606878855)"
-7163421,HR570438,10/04/2009 09:25:00 AM,120XX S UNION AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENTIAL YARD (FRONT/BACK),false,true,0523,005,34,53,08B,1173779,1825068,2009,10/11/2009 07:37:20 AM,41.67539226,-87.639572205,"(41.67539226, -87.639572205)"
-7160570,HR570007,10/03/2009 11:44:00 PM,009XX N LAWNDALE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1112,011,27,23,06,1151577,1906239,2009,10/06/2009 10:47:49 AM,41.898599689,-87.718712841,"(41.898599689, -87.718712841)"
-7160297,HR569533,10/03/2009 05:20:00 PM,061XX S EBERHART AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,0313,003,20,42,08B,1180673,1864520,2009,10/04/2009 04:43:07 AM,41.783498241,-87.613130734,"(41.783498241, -87.613130734)"
-7241496,HR609079,10/02/2009 07:00:00 PM,078XX S EMERALD AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0621,006,17,71,26,1172700,1852863,2009,11/25/2009 08:11:04 AM,41.751689573,-87.642705533,"(41.751689573, -87.642705533)"
-7159296,HR564621,09/30/2009 07:00:00 PM,085XX S ABERDEEN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,true,true,0613,006,21,71,08B,1170430,1848375,2009,10/08/2009 08:03:33 AM,41.739423594,-87.651154397,"(41.739423594, -87.651154397)"
-7155683,HR564236,09/29/2009 08:00:00 PM,068XX S CALUMET AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0322,003,20,69,14,1179704,1859531,2009,10/01/2009 05:58:02 AM,41.769830142,-87.61683579,"(41.769830142, -87.61683579)"
-7153949,HR562886,09/29/2009 06:45:00 PM,058XX S PEORIA ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,true,false,0712,007,16,68,14,1171350,1865939,2009,09/30/2009 11:25:45 AM,41.787601339,-87.647270418,"(41.787601339, -87.647270418)"
-7168263,HR562792,09/29/2009 03:00:00 PM,011XX E 46TH ST,0810,THEFT,OVER $500,STREET,false,false,2123,002,4,39,06,1184588,1874697,2009,10/12/2009 12:07:22 PM,41.811333856,-87.598458182,"(41.811333856, -87.598458182)"
-7153087,HR561869,09/29/2009 07:30:00 AM,079XX S RACINE AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0612,006,21,71,08B,1169654,1852337,2009,10/02/2009 12:38:59 PM,41.750312711,-87.653882912,"(41.750312711, -87.653882912)"
-7151828,HR560700,09/28/2009 01:00:00 PM,006XX S INDEPENDENCE BLVD,0820,THEFT,$500 AND UNDER,APARTMENT,false,false,1133,011,24,26,06,1151161,1896816,2009,09/29/2009 10:07:40 AM,41.872750148,-87.720487967,"(41.872750148, -87.720487967)"
-7150036,HR559757,09/26/2009 10:30:00 PM,026XX W EVERGREEN AVE,0810,THEFT,OVER $500,STREET,false,false,1423,014,26,24,06,1158498,1908836,2009,09/28/2009 09:47:27 AM,41.905587203,-87.693221159,"(41.905587203, -87.693221159)"
-7149017,HR557904,09/26/2009 05:54:08 PM,008XX W LAKESIDE PL,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,true,false,2312,019,46,3,14,1169850,1931718,2009,09/29/2009 08:48:17 AM,41.968136389,-87.65085242,"(41.968136389, -87.65085242)"
-7149066,HR557476,09/26/2009 12:30:00 PM,042XX N PAULINA ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1922,019,47,6,06,1164421,1928367,2009,09/28/2009 09:32:50 AM,41.959058038,-87.67090967,"(41.959058038, -87.67090967)"
-7146921,HR555371,09/24/2009 05:30:00 PM,009XX N HOMAN AVE,0610,BURGLARY,FORCIBLE ENTRY,OTHER,false,false,1121,011,27,23,05,1153573,1906159,2009,01/07/2010 08:31:30 PM,41.898340689,-87.711383739,"(41.898340689, -87.711383739)"
-7145292,HR553881,09/24/2009 12:00:00 PM,064XX S COTTAGE GROVE AVE,0890,THEFT,FROM BUILDING,GROCERY FOOD STORE,false,false,0312,003,20,42,06,1182637,1862170,2009,09/25/2009 06:20:25 AM,41.777004258,-87.606002983,"(41.777004258, -87.606002983)"
-7139831,HR549240,09/21/2009 03:30:00 PM,003XX E HURON ST,0890,THEFT,FROM BUILDING,OTHER,false,false,1834,018,42,8,06,1178708,1905187,2009,09/22/2009 12:17:53 PM,41.895136359,-87.61909519,"(41.895136359, -87.61909519)"
-7139255,HR548582,09/20/2009 01:57:00 PM,054XX N SHERIDAN RD,0820,THEFT,$500 AND UNDER,OTHER,false,false,2023,020,48,77,06,1168611,1936471,2009,09/22/2009 09:39:15 AM,41.981205746,-87.655269782,"(41.981205746, -87.655269782)"
-7146043,HR546124,09/19/2009 10:30:00 PM,084XX S MACKINAW AVE,4510,OTHER OFFENSE,PROBATION VIOLATION,RESIDENCE,true,false,0424,004,10,46,26,1199975,1849722,2009,09/26/2009 04:51:06 AM,41.742426198,-87.542862985,"(41.742426198, -87.542862985)"
-7136645,HR545633,09/19/2009 04:45:00 PM,064XX S LONG AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE PORCH/HALLWAY,false,true,0813,008,13,64,08B,1141554,1861393,2009,09/26/2009 10:28:18 AM,41.775726821,-87.756633079,"(41.775726821, -87.756633079)"
-7137630,HR546977,09/19/2009 02:56:00 PM,023XX W 22ND PL,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,ATM (AUTOMATIC TELLER MACHINE),false,false,1034,010,25,31,11,1161062,1888972,2009,10/11/2009 10:39:39 AM,41.851025707,-87.684354452,"(41.851025707, -87.684354452)"
-7135559,HR544416,09/18/2009 09:45:00 PM,046XX N MILWAUKEE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,1623,016,45,15,03,1140804,1930049,2009,09/24/2009 06:28:56 PM,41.964141739,-87.757694751,"(41.964141739, -87.757694751)"
-7133776,HR542121,09/17/2009 05:30:00 PM,120XX S EMERALD AVE,033A,ROBBERY,ATTEMPT: ARMED-HANDGUN,STREET,false,false,0523,005,34,53,03,1173378,1825210,2009,10/09/2009 08:08:27 AM,41.675790781,-87.641035766,"(41.675790781, -87.641035766)"
-7133203,HR542102,09/16/2009 09:10:00 PM,019XX W 103RD ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,2213,022,19,72,06,1165288,1836401,2009,09/18/2009 07:43:33 AM,41.706675367,-87.670331753,"(41.706675367, -87.670331753)"
-7133849,HR537957,09/15/2009 11:25:00 AM,003XX N LAKE SHORE DR SB,0320,ROBBERY,STRONGARM - NO WEAPON,PARK PROPERTY,false,false,0124,001,42,32,03,1180048,1901941,2009,09/27/2009 01:52:40 PM,41.886198476,-87.61427369,"(41.886198476, -87.61427369)"
-7128697,HR537805,09/15/2009 10:45:00 AM,074XX N WOLCOTT AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,2424,024,49,1,06,1162464,1949572,2009,09/16/2009 07:37:18 AM,42.017286567,-87.677507416,"(42.017286567, -87.677507416)"
-7129118,HR538192,09/13/2009 10:39:00 PM,035XX W 85TH ST,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,false,true,0834,008,18,70,26,1154439,1848041,2009,09/17/2009 09:00:36 AM,41.73883995,-87.709752111,"(41.73883995, -87.709752111)"
-7127832,HR535776,09/13/2009 10:00:00 PM,009XX W BELMONT AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESTAURANT,false,false,1924,019,44,6,05,1169516,1921478,2009,09/27/2009 01:46:40 PM,41.940044754,-87.65237968,"(41.940044754, -87.65237968)"
-7129569,HR538404,09/12/2009 08:00:00 PM,050XX N NORTHWEST HWY,0820,THEFT,$500 AND UNDER,OTHER RAILROAD PROP / TRAIN DEPOT,false,false,1623,016,45,11,06,1139381,1932848,2009,09/16/2009 10:40:28 AM,41.971848553,-87.762858251,"(41.971848553, -87.762858251)"
-7124329,HR533650,09/12/2009 05:15:00 PM,002XX W 103RD ST,2022,NARCOTICS,POSS: COCAINE,PARKING LOT/GARAGE(NON.RESID.),true,false,0512,005,34,49,18,1176656,1836605,2009,09/12/2009 08:03:15 PM,41.706987453,-87.628696428,"(41.706987453, -87.628696428)"
-7123261,HR531878,09/11/2009 05:00:00 PM,112XX S AVENUE N,1780,OFFENSE INVOLVING CHILDREN,OTHER OFFENSE,RESIDENCE,false,true,0433,004,10,52,26,1201271,1830817,2009,09/16/2009 05:50:09 PM,41.690516606,-87.538753814,"(41.690516606, -87.538753814)"
-7122494,HR531114,09/11/2009 12:20:00 PM,026XX N HARDING AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE PORCH/HALLWAY,true,true,2524,025,30,22,08B,1149637,1917495,2009,09/12/2009 10:41:55 AM,41.929525138,-87.725545424,"(41.929525138, -87.725545424)"
-7123873,HR532901,09/11/2009 12:00:00 PM,014XX N DAYTON ST,0810,THEFT,OVER $500,STREET,false,false,1822,018,32,8,06,1170372,1909761,2009,09/14/2009 08:10:41 AM,41.907874031,-87.649577011,"(41.907874031, -87.649577011)"
-7121797,HR530227,09/10/2009 08:30:00 PM,049XX N MILWAUKEE AVE,2850,PUBLIC PEACE VIOLATION,BOMB THREAT,OTHER,false,false,1623,016,45,11,26,1139458,1932237,2009,12/14/2009 09:34:14 AM,41.97017051,-87.762590075,"(41.97017051, -87.762590075)"
-7120885,HR529971,09/10/2009 10:00:00 AM,110XX S AVENUE F,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE-GARAGE,false,false,0433,004,10,52,14,1203474,1832389,2009,09/11/2009 07:51:12 AM,41.694774263,-87.530635093,"(41.694774263, -87.530635093)"
-7126214,HR526933,09/08/2009 11:43:00 PM,079XX S ESSEX AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,VEHICLE NON-COMMERCIAL,true,false,0422,004,7,46,15,1194201,1852503,2009,09/15/2009 05:10:28 AM,41.750201087,-87.56392739,"(41.750201087, -87.56392739)"
-7117072,HR526080,09/08/2009 03:00:00 PM,066XX S GREENWOOD AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0321,003,5,42,03,1184329,1860863,2009,09/16/2009 08:12:40 PM,41.773378294,-87.599841094,"(41.773378294, -87.599841094)"
-7117404,HR526834,09/08/2009 09:50:00 AM,008XX W BELDEN AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1812,018,43,7,05,1170081,1915452,2009,09/20/2009 02:33:19 PM,41.9234968,-87.650479533,"(41.9234968, -87.650479533)"
-7115816,HR525217,09/07/2009 09:00:00 PM,028XX W GRACE ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1733,017,33,16,06,1156574,1925163,2009,09/09/2009 10:39:39 AM,41.950428894,-87.699845583,"(41.950428894, -87.699845583)"
-7115286,HR524685,09/07/2009 07:05:00 PM,018XX W 21ST ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ABANDONED BUILDING,true,false,1223,012,25,31,18,1164276,1890141,2009,09/07/2009 07:45:49 PM,41.854166296,-87.672525371,"(41.854166296, -87.672525371)"
-7115090,HR524223,09/07/2009 12:55:00 PM,002XX W 87TH ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,0634,006,21,44,06,1176402,1847173,2009,09/08/2009 06:14:43 AM,41.735993165,-87.629310087,"(41.735993165, -87.629310087)"
-7114987,HR524146,09/07/2009 12:15:00 PM,057XX W NORTH AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,2531,025,29,25,16,1137798,1910015,2009,09/07/2009 01:08:44 PM,41.909221217,-87.769231773,"(41.909221217, -87.769231773)"
-7115524,HR524903,09/06/2009 09:00:00 PM,065XX S BISHOP ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0725,007,17,67,06,1167746,1861500,2009,09/11/2009 08:22:26 AM,41.775498331,-87.660612102,"(41.775498331, -87.660612102)"
-7125103,HR534474,09/06/2009 03:00:00 PM,027XX N HAMLIN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,2524,025,35,22,14,1150536,1918155,2009,09/14/2009 10:21:34 AM,41.9313187,-87.722224545,"(41.9313187, -87.722224545)"
-7120732,HR529621,09/06/2009 01:37:00 PM,095XX S OAKLEY AVE,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,false,false,2213,022,19,72,26,1162766,1841014,2009,09/14/2009 11:45:28 AM,41.719387121,-87.679439008,"(41.719387121, -87.679439008)"
-7184093,HR518720,09/03/2009 09:40:00 PM,071XX S ROCKWELL ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0831,008,18,66,18,1160234,1857245,2009,11/04/2009 09:42:22 AM,41.763979949,-87.688267384,"(41.763979949, -87.688267384)"
-7119717,HR518764,09/03/2009 12:00:00 AM,088XX S COMMERCIAL AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0423,004,10,46,16,1197632,1847130,2009,09/12/2009 08:35:09 AM,41.735372288,-87.551533824,"(41.735372288, -87.551533824)"
-7116271,HR516854,09/02/2009 08:30:00 PM,100XX W OHARE ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,AIRPORT/AIRCRAFT,true,false,1651,016,41,76,26,1100635,1934208,2009,09/10/2009 01:11:02 PM,41.976200173,-87.905312411,"(41.976200173, -87.905312411)"
-7104551,HR513788,09/01/2009 02:00:00 AM,046XX W ADAMS ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1113,011,28,25,26,1145217,1898675,2009,09/01/2009 02:40:39 AM,41.877965854,-87.742264346,"(41.877965854, -87.742264346)"
-7104091,HR512775,08/31/2009 02:20:00 PM,017XX W TOUHY AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENTIAL YARD (FRONT/BACK),false,false,2423,024,49,1,26,1163628,1947934,2009,09/03/2009 12:08:15 PM,42.012767312,-87.673270659,"(42.012767312, -87.673270659)"
-7111892,HR520178,08/29/2009 05:00:00 PM,003XX W NORMAL PKWY,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0722,007,6,68,05,1175115,1860133,2009,09/06/2009 12:42:24 PM,41.771585772,-87.633639023,"(41.771585772, -87.633639023)"
-7100861,HR509503,08/29/2009 11:55:00 AM,005XX N PINE AVE,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,true,false,1523,015,37,25,26,1139401,1902948,2009,08/30/2009 08:19:46 AM,41.889799438,-87.763515416,"(41.889799438, -87.763515416)"
-7098184,HR506360,08/27/2009 03:10:00 PM,053XX W FULLERTON AVE,0560,ASSAULT,SIMPLE,SIDEWALK,true,true,2515,025,37,19,08A,1140449,1915402,2009,08/28/2009 08:44:53 AM,41.923955485,-87.759360607,"(41.923955485, -87.759360607)"
-7164846,HR574301,08/27/2009 03:00:00 PM,086XX W FOSTER AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,1614,016,41,76,26,1117493,1933366,2009,10/08/2009 11:41:03 AM,41.973641746,-87.843334694,"(41.973641746, -87.843334694)"
-7149458,HR505280,08/26/2009 10:00:00 PM,102XX S LA SALLE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0511,005,9,49,18,1177039,1836942,2009,09/27/2009 03:11:08 PM,41.70790362,-87.627283774,"(41.70790362, -87.627283774)"
-7096060,HR504592,08/26/2009 02:30:00 PM,083XX S EXCHANGE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0424,004,10,46,18,1197321,1850481,2009,08/26/2009 03:42:47 PM,41.744575439,-87.552561797,"(41.744575439, -87.552561797)"
-7095666,HR504350,08/25/2009 06:00:00 PM,047XX W WASHINGTON BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1113,011,28,25,14,1144621,1900117,2009,08/26/2009 01:45:54 PM,41.881934118,-87.744416407,"(41.881934118, -87.744416407)"
-7094118,HR502715,08/25/2009 03:00:00 PM,100XX S UNION AVE,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,2232,022,34,73,03,1173363,1838490,2009,10/03/2009 08:40:57 AM,41.71223348,-87.640699803,"(41.71223348, -87.640699803)"
-7093240,HR502046,08/25/2009 08:00:00 AM,012XX S LAWNDALE AVE,1330,CRIMINAL TRESPASS,TO LAND,APARTMENT,true,false,1011,010,24,29,26,1151866,1894048,2009,08/25/2009 12:30:06 PM,41.865140603,-87.717972421,"(41.865140603, -87.717972421)"
-7093669,HR502298,08/24/2009 10:00:00 PM,026XX W 15TH ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1023,010,28,29,06,1158683,1892657,2009,08/25/2009 12:30:33 PM,41.861186742,-87.692985048,"(41.861186742, -87.692985048)"
-7092171,HR499278,08/23/2009 01:50:00 PM,015XX N LONG AVE,0460,BATTERY,SIMPLE,STREET,false,false,2532,025,37,25,08B,1140098,1910043,2009,08/27/2009 11:27:52 PM,41.909256234,-87.760781797,"(41.909256234, -87.760781797)"
-7088471,HR496485,08/21/2009 07:30:00 AM,080XX S ESSEX AVE,0820,THEFT,$500 AND UNDER,DRIVEWAY - RESIDENTIAL,false,false,0422,004,7,46,06,1194204,1852336,2009,08/23/2009 09:53:00 AM,41.749742752,-87.56392187,"(41.749742752, -87.56392187)"
-7087058,HR494700,08/20/2009 05:56:00 PM,013XX S CANAL ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,0131,001,2,28,06,1173296,1893975,2009,08/25/2009 08:22:28 AM,41.864491821,-87.639304867,"(41.864491821, -87.639304867)"
-7086373,HR494502,08/19/2009 09:36:00 PM,069XX S KIMBARK AVE,5000,OTHER OFFENSE,OTHER CRIME AGAINST PERSON,RESIDENCE,false,false,0321,003,5,69,26,1185886,1859457,2009,08/21/2009 04:47:42 PM,41.769483523,-87.594177863,"(41.769483523, -87.594177863)"
-7096064,HR492730,08/19/2009 03:00:00 PM,027XX W LOGAN BLVD,0810,THEFT,OVER $500,STREET,false,false,1431,014,35,22,06,1157683,1917132,2009,08/27/2009 09:53:44 AM,41.928368724,-87.695988469,"(41.928368724, -87.695988469)"
-7084042,HR492083,08/19/2009 10:45:00 AM,057XX S ASHLAND AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,0715,007,15,67,18,1166607,1866849,2009,08/19/2009 01:02:11 PM,41.790201031,-87.66463506,"(41.790201031, -87.66463506)"
-7082157,HR490401,08/18/2009 01:35:00 PM,047XX W NORTH AVE,1330,CRIMINAL TRESPASS,TO LAND,RESTAURANT,true,false,2533,025,37,25,26,1144231,1910168,2009,08/19/2009 11:14:59 AM,41.90952254,-87.745595672,"(41.90952254, -87.745595672)"
-7080633,HR489273,08/17/2009 08:05:00 PM,104XX S GREEN ST,0560,ASSAULT,SIMPLE,RESIDENCE,true,true,2233,022,34,73,08A,1172455,1835554,2009,08/18/2009 05:03:25 AM,41.704196653,-87.644111217,"(41.704196653, -87.644111217)"
-7079280,HR487541,08/16/2009 08:10:00 PM,024XX W 46TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,0914,009,12,58,18,1160865,1874148,2009,08/16/2009 09:57:46 PM,41.81035105,-87.685487789,"(41.81035105, -87.685487789)"
-7078973,HR486788,08/16/2009 12:00:00 PM,074XX S WABASH AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0323,003,6,69,08B,1177968,1855660,2009,08/17/2009 06:07:29 AM,41.759247201,-87.623316291,"(41.759247201, -87.623316291)"
-7127290,HR536788,08/16/2009 09:00:00 AM,016XX N CENTRAL AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE PORCH/HALLWAY,false,false,2532,025,37,25,06,1138840,1910438,2009,09/22/2009 04:18:40 PM,41.91036311,-87.765393586,"(41.91036311, -87.765393586)"
-7179548,HR483776,08/14/2009 04:30:00 PM,099XX S PRINCETON AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,0511,005,9,49,05,1176080,1839026,2009,10/21/2009 07:45:09 AM,41.713643932,-87.630733386,"(41.713643932, -87.630733386)"
-7067128,HR474919,08/09/2009 02:30:00 PM,008XX E 59TH ST,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,2133,002,5,41,06,1183059,1866116,2009,08/10/2009 11:48:46 AM,41.787822632,-87.604333316,"(41.787822632, -87.604333316)"
-7068509,HR475894,08/09/2009 09:30:00 AM,076XX S CICERO AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,SMALL RETAIL STORE,false,false,0833,008,13,65,26,1145766,1853738,2009,08/25/2009 07:28:26 PM,41.75464162,-87.741385158,"(41.75464162, -87.741385158)"
-7066332,HR473747,08/08/2009 10:40:00 PM,057XX W AUGUSTA BLVD,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1511,015,29,25,14,1138138,1906136,2009,08/10/2009 09:07:12 AM,41.898570631,-87.768076643,"(41.898570631, -87.768076643)"
-7065321,HR470275,08/07/2009 10:15:00 AM,079XX S WOOD ST,1330,CRIMINAL TRESPASS,TO LAND,APARTMENT,true,false,0611,006,21,71,26,1165767,1852105,2009,08/09/2009 07:44:45 AM,41.749759419,-87.668133247,"(41.749759419, -87.668133247)"
-7069534,HR469816,08/06/2009 11:20:00 PM,072XX S HALSTED ST,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,SIDEWALK,false,false,0732,007,17,68,04B,1172266,1856616,2009,09/14/2009 07:11:46 PM,41.761997827,-87.644185767,"(41.761997827, -87.644185767)"
-7062844,HR469570,08/06/2009 09:15:00 PM,008XX N HUDSON AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1823,018,27,8,18,1172994,1905886,2009,08/06/2009 10:01:27 PM,41.897183065,-87.640060284,"(41.897183065, -87.640060284)"
-7062494,HR469007,08/06/2009 04:05:00 PM,050XX W JACKSON BLVD,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,1533,015,28,25,18,1143050,1898165,2009,08/06/2009 04:41:09 PM,41.876607026,-87.750233867,"(41.876607026, -87.750233867)"
-7061517,HR468330,08/06/2009 08:19:00 AM,041XX N MC VICKER AVE,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,RESIDENCE,false,false,1624,016,38,15,06,1135308,1927230,2009,08/12/2009 08:32:46 AM,41.956505668,-87.777969416,"(41.956505668, -87.777969416)"
-7061123,HR467935,08/05/2009 10:00:00 PM,038XX W MAYPOLE AVE,1661,GAMBLING,GAME/DICE,SIDEWALK,true,false,1122,,28,26,19,,,2009,08/05/2009 11:30:39 PM,,,
-7060900,HR467371,08/05/2009 04:30:00 PM,035XX S RHODES AVE,0560,ASSAULT,SIMPLE,POLICE FACILITY/VEH PARKING LOT,false,false,0212,002,4,35,08A,1180131,1881331,2009,08/06/2009 10:29:05 AM,41.829641493,-87.614602448,"(41.829641493, -87.614602448)"
-7066519,HR468511,08/04/2009 08:30:00 AM,032XX N LAKE SHORE DR,0460,BATTERY,SIMPLE,CTA BUS,true,false,2332,019,44,6,08B,1173048,1921825,2009,10/30/2009 11:23:12 AM,41.94091921,-87.63938824,"(41.94091921, -87.63938824)"
-7080357,HR488499,08/04/2009 12:01:00 AM,039XX W 26TH ST,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,MEDICAL/DENTAL OFFICE,false,false,1013,010,22,30,06,1150403,1886470,2009,09/28/2009 09:35:00 AM,41.844374296,-87.723540734,"(41.844374296, -87.723540734)"
-7058453,HR462677,08/03/2009 12:25:00 AM,033XX N DAMEN AVE,0890,THEFT,FROM BUILDING,BAR OR TAVERN,false,false,1924,019,32,5,06,1162461,1922279,2009,08/05/2009 07:18:35 AM,41.942393599,-87.67828649,"(41.942393599, -87.67828649)"
-7054739,HR459584,08/01/2009 02:25:00 AM,001XX W ONTARIO ST,0460,BATTERY,SIMPLE,STREET,false,false,1832,018,42,8,08B,1174816,1904408,2009,08/19/2009 07:58:56 PM,41.893086758,-87.633412684,"(41.893086758, -87.633412684)"
-7053408,HR459285,07/31/2009 09:50:00 PM,007XX N LOREL AVE,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),SIDEWALK,true,false,1524,015,37,25,18,1140495,1904543,2009,07/31/2009 11:29:53 PM,41.894156307,-87.759458536,"(41.894156307, -87.759458536)"
-7053176,HR458890,07/31/2009 06:05:00 PM,011XX S WESTERN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,true,true,1135,011,28,28,08B,1160518,1894700,2009,08/01/2009 10:31:45 AM,41.86675518,-87.686192595,"(41.86675518, -87.686192595)"
-7052963,HR458404,07/30/2009 02:15:00 PM,015XX N LAWNDALE AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,2535,025,26,23,05,1151373,1910159,2009,08/27/2009 11:31:05 PM,41.909360556,-87.719359076,"(41.909360556, -87.719359076)"
-7050493,HR456184,07/30/2009 12:00:00 AM,069XX S DR MARTIN LUTHER KING JR DR,0920,MOTOR VEHICLE THEFT,ATT: AUTOMOBILE,STREET,false,false,0322,,6,69,07,,,2009,07/31/2009 08:51:57 AM,,,
-7047238,HR454225,07/29/2009 02:00:00 AM,119XX S MICHIGAN AVE,0610,BURGLARY,FORCIBLE ENTRY,TAVERN/LIQUOR STORE,false,false,0532,005,9,53,05,1178956,1825469,2009,09/23/2009 08:44:16 AM,41.67637672,-87.620611316,"(41.67637672, -87.620611316)"
-7047347,HR453717,07/28/2009 07:15:00 PM,049XX S WOLCOTT AVE,0460,BATTERY,SIMPLE,STREET,false,false,0915,009,16,61,08B,1164472,1872179,2009,07/30/2009 10:49:49 AM,41.804872506,-87.672313257,"(41.804872506, -87.672313257)"
-7053211,HR459052,07/28/2009 05:00:00 PM,005XX E 67TH ST,4255,KIDNAPPING,UNLAWFUL INTERFERE/VISITATION,RESIDENCE,false,true,0321,003,20,42,26,1181231,1860755,2009,08/10/2009 05:24:44 PM,41.773153876,-87.61120085,"(41.773153876, -87.61120085)"
-7046878,HR453698,07/27/2009 06:00:00 AM,005XX N KINGSBURY ST,0810,THEFT,OVER $500,SIDEWALK,false,false,1831,018,42,8,06,1173185,1903533,2009,07/30/2009 02:02:11 PM,41.89072206,-87.639428667,"(41.89072206, -87.639428667)"
-7043151,HR450623,07/27/2009 03:25:00 AM,008XX W JACKSON BLVD,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,VEHICLE NON-COMMERCIAL,true,false,1213,012,27,28,15,1170957,1898816,2009,07/28/2009 09:08:05 AM,41.877827447,-87.647749304,"(41.877827447, -87.647749304)"
-7047220,HR450435,07/26/2009 11:40:00 PM,010XX W MAXWELL ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,1232,012,2,28,08B,1169540,1894053,2009,08/27/2009 10:52:27 AM,41.864788355,-87.653090734,"(41.864788355, -87.653090734)"
-7044638,HR451358,07/26/2009 05:00:00 AM,032XX S UNION AVE,0810,THEFT,OVER $500,RESIDENCE-GARAGE,false,false,0924,009,11,60,06,1172201,1883426,2009,08/04/2009 10:53:23 AM,41.835568733,-87.643635589,"(41.835568733, -87.643635589)"
-7042318,HR448838,07/26/2009 12:05:00 AM,060XX S INDIANA AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,0311,003,20,40,08B,1178593,1865006,2009,08/05/2009 02:22:03 PM,41.784879428,-87.620741902,"(41.784879428, -87.620741902)"
-7040451,HR447402,07/25/2009 02:54:00 AM,031XX W DIVERSEY AVE,0610,BURGLARY,FORCIBLE ENTRY,BARBERSHOP,false,false,1411,014,35,22,05,1155193,1918411,2009,08/06/2009 07:14:59 PM,41.931928823,-87.70510394,"(41.931928823, -87.70510394)"
-7071029,HR446547,07/24/2009 04:50:00 PM,008XX W 53RD ST,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,0934,009,3,61,08A,1171491,1869704,2009,08/12/2009 01:40:02 PM,41.797929826,-87.646643165,"(41.797929826, -87.646643165)"
-7039040,HR445379,07/24/2009 01:14:13 AM,049XX W 43RD ST,0496,BATTERY,AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST,CHA APARTMENT,false,false,0814,008,23,56,04B,1144131,1875470,2009,07/25/2009 04:47:53 PM,41.814308588,-87.746833803,"(41.814308588, -87.746833803)"
-7038333,HR445150,07/23/2009 07:00:00 PM,104XX S MICHIGAN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0512,005,9,49,14,1178929,1835882,2009,07/24/2009 06:30:50 AM,41.704952102,-87.620394694,"(41.704952102, -87.620394694)"
-7038070,HR444657,07/23/2009 03:30:00 PM,042XX S WENTWORTH AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0935,009,3,37,03,1175598,1876717,2009,08/02/2009 09:12:49 AM,41.817083142,-87.631372123,"(41.817083142, -87.631372123)"
-7037754,HR444308,07/23/2009 11:30:00 AM,012XX W 59TH ST,0460,BATTERY,SIMPLE,APARTMENT,true,false,0713,007,16,67,08B,1169143,1865617,2009,08/12/2009 01:58:13 PM,41.786765775,-87.655371861,"(41.786765775, -87.655371861)"
-7042261,HR445922,07/23/2009 11:15:00 AM,069XX S MERRILL AVE,0810,THEFT,OVER $500,RESIDENTIAL YARD (FRONT/BACK),false,false,0331,003,5,43,06,1191779,1859499,2009,11/08/2009 06:02:22 PM,41.769457769,-87.572575803,"(41.769457769, -87.572575803)"
-7031821,HR439177,07/20/2009 04:25:00 PM,012XX N LOCKWOOD AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,2532,025,37,25,18,1140835,1907856,2009,07/20/2009 05:22:16 PM,41.903241324,-87.758128225,"(41.903241324, -87.758128225)"
-7054108,HR436380,07/18/2009 09:50:00 PM,006XX N HOMAN AVE,2027,NARCOTICS,POSS: CRACK,SMALL RETAIL STORE,true,false,1121,011,27,23,18,1153548,1904372,2009,08/01/2009 01:01:32 PM,41.893437486,-87.711523132,"(41.893437486, -87.711523132)"
-7030252,HR436020,07/18/2009 04:00:00 PM,081XX S SANGAMON ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0613,006,21,71,14,1171346,1851055,2009,07/20/2009 06:35:57 AM,41.746757901,-87.647720096,"(41.746757901, -87.647720096)"
-7049243,HR437394,07/18/2009 02:00:00 PM,044XX N KENNETH AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1722,017,45,16,07,1145709,1928991,2009,07/31/2009 10:41:23 AM,41.961146662,-87.739687265,"(41.961146662, -87.739687265)"
-7028418,HR435367,07/18/2009 01:30:00 AM,014XX W OHIO ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1324,012,27,24,14,1166286,1904153,2009,07/20/2009 10:34:58 AM,41.892573688,-87.664747299,"(41.892573688, -87.664747299)"
-7035387,HR442246,07/17/2009 10:00:00 AM,017XX W CHICAGO AVE,0810,THEFT,OVER $500,CONSTRUCTION SITE,false,false,1324,012,1,24,06,1164887,1905362,2009,07/23/2009 10:49:41 AM,41.895921074,-87.669850909,"(41.895921074, -87.669850909)"
-7025580,HR429952,07/15/2009 10:50:00 AM,0000X N MICHIGAN AVE,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,0122,001,42,32,06,1177265,1900589,2009,07/17/2009 09:59:13 AM,41.882552053,-87.624534384,"(41.882552053, -87.624534384)"
-7021658,HR429268,07/14/2009 09:10:00 PM,021XX W 99TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2213,022,19,72,14,1163811,1838927,2009,07/15/2009 08:03:15 AM,41.713638209,-87.675669862,"(41.713638209, -87.675669862)"
-7021720,HR429295,07/14/2009 08:42:00 PM,012XX E 52ND ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,SIDEWALK,true,false,2124,002,4,41,24,1185384,1870952,2009,07/15/2009 09:28:38 AM,41.801038586,-87.595656445,"(41.801038586, -87.595656445)"
-7020696,HR428154,07/14/2009 11:10:00 AM,055XX S HALSTED ST,0560,ASSAULT,SIMPLE,STREET,false,false,0712,007,20,68,08A,1171871,1868181,2009,07/14/2009 01:13:08 PM,41.793742211,-87.645294347,"(41.793742211, -87.645294347)"
-7019548,HR427605,07/13/2009 11:25:00 PM,040XX W LAKE ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CTA PLATFORM,true,false,1114,011,28,26,26,1149614,1901481,2009,07/14/2009 08:56:21 AM,41.885581599,-87.726046569,"(41.885581599, -87.726046569)"
-7019726,HR427627,07/13/2009 11:00:00 AM,040XX W JACKSON BLVD,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,1132,011,28,26,08A,1149319,1898335,2009,07/16/2009 04:11:24 PM,41.876954348,-87.727211468,"(41.876954348, -87.727211468)"
-7016052,HR423313,07/11/2009 11:55:00 AM,064XX S CALUMET AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENTIAL YARD (FRONT/BACK),true,false,0312,003,20,69,26,1179601,1862390,2009,07/12/2009 06:36:10 AM,41.777677883,-87.617126065,"(41.777677883, -87.617126065)"
-7016219,HR423767,07/11/2009 11:00:00 AM,020XX N CLARK ST,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,1814,018,43,7,06,1174033,1913685,2009,07/13/2009 07:01:11 AM,41.918560801,-87.636011353,"(41.918560801, -87.636011353)"
-7015370,HR422684,07/10/2009 11:27:00 PM,010XX E 79TH ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,0624,006,8,69,15,1184624,1852878,2009,07/11/2009 06:09:42 AM,41.751459775,-87.599009582,"(41.751459775, -87.599009582)"
-7028884,HR422264,07/10/2009 07:35:00 PM,117XX S YALE AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,0522,005,34,53,08A,1176680,1827030,2009,07/20/2009 08:33:30 AM,41.680711705,-87.628895299,"(41.680711705, -87.628895299)"
-7013209,HR420344,07/09/2009 07:20:00 PM,001XX S PARKSIDE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,false,false,1513,015,29,25,26,1138591,1898962,2009,07/11/2009 08:57:08 AM,41.878876061,-87.76658681,"(41.878876061, -87.76658681)"
-7013556,HR419653,07/09/2009 12:00:00 PM,014XX W TAYLOR ST,0860,THEFT,RETAIL THEFT,OTHER,false,false,1213,012,25,28,06,1166827,1895722,2009,07/14/2009 02:47:03 PM,41.869426789,-87.663002281,"(41.869426789, -87.663002281)"
-7007527,HR414753,07/06/2009 05:30:00 PM,061XX W 54TH ST,0560,ASSAULT,SIMPLE,RESIDENCE PORCH/HALLWAY,false,true,0811,008,23,56,08A,1136134,1868101,2009,07/20/2009 07:02:15 AM,41.794232812,-87.776343397,"(41.794232812, -87.776343397)"
-7005529,HR413027,07/05/2009 08:25:00 PM,092XX S BLACKSTONE AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENTIAL YARD (FRONT/BACK),true,false,0413,004,8,48,26,1187763,1844211,2009,07/06/2009 07:40:54 AM,41.727602565,-87.587781963,"(41.727602565, -87.587781963)"
-7005087,HR412117,07/05/2009 10:05:00 AM,039XX W VAN BUREN ST,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,APARTMENT,true,false,1132,011,24,26,04B,1150246,1897695,2009,07/08/2009 08:05:37 AM,41.875180102,-87.723824462,"(41.875180102, -87.723824462)"
-7006114,HR411611,07/05/2009 12:03:12 AM,076XX N SHERIDAN RD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,DRIVEWAY - RESIDENTIAL,false,false,2422,024,49,1,08B,1165527,1950339,2009,07/07/2009 02:06:30 PM,42.019326273,-87.666214339,"(42.019326273, -87.666214339)"
-7004221,HR410774,07/03/2009 07:00:00 PM,081XX S ELIZABETH ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0613,006,21,71,06,1169370,1850672,2009,07/05/2009 07:20:41 AM,41.745749874,-87.654971719,"(41.745749874, -87.654971719)"
-7002368,HR408605,07/03/2009 02:03:00 AM,015XX S KEELER AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1012,010,24,29,18,1148592,1892257,2009,07/03/2009 03:24:12 AM,41.860289651,-87.730037659,"(41.860289651, -87.730037659)"
-7002159,HR408151,07/02/2009 06:30:00 PM,063XX S ASHLAND AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0725,007,16,67,08B,1166712,1862870,2009,07/13/2009 09:38:31 AM,41.77927992,-87.664363567,"(41.77927992, -87.664363567)"
-6998760,HR403866,06/30/2009 01:00:00 PM,016XX W EDGEWATER AVE,0610,BURGLARY,FORCIBLE ENTRY,OTHER,false,false,2012,020,40,77,05,1164362,1938116,2009,07/14/2009 02:55:36 PM,41.985810914,-87.670849415,"(41.985810914, -87.670849415)"
-6997088,HR403288,06/29/2009 07:00:00 PM,105XX S LAWNDALE AVE,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,2211,022,19,74,06,1153494,1834646,2009,06/30/2009 09:50:33 AM,41.702100379,-87.713567747,"(41.702100379, -87.713567747)"
-6995439,HR401104,06/29/2009 03:51:42 AM,072XX N CLARK ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2424,024,49,1,14,1163206,1947956,2009,07/01/2009 10:32:12 AM,42.012836599,-87.674822784,"(42.012836599, -87.674822784)"
-6994494,HR400743,06/28/2009 08:20:00 PM,039XX S LAKE SHORE DR SB,0460,BATTERY,SIMPLE,PARK PROPERTY,false,false,2122,002,4,36,08B,1183987,1879902,2009,07/20/2009 01:15:23 PM,41.825630825,-87.600499757,"(41.825630825, -87.600499757)"
-7005735,HR404035,06/28/2009 03:00:00 PM,087XX S COMMERCIAL AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,SMALL RETAIL STORE,false,false,0423,004,10,46,14,1197618,1847684,2009,07/06/2009 07:54:02 AM,41.736892857,-87.551566677,"(41.736892857, -87.551566677)"
-6993727,HR399606,06/28/2009 03:45:00 AM,015XX N MILWAUKEE AVE,0890,THEFT,FROM BUILDING,BAR OR TAVERN,false,false,1424,014,1,24,06,1162776,1910563,2009,06/29/2009 08:20:50 AM,41.910237541,-87.677458103,"(41.910237541, -87.677458103)"
-6994064,HR400051,06/28/2009 12:00:00 AM,112XX S AVENUE M,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0433,004,10,52,05,1201518,1831020,2009,08/03/2009 03:05:20 PM,41.691067401,-87.537842693,"(41.691067401, -87.537842693)"
-6992518,HR395338,06/25/2009 09:30:00 PM,006XX E 75TH ST,0496,BATTERY,AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST,SIDEWALK,false,true,0624,006,6,69,04B,1182160,1855395,2009,08/03/2009 03:11:37 PM,41.758424042,-87.607961096,"(41.758424042, -87.607961096)"
-6990050,HR395354,06/25/2009 09:00:00 PM,027XX N RUTHERFORD AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,2512,025,36,18,26,1130911,1917491,2009,06/28/2009 01:26:33 PM,41.929857798,-87.794359384,"(41.929857798, -87.794359384)"
-6992266,HR395871,06/25/2009 08:30:00 PM,013XX W 109TH PL,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,2234,022,34,75,05,1169031,1832102,2009,06/29/2009 07:20:16 AM,41.694798325,-87.656748637,"(41.694798325, -87.656748637)"
-7257479,HR663616,06/25/2009 07:00:00 PM,032XX S WALLACE ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,0924,009,11,60,06,1172792,1883112,2009,12/18/2009 08:53:26 AM,41.834694047,-87.641476311,"(41.834694047, -87.641476311)"
-6990752,HR394825,06/25/2009 04:15:00 PM,022XX N LONG AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,RESIDENCE,false,false,2515,025,37,19,14,1140057,1914351,2009,06/28/2009 01:27:15 PM,41.921078619,-87.760826773,"(41.921078619, -87.760826773)"
-6988148,HR393250,06/24/2009 07:00:00 PM,059XX S DAMEN AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,false,0714,007,15,67,08A,1164083,1865260,2009,06/25/2009 11:49:53 AM,41.785894111,-87.673934592,"(41.785894111, -87.673934592)"
-6987873,HR390834,06/23/2009 10:15:00 AM,063XX S MORGAN ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0723,007,16,68,14,1170775,1862706,2009,06/25/2009 01:29:12 PM,41.778742182,-87.649472976,"(41.778742182, -87.649472976)"
-7088981,HR389693,06/22/2009 08:30:00 PM,080XX S EVANS AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0631,006,6,44,05,1182581,1851854,2009,08/14/2010 10:37:20 AM,41.748697414,-87.606527813,"(41.748697414, -87.606527813)"
-6982545,HR387896,06/22/2009 12:35:00 AM,044XX S DREXEL BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,true,false,2123,002,4,39,14,1182896,1875848,2009,06/23/2009 01:44:07 PM,41.814531823,-87.604628451,"(41.814531823, -87.604628451)"
-6982853,HR388160,06/22/2009 12:00:00 AM,049XX S DR MARTIN LUTHER KING JR DR,0820,THEFT,$500 AND UNDER,RESIDENCE PORCH/HALLWAY,false,false,0223,002,4,38,06,1179738,1872205,2009,06/23/2009 10:44:50 AM,41.804608026,-87.616323735,"(41.804608026, -87.616323735)"
-6983008,HR386869,06/21/2009 11:30:00 AM,021XX N KEYSTONE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,2525,025,30,20,14,1149067,1914101,2009,06/24/2009 12:02:47 PM,41.920222763,-87.727728114,"(41.920222763, -87.727728114)"
-7719458,HR386495,06/21/2009 04:32:00 AM,001XX N LECLAIRE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1532,,28,25,08B,,,2009,09/28/2010 12:32:08 PM,,,
-6981745,HR386759,06/20/2009 11:00:00 PM,027XX S RIDGEWAY AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1031,010,22,30,07,1151856,1885454,2009,06/22/2009 10:17:19 AM,41.841557831,-87.718235125,"(41.841557831, -87.718235125)"
-7006275,HR386679,06/20/2009 06:00:00 PM,032XX S PRAIRIE AVE,0810,THEFT,OVER $500,STREET,false,false,2112,002,3,35,06,1178510,1883365,2009,07/27/2009 01:34:01 PM,41.835260004,-87.620487884,"(41.835260004, -87.620487884)"
-7012140,HR385472,06/20/2009 03:45:00 PM,061XX S DR MARTIN LUTHER KING JR DR,0554,ASSAULT,AGG PO HANDS NO/MIN INJURY,STREET,true,false,0313,003,20,42,08A,1180009,1864589,2009,07/11/2009 06:49:18 AM,41.78370282,-87.615563053,"(41.78370282, -87.615563053)"
-6980153,HR384704,06/19/2009 02:00:00 PM,076XX S PARNELL AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0621,006,17,69,06,1173902,1854172,2009,06/20/2009 06:11:48 AM,41.755255071,-87.638262025,"(41.755255071, -87.638262025)"
-6978176,HR382577,06/18/2009 08:35:00 PM,006XX S WABASH AVE,1330,CRIMINAL TRESPASS,TO LAND,BAR OR TAVERN,false,false,0132,001,2,32,26,1176860,1897273,2009,06/19/2009 08:59:24 AM,41.873461928,-87.626121868,"(41.873461928, -87.626121868)"
-6976297,HR381019,06/17/2009 11:54:00 PM,016XX N TRIPP AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2534,025,30,23,18,1147844,1910509,2009,06/18/2009 01:46:00 AM,41.910389576,-87.732314164,"(41.910389576, -87.732314164)"
-6989112,HR380955,06/17/2009 08:30:00 PM,066XX S MOZART ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0831,008,15,66,14,1158482,1860461,2009,06/26/2009 11:34:56 AM,41.772841006,-87.694601383,"(41.772841006, -87.694601383)"
-6978879,HR378990,06/16/2009 08:01:40 PM,015XX S SPAULDING AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1022,010,24,29,14,1154653,1892324,2009,06/23/2009 11:22:45 AM,41.860354482,-87.707787333,"(41.860354482, -87.707787333)"
-7019418,HR414230,06/16/2009 08:00:00 AM,086XX S LOOMIS BLVD,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,RESIDENCE,false,false,0613,006,21,71,11,1168539,1847660,2009,07/14/2009 11:45:05 AM,41.737502457,-87.658103265,"(41.737502457, -87.658103265)"
-6974911,HR377705,06/16/2009 06:43:00 AM,037XX W CULLOM AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,false,true,1723,017,39,16,26,1150452,1928352,2009,06/24/2009 11:31:33 AM,41.959301708,-87.722266088,"(41.959301708, -87.722266088)"
-6974515,HR377765,06/16/2009 03:00:00 AM,063XX W DEVON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1611,016,45,10,14,1133065,1942070,2009,06/19/2009 11:16:08 AM,41.997267614,-87.785866758,"(41.997267614, -87.785866758)"
-6973985,HR378600,06/16/2009 02:00:00 AM,063XX N WINTHROP AVE,0560,ASSAULT,SIMPLE,OTHER,false,false,2433,024,48,77,08A,1167666,1942230,2009,06/19/2009 08:45:57 PM,41.997029049,-87.658578303,"(41.997029049, -87.658578303)"
-6974004,HR377126,06/15/2009 06:33:00 PM,052XX W JACKSON BLVD,1330,CRIMINAL TRESPASS,TO LAND,GAS STATION,true,false,1522,015,29,25,26,1141700,1898133,2009,06/17/2009 10:17:16 AM,41.876544275,-87.755191494,"(41.876544275, -87.755191494)"
-7891492,HT121090,06/15/2009 04:00:00 PM,088XX S HERMITAGE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2221,,21,71,26,,,2009,01/19/2011 10:31:13 AM,,,
-6972485,HR375587,06/14/2009 08:50:00 PM,069XX W HIGGINS AVE,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),true,false,1613,016,41,10,06,1128707,1935806,2009,06/18/2009 10:17:56 AM,41.980153938,-87.802041416,"(41.980153938, -87.802041416)"
-6980865,HR379010,06/13/2009 03:30:00 AM,053XX S SAWYER AVE,0810,THEFT,OVER $500,STREET,false,false,0822,008,14,63,06,1155665,1868846,2009,06/21/2009 02:55:01 PM,41.79590762,-87.704703137,"(41.79590762, -87.704703137)"
-6968046,HR372785,06/13/2009 12:30:00 AM,072XX S HARVARD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0731,007,17,69,08B,1175251,1856903,2009,06/19/2009 10:32:38 AM,41.762719245,-87.633236868,"(41.762719245, -87.633236868)"
-7120026,HR529010,06/12/2009 09:00:00 AM,007XX N ADA ST,0810,THEFT,OVER $500,"SCHOOL, PUBLIC, BUILDING",false,false,1324,012,27,24,06,1167232,1905220,2009,09/11/2009 07:34:55 AM,41.895481331,-87.661242342,"(41.895481331, -87.661242342)"
-6964586,HR369239,06/11/2009 04:00:00 AM,019XX S TROY ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,true,true,1022,010,24,29,26,1155627,1890250,2009,06/19/2009 01:33:24 PM,41.854643666,-87.704267784,"(41.854643666, -87.704267784)"
-6976501,HR367926,06/10/2009 01:15:00 PM,045XX S CHRISTIANA AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,STREET,true,false,0821,008,14,58,26,1154840,1874215,2009,06/19/2009 10:39:22 AM,41.810657416,-87.707585148,"(41.810657416, -87.707585148)"
-7068607,HR476106,06/10/2009 12:00:00 AM,004XX W WELLINGTON AVE,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,COMMERCIAL / BUSINESS OFFICE,false,false,2333,019,44,6,11,1172583,1920194,2009,09/20/2009 02:37:05 PM,41.936454003,-87.641145673,"(41.936454003, -87.641145673)"
-6961286,HR365809,06/09/2009 12:01:00 AM,001XX N LATROBE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENTIAL YARD (FRONT/BACK),false,true,1523,015,28,25,08B,1141388,1900811,2009,06/11/2009 11:47:00 AM,41.883898807,-87.756270958,"(41.883898807, -87.756270958)"
-6959692,HR365145,06/08/2009 09:00:00 PM,027XX W 62ND ST,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",false,false,0825,008,15,66,06,1159385,1863390,2009,06/23/2009 11:48:54 AM,41.780860148,-87.691211026,"(41.780860148, -87.691211026)"
-6965760,HR366346,06/08/2009 03:30:00 PM,040XX N ROCKWELL ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,FACTORY/MANUFACTURING BUILDING,false,true,1912,019,47,5,26,1158309,1927001,2009,06/24/2009 04:08:25 PM,41.955437108,-87.693417409,"(41.955437108, -87.693417409)"
-6961112,HR363345,06/07/2009 10:03:00 PM,064XX S LOREL AVE,2022,NARCOTICS,POSS: COCAINE,ALLEY,true,false,0813,008,13,64,18,1141887,1861315,2009,06/09/2009 02:39:06 PM,41.775506641,-87.755414232,"(41.775506641, -87.755414232)"
-6957164,HR362586,06/06/2009 11:30:00 PM,023XX N ELSTON AVE,0810,THEFT,OVER $500,STREET,false,false,1432,014,32,22,06,1163098,1915573,2009,06/08/2009 09:25:47 AM,41.923978555,-87.676134165,"(41.923978555, -87.676134165)"
-7602964,HR360741,06/06/2009 05:00:00 AM,081XX S YATES BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0414,004,7,46,14,1193563,1851297,2009,07/15/2010 08:19:11 AM,41.746907356,-87.566304658,"(41.746907356, -87.566304658)"
-6956672,HR360623,06/06/2009 02:31:51 AM,017XX W GREENLEAF AVE,1460,WEAPONS VIOLATION,POSS FIREARM/AMMO:NO FOID CARD,BAR OR TAVERN,true,false,2424,024,49,1,15,1163159,1947031,2009,06/12/2009 02:09:51 PM,42.010299371,-87.675021894,"(42.010299371, -87.675021894)"
-6957118,HR360912,06/05/2009 03:45:00 PM,009XX E 104TH ST,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0512,005,9,50,05,1184211,1836150,2009,06/26/2009 12:15:52 AM,41.705565903,-87.601044511,"(41.705565903, -87.601044511)"
-7069873,HR475932,06/05/2009 10:00:00 AM,052XX N CENTRAL AVE,1120,DECEPTIVE PRACTICE,FORGERY,BANK,false,false,1622,016,45,11,10,1137803,1934268,2009,08/14/2009 10:31:20 PM,41.975773845,-87.768626458,"(41.975773845, -87.768626458)"
-6959545,HR364890,06/04/2009 01:01:00 PM,006XX N FAIRBANKS CT,0843,THEFT,ATTEMPT FINANCIAL IDENTITY THEFT,BANK,false,false,1834,018,42,8,06,1178428,1904746,2009,06/09/2009 10:45:46 AM,41.893932627,-87.620137008,"(41.893932627, -87.620137008)"
-6980764,HR357765,06/03/2009 03:00:00 PM,012XX N CLARK ST,0330,ROBBERY,AGGRAVATED,STREET,false,false,1821,018,42,8,03,1175274,1908508,2009,06/29/2009 09:45:16 PM,41.904327102,-87.631607477,"(41.904327102, -87.631607477)"
-6949579,HR355146,06/02/2009 11:30:00 PM,016XX E 86TH PL,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,0412,004,8,45,15,1188772,1847992,2009,06/07/2009 07:15:38 AM,41.73795397,-87.583965299,"(41.73795397, -87.583965299)"
-6947653,HR353548,06/01/2009 08:00:00 PM,077XX S TROY ST,0810,THEFT,OVER $500,STREET,false,false,0835,008,18,70,06,1156686,1852893,2009,06/02/2009 01:36:08 PM,41.752109656,-87.701388914,"(41.752109656, -87.701388914)"
-7241446,HR655568,06/01/2009 07:00:00 AM,063XX N FRANCISCO AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,2413,024,50,2,06,1155891,1942093,2009,12/08/2009 01:52:32 PM,41.996899555,-87.701897533,"(41.996899555, -87.701897533)"
-6973677,HR351670,06/01/2009 03:45:00 AM,055XX S ASHLAND AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,false,false,0715,007,15,67,14,1166578,1867940,2009,06/16/2009 02:24:36 PM,41.793195486,-87.664710283,"(41.793195486, -87.664710283)"
-7649487,HS454063,06/01/2009 12:01:00 AM,014XX W 72ND ST,1562,SEX OFFENSE,AGG CRIMINAL SEXUAL ABUSE,RESIDENCE,true,false,0734,007,17,67,17,1167867,1856958,2009,08/11/2010 04:17:03 PM,41.763031898,-87.660298822,"(41.763031898, -87.660298822)"
-6948654,HR353710,05/31/2009 09:30:00 PM,053XX S LOWE AVE,0560,ASSAULT,SIMPLE,RESIDENTIAL YARD (FRONT/BACK),false,false,0934,009,3,61,08A,1172832,1869440,2009,06/03/2009 11:05:29 AM,41.797175881,-87.641733297,"(41.797175881, -87.641733297)"
-6945599,HR350995,05/31/2009 05:07:56 PM,036XX N WESTERN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESTAURANT,true,false,1912,019,47,5,14,1159692,1923994,2009,06/02/2009 08:11:25 AM,41.947157276,-87.688416397,"(41.947157276, -87.688416397)"
-6960945,HR348163,05/29/2009 08:09:00 PM,008XX W 75TH ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0621,006,17,71,16,1171782,1855007,2009,06/16/2009 10:01:56 AM,41.757593156,-87.646006799,"(41.757593156, -87.646006799)"
-6943068,HR348271,05/29/2009 07:00:00 PM,030XX W 25TH ST,0460,BATTERY,SIMPLE,STREET,false,false,1033,010,12,30,08B,1156457,1887285,2009,06/01/2009 01:05:31 PM,41.846490646,-87.701301419,"(41.846490646, -87.701301419)"
-6942681,HR347706,05/29/2009 04:55:00 PM,021XX S WABASH AVE,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,0134,001,2,33,18,1177127,1889933,2009,05/29/2009 05:48:26 PM,41.853314459,-87.625363821,"(41.853314459, -87.625363821)"
-6943014,HR348086,05/29/2009 03:30:00 PM,036XX W 26TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1032,010,22,30,08B,1152378,1886439,2009,06/28/2009 12:58:22 PM,41.844250519,-87.716293572,"(41.844250519, -87.716293572)"
-6943842,HR348934,05/29/2009 10:00:00 AM,014XX W GARFIELD BLVD,2820,OTHER OFFENSE,TELEPHONE THREAT,STREET,false,false,0933,009,16,61,26,1167458,1868309,2009,05/31/2009 02:41:00 PM,41.794189239,-87.661472835,"(41.794189239, -87.661472835)"
-6943295,HR346852,05/29/2009 09:15:00 AM,001XX S HALSTED ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,1213,012,27,28,06,1171109,1899644,2009,06/01/2009 09:27:34 AM,41.880096203,-87.647166896,"(41.880096203, -87.647166896)"
-6942847,HR347569,05/29/2009 04:30:00 AM,061XX W NEWPORT AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,false,true,1633,016,38,17,26,1134673,1922336,2009,06/03/2009 04:38:38 PM,41.94308729,-87.780419953,"(41.94308729, -87.780419953)"
-6946878,HR346278,05/28/2009 06:00:00 PM,079XX S MARSHFIELD AVE,0810,THEFT,OVER $500,RESIDENTIAL YARD (FRONT/BACK),false,false,0611,006,21,71,06,1166690,1851761,2009,10/31/2014 03:20:56 PM,41.748795803,-87.664760772,"(41.748795803, -87.664760772)"
-6938604,HR344145,05/27/2009 06:50:00 PM,061XX S GREENWOOD AVE,0820,THEFT,$500 AND UNDER,APARTMENT,false,false,0314,003,20,42,06,1184430,1864459,2009,05/31/2009 06:22:59 AM,41.783243673,-87.599358344,"(41.783243673, -87.599358344)"
-6937110,HR342681,05/26/2009 10:47:00 PM,078XX S SAGINAW AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,0421,004,7,43,08B,1195319,1853568,2009,05/27/2009 04:44:34 AM,41.753096021,-87.559795536,"(41.753096021, -87.559795536)"
-6949898,HR341578,05/26/2009 12:30:00 PM,042XX S WELLS ST,0810,THEFT,OVER $500,STREET,false,false,0935,009,3,37,06,1175369,1876546,2009,06/03/2009 12:46:18 PM,41.816619033,-87.632217268,"(41.816619033, -87.632217268)"
-6934641,HR340335,05/25/2009 04:25:00 PM,066XX S EVANS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0321,003,20,42,08B,1182324,1860963,2009,05/28/2009 08:37:38 AM,41.773699393,-87.607187783,"(41.773699393, -87.607187783)"
-6933670,HR339456,05/25/2009 12:50:00 AM,054XX S KARLOV AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0815,008,23,62,14,1149929,1867878,2009,05/25/2009 11:29:36 AM,41.793364459,-87.725762696,"(41.793364459, -87.725762696)"
-6941509,HR338396,05/24/2009 10:50:00 AM,040XX W LAKE ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,true,false,1114,011,28,26,11,1149614,1901481,2009,06/01/2009 09:44:04 AM,41.885581599,-87.726046569,"(41.885581599, -87.726046569)"
-6948031,HR353649,05/23/2009 12:01:00 AM,068XX S MICHIGAN AVE,1122,DECEPTIVE PRACTICE,COUNTERFEIT CHECK,RESIDENCE,false,false,0322,003,20,69,10,1178302,1859708,2009,07/05/2009 11:42:51 AM,41.770347784,-87.621969508,"(41.770347784, -87.621969508)"
-6931880,HR336728,05/22/2009 11:00:00 PM,059XX S LAWNDALE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0822,008,13,65,06,1152679,1865199,2009,05/23/2009 12:33:50 PM,41.785959107,-87.715749146,"(41.785959107, -87.715749146)"
-6931988,HR334207,05/21/2009 09:15:00 PM,036XX W BELMONT AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,2523,025,35,21,26,1151775,1921000,2009,05/23/2009 01:11:46 PM,41.939101286,-87.717596326,"(41.939101286, -87.717596326)"
-6930102,HR334796,05/21/2009 08:00:00 PM,009XX W ARMITAGE AVE,0890,THEFT,FROM BUILDING,TAVERN/LIQUOR STORE,false,false,1812,018,43,7,06,1169424,1913524,2009,05/22/2009 11:35:32 AM,41.918220598,-87.652949783,"(41.918220598, -87.652949783)"
-6933322,HR338781,05/21/2009 06:00:00 PM,017XX W HOWARD ST,0880,THEFT,PURSE-SNATCHING,STREET,false,false,2422,024,49,1,06,1163523,1950306,2009,05/25/2009 08:09:05 AM,42.019278344,-87.673589736,"(42.019278344, -87.673589736)"
-6931718,HR332379,05/20/2009 10:35:00 PM,022XX S SPRINGFIELD AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1013,010,22,30,18,1150783,1888547,2009,05/23/2009 08:51:24 AM,41.850066432,-87.722091933,"(41.850066432, -87.722091933)"
-6927265,HR332355,05/20/2009 10:10:00 PM,065XX S WINCHESTER AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,0726,007,15,67,05,1164527,1861089,2009,06/15/2009 08:34:52 AM,41.774438986,-87.672424208,"(41.774438986, -87.672424208)"
-6932228,HR332208,05/20/2009 08:40:00 PM,016XX W HOWARD ST,0340,ROBBERY,ATTEMPT: STRONGARM-NO WEAPON,SIDEWALK,false,false,2422,024,49,1,03,1164152,1950385,2009,05/26/2009 09:01:40 PM,42.019481794,-87.67127285,"(42.019481794, -87.67127285)"
-6928144,HR331317,05/20/2009 12:50:00 PM,0000X W LAKE ST,2890,PUBLIC PEACE VIOLATION,OTHER VIOLATION,OTHER,false,false,0122,001,42,32,26,1176250,1901786,2009,05/22/2009 07:01:41 AM,41.885859631,-87.628225327,"(41.885859631, -87.628225327)"
-6922902,HR328242,05/18/2009 06:27:00 PM,035XX S RHODES AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,OTHER,false,false,0212,002,4,35,26,1180121,1881754,2009,05/20/2009 07:07:11 AM,41.830802465,-87.614626154,"(41.830802465, -87.614626154)"
-6920859,HR326581,05/17/2009 08:56:00 PM,006XX N ORLEANS ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1831,018,42,8,14,1173841,1904979,2009,05/18/2009 07:26:00 AM,41.894675384,-87.636976438,"(41.894675384, -87.636976438)"
-6920869,HR326550,05/17/2009 08:05:00 PM,002XX E 35TH ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,0211,002,2,35,18,1178558,1881799,2009,05/17/2009 09:27:55 PM,41.830961689,-87.620359451,"(41.830961689, -87.620359451)"
-6953816,HR325531,05/17/2009 04:45:00 AM,016XX W NORTH AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,RESTAURANT,false,false,1433,014,1,24,04B,1165401,1910685,2009,06/30/2009 06:34:27 PM,41.910516831,-87.66781143,"(41.910516831, -87.66781143)"
-6919612,HR324785,05/16/2009 06:30:00 PM,024XX W COLUMBUS AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,SIDEWALK,false,false,0835,008,18,66,04B,1161400,1855271,2009,05/23/2009 03:24:18 PM,41.75853893,-87.684048307,"(41.75853893, -87.684048307)"
-7033755,HR429358,05/16/2009 05:00:00 PM,099XX S EMERALD AVE,1562,SEX OFFENSE,AGG CRIMINAL SEXUAL ABUSE,RESIDENCE,false,true,2232,022,34,73,17,1173023,1838851,2009,07/28/2009 10:53:34 AM,41.713231615,-87.641934363,"(41.713231615, -87.641934363)"
-6919037,HR323762,05/16/2009 04:02:00 AM,003XX N STATE ST,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,false,false,1831,018,42,8,04B,1176248,1902885,2009,05/26/2009 10:23:56 PM,41.888875392,-87.628199509,"(41.888875392, -87.628199509)"
-6918608,HR323066,05/15/2009 06:10:00 PM,056XX S ARTESIAN AVE,0330,ROBBERY,AGGRAVATED,SIDEWALK,false,false,0824,008,16,63,03,1160945,1867398,2009,05/25/2009 02:40:29 PM,41.791826532,-87.685380992,"(41.791826532, -87.685380992)"
-6918420,HR322930,05/15/2009 08:00:00 AM,004XX N CAMPBELL AVE,0810,THEFT,OVER $500,STREET,false,false,1313,012,27,24,06,1159612,1903156,2009,10/21/2010 02:59:09 PM,41.889977924,-87.689285681,"(41.889977924, -87.689285681)"
-6920825,HR326173,05/15/2009 02:00:00 AM,006XX W CORNELIA AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,2331,019,46,6,03,1171416,1923536,2009,06/06/2009 03:34:40 PM,41.945650355,-87.645335918,"(41.945650355, -87.645335918)"
-6916704,HR320757,05/14/2009 03:00:00 PM,069XX S DORCHESTER AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,0332,003,5,43,05,1186781,1859058,2009,04/20/2010 02:40:46 PM,41.768367478,-87.59090987,"(41.768367478, -87.59090987)"
-6918805,HR320613,05/14/2009 02:20:00 PM,041XX N SHERIDAN RD,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE PORCH/HALLWAY,true,false,2322,019,46,3,26,1168809,1927966,2009,10/31/2014 03:20:56 PM,41.957863467,-87.654789333,"(41.957863467, -87.654789333)"
-6916448,HR319775,05/14/2009 12:20:00 AM,039XX N AVONDALE AVE,0460,BATTERY,SIMPLE,OTHER RAILROAD PROP / TRAIN DEPOT,true,false,1732,017,38,16,08B,1147816,1926257,2009,05/18/2009 08:54:30 AM,41.953604038,-87.732011274,"(41.953604038, -87.732011274)"
-6915670,HR319473,05/13/2009 07:51:53 PM,118XX S VINCENNES AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2212,022,34,75,18,1164981,1825910,2009,05/14/2009 07:45:42 AM,41.677892734,-87.67175105,"(41.677892734, -87.67175105)"
-6915145,HR319175,05/13/2009 04:00:00 PM,040XX W LAKE ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,true,false,1114,011,28,26,11,1149614,1901481,2009,05/14/2009 10:14:26 AM,41.885581599,-87.726046569,"(41.885581599, -87.726046569)"
-6928552,HR317187,05/12/2009 03:44:00 PM,068XX S ROCKWELL ST,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,SIDEWALK,true,false,0832,,15,66,18,,,2009,03/06/2012 09:08:19 AM,,,
-6913020,HR316764,05/12/2009 01:45:00 PM,076XX S ESSEX AVE,0890,THEFT,FROM BUILDING,ABANDONED BUILDING,true,false,0421,004,7,43,06,1194149,1854769,2009,05/13/2009 06:54:59 AM,41.756420445,-87.564043691,"(41.756420445, -87.564043691)"
-6912628,HR316612,05/12/2009 12:18:00 PM,048XX W GLADYS AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1533,015,24,25,26,1144158,1897943,2009,05/12/2009 01:22:02 PM,41.875977103,-87.746171171,"(41.875977103, -87.746171171)"
-6930452,HR333223,05/12/2009 12:00:00 PM,005XX W 62ND ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,0711,007,20,68,26,1173821,1863855,2009,05/28/2009 11:57:42 AM,41.781828152,-87.638272102,"(41.781828152, -87.638272102)"
-6923849,HR328988,05/12/2009 09:00:00 AM,018XX N LAWNDALE AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,ALLEY,false,false,2535,025,26,22,14,1151377,1912144,2009,05/20/2009 10:24:47 AM,41.914807501,-87.719292177,"(41.914807501, -87.719292177)"
-6905453,HR313328,05/10/2009 09:00:00 AM,032XX W MONROE ST,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,1124,011,28,,06,1154528,1899388,2009,05/11/2009 01:10:44 PM,41.879741351,-87.708057298,"(41.879741351, -87.708057298)"
-6942380,HR312535,05/09/2009 09:59:20 PM,039XX W OHIO ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1122,011,27,23,18,1150205,1903681,2009,06/03/2009 11:03:15 AM,41.891607135,-87.723818899,"(41.891607135, -87.723818899)"
-6914570,HR312038,05/09/2009 03:32:53 PM,055XX W THOMAS ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,true,false,1524,015,37,25,26,1139238,1906826,2009,07/20/2009 07:29:04 AM,41.900444115,-87.764019531,"(41.900444115, -87.764019531)"
-6907001,HR311499,05/09/2009 10:10:00 AM,017XX W PETERSON AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,false,true,2012,020,40,77,26,1163633,1939883,2009,05/20/2009 03:44:36 PM,41.99067506,-87.673480562,"(41.99067506, -87.673480562)"
-6903806,HR310676,05/08/2009 07:55:00 PM,103XX S WALLACE ST,0460,BATTERY,SIMPLE,RESIDENCE,true,false,2232,022,34,49,08B,1174099,1835970,2009,05/20/2009 06:12:08 AM,41.705301955,-87.638078881,"(41.705301955, -87.638078881)"
-6937177,HR307225,05/07/2009 12:15:00 AM,025XX N MILWAUKEE AVE,0810,THEFT,OVER $500,APARTMENT,false,false,1414,014,35,22,06,1155520,1916651,2009,06/09/2009 06:56:20 PM,41.927092667,-87.703949735,"(41.927092667, -87.703949735)"
-6901017,HR304555,05/05/2009 03:24:24 PM,009XX N DRAKE AVE,0560,ASSAULT,SIMPLE,STREET,false,true,1121,011,27,23,08A,1152570,1906369,2009,05/11/2009 01:18:16 PM,41.898936843,-87.715062147,"(41.898936843, -87.715062147)"
-6900475,HR304310,05/05/2009 12:51:12 PM,049XX W QUINCY ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,1533,015,28,25,08B,1143539,1898588,2009,05/10/2009 06:25:07 PM,41.87775866,-87.748427808,"(41.87775866, -87.748427808)"
-6896343,HR303545,05/04/2009 09:30:00 PM,114XX S MICHIGAN AVE,0313,ROBBERY,ARMED: OTHER DANGEROUS WEAPON,SIDEWALK,true,false,0531,005,9,49,03,1178801,1829332,2009,05/05/2009 05:37:04 AM,41.686980882,-87.621061757,"(41.686980882, -87.621061757)"
-6916384,HR296975,05/01/2009 09:30:00 AM,033XX W FILLMORE ST,2093,NARCOTICS,FOUND SUSPECT NARCOTICS,POLICE FACILITY/VEH PARKING LOT,true,false,1134,,24,29,26,,,2009,07/14/2011 02:00:24 PM,,,
-6890673,HR296786,05/01/2009 04:00:00 AM,026XX W CATALPA AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2011,020,40,4,08B,1157822,1936389,2009,05/07/2009 04:03:50 PM,41.981208241,-87.694950569,"(41.981208241, -87.694950569)"
-6923008,HR328128,04/30/2009 06:00:00 PM,062XX N ST LOUIS AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,1711,017,50,13,05,1151873,1941218,2009,05/22/2009 01:04:51 PM,41.994578862,-87.71670136,"(41.994578862, -87.71670136)"
-6915374,HR316917,04/30/2009 09:00:00 AM,001XX S MICHIGAN AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,BANK,false,false,0123,001,42,32,06,1177283,1899916,2009,05/18/2009 12:47:08 PM,41.880704896,-87.6244887,"(41.880704896, -87.6244887)"
-6889026,HR295058,04/30/2009 07:45:00 AM,050XX W ALTGELD ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2521,025,31,19,07,1142275,1916204,2009,05/20/2009 10:57:51 AM,41.926122521,-87.752631116,"(41.926122521, -87.752631116)"
-6888423,HR294383,04/29/2009 05:00:00 PM,099XX S HALSTED ST,2820,OTHER OFFENSE,TELEPHONE THREAT,STREET,false,true,2232,022,34,73,26,1172691,1839138,2009,05/13/2009 03:46:42 PM,41.714026494,-87.643141834,"(41.714026494, -87.643141834)"
-6882653,HR289436,04/27/2009 12:30:00 AM,035XX W POLK ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1133,011,24,27,08B,1152700,1896176,2009,05/09/2009 02:45:43 PM,41.870963627,-87.714854483,"(41.870963627, -87.714854483)"
-6880794,HR286686,04/25/2009 10:25:00 AM,073XX S ARTESIAN AVE,031B,ROBBERY,ARMED: OTHER FIREARM,RESIDENCE,false,false,0835,008,18,66,03,1161352,1855966,2009,05/12/2009 02:13:59 PM,41.760447106,-87.684205005,"(41.760447106, -87.684205005)"
-6880268,HR284719,04/24/2009 10:05:00 AM,021XX S ARCHER AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,MEDICAL/DENTAL OFFICE,false,false,2111,009,25,34,26,1174800,1889958,2009,05/23/2009 10:21:21 AM,41.85343539,-87.633903884,"(41.85343539, -87.633903884)"
-6890507,HR284126,04/23/2009 09:10:00 PM,035XX S RHODES AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0212,002,4,35,14,1180131,1881331,2009,05/01/2009 10:36:05 AM,41.829641493,-87.614602448,"(41.829641493, -87.614602448)"
-6883567,HR283619,04/23/2009 05:00:00 PM,057XX S CICERO AVE,0890,THEFT,FROM BUILDING,AIRPORT/AIRCRAFT,true,false,0813,008,23,56,06,1145609,1866345,2009,05/02/2009 08:17:17 PM,41.789240349,-87.741642671,"(41.789240349, -87.741642671)"
-6875822,HR281614,04/22/2009 02:59:00 PM,004XX W 58TH ST,2024,NARCOTICS,POSS: HEROIN(WHITE),CHA PARKING LOT/GROUNDS,true,false,0711,007,20,68,18,1174454,1866532,2009,04/22/2009 04:31:26 PM,41.789160063,-87.635871762,"(41.789160063, -87.635871762)"
-6887410,HR281488,04/22/2009 01:00:00 PM,082XX S INGLESIDE AVE,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,false,false,0631,006,8,44,26,1184040,1850483,2009,05/12/2009 09:59:06 PM,41.744901303,-87.601224312,"(41.744901303, -87.601224312)"
-6989783,HR283093,04/22/2009 09:00:00 AM,007XX E 63RD ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0312,003,20,42,06,1182401,1863364,2009,06/26/2009 06:05:44 AM,41.780286179,-87.606831164,"(41.780286179, -87.606831164)"
-6873978,HR280649,04/21/2009 09:45:00 PM,030XX W 53RD ST,1121,DECEPTIVE PRACTICE,COUNTERFEITING DOCUMENT,STREET,true,false,0911,009,14,63,10,1157222,1869386,2009,04/25/2009 11:48:07 AM,41.797358067,-87.698978874,"(41.797358067, -87.698978874)"
-6873891,HR280481,04/21/2009 09:10:00 PM,061XX S COTTAGE GROVE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,0313,003,20,42,18,1182575,1864672,2009,04/21/2009 09:40:42 PM,41.783871415,-87.606152705,"(41.783871415, -87.606152705)"
-6872173,HR277740,04/20/2009 12:00:00 PM,001XX N KOSTNER AVE,0320,ROBBERY,STRONGARM - NO WEAPON,APARTMENT,false,true,1114,011,28,26,03,1147080,1900271,2009,05/19/2009 09:31:01 PM,41.882310052,-87.73538296,"(41.882310052, -87.73538296)"
-6869764,HR275368,04/18/2009 12:00:00 PM,083XX S SANGAMON ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0613,006,21,71,06,1171380,1849417,2009,04/20/2009 06:20:34 AM,41.742262266,-87.647643338,"(41.742262266, -87.647643338)"
-6870207,HR273992,04/17/2009 09:00:00 PM,114XX S HARVARD AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0522,005,34,49,08B,1176039,1829136,2009,04/25/2009 10:40:58 AM,41.686505251,-87.63117884,"(41.686505251, -87.63117884)"
-6867348,HR272857,04/17/2009 09:30:00 AM,005XX S CENTRAL AVE,0890,THEFT,FROM BUILDING,COMMERCIAL / BUSINESS OFFICE,false,false,1513,015,29,25,06,1139098,1897087,2009,04/19/2009 08:47:03 AM,41.87372161,-87.76477078,"(41.87372161, -87.76477078)"
-6867907,HR272442,04/17/2009 08:10:00 AM,018XX E 71ST ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,true,true,0324,003,5,43,08B,1189920,1858199,2009,04/20/2009 04:35:00 AM,41.765935383,-87.579431702,"(41.765935383, -87.579431702)"
-6864488,HR269683,04/15/2009 05:00:00 PM,080XX S GREEN ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,0621,006,21,71,04B,1171988,1851737,2009,04/20/2009 07:56:20 PM,41.748615337,-87.645347669,"(41.748615337, -87.645347669)"
-6863691,HR269218,04/15/2009 12:05:00 PM,016XX N MONITOR AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2531,025,29,25,26,1137139,1910399,2009,04/20/2009 10:08:06 AM,41.910286827,-87.771643444,"(41.910286827, -87.771643444)"
-6861273,HR267330,04/14/2009 12:07:00 PM,045XX N WOLCOTT AVE,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,RESIDENCE,false,false,1922,019,47,4,06,1162960,1930248,2009,04/15/2009 01:49:20 PM,41.964250454,-87.676227865,"(41.964250454, -87.676227865)"
-6865961,HR271503,04/13/2009 11:00:00 PM,005XX E 89TH PL,0560,ASSAULT,SIMPLE,APARTMENT,false,true,0633,006,6,44,08A,1181539,1845719,2009,05/21/2009 02:23:31 PM,41.731886365,-87.610534859,"(41.731886365, -87.610534859)"
-6861557,HR266601,04/10/2009 06:00:00 PM,001XX E ONTARIO ST,0497,BATTERY,AGGRAVATED DOMESTIC BATTERY: OTHER DANG WEAPON,HOTEL/MOTEL,false,true,1834,018,42,8,04B,1177661,1904570,2009,04/26/2009 01:48:23 PM,41.893467135,-87.622959264,"(41.893467135, -87.622959264)"
-6854300,HR260570,04/10/2009 12:00:00 AM,109XX S AVENUE G,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0432,004,10,52,14,1203146,1833193,2009,04/13/2009 07:50:59 AM,41.696988889,-87.531808573,"(41.696988889, -87.531808573)"
-6853099,HR259187,04/08/2009 09:00:00 AM,050XX S ST LAWRENCE AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,STREET,false,false,0223,002,4,38,26,1181071,1871629,2009,04/21/2009 04:56:02 PM,41.802996815,-87.611452682,"(41.802996815, -87.611452682)"
-6860919,HR257087,04/08/2009 01:00:14 AM,049XX W LAKE ST,1360,CRIMINAL TRESPASS,TO VEHICLE,STREET,true,false,1532,015,28,25,26,1143485,1901844,2009,04/16/2009 10:14:54 AM,41.88669453,-87.74854462,"(41.88669453, -87.74854462)"
-6852857,HR255862,04/07/2009 10:30:00 AM,011XX S HAMILTON AVE,2093,NARCOTICS,FOUND SUSPECT NARCOTICS,JAIL / LOCK-UP FACILITY,true,false,1224,012,2,28,26,1162078,1895078,2009,04/12/2009 08:55:35 AM,41.867760034,-87.680455099,"(41.867760034, -87.680455099)"
-6877687,HR260174,04/06/2009 03:00:00 PM,080XX S JUSTINE ST,1151,DECEPTIVE PRACTICE,ILLEGAL POSSESSION CASH CARD,APARTMENT,false,false,0612,006,21,71,11,1167362,1851427,2009,04/28/2009 12:39:13 PM,41.747864905,-87.662307857,"(41.747864905, -87.662307857)"
-6847421,HR254473,04/06/2009 02:00:00 PM,007XX E 95TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,0633,006,9,49,14,1182863,1842179,2009,04/13/2009 05:47:34 AM,41.722141576,-87.605794166,"(41.722141576, -87.605794166)"
-6850011,HR254802,04/06/2009 03:00:00 AM,031XX N BROADWAY,0560,ASSAULT,SIMPLE,RESIDENCE PORCH/HALLWAY,true,false,2332,019,44,6,08A,1171728,1921463,2009,04/10/2009 10:12:16 AM,41.939955089,-87.644250381,"(41.939955089, -87.644250381)"
-6848241,HR251490,04/04/2009 01:41:43 PM,120XX S YALE AVE,4650,OTHER OFFENSE,SEX OFFENDER: FAIL TO REGISTER,RESIDENCE,true,false,0523,005,9,53,26,1176815,1825190,2009,09/12/2009 09:55:54 AM,41.675659431,-87.628456234,"(41.675659431, -87.628456234)"
-6846826,HR251161,04/04/2009 09:30:17 AM,053XX S INDIANA AVE,031A,ROBBERY,ARMED: HANDGUN,RESIDENCE PORCH/HALLWAY,false,false,0232,002,3,40,03,1178468,1869582,2009,04/16/2009 03:48:40 PM,41.797439253,-87.621061201,"(41.797439253, -87.621061201)"
-7556851,HR250927,04/04/2009 03:38:00 AM,117XX S WENTWORTH AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0522,,34,53,14,,,2009,06/16/2010 06:22:45 AM,,,
-6844561,HR251251,04/04/2009 02:30:00 AM,086XX S RHODES AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0632,006,6,44,08B,1181261,1848011,2009,04/05/2009 06:42:26 AM,41.738182283,-87.611482854,"(41.738182283, -87.611482854)"
-6846373,HR250807,04/04/2009 01:21:47 AM,069XX S OAKLEY AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,false,false,0832,008,17,66,26,1162308,1858445,2009,04/07/2009 11:45:59 AM,41.767229988,-87.680632274,"(41.767229988, -87.680632274)"
-6844819,HR251960,04/03/2009 11:00:00 AM,009XX E 79TH ST,0890,THEFT,FROM BUILDING,HOTEL/MOTEL,false,false,0624,006,8,69,06,1183776,1852860,2009,04/05/2009 06:02:47 AM,41.7514302,-87.60211762,"(41.7514302, -87.60211762)"
-6843105,HR249320,04/03/2009 10:30:00 AM,075XX S LAFAYETTE AVE,0560,ASSAULT,SIMPLE,ALLEY,false,false,0623,006,6,69,08A,1177104,1855009,2009,04/06/2009 07:26:45 AM,41.757480307,-87.626502402,"(41.757480307, -87.626502402)"
-6849782,HR256535,04/02/2009 01:00:00 PM,077XX S TROY ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0835,008,18,70,06,1156762,1853036,2009,05/06/2009 11:16:23 AM,41.752500539,-87.701106554,"(41.752500539, -87.701106554)"
-6839911,HR247128,04/01/2009 10:30:00 PM,029XX W 63RD ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0823,008,15,66,08B,1157702,1862760,2009,04/17/2009 12:50:14 PM,41.779165659,-87.697398361,"(41.779165659, -87.697398361)"
-6837547,HR245109,03/31/2009 05:09:00 PM,057XX S INDIANA AVE,0820,THEFT,$500 AND UNDER,ALLEY,true,false,0233,002,20,40,06,1178537,1867018,2009,04/01/2009 12:06:32 PM,41.790401826,-87.620886093,"(41.790401826, -87.620886093)"
-6837545,HR245160,03/31/2009 03:45:00 PM,032XX S RIDGEWAY AVE,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,false,false,1031,010,22,30,26,1151846,1882899,2009,04/01/2009 08:26:54 AM,41.834546782,-87.718338954,"(41.834546782, -87.718338954)"
-6835248,HR243382,03/30/2009 07:30:00 AM,074XX S EXCHANGE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,GOVERNMENT BUILDING/PROPERTY,false,true,0334,003,7,43,26,1194914,1856037,2009,04/02/2009 07:06:50 AM,41.759881126,-87.561198467,"(41.759881126, -87.561198467)"
-6834458,HR242285,03/30/2009 06:00:00 AM,014XX N FAIRFIELD AVE,0330,ROBBERY,AGGRAVATED,OTHER,false,false,1423,014,26,24,03,1157859,1909464,2009,04/07/2009 10:59:20 AM,41.907323548,-87.695551278,"(41.907323548, -87.695551278)"
-6833403,HR242008,03/29/2009 08:50:00 PM,032XX S MAY ST,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,0924,009,11,60,26,1169219,1883235,2009,04/19/2009 08:01:06 AM,41.835109793,-87.654583013,"(41.835109793, -87.654583013)"
-6833150,HR241479,03/29/2009 12:30:00 PM,006XX N CENTRAL PARK AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,true,1122,011,27,23,08A,1152224,1904210,2009,04/01/2009 08:21:03 AM,41.893019169,-87.716390022,"(41.893019169, -87.716390022)"
-6832237,HR240302,03/28/2009 04:00:00 AM,042XX W BERTEAU AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1722,017,38,16,14,1147609,1927622,2009,03/29/2009 08:30:48 AM,41.957353688,-87.732737066,"(41.957353688, -87.732737066)"
-6831036,HR238385,03/27/2009 09:30:00 AM,030XX N HAUSSEN CT,0820,THEFT,$500 AND UNDER,STREET,false,false,2523,025,30,21,06,1150125,1919903,2009,03/28/2009 08:55:25 AM,41.936123388,-87.723689228,"(41.936123388, -87.723689228)"
-6831875,HR238360,03/27/2009 03:45:00 AM,045XX S CHRISTIANA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0821,008,14,58,14,1154761,1874178,2009,04/01/2009 09:51:36 AM,41.810557461,-87.707875902,"(41.810557461, -87.707875902)"
-6830910,HR238347,03/26/2009 10:00:00 PM,013XX S KEDVALE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1011,010,24,29,14,1148893,1893339,2009,03/30/2009 09:42:39 AM,41.863252981,-87.728904792,"(41.863252981, -87.728904792)"
-6826571,HR234615,03/25/2009 01:50:00 PM,012XX N LARRABEE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,1822,018,27,8,18,1172064,1908588,2009,03/25/2009 03:20:27 PM,41.904618077,-87.643396202,"(41.904618077, -87.643396202)"
-6860566,HR238341,03/25/2009 12:45:00 PM,002XX W WACKER DR,0820,THEFT,$500 AND UNDER,STREET,false,false,0113,001,42,32,06,1174481,1902081,2009,04/16/2009 07:46:09 AM,41.886708831,-87.634712582,"(41.886708831, -87.634712582)"
-6822385,HR231143,03/23/2009 01:15:00 AM,010XX N RIDGEWAY AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,true,1112,011,27,23,08A,1151152,1906639,2009,03/26/2009 10:07:27 AM,41.899705673,-87.720263353,"(41.899705673, -87.720263353)"
-6819291,HR228680,03/22/2009 12:45:00 AM,041XX W CERMAK RD,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1012,010,24,29,08B,1148863,1889089,2009,03/24/2009 04:02:18 PM,41.851591035,-87.729124709,"(41.851591035, -87.729124709)"
-6818797,HR227417,03/21/2009 09:38:00 AM,072XX S COLES AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,APARTMENT,true,false,0334,003,7,43,15,1194252,1857863,2009,03/23/2009 09:16:51 AM,41.764908085,-87.56356476,"(41.764908085, -87.56356476)"
-6816034,HR222807,03/18/2009 02:10:00 PM,035XX W 81ST PL,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0834,008,18,70,05,1154264,1850364,2009,03/30/2009 04:07:04 PM,41.745218134,-87.710331667,"(41.745218134, -87.710331667)"
-6825623,HR222313,03/18/2009 12:13:04 PM,001XX N PINE AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,RESIDENCE PORCH/HALLWAY,false,false,1523,015,29,25,04A,1139429,1900818,2009,03/28/2009 12:54:00 PM,41.883953938,-87.763464534,"(41.883953938, -87.763464534)"
-6811061,HR221520,03/17/2009 11:45:00 PM,068XX S PEORIA ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,RESIDENCE PORCH/HALLWAY,true,false,0723,007,17,68,18,1171523,1859612,2009,03/18/2009 12:29:15 AM,41.770235522,-87.646821304,"(41.770235522, -87.646821304)"
-6808721,HR219705,03/17/2009 01:10:00 AM,020XX W TOUHY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,2424,024,49,1,08B,1161619,1947871,2009,10/31/2014 03:20:56 PM,42.012636708,-87.680664539,"(42.012636708, -87.680664539)"
-6809917,HR210518,03/11/2009 06:50:47 PM,006XX S ST LOUIS AVE,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,false,false,1133,011,24,27,04B,1153099,1897104,2009,03/22/2009 10:05:13 PM,41.873502261,-87.713364995,"(41.873502261, -87.713364995)"
-6799560,HR208043,03/10/2009 11:55:00 AM,100XX W OHARE ST,0890,THEFT,FROM BUILDING,AIRPORT/AIRCRAFT,false,false,1651,016,41,76,06,1100635,1934208,2009,03/16/2009 01:15:15 PM,41.976200173,-87.905312411,"(41.976200173, -87.905312411)"
-6793473,HR204895,03/08/2009 02:20:00 PM,019XX E 73RD PL,0820,THEFT,$500 AND UNDER,RESIDENCE PORCH/HALLWAY,false,false,0333,003,5,43,06,1190517,1856581,2009,04/24/2009 04:20:28 PM,41.761481078,-87.577295653,"(41.761481078, -87.577295653)"
-6807495,HR204475,03/08/2009 05:15:00 AM,007XX W 79TH ST,0460,BATTERY,SIMPLE,BAR OR TAVERN,false,false,0621,,17,71,08B,,,2009,03/23/2009 09:21:23 AM,,,
-6802940,HR202610,03/06/2009 08:40:00 PM,053XX W DIVISION ST,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,2532,025,37,25,03,1140769,1907523,2009,04/04/2009 06:37:40 PM,41.902328747,-87.758378858,"(41.902328747, -87.758378858)"
-6791617,HR201186,03/06/2009 09:15:00 AM,019XX N HAMLIN AVE,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,2535,025,26,22,08B,1150696,1912604,2009,03/10/2009 11:53:29 AM,41.916083135,-87.72178205,"(41.916083135, -87.72178205)"
-6789864,HR200784,03/06/2009 01:00:00 AM,014XX E 67TH PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,false,0332,003,5,43,08B,1187040,1860565,2009,03/11/2009 05:50:29 AM,41.772496678,-87.589912776,"(41.772496678, -87.589912776)"
-6787140,HR200841,03/05/2009 10:00:00 PM,036XX W SUNNYSIDE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1723,017,33,14,06,1151544,1929706,2009,03/07/2009 09:33:25 AM,41.962995739,-87.718215671,"(41.962995739, -87.718215671)"
-6792867,HR199209,03/04/2009 08:28:53 PM,002XX N HOMAN AVE,2220,LIQUOR LAW VIOLATION,ILLEGAL POSSESSION BY MINOR,STREET,true,false,1123,011,28,27,22,1153634,1901391,2009,03/22/2009 01:12:51 PM,41.885255615,-87.711286649,"(41.885255615, -87.711286649)"
-6784664,HR198697,03/04/2009 11:40:00 AM,023XX N KEELER AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2525,025,31,20,14,1148037,1915172,2009,03/05/2009 09:03:23 AM,41.923181585,-87.731484936,"(41.923181585, -87.731484936)"
-6783695,HR198094,03/03/2009 06:00:00 PM,033XX N MEADE AVE,0810,THEFT,OVER $500,STREET,false,false,1633,016,36,17,06,1135102,1921336,2009,03/07/2009 09:33:04 AM,41.940335578,-87.778866913,"(41.940335578, -87.778866913)"
-6779714,HR192584,02/28/2009 12:45:00 PM,121XX S EMERALD AVE,0560,ASSAULT,SIMPLE,STREET,false,false,0523,005,34,53,08A,1173548,1824546,2009,03/05/2009 05:37:16 AM,41.673964907,-87.640433073,"(41.673964907, -87.640433073)"
-6766970,HR184367,02/23/2009 01:00:00 PM,044XX S LOWE AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0935,009,11,61,26,1172738,1875656,2009,02/27/2009 09:39:45 AM,41.814235289,-87.641894624,"(41.814235289, -87.641894624)"
-6767839,HR183551,02/22/2009 07:30:00 PM,001XX W 87TH ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0634,006,21,44,06,1176799,1847235,2009,02/25/2009 08:41:19 AM,41.73615438,-87.627853768,"(41.73615438, -87.627853768)"
-6791296,HR182299,02/21/2009 08:30:00 PM,029XX W IRVING PARK RD,0560,ASSAULT,SIMPLE,COMMERCIAL / BUSINESS OFFICE,true,false,1733,017,33,16,08A,1155800,1926401,2009,03/10/2009 01:10:27 PM,41.953841709,-87.702657263,"(41.953841709, -87.702657263)"
-6764147,HR180671,02/20/2009 06:20:00 PM,081XX S RACINE AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,0613,006,21,71,06,1169784,1850555,2009,02/23/2009 07:00:53 AM,41.745419843,-87.65345813,"(41.745419843, -87.65345813)"
-6763204,HR180260,02/20/2009 03:00:00 PM,044XX S WOODLAWN AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2123,002,4,39,07,1184959,1875702,2009,02/24/2009 10:22:35 AM,41.814082934,-87.597065814,"(41.814082934, -87.597065814)"
-6761650,HR179524,02/20/2009 12:00:00 AM,043XX W 63RD ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0813,008,13,65,26,1148715,1862517,2009,02/23/2009 10:33:42 AM,41.778676483,-87.730352249,"(41.778676483, -87.730352249)"
-6760832,HR177989,02/18/2009 09:30:00 PM,054XX N MASON AVE,0810,THEFT,OVER $500,STREET,false,false,1622,016,45,11,06,1135510,1936051,2009,02/21/2009 08:55:05 AM,41.980707725,-87.77701623,"(41.980707725, -87.77701623)"
-6759723,HR176230,02/18/2009 09:30:00 AM,035XX N OAK PARK AVE,1170,DECEPTIVE PRACTICE,IMPERSONATION,OTHER,true,false,1632,016,36,17,11,1130476,1923078,2009,02/23/2009 01:00:42 PM,41.945196674,-87.795829198,"(41.945196674, -87.795829198)"
-6757667,HR174368,02/17/2009 12:15:00 PM,059XX S RICHMOND ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,0824,008,16,66,08B,1157779,1864780,2009,02/19/2009 11:09:36 AM,41.784707266,-87.697061271,"(41.784707266, -87.697061271)"
-6764221,HR173495,02/16/2009 06:26:00 PM,008XX E 79TH ST,2091,NARCOTICS,FORFEIT PROPERTY,STREET,true,false,0624,006,6,69,26,1183006,1852842,2009,02/21/2009 11:27:56 AM,41.751398729,-87.604939826,"(41.751398729, -87.604939826)"
-6758030,HR172414,02/16/2009 03:54:23 AM,006XX E 100TH PL,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,0511,005,9,49,26,1182324,1838426,2009,02/22/2009 06:12:14 AM,41.711855363,-87.607884253,"(41.711855363, -87.607884253)"
-6754221,HR171936,02/15/2009 05:45:00 PM,079XX S ASHLAND AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0611,006,21,71,08B,1167009,1852265,2009,02/20/2009 07:00:37 AM,41.750172042,-87.66357746,"(41.750172042, -87.66357746)"
-6751778,HR168912,02/13/2009 06:05:00 PM,063XX S PULASKI RD,0320,ROBBERY,STRONGARM - NO WEAPON,PARKING LOT/GARAGE(NON.RESID.),false,false,0823,008,13,65,03,1150827,1862447,2009,06/24/2009 08:01:59 AM,41.778443492,-87.722611228,"(41.778443492, -87.722611228)"
-6752565,HR170127,02/13/2009 05:30:00 PM,072XX S RIDGEWAY AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0833,008,13,65,14,1152592,1856008,2009,02/15/2009 11:45:11 AM,41.76073927,-87.716309908,"(41.76073927, -87.716309908)"
-6749708,HR166687,02/12/2009 11:00:00 AM,012XX N CALIFORNIA AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1423,014,26,24,07,1157544,1908008,2009,02/20/2009 10:49:04 AM,41.903334582,-87.696748107,"(41.903334582, -87.696748107)"
-6750199,HR167510,02/12/2009 12:01:00 AM,123XX S LOWE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0523,005,34,53,06,1174178,1822941,2009,02/16/2009 08:50:40 AM,41.6695466,-87.638174594,"(41.6695466, -87.638174594)"
-6758424,HR160005,02/08/2009 10:00:00 AM,036XX W GRAND AVE,0620,BURGLARY,UNLAWFUL ENTRY,CHURCH/SYNAGOGUE/PLACE OF WORSHIP,false,false,2535,025,27,23,05,1151480,1907878,2009,02/21/2009 11:33:07 AM,41.903099172,-87.719026012,"(41.903099172, -87.719026012)"
-6751015,HR168139,02/07/2009 01:00:00 PM,011XX W WILSON AVE,0460,BATTERY,SIMPLE,COLLEGE/UNIVERSITY GROUNDS,false,false,2311,019,46,3,08B,1167577,1930654,2009,02/16/2009 07:08:53 AM,41.965266117,-87.659240822,"(41.965266117, -87.659240822)"
-6745066,HR159485,02/07/2009 03:00:00 AM,013XX W 14TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA APARTMENT,false,true,1231,012,2,28,08B,1167605,1893558,2009,02/13/2009 09:28:23 AM,41.863471887,-87.66020834,"(41.863471887, -87.66020834)"
-6740534,HR157585,02/06/2009 03:00:00 PM,091XX S COTTAGE GROVE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0633,006,8,44,07,1183201,1844512,2009,02/07/2009 08:09:14 AM,41.728535757,-87.604483781,"(41.728535757, -87.604483781)"
-6741158,HR157020,02/06/2009 02:30:00 PM,044XX S DR MARTIN LUTHER KING JR DR,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,"SCHOOL, PUBLIC, GROUNDS",false,false,0222,002,3,38,07,1179638,1875733,2009,02/16/2009 08:43:04 AM,41.814291445,-87.616582575,"(41.814291445, -87.616582575)"
-6744521,HR162302,02/06/2009 12:00:00 PM,074XX S RHODES AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,0323,003,6,69,14,1181159,1855580,2009,02/10/2009 09:41:22 AM,41.758954805,-87.611623954,"(41.758954805, -87.611623954)"
-6765667,HR183410,02/05/2009 05:00:00 PM,062XX S STEWART AVE,0820,THEFT,$500 AND UNDER,"SCHOOL, PUBLIC, BUILDING",false,false,0711,,20,68,06,,,2009,02/23/2009 12:16:20 PM,,,
-6742944,HR155205,02/05/2009 03:12:03 PM,007XX N LOREL AVE,0454,BATTERY,AGG PO HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,1524,015,37,25,08B,1140502,1904362,2009,02/09/2009 12:37:01 PM,41.893659493,-87.759437273,"(41.893659493, -87.759437273)"
-6737824,HR154855,02/04/2009 11:00:00 PM,008XX N SACRAMENTO BLVD,0820,THEFT,$500 AND UNDER,STREET,false,false,1311,012,26,23,06,1156105,1905668,2009,03/07/2009 07:53:05 AM,41.896942594,-87.702097121,"(41.896942594, -87.702097121)"
-6736905,HR154475,02/04/2009 07:00:00 PM,055XX W HENDERSON ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1633,016,38,15,07,1138614,1921758,2009,02/15/2009 03:13:48 PM,41.941430529,-87.765948665,"(41.941430529, -87.765948665)"
-6747393,HR161303,02/04/2009 01:30:00 PM,024XX S LEAVITT ST,1375,CRIMINAL DAMAGE,INSTITUTIONAL VANDALISM,"SCHOOL, PUBLIC, BUILDING",false,false,1034,010,25,31,14,1162043,1887909,2009,02/12/2009 08:48:19 AM,41.848088325,-87.680783616,"(41.848088325, -87.680783616)"
-6736084,HR153419,02/04/2009 12:30:00 PM,076XX S EGGLESTON AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,OTHER,false,false,0621,,17,69,26,,,2009,02/06/2009 06:18:23 AM,,,
-6735717,HR153298,02/04/2009 11:20:00 AM,115XX S HARVARD AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0522,005,34,53,18,1176058,1828602,2009,02/04/2009 12:06:10 PM,41.685039448,-87.631125219,"(41.685039448, -87.631125219)"
-6739139,HR151193,02/02/2009 11:15:00 PM,080XX S ADA ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0612,006,21,71,18,1168680,1851646,2009,02/09/2009 09:43:02 AM,41.748437564,-87.657471982,"(41.748437564, -87.657471982)"
-6730997,HR148840,02/01/2009 10:15:00 AM,013XX W SCHOOL ST,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,RESIDENCE,false,false,1924,019,44,6,11,1166705,1922062,2009,02/08/2009 03:03:16 PM,41.941708087,-87.662694158,"(41.941708087, -87.662694158)"
-6730699,HR147449,01/31/2009 12:50:00 PM,065XX S STEWART AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0722,007,20,68,08B,1174786,1861707,2009,02/21/2009 10:05:51 AM,41.775912344,-87.634798158,"(41.775912344, -87.634798158)"
-6731609,HR149260,01/31/2009 04:00:00 AM,094XX S ADA ST,031A,ROBBERY,ARMED: HANDGUN,RESIDENCE PORCH/HALLWAY,false,false,2222,022,21,73,03,1168946,1842126,2009,02/19/2009 06:22:43 AM,41.722307581,-87.656771493,"(41.722307581, -87.656771493)"
-6728315,HR144773,01/29/2009 02:00:00 PM,062XX S BISHOP ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0713,007,16,67,05,1167690,1863508,2009,02/03/2009 08:34:24 AM,41.781009737,-87.660759819,"(41.781009737, -87.660759819)"
-6725895,HR143076,01/28/2009 05:30:00 PM,039XX W 68TH ST,0880,THEFT,PURSE-SNATCHING,RESIDENCE-GARAGE,false,false,0833,008,13,65,06,1151106,1859174,2009,01/31/2009 12:20:24 PM,41.769456414,-87.721673725,"(41.769456414, -87.721673725)"
-6731130,HR142915,01/28/2009 04:00:00 PM,028XX W WILCOX ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1124,011,2,27,18,1157478,1899188,2009,02/01/2009 01:47:25 PM,41.879133044,-87.697230716,"(41.879133044, -87.697230716)"
-6723453,HR140531,01/27/2009 08:35:00 AM,029XX S MICHIGAN AVE,0460,BATTERY,SIMPLE,STREET,true,false,2113,001,2,35,08B,1177618,1885722,2009,01/29/2009 12:43:38 PM,41.841748055,-87.623689419,"(41.841748055, -87.623689419)"
-6721918,HR139885,01/26/2009 06:45:00 PM,038XX W ROOSEVELT RD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1011,010,24,29,18,1151045,1894398,2009,01/26/2009 07:38:15 PM,41.866117153,-87.720977189,"(41.866117153, -87.720977189)"
-6726468,HR140085,01/26/2009 06:35:00 PM,108XX S EBERHART AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE-GARAGE,false,false,0513,005,9,49,14,1181538,1833295,2009,01/30/2009 07:17:37 AM,41.697793362,-87.610920402,"(41.697793362, -87.610920402)"
-6725333,HR138041,01/25/2009 01:30:00 PM,0000X W 95TH ST,141C,WEAPONS VIOLATION,UNLAWFUL USE OTHER DANG WEAPON,CTA PLATFORM,true,false,0634,006,21,49,15,1177743,1841988,2009,02/01/2009 06:58:29 AM,41.721734655,-87.624553594,"(41.721734655, -87.624553594)"
-6721275,HR137304,01/24/2009 11:00:00 PM,051XX S ROCKWELL ST,0325,ROBBERY,VEHICULAR HIJACKING,STREET,false,false,0911,009,14,63,03,1159862,1870692,2009,02/04/2009 09:37:55 AM,41.800888034,-87.689261685,"(41.800888034, -87.689261685)"
-6721638,HR137229,01/24/2009 08:59:00 PM,041XX S ASHLAND AVE,0560,ASSAULT,SIMPLE,RESTAURANT,true,false,0914,009,12,61,08A,1166337,1876935,2009,01/27/2009 01:28:35 PM,41.817883938,-87.665337653,"(41.817883938, -87.665337653)"
-6719967,HR136688,01/24/2009 02:15:33 PM,002XX N LOREL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1523,015,28,25,08B,1140708,1901184,2009,02/07/2009 02:04:13 PM,41.884934886,-87.758758849,"(41.884934886, -87.758758849)"
-6718020,HR132748,01/22/2009 07:09:00 AM,046XX S WASHTENAW AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,0912,009,12,58,16,1159118,1873459,2009,01/23/2009 11:14:13 AM,41.808496306,-87.691914473,"(41.808496306, -87.691914473)"
-6716231,HR131073,01/21/2009 08:20:00 AM,038XX W POLK ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1133,011,24,26,08B,1150640,1896045,2009,01/24/2009 02:03:31 PM,41.870644628,-87.722420958,"(41.870644628, -87.722420958)"
-6735540,HR129853,01/20/2009 12:27:41 PM,048XX S ASHLAND AVE,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,0931,009,20,61,06,1166456,1872384,2009,03/04/2009 08:29:14 AM,41.805392947,-87.665030951,"(41.805392947, -87.665030951)"
-6714020,HR130611,01/20/2009 12:00:00 PM,057XX S WENTWORTH AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0233,002,20,68,06,1175997,1866543,2009,01/26/2009 09:14:20 AM,41.78915576,-87.630213795,"(41.78915576, -87.630213795)"
-6712066,HR127921,01/18/2009 10:00:00 PM,019XX N CLYBOURN AVE,0610,BURGLARY,FORCIBLE ENTRY,GROCERY FOOD STORE,false,false,1811,018,32,7,05,1168492,1912598,2009,02/04/2009 04:50:11 PM,41.915699841,-87.656400847,"(41.915699841, -87.656400847)"
-6712254,HR126733,01/18/2009 05:16:59 AM,017XX W ALBION AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,false,2432,024,40,1,08B,1163464,1943927,2009,01/21/2009 08:41:25 AM,42.001775481,-87.673987647,"(42.001775481, -87.673987647)"
-6710777,HR126082,01/17/2009 06:00:00 PM,046XX W NORTH AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,false,false,2533,025,37,25,06,1144852,1910260,2009,01/19/2009 07:39:31 AM,41.909763298,-87.743312036,"(41.909763298, -87.743312036)"
-6708095,HR123388,01/16/2009 12:00:00 AM,016XX W OGDEN AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1211,012,2,28,07,1165710,1899874,2009,01/21/2009 12:52:00 PM,41.880844081,-87.666984737,"(41.880844081, -87.666984737)"
-6708068,HR122908,01/15/2009 06:00:00 PM,044XX W MADISON ST,0810,THEFT,OVER $500,STREET,false,false,1113,011,28,26,06,1146991,1899690,2009,02/04/2009 09:30:49 AM,41.880717422,-87.735724628,"(41.880717422, -87.735724628)"
-6707940,HR122497,01/15/2009 02:18:00 PM,004XX W 57TH ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,false,false,0711,007,20,68,26,1174342,1867096,2009,01/16/2009 04:11:01 PM,41.790710231,-87.63626566,"(41.790710231, -87.63626566)"
-6706492,HR121768,01/15/2009 12:39:00 AM,003XX E 111TH ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,0531,,9,49,15,,,2009,01/16/2009 06:51:14 AM,,,
-6704740,HR117958,01/12/2009 02:32:00 PM,046XX S LAMON AVE,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,0814,008,23,56,08B,1144437,1873254,2009,01/15/2009 11:04:38 AM,41.808221805,-87.74576692,"(41.808221805, -87.74576692)"
-6701331,HR116180,01/11/2009 12:01:00 AM,005XX E 49TH ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0223,002,3,38,06,1180497,1872681,2009,01/11/2009 12:32:46 PM,41.805896802,-87.613525471,"(41.805896802, -87.613525471)"
-6700681,HR115400,01/10/2009 04:20:00 PM,054XX S LOOMIS BLVD,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,false,true,0933,009,16,61,26,1167878,1868820,2009,02/05/2009 12:28:59 PM,41.795582465,-87.659918035,"(41.795582465, -87.659918035)"
-6699914,HR114647,01/10/2009 05:15:00 AM,064XX S COTTAGE GROVE AVE,0312,ROBBERY,ARMED:KNIFE/CUTTING INSTRUMENT,SIDEWALK,false,false,0312,003,20,42,03,1182712,1862392,2009,01/22/2009 05:04:56 PM,41.777611705,-87.605721152,"(41.777611705, -87.605721152)"
-6697739,HR112384,01/08/2009 06:42:00 PM,101XX S MICHIGAN AVE,031A,ROBBERY,ARMED: HANDGUN,APARTMENT,false,false,0511,005,9,49,03,1179086,1837403,2009,03/03/2009 07:29:58 AM,41.709122364,-87.619773631,"(41.709122364, -87.619773631)"
-6697602,HR112128,01/08/2009 03:39:00 PM,025XX W AUGUSTA BLVD,1821,NARCOTICS,MANU/DEL:CANNABIS 10GM OR LESS,APARTMENT,true,false,1312,012,1,24,18,1159173,1906604,2009,01/08/2009 06:26:03 PM,41.899448563,-87.690803059,"(41.899448563, -87.690803059)"
-6695919,HR111090,01/07/2009 11:00:00 PM,006XX N CHRISTIANA AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,false,false,1121,011,27,23,26,1153893,1903940,2009,01/18/2009 10:39:54 AM,41.892245168,-87.710267584,"(41.892245168, -87.710267584)"
-6694683,HR108280,01/06/2009 10:10:00 AM,038XX W OGDEN AVE,0810,THEFT,OVER $500,OTHER,true,false,1014,010,24,29,06,1151213,1889540,2009,01/08/2009 09:06:45 AM,41.852782936,-87.720487741,"(41.852782936, -87.720487741)"
-6690906,HR106240,01/05/2009 02:00:00 AM,016XX N SPRINGFIELD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,2535,025,30,23,14,1150166,1910749,2009,01/06/2009 11:43:14 AM,41.911003195,-87.723777691,"(41.911003195, -87.723777691)"
-6693140,HR105793,01/04/2009 06:13:25 PM,004XX E 71ST ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0322,003,6,69,18,1180177,1858075,2009,01/06/2009 09:59:56 AM,41.765823901,-87.615146556,"(41.765823901, -87.615146556)"
-6690620,HR105985,01/04/2009 03:00:00 PM,072XX S JEFFERY BLVD,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0333,003,5,43,07,1190750,1857588,2009,02/14/2009 07:23:00 AM,41.764238746,-87.576409226,"(41.764238746, -87.576409226)"
-6705867,HR103516,01/03/2009 03:34:00 AM,056XX S FAIRFIELD AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,true,false,0824,008,16,63,14,1159052,1866810,2009,03/16/2009 07:56:21 AM,41.790251921,-87.692338403,"(41.790251921, -87.692338403)"
-6992429,HR100653,01/01/2009 09:59:00 AM,058XX N RIDGE AVE,0460,BATTERY,SIMPLE,NURSING HOME/RETIREMENT HOME,false,false,2013,020,48,77,08B,1164952,1939187,2009,06/30/2009 11:49:00 AM,41.988737226,-87.668648861,"(41.988737226, -87.668648861)"
-6685249,HR100018,12/31/2008 11:40:00 PM,021XX N MILWAUKEE AVE,1330,CRIMINAL TRESPASS,TO LAND,OTHER,true,false,1431,014,1,22,26,1158676,1914100,2008,01/01/2009 10:31:22 AM,41.920028383,-87.692422811,"(41.920028383, -87.692422811)"
-6685397,HP759820,12/31/2008 03:40:00 PM,100XX S PULASKI RD,0620,BURGLARY,UNLAWFUL ENTRY,OTHER,false,false,2211,022,19,74,05,1151458,1837934,2008,01/26/2009 06:45:39 AM,41.711163247,-87.720937475,"(41.711163247, -87.720937475)"
-6684447,HP757966,12/30/2008 03:35:00 PM,055XX S PULASKI RD,0560,ASSAULT,SIMPLE,DRIVEWAY - RESIDENTIAL,false,false,0813,008,13,62,08A,1150605,1867610,2008,01/05/2009 08:00:35 AM,41.79261589,-87.723290808,"(41.79261589, -87.723290808)"
-6686216,HP756013,12/29/2008 02:11:07 PM,036XX S CALIFORNIA AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0913,009,12,58,18,1158255,1880375,2008,01/09/2009 12:12:25 PM,41.827492305,-87.694891308,"(41.827492305, -87.694891308)"
-6678658,HP754977,12/28/2008 08:36:00 PM,071XX S ADA ST,1821,NARCOTICS,MANU/DEL:CANNABIS 10GM OR LESS,SIDEWALK,true,false,0734,007,17,67,18,1168626,1857648,2008,12/28/2008 10:54:46 PM,41.764909018,-87.65749708,"(41.764909018, -87.65749708)"
-6679833,HP754613,12/28/2008 03:00:00 PM,015XX N WALLER AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2531,025,29,25,07,1138091,1909778,2008,04/02/2009 10:48:13 AM,41.908565569,-87.768161143,"(41.908565569, -87.768161143)"
-6682763,HP754555,12/28/2008 02:55:00 PM,039XX W POLK ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1132,011,24,26,08B,1150260,1896116,2008,01/04/2009 10:28:50 PM,41.870846873,-87.723814227,"(41.870846873, -87.723814227)"
-6678927,HP753480,12/27/2008 07:55:00 PM,073XX S CALUMET AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0323,003,6,69,14,1179707,1856456,2008,12/30/2008 08:06:43 AM,41.761391948,-87.616918674,"(41.761391948, -87.616918674)"
-6677524,HP752228,12/27/2008 12:35:00 AM,002XX N LARAMIE AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),VEHICLE NON-COMMERCIAL,true,false,1523,015,28,25,18,1141624,1901296,2008,12/27/2008 02:22:57 AM,41.885225349,-87.755392337,"(41.885225349, -87.755392337)"
-6677440,HP752133,12/26/2008 10:25:00 PM,052XX S SPRINGFIELD AVE,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,true,false,0822,008,23,62,04A,1151225,1869321,2008,12/29/2008 10:18:45 AM,41.797299034,-87.720972631,"(41.797299034, -87.720972631)"
-6678420,HP750104,12/24/2008 01:00:00 PM,087XX S STATE ST,2820,OTHER OFFENSE,TELEPHONE THREAT,VEHICLE NON-COMMERCIAL,false,false,0634,006,21,44,26,1177768,1847213,2008,01/01/2009 07:47:00 AM,41.736072158,-87.624304378,"(41.736072158, -87.624304378)"
-6679793,HP747587,12/23/2008 02:50:00 PM,026XX W 23RD ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1034,010,28,30,18,1159306,1888676,2008,01/07/2009 12:12:51 PM,41.850249688,-87.69080749,"(41.850249688, -87.69080749)"
-6691641,HP747507,12/21/2008 10:30:00 PM,021XX N DAMEN AVE,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,RESTAURANT,false,false,1432,014,32,22,05,1162640,1914160,2008,01/10/2009 04:55:33 PM,41.92011081,-87.677856723,"(41.92011081, -87.677856723)"
-6672755,HP744876,12/21/2008 03:55:00 PM,112XX S BISHOP ST,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,2234,022,34,75,04A,1168515,1830452,2008,01/04/2009 11:42:49 AM,41.690281559,-87.658685185,"(41.690281559, -87.658685185)"
-6669105,HP742677,12/20/2008 02:15:00 AM,018XX N ROCKWELL ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,1434,014,1,22,08B,1158789,1912007,2008,12/20/2008 07:02:47 AM,41.914282715,-87.69206513,"(41.914282715, -87.69206513)"
-6669370,HP741360,12/18/2008 02:55:00 PM,044XX S EVANS AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, GROUNDS",false,false,0222,002,3,38,08B,1181860,1875528,2008,01/09/2009 08:14:57 AM,41.813677763,-87.608438479,"(41.813677763, -87.608438479)"
-6664034,HP737744,12/17/2008 12:00:00 AM,039XX W IRVING PARK RD,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,1723,017,39,16,06,1149195,1926326,2008,12/17/2008 08:16:57 AM,41.953766716,-87.726940097,"(41.953766716, -87.726940097)"
-6663088,HP735722,12/15/2008 05:00:00 PM,062XX S ASHLAND AVE,0460,BATTERY,SIMPLE,RESTAURANT,false,false,0714,007,16,67,08B,1166707,1863058,2008,12/18/2008 02:02:15 PM,41.779795922,-87.664376536,"(41.779795922, -87.664376536)"
-6920809,HR325982,12/15/2008 12:00:00 PM,014XX S MICHIGAN AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0132,,2,33,06,,,2008,05/21/2009 07:11:05 PM,,,
-6662294,HP735950,12/15/2008 08:00:00 AM,020XX N HOYNE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1432,014,32,22,14,1162063,1913708,2008,12/18/2008 10:53:21 AM,41.918882574,-87.679989372,"(41.918882574, -87.679989372)"
-6860779,HR261467,12/12/2008 08:24:00 PM,020XX N OAKLEY AVE,1240,DECEPTIVE PRACTICE,UNLAWFUL USE OF A COMPUTER,RESIDENCE,false,false,1432,014,32,22,11,1160662,1913621,2008,06/01/2009 09:15:29 PM,41.918673013,-87.685139207,"(41.918673013, -87.685139207)"
-6663261,HP730863,12/12/2008 07:30:00 PM,064XX W FULLERTON AVE,0890,THEFT,FROM BUILDING,OTHER,false,false,2512,025,36,19,06,1133118,1915290,2008,12/17/2008 08:38:00 AM,41.923779638,-87.786300692,"(41.923779638, -87.786300692)"
-6656630,HP727764,12/11/2008 08:40:00 AM,005XX N CENTRAL AVE,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,true,false,1523,015,37,25,26,1138986,1902826,2008,12/13/2008 10:58:23 AM,41.889472207,-87.765042468,"(41.889472207, -87.765042468)"
-6653475,HP726004,12/10/2008 08:00:00 AM,066XX N LAKEWOOD AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2432,024,40,1,14,1166400,1944217,2008,12/10/2008 10:23:49 AM,42.002508698,-87.663178176,"(42.002508698, -87.663178176)"
-6654758,HP725485,12/09/2008 10:25:14 PM,001XX E PERSHING RD,031A,ROBBERY,ARMED: HANDGUN,TAVERN/LIQUOR STORE,false,false,0211,002,3,35,03,1178113,1879214,2008,01/17/2009 07:07:42 PM,41.823878367,-87.62207069,"(41.823878367, -87.62207069)"
-6655894,HP726236,12/09/2008 04:10:00 PM,014XX E 61ST PL,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,"SCHOOL, PUBLIC, GROUNDS",false,false,0314,003,20,42,14,1186763,1864534,2008,12/15/2008 06:20:16 AM,41.783394513,-87.590802538,"(41.783394513, -87.590802538)"
-6652044,HP724432,12/09/2008 12:00:00 AM,067XX S JEFFERY BLVD,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,false,false,0332,003,5,43,26,1190664,1860694,2008,12/11/2008 05:51:52 AM,41.772763933,-87.576624274,"(41.772763933, -87.576624274)"
-6685222,HP723627,12/08/2008 02:30:00 PM,019XX N HUMBOLDT BLVD,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1421,014,35,22,05,1156098,1912909,2008,01/06/2009 06:28:50 PM,41.916812663,-87.701927067,"(41.916812663, -87.701927067)"
-6662060,HP719901,12/06/2008 03:45:00 PM,049XX S PRAIRIE AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0224,002,3,38,16,1178835,1872362,2008,12/19/2008 01:16:27 PM,41.805059472,-87.619630723,"(41.805059472, -87.619630723)"
-6647482,HP719499,12/05/2008 02:30:00 PM,101XX S GREEN ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,2232,022,34,73,05,1172477,1837636,2008,12/08/2008 07:34:33 AM,41.70990949,-87.643969625,"(41.70990949, -87.643969625)"
-6638653,HP711966,12/01/2008 07:15:00 PM,050XX W 47TH ST,0810,THEFT,OVER $500,STREET,false,false,0814,008,23,56,06,1143404,1873038,2008,12/02/2008 11:37:20 AM,41.807648385,-87.749561159,"(41.807648385, -87.749561159)"
-6639018,HP711653,12/01/2008 12:45:00 PM,102XX S WOOD ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2213,022,19,72,05,1166122,1836572,2008,12/05/2008 07:48:31 AM,41.707126958,-87.667272813,"(41.707126958, -87.667272813)"
-6646302,HP718151,12/01/2008 05:00:00 AM,054XX W ARMSTRONG AVE,0460,BATTERY,SIMPLE,CTA GARAGE / OTHER PROPERTY,false,false,1621,016,45,11,08B,1138965,1937391,2008,12/06/2008 01:20:49 PM,41.984322515,-87.76427699,"(41.984322515, -87.76427699)"
-6728721,HR145749,12/01/2008 12:00:00 AM,003XX N WABASH AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,COMMERCIAL / BUSINESS OFFICE,false,true,0122,001,42,32,26,1176617,1902438,2008,02/03/2009 03:19:39 PM,41.887640471,-87.62685793,"(41.887640471, -87.62685793)"
-6636767,HP710112,11/30/2008 10:00:00 PM,048XX N SHERIDAN RD,1330,CRIMINAL TRESPASS,TO LAND,PARKING LOT/GARAGE(NON.RESID.),true,false,2024,020,46,3,26,1168713,1932579,2008,12/01/2008 10:52:19 AM,41.970523776,-87.655008001,"(41.970523776, -87.655008001)"
-6636808,HP709772,11/30/2008 05:35:00 PM,002XX W GARFIELD BLVD,031A,ROBBERY,ARMED: HANDGUN,GAS STATION,false,false,0934,009,3,37,03,1175416,1868536,2008,12/17/2008 03:05:57 PM,41.794637775,-87.632284526,"(41.794637775, -87.632284526)"
-6648895,HP721236,11/29/2008 02:00:00 PM,050XX N MONTCLARE AVE,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,RESIDENCE,false,false,1613,016,41,10,06,1127941,1932857,2008,01/08/2009 11:49:17 AM,41.972074582,-87.804925436,"(41.972074582, -87.804925436)"
-6633994,HP706551,11/28/2008 09:45:00 AM,031XX W OHIO ST,0890,THEFT,FROM BUILDING,APARTMENT,false,false,1313,012,27,23,06,1155168,1903865,2008,12/01/2008 11:03:41 AM,41.892013859,-87.705587042,"(41.892013859, -87.705587042)"
-6633095,HP705214,11/27/2008 03:00:00 PM,053XX S RACINE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0934,009,16,61,08B,1169270,1869394,2008,02/04/2009 11:54:27 AM,41.797127549,-87.65479691,"(41.797127549, -87.65479691)"
-6630349,HP701493,11/25/2008 01:15:00 PM,095XX S LA SALLE ST,0460,BATTERY,SIMPLE,STREET,false,false,0511,005,21,49,08B,1176914,1841892,2008,12/01/2008 11:47:27 AM,41.721489907,-87.627592945,"(41.721489907, -87.627592945)"
-6627754,HP700408,11/24/2008 07:50:00 PM,005XX W WINNECONNA PKWY,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0621,006,17,69,18,1174053,1852885,2008,11/24/2008 08:51:38 PM,41.751720034,-87.637746785,"(41.751720034, -87.637746785)"
-6628038,HP699542,11/24/2008 11:56:20 AM,057XX W ROOSEVELT RD,0460,BATTERY,SIMPLE,HOSPITAL BUILDING/GROUNDS,false,false,1513,015,29,25,08B,1138207,1894141,2008,11/27/2008 12:11:52 PM,41.865653532,-87.768113372,"(41.865653532, -87.768113372)"
-6627087,HP698897,11/24/2008 12:14:00 AM,071XX S JEFFERY BLVD,0560,ASSAULT,SIMPLE,SIDEWALK,false,true,0333,003,5,43,08A,1190737,1858150,2008,12/01/2008 11:41:39 AM,41.765781234,-87.576438747,"(41.765781234, -87.576438747)"
-6625131,HP696588,11/22/2008 01:43:50 PM,007XX N CENTRAL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1524,015,37,25,08B,1138931,1904380,2008,11/26/2008 01:33:27 PM,41.893737577,-87.765206667,"(41.893737577, -87.765206667)"
-6624440,HP696314,11/22/2008 09:50:00 AM,045XX N CLARK ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,1922,019,47,3,06,1165539,1930383,2008,11/24/2008 09:03:09 AM,41.964566235,-87.666741798,"(41.964566235, -87.666741798)"
-6623258,HP694898,11/21/2008 12:00:00 PM,040XX S OAKENWALD AVE,0810,THEFT,OVER $500,ALLEY,false,false,2123,002,4,36,06,1184100,1878322,2008,11/22/2008 09:56:06 AM,41.821292552,-87.60013466,"(41.821292552, -87.60013466)"
-6629641,HP694345,11/21/2008 08:00:00 AM,017XX N CLYBOURN AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,1813,018,43,7,06,1169955,1911324,2008,11/27/2008 07:45:11 AM,41.912172102,-87.651063168,"(41.912172102, -87.651063168)"
-6623928,HP690432,11/18/2008 11:36:54 PM,056XX S ASHLAND AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,0715,007,15,67,18,1166591,1867439,2008,11/22/2008 08:03:00 AM,41.791820404,-87.664676902,"(41.791820404, -87.664676902)"
-6612646,HP685205,11/15/2008 05:20:00 PM,079XX S SOUTH SHORE DR,0486,BATTERY,DOMESTIC BATTERY SIMPLE,PARKING LOT/GARAGE(NON.RESID.),false,true,0422,004,7,46,08B,1198478,1853116,2008,11/19/2008 09:13:01 AM,41.75177718,-87.548234406,"(41.75177718, -87.548234406)"
-6613097,HP686172,11/15/2008 08:00:00 AM,009XX W WEBSTER AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1812,018,43,7,14,1169690,1914857,2008,11/17/2008 10:09:39 AM,41.921872624,-87.651933574,"(41.921872624, -87.651933574)"
-6618756,HP691117,11/14/2008 10:00:00 PM,006XX N MONTICELLO AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,1122,011,27,23,26,1151896,1904050,2008,11/23/2008 10:00:04 AM,41.892586578,-87.717598866,"(41.892586578, -87.717598866)"
-6610665,HP683428,11/14/2008 02:30:00 PM,023XX W BLOOMINGDALE AVE,0810,THEFT,OVER $500,STREET,false,false,1434,014,1,22,06,1160311,1911958,2008,11/18/2008 09:30:54 AM,41.914116892,-87.686474898,"(41.914116892, -87.686474898)"
-6609805,HP682076,11/13/2008 11:50:00 PM,024XX S WHIPPLE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,false,1033,010,12,30,08B,1156376,1887563,2008,11/18/2008 11:10:38 AM,41.847255145,-87.701591182,"(41.847255145, -87.701591182)"
-6609759,HP682292,11/13/2008 11:30:00 PM,056XX S MELVINA AVE,0810,THEFT,OVER $500,STREET,false,false,0811,008,23,56,06,1135962,1866711,2008,11/23/2008 12:16:58 PM,41.790421461,-87.777007112,"(41.790421461, -87.777007112)"
-6621069,HP681949,11/13/2008 09:28:00 PM,048XX W WABANSIA AVE,2017,NARCOTICS,MANU/DELIVER:CRACK,ALLEY,true,false,2533,,37,25,18,,,2008,08/23/2010 12:10:32 PM,,,
-6611470,HP678755,11/12/2008 07:40:06 AM,007XX E 111TH ST,2022,NARCOTICS,POSS: COCAINE,JAIL / LOCK-UP FACILITY,false,false,0531,,9,50,18,,,2008,11/08/1999 03:39:40 PM,,,
-6604802,HP677855,11/11/2008 01:15:00 PM,0000X W RANDOLPH ST,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0122,001,42,32,06,1175960,1901321,2008,11/12/2008 08:49:21 AM,41.884590179,-87.629304266,"(41.884590179, -87.629304266)"
-6624905,HP687723,11/11/2008 09:00:00 AM,096XX S LA SALLE ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,OTHER,false,false,0511,,21,49,06,,,2008,12/03/2008 08:06:38 AM,,,
-6602453,HP674673,11/09/2008 05:00:00 PM,059XX N KNOX AVE,0810,THEFT,OVER $500,DRIVEWAY - RESIDENTIAL,false,true,1711,017,39,12,06,1144319,1939382,2008,11/13/2008 12:10:03 PM,41.989686657,-87.744535119,"(41.989686657, -87.744535119)"
-6609526,HP674616,11/09/2008 03:55:00 PM,021XX E 71ST ST,1330,CRIMINAL TRESPASS,TO LAND,SMALL RETAIL STORE,true,false,0333,003,5,43,26,1191967,1858243,2008,11/16/2008 08:03:15 AM,41.766006643,-87.571927449,"(41.766006643, -87.571927449)"
-6712285,HR128230,11/08/2008 12:00:00 PM,016XX W PIERCE AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,1433,014,1,24,06,1165146,1910481,2008,01/31/2009 01:06:28 PM,41.909962467,-87.668754008,"(41.909962467, -87.668754008)"
-6640410,HP672495,11/08/2008 10:06:00 AM,038XX W AUGUSTA BLVD,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),VEHICLE NON-COMMERCIAL,true,false,1112,,27,23,18,,,2008,06/08/2010 11:59:10 AM,,,
-6604356,HP672189,11/08/2008 03:26:05 AM,028XX E 77TH ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,APARTMENT,false,false,0421,004,7,43,04B,1196731,1854848,2008,11/22/2008 12:52:58 PM,41.75657348,-87.554578757,"(41.75657348, -87.554578757)"
-6599583,HP671302,11/07/2008 03:45:00 PM,080XX S CLYDE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0414,004,8,46,05,1191550,1852127,2008,12/15/2008 09:12:51 AM,41.749233952,-87.573653828,"(41.749233952, -87.573653828)"
-6600239,HP672426,11/06/2008 08:30:00 PM,016XX W 101ST ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,2213,022,19,72,14,1167080,1837611,2008,11/09/2008 07:09:44 AM,41.709957753,-87.663735023,"(41.709957753, -87.663735023)"
-6600036,HP668836,11/06/2008 11:40:00 AM,009XX N DRAKE AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1121,011,27,23,26,1152587,1905838,2008,11/09/2008 07:27:47 AM,41.897479391,-87.715013763,"(41.897479391, -87.715013763)"
-6597621,HP667089,11/05/2008 12:55:00 PM,031XX S ARCHER AVE,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,OTHER,false,false,0922,009,11,59,11,1165838,1883820,2008,11/13/2008 08:34:59 AM,41.836787733,-87.666972243,"(41.836787733, -87.666972243)"
-6594106,HP666701,11/04/2008 02:35:00 PM,019XX W 23RD ST,0810,THEFT,OVER $500,"SCHOOL, PUBLIC, BUILDING",false,false,1034,010,25,31,06,1164022,1888805,2008,12/05/2008 09:58:43 AM,41.850505543,-87.673495331,"(41.850505543, -87.673495331)"
-6592576,HP664815,11/03/2008 06:30:00 PM,115XX S EGGLESTON AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,0522,005,34,53,26,1175409,1828238,2008,11/11/2008 06:19:45 AM,41.684055068,-87.633511858,"(41.684055068, -87.633511858)"
-6594326,HP660477,11/01/2008 04:45:00 PM,123XX S PRINCETON AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0523,005,34,53,08B,1176469,1823206,2008,11/10/2008 07:50:55 AM,41.670222782,-87.629781953,"(41.670222782, -87.629781953)"
-6869736,HR276562,11/01/2008 03:00:00 PM,101XX S CALUMET AVE,1195,DECEPTIVE PRACTICE,FINAN EXPLOIT-ELDERLY/DISABLED,RESIDENCE,false,false,0511,005,9,49,11,1180411,1837736,2008,06/09/2009 01:30:25 PM,41.710005928,-87.614911195,"(41.710005928, -87.614911195)"
-6584355,HP656536,10/30/2008 06:00:00 PM,016XX W 95TH ST,0890,THEFT,FROM BUILDING,COMMERCIAL / BUSINESS OFFICE,false,false,2221,022,21,73,06,1167014,1841746,2008,10/31/2008 10:02:08 AM,41.72130625,-87.663858979,"(41.72130625, -87.663858979)"
-6585399,HP656512,10/30/2008 05:30:00 PM,115XX S UNION AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0524,005,34,53,14,1173569,1828313,2008,11/01/2008 10:09:58 AM,41.684301698,-87.640245301,"(41.684301698, -87.640245301)"
-6585182,HP656300,10/30/2008 04:30:00 PM,070XX S EBERHART AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,0322,003,6,69,05,1180757,1858319,2008,11/29/2008 03:48:42 PM,41.766480153,-87.61301319,"(41.766480153, -87.61301319)"
-6596147,HP657548,10/30/2008 03:10:00 PM,018XX W 77TH ST,0460,BATTERY,SIMPLE,STREET,false,false,0611,006,17,71,08B,1165652,1853664,2008,11/13/2008 07:02:48 AM,41.754039975,-87.668510513,"(41.754039975, -87.668510513)"
-6585877,HP656005,10/30/2008 01:30:00 PM,002XX W MONROE ST,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,0112,001,2,32,06,1174504,1899912,2008,11/04/2008 11:41:17 AM,41.880756452,-87.634692965,"(41.880756452, -87.634692965)"
-6592864,HP662904,10/30/2008 12:30:00 PM,029XX W POLK ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,1135,011,28,27,08B,1156782,1896186,2008,11/29/2008 09:22:02 AM,41.870909382,-87.699867667,"(41.870909382, -87.699867667)"
-6581987,HP652684,10/28/2008 04:05:00 PM,051XX W POTOMAC AVE,2093,NARCOTICS,FOUND SUSPECT NARCOTICS,RESIDENCE PORCH/HALLWAY,true,false,2533,025,37,25,26,1141934,1908129,2008,10/29/2008 02:22:45 PM,41.903970175,-87.754084545,"(41.903970175, -87.754084545)"
-6611003,HP682851,10/27/2008 10:00:00 AM,0000X S MORGAN ST,0890,THEFT,FROM BUILDING,CONSTRUCTION SITE,false,false,1212,012,2,28,06,1169747,1899958,2008,12/13/2008 06:07:26 PM,41.880987614,-87.652158814,"(41.880987614, -87.652158814)"
-6577155,HP648810,10/26/2008 12:15:00 PM,064XX S RICHARDS DR,0810,THEFT,OVER $500,STREET,false,false,0331,003,5,42,06,1189824,1862778,2008,11/21/2008 04:05:11 PM,41.778502832,-87.579636486,"(41.778502832, -87.579636486)"
diff --git a/work-with-data/dataprep/data/crime_partfiles/part-00003-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv b/work-with-data/dataprep/data/crime_partfiles/part-00003-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv
deleted file mode 100644
index 17aaf10b..00000000
--- a/work-with-data/dataprep/data/crime_partfiles/part-00003-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv
+++ /dev/null
@@ -1,953 +0,0 @@
-6575372,HP647405,10/25/2008 03:05:00 PM,065XX W DIVERSEY AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,2512,025,36,19,06,1132342,1917855,2008,10/27/2008 07:41:19 AM,41.930831853,-87.789092247,"(41.930831853, -87.789092247)"
-6577639,HP646072,10/24/2008 07:02:55 PM,011XX N LAWNDALE AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1112,011,27,23,18,1151459,1907420,2008,10/31/2008 12:33:49 PM,41.901842788,-87.719115195,"(41.901842788, -87.719115195)"
-6581023,HP644120,10/23/2008 07:05:00 PM,046XX N ST LOUIS AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1723,017,33,14,14,1152171,1930764,2008,12/03/2008 07:46:30 PM,41.965886586,-87.715882373,"(41.965886586, -87.715882373)"
-6573192,HP643972,10/23/2008 06:22:49 PM,009XX N HOMAN AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1121,011,27,23,18,1153564,1906421,2008,10/31/2008 12:35:30 PM,41.899059821,-87.71140982,"(41.899059821, -87.71140982)"
-6575004,HP647317,10/23/2008 05:00:00 PM,001XX W 110TH PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,0513,005,34,49,08B,1177258,1831637,2008,10/28/2008 05:55:45 PM,41.693341013,-87.626641201,"(41.693341013, -87.626641201)"
-6575433,HP643808,10/23/2008 04:45:00 PM,052XX W BLOOMINGDALE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2532,025,37,25,08B,1141308,1911510,2008,10/31/2008 02:08:00 PM,41.913259596,-87.756300482,"(41.913259596, -87.756300482)"
-6570277,HP642742,10/23/2008 01:07:18 AM,049XX W MONROE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1533,015,28,25,18,1143744,1899253,2008,10/26/2008 11:38:26 AM,41.879579666,-87.747658429,"(41.879579666, -87.747658429)"
-6571206,HP643357,10/22/2008 05:30:00 PM,058XX N ROCKWELL ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2011,020,40,2,06,1157988,1938544,2008,10/24/2008 07:41:49 AM,41.987118261,-87.694280938,"(41.987118261, -87.694280938)"
-6569513,HP642562,10/22/2008 07:30:00 AM,023XX W BELLE PLAINE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1912,019,47,5,26,1159899,1927126,2008,12/11/2008 12:50:17 PM,41.955747386,-87.687568778,"(41.955747386, -87.687568778)"
-6568089,HP640873,10/21/2008 10:30:00 PM,036XX N LAKE SHORE DR,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2323,019,46,6,08B,1171850,1924677,2008,10/24/2008 06:53:00 AM,41.948771728,-87.64370696,"(41.948771728, -87.64370696)"
-6567694,HP640791,10/21/2008 10:10:00 PM,036XX W ARMITAGE AVE,1330,CRIMINAL TRESPASS,TO LAND,GROCERY FOOD STORE,true,false,2535,025,26,22,26,1151653,1913001,2008,10/22/2008 08:16:49 AM,41.917153759,-87.718255602,"(41.917153759, -87.718255602)"
-6580994,HP637970,10/20/2008 01:22:00 PM,110XX S MICHIGAN AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,0513,005,9,49,06,1178748,1831610,2008,10/30/2008 08:20:43 AM,41.693233246,-87.621186839,"(41.693233246, -87.621186839)"
-6562518,HP636007,10/18/2008 08:45:00 PM,007XX W GRAND AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1323,012,27,24,06,1171117,1903727,2008,10/22/2008 10:43:41 AM,41.891300049,-87.647017617,"(41.891300049, -87.647017617)"
-6563441,HP635271,10/18/2008 06:45:43 PM,006XX N SPAULDING AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1121,011,27,23,26,1154212,1904405,2008,10/31/2008 12:38:19 PM,41.89351481,-87.709083601,"(41.89351481, -87.709083601)"
-6569355,HP633189,10/17/2008 09:00:00 AM,008XX N MASSASOIT AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,true,1511,015,29,25,14,1137908,1905230,2008,10/24/2008 11:21:20 AM,41.89608861,-87.768943324,"(41.89608861, -87.768943324)"
-6581523,HP632069,10/17/2008 06:05:00 AM,030XX W MADISON ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1331,012,2,27,16,1155826,1899898,2008,11/03/2008 11:50:24 AM,41.881114793,-87.703277455,"(41.881114793, -87.703277455)"
-6559228,HP631981,10/17/2008 01:45:00 AM,001XX W 103RD ST,1330,CRIMINAL TRESPASS,TO LAND,TAVERN/LIQUOR STORE,true,false,0511,005,9,49,26,1176914,1836691,2008,10/18/2008 06:41:17 AM,41.707217652,-87.627749059,"(41.707217652, -87.627749059)"
-6556668,HP629642,10/15/2008 08:42:00 PM,018XX E 71ST ST,1320,CRIMINAL DAMAGE,TO VEHICLE,CTA BUS,false,false,0324,003,5,43,14,1189753,1858195,2008,10/17/2008 08:32:01 AM,41.765928421,-87.580043932,"(41.765928421, -87.580043932)"
-6907793,HR314911,10/15/2008 01:00:00 PM,015XX W HOLLYWOOD AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2012,020,40,77,26,1164724,1937827,2008,06/10/2009 10:27:05 PM,41.985010198,-87.66952624,"(41.985010198, -87.66952624)"
-6557234,HP627032,10/14/2008 04:00:00 PM,006XX W 95TH ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,2223,022,21,73,06,1173765,1841835,2008,10/17/2008 10:54:01 AM,41.721403752,-87.639128792,"(41.721403752, -87.639128792)"
-6553414,HP626854,10/14/2008 01:45:00 PM,033XX N LOCKWOOD AVE,1330,CRIMINAL TRESPASS,TO LAND,"SCHOOL, PRIVATE, GROUNDS",true,false,1634,016,38,15,26,1140400,1921793,2008,10/15/2008 09:10:24 AM,41.941493945,-87.759383448,"(41.941493945, -87.759383448)"
-6555052,HP625875,10/13/2008 10:40:00 PM,009XX N CENTRAL PARK AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1112,011,27,23,18,1152158,1906360,2008,10/15/2008 12:10:09 PM,41.898920283,-87.716575654,"(41.898920283, -87.716575654)"
-6557669,HP625851,10/13/2008 10:30:00 PM,079XX S ASHLAND AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0611,006,21,71,18,1167009,1852265,2008,10/31/2008 01:06:59 PM,41.750172042,-87.66357746,"(41.750172042, -87.66357746)"
-6552731,HP624778,10/13/2008 12:12:00 PM,064XX S CARPENTER ST,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,0724,007,16,68,08A,1170464,1861944,2008,10/15/2008 12:20:30 PM,41.776657944,-87.650635301,"(41.776657944, -87.650635301)"
-6550929,HP623613,10/12/2008 06:08:00 PM,015XX W 59TH ST,0820,THEFT,$500 AND UNDER,APARTMENT,false,false,0713,007,15,67,06,1166893,1865636,2008,10/16/2008 09:07:19 AM,41.786866302,-87.663621021,"(41.786866302, -87.663621021)"
-6556806,HP622841,10/11/2008 11:00:00 PM,015XX E 71ST PL,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0324,003,5,43,14,1187517,1857844,2008,10/17/2008 10:30:09 AM,41.76501869,-87.58825066,"(41.76501869, -87.58825066)"
-6549239,HP620806,10/11/2008 06:51:22 AM,066XX S SEELEY AVE,1020,ARSON,BY FIRE,SIDEWALK,false,false,0726,007,15,67,09,1163874,1860777,2008,02/27/2009 11:43:43 AM,41.773596561,-87.67482676,"(41.773596561, -87.67482676)"
-6555935,HP621609,10/10/2008 11:45:00 PM,035XX N CLARK ST,0890,THEFT,FROM BUILDING,BAR OR TAVERN,false,false,2331,019,44,6,06,1168545,1923866,2008,10/22/2008 12:01:01 PM,41.946618639,-87.655879029,"(41.946618639, -87.655879029)"
-6557159,HP620346,10/10/2008 11:00:00 PM,042XX W 56TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0813,008,13,62,18,1149286,1867178,2008,10/17/2008 12:05:35 PM,41.791455997,-87.72813862,"(41.791455997, -87.72813862)"
-6547087,HP617934,10/09/2008 05:50:00 PM,0000X E 71ST ST,0560,ASSAULT,SIMPLE,DEPARTMENT STORE,false,false,0323,003,6,69,08A,1178077,1857919,2008,10/13/2008 09:46:26 AM,41.765443678,-87.622848431,"(41.765443678, -87.622848431)"
-6546487,HP617442,10/09/2008 01:01:37 PM,0000X N STATE ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0122,001,42,32,06,1176328,1900431,2008,10/15/2008 07:56:21 AM,41.882139677,-87.627979796,"(41.882139677, -87.627979796)"
-6559579,HP615180,10/08/2008 09:45:00 AM,039XX W THOMAS ST,2017,NARCOTICS,MANU/DELIVER:CRACK,VEHICLE NON-COMMERCIAL,true,false,1112,011,27,23,18,1149553,1907004,2008,10/21/2008 09:54:32 AM,41.900738476,-87.726127044,"(41.900738476, -87.726127044)"
-6767798,HP613661,10/07/2008 12:00:00 PM,055XX N WINTHROP AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,2023,020,48,77,26,1167904,1937091,2008,02/26/2009 08:19:57 AM,41.98292237,-87.657851909,"(41.98292237, -87.657851909)"
-6547384,HP618050,10/07/2008 11:00:00 AM,049XX S KOSTNER AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENTIAL YARD (FRONT/BACK),false,false,0815,008,23,57,05,1147821,1871503,2008,12/01/2008 08:23:16 AM,41.803352641,-87.733399876,"(41.803352641, -87.733399876)"
-6566064,HP633509,10/06/2008 09:00:00 AM,031XX N RICHMOND ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1411,014,33,21,07,1156205,1920696,2008,11/13/2008 11:30:40 AM,41.938178619,-87.701323073,"(41.938178619, -87.701323073)"
-6553151,HP613397,10/05/2008 10:30:00 PM,071XX S RICHMOND ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,true,0831,008,18,66,26,1157901,1857121,2008,10/15/2008 07:58:48 AM,41.763687367,-87.696821801,"(41.763687367, -87.696821801)"
-6540432,HP610897,10/05/2008 09:05:00 PM,060XX S CHAMPLAIN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,0313,003,20,42,08B,1181562,1865119,2008,10/23/2008 09:21:38 AM,41.785121473,-87.609852906,"(41.785121473, -87.609852906)"
-6545210,HP609100,10/04/2008 05:30:00 AM,081XX S EVANS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0631,006,6,44,08B,1182599,1851279,2008,10/13/2008 09:44:43 AM,41.747119135,-87.606479654,"(41.747119135, -87.606479654)"
-6539379,HP606845,10/03/2008 01:00:00 PM,100XX W OHARE ST,0810,THEFT,OVER $500,AIRPORT/AIRCRAFT,false,false,1651,016,41,76,06,1100635,1934208,2008,10/09/2008 11:41:08 AM,41.976200173,-87.905312411,"(41.976200173, -87.905312411)"
-6548073,HP619012,10/03/2008 10:30:00 AM,077XX S WINCHESTER AVE,0890,THEFT,FROM BUILDING,OTHER,false,false,0611,006,18,71,06,1164738,1853427,2008,10/12/2008 07:33:00 AM,41.753408946,-87.671866707,"(41.753408946, -87.671866707)"
-6536668,HP610256,10/03/2008 01:00:00 AM,007XX W ALDINE AVE,0810,THEFT,OVER $500,STREET,false,false,2332,019,44,6,06,1170854,1922097,2008,10/06/2008 11:04:11 AM,41.941714043,-87.647443936,"(41.941714043, -87.647443936)"
-6528369,HP602555,09/30/2008 10:30:00 PM,021XX W CHICAGO AVE,0810,THEFT,OVER $500,STREET,false,false,1312,012,32,24,06,1161879,1905351,2008,10/03/2008 12:57:24 PM,41.895954188,-87.680898948,"(41.895954188, -87.680898948)"
-6531670,HP601401,09/30/2008 12:40:00 PM,007XX N PULASKI RD,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,1111,011,28,23,08B,1149548,1904720,2008,10/03/2008 11:07:30 AM,41.894471042,-87.726204778,"(41.894471042, -87.726204778)"
-6526364,HP600349,09/29/2008 06:31:00 PM,039XX W MADISON ST,0460,BATTERY,SIMPLE,DEPARTMENT STORE,true,false,1122,011,28,26,08B,1149793,1899754,2008,10/01/2008 12:21:48 PM,41.880839044,-87.725434164,"(41.880839044, -87.725434164)"
-6525642,HP599340,09/29/2008 08:45:00 AM,052XX S MARSHFIELD AVE,0454,BATTERY,AGG PO HANDS NO/MIN INJURY,STREET,false,false,0932,009,16,61,08B,1166188,1870050,2008,10/20/2008 10:20:21 AM,41.798993893,-87.66608032,"(41.798993893, -87.66608032)"
-6525247,HP598111,09/28/2008 02:40:00 PM,067XX S MAY ST,0810,THEFT,OVER $500,STREET,false,false,0724,007,17,68,06,1169779,1859844,2008,09/30/2008 12:20:02 PM,41.770910181,-87.65320736,"(41.770910181, -87.65320736)"
-6526452,HP598072,09/28/2008 01:55:00 PM,0000X N WESTERN AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,false,false,1332,012,2,28,04A,1160452,1899984,2008,10/03/2008 12:01:43 PM,41.881256335,-87.686288658,"(41.881256335, -87.686288658)"
-6528127,HP598096,09/28/2008 01:49:00 PM,049XX S KEDZIE AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,0821,008,14,63,06,1155841,1871546,2008,10/20/2008 01:26:12 PM,41.80331326,-87.703985238,"(41.80331326, -87.703985238)"
-6536838,HP610239,09/28/2008 12:00:00 PM,0000X W ELM ST,0890,THEFT,FROM BUILDING,APARTMENT,false,false,1824,018,42,8,06,1175967,1908131,2008,10/06/2008 10:11:44 AM,41.903277012,-87.629073298,"(41.903277012, -87.629073298)"
-6539567,HP597567,09/28/2008 06:00:00 AM,047XX W CHICAGO AVE,2250,LIQUOR LAW VIOLATION,LIQUOR LICENSE VIOLATION,OTHER,true,false,1111,011,28,25,22,1144494,1904846,2008,10/07/2008 11:48:21 AM,41.894913437,-87.744763648,"(41.894913437, -87.744763648)"
-6523903,HP596365,09/27/2008 01:45:00 PM,001XX W 87TH ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0634,006,21,44,06,1176799,1847235,2008,09/30/2008 06:11:44 AM,41.73615438,-87.627853768,"(41.73615438, -87.627853768)"
-6522778,HP596247,09/27/2008 12:45:00 PM,053XX N OAK PARK AVE,502R,OTHER OFFENSE,VEHICLE TITLE/REG OFFENSE,ALLEY,false,false,1613,016,41,10,26,1130229,1934994,2008,10/20/2008 09:11:18 AM,41.977899689,-87.796462673,"(41.977899689, -87.796462673)"
-6523767,HP595901,09/27/2008 09:20:00 AM,008XX W 88TH ST,0810,THEFT,OVER $500,STREET,false,false,2223,,21,71,06,,,2008,09/29/2008 06:13:48 AM,,,
-6521924,HP595165,09/26/2008 11:30:00 AM,034XX W 51ST ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0822,008,14,63,05,1154340,1870558,2008,09/29/2008 01:48:43 PM,41.800632077,-87.70951647,"(41.800632077, -87.70951647)"
-6528339,HP601993,09/26/2008 11:00:00 AM,049XX S BLACKSTONE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2124,002,4,39,07,1186723,1872346,2008,10/01/2008 08:05:33 AM,41.804832184,-87.590701769,"(41.804832184, -87.590701769)"
-6517912,HP591247,09/24/2008 08:05:00 PM,001XX N KILDARE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1114,011,28,26,08B,1147662,1900464,2008,10/03/2008 11:56:00 AM,41.882828519,-87.733240875,"(41.882828519, -87.733240875)"
-6517989,HP589842,09/24/2008 05:23:00 AM,061XX S MICHIGAN AVE,0610,BURGLARY,FORCIBLE ENTRY,"SCHOOL, PUBLIC, BUILDING",false,false,0311,003,20,40,05,1178245,1864514,2008,10/01/2008 10:47:24 PM,41.783537238,-87.622032731,"(41.783537238, -87.622032731)"
-6514844,HP589276,09/23/2008 08:20:00 PM,010XX E 95TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0413,004,8,47,14,1184923,1842237,2008,09/24/2008 09:07:17 AM,41.722252713,-87.598246962,"(41.722252713, -87.598246962)"
-6517611,HP588048,09/23/2008 10:03:29 AM,044XX S MICHIGAN AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,OTHER,false,false,0221,002,3,38,04B,1177865,1875401,2008,09/26/2008 01:32:41 PM,41.813420805,-87.623096133,"(41.813420805, -87.623096133)"
-6520246,HP587630,09/22/2008 11:30:00 PM,065XX S MINERVA AVE,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,0321,003,20,42,06,1184963,1862074,2008,09/27/2008 07:17:42 AM,41.776686524,-87.597479042,"(41.776686524, -87.597479042)"
-6513205,HP587908,09/22/2008 10:00:00 PM,070XX S ROCKWELL ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0832,008,18,66,05,1160292,1858039,2008,09/26/2008 08:54:03 AM,41.766157608,-87.688032966,"(41.766157608, -87.688032966)"
-6510285,HP585700,09/22/2008 12:00:00 AM,007XX N PARKSIDE AVE,0810,THEFT,OVER $500,STREET,false,false,1511,015,29,25,06,1138497,1904575,2008,09/23/2008 09:51:04 AM,41.894280556,-87.766795898,"(41.894280556, -87.766795898)"
-6513473,HP585474,09/21/2008 10:35:00 PM,063XX S CENTRAL AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,true,0813,008,13,64,06,1140220,1861647,2008,10/20/2008 08:53:35 AM,41.776448287,-87.761517297,"(41.776448287, -87.761517297)"
-6512834,HP583583,09/20/2008 09:20:00 PM,047XX W HARRISON ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,true,false,1131,011,24,25,15,1144547,1896955,2008,09/25/2008 12:04:58 PM,41.873258598,-87.74476776,"(41.873258598, -87.74476776)"
-6516044,HP583506,09/20/2008 08:25:00 PM,057XX S BISHOP ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0713,007,16,67,18,1167599,1866876,2008,09/29/2008 11:06:03 AM,41.790253892,-87.660996889,"(41.790253892, -87.660996889)"
-6513663,HP582905,09/20/2008 02:15:00 PM,013XX W 78TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0612,006,17,71,14,1168874,1853086,2008,09/25/2008 07:11:00 AM,41.752384936,-87.656719603,"(41.752384936, -87.656719603)"
-6519980,HP581044,09/19/2008 10:00:00 PM,115XX S MAY ST,0281,CRIM SEXUAL ASSAULT,NON-AGGRAVATED,RESIDENCE,false,false,0524,005,34,53,02,1170684,1828465,2008,10/13/2008 08:11:04 PM,41.684782017,-87.650802016,"(41.684782017, -87.650802016)"
-6516941,HP590155,09/19/2008 09:00:00 PM,089XX S CHAPPEL AVE,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,RESIDENCE,false,false,0413,004,8,48,11,1191356,1846298,2008,10/07/2008 08:11:37 AM,41.733243343,-87.574553043,"(41.733243343, -87.574553043)"
-6504842,HP579511,09/18/2008 07:00:00 PM,062XX S ROCKWELL ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0825,008,15,66,05,1160150,1863238,2008,10/06/2008 08:39:50 AM,41.780427329,-87.688410549,"(41.780427329, -87.688410549)"
-6502940,HP578190,09/17/2008 08:00:00 PM,023XX W JARVIS AVE,0810,THEFT,OVER $500,STREET,false,false,2411,024,49,2,06,1159051,1948914,2008,09/18/2008 07:28:28 AM,42.015552067,-87.690084649,"(42.015552067, -87.690084649)"
-6500323,HP575269,09/15/2008 04:30:00 PM,074XX N ARTESIAN AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2411,024,50,2,06,1158735,1949658,2008,09/18/2008 01:11:19 PM,42.017600134,-87.691226887,"(42.017600134, -87.691226887)"
-6494594,HP570731,09/13/2008 10:00:00 PM,036XX N CLARK ST,0870,THEFT,POCKET-PICKING,SIDEWALK,false,false,1923,019,44,6,06,1168310,1924097,2008,09/16/2008 02:36:50 PM,41.947257606,-87.65673611,"(41.947257606, -87.65673611)"
-6497963,HP574011,09/13/2008 12:00:00 AM,007XX N LATROBE AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1524,015,28,25,26,1141178,1904572,2008,09/19/2008 10:40:07 AM,41.894223323,-87.756949341,"(41.894223323, -87.756949341)"
-6493239,HP568800,09/12/2008 10:20:00 PM,073XX S CALIFORNIA AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0835,008,18,66,03,1158939,1855825,2008,09/16/2008 02:55:57 PM,41.760109807,-87.693052634,"(41.760109807, -87.693052634)"
-6494075,HP568214,09/12/2008 05:30:00 PM,011XX E 54TH PL,0337,ROBBERY,ATTEMPT: ARMED-OTHER DANG WEAP,ALLEY,true,false,2131,002,4,41,03,1184542,1869336,2008,09/15/2008 11:06:05 AM,41.796623943,-87.598794952,"(41.796623943, -87.598794952)"
-6496845,HP568218,09/12/2008 04:00:00 PM,006XX W WASHINGTON BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,0111,001,27,28,08B,1171775,1900726,2008,09/18/2008 03:13:49 PM,41.883050646,-87.644689562,"(41.883050646, -87.644689562)"
-6495168,HP567769,09/12/2008 02:03:13 PM,058XX W MELROSE ST,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1633,016,38,15,08B,1137046,1920975,2008,10/16/2008 01:11:45 PM,41.93931023,-87.771730635,"(41.93931023, -87.771730635)"
-6496591,HP568757,09/12/2008 11:00:00 AM,012XX N NOBLE ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1433,014,32,24,05,1166864,1908508,2008,09/21/2008 04:30:20 PM,41.904511728,-87.662499485,"(41.904511728, -87.662499485)"
-6497004,HP567413,09/12/2008 10:30:00 AM,085XX S COTTAGE GROVE AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0632,006,6,44,06,1183016,1848397,2008,09/16/2008 07:20:14 AM,41.739200936,-87.605041045,"(41.739200936, -87.605041045)"
-6519033,HP565828,09/11/2008 03:12:00 PM,048XX W ADAMS ST,2017,NARCOTICS,MANU/DELIVER:CRACK,VEHICLE NON-COMMERCIAL,true,false,1533,,28,25,18,,,2008,06/18/2010 04:43:24 PM,,,
-6549469,HP621240,09/11/2008 03:00:00 PM,071XX S NORMAL BLVD,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0732,007,6,68,05,1174233,1857684,2008,10/27/2008 08:52:50 PM,41.764885076,-87.636944812,"(41.764885076, -87.636944812)"
-6489511,HP565204,09/11/2008 09:00:00 AM,013XX N PAULINA ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,1433,014,1,24,06,1164854,1909126,2008,09/12/2008 12:17:20 PM,41.906250459,-87.669865196,"(41.906250459, -87.669865196)"
-6496371,HP563537,09/10/2008 11:30:44 AM,067XX S LAFLIN ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,true,false,0725,007,17,67,05,1167463,1859744,2008,10/19/2008 09:31:01 AM,41.770685712,-87.661699822,"(41.770685712, -87.661699822)"
-6487114,HP563447,09/10/2008 10:45:00 AM,004XX N CAMPBELL AVE,0810,THEFT,OVER $500,STREET,false,false,1313,012,26,24,06,1159694,1903083,2008,09/11/2008 11:11:24 AM,41.889775916,-87.688986553,"(41.889775916, -87.688986553)"
-6498728,HP563314,09/10/2008 07:00:00 AM,053XX N OLCOTT AVE,0810,THEFT,OVER $500,STREET,false,false,1613,016,41,10,06,1125387,1934909,2008,09/18/2008 11:32:44 AM,41.977748333,-87.814271395,"(41.977748333, -87.814271395)"
-6486566,HP561788,09/09/2008 01:30:00 PM,063XX S HALSTED PKWY,0460,BATTERY,SIMPLE,STREET,false,false,0723,007,20,68,08B,1172680,1863029,2008,09/17/2008 09:39:15 AM,41.779586747,-87.642479609,"(41.779586747, -87.642479609)"
-6489550,HP561790,09/09/2008 01:20:00 PM,094XX S STATE ST,0560,ASSAULT,SIMPLE,SMALL RETAIL STORE,true,false,0634,006,6,49,08A,1177989,1842459,2008,09/12/2008 05:58:21 AM,41.723021579,-87.623638321,"(41.723021579, -87.623638321)"
-6487595,HP561603,09/09/2008 12:00:00 PM,004XX N HAMLIN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,false,1122,011,27,23,08B,1150934,1902819,2008,09/13/2008 10:25:26 AM,41.889227476,-87.721164183,"(41.889227476, -87.721164183)"
-6481901,HP559265,09/08/2008 01:35:00 AM,001XX W ILLINOIS ST,4310,OTHER OFFENSE,POSSESSION OF BURGLARY TOOLS,PARKING LOT/GARAGE(NON.RESID.),false,false,1831,018,42,8,26,1174863,1903533,2008,09/09/2008 07:05:24 AM,41.890684658,-87.633266291,"(41.890684658, -87.633266291)"
-6481348,HP558792,09/07/2008 06:00:00 PM,002XX N PARKSIDE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,true,1512,015,29,25,14,1138597,1901016,2008,09/11/2008 10:12:08 AM,41.884512396,-87.766514967,"(41.884512396, -87.766514967)"
-6482023,HP558456,09/07/2008 03:44:16 PM,056XX S DR MARTIN LUTHER KING JR DR,0496,BATTERY,AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST,APARTMENT,false,true,0234,002,20,40,04B,1179847,1867688,2008,09/19/2008 12:33:47 PM,41.792210478,-87.616062209,"(41.792210478, -87.616062209)"
-6480370,HP555300,09/05/2008 08:07:00 PM,013XX W 51ST ST,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0933,009,16,61,03,1168492,1870910,2008,09/09/2008 05:44:14 PM,41.801304442,-87.657606228,"(41.801304442, -87.657606228)"
-6478673,HP555185,09/05/2008 06:15:00 PM,024XX W 103RD ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,VEHICLE NON-COMMERCIAL,true,false,2211,022,19,72,18,1161762,1836305,2008,09/06/2008 06:31:21 AM,41.706485697,-87.683246597,"(41.706485697, -87.683246597)"
-6479280,HP554201,09/05/2008 09:30:00 AM,034XX W WILSON AVE,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",true,false,1723,017,33,14,08B,1152451,1930390,2008,09/08/2008 12:57:05 PM,41.964854764,-87.714862788,"(41.964854764, -87.714862788)"
-6478528,HP555489,09/04/2008 06:00:00 PM,032XX W DIVERSEY AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1412,014,35,22,14,1154324,1918399,2008,09/06/2008 09:01:50 AM,41.931913322,-87.708297723,"(41.931913322, -87.708297723)"
-6476043,HP553350,09/03/2008 11:00:00 PM,109XX S GREEN BAY AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0432,004,10,52,07,1200503,1832784,2008,09/05/2008 05:11:22 PM,41.695933642,-87.541499236,"(41.695933642, -87.541499236)"
-6474939,HP551637,09/03/2008 06:30:00 PM,044XX S MARSHFIELD AVE,2025,NARCOTICS,POSS: HALLUCINOGENS,PARK PROPERTY,true,false,0914,009,12,61,18,1166042,1875192,2008,09/05/2008 01:44:56 PM,41.813107242,-87.666469425,"(41.813107242, -87.666469425)"
-6484506,HP549953,09/02/2008 09:28:12 PM,058XX S WESTERN AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0824,008,16,63,18,1161338,1865609,2008,09/12/2008 10:14:48 AM,41.786909141,-87.683989469,"(41.786909141, -87.683989469)"
-6471139,HP549344,09/02/2008 04:25:55 PM,058XX W WASHINGTON BLVD,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1512,015,29,25,05,1137394,1900191,2008,09/06/2008 06:39:34 PM,41.882270207,-87.770952468,"(41.882270207, -87.770952468)"
-6468546,HP547552,08/31/2008 09:00:00 PM,018XX N WILMOT AVE,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,1434,014,32,22,06,1161051,1912503,2008,09/02/2008 08:51:27 AM,41.915597064,-87.683741098,"(41.915597064, -87.683741098)"
-6468393,HP546864,08/31/2008 03:30:00 PM,001XX W 75TH ST,0810,THEFT,OVER $500,STREET,false,false,0731,007,6,69,06,1176438,1855293,2008,09/02/2008 12:14:30 PM,41.758274626,-87.628934664,"(41.758274626, -87.628934664)"
-6590955,HP554289,08/29/2008 05:30:00 PM,029XX N PARKSIDE AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,2514,025,31,19,06,1138226,1919296,2008,11/05/2008 08:29:19 AM,41.934681583,-87.767434475,"(41.934681583, -87.767434475)"
-6515313,HP541796,08/29/2008 02:15:00 PM,044XX W WASHINGTON BLVD,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),VEHICLE NON-COMMERCIAL,true,false,1113,,28,26,18,,,2008,06/18/2010 04:41:41 PM,,,
-6468121,HP541320,08/29/2008 09:20:00 AM,030XX W FLOURNOY ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,true,false,1134,011,28,27,05,1156331,1896919,2008,10/26/2008 10:37:31 AM,41.87292993,-87.701503645,"(41.87292993, -87.701503645)"
-6465943,HP540025,08/28/2008 02:45:00 PM,023XX N CICERO AVE,0545,ASSAULT,PRO EMP HANDS NO/MIN INJURY,SIDEWALK,false,false,2522,025,31,19,08A,1144049,1915287,2008,12/19/2008 01:47:28 PM,41.923573028,-87.746135514,"(41.923573028, -87.746135514)"
-6467019,HP540140,08/28/2008 11:00:00 AM,011XX W 17TH ST,2820,OTHER OFFENSE,TELEPHONE THREAT,STREET,false,true,1233,012,25,31,26,1169176,1891939,2008,09/10/2008 06:21:24 PM,41.858995273,-87.654488334,"(41.858995273, -87.654488334)"
-6477557,HP538410,08/27/2008 06:10:00 PM,051XX S ASHLAND AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0932,009,16,61,16,1166498,1870832,2008,09/15/2008 02:21:23 PM,41.801133184,-87.664921177,"(41.801133184, -87.664921177)"
-6459468,HP537307,08/27/2008 08:15:00 AM,023XX S DEARBORN ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA HALLWAY/STAIRWELL/ELEVATOR,false,false,0134,001,2,33,26,1176323,1888852,2008,09/02/2008 10:04:51 AM,41.85036627,-87.628347321,"(41.85036627, -87.628347321)"
-6459188,HP538191,08/26/2008 10:00:00 PM,031XX N WESTERN AVE,0810,THEFT,OVER $500,RESIDENCE PORCH/HALLWAY,false,false,1913,019,1,5,06,1159750,1920667,2008,08/28/2008 03:04:37 PM,41.938026586,-87.68829523,"(41.938026586, -87.68829523)"
-6456985,HP536817,08/26/2008 08:40:00 PM,045XX N NARRAGANSETT AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1622,016,38,15,14,1132910,1929398,2008,08/29/2008 09:46:20 AM,41.962497188,-87.786734419,"(41.962497188, -87.786734419)"
-6457050,HP536734,08/26/2008 07:55:00 PM,038XX S CALUMET AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0211,002,3,35,18,1179121,1879320,2008,08/26/2008 11:14:19 PM,41.824146281,-87.618369486,"(41.824146281, -87.618369486)"
-6454685,HP534213,08/25/2008 02:49:37 PM,055XX W ADAMS ST,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,1522,015,29,25,04A,1139322,1898818,2008,08/26/2008 11:56:55 AM,41.878467628,-87.763906184,"(41.878467628, -87.763906184)"
-6458065,HP533366,08/25/2008 12:10:00 AM,087XX S HERMITAGE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,2221,022,21,71,08B,1166239,1846839,2008,09/18/2008 03:15:15 PM,41.735298722,-87.666553155,"(41.735298722, -87.666553155)"
-6462810,HP532627,08/24/2008 05:45:00 PM,076XX S MARYLAND AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0624,006,6,69,08B,1183268,1854443,2008,08/31/2008 10:09:42 AM,41.755785948,-87.603929994,"(41.755785948, -87.603929994)"
-6451296,HP531376,08/23/2008 11:35:00 PM,007XX E BOWEN AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,false,0213,002,4,38,06,1181905,1877599,2008,09/06/2008 11:21:57 AM,41.819359703,-87.608209313,"(41.819359703, -87.608209313)"
-6452747,HP531968,08/23/2008 07:30:00 PM,017XX N LATROBE AVE,0820,THEFT,$500 AND UNDER,APARTMENT,false,true,2532,025,37,25,06,1141162,1911181,2008,08/27/2008 09:25:36 AM,41.912359478,-87.756844986,"(41.912359478, -87.756844986)"
-6457868,HP529727,08/23/2008 02:19:27 AM,054XX S ARTESIAN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0911,009,14,63,14,1160762,1868578,2008,08/29/2008 09:25:10 AM,41.79506839,-87.686019443,"(41.79506839, -87.686019443)"
-6456339,HP529289,08/22/2008 09:26:07 PM,022XX S WHIPPLE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1033,010,24,30,18,1156341,1888756,2008,08/26/2008 02:06:03 PM,41.850529579,-87.701687428,"(41.850529579, -87.701687428)"
-6449211,HP528772,08/22/2008 09:00:00 AM,081XX S SPAULDING AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,0834,008,18,70,14,1155759,1850431,2008,08/24/2008 03:03:05 PM,41.74537216,-87.704851891,"(41.74537216, -87.704851891)"
-6444593,HP525562,08/21/2008 12:34:34 AM,029XX S ARCHER AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,STREET,false,false,0923,009,11,60,04A,1168310,1885676,2008,09/01/2008 08:31:18 PM,41.841827789,-87.657847958,"(41.841827789, -87.657847958)"
-6453657,HP533674,08/20/2008 12:00:00 PM,067XX S COTTAGE GROVE AVE,0820,THEFT,$500 AND UNDER,GAS STATION,false,false,0321,003,20,42,06,1182674,1860695,2008,08/26/2008 08:00:10 AM,41.772955859,-87.605913081,"(41.772955859, -87.605913081)"
-6443700,HP524183,08/20/2008 11:45:00 AM,037XX W GEORGE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2523,025,30,21,18,1150677,1919057,2008,08/20/2008 01:16:48 PM,41.933791105,-87.721682744,"(41.933791105, -87.721682744)"
-6442156,HP523598,08/20/2008 02:00:00 AM,023XX W DICKENS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,1432,014,32,22,08B,1160500,1913823,2008,08/22/2008 08:18:57 AM,41.919230673,-87.685728806,"(41.919230673, -87.685728806)"
-6452457,HP523275,08/19/2008 09:10:00 PM,015XX W 63RD ST,031A,ROBBERY,ARMED: HANDGUN,ALLEY,false,false,0725,007,16,67,03,1166918,1862905,2008,08/28/2008 07:38:37 PM,41.779371564,-87.663607349,"(41.779371564, -87.663607349)"
-6442053,HP523401,08/19/2008 06:30:00 PM,068XX S TALMAN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0831,008,15,66,14,1159846,1859276,2008,09/02/2008 10:57:39 AM,41.769561287,-87.689633794,"(41.769561287, -87.689633794)"
-6475931,HP554039,08/19/2008 07:30:00 AM,008XX E 82ND ST,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,false,false,0631,006,8,44,26,1183566,1850853,2008,09/09/2008 11:43:03 AM,41.745927675,-87.602949591,"(41.745927675, -87.602949591)"
-6443536,HP522611,08/18/2008 11:00:00 PM,033XX W WARREN BLVD,0330,ROBBERY,AGGRAVATED,VEHICLE NON-COMMERCIAL,false,false,1123,011,28,27,03,1154270,1900191,2008,09/21/2008 06:07:54 PM,41.88195002,-87.708983193,"(41.88195002, -87.708983193)"
-6439571,HP521473,08/18/2008 10:00:00 PM,026XX N ORCHARD ST,0810,THEFT,OVER $500,STREET,false,false,1933,019,43,7,06,1171106,1917840,2008,08/21/2008 12:35:40 PM,41.930027122,-87.646643063,"(41.930027122, -87.646643063)"
-6443342,HP521000,08/18/2008 06:32:00 PM,069XX S PAXTON AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0331,003,5,43,18,1192045,1859101,2008,08/20/2008 11:19:18 AM,41.768359166,-87.571613707,"(41.768359166, -87.571613707)"
-6439309,HP520675,08/18/2008 04:30:00 PM,012XX N PARKSIDE AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,2531,025,29,25,18,1138407,1907771,2008,08/18/2008 05:45:28 PM,41.903052402,-87.767048967,"(41.903052402, -87.767048967)"
-6441969,HP520153,08/18/2008 12:05:00 PM,088XX S THROOP ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,2222,022,21,71,18,1169176,1845854,2008,08/19/2008 08:00:43 PM,41.732532796,-87.655821557,"(41.732532796, -87.655821557)"
-6440108,HP519837,08/18/2008 08:30:00 AM,079XX S CLYDE AVE,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,RESIDENCE,false,false,0414,004,8,46,05,1191622,1852517,2008,09/14/2008 07:00:16 AM,41.750302401,-87.573377374,"(41.750302401, -87.573377374)"
-6436756,HP517933,08/17/2008 02:55:00 AM,003XX N CENTRAL PARK AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,1123,011,28,27,08B,1152291,1901795,2008,08/20/2008 10:27:12 AM,41.886390841,-87.716207743,"(41.886390841, -87.716207743)"
-6437659,HP518319,08/16/2008 11:00:00 PM,010XX N HERMITAGE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1322,012,1,24,14,1164481,1907588,2008,08/19/2008 11:45:36 AM,41.902037986,-87.671278966,"(41.902037986, -87.671278966)"
-6444155,HP517493,08/16/2008 07:00:00 PM,006XX N LOREL AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,1524,015,37,25,06,1140530,1903517,2008,08/22/2008 09:20:11 AM,41.891340196,-87.759355197,"(41.891340196, -87.759355197)"
-6447623,HP517181,08/16/2008 06:15:00 PM,008XX N STATE ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,true,false,1832,018,42,8,11,1176183,1905757,2008,08/26/2008 10:07:07 AM,41.896757773,-87.628351553,"(41.896757773, -87.628351553)"
-6437067,HP516910,08/16/2008 03:00:00 PM,064XX W FULLERTON AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,2512,025,36,19,06,1133118,1915290,2008,08/19/2008 01:03:22 PM,41.923779638,-87.786300692,"(41.923779638, -87.786300692)"
-6447111,HP520557,08/16/2008 02:00:00 PM,070XX S MARSHFIELD AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0735,007,17,67,06,1166516,1858113,2008,08/24/2008 02:47:14 PM,41.766230274,-87.665217606,"(41.766230274, -87.665217606)"
-6440410,HP515604,08/15/2008 07:30:00 PM,026XX N MOBILE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,false,2512,025,29,19,08B,1133999,1916967,2008,09/12/2008 08:53:32 PM,41.928366054,-87.783023953,"(41.928366054, -87.783023953)"
-6435950,HP515410,08/15/2008 07:15:00 PM,020XX W DIVERSEY PKWY,1340,CRIMINAL DAMAGE,TO STATE SUP PROP,CHA APARTMENT,false,false,1913,019,1,7,14,1162222,1918548,2008,08/19/2008 08:34:04 AM,41.932160522,-87.679269547,"(41.932160522, -87.679269547)"
-6438009,HP514560,08/15/2008 11:59:04 AM,071XX S YALE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,0731,007,6,69,18,1175907,1857293,2008,08/18/2008 11:49:06 AM,41.763774773,-87.63082086,"(41.763774773, -87.63082086)"
-6433202,HP514090,08/15/2008 04:21:56 AM,064XX S EVANS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0312,003,20,42,08B,1182372,1862254,2008,08/19/2008 08:11:18 AM,41.777240908,-87.606971855,"(41.777240908, -87.606971855)"
-6432360,HP514585,08/15/2008 12:00:00 AM,036XX S SANGAMON ST,0810,THEFT,OVER $500,CONSTRUCTION SITE,false,false,0922,,11,60,06,,,2008,08/15/2008 12:30:38 PM,,,
-6433605,HP513637,08/14/2008 08:05:00 PM,044XX S CICERO AVE,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,true,false,0814,008,23,56,26,1145057,1874967,2008,08/17/2008 02:03:36 PM,41.812910886,-87.743449749,"(41.812910886, -87.743449749)"
-6438082,HP512834,08/14/2008 01:00:00 PM,100XX W OHARE ST,1206,DECEPTIVE PRACTICE,"THEFT BY LESSEE,MOTOR VEH",AIRPORT/AIRCRAFT,true,false,1651,016,41,76,11,1100635,1934208,2008,09/01/2008 10:07:39 AM,41.976200173,-87.905312411,"(41.976200173, -87.905312411)"
-6433344,HP511888,08/13/2008 10:26:00 PM,060XX S LOOMIS BLVD,0610,BURGLARY,FORCIBLE ENTRY,GROCERY FOOD STORE,false,false,0713,007,16,67,05,1167985,1864896,2008,08/21/2008 03:22:48 PM,41.784812242,-87.659638419,"(41.784812242, -87.659638419)"
-6437645,HP511467,08/13/2008 06:50:09 PM,041XX W MADISON ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1115,011,28,26,18,1148602,1899725,2008,08/18/2008 12:58:06 PM,41.880782531,-87.729808212,"(41.880782531, -87.729808212)"
-6429396,HP511349,08/13/2008 03:00:00 PM,059XX W WABANSIA AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2531,025,29,25,07,1136452,1910728,2008,10/19/2008 08:30:45 AM,41.91120196,-87.774159377,"(41.91120196, -87.774159377)"
-6474645,HP509916,08/12/2008 10:25:00 PM,003XX N LOREL AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,1523,015,28,25,18,1140695,1901543,2008,09/10/2008 02:54:36 PM,41.885920265,-87.758797762,"(41.885920265, -87.758797762)"
-6439184,HP520578,08/12/2008 07:00:00 PM,007XX W FULLERTON AVE,0890,THEFT,FROM BUILDING,HOSPITAL BUILDING/GROUNDS,false,false,1812,018,43,7,06,1171133,1916146,2008,08/25/2008 10:33:39 AM,41.925378115,-87.646593703,"(41.925378115, -87.646593703)"
-6430096,HP508596,08/12/2008 10:10:57 AM,051XX S MICHIGAN AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,0232,002,3,40,08A,1177999,1870570,2008,08/18/2008 10:25:17 PM,41.800161069,-87.622751126,"(41.800161069, -87.622751126)"
-6425255,HP508497,08/11/2008 05:00:00 PM,089XX S COMMERCIAL AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0423,004,10,46,14,1197650,1846464,2008,08/13/2008 07:26:17 AM,41.733544282,-87.551490045,"(41.733544282, -87.551490045)"
-6425408,HP506910,08/11/2008 12:33:31 PM,021XX S STATE ST,0560,ASSAULT,SIMPLE,RESTAURANT,false,false,0134,001,3,33,08A,1176691,1890105,2008,08/22/2008 04:03:12 PM,41.853796292,-87.626958881,"(41.853796292, -87.626958881)"
-6438657,HP520092,08/10/2008 08:00:00 PM,020XX W FULTON ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,WAREHOUSE,false,false,1332,012,27,28,26,1162955,1902051,2008,08/28/2008 11:06:01 AM,41.886876203,-87.677039735,"(41.886876203, -87.677039735)"
-6422786,HP506193,08/10/2008 05:00:00 PM,078XX S KILPATRICK AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0834,008,13,70,05,1146478,1852138,2008,08/13/2008 09:11:33 AM,41.750237461,-87.738816365,"(41.750237461, -87.738816365)"
-6488341,HP564760,08/10/2008 01:00:00 PM,078XX S WOODLAWN AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,RESIDENCE,false,false,0411,004,8,45,26,1185614,1853221,2008,09/24/2008 07:09:30 AM,41.752377758,-87.595370973,"(41.752377758, -87.595370973)"
-6421988,HP505368,08/10/2008 03:00:00 AM,061XX N KEDVALE AVE,0810,THEFT,OVER $500,STREET,false,false,1711,017,39,12,06,1147536,1940656,2008,08/14/2008 08:52:39 AM,41.993121289,-87.732669453,"(41.993121289, -87.732669453)"
-6441217,HP501660,08/08/2008 01:00:00 PM,080XX S UNION AVE,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),VEHICLE NON-COMMERCIAL,true,false,0621,,21,71,18,,,2008,04/22/2010 02:13:39 PM,,,
-6417188,HP500676,08/07/2008 11:15:00 PM,024XX W MADISON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1125,011,2,28,08B,1160092,1899902,2008,08/08/2008 01:20:41 PM,41.88103876,-87.687612825,"(41.88103876, -87.687612825)"
-6422706,HP500032,08/07/2008 06:18:10 PM,055XX S RACINE AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,0712,007,16,68,08A,1169309,1867860,2008,08/18/2008 08:08:21 PM,41.792917234,-87.654698301,"(41.792917234, -87.654698301)"
-6417657,HP500314,08/07/2008 06:30:00 AM,030XX N LOWELL AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,2523,025,31,20,05,1146825,1920133,2008,08/16/2008 09:42:56 AM,41.936818284,-87.735811258,"(41.936818284, -87.735811258)"
-6415067,HP498719,08/07/2008 01:35:00 AM,060XX W ADDISON ST,0860,THEFT,RETAIL THEFT,GAS STATION,false,false,1633,016,38,17,06,1135665,1923359,2008,08/08/2008 10:56:55 AM,41.945876893,-87.776749385,"(41.945876893, -87.776749385)"
-6416567,HP499548,08/06/2008 12:00:00 AM,055XX S LA SALLE ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,OTHER,false,true,0233,002,3,68,26,1176284,1867882,2008,09/03/2008 09:17:57 AM,41.792823665,-87.629121251,"(41.792823665, -87.629121251)"
-6412772,HP495295,08/05/2008 11:55:00 AM,002XX W GARFIELD BLVD,0497,BATTERY,AGGRAVATED DOMESTIC BATTERY: OTHER DANG WEAPON,PARKING LOT/GARAGE(NON.RESID.),false,true,0934,002,3,37,04B,1175886,1868551,2008,08/08/2008 08:25:17 AM,41.794668405,-87.630560606,"(41.794668405, -87.630560606)"
-6418702,HP493790,08/04/2008 03:21:01 PM,032XX W DOUGLAS BLVD,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1022,010,24,29,26,1154884,1893310,2008,08/08/2008 01:56:25 PM,41.863055553,-87.706912978,"(41.863055553, -87.706912978)"
-6409049,HP493564,08/02/2008 10:00:00 PM,008XX N LAWNDALE AVE,0810,THEFT,OVER $500,STREET,false,false,1112,011,27,23,06,1151517,1905609,2008,08/05/2008 01:38:48 PM,41.896872086,-87.71894979,"(41.896872086, -87.71894979)"
-6406057,HP490160,08/02/2008 04:05:00 PM,014XX W MORSE AVE,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,2431,024,49,1,18,1165158,1946123,2008,08/02/2008 05:12:09 PM,42.007765385,-87.667692851,"(42.007765385, -87.667692851)"
-6405504,HP489484,08/02/2008 07:00:00 AM,045XX S ALBANY AVE,1780,OFFENSE INVOLVING CHILDREN,OTHER OFFENSE,OTHER,false,false,0912,009,14,58,26,1156420,1874464,2008,09/04/2008 10:34:32 AM,41.811308989,-87.701783111,"(41.811308989, -87.701783111)"
-6406435,HP489371,08/02/2008 03:59:00 AM,006XX W FULTON ST,0610,BURGLARY,FORCIBLE ENTRY,CONVENIENCE STORE,true,false,1212,012,42,28,05,1171937,1902135,2008,01/13/2010 06:58:11 PM,41.886913464,-87.644053137,"(41.886913464, -87.644053137)"
-6405736,HP489541,08/02/2008 12:00:00 AM,047XX S ASHLAND AVE,0890,THEFT,FROM BUILDING,OTHER,false,false,0931,009,20,61,06,1166424,1873486,2008,08/02/2008 02:13:29 PM,41.808417643,-87.66511689,"(41.808417643, -87.66511689)"
-6430476,HP487338,07/31/2008 10:00:00 PM,046XX W 59TH ST,0620,BURGLARY,UNLAWFUL ENTRY,GROCERY FOOD STORE,false,false,0813,008,23,62,05,1146459,1865053,2008,09/02/2008 10:00:50 AM,41.785678796,-87.738558722,"(41.785678796, -87.738558722)"
-6404162,HP485933,07/31/2008 01:05:00 PM,013XX E 79TH ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,false,false,0411,004,8,45,06,1186630,1852923,2008,08/02/2008 07:48:34 AM,41.751536043,-87.591657232,"(41.751536043, -87.591657232)"
-6439350,HP521004,07/31/2008 10:00:00 AM,011XX S CANAL ST,0560,ASSAULT,SIMPLE,MEDICAL/DENTAL OFFICE,false,false,0131,001,2,28,08A,1173340,1895546,2008,09/11/2008 02:51:22 PM,41.86880178,-87.639096707,"(41.86880178, -87.639096707)"
-6400467,HP484968,07/30/2008 11:45:00 PM,075XX N CLARK ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,2422,024,49,1,18,1162893,1950122,2008,07/31/2008 01:18:12 AM,42.018786747,-87.675913256,"(42.018786747, -87.675913256)"
-6398107,HP483143,07/29/2008 10:00:00 PM,012XX N MILWAUKEE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1433,014,1,24,14,1165309,1908532,2008,08/01/2008 11:28:39 AM,41.904610813,-87.668210732,"(41.904610813, -87.668210732)"
-6481026,HP482696,07/29/2008 08:05:00 PM,076XX S WOOD ST,1661,GAMBLING,GAME/DICE,RESIDENCE PORCH/HALLWAY,true,false,0611,006,17,71,19,1165711,1854169,2008,09/07/2008 05:01:12 PM,41.755424515,-87.668279991,"(41.755424515, -87.668279991)"
-6398188,HP482526,07/29/2008 07:43:16 PM,023XX S HAMLIN AVE,031A,ROBBERY,ARMED: HANDGUN,RESIDENCE PORCH/HALLWAY,false,false,1013,010,22,30,03,1151368,1888199,2008,09/12/2008 10:54:07 AM,41.849100031,-87.719953995,"(41.849100031, -87.719953995)"
-6395035,HP480203,07/28/2008 07:00:00 AM,023XX N STOCKTON DR,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1814,018,43,7,07,1174057,1916222,2008,07/29/2008 09:21:32 AM,41.9255219,-87.635847361,"(41.9255219, -87.635847361)"
-6396712,HP477214,07/27/2008 03:48:30 AM,042XX W JACKSON BLVD,0460,BATTERY,SIMPLE,STREET,false,false,1115,011,28,26,08B,1148369,1898393,2008,08/08/2008 09:59:58 AM,41.877131864,-87.73069812,"(41.877131864, -87.73069812)"
-6391176,HP475973,07/26/2008 09:00:00 AM,051XX W WINONA ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1623,016,45,11,14,1140979,1933717,2008,07/27/2008 11:00:02 AM,41.974203811,-87.756960591,"(41.974203811, -87.756960591)"
-6402809,HP474280,07/23/2008 04:00:00 PM,089XX S YATES BLVD,0842,THEFT,AGG: FINANCIAL ID THEFT,RESIDENCE,false,false,0413,004,7,48,06,1193691,1845865,2008,08/08/2008 06:07:56 PM,41.731998345,-87.566013112,"(41.731998345, -87.566013112)"
-6385864,HP469064,07/23/2008 02:25:06 AM,016XX W HOWARD ST,0460,BATTERY,SIMPLE,CTA PLATFORM,true,false,2422,024,49,1,08B,1163711,1950306,2008,07/25/2008 08:50:07 AM,42.019274366,-87.672897921,"(42.019274366, -87.672897921)"
-6389060,HP468772,07/22/2008 10:33:21 PM,078XX S COLFAX AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0421,004,7,43,18,1194844,1853319,2008,08/02/2008 02:12:59 PM,41.752424452,-87.561544376,"(41.752424452, -87.561544376)"
-6389668,HP473581,07/22/2008 04:00:00 PM,006XX E 50TH ST,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,RESIDENCE,false,false,0223,002,4,38,06,1181605,1872050,2008,08/10/2008 11:58:54 AM,41.80413975,-87.609481277,"(41.80413975, -87.609481277)"
-6379603,HP467119,07/21/2008 10:00:00 PM,030XX N CHRISTIANA AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1412,014,35,21,06,1153485,1920007,2008,07/26/2008 08:57:40 AM,41.936342542,-87.711338068,"(41.936342542, -87.711338068)"
-6390863,HP475833,07/20/2008 08:00:00 PM,010XX W HOLLYWOOD AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,2022,020,48,77,06,1168317,1938078,2008,07/28/2008 10:45:10 AM,41.985621774,-87.656304299,"(41.985621774, -87.656304299)"
-6377185,HP464482,07/20/2008 07:30:00 PM,023XX S STATE ST,0820,THEFT,$500 AND UNDER,CHA HALLWAY/STAIRWELL/ELEVATOR,false,false,0134,001,3,33,06,1176644,1888767,2008,07/22/2008 08:05:08 AM,41.850125786,-87.627171772,"(41.850125786, -87.627171772)"
-6382928,HP463945,07/20/2008 12:30:00 PM,086XX S WABASH AVE,143B,WEAPONS VIOLATION,UNLAWFUL POSS OTHER FIREARM,RESIDENCE,true,false,0632,006,6,44,15,1178253,1847929,2008,07/24/2008 09:16:14 AM,41.738025971,-87.622505857,"(41.738025971, -87.622505857)"
-6379788,HP463640,07/20/2008 09:45:00 AM,023XX S STATE ST,2027,NARCOTICS,POSS: CRACK,CHA PARKING LOT/GROUNDS,true,false,0134,001,3,33,18,1176649,1888583,2008,08/20/2008 05:40:34 AM,41.849620764,-87.627158975,"(41.849620764, -87.627158975)"
-6375914,HP460943,07/18/2008 09:25:00 PM,032XX S LAKE SHORE DR NB,0460,BATTERY,SIMPLE,PARK PROPERTY,false,false,2122,002,4,35,08B,1181998,1883638,2008,07/22/2008 12:56:37 PM,41.835928991,-87.607681036,"(41.835928991, -87.607681036)"
-6372355,HP459261,07/18/2008 01:55:00 AM,038XX S WOOD ST,1020,ARSON,BY FIRE,RESIDENCE-GARAGE,false,false,0922,009,11,59,09,1164939,1879407,2008,08/17/2008 02:43:50 AM,41.824697087,-87.670395991,"(41.824697087, -87.670395991)"
-6398522,HP458241,07/17/2008 03:00:00 PM,015XX S KENNETH AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),VEHICLE NON-COMMERCIAL,true,false,1012,010,24,29,18,1147006,1892270,2008,07/30/2008 10:23:06 AM,41.860355759,-87.735859189,"(41.860355759, -87.735859189)"
-6370000,HP457077,07/16/2008 09:00:00 PM,073XX N HOYNE AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,true,2424,024,49,1,26,1161013,1948868,2008,07/18/2008 09:20:21 AM,42.015385157,-87.682866436,"(42.015385157, -87.682866436)"
-6372403,HP456934,07/16/2008 08:59:45 PM,008XX N PARKSIDE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,SIDEWALK,false,false,1511,015,29,25,14,1138571,1905229,2008,07/19/2008 10:07:31 AM,41.896073872,-87.766508246,"(41.896073872, -87.766508246)"
-6373620,HP455088,07/15/2008 10:25:00 PM,059XX S WENTWORTH AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0233,007,20,68,03,1175935,1865807,2008,08/16/2008 04:13:42 PM,41.787137491,-87.630463188,"(41.787137491, -87.630463188)"
-6365375,HP452822,07/14/2008 08:00:00 PM,014XX W 117TH ST,0460,BATTERY,SIMPLE,ALLEY,false,false,0524,005,34,53,08B,1169069,1827124,2008,07/17/2008 10:47:20 PM,41.681137046,-87.656752653,"(41.681137046, -87.656752653)"
-6361663,HP449259,07/13/2008 12:01:00 AM,111XX S MICHIGAN AVE,031A,ROBBERY,ARMED: HANDGUN,RESTAURANT,false,false,0531,005,9,49,03,1178828,1831329,2008,07/16/2008 09:32:03 AM,41.692460326,-87.620902453,"(41.692460326, -87.620902453)"
-6365337,HP450942,07/12/2008 10:00:00 PM,032XX W 61ST PL,0460,BATTERY,SIMPLE,SIDEWALK,true,false,0823,008,15,66,08B,1155861,1863626,2008,07/18/2008 12:06:37 PM,41.781579254,-87.704124478,"(41.781579254, -87.704124478)"
-6423427,HP498684,07/12/2008 12:01:00 AM,012XX S KEELER AVE,0265,CRIM SEXUAL ASSAULT,AGGRAVATED: OTHER,APARTMENT,false,false,1011,010,24,29,02,1148621,1893983,2008,09/25/2008 10:32:03 AM,41.865025446,-87.729886673,"(41.865025446, -87.729886673)"
-6361806,HP447827,07/11/2008 01:35:00 AM,001XX W 109TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,0513,005,34,49,14,1177397,1832637,2008,07/14/2008 08:47:12 AM,41.696082028,-87.626102233,"(41.696082028, -87.626102233)"
-6363209,HP445121,07/10/2008 10:13:55 PM,067XX S BISHOP ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0725,007,17,67,14,1167789,1859954,2008,07/15/2008 02:56:30 PM,41.771254987,-87.660498808,"(41.771254987, -87.660498808)"
-6369675,HP445095,07/10/2008 09:30:00 PM,049XX W HUBBARD ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1532,015,28,25,18,1143113,1902514,2008,07/21/2008 09:37:55 AM,41.888540039,-87.749893988,"(41.888540039, -87.749893988)"
-6377436,HP444430,07/10/2008 10:00:00 AM,011XX W GRANVILLE AVE,0460,BATTERY,SIMPLE,CTA TRAIN,false,false,2433,024,48,77,08B,1167492,1941290,2008,07/21/2008 09:58:39 AM,41.994453431,-87.659245596,"(41.994453431, -87.659245596)"
-6459166,HP442935,07/09/2008 08:45:00 PM,077XX S BISHOP ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0612,006,17,71,18,1167938,1853543,2008,08/27/2008 07:47:16 PM,41.753659155,-87.660136542,"(41.753659155, -87.660136542)"
-6355717,HP442577,07/09/2008 05:43:00 PM,054XX S HALSTED ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,0934,009,20,61,18,1171864,1868438,2008,07/10/2008 01:23:42 PM,41.794447601,-87.645312473,"(41.794447601, -87.645312473)"
-6372999,HP439977,07/08/2008 12:20:00 PM,055XX W ADAMS ST,2017,NARCOTICS,MANU/DELIVER:CRACK,STREET,true,false,1522,015,29,25,18,1139647,1898745,2008,07/23/2008 10:27:49 AM,41.878261384,-87.762714617,"(41.878261384, -87.762714617)"
-6349922,HP438915,07/07/2008 01:00:00 PM,038XX S GILES AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0211,002,3,35,14,1178932,1879554,2008,07/09/2008 11:11:38 AM,41.824792709,-87.619055721,"(41.824792709, -87.619055721)"
-6346351,HP434098,07/05/2008 09:58:16 AM,095XX S JEFFERY AVE,0620,BURGLARY,UNLAWFUL ENTRY,SMALL RETAIL STORE,false,false,0431,004,7,51,05,1191218,1842095,2008,08/31/2008 04:10:26 PM,41.721713235,-87.575194208,"(41.721713235, -87.575194208)"
-6345811,HP434080,07/05/2008 04:40:00 AM,005XX W ARLINGTON PL,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,false,false,1933,019,43,7,11,1172443,1916656,2008,07/11/2008 02:15:48 PM,41.926748687,-87.641765065,"(41.926748687, -87.641765065)"
-6389156,HP436781,07/04/2008 08:00:00 PM,120XX S AVENUE O,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0433,004,10,55,07,1200929,1825906,2008,07/28/2008 11:58:11 AM,41.677048999,-87.54017139,"(41.677048999, -87.54017139)"
-6346458,HP432796,07/04/2008 01:58:13 PM,022XX S STATE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA APARTMENT,false,true,0134,001,3,33,08B,1176624,1889501,2008,07/09/2008 10:41:15 AM,41.852140386,-87.627223023,"(41.852140386, -87.627223023)"
-6343490,HP432118,07/04/2008 12:30:00 AM,002XX N LOCKWOOD AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1523,015,28,25,07,1140960,1901207,2008,07/16/2008 01:49:28 PM,41.884993367,-87.757832886,"(41.884993367, -87.757832886)"
-6344452,HP432392,07/03/2008 09:00:00 AM,100XX S WENTWORTH AVE,0820,THEFT,$500 AND UNDER,RESIDENCE-GARAGE,false,false,0511,005,9,49,06,1176679,1838169,2008,07/05/2008 07:01:54 AM,41.711278772,-87.628565327,"(41.711278772, -87.628565327)"
-6344482,HP428312,07/02/2008 01:40:00 AM,025XX W VAN BUREN ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,false,false,1125,011,2,28,15,1159763,1898126,2008,07/07/2008 10:43:36 AM,41.876172038,-87.688869862,"(41.876172038, -87.688869862)"
-6338477,HP425984,06/30/2008 10:00:00 PM,074XX S HALSTED ST,0460,BATTERY,SIMPLE,STREET,false,false,0733,007,17,68,08B,1172216,1855472,2008,07/02/2008 11:00:21 AM,41.758859648,-87.644402602,"(41.758859648, -87.644402602)"
-6336777,HP425962,06/29/2008 07:30:00 PM,075XX S WINCHESTER AVE,0810,THEFT,OVER $500,RESIDENTIAL YARD (FRONT/BACK),false,false,0611,006,18,71,06,1164711,1854380,2008,07/01/2008 07:56:33 AM,41.756024684,-87.671938804,"(41.756024684, -87.671938804)"
-6334746,HP423400,06/29/2008 03:05:00 PM,105XX S HOMAN AVE,0560,ASSAULT,SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,2211,022,19,74,08A,1155505,1834418,2008,07/02/2008 06:24:14 AM,41.701434755,-87.706210038,"(41.701434755, -87.706210038)"
-6364628,HP420512,06/28/2008 12:25:00 AM,038XX W MADISON ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1122,011,28,26,16,1150998,1899783,2008,07/22/2008 02:33:59 PM,41.880895116,-87.721008704,"(41.880895116, -87.721008704)"
-6396627,HP420412,06/27/2008 11:21:36 PM,059XX S WHIPPLE ST,2022,NARCOTICS,POSS: COCAINE,APARTMENT,true,false,0824,008,16,66,18,1157099,1865292,2008,07/29/2008 12:04:43 PM,41.786126048,-87.699540616,"(41.786126048, -87.699540616)"
-6332601,HP419842,06/27/2008 06:20:17 PM,005XX W ENGLEWOOD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,true,true,0711,007,20,68,08B,1173815,1863435,2008,12/19/2010 09:50:33 AM,41.780675758,-87.638306546,"(41.780675758, -87.638306546)"
-6341546,HP419573,06/27/2008 03:58:00 PM,055XX S TRIPP AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0813,008,13,62,06,1149016,1867520,2008,07/04/2008 12:19:16 PM,41.79239971,-87.729119856,"(41.79239971, -87.729119856)"
-6396050,HP419337,06/27/2008 02:07:51 PM,031XX W 63RD ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0823,008,15,66,18,1156659,1862652,2008,07/29/2008 12:00:25 PM,41.778890394,-87.701225045,"(41.778890394, -87.701225045)"
-6333922,HP418364,06/26/2008 11:15:00 PM,054XX N EAST RIVER RD,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,APARTMENT,false,true,1614,016,41,76,26,1116672,1934828,2008,07/12/2008 08:06:15 PM,41.977666497,-87.846323236,"(41.977666497, -87.846323236)"
-6333473,HP416848,06/26/2008 08:30:00 AM,019XX W BELLE PLAINE AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1923,019,47,5,26,1162802,1927203,2008,07/01/2008 11:24:03 AM,41.955898155,-87.67689455,"(41.955898155, -87.67689455)"
-6325668,HP414841,06/24/2008 08:00:00 PM,108XX S HOYNE AVE,0810,THEFT,OVER $500,STREET,false,false,2212,022,19,75,06,1164323,1832709,2008,06/26/2008 07:24:11 AM,41.696564228,-87.673969083,"(41.696564228, -87.673969083)"
-6326867,HP412355,06/23/2008 08:59:27 PM,004XX W 76TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,false,0621,006,17,69,08B,1174464,1854570,2008,06/27/2008 05:48:06 AM,41.756334753,-87.636190632,"(41.756334753, -87.636190632)"
-6336493,HP425696,06/23/2008 05:00:00 PM,019XX W ARMITAGE AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,false,1434,014,32,22,06,1163096,1913275,2008,07/01/2008 09:38:20 AM,41.917672735,-87.676206217,"(41.917672735, -87.676206217)"
-6328635,HP408964,06/22/2008 12:29:45 AM,0000X W CHICAGO AVE,0560,ASSAULT,SIMPLE,TAVERN/LIQUOR STORE,false,false,1832,018,42,8,08A,1176140,1905765,2008,07/01/2008 08:30:38 AM,41.896780695,-87.628509241,"(41.896780695, -87.628509241)"
-6319624,HP409355,06/21/2008 09:00:00 PM,013XX W 72ND PL,0810,THEFT,OVER $500,STREET,false,false,0734,007,17,67,06,1168434,1856642,2008,06/23/2008 10:02:51 AM,41.76215256,-87.658229754,"(41.76215256, -87.658229754)"
-6317746,HP406652,06/20/2008 07:00:00 PM,021XX N NARRAGANSETT AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2512,025,36,19,07,1133352,1913861,2008,10/08/2008 10:28:52 AM,41.919854191,-87.785474416,"(41.919854191, -87.785474416)"
-6318125,HP406055,06/20/2008 01:15:00 PM,007XX N TROY ST,4388,OTHER OFFENSE,VIO BAIL BOND: DOM VIOLENCE,RESIDENCE PORCH/HALLWAY,true,true,1313,012,27,23,26,1155200,1904779,2008,07/08/2008 12:17:56 PM,41.894521319,-87.705444955,"(41.894521319, -87.705444955)"
-6318930,HP405312,06/20/2008 02:04:48 AM,083XX S MACKINAW AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0424,004,10,46,08B,1199885,1850118,2008,06/23/2008 06:10:42 AM,41.743515118,-87.543179415,"(41.743515118, -87.543179415)"
-6315270,HP404249,06/19/2008 12:00:00 PM,004XX W 44TH PL,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,0935,009,11,61,06,1173952,1875388,2008,07/14/2008 04:15:27 PM,41.813472981,-87.637449531,"(41.813472981, -87.637449531)"
-6315744,HP404528,06/19/2008 10:00:00 AM,006XX W OAKDALE AVE,0810,THEFT,OVER $500,STREET,false,false,2333,019,44,6,06,1171147,1919892,2008,06/20/2008 10:15:37 AM,41.935656997,-87.646431986,"(41.935656997, -87.646431986)"
-6313871,HP402725,06/18/2008 05:15:00 PM,097XX S GREENWOOD AVE,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, GROUNDS",false,false,0511,005,8,50,08A,1185296,1840679,2008,06/22/2008 07:53:41 AM,41.717968633,-87.596929546,"(41.717968633, -87.596929546)"
-6314497,HP403644,06/18/2008 04:30:00 PM,012XX W ADAMS ST,1340,CRIMINAL DAMAGE,TO STATE SUP PROP,GOVERNMENT BUILDING/PROPERTY,false,false,1213,012,27,28,14,1168061,1899194,2008,06/20/2008 12:40:58 PM,41.878927695,-87.65837172,"(41.878927695, -87.65837172)"
-6309726,HP399689,06/17/2008 02:12:00 AM,031XX N PINE GROVE AVE,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,STREET,false,false,2332,019,44,6,11,1172392,1921313,2008,06/18/2008 09:50:43 AM,41.939528811,-87.641814441,"(41.939528811, -87.641814441)"
-6318273,HP400600,06/16/2008 08:01:00 PM,001XX N STATE ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,DEPARTMENT STORE,false,false,0122,001,42,32,11,1176390,1900949,2008,07/14/2008 03:03:08 PM,41.883559699,-87.627736496,"(41.883559699, -87.627736496)"
-6311634,HP398166,06/16/2008 10:37:00 AM,064XX S OAKLEY AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,false,false,0825,008,15,66,24,1162215,1862077,2008,06/20/2008 03:28:21 PM,41.777198638,-87.680872163,"(41.777198638, -87.680872163)"
-6309924,HP397781,06/16/2008 03:45:00 AM,121XX S PARNELL AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0523,005,34,53,14,1174864,1824506,2008,06/18/2008 08:12:59 AM,41.673826,-87.635617558,"(41.673826, -87.635617558)"
-6306693,HP397040,06/15/2008 05:00:00 PM,030XX N FRANCISCO AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1411,014,33,21,08B,1156479,1919977,2008,06/17/2008 01:26:19 PM,41.936200085,-87.700335573,"(41.936200085, -87.700335573)"
-6308006,HP396985,06/15/2008 04:31:37 PM,067XX S LAFAYETTE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,false,0722,007,6,69,08B,1176963,1859966,2008,06/26/2008 11:39:50 AM,41.77108605,-87.62686994,"(41.77108605, -87.62686994)"
-6312470,HP396646,06/15/2008 11:00:40 AM,005XX W 119TH ST,2091,NARCOTICS,FORFEIT PROPERTY,STREET,true,false,0524,005,34,53,26,1174744,1826021,2008,07/31/2008 12:28:32 PM,41.677986068,-87.636011894,"(41.677986068, -87.636011894)"
-6306502,HP396389,06/15/2008 09:30:00 AM,034XX N GREENVIEW AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1924,019,44,6,26,1165662,1922839,2008,07/31/2008 11:37:43 AM,41.943862541,-87.666505385,"(41.943862541, -87.666505385)"
-6335684,HP424622,06/13/2008 06:00:00 AM,035XX S HAMILTON AVE,1582,OFFENSE INVOLVING CHILDREN,CHILD PORNOGRAPHY,RESIDENCE,false,false,0913,009,11,59,17,1162566,1881011,2008,07/04/2008 04:06:52 PM,41.829148558,-87.679057023,"(41.829148558, -87.679057023)"
-6304659,HP391667,06/12/2008 08:24:00 PM,060XX W BYRON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE-GARAGE,false,true,1633,016,38,17,08B,1135561,1925259,2008,06/18/2008 10:46:16 AM,41.951092542,-87.777086336,"(41.951092542, -87.777086336)"
-6303395,HP389998,06/12/2008 12:55:00 AM,057XX W BLOOMINGDALE AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,2531,025,29,25,08B,1138223,1911224,2008,06/16/2008 01:43:37 PM,41.912531174,-87.767641198,"(41.912531174, -87.767641198)"
-6300534,HP389435,06/11/2008 06:35:00 PM,0000X N STATE ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0122,001,42,32,06,1176398,1900682,2008,06/13/2008 08:29:28 AM,41.882826856,-87.627715181,"(41.882826856, -87.627715181)"
-6297760,HP385627,06/09/2008 06:30:00 PM,028XX W 85TH ST,0560,ASSAULT,SIMPLE,STREET,false,true,0835,008,18,70,08A,1158927,1848234,2008,06/15/2008 09:12:30 AM,41.739279158,-87.693303566,"(41.739279158, -87.693303566)"
-6293373,HP383611,06/08/2008 10:15:00 PM,0000X W MONROE ST,0890,THEFT,FROM BUILDING,TAVERN/LIQUOR STORE,false,false,0123,001,42,32,06,1176056,1899873,2008,06/10/2008 03:19:44 PM,41.880614623,-87.628995384,"(41.880614623, -87.628995384)"
-6292423,HP382302,06/08/2008 06:00:00 AM,028XX N PINE GROVE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,2333,019,44,6,14,1172489,1919193,2008,06/09/2008 11:42:05 AM,41.933709302,-87.641520817,"(41.933709302, -87.641520817)"
-6316879,HP379151,06/06/2008 02:40:00 PM,039XX W MADISON ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1122,011,28,26,16,1150320,1899766,2008,06/23/2008 01:24:18 PM,41.880861713,-87.723498732,"(41.880861713, -87.723498732)"
-6303156,HP379123,06/06/2008 02:30:00 PM,006XX N MICHIGAN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,BANK,false,true,1834,018,42,8,08B,1177367,1904310,2008,06/17/2008 04:07:28 PM,41.892760356,-87.624046903,"(41.892760356, -87.624046903)"
-6289704,HP378094,06/06/2008 01:20:00 AM,053XX W BELMONT AVE,0460,BATTERY,SIMPLE,BAR OR TAVERN,false,false,2514,025,30,19,08B,1140408,1920722,2008,06/14/2008 12:43:51 PM,41.938554868,-87.7593804,"(41.938554868, -87.7593804)"
-6290227,HP378450,06/05/2008 11:30:00 AM,030XX S PRINCETON AVE,0810,THEFT,OVER $500,RESIDENCE,false,false,2113,009,11,34,06,1174740,1884794,2008,06/11/2008 10:37:38 AM,41.839266316,-87.634278385,"(41.839266316, -87.634278385)"
-6287153,HP375018,06/04/2008 03:15:00 PM,033XX N AUSTIN AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,1633,016,36,17,03,1135763,1921351,2008,06/24/2008 01:50:24 PM,41.940364982,-87.776437117,"(41.940364982, -87.776437117)"
-6298294,HP386443,06/04/2008 07:30:00 AM,046XX N LAWLER AVE,5001,OTHER OFFENSE,OTHER CRIME INVOLVING PROPERTY,RESIDENCE,false,true,1623,016,45,15,26,1141827,1930424,2008,06/15/2008 10:53:40 AM,41.965151849,-87.75392411,"(41.965151849, -87.75392411)"
-6294378,HP372160,06/03/2008 03:45:00 AM,024XX S SACRAMENTO AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1033,010,12,30,18,1156716,1887317,2008,06/09/2008 10:34:05 AM,41.846573223,-87.700350031,"(41.846573223, -87.700350031)"
-6274249,HP361687,05/28/2008 06:40:00 PM,029XX W MADISON ST,0880,THEFT,PURSE-SNATCHING,SIDEWALK,false,false,1331,012,2,27,06,1156996,1899922,2008,05/30/2008 11:55:15 AM,41.881157001,-87.698980621,"(41.881157001, -87.698980621)"
-6276547,HP362665,05/28/2008 02:30:00 PM,124XX S NORMAL AVE,0460,BATTERY,SIMPLE,STREET,true,false,0523,005,34,53,08B,1175262,1822430,2008,05/31/2008 06:25:36 AM,41.668120269,-87.634222483,"(41.668120269, -87.634222483)"
-6345665,HP434308,05/28/2008 12:00:00 AM,016XX N MOODY AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2513,025,29,25,26,1135201,1910447,2008,07/08/2008 11:23:42 AM,41.910453149,-87.778761884,"(41.910453149, -87.778761884)"
-6275676,HP359271,05/27/2008 02:00:00 PM,004XX W WASHINGTON ST,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,0111,001,42,28,06,1173485,1900749,2008,05/30/2008 08:53:07 AM,41.883075934,-87.638409728,"(41.883075934, -87.638409728)"
-6274102,HP359328,05/27/2008 01:00:00 PM,018XX N KEDVALE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2534,025,30,20,14,1148383,1912078,2008,06/10/2008 06:29:29 PM,41.914684685,-87.730293541,"(41.914684685, -87.730293541)"
-6275257,HP356730,05/26/2008 02:05:00 AM,105XX S CALUMET AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,0512,005,9,49,26,1180398,1835350,2008,06/17/2008 07:44:12 AM,41.703458724,-87.615031688,"(41.703458724, -87.615031688)"
-6269540,HP357099,05/26/2008 12:00:00 AM,020XX N MILWAUKEE AVE,0810,THEFT,OVER $500,STREET,false,false,1431,014,1,22,06,1159459,1913368,2008,06/02/2008 07:06:17 AM,41.918003628,-87.689566121,"(41.918003628, -87.689566121)"
-6268820,HP354854,05/24/2008 09:35:00 PM,015XX W 21ST ST,0460,BATTERY,SIMPLE,STREET,false,false,1222,012,25,31,08B,1166260,1890124,2008,06/23/2008 10:13:11 AM,41.854077517,-87.665243812,"(41.854077517, -87.665243812)"
-6269559,HP355688,05/24/2008 09:00:00 PM,026XX W GUNNISON ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2031,020,40,4,14,1157640,1932136,2008,05/27/2008 09:15:31 AM,41.969541517,-87.695736326,"(41.969541517, -87.695736326)"
-6268147,HP354720,05/24/2008 07:54:29 PM,071XX S HARVARD AVE,0810,THEFT,OVER $500,RESIDENCE,false,false,0731,007,6,69,06,1175238,1857420,2008,05/28/2008 12:33:01 PM,41.764138243,-87.633269093,"(41.764138243, -87.633269093)"
-6292905,HP354332,05/24/2008 03:55:51 PM,083XX S HERMITAGE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0614,006,18,71,18,1166085,1849585,2008,06/08/2008 11:43:25 AM,41.742837424,-87.667039462,"(41.742837424, -87.667039462)"
-6266582,HP351840,05/23/2008 08:50:00 AM,111XX S DR MARTIN LUTHER KING JR DR,0454,BATTERY,AGG PO HANDS NO/MIN INJURY,STREET,true,false,0531,005,9,49,08B,1180931,1831161,2008,07/25/2008 02:29:25 PM,41.691951322,-87.613208221,"(41.691951322, -87.613208221)"
-6266039,HP351935,05/23/2008 12:01:00 AM,014XX W 114TH PL,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,2234,022,34,75,14,1169004,1828860,2008,05/25/2008 06:38:27 AM,41.685902323,-87.656940696,"(41.685902323, -87.656940696)"
-6341246,HP350890,05/22/2008 04:30:00 PM,019XX N LOWELL AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,2534,,31,20,05,,,2008,07/04/2008 11:48:08 AM,,,
-6266425,HP349111,05/21/2008 06:53:00 PM,063XX S CALIFORNIA AVE,0340,ROBBERY,ATTEMPT: STRONGARM-NO WEAPON,SIDEWALK,false,false,0823,008,15,66,03,1158760,1862535,2008,06/24/2008 08:04:44 AM,41.778526691,-87.693525737,"(41.778526691, -87.693525737)"
-6259230,HP346495,05/20/2008 08:30:00 AM,053XX S LOOMIS BLVD,0810,THEFT,OVER $500,STREET,false,false,0933,009,16,61,06,1167859,1869539,2008,05/21/2008 09:12:12 AM,41.797555892,-87.659967055,"(41.797555892, -87.659967055)"
-6255908,HP344002,05/18/2008 10:15:00 PM,012XX W BARRY AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1932,019,32,6,14,1167539,1920679,2008,05/21/2008 10:38:35 AM,41.937895124,-87.659668823,"(41.937895124, -87.659668823)"
-6254999,HP342365,05/18/2008 08:38:23 AM,006XX N LEAMINGTON AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1532,015,28,25,05,1141956,1903672,2008,05/26/2008 10:06:24 AM,41.891739239,-87.754114263,"(41.891739239, -87.754114263)"
-6253998,HP341044,05/17/2008 01:10:00 PM,001XX W LAKE ST,0870,THEFT,POCKET-PICKING,CTA TRAIN,false,false,0113,001,42,32,06,1175354,1901695,2008,05/20/2008 02:15:36 PM,41.885630077,-87.631518326,"(41.885630077, -87.631518326)"
-6256350,HP344206,05/17/2008 10:00:00 AM,040XX W MADISON ST,0890,THEFT,FROM BUILDING,SMALL RETAIL STORE,false,false,1115,011,28,26,06,1149571,1899668,2008,05/20/2008 09:34:13 AM,41.880607362,-87.726251572,"(41.880607362, -87.726251572)"
-6276530,HP353847,05/16/2008 12:00:00 PM,019XX W ADDISON ST,0890,THEFT,FROM BUILDING,RESIDENCE-GARAGE,false,false,1923,019,47,5,06,1162916,1923880,2008,06/12/2008 07:11:47 AM,41.946777276,-87.676569074,"(41.946777276, -87.676569074)"
-6254093,HP338742,05/16/2008 10:00:00 AM,096XX S MICHIGAN AVE,5007,OTHER OFFENSE,OTHER WEAPONS VIOLATION,"SCHOOL, PUBLIC, BUILDING",true,false,0511,005,6,49,26,1178815,1840807,2008,05/19/2008 04:49:31 AM,41.718469551,-87.620662877,"(41.718469551, -87.620662877)"
-6253531,HP338596,05/16/2008 09:00:00 AM,072XX S MICHIGAN AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0323,003,6,69,05,1178372,1857130,2008,05/24/2008 03:50:32 PM,41.763271884,-87.621791093,"(41.763271884, -87.621791093)"
-6256819,HP338124,05/15/2008 11:00:00 PM,062XX S MARSHFIELD AVE,0313,ROBBERY,ARMED: OTHER DANGEROUS WEAPON,STREET,false,false,0714,007,15,67,03,1166365,1863527,2008,06/02/2008 04:31:11 PM,41.781090212,-87.665617005,"(41.781090212, -87.665617005)"
-6250156,HP337169,05/15/2008 03:05:00 PM,029XX N MELVINA AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,true,false,2511,025,29,19,03,1134527,1918687,2008,06/20/2008 09:48:39 AM,41.933076614,-87.781042991,"(41.933076614, -87.781042991)"
-6271230,HP358720,05/15/2008 08:00:00 AM,099XX S NORMAL AVE,1755,OFFENSE INVOLVING CHILDREN,CHILD ABANDONMENT,RESIDENCE,false,true,2232,022,9,73,26,1174755,1839066,2008,05/30/2008 12:35:41 PM,41.713783265,-87.635584819,"(41.713783265, -87.635584819)"
-6251791,HP336361,05/14/2008 04:00:00 PM,047XX S WOLCOTT AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,0931,009,20,61,08A,1164527,1873136,2008,05/22/2008 07:33:02 AM,41.807497466,-87.672084537,"(41.807497466, -87.672084537)"
-6246706,HP332696,05/13/2008 08:42:12 AM,069XX S DAMEN AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,0735,007,17,67,07,1164268,1858443,2008,05/14/2008 10:10:02 AM,41.76718346,-87.673448083,"(41.76718346, -87.673448083)"
-6245516,HP331515,05/12/2008 01:45:00 PM,003XX S CENTRAL PARK BLVD,0810,THEFT,OVER $500,STREET,false,false,1133,011,28,27,06,1152395,1898393,2008,05/15/2008 10:04:07 AM,41.877053341,-87.715915706,"(41.877053341, -87.715915706)"
-6245443,HP331815,05/12/2008 10:40:00 AM,039XX W 79TH ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",true,false,0834,008,18,70,08B,1151306,1851847,2008,06/29/2008 03:43:05 PM,41.749346,-87.72113174,"(41.749346, -87.72113174)"
-6241325,HP329342,05/11/2008 03:00:00 AM,118XX S STEWART AVE,0810,THEFT,OVER $500,RESIDENCE,false,false,0522,005,34,53,06,1175718,1826289,2008,05/12/2008 06:54:23 AM,41.678699816,-87.632438751,"(41.678699816, -87.632438751)"
-6241297,HP328423,05/10/2008 04:16:00 PM,059XX S ELIZABETH ST,0610,BURGLARY,FORCIBLE ENTRY,ABANDONED BUILDING,false,false,0713,007,16,67,05,1169047,1865295,2008,05/15/2008 07:51:42 AM,41.785884245,-87.655733152,"(41.785884245, -87.655733152)"
-6237622,HP324032,05/08/2008 10:30:00 AM,059XX N NORTHWEST HWY,1780,OFFENSE INVOLVING CHILDREN,OTHER OFFENSE,DRIVEWAY - RESIDENTIAL,false,false,1611,016,41,10,26,1130909,1939238,2008,05/11/2008 06:53:13 PM,41.989533904,-87.793863624,"(41.989533904, -87.793863624)"
-6244566,HP323145,05/07/2008 08:50:00 PM,033XX W ARMITAGE AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1413,014,35,22,16,1153660,1913128,2008,05/14/2008 11:12:41 AM,41.917462523,-87.710878446,"(41.917462523, -87.710878446)"
-6237480,HP322574,05/07/2008 03:25:00 PM,038XX W CHICAGO AVE,2093,NARCOTICS,FOUND SUSPECT NARCOTICS,SMALL RETAIL STORE,true,false,1112,011,27,23,26,1150612,1905026,2008,05/09/2008 12:22:34 PM,41.89529001,-87.722288986,"(41.89529001, -87.722288986)"
-6233442,HP320495,05/06/2008 03:36:14 PM,025XX W 63RD ST,0460,BATTERY,SIMPLE,STREET,false,false,0825,008,15,66,08B,1160239,1862749,2008,05/09/2008 11:49:23 AM,41.779083612,-87.68809771,"(41.779083612, -87.68809771)"
-6230277,HP318487,05/05/2008 03:00:00 PM,005XX S ASHLAND AVE,0890,THEFT,FROM BUILDING,OTHER,false,false,1211,012,2,28,06,1165788,1897659,2008,05/06/2008 07:36:25 AM,41.87476428,-87.666761495,"(41.87476428, -87.666761495)"
-6232814,HP321041,05/05/2008 12:00:00 PM,105XX S AVENUE O,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE-GARAGE,false,false,0432,004,10,52,14,1200896,1835774,2008,05/07/2008 05:52:33 AM,41.704128544,-87.53995951,"(41.704128544, -87.53995951)"
-6238994,HP318596,05/05/2008 12:00:00 AM,031XX W HARRISON ST,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,OTHER,false,false,1134,011,24,27,11,1155268,1897149,2008,05/20/2008 01:00:12 PM,41.87358248,-87.705400257,"(41.87358248, -87.705400257)"
-6229960,HP316713,05/04/2008 02:59:55 PM,009XX N ST LOUIS AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1121,011,27,23,14,1152915,1905954,2008,05/31/2008 04:07:47 PM,41.897791213,-87.713805975,"(41.897791213, -87.713805975)"
-6230917,HP316013,05/04/2008 02:00:00 AM,059XX W 63RD ST,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,false,false,0812,008,13,64,14,1137700,1862110,2008,05/07/2008 11:26:03 AM,41.777764453,-87.770744565,"(41.777764453, -87.770744565)"
-6228044,HP315813,05/03/2008 11:20:31 PM,064XX S LOWE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0723,007,16,68,08B,1173185,1862165,2008,05/11/2008 11:12:23 AM,41.777204684,-87.640653738,"(41.777204684, -87.640653738)"
-6231900,HP314855,05/03/2008 12:40:00 PM,046XX S LECLAIRE AVE,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,0814,008,23,56,06,1143185,1873186,2008,05/08/2008 02:22:23 PM,41.808058599,-87.750360722,"(41.808058599, -87.750360722)"
-6229667,HP314350,05/03/2008 03:45:00 AM,022XX E 68TH ST,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,SIDEWALK,true,false,0331,003,5,43,08B,1192111,1860352,2008,06/05/2008 04:12:42 AM,41.7717904,-87.571331163,"(41.7717904, -87.571331163)"
-6225982,HP313571,05/02/2008 03:00:00 AM,007XX E 76TH ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0624,006,6,69,07,1182802,1854831,2008,05/03/2008 08:05:01 AM,41.75686149,-87.605625729,"(41.75686149, -87.605625729)"
-6227178,HP311976,05/01/2008 09:30:00 PM,079XX S SOUTH SHORE DR,1563,SEX OFFENSE,CRIMINAL SEXUAL ABUSE,OTHER,false,true,0422,004,7,46,17,1198478,1853116,2008,05/30/2008 12:33:29 PM,41.75177718,-87.548234406,"(41.75177718, -87.548234406)"
-6224661,HP310655,05/01/2008 09:52:58 AM,053XX S DR MARTIN LUTHER KING JR DR,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0234,002,3,40,08B,1179800,1869388,2008,05/03/2008 01:49:10 PM,41.79687651,-87.616182547,"(41.79687651, -87.616182547)"
-6222199,HP308088,04/29/2008 06:50:00 PM,0000X E JACKSON BLVD,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,0123,001,2,32,06,1176462,1898963,2008,05/02/2008 08:27:00 AM,41.878108377,-87.627532085,"(41.878108377, -87.627532085)"
-6234443,HP304004,04/27/2008 03:37:32 PM,036XX W 63RD ST,2022,NARCOTICS,POSS: COCAINE,STREET,true,false,0823,008,13,65,18,1153286,1862637,2008,05/08/2008 11:21:37 AM,41.778916604,-87.713591268,"(41.778916604, -87.713591268)"
-6220381,HP303767,04/27/2008 12:55:00 PM,045XX N KENNETH AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1722,017,45,16,08B,1145675,1930096,2008,05/03/2008 12:12:46 PM,41.964179515,-87.739784127,"(41.964179515, -87.739784127)"
-6221258,HP302387,04/26/2008 01:50:00 PM,062XX S HAMLIN AVE,5007,OTHER OFFENSE,OTHER WEAPONS VIOLATION,STREET,false,false,0823,008,13,65,26,1152159,1862739,2008,05/05/2008 01:41:23 PM,41.779218722,-87.717720316,"(41.779218722, -87.717720316)"
-6216101,HP302079,04/26/2008 01:25:00 PM,049XX N DRAKE AVE,0460,BATTERY,SIMPLE,STREET,false,false,1712,017,39,14,08B,1151865,1932596,2008,04/30/2008 10:29:00 AM,41.970919757,-87.716959022,"(41.970919757, -87.716959022)"
-6214550,HP301774,04/26/2008 09:45:00 AM,041XX S CAMPBELL AVE,0560,ASSAULT,SIMPLE,GROCERY FOOD STORE,false,false,0914,009,12,58,08A,1160428,1876804,2008,05/06/2008 12:32:51 PM,41.817648468,-87.687017391,"(41.817648468, -87.687017391)"
-6213539,HP299876,04/25/2008 01:10:00 PM,042XX W AUGUSTA BLVD,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,STREET,true,false,1111,011,37,23,18,1147689,1906282,2008,04/26/2008 11:00:13 AM,41.898793231,-87.732992255,"(41.898793231, -87.732992255)"
-6220605,HP302841,04/24/2008 09:45:00 PM,090XX S ELIZABETH ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,APARTMENT,false,false,2222,022,21,73,04A,1169622,1844711,2008,05/04/2008 07:44:09 PM,41.729386594,-87.654220699,"(41.729386594, -87.654220699)"
-6210931,HP295897,04/23/2008 08:07:00 AM,042XX W WASHINGTON BLVD,1360,CRIMINAL TRESPASS,TO VEHICLE,STREET,true,false,1115,011,28,26,26,1147890,1900110,2008,04/28/2008 09:39:36 AM,41.881852725,-87.732412745,"(41.881852725, -87.732412745)"
-6206271,HP295689,04/23/2008 04:10:00 AM,014XX N SEDGWICK ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,false,1821,018,27,8,08B,1173336,1910187,2008,04/25/2008 06:51:23 AM,41.908977646,-87.638676259,"(41.908977646, -87.638676259)"
-6200401,HP289541,04/19/2008 08:00:00 PM,106XX S HALSTED ST,0860,THEFT,RETAIL THEFT,GAS STATION,false,false,2233,022,34,49,06,1172915,1834001,2008,04/21/2008 07:58:03 AM,41.699924871,-87.642472396,"(41.699924871, -87.642472396)"
-6203792,HP291728,04/19/2008 02:00:00 PM,019XX W IRVING PARK RD,1310,CRIMINAL DAMAGE,TO PROPERTY,BANK,false,false,1923,019,47,5,14,1162475,1926608,2008,04/23/2008 07:22:36 AM,41.954272312,-87.678113399,"(41.954272312, -87.678113399)"
-6200213,HP287322,04/18/2008 05:20:00 PM,109XX S CHURCH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,true,2212,022,19,75,14,1167363,1832273,2008,04/22/2008 10:12:42 AM,41.695303369,-87.662850823,"(41.695303369, -87.662850823)"
-6204455,HP286817,04/18/2008 12:15:00 PM,101XX S PRAIRIE AVE,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",true,false,0511,005,9,49,08B,1179743,1837863,2008,04/23/2008 06:28:49 AM,41.7103697,-87.617353634,"(41.7103697, -87.617353634)"
-6201232,HP285321,04/17/2008 02:00:00 PM,037XX W DICKENS AVE,0560,ASSAULT,SIMPLE,OTHER,false,false,2525,025,26,22,08A,1150898,1913730,2008,04/28/2008 03:27:31 PM,41.919169026,-87.721010371,"(41.919169026, -87.721010371)"
-6192593,HP281344,04/15/2008 04:20:00 PM,027XX E 89TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,POLICE FACILITY/VEH PARKING LOT,false,false,0423,004,7,46,14,1195945,1846527,2008,04/17/2008 08:38:33 AM,41.733759491,-87.557734098,"(41.733759491, -87.557734098)"
-6191833,HP281462,04/14/2008 11:00:00 PM,081XX S CALUMET AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,0631,006,6,44,07,1179924,1851214,2008,04/19/2008 06:00:49 PM,41.747002357,-87.616283482,"(41.747002357, -87.616283482)"
-6191235,HP279024,04/14/2008 02:16:39 PM,042XX S MICHIGAN AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE PORCH/HALLWAY,true,false,0214,002,3,38,26,1177822,1876946,2008,04/16/2008 11:12:52 AM,41.817661392,-87.623207025,"(41.817661392, -87.623207025)"
-6190165,HP278941,04/14/2008 01:20:00 PM,020XX E 91ST ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,RESIDENCE PORCH/HALLWAY,true,false,0413,004,8,48,18,1191604,1845080,2008,04/16/2008 12:16:25 PM,41.729895032,-87.573683896,"(41.729895032, -87.573683896)"
-6188015,HP277790,04/13/2008 05:45:00 PM,038XX W 59TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0822,008,13,62,14,1151944,1865251,2008,04/22/2008 12:01:23 PM,41.786116259,-87.718442682,"(41.786116259, -87.718442682)"
-6187591,HP276885,04/13/2008 03:30:00 AM,047XX S WASHTENAW AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0911,009,14,58,18,1159121,1873329,2008,04/13/2008 11:51:21 AM,41.808139508,-87.691907028,"(41.808139508, -87.691907028)"
-6187521,HP273965,04/11/2008 01:30:00 PM,117XX S PERRY AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,"SCHOOL, PUBLIC, GROUNDS",false,false,0522,005,34,53,14,1177728,1827089,2008,04/14/2008 08:53:28 AM,41.680850033,-87.62505734,"(41.680850033, -87.62505734)"
-6182074,HP270607,04/09/2008 12:00:00 PM,047XX W SCHUBERT AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2521,025,31,19,05,1144324,1917420,2008,04/14/2008 09:39:42 PM,41.929421019,-87.745071281,"(41.929421019, -87.745071281)"
-6175567,HP265799,04/07/2008 02:42:00 AM,009XX W NEWPORT AVE,1330,CRIMINAL TRESPASS,TO LAND,APARTMENT,false,false,2331,019,44,6,26,1169428,1923133,2008,04/08/2008 08:33:34 AM,41.944588061,-87.652654805,"(41.944588061, -87.652654805)"
-6176186,HP265318,04/06/2008 07:45:00 PM,055XX N HARLEM AVE,0460,BATTERY,SIMPLE,RESTAURANT,false,false,1613,016,41,10,08B,1127411,1936253,2008,04/24/2008 11:06:04 PM,41.981402511,-87.806797616,"(41.981402511, -87.806797616)"
-6175423,HP265014,04/06/2008 07:00:00 AM,034XX W 116TH PL,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,2211,022,19,74,05,1155481,1827087,2008,06/24/2008 07:42:15 AM,41.681317565,-87.706493262,"(41.681317565, -87.706493262)"
-6175737,HP264039,04/06/2008 12:45:00 AM,039XX N OCONTO AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1631,016,38,17,08B,1126985,1925604,2008,04/10/2008 09:43:48 AM,41.952187671,-87.808604205,"(41.952187671, -87.808604205)"
-6187610,HP277192,04/06/2008 12:00:00 AM,021XX W CORTLAND ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,1434,014,32,22,06,1161623,1912670,2008,05/16/2008 03:33:53 PM,41.916043416,-87.681634957,"(41.916043416, -87.681634957)"
-6173089,HP261066,04/04/2008 11:37:11 AM,023XX W 21ST PL,0820,THEFT,$500 AND UNDER,"SCHOOL, PUBLIC, BUILDING",false,false,1223,012,25,31,06,1161300,1889646,2008,04/08/2008 10:11:27 AM,41.852870293,-87.683462218,"(41.852870293, -87.683462218)"
-6181197,HP260428,04/04/2008 07:20:00 AM,027XX W MADISON ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1331,012,2,27,16,1157810,1899940,2008,04/10/2008 02:10:22 PM,41.881189845,-87.695991162,"(41.881189845, -87.695991162)"
-6172807,HP260234,04/02/2008 11:00:00 PM,063XX S MOZART ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0823,008,15,66,14,1158437,1862179,2008,04/06/2008 11:10:47 AM,41.777556364,-87.694719576,"(41.777556364, -87.694719576)"
-6171926,HP258149,04/02/2008 08:25:00 PM,041XX W LAKE ST,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,1114,011,28,26,08A,1148520,1901557,2008,04/19/2008 06:06:42 PM,41.885811326,-87.730062024,"(41.885811326, -87.730062024)"
-6172564,HP257996,04/02/2008 02:45:00 PM,070XX S CLYDE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,true,true,0331,003,5,43,08B,1191470,1858641,2008,04/05/2008 05:46:06 AM,41.767110845,-87.573736211,"(41.767110845, -87.573736211)"
-6166853,HP255552,04/01/2008 02:00:00 PM,018XX W 103RD ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,2212,022,19,72,06,1165897,1836338,2008,04/03/2008 08:33:31 AM,41.706489596,-87.668103385,"(41.706489596, -87.668103385)"
-6166260,HP255550,04/01/2008 12:00:00 AM,049XX N ST LOUIS AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1712,017,39,14,14,1152123,1932457,2008,04/02/2008 08:28:16 AM,41.970533235,-87.716014009,"(41.970533235, -87.716014009)"
-6165207,HP254680,03/31/2008 11:35:00 PM,033XX W LAWRENCE AVE,2022,NARCOTICS,POSS: COCAINE,STREET,true,false,1713,017,39,14,18,1152881,1931725,2008,04/01/2008 09:39:17 AM,41.96850956,-87.713246255,"(41.96850956, -87.713246255)"
-6165343,HP255021,03/31/2008 05:00:00 PM,017XX N FRANCISCO AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1421,014,35,24,14,1156822,1911409,2008,04/05/2008 08:12:37 AM,41.912681884,-87.699307833,"(41.912681884, -87.699307833)"
-6173843,HP261122,03/30/2008 02:30:00 PM,006XX W ROSCOE ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2331,019,44,6,06,1171157,1922770,2008,04/07/2008 10:28:52 AM,41.943554123,-87.646310469,"(41.943554123, -87.646310469)"
-6163565,HP250889,03/29/2008 06:55:00 PM,026XX W CERMAK RD,1310,CRIMINAL DAMAGE,TO PROPERTY,RESTAURANT,false,false,1034,010,28,30,14,1158828,1889249,2008,04/01/2008 11:36:42 AM,41.851831862,-87.692546144,"(41.851831862, -87.692546144)"
-6165902,HP252641,03/29/2008 02:30:00 PM,053XX S MARYLAND AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,2131,002,5,41,06,1182776,1869711,2008,04/17/2008 10:19:38 AM,41.797694201,-87.605259317,"(41.797694201, -87.605259317)"
-6161279,HP250617,03/28/2008 11:15:00 PM,018XX S CANALPORT AVE,0610,BURGLARY,FORCIBLE ENTRY,COMMERCIAL / BUSINESS OFFICE,false,false,1233,012,25,31,05,1172677,1891230,2008,04/22/2008 03:39:04 PM,41.856973045,-87.641658371,"(41.856973045, -87.641658371)"
-6234135,HP318978,03/28/2008 11:00:00 PM,023XX N KIMBALL AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1413,014,26,22,14,1153273,1915524,2008,05/12/2008 09:13:13 AM,41.924045049,-87.712236543,"(41.924045049, -87.712236543)"
-6162136,HP249101,03/28/2008 06:25:00 PM,036XX S HERMITAGE AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,SIDEWALK,false,false,0922,009,11,59,14,1165314,1880761,2008,03/31/2008 11:49:10 AM,41.828404659,-87.668981815,"(41.828404659, -87.668981815)"
-6158572,HP247175,03/27/2008 05:16:55 PM,002XX W 43RD ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,true,0935,009,3,37,18,1175615,1876514,2008,03/28/2008 11:18:38 AM,41.816525711,-87.631315846,"(41.816525711, -87.631315846)"
-6158702,HP247925,03/27/2008 04:00:00 PM,039XX N CLARK ST,0870,THEFT,POCKET-PICKING,CTA BUS,false,false,2324,019,44,6,06,1166862,1926635,2008,03/31/2008 03:36:59 PM,41.95425323,-87.66198547,"(41.95425323, -87.66198547)"
-6154681,HP244948,03/25/2008 06:00:00 PM,0000X E GRAND AVE,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,1834,018,42,8,06,1176699,1903953,2008,04/05/2008 02:46:31 PM,41.891795857,-87.626510975,"(41.891795857, -87.626510975)"
-6150658,HP241603,03/25/2008 12:00:00 AM,001XX N MAYFIELD AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,APARTMENT,false,false,1512,015,29,25,06,1137091,1900665,2008,04/03/2008 06:37:02 PM,41.883576369,-87.77205372,"(41.883576369, -87.77205372)"
-6152030,HP240169,03/23/2008 04:59:00 PM,030XX S LAWNDALE AVE,0460,BATTERY,SIMPLE,STREET,true,false,1031,010,22,30,08B,1152139,1884400,2008,03/27/2008 09:28:07 AM,41.838659956,-87.717224345,"(41.838659956, -87.717224345)"
-6162097,HP244814,03/22/2008 07:35:00 PM,031XX W ROOSEVELT RD,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1022,010,24,29,14,1155411,1894498,2008,04/30/2008 08:05:35 AM,41.866304985,-87.70494648,"(41.866304985, -87.70494648)"
-6149995,HP238747,03/22/2008 02:00:00 PM,092XX S LAFLIN ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,2222,022,21,73,07,1167997,1843439,2008,03/27/2008 07:44:57 PM,41.725931064,-87.660209947,"(41.725931064, -87.660209947)"
-6149264,HP235897,03/20/2008 01:00:00 PM,100XX W OHARE ST,0810,THEFT,OVER $500,AIRPORT/AIRCRAFT,false,false,1651,016,41,76,06,1100635,1934208,2008,03/26/2008 10:44:11 AM,41.976200173,-87.905312411,"(41.976200173, -87.905312411)"
-6140745,HP232040,03/18/2008 12:45:00 PM,035XX W CONGRESS PKWY,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,1133,011,28,27,08B,1152803,1897503,2008,03/21/2008 09:25:45 AM,41.874603022,-87.714441204,"(41.874603022, -87.714441204)"
-6145113,HP228437,03/16/2008 01:10:00 PM,075XX S NORMAL AVE,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,false,false,0621,006,17,69,26,1174122,1854872,2008,03/22/2008 07:11:01 AM,41.757171076,-87.637435036,"(41.757171076, -87.637435036)"
-6134602,HP227016,03/15/2008 04:00:00 PM,028XX S SPRINGFIELD AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,1031,010,22,30,07,1150871,1885033,2008,03/17/2008 08:30:25 AM,41.840421854,-87.721860782,"(41.840421854, -87.721860782)"
-6134585,HP226614,03/15/2008 10:46:35 AM,061XX S EBERHART AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,false,false,0313,003,20,42,18,1180594,1864462,2008,11/11/1999 05:36:42 PM,41.783340899,-87.613422153,"(41.783340899, -87.613422153)"
-6130233,HP219933,03/11/2008 04:47:00 PM,063XX S WESTERN AVE,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,SMALL RETAIL STORE,false,false,0825,008,15,66,11,1161416,1862739,2008,03/17/2008 10:39:55 AM,41.779031855,-87.68378297,"(41.779031855, -87.68378297)"
-6131453,HP219488,03/11/2008 04:03:23 PM,007XX N CENTRAL AVE,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,1524,015,37,25,18,1138920,1904680,2008,03/14/2008 12:57:13 PM,41.894561014,-87.765239771,"(41.894561014, -87.765239771)"
-6126974,HP220000,03/11/2008 04:00:00 PM,052XX S MOODY AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0811,008,23,56,14,1136220,1869161,2008,03/13/2008 02:09:00 PM,41.79714011,-87.776002831,"(41.79714011, -87.776002831)"
-6131439,HP219061,03/11/2008 08:00:00 AM,103XX S ELIZABETH ST,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",false,false,2232,022,21,73,06,1169808,1836140,2008,03/15/2008 07:40:49 AM,41.705862453,-87.653787153,"(41.705862453, -87.653787153)"
-6122028,HP216249,03/09/2008 03:30:00 PM,065XX S HERMITAGE AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,0725,007,15,67,08A,1165839,1861121,2008,03/12/2008 08:48:21 AM,41.774499029,-87.66761372,"(41.774499029, -87.66761372)"
-6238424,HP324145,03/08/2008 11:00:00 AM,012XX N CLARK ST,0820,THEFT,$500 AND UNDER,GROCERY FOOD STORE,false,false,1821,018,42,8,06,1175274,1908508,2008,05/12/2008 09:14:19 AM,41.904327102,-87.631607477,"(41.904327102, -87.631607477)"
-6125147,HP213947,03/08/2008 06:50:00 AM,100XX W OHARE ST,0810,THEFT,OVER $500,AIRPORT/AIRCRAFT,false,false,1651,016,41,76,06,1100635,1934208,2008,04/11/2008 11:28:08 AM,41.976200173,-87.905312411,"(41.976200173, -87.905312411)"
-6152685,HP243742,03/07/2008 07:30:00 AM,091XX S GREENWOOD AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0413,004,8,47,06,1185105,1844523,2008,03/27/2008 07:50:12 AM,41.72852149,-87.597508756,"(41.72852149, -87.597508756)"
-6118412,HP212130,03/07/2008 12:00:00 AM,024XX S CALIFORNIA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1033,010,12,30,14,1158052,1887427,2008,03/10/2008 11:18:15 AM,41.846847949,-87.695443949,"(41.846847949, -87.695443949)"
-6119122,HP208737,03/05/2008 07:00:00 AM,069XX W MEDILL AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,2512,025,36,18,08A,1129793,1914709,2008,03/19/2008 01:53:22 PM,41.922242857,-87.798531583,"(41.922242857, -87.798531583)"
-6158692,HP246351,03/04/2008 12:30:00 PM,035XX S GILES AVE,0810,THEFT,OVER $500,"SCHOOL, PUBLIC, BUILDING",false,false,0211,002,2,35,06,1178880,1881656,2008,04/06/2008 04:34:45 PM,41.830561946,-87.619182387,"(41.830561946, -87.619182387)"
-6109568,HP204863,03/02/2008 09:00:00 PM,012XX W 110TH PL,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,true,true,2234,022,34,75,26,1169842,1831540,2008,03/09/2008 01:54:57 PM,41.693238586,-87.653795555,"(41.693238586, -87.653795555)"
-6109137,HP203592,03/02/2008 01:27:00 AM,027XX W 83RD ST,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0835,008,18,70,08B,1159208,1849428,2008,03/04/2008 01:08:13 PM,41.742549946,-87.692241433,"(41.742549946, -87.692241433)"
-6109105,HP203127,03/01/2008 04:45:00 PM,079XX S HALSTED ST,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),false,false,0621,006,17,71,07,1172301,1852337,2008,03/03/2008 10:53:09 AM,41.750254941,-87.644183117,"(41.750254941, -87.644183117)"
-7626207,HS430577,03/01/2008 09:00:00 AM,016XX E 50TH ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,2132,002,4,39,06,1188327,1872154,2008,09/13/2010 02:05:24 PM,41.804267141,-87.584825224,"(41.804267141, -87.584825224)"
-6111471,HP201195,02/29/2008 04:50:25 PM,041XX S CALIFORNIA AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0912,009,12,58,04B,1158413,1877314,2008,03/25/2008 08:34:11 PM,41.819089324,-87.694395141,"(41.819089324, -87.694395141)"
-6105776,HP198570,02/28/2008 08:08:53 AM,002XX W JACKSON BLVD,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,0112,001,2,32,08A,1174547,1898907,2008,03/03/2008 08:40:16 AM,41.877997709,-87.63456512,"(41.877997709, -87.63456512)"
-6101855,HP195863,02/26/2008 03:37:55 PM,057XX S MAY ST,2017,NARCOTICS,MANU/DELIVER:CRACK,STREET,true,false,0712,007,16,68,18,1169595,1866600,2008,02/27/2008 01:54:12 PM,41.789453449,-87.653686099,"(41.789453449, -87.653686099)"
-6103190,HP198655,02/24/2008 10:45:00 PM,052XX W POTOMAC AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2532,025,37,25,26,1140990,1908108,2008,03/07/2008 10:44:02 AM,41.903929988,-87.757552657,"(41.903929988, -87.757552657)"
-6097933,HP191097,02/23/2008 06:50:00 PM,027XX S KEELER AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,APARTMENT,true,false,1031,010,22,30,04B,1148864,1885617,2008,02/27/2008 10:27:52 AM,41.8420634,-87.729210681,"(41.8420634, -87.729210681)"
-6094751,HP190118,02/22/2008 07:00:00 PM,003XX S KOSTNER AVE,0610,BURGLARY,FORCIBLE ENTRY,GROCERY FOOD STORE,false,false,1131,011,24,26,05,1147066,1898222,2008,04/26/2008 09:54:25 PM,41.876687625,-87.73548678,"(41.876687625, -87.73548678)"
-6089215,HP187178,02/21/2008 03:00:00 PM,0000X N STATE ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,false,false,0122,001,42,32,06,1176321,1900593,2008,02/22/2008 08:31:27 AM,41.882584372,-87.628000611,"(41.882584372, -87.628000611)"
-6090627,HP186730,02/21/2008 11:30:00 AM,028XX S SACRAMENTO AVE,0810,THEFT,OVER $500,STREET,false,false,1033,010,12,30,06,1156865,1884997,2008,02/25/2008 12:17:42 PM,41.84020386,-87.699866005,"(41.84020386, -87.699866005)"
-6086993,HP185410,02/20/2008 03:45:00 PM,001XX N STATE ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0122,001,42,32,06,1176390,1900949,2008,02/22/2008 07:41:34 AM,41.883559699,-87.627736496,"(41.883559699, -87.627736496)"
-6087823,HP185275,02/20/2008 12:30:00 PM,028XX S KOMENSKY AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,1031,010,22,30,08B,1149878,1884877,2008,02/22/2008 10:54:00 AM,41.840013111,-87.725508787,"(41.840013111, -87.725508787)"
-6085784,HP183567,02/19/2008 02:19:00 PM,124XX S STEWART AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0523,005,34,53,08B,1175833,1822623,2008,02/27/2008 07:18:26 AM,41.668637162,-87.632127005,"(41.668637162, -87.632127005)"
-6084181,HP182916,02/19/2008 04:30:00 AM,053XX W DEMING PL,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2515,025,31,19,14,1140108,1916482,2008,02/20/2008 08:17:12 AM,41.926925377,-87.760587087,"(41.926925377, -87.760587087)"
-6085633,HP182093,02/18/2008 03:00:00 PM,0000X N STATE ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0122,001,42,32,06,1176328,1900431,2008,02/21/2008 06:49:58 AM,41.882139677,-87.627979796,"(41.882139677, -87.627979796)"
-6095803,HP184988,02/18/2008 12:01:00 AM,018XX N WOLCOTT AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,STREET,false,false,1434,014,32,22,11,1163434,1912383,2008,03/14/2008 10:00:48 AM,41.915217913,-87.674989553,"(41.915217913, -87.674989553)"
-6079478,HP179864,02/16/2008 11:30:00 PM,070XX W GRAND AVE,041A,BATTERY,AGGRAVATED: HANDGUN,CTA GARAGE / OTHER PROPERTY,false,false,2512,025,36,18,04B,1128939,1915219,2008,02/17/2008 06:20:06 AM,41.923656934,-87.80165788,"(41.923656934, -87.80165788)"
-6080497,HP179366,02/16/2008 06:41:33 PM,0000X N LATROBE AVE,5001,OTHER OFFENSE,OTHER CRIME INVOLVING PROPERTY,STREET,true,false,1522,015,28,25,26,1141336,1900023,2008,02/19/2008 09:30:16 AM,41.881737395,-87.75648136,"(41.881737395, -87.75648136)"
-6079643,HP178304,02/16/2008 08:43:08 AM,053XX W MONROE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1522,015,29,25,14,1140792,1899103,2008,02/17/2008 10:47:17 AM,41.879222815,-87.75850157,"(41.879222815, -87.75850157)"
-6082523,HP182300,02/15/2008 05:00:00 PM,022XX N MENARD AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2515,025,37,19,07,1137321,1914162,2008,04/04/2008 10:21:58 AM,41.920609664,-87.770884178,"(41.920609664, -87.770884178)"
-6075250,HP174724,02/14/2008 12:00:00 AM,015XX E 53RD ST,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,SMALL RETAIL STORE,false,false,2132,002,4,41,14,1187270,1870464,2008,02/27/2008 09:16:11 AM,41.799654855,-87.588755435,"(41.799654855, -87.588755435)"
-6072564,HP171926,02/12/2008 03:08:32 PM,050XX W CHICAGO AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1531,015,28,25,18,1142395,1904805,2008,02/13/2008 11:43:04 AM,41.894840181,-87.752473824,"(41.894840181, -87.752473824)"
-6073190,HP171331,02/11/2008 07:00:00 PM,099XX S PERRY AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0511,005,9,49,07,1177317,1838976,2008,02/14/2008 11:44:29 AM,41.713478936,-87.626204563,"(41.713478936, -87.626204563)"
-6070921,HP170580,02/11/2008 05:09:50 PM,064XX S LANGLEY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,VEHICLE NON-COMMERCIAL,false,true,0312,003,20,42,08B,1181959,1862478,2008,02/13/2008 07:08:07 AM,41.777865148,-87.608478971,"(41.777865148, -87.608478971)"
-6071483,HP170097,02/11/2008 10:00:00 AM,001XX E CULLERTON ST,0810,THEFT,OVER $500,STREET,false,false,0134,001,2,33,06,1177548,1890690,2008,02/15/2008 11:57:56 AM,41.855382182,-87.623795664,"(41.855382182, -87.623795664)"
-6071696,HP171576,02/10/2008 10:00:00 AM,014XX N CICERO AVE,0890,THEFT,FROM BUILDING,COMMERCIAL / BUSINESS OFFICE,false,false,2533,025,37,25,06,1144158,1908946,2008,02/25/2008 10:15:20 PM,41.906170612,-87.745894589,"(41.906170612, -87.745894589)"
-6133017,HP221408,02/09/2008 12:00:00 PM,055XX N CHRISTIANA AVE,5001,OTHER OFFENSE,OTHER CRIME INVOLVING PROPERTY,RESIDENCE,false,false,1712,017,40,13,26,1153088,1936804,2008,05/14/2008 12:13:46 PM,41.98244252,-87.712349743,"(41.98244252, -87.712349743)"
-6068240,HP166755,02/09/2008 02:00:00 AM,007XX W 71ST ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0732,007,6,68,14,1172837,1857762,2008,02/15/2008 11:52:30 AM,41.765130018,-87.642059235,"(41.765130018, -87.642059235)"
-6067026,HP166240,02/08/2008 07:10:00 PM,110XX S TROY ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2211,022,19,74,18,1157291,1830902,2008,02/09/2008 08:08:27 AM,41.691750324,-87.699764923,"(41.691750324, -87.699764923)"
-6063043,HP163044,02/07/2008 01:58:18 AM,001XX N PINE AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1523,015,28,25,18,1139520,1900458,2008,02/07/2008 11:54:36 AM,41.882964393,-87.763139151,"(41.882964393, -87.763139151)"
-6061079,HP161221,02/05/2008 10:00:00 PM,062XX S COTTAGE GROVE AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE PORCH/HALLWAY,true,false,0313,003,20,42,26,1182594,1863879,2008,02/07/2008 07:34:10 AM,41.78169491,-87.606107635,"(41.78169491, -87.606107635)"
-6059508,HP159862,02/04/2008 08:00:00 PM,046XX N WOLCOTT AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1922,019,47,4,06,1162942,1930857,2008,02/06/2008 07:29:59 AM,41.965921956,-87.676276875,"(41.965921956, -87.676276875)"
-6057994,HP159485,02/04/2008 05:45:00 PM,034XX N ASHLAND AVE,0810,THEFT,OVER $500,RESIDENCE-GARAGE,false,false,1924,019,32,6,06,1164999,1922985,2008,02/05/2008 08:37:17 AM,41.944277299,-87.668938099,"(41.944277299, -87.668938099)"
-6083324,HP158356,02/04/2008 08:30:00 AM,100XX W OHARE ST,5007,OTHER OFFENSE,OTHER WEAPONS VIOLATION,AIRPORT/AIRCRAFT,false,false,1651,016,41,76,26,1100635,1934208,2008,02/20/2008 01:25:46 PM,41.976200173,-87.905312411,"(41.976200173, -87.905312411)"
-6057968,HP159329,02/04/2008 12:00:00 AM,050XX W GLADYS AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1533,015,28,25,26,1142526,1897904,2008,02/12/2008 10:32:58 AM,41.875900562,-87.752164342,"(41.875900562, -87.752164342)"
-6057745,HP158561,02/03/2008 10:00:00 PM,001XX N CENTRAL PARK DR,1320,CRIMINAL DAMAGE,TO VEHICLE,OTHER,false,false,1122,011,28,27,14,1152437,1900703,2008,02/07/2008 12:31:41 PM,41.883391397,-87.715700454,"(41.883391397, -87.715700454)"
-6421999,HP497567,02/02/2008 12:01:00 AM,091XX S EGGLESTON AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,2223,022,21,73,06,1174850,1844385,2008,09/01/2008 07:29:30 PM,41.728377222,-87.635078895,"(41.728377222, -87.635078895)"
-6054807,HP156076,02/01/2008 05:00:00 PM,015XX N WESTERN AVE,0610,BURGLARY,FORCIBLE ENTRY,COMMERCIAL / BUSINESS OFFICE,false,false,1424,014,1,24,05,1160164,1910360,2008,02/10/2008 08:37:26 PM,41.9097349,-87.687059188,"(41.9097349, -87.687059188)"
-6053730,HP154724,01/31/2008 08:00:00 PM,013XX N KEELER AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2534,025,37,23,07,1148092,1908689,2008,02/14/2008 12:03:08 PM,41.905390541,-87.731450014,"(41.905390541, -87.731450014)"
-6054059,HP153786,01/31/2008 12:00:00 PM,050XX N MARINE DR,0810,THEFT,OVER $500,STREET,false,false,2024,020,48,3,06,1169662,1933934,2008,02/04/2008 08:57:22 AM,41.974221261,-87.651478828,"(41.974221261, -87.651478828)"
-6051653,HP152452,01/30/2008 10:00:00 PM,017XX N NAGLE AVE,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,2513,025,36,25,06,1132985,1911186,2008,02/02/2008 08:26:22 AM,41.912520086,-87.786885481,"(41.912520086, -87.786885481)"
-6048557,HP148252,01/28/2008 09:00:00 PM,0000X W HURON ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,false,false,1832,018,42,8,06,1175515,1905092,2008,02/01/2008 10:11:23 AM,41.894948015,-87.630824978,"(41.894948015, -87.630824978)"
-6050617,HP148946,01/28/2008 05:45:00 PM,024XX W ROSEMONT AVE,0810,THEFT,OVER $500,STREET,false,false,2413,024,50,2,06,1159141,1941796,2008,02/01/2008 12:55:52 PM,41.99601819,-87.689950309,"(41.99601819, -87.689950309)"
-6048493,HP148365,01/28/2008 10:00:00 AM,097XX S UNION AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,false,false,2223,022,21,73,26,1173392,1840475,2008,02/01/2008 11:40:34 AM,41.717679965,-87.640535103,"(41.717679965, -87.640535103)"
-6051700,HP148211,01/28/2008 12:00:00 AM,001XX N AUSTIN BLVD,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,1512,015,29,25,08A,1136391,1900669,2008,02/05/2008 10:05:28 AM,41.88359988,-87.774624123,"(41.88359988, -87.774624123)"
-6048193,HP149443,01/27/2008 02:30:00 PM,042XX S ASHLAND AVE,0460,BATTERY,SIMPLE,OTHER,false,false,0914,009,12,61,08B,1166338,1876850,2008,01/31/2008 05:29:24 AM,41.817650668,-87.665336409,"(41.817650668, -87.665336409)"
-6043685,HP145781,01/27/2008 01:00:00 PM,001XX N KEDZIE AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1331,012,27,27,18,1155069,1900663,2008,01/28/2008 07:44:48 AM,41.883229246,-87.706036593,"(41.883229246, -87.706036593)"
-6041889,HP144012,01/26/2008 10:30:00 AM,023XX W JACKSON BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,1211,012,2,28,08B,1160845,1898601,2008,01/28/2008 11:50:44 AM,41.877453118,-87.684883951,"(41.877453118, -87.684883951)"
-6041743,HP144909,01/25/2008 08:30:00 PM,008XX W OAKDALE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1932,019,44,6,06,1170061,1919841,2008,01/29/2008 08:21:54 AM,41.935540858,-87.650424573,"(41.935540858, -87.650424573)"
-6041975,HP142917,01/25/2008 05:25:00 PM,060XX S RACINE AVE,0325,ROBBERY,VEHICULAR HIJACKING,STREET,false,false,0713,007,16,67,03,1169323,1864401,2008,01/31/2008 07:23:24 PM,41.78342503,-87.654747073,"(41.78342503, -87.654747073)"
-6037595,HP139727,01/23/2008 11:12:46 PM,041XX W HENDERSON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1731,017,30,16,08B,1148091,1921979,2008,01/28/2008 01:32:58 PM,41.941859558,-87.731110835,"(41.941859558, -87.731110835)"
-6038698,HP140590,01/23/2008 06:15:00 PM,114XX S EMERALD AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2233,022,34,49,06,1173404,1828998,2008,01/25/2008 08:26:26 AM,41.686185088,-87.640829159,"(41.686185088, -87.640829159)"
-6031192,HP134970,01/20/2008 11:30:00 AM,001XX W CHESTNUT ST,0820,THEFT,$500 AND UNDER,OTHER,false,false,1832,018,42,8,06,1175341,1906231,2008,01/21/2008 11:30:46 AM,41.898077397,-87.631429803,"(41.898077397, -87.631429803)"
-6029341,HP132725,01/19/2008 02:56:23 PM,070XX S INDIANA AVE,1710,OFFENSE INVOLVING CHILDREN,ENDANGERING LIFE/HEALTH CHILD,APARTMENT,false,true,0322,003,6,69,26,1178859,1858127,2008,05/14/2008 11:38:53 PM,41.765996691,-87.619975852,"(41.765996691, -87.619975852)"
-6023197,HP125328,01/15/2008 01:40:00 PM,065XX S PEORIA ST,0560,ASSAULT,SIMPLE,APARTMENT,false,false,0723,007,17,68,08A,1171397,1861251,2008,01/24/2008 09:10:11 AM,41.774735894,-87.647235239,"(41.774735894, -87.647235239)"
-6024241,HP124314,01/14/2008 09:00:00 PM,071XX S MORGAN ST,0460,BATTERY,SIMPLE,SIDEWALK,true,false,0733,007,17,68,08B,1170847,1857148,2008,01/21/2008 05:30:58 PM,41.76348878,-87.64937106,"(41.76348878, -87.64937106)"
-6016460,HP122115,01/13/2008 02:00:00 PM,009XX W BELMONT AVE,0460,BATTERY,SIMPLE,CTA TRAIN,false,false,1932,019,44,6,08B,1169257,1921390,2008,01/14/2008 08:27:21 AM,41.939808919,-87.653334147,"(41.939808919, -87.653334147)"
-6017735,HP121062,01/12/2008 09:50:00 PM,037XX W CHICAGO AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1112,011,27,23,18,1151189,1905039,2008,01/14/2008 11:05:41 AM,41.895314388,-87.720169452,"(41.895314388, -87.720169452)"
-6021252,HP120662,01/12/2008 05:10:00 PM,023XX W LOGAN BLVD,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1432,014,1,22,14,1159964,1917231,2008,01/16/2008 08:40:40 AM,41.928593555,-87.687603847,"(41.928593555, -87.687603847)"
-6018122,HP123090,01/11/2008 04:40:00 PM,009XX N DAMEN AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1322,012,32,24,05,1162935,1906411,2008,01/20/2008 04:18:26 PM,41.898840809,-87.676990704,"(41.898840809, -87.676990704)"
-6016258,HP118210,01/11/2008 11:10:00 AM,079XX S DAMEN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,GAS STATION,false,false,0611,006,18,71,14,1164442,1852234,2008,01/15/2008 08:39:17 AM,41.750141421,-87.672984996,"(41.750141421, -87.672984996)"
-6011898,HP116012,01/10/2008 08:22:47 AM,007XX W RANDOLPH ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,STREET,false,false,1212,012,27,28,11,1171239,1901177,2008,02/09/2008 09:36:32 AM,41.884300006,-87.646644515,"(41.884300006, -87.646644515)"
-6005018,HP110823,01/07/2008 10:55:00 AM,080XX S UNION AVE,0460,BATTERY,SIMPLE,APARTMENT,false,false,0621,006,21,71,08B,1173078,1851346,2008,01/10/2008 07:21:10 AM,41.747518397,-87.641365051,"(41.747518397, -87.641365051)"
-6002597,HP110714,01/06/2008 08:30:00 PM,054XX S CORNELL AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2132,002,5,41,07,1188146,1869223,2008,01/08/2008 10:20:35 AM,41.796228596,-87.585582548,"(41.796228596, -87.585582548)"
-6002697,HP108963,01/06/2008 03:30:00 AM,071XX W HIGGINS AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1613,016,41,10,14,1127720,1936004,2008,01/09/2008 11:59:29 AM,41.980714012,-87.805666824,"(41.980714012, -87.805666824)"
-6005671,HP106401,01/04/2008 05:50:00 PM,008XX W 63RD ST,1120,DECEPTIVE PRACTICE,FORGERY,BANK,true,false,0723,007,20,68,10,1171877,1863038,2008,01/13/2008 01:48:35 PM,41.779629109,-87.645423234,"(41.779629109, -87.645423234)"
-6002515,HP103250,01/02/2008 09:15:00 PM,077XX S COLES AVE,1822,NARCOTICS,MANU/DEL:CANNABIS OVER 10 GMS,SIDEWALK,true,false,0421,004,7,43,18,1196659,1854676,2008,01/11/2008 09:10:55 AM,41.756103288,-87.554848321,"(41.756103288, -87.554848321)"
-5992611,HP100707,01/01/2008 09:30:00 AM,089XX S LOWE AVE,0281,CRIM SEXUAL ASSAULT,NON-AGGRAVATED,RESIDENCE,false,true,2223,022,21,71,02,1173502,1845434,2008,04/05/2009 03:04:15 PM,41.731285722,-87.639985929,"(41.731285722, -87.639985929)"
-6014743,HN787147,12/31/2007 10:54:00 PM,032XX W HARRISON ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,true,false,1134,011,24,27,14,1154733,1897137,2007,01/23/2008 02:56:07 PM,41.873560274,-87.707364844,"(41.873560274, -87.707364844)"
-5992539,HN786587,12/31/2007 03:57:55 PM,029XX W FILLMORE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1135,011,28,29,08B,1157079,1895278,2007,01/13/2008 01:05:28 AM,41.868411718,-87.698801913,"(41.868411718, -87.698801913)"
-5989290,HN785485,12/30/2007 08:15:00 PM,056XX W NORTH AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2531,025,29,25,05,1138351,1910028,2007,01/07/2008 01:04:14 AM,41.909246893,-87.767199953,"(41.909246893, -87.767199953)"
-5988344,HN783684,12/29/2007 06:00:00 AM,002XX N FRANCISCO AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1331,012,2,27,05,1156981,1901265,2007,11/27/2008 01:04:05 AM,41.884842626,-87.698999249,"(41.884842626, -87.698999249)"
-5987525,HN782816,12/29/2007 03:29:38 AM,024XX W LUNT AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2411,024,50,2,08B,1158820,1946432,2007,01/02/2008 01:04:12 AM,42.008746146,-87.691003182,"(42.008746146, -87.691003182)"
-5987141,HN782557,12/28/2007 09:46:11 PM,014XX S ST LOUIS AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,false,false,1021,010,24,29,04B,1153227,1892951,2007,01/03/2008 01:04:43 AM,41.862103432,-87.713005224,"(41.862103432, -87.713005224)"
-6191382,HN781364,12/28/2007 09:54:47 AM,034XX W ROOSEVELT RD,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,1133,011,24,29,04B,1153819,1894541,2007,04/18/2008 01:05:57 AM,41.866454811,-87.710789743,"(41.866454811, -87.710789743)"
-5991409,HN779844,12/27/2007 11:15:00 AM,066XX N SEELEY AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,2412,024,50,2,26,1161386,1944080,2007,01/11/2008 01:05:21 AM,42.002238974,-87.681628045,"(42.002238974, -87.681628045)"
-5993702,HN779567,12/27/2007 05:28:00 AM,042XX W 13TH ST,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1011,010,24,29,18,1148060,1893655,2007,01/06/2008 01:05:00 AM,41.864136176,-87.731954568,"(41.864136176, -87.731954568)"
-5992368,HN778916,12/26/2007 06:14:54 PM,067XX S LOOMIS BLVD,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,0725,007,17,67,18,1168127,1859725,2007,01/06/2008 01:05:00 AM,41.770619319,-87.659266397,"(41.770619319, -87.659266397)"
-5981302,HN777509,12/25/2007 02:00:00 PM,005XX N OGDEN AVE,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,1324,012,27,24,06,1167953,1903861,2007,01/04/2008 01:05:01 AM,41.891736613,-87.658633543,"(41.891736613, -87.658633543)"
-5994826,HP103019,12/24/2007 12:00:00 PM,035XX N OTTAWA AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,false,true,1631,016,36,17,26,1124493,1922694,2007,01/12/2008 01:05:03 AM,41.944243779,-87.817829391,"(41.944243779, -87.817829391)"
-5982038,HN775486,12/24/2007 07:25:37 AM,024XX W TOUHY AVE,0460,BATTERY,SIMPLE,NURSING HOME/RETIREMENT HOME,false,false,2411,024,50,2,08B,1158504,1947662,2007,01/18/2008 01:04:32 AM,42.012127804,-87.692131936,"(42.012127804, -87.692131936)"
-5987018,HN775531,12/24/2007 06:20:00 AM,088XX S COMMERCIAL AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,0423,004,10,46,16,1197632,1847130,2007,01/02/2008 01:04:12 AM,41.735372288,-87.551533824,"(41.735372288, -87.551533824)"
-6023224,HP123819,12/24/2007 12:00:00 AM,012XX W PRATT BLVD,1110,DECEPTIVE PRACTICE,BOGUS CHECK,BANK,false,false,2432,024,49,1,11,1166357,1945267,2007,01/26/2008 01:04:36 AM,42.00539084,-87.663306139,"(42.00539084, -87.663306139)"
-5999153,HN775174,12/23/2007 09:26:03 PM,045XX W WILCOX ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1113,011,28,26,08B,1146151,1898992,2007,01/11/2008 01:05:21 AM,41.878818037,-87.738826824,"(41.878818037, -87.738826824)"
-5977274,HN773656,12/22/2007 10:45:00 PM,090XX S WALLACE ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,2223,022,21,71,18,1173840,1845233,2007,12/26/2007 01:03:36 AM,41.730726672,-87.638753655,"(41.730726672, -87.638753655)"
-5977278,HN773847,12/22/2007 09:30:00 PM,024XX W POPE JOHN PAUL II DR,0820,THEFT,$500 AND UNDER,STREET,false,false,0914,009,12,58,06,1160865,1876051,2007,12/04/2014 12:43:35 PM,41.815573115,-87.685435165,"(41.815573115, -87.685435165)"
-6003708,HP111792,12/21/2007 09:00:00 AM,055XX N WESTERN AVE,0890,THEFT,FROM BUILDING,COMMERCIAL / BUSINESS OFFICE,false,false,2011,020,40,4,06,1159293,1936651,2007,02/01/2008 01:05:02 AM,41.981896969,-87.689533445,"(41.981896969, -87.689533445)"
-5976412,HN769964,12/20/2007 10:30:00 PM,039XX N CENTRAL AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1633,016,38,15,06,1138266,1925806,2007,12/04/2014 12:43:35 PM,41.952544957,-87.76712944,"(41.952544957, -87.76712944)"
-6101442,HP190712,12/20/2007 08:45:00 PM,058XX S WELLS ST,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,0711,007,20,68,06,1175571,1865986,2007,12/04/2014 12:43:35 PM,41.787636844,-87.631792451,"(41.787636844, -87.631792451)"
-5995109,HP103690,12/20/2007 06:30:00 AM,013XX W MELROSE ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1924,019,44,6,26,1166678,1921726,2007,01/30/2008 01:05:27 AM,41.940786666,-87.662803054,"(41.940786666, -87.662803054)"
-5972174,HN768002,12/19/2007 07:45:00 PM,006XX E 103RD ST,0554,ASSAULT,AGG PO HANDS NO/MIN INJURY,STREET,true,false,0512,005,9,50,08A,1182604,1836773,2007,12/23/2007 01:04:00 AM,41.707312844,-87.606909897,"(41.707312844, -87.606909897)"
-5976146,HN770701,12/19/2007 04:00:00 PM,020XX N PULASKI RD,0820,THEFT,$500 AND UNDER,RESTAURANT,false,false,2525,025,30,20,06,1149420,1913394,2007,12/04/2014 12:43:35 PM,41.918275841,-87.7264495,"(41.918275841, -87.7264495)"
-6176145,HN765101,12/17/2007 09:00:00 PM,004XX W WELLINGTON AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,2333,019,44,6,06,1172492,1920192,2007,12/04/2014 12:43:35 PM,41.93645053,-87.641480165,"(41.93645053, -87.641480165)"
-5970352,HN765263,12/16/2007 07:00:00 PM,028XX W FOSTER AVE,0560,ASSAULT,SIMPLE,NURSING HOME/RETIREMENT HOME,false,false,2011,020,40,4,08A,1156324,1934449,2007,12/22/2007 01:04:10 AM,41.975915286,-87.700512466,"(41.975915286, -87.700512466)"
-5965733,HN761134,12/15/2007 05:12:05 PM,020XX E 68TH ST,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,false,false,0331,003,5,43,26,1190891,1860324,2007,12/19/2007 01:04:46 AM,41.771743141,-87.575804114,"(41.771743141, -87.575804114)"
-5968430,HN761048,12/15/2007 03:00:00 PM,022XX W CERMAK RD,1345,CRIMINAL DAMAGE,TO CITY OF CHICAGO PROPERTY,SIDEWALK,true,false,1223,012,25,31,14,1161893,1889406,2007,12/21/2007 01:04:15 AM,41.852199373,-87.681292406,"(41.852199373, -87.681292406)"
-5970536,HN766760,12/14/2007 05:00:00 PM,002XX S WACKER DR,0890,THEFT,FROM BUILDING,COMMERCIAL / BUSINESS OFFICE,false,false,0112,001,2,32,06,1174011,1899114,2007,12/21/2007 01:04:15 AM,41.878577688,-87.636526993,"(41.878577688, -87.636526993)"
-5962110,HN757159,12/13/2007 01:30:00 PM,044XX W PALMER ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2522,025,31,20,06,1146618,1914219,2007,12/04/2014 12:43:35 PM,41.920593672,-87.736723253,"(41.920593672, -87.736723253)"
-5997367,HN779957,12/13/2007 12:00:00 AM,071XX S UNION AVE,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,APARTMENT,false,true,0732,007,6,68,20,1172945,1857464,2007,01/10/2008 01:05:01 AM,41.764309888,-87.641672167,"(41.764309888, -87.641672167)"
-5962739,HN756638,12/12/2007 11:30:00 PM,035XX W 65TH ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0831,008,15,66,07,1154034,1861247,2007,12/18/2007 02:43:27 PM,41.775087406,-87.710885892,"(41.775087406, -87.710885892)"
-5972625,HN756315,12/12/2007 11:15:00 PM,019XX W GARFIELD BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0715,007,15,67,18,1164061,1868010,2007,12/30/2007 01:04:20 AM,41.793440923,-87.673937923,"(41.793440923, -87.673937923)"
-5960369,HN755977,12/12/2007 07:00:01 PM,091XX S COMMERCIAL AVE,0326,ROBBERY,AGGRAVATED VEHICULAR HIJACKING,VEHICLE NON-COMMERCIAL,false,false,0423,004,10,46,03,1197686,1845133,2007,03/01/2008 01:04:38 AM,41.729891013,-87.551402459,"(41.729891013, -87.551402459)"
-5960400,HN754892,12/12/2007 08:48:00 AM,019XX E 87TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0412,004,8,45,18,1190639,1847713,2007,12/19/2007 01:04:46 AM,41.737143554,-87.577134169,"(41.737143554, -87.577134169)"
-5956756,HN753759,12/10/2007 11:50:00 PM,0000X E 70TH ST,2830,OTHER OFFENSE,OBSCENE TELEPHONE CALLS,APARTMENT,false,false,0322,003,6,69,17,1177763,1858651,2007,12/18/2007 02:43:27 PM,41.767459478,-87.623977205,"(41.767459478, -87.623977205)"
-5952310,HN750733,12/09/2007 08:13:56 PM,066XX S HOYNE AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,RESIDENCE PORCH/HALLWAY,true,false,0726,007,15,67,15,1163470,1860493,2007,12/13/2007 01:04:49 AM,41.772825706,-87.676315695,"(41.772825706, -87.676315695)"
-5951078,HN749838,12/09/2007 08:40:00 AM,049XX N KEDZIE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1713,017,33,14,26,1154192,1932663,2007,12/13/2007 01:04:49 AM,41.97105735,-87.708400585,"(41.97105735, -87.708400585)"
-5952111,HN749749,12/09/2007 06:00:00 AM,063XX S KOMENSKY AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,true,0813,008,13,65,08B,1150503,1862140,2007,12/18/2007 02:43:27 PM,41.777607343,-87.723807023,"(41.777607343, -87.723807023)"
-5948577,HN746760,12/07/2007 12:20:00 PM,029XX W FITCH AVE,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,false,false,2411,024,50,2,07,1155411,1947298,2007,12/12/2007 01:05:15 AM,42.011191984,-87.703522413,"(42.011191984, -87.703522413)"
-5998278,HN744429,12/06/2007 01:26:26 AM,075XX S CALUMET AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,OTHER,true,false,0623,006,6,69,18,1179817,1855256,2007,01/09/2008 01:04:52 AM,41.758096503,-87.616552164,"(41.758096503, -87.616552164)"
-5998274,HN744338,12/05/2007 11:05:00 PM,080XX S LAFLIN ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,OTHER,true,false,0612,006,21,71,18,1167687,1851619,2007,01/09/2008 01:04:52 AM,41.748384818,-87.661111456,"(41.748384818, -87.661111456)"
-5946496,HN744765,12/05/2007 03:20:00 PM,011XX S STATE ST,0460,BATTERY,SIMPLE,STREET,false,false,0132,001,2,32,08B,1176562,1895133,2007,12/21/2007 01:04:15 AM,41.867596369,-87.627280588,"(41.867596369, -87.627280588)"
-5944224,HN743721,12/05/2007 01:15:00 PM,111XX S VINCENNES AVE,0810,THEFT,OVER $500,STREET,false,false,2234,022,34,75,06,1167256,1831056,2007,12/04/2014 12:43:35 PM,41.691966003,-87.663277248,"(41.691966003, -87.663277248)"
-5943232,HN741425,12/04/2007 10:25:00 AM,060XX S SANGAMON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0712,007,16,68,08B,1170973,1864665,2007,12/24/2007 01:03:56 AM,41.784113584,-87.648689912,"(41.784113584, -87.648689912)"
-5942578,HN741343,12/03/2007 12:00:00 PM,048XX N LINDER AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1623,016,45,11,14,1138747,1931793,2007,12/10/2007 01:03:40 AM,41.968965102,-87.765215322,"(41.968965102, -87.765215322)"
-5943314,HN738069,12/02/2007 08:52:00 AM,032XX W GEORGE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1412,014,35,21,14,1154028,1919070,2007,12/09/2007 01:04:39 AM,41.933760515,-87.709367538,"(41.933760515, -87.709367538)"
-4345,HN737868,12/02/2007 03:49:00 AM,045XX W VAN BUREN ST,0110,HOMICIDE,FIRST DEGREE MURDER,STREET,false,false,1131,011,24,26,01A,1146369,1897602,2007,11/17/2011 12:01:47 PM,41.874999565,-87.738061759,"(41.874999565, -87.738061759)"
-5940691,HN737917,12/01/2007 02:42:00 AM,015XX E 67TH PL,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0332,003,5,43,05,1187701,1860580,2007,12/09/2007 01:04:39 AM,41.772522135,-87.587489296,"(41.772522135, -87.587489296)"
-5936409,HN734117,11/29/2007 10:14:30 PM,032XX W FLOURNOY ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,RESIDENCE,true,false,1134,011,24,27,15,1154761,1896807,2007,12/05/2007 01:05:24 AM,41.872654159,-87.707270879,"(41.872654159, -87.707270879)"
-5935034,HN731369,11/28/2007 10:55:00 AM,013XX W 78TH ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0612,006,17,71,05,1168988,1853009,2007,12/29/2007 01:04:11 AM,41.752171177,-87.656304062,"(41.752171177, -87.656304062)"
-5936840,HN731719,11/28/2007 07:45:00 AM,032XX N LECLAIRE AVE,0820,THEFT,$500 AND UNDER,"SCHOOL, PUBLIC, BUILDING",false,false,1634,016,38,15,06,1141754,1921160,2007,12/04/2014 12:43:35 PM,41.939731947,-87.754422599,"(41.939731947, -87.754422599)"
-5931950,HN730526,11/27/2007 10:28:00 PM,072XX S MERRILL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0333,003,5,43,08B,1191849,1857348,2007,12/09/2007 01:04:39 AM,41.763553558,-87.572388973,"(41.763553558, -87.572388973)"
-5932414,HN730827,11/27/2007 11:00:00 AM,039XX W BRYN MAWR AVE,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,1711,017,39,13,06,1149404,1936964,2007,12/04/2014 12:43:35 PM,41.982954062,-87.725894522,"(41.982954062, -87.725894522)"
-5933014,HN729239,11/27/2007 10:30:00 AM,015XX N KEATING AVE,1330,CRIMINAL TRESPASS,TO LAND,APARTMENT,false,false,2533,025,37,25,26,1144484,1909820,2007,12/04/2007 01:04:29 AM,41.90856283,-87.744675022,"(41.90856283, -87.744675022)"
-5929804,HN730141,11/26/2007 06:00:00 PM,021XX W PIERCE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1424,014,32,24,06,1161816,1910175,2007,12/04/2014 12:43:35 PM,41.90919293,-87.680995605,"(41.90919293, -87.680995605)"
-5928179,HN728238,11/26/2007 05:57:00 PM,003XX W HURON ST,031A,ROBBERY,ARMED: HANDGUN,PARKING LOT/GARAGE(NON.RESID.),false,false,1831,018,42,8,03,1173598,1904961,2007,12/30/2007 01:04:20 AM,41.894631399,-87.637869436,"(41.894631399, -87.637869436)"
-5939213,HN727770,11/26/2007 01:58:00 PM,030XX S DR MARTIN LUTHER KING JR DR,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",true,false,2112,001,2,35,08B,1179263,1885133,2007,12/08/2007 01:04:59 AM,41.840094342,-87.617670874,"(41.840094342, -87.617670874)"
-5925027,HN725588,11/25/2007 01:35:00 AM,025XX S HALSTED ST,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,SIDEWALK,true,false,0923,009,11,60,14,1171438,1887322,2007,12/02/2007 01:04:24 AM,41.846276473,-87.646320921,"(41.846276473, -87.646320921)"
-5924840,HN725555,11/25/2007 12:01:00 AM,026XX N LINCOLN AVE,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,1933,019,32,7,06,1169114,1917456,2007,12/04/2014 12:43:35 PM,41.929016952,-87.653974285,"(41.929016952, -87.653974285)"
-5925745,HN726033,11/24/2007 11:45:00 PM,018XX S WASHTENAW AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,1023,010,28,29,07,1158609,1891066,2007,12/01/2007 01:05:44 AM,41.856822386,-87.693300224,"(41.856822386, -87.693300224)"
-5924050,HN724065,11/24/2007 01:40:00 AM,033XX W 38TH ST,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,true,false,0913,009,12,58,26,1154758,1879165,2007,11/29/2007 01:05:58 AM,41.824242507,-87.707753736,"(41.824242507, -87.707753736)"
-5928074,HN724040,11/24/2007 01:26:00 AM,037XX W 68TH ST,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,SIDEWALK,true,false,0833,008,13,65,14,1152277,1859206,2007,11/30/2007 01:06:01 AM,41.769521295,-87.717380479,"(41.769521295, -87.717380479)"
-5930556,HN723000,11/23/2007 12:23:23 PM,049XX W IOWA ST,2017,NARCOTICS,MANU/DELIVER:CRACK,SIDEWALK,true,false,1531,015,37,25,18,1142945,1905482,2007,12/05/2007 01:05:24 AM,41.896687712,-87.750436907,"(41.896687712, -87.750436907)"
-5925277,HN722965,11/23/2007 11:56:11 AM,092XX S DOBSON AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,ALLEY,true,false,0413,004,8,47,26,1184875,1843741,2007,11/28/2007 01:05:48 AM,41.726380986,-87.598375744,"(41.726380986, -87.598375744)"
-5923121,HN721351,11/22/2007 12:05:00 AM,0000X W 69TH ST,0545,ASSAULT,PRO EMP HANDS NO/MIN INJURY,CTA TRAIN,true,false,0722,007,6,69,08A,1177310,1859316,2007,11/28/2007 01:05:48 AM,41.769294551,-87.625617578,"(41.769294551, -87.625617578)"
-5920638,HN718998,11/20/2007 06:26:16 PM,088XX S UNION AVE,0460,BATTERY,SIMPLE,STREET,false,false,2223,022,21,71,08B,1173141,1846474,2007,11/24/2007 01:04:48 AM,41.734147595,-87.641277768,"(41.734147595, -87.641277768)"
-5927681,HN727951,11/20/2007 01:00:00 PM,015XX S WABASH AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0132,001,3,33,06,1176973,1892884,2007,11/30/2007 01:06:01 AM,41.861415683,-87.625839799,"(41.861415683, -87.625839799)"
-5918839,HN717713,11/19/2007 06:00:00 PM,079XX W BELMONT AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,COMMERCIAL / BUSINESS OFFICE,false,false,1631,016,36,17,14,1122314,1920355,2007,11/24/2007 01:04:48 AM,41.937860942,-87.825889526,"(41.937860942, -87.825889526)"
-5919011,HN715983,11/19/2007 08:45:00 AM,022XX S LAWNDALE AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,1013,010,22,30,06,1152015,1888857,2007,12/04/2014 12:43:35 PM,41.850892957,-87.717562102,"(41.850892957, -87.717562102)"
-5919183,HN714093,11/18/2007 09:30:00 AM,100XX W OHARE ST,5007,OTHER OFFENSE,OTHER WEAPONS VIOLATION,AIRPORT/AIRCRAFT,false,false,1651,016,41,76,26,1100635,1934208,2007,11/24/2007 01:04:48 AM,41.976200173,-87.905312411,"(41.976200173, -87.905312411)"
-5915895,HN713903,11/18/2007 02:30:00 AM,032XX W ROOSEVELT RD,0560,ASSAULT,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1134,011,24,29,08A,1155160,1894571,2007,11/21/2007 01:04:07 AM,41.866510343,-87.70586597,"(41.866510343, -87.70586597)"
-5919177,HN711384,11/16/2007 08:00:00 PM,011XX S HOMAN AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,1134,011,24,29,03,1153925,1894538,2007,12/21/2007 01:04:15 AM,41.866444468,-87.710400684,"(41.866444468, -87.710400684)"
-5914976,HN710941,11/16/2007 04:26:24 PM,065XX S WOODLAWN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,0321,003,20,42,08B,1185295,1862080,2007,11/28/2007 01:05:48 AM,41.776695187,-87.596261771,"(41.776695187, -87.596261771)"
-5910843,HN707819,11/14/2007 10:00:00 PM,0000X E LAKE ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,0122,001,42,32,06,1176882,1901783,2007,11/21/2007 01:04:07 AM,41.885837125,-87.625904608,"(41.885837125, -87.625904608)"
-5910240,HN707039,11/14/2007 02:25:00 PM,011XX W SUNNYSIDE AVE,2170,NARCOTICS,POSSESSION OF DRUG EQUIPMENT,SIDEWALK,true,false,2311,019,46,3,18,1167517,1929779,2007,11/18/2007 01:03:46 AM,41.962866381,-87.659486728,"(41.962866381, -87.659486728)"
-5909814,HN705850,11/13/2007 09:25:42 PM,008XX N LAWLER AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1531,015,37,25,26,1142488,1905426,2007,11/21/2007 01:04:07 AM,41.89654255,-87.752116806,"(41.89654255, -87.752116806)"
-5986881,HN757907,11/13/2007 08:40:00 PM,050XX S KEDZIE AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,0821,008,14,63,06,1155863,1870771,2007,01/01/2008 01:04:14 AM,41.801186112,-87.703925366,"(41.801186112, -87.703925366)"
-5906076,HN703671,11/12/2007 06:00:00 PM,059XX S HALSTED ST,0860,THEFT,RETAIL THEFT,GAS STATION,false,false,0711,007,16,68,06,1172021,1865668,2007,08/31/2010 03:21:15 PM,41.786842966,-87.644818107,"(41.786842966, -87.644818107)"
-5906276,HN703601,11/12/2007 05:32:00 PM,096XX S WINCHESTER AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,2213,022,19,72,08A,1165121,1840616,2007,11/16/2007 01:04:24 AM,41.718245538,-87.670824542,"(41.718245538, -87.670824542)"
-5905790,HN702920,11/12/2007 12:00:00 PM,011XX N CALIFORNIA AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,1311,012,26,24,08B,1157557,1907554,2007,11/16/2007 01:04:24 AM,41.902088503,-87.69671273,"(41.902088503, -87.69671273)"
-5904979,HN703557,11/12/2007 09:00:00 AM,055XX S WENTWORTH AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0233,007,3,68,07,1175869,1868108,2007,08/31/2010 03:21:15 PM,41.79345315,-87.630636223,"(41.79345315, -87.630636223)"
-5909175,HN706742,11/11/2007 03:00:00 PM,023XX W ST PAUL AVE,0810,THEFT,OVER $500,STREET,false,false,1434,014,1,24,06,1160147,1911685,2007,12/04/2014 12:43:35 PM,41.913371152,-87.687084964,"(41.913371152, -87.687084964)"
-5902217,HN699063,11/10/2007 03:03:38 AM,004XX E 71ST ST,502R,OTHER OFFENSE,VEHICLE TITLE/REG OFFENSE,STREET,false,false,0323,003,6,69,26,1180200,1857995,2007,11/13/2007 01:03:20 AM,41.765603845,-87.615064702,"(41.765603845, -87.615064702)"
-4323,HN696765,11/09/2007 04:10:00 AM,005XX S KOSTNER AVE,0110,HOMICIDE,FIRST DEGREE MURDER,PARKING LOT,true,false,1131,011,24,26,01A,1147099,1897319,2007,10/31/2014 03:20:56 PM,41.874209056,-87.735388711,"(41.874209056, -87.735388711)"
-5901390,HN696804,11/08/2007 09:40:00 PM,077XX S SAGINAW AVE,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,STREET,true,true,0421,004,7,43,04A,1195221,1854354,2007,12/21/2007 01:04:15 AM,41.755255282,-87.560128774,"(41.755255282, -87.560128774)"
-5908247,HN695268,11/08/2007 03:10:00 AM,006XX W 129TH PL,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,false,false,0523,005,34,53,07,1174494,1818769,2007,08/31/2010 03:21:15 PM,41.658090937,-87.637141419,"(41.658090937, -87.637141419)"
-5899702,HN695111,11/07/2007 10:39:00 PM,111XX S NORMAL AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,2233,022,34,49,08A,1174991,1830952,2007,11/14/2007 01:04:36 AM,41.691512009,-87.634961479,"(41.691512009, -87.634961479)"
-5896516,HN692393,11/06/2007 02:35:00 PM,038XX W ROOSEVELT RD,2024,NARCOTICS,POSS: HEROIN(WHITE),VACANT LOT/LAND,true,false,1133,011,24,29,18,1150863,1894474,2007,11/11/2007 01:03:25 AM,41.866329266,-87.721643342,"(41.866329266, -87.721643342)"
-5892518,HN691670,11/06/2007 06:00:00 AM,103XX S UNION AVE,0890,THEFT,FROM BUILDING,RESIDENCE-GARAGE,false,false,2232,022,34,49,06,1173429,1836204,2007,11/11/2007 01:03:25 AM,41.705958903,-87.640525451,"(41.705958903, -87.640525451)"
-5897652,HN695301,11/04/2007 06:00:00 PM,042XX W DIVISION ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,1111,011,37,23,07,1147651,1907615,2007,12/01/2007 01:05:44 AM,41.902451853,-87.733097572,"(41.902451853, -87.733097572)"
-5880878,HN687167,11/03/2007 03:45:00 PM,071XX S CLYDE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,0333,003,5,43,08B,1191401,1858240,2007,11/09/2007 09:43:39 AM,41.766012141,-87.574002096,"(41.766012141, -87.574002096)"
-5892531,HN686676,11/03/2007 11:15:00 AM,003XX W CHICAGO AVE,0810,THEFT,OVER $500,GAS STATION,false,false,1823,018,27,8,06,1173636,1905698,2007,12/04/2014 12:43:35 PM,41.896652923,-87.637707928,"(41.896652923, -87.637707928)"
-5878327,HN685060,11/02/2007 02:40:00 PM,017XX W WINONA ST,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,2032,020,47,3,26,1163966,1934289,2007,12/03/2007 01:04:01 AM,41.97531789,-87.672414486,"(41.97531789, -87.672414486)"
-5895933,HN681509,10/31/2007 06:40:00 PM,029XX W FILLMORE ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1135,011,28,29,18,1156746,1895270,2007,11/11/2007 01:03:25 AM,41.868396513,-87.700024647,"(41.868396513, -87.700024647)"
-5874198,HN680832,10/31/2007 01:50:17 PM,029XX E 91ST ST,0460,BATTERY,SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,0423,004,10,46,08B,1197093,1845224,2007,11/09/2007 09:43:39 AM,41.730155487,-87.553571723,"(41.730155487, -87.553571723)"
-5874173,HN679864,10/30/2007 11:55:00 PM,045XX W WILCOX ST,0560,ASSAULT,SIMPLE,APARTMENT,false,false,1113,011,28,26,08A,1146260,1898996,2007,11/09/2007 09:43:39 AM,41.878826941,-87.738426491,"(41.878826941, -87.738426491)"
-5871442,HN679456,10/30/2007 07:16:32 PM,005XX N CENTRAL AVE,5004,SEX OFFENSE,ATT CRIM SEXUAL ABUSE,OTHER,false,false,1523,015,37,25,17,1138986,1902826,2007,11/09/2007 09:43:39 AM,41.889472207,-87.765042468,"(41.889472207, -87.765042468)"
-5875792,HN678511,10/30/2007 12:40:00 PM,076XX S EGGLESTON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0621,006,17,69,08B,1174666,1853937,2007,12/18/2007 02:43:27 PM,41.75459323,-87.635469164,"(41.75459323, -87.635469164)"
-5896560,HN676880,10/29/2007 03:30:00 PM,034XX W 63RD ST,2093,NARCOTICS,FOUND SUSPECT NARCOTICS,PARKING LOT/GARAGE(NON.RESID.),true,false,0823,008,15,66,26,1154564,1862671,2007,11/11/2007 01:03:25 AM,41.778984535,-87.708905058,"(41.778984535, -87.708905058)"
-5870499,HN676434,10/29/2007 11:30:00 AM,024XX W 63RD ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,APARTMENT,true,true,0825,008,15,66,04A,1160952,1862848,2007,11/09/2007 09:43:39 AM,41.779340571,-87.685481035,"(41.779340571, -87.685481035)"
-5877822,HN676200,10/28/2007 05:00:00 PM,004XX E GRAND AVE,0610,BURGLARY,FORCIBLE ENTRY,SMALL RETAIL STORE,false,false,1834,018,42,8,05,1179438,1904041,2007,11/09/2007 09:43:39 AM,41.891974978,-87.616449292,"(41.891974978, -87.616449292)"
-5871141,HN675041,10/28/2007 01:39:00 PM,002XX W 87TH ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,0622,006,21,44,06,1176403,1847255,2007,11/09/2007 09:43:39 AM,41.736218161,-87.629303966,"(41.736218161, -87.629303966)"
-5869995,HN674696,10/28/2007 11:11:07 AM,076XX N SHERIDAN RD,0498,BATTERY,AGGRAVATED DOMESTIC BATTERY: HANDS/FIST/FEET SERIOUS INJURY,APARTMENT,true,false,2422,024,49,1,04B,1165415,1950829,2007,11/09/2007 09:43:39 AM,42.020673234,-87.666612438,"(42.020673234, -87.666612438)"
-5866750,HN673776,10/27/2007 07:00:00 PM,074XX N HARLEM AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,false,true,1611,016,41,9,26,1127351,1948885,2007,11/09/2007 09:43:39 AM,42.016066904,-87.80673257,"(42.016066904, -87.80673257)"
-5866804,HN673609,10/27/2007 06:30:00 AM,030XX W LAKE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1331,012,2,27,14,1156091,1901037,2007,11/01/2007 01:04:07 AM,41.884234978,-87.702273633,"(41.884234978, -87.702273633)"
-5931932,HN729616,10/26/2007 07:30:00 PM,023XX N KEELER AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2522,025,31,20,26,1147956,1915225,2007,12/03/2007 01:04:01 AM,41.923328582,-87.731781195,"(41.923328582, -87.731781195)"
-5867968,HN668434,10/25/2007 08:33:06 AM,091XX S STONY ISLAND AVE,0820,THEFT,$500 AND UNDER,GAS STATION,false,false,0413,004,8,48,06,1188557,1844370,2007,12/04/2014 12:43:35 PM,41.728019969,-87.58486839,"(41.728019969, -87.58486839)"
-5951014,HN748508,10/25/2007 12:01:00 AM,098XX S WINSTON AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2213,022,21,73,26,1168504,1839708,2007,12/18/2007 02:43:27 PM,41.715681741,-87.658459907,"(41.715681741, -87.658459907)"
-5864191,HN671534,10/24/2007 09:00:00 PM,019XX N HOWE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1813,018,43,7,14,1171595,1913009,2007,10/31/2007 01:04:04 AM,41.916759872,-87.644988633,"(41.916759872, -87.644988633)"
-5859516,HN667700,10/24/2007 06:28:00 PM,020XX W CULLERTON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,false,1223,012,25,31,08B,1163262,1890448,2007,10/31/2007 01:04:04 AM,41.855030087,-87.676238525,"(41.855030087, -87.676238525)"
-5860444,HN666541,10/24/2007 08:35:08 AM,039XX S DR MARTIN LUTHER KING JR DR,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0213,002,3,38,03,1179457,1879165,2007,11/09/2007 09:43:39 AM,41.82371327,-87.617141566,"(41.82371327, -87.617141566)"
-6057328,HP107749,10/21/2007 05:00:00 PM,034XX E 95TH ST,0810,THEFT,OVER $500,BOAT/WATERCRAFT,false,false,0432,004,10,52,06,1200578,1842567,2007,12/04/2014 12:43:35 PM,41.722777131,-87.540895011,"(41.722777131, -87.540895011)"
-5850957,HN660427,10/20/2007 11:02:03 PM,056XX W THOMAS ST,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,STREET,false,false,1511,015,29,25,08B,1138776,1906816,2007,10/24/2007 01:04:16 AM,41.900425075,-87.765716747,"(41.900425075, -87.765716747)"
-5909724,HN707975,10/20/2007 01:05:00 PM,039XX N SHERIDAN RD,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,CTA PLATFORM,false,false,2324,019,44,6,14,1168850,1926543,2007,11/18/2007 01:03:46 AM,41.953957812,-87.654680033,"(41.953957812, -87.654680033)"
-5849128,HN657083,10/19/2007 04:00:00 AM,037XX N RACINE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,CHA PARKING LOT/GROUNDS,false,false,2324,019,44,6,14,1167654,1925325,2007,10/24/2007 01:04:16 AM,41.950641475,-87.659111871,"(41.950641475, -87.659111871)"
-5849459,HN656397,10/18/2007 07:55:00 PM,050XX W HURON ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1532,015,28,25,18,1142795,1904139,2007,10/24/2007 01:04:16 AM,41.893005159,-87.751021316,"(41.893005159, -87.751021316)"
-5849436,HN655903,10/18/2007 03:43:58 PM,060XX N BROADWAY,0460,BATTERY,SIMPLE,SIDEWALK,true,false,2433,024,48,77,08B,1167250,1940209,2007,10/24/2007 01:04:16 AM,41.991492367,-87.660167034,"(41.991492367, -87.660167034)"
-5847909,HN655431,10/18/2007 12:00:00 PM,102XX S STATE ST,0810,THEFT,OVER $500,CONSTRUCTION SITE,false,false,0511,005,9,49,06,1178024,1836807,2007,12/04/2014 12:43:35 PM,41.707510942,-87.623680757,"(41.707510942, -87.623680757)"
-5849154,HN653586,10/17/2007 02:00:00 PM,007XX N ADA ST,0460,BATTERY,SIMPLE,OTHER,true,false,1324,012,27,24,08B,1167232,1905220,2007,11/21/2007 01:04:07 AM,41.895481331,-87.661242342,"(41.895481331, -87.661242342)"
-5844141,HN653380,10/17/2007 01:04:08 PM,107XX S AVENUE M,0460,BATTERY,SIMPLE,RESIDENCE,true,false,0432,004,10,52,08B,1201566,1834329,2007,10/22/2007 01:03:26 AM,41.700146375,-87.537555041,"(41.700146375, -87.537555041)"
-5847534,HN653516,10/16/2007 05:20:00 PM,026XX E 79TH ST,0810,THEFT,OVER $500,CTA BUS,false,false,0421,004,7,43,06,1194921,1853130,2007,12/04/2014 12:43:35 PM,41.751903926,-87.561268425,"(41.751903926, -87.561268425)"
-5843811,HN650896,10/16/2007 09:00:00 AM,026XX N LARAMIE AVE,1365,CRIMINAL TRESPASS,TO RESIDENCE,APARTMENT,true,true,2514,025,31,19,26,1141232,1916942,2007,10/21/2007 01:04:02 AM,41.928166988,-87.756445446,"(41.928166988, -87.756445446)"
-5835802,HN645367,10/12/2007 07:00:00 PM,089XX S BURLEY AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0424,004,10,46,06,1199187,1846141,2007,12/04/2014 12:43:35 PM,41.732619494,-87.545870205,"(41.732619494, -87.545870205)"
-5837752,HN643902,10/12/2007 01:30:00 PM,049XX N SAWYER AVE,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,"SCHOOL, PUBLIC, GROUNDS",true,false,1713,017,39,14,26,1153775,1932800,2007,10/18/2007 01:04:23 AM,41.971441623,-87.709930278,"(41.971441623, -87.709930278)"
-5832224,HN642986,10/12/2007 03:04:20 AM,011XX W WILSON AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,2311,019,46,3,18,1167811,1930740,2007,10/14/2007 01:03:27 AM,41.965497049,-87.658377972,"(41.965497049, -87.658377972)"
-5855780,HN641405,10/11/2007 04:00:00 AM,041XX W MADISON ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1115,011,28,26,16,1148774,1899650,2007,11/09/2007 09:43:39 AM,41.880573401,-87.729178575,"(41.880573401, -87.729178575)"
-5834923,HN641901,10/10/2007 02:00:00 PM,025XX W 79TH ST,033A,ROBBERY,ATTEMPT: ARMED-HANDGUN,RESTAURANT,true,false,0835,008,18,70,03,1161052,1852117,2007,01/25/2008 01:04:41 AM,41.74989108,-87.685410761,"(41.74989108, -87.685410761)"
-5830563,HN639541,10/10/2007 09:50:00 AM,030XX S KEDZIE AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,1033,010,12,30,06,1155545,1884503,2007,12/04/2014 12:43:35 PM,41.838874878,-87.704723163,"(41.838874878, -87.704723163)"
-5825686,HN636898,10/08/2007 10:53:31 PM,010XX N LAVERGNE AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1531,015,37,25,18,1142803,1906222,2007,10/14/2007 01:03:27 AM,41.898721004,-87.750940006,"(41.898721004, -87.750940006)"
-5833183,HN636598,10/08/2007 07:45:00 PM,122XX S GREEN ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0524,005,34,53,18,1172844,1823423,2007,12/09/2007 01:04:39 AM,41.670898715,-87.643042718,"(41.670898715, -87.643042718)"
-5824281,HN634776,10/07/2007 09:00:42 PM,050XX W MAYPOLE AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,1532,015,28,25,08A,1142850,1900955,2007,10/13/2007 01:04:06 AM,41.884266857,-87.750898691,"(41.884266857, -87.750898691)"
-5823999,HN634438,10/07/2007 02:48:00 PM,063XX S DR MARTIN LUTHER KING JR DR,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0312,003,20,69,08B,1179974,1862856,2007,10/14/2007 01:03:27 AM,41.778948101,-87.615744398,"(41.778948101, -87.615744398)"
-5823263,HN633621,10/07/2007 02:29:00 AM,021XX S LUMBER ST,041A,BATTERY,AGGRAVATED: HANDGUN,SIDEWALK,false,false,1233,012,25,31,04B,1172782,1889780,2007,01/08/2008 01:04:49 AM,41.852991812,-87.64131586,"(41.852991812, -87.64131586)"
-5824805,HN634343,10/06/2007 09:00:00 PM,032XX W 55TH ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),false,false,0822,008,14,63,07,1155412,1867920,2007,10/20/2007 01:03:54 AM,41.793371617,-87.705655703,"(41.793371617, -87.705655703)"
-5825692,HN633908,10/06/2007 09:00:00 PM,011XX W 18TH PL,0560,ASSAULT,SIMPLE,ALLEY,true,false,1233,012,25,31,08A,1169262,1891362,2007,10/14/2007 01:03:27 AM,41.857410071,-87.654189411,"(41.857410071, -87.654189411)"
-5822529,HN632364,10/06/2007 12:00:00 AM,076XX N PAULINA ST,0890,THEFT,FROM BUILDING,OTHER,false,false,2422,024,49,1,06,1163617,1950699,2007,10/12/2007 01:03:28 AM,42.020354754,-87.673232675,"(42.020354754, -87.673232675)"
-5829269,HN631243,10/05/2007 09:00:00 PM,054XX W HIGGINS AVE,2012,NARCOTICS,MANU/DELIVER:COCAINE,STREET,true,false,1623,016,45,11,18,1139758,1931749,2007,10/14/2007 01:03:27 AM,41.968825906,-87.761498921,"(41.968825906, -87.761498921)"
-5845427,HN631207,10/05/2007 08:45:00 PM,065XX S EBERHART AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0321,003,20,42,18,1180743,1861593,2007,10/28/2007 01:04:10 AM,41.775464661,-87.612963991,"(41.775464661, -87.612963991)"
-5823131,HN630648,10/05/2007 04:35:45 PM,002XX S LOTUS AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1522,015,29,25,18,1139877,1898515,2007,10/10/2007 01:49:09 AM,41.877626034,-87.761875719,"(41.877626034, -87.761875719)"
-5819828,HN628737,10/04/2007 11:00:00 AM,109XX S EGGLESTON AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,2233,022,34,49,06,1175280,1832139,2007,10/11/2007 01:04:03 AM,41.694762881,-87.633868132,"(41.694762881, -87.633868132)"
-5817270,HN627106,10/03/2007 07:39:22 PM,036XX S RHODES AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0212,002,4,35,14,1180140,1880977,2007,10/07/2007 01:38:50 AM,41.828669884,-87.614580294,"(41.828669884, -87.614580294)"
-5816931,HN625290,10/02/2007 09:07:01 PM,007XX W LAWRENCE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,GAS STATION,false,false,2312,019,46,3,03,1170134,1932053,2007,12/04/2007 01:04:29 AM,41.969049425,-87.649798351,"(41.969049425, -87.649798351)"
-5814367,HN625155,10/02/2007 08:20:00 PM,028XX E 76TH PL,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,false,false,0421,004,7,43,26,1196272,1854967,2007,10/05/2007 01:47:14 AM,41.756911414,-87.556256929,"(41.756911414, -87.556256929)"
-5816128,HN624843,10/02/2007 04:45:00 PM,081XX S MAY ST,0460,BATTERY,SIMPLE,STREET,false,false,0613,006,21,71,08B,1170110,1850747,2007,10/07/2007 01:38:50 AM,41.745939643,-87.652258041,"(41.745939643, -87.652258041)"
-5814184,HN624224,10/01/2007 10:00:00 PM,017XX W 35TH ST,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE,false,false,0922,009,11,59,14,1165077,1881544,2007,10/08/2007 01:39:25 AM,41.830558319,-87.66982916,"(41.830558319, -87.66982916)"
-5854976,HN655427,10/01/2007 12:00:00 PM,004XX W FULLERTON PKWY,1110,DECEPTIVE PRACTICE,BOGUS CHECK,COMMERCIAL / BUSINESS OFFICE,false,false,1933,019,43,7,11,1172664,1916270,2007,11/01/2007 01:04:07 AM,41.925684592,-87.640964448,"(41.925684592, -87.640964448)"
-5810685,HN621771,09/30/2007 04:00:00 PM,078XX S SPAULDING AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,false,0835,008,18,70,08A,1155706,1852330,2007,10/05/2007 01:47:14 AM,41.750584389,-87.704995282,"(41.750584389, -87.704995282)"
-5809541,HN620732,09/30/2007 04:59:00 AM,005XX E RANDOLPH ST,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,0124,001,42,32,06,1180199,1901323,2007,12/04/2014 12:43:35 PM,41.884499182,-87.613738222,"(41.884499182, -87.613738222)"
-5808559,HN619849,09/30/2007 02:00:00 AM,001XX N COLUMBUS DR,0810,THEFT,OVER $500,STREET,false,false,0124,001,42,32,06,1178280,1901256,2007,12/04/2014 12:43:35 PM,41.884359271,-87.620786993,"(41.884359271, -87.620786993)"
-5808572,HN619874,09/30/2007 12:00:00 AM,002XX E GRAND AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1834,018,42,8,06,1177777,1903989,2007,12/04/2014 12:43:35 PM,41.891870207,-87.622550919,"(41.891870207, -87.622550919)"
-5810342,HN613930,09/26/2007 08:00:00 AM,037XX W DOUGLAS BLVD,0820,THEFT,$500 AND UNDER,"SCHOOL, PUBLIC, BUILDING",false,false,1011,010,24,29,06,1151792,1892995,2007,12/04/2014 12:43:35 PM,41.862252505,-87.718271773,"(41.862252505, -87.718271773)"
-5830274,HN611719,09/25/2007 11:40:00 PM,080XX S PHILLIPS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,0422,004,7,46,08B,1193877,1852093,2007,10/17/2007 01:04:36 AM,41.749083958,-87.565128062,"(41.749083958, -87.565128062)"
-5843850,HN650026,09/25/2007 08:42:00 PM,056XX W BELMONT AVE,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,ATM (AUTOMATIC TELLER MACHINE),false,false,2514,025,30,19,11,1138438,1920675,2007,12/09/2007 01:04:39 AM,41.93846186,-87.76662186,"(41.93846186, -87.76662186)"
-5803448,HN610943,09/25/2007 04:42:02 PM,004XX W GARFIELD BLVD,0320,ROBBERY,STRONGARM - NO WEAPON,CTA TRAIN,true,false,0934,009,3,61,03,1174446,1868506,2007,10/31/2007 01:04:04 AM,41.794577103,-87.635842373,"(41.794577103, -87.635842373)"
-5801707,HN610802,09/25/2007 04:22:00 PM,012XX W 74TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,0734,007,17,67,14,1169107,1855744,2007,08/31/2010 03:21:15 PM,41.75967381,-87.655789046,"(41.75967381, -87.655789046)"
-5802557,HN610834,09/25/2007 04:00:00 PM,060XX S ARCHER AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0811,008,23,56,06,1138349,1868370,2007,12/04/2014 12:43:35 PM,41.794931326,-87.768214429,"(41.794931326, -87.768214429)"
-5800743,HN609345,09/24/2007 09:20:00 PM,118XX S LAFAYETTE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0522,005,34,53,18,1177995,1826654,2007,09/30/2007 01:49:21 AM,41.679650299,-87.624093097,"(41.679650299, -87.624093097)"
-5797961,HN609552,09/24/2007 02:00:00 PM,073XX N RIDGE BLVD,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2424,024,49,1,07,1160737,1948730,2007,09/29/2007 01:49:32 AM,42.015012232,-87.683885872,"(42.015012232, -87.683885872)"
-5809820,HN608196,09/24/2007 11:40:00 AM,012XX N BURLING ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,1822,018,27,8,26,1171043,1908530,2007,10/05/2007 01:47:14 AM,41.904481394,-87.647148303,"(41.904481394, -87.647148303)"
-5807437,HN610417,09/24/2007 07:00:00 AM,033XX W 84TH PL,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0834,008,18,70,05,1155549,1848401,2007,10/05/2007 01:47:14 AM,41.739805708,-87.705675629,"(41.739805708, -87.705675629)"
-5795173,HN606046,09/22/2007 11:00:00 PM,048XX N HERMITAGE AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,2032,020,47,3,06,1163948,1932815,2007,12/04/2014 12:43:35 PM,41.971273558,-87.672522489,"(41.971273558, -87.672522489)"
-5795990,HN599511,09/20/2007 12:15:00 PM,008XX N KEYSTONE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,1111,011,37,23,08B,1149268,1905544,2007,09/29/2007 01:49:32 AM,41.896737616,-87.727211764,"(41.896737616, -87.727211764)"
-5787202,HN597510,09/19/2007 01:03:55 AM,006XX N LOREL AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,true,false,1524,015,37,25,24,1140602,1903738,2007,09/21/2007 01:48:17 AM,41.891945325,-87.75908534,"(41.891945325, -87.75908534)"
-5787218,HN596986,09/18/2007 07:45:00 PM,059XX S WASHTENAW AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0824,008,16,66,08B,1159354,1865064,2007,09/28/2007 01:47:12 AM,41.785454473,-87.691278852,"(41.785454473, -87.691278852)"
-5789585,HN596919,09/18/2007 06:50:00 PM,047XX S MICHIGAN AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,0231,002,3,38,18,1177922,1873388,2007,09/23/2007 01:51:11 AM,41.807895663,-87.622948091,"(41.807895663, -87.622948091)"
-5784100,HN590883,09/15/2007 02:45:00 PM,027XX N CLYBOURN AVE,1330,CRIMINAL TRESPASS,TO LAND,GROCERY FOOD STORE,true,false,1931,019,32,7,26,1162877,1918139,2007,09/21/2007 01:48:17 AM,41.931024465,-87.676874021,"(41.931024465, -87.676874021)"
-5781266,HN590360,09/15/2007 10:17:02 AM,030XX S WALLACE ST,0560,ASSAULT,SIMPLE,APARTMENT,false,true,0923,009,11,60,08A,1172752,1884604,2007,01/04/2008 01:05:01 AM,41.838789108,-87.641578992,"(41.838789108, -87.641578992)"
-5784933,HN593901,09/14/2007 12:00:00 PM,043XX S HERMITAGE AVE,0560,ASSAULT,SIMPLE,STREET,true,false,0914,009,12,61,08A,1165444,1875953,2007,10/15/2007 01:03:25 AM,41.815208224,-87.668641322,"(41.815208224, -87.668641322)"
-5777337,HN586471,09/13/2007 11:00:00 AM,007XX N BISHOP ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1324,012,27,24,14,1166443,1904973,2007,09/21/2007 01:48:17 AM,41.894820469,-87.664147216,"(41.894820469, -87.664147216)"
-5795370,HN598424,09/11/2007 11:30:00 PM,054XX W BERENICE AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,false,1633,016,38,15,08B,1139121,1925048,2007,07/02/2008 01:04:10 AM,41.950449389,-87.764004892,"(41.950449389, -87.764004892)"
-5785179,HN583784,09/11/2007 09:15:00 PM,003XX W 35TH ST,0460,BATTERY,SIMPLE,SPORTS ARENA/STADIUM,true,false,0925,009,11,34,08B,1174472,1881700,2007,09/21/2007 01:48:17 AM,41.830782103,-87.635354068,"(41.830782103, -87.635354068)"
-5777463,HN583385,09/11/2007 05:00:00 PM,010XX E 81ST ST,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,ALLEY,false,false,0631,006,8,44,04B,1184769,1851548,2007,11/09/2007 09:43:39 AM,41.747806722,-87.598519857,"(41.747806722, -87.598519857)"
-5773333,HN583592,09/11/2007 09:00:00 AM,0000X N FRANKLIN ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0113,001,42,32,06,1174371,1900363,2007,12/04/2014 12:43:35 PM,41.881996993,-87.635167853,"(41.881996993, -87.635167853)"
-5803684,HN610567,09/10/2007 11:51:00 AM,006XX N DEARBORN ST,1122,DECEPTIVE PRACTICE,COUNTERFEIT CHECK,BANK,true,false,1832,018,42,8,10,1175865,1904552,2007,10/26/2007 01:04:16 AM,41.893458356,-87.629555804,"(41.893458356, -87.629555804)"
-5773484,HN580270,09/10/2007 08:18:57 AM,019XX E 95TH ST,0610,BURGLARY,FORCIBLE ENTRY,RESTAURANT,false,false,0413,004,8,51,05,1190933,1842403,2007,06/02/2010 10:34:17 AM,41.722565301,-87.576228175,"(41.722565301, -87.576228175)"
-5779185,HN579845,09/09/2007 10:30:00 PM,079XX S MARSHFIELD AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0611,006,21,71,18,1166677,1852255,2007,09/19/2007 01:46:52 AM,41.750151686,-87.664794344,"(41.750151686, -87.664794344)"
-5766636,HN576142,09/07/2007 10:00:00 PM,082XX S DR MARTIN LUTHER KING JR DR,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0631,006,6,44,03,1180306,1850117,2007,09/25/2007 02:22:43 AM,41.74398332,-87.614917304,"(41.74398332, -87.614917304)"
-5768518,HN575805,09/07/2007 06:27:00 PM,012XX N LARRABEE ST,0496,BATTERY,AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST,CHA HALLWAY/STAIRWELL/ELEVATOR,true,true,1822,018,27,8,04B,1172064,1908588,2007,09/13/2007 01:47:03 AM,41.904618077,-87.643396202,"(41.904618077, -87.643396202)"
-5763978,HN574129,09/06/2007 10:38:00 PM,019XX W WASHINGTON BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,true,true,1333,012,27,28,08B,1163328,1900740,2007,09/27/2007 02:26:25 AM,41.883270875,-87.675706875,"(41.883270875, -87.675706875)"
-5767286,HN574145,09/06/2007 10:00:00 PM,011XX S KEELER AVE,0820,THEFT,$500 AND UNDER,CONSTRUCTION SITE,true,false,1132,011,24,29,06,1148521,1894630,2007,12/04/2014 12:43:35 PM,41.86680282,-87.730237092,"(41.86680282, -87.730237092)"
-5765771,HN575094,09/06/2007 12:00:00 PM,043XX W CORTEZ ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,OTHER,false,false,1111,011,37,23,11,1146943,1906594,2007,09/27/2007 02:26:25 AM,41.899663685,-87.735724321,"(41.899663685, -87.735724321)"
-5763633,HN573264,09/06/2007 09:00:00 AM,023XX S WABASH AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0134,001,2,33,14,1177069,1889054,2007,09/09/2007 01:53:39 AM,41.850903734,-87.625603289,"(41.850903734, -87.625603289)"
-5761434,HN572341,09/06/2007 12:00:00 AM,032XX W ARMITAGE AVE,0820,THEFT,$500 AND UNDER,APARTMENT,false,false,1421,014,35,22,06,1154640,1913072,2007,12/04/2014 12:43:35 PM,41.917289279,-87.707279389,"(41.917289279, -87.707279389)"
-5763750,HN574072,09/05/2007 10:30:00 PM,055XX S EMERALD AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,OTHER,false,false,0711,007,3,68,07,1172284,1868158,2007,09/10/2007 01:45:18 AM,41.793670016,-87.643780586,"(41.793670016, -87.643780586)"
-5760570,HN570315,09/04/2007 11:30:00 PM,063XX W ROSEDALE AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,STREET,false,false,1622,016,45,10,14,1132699,1938799,2007,09/09/2007 01:53:39 AM,41.988298119,-87.78728988,"(41.988298119, -87.78728988)"
-5785733,HN565993,09/02/2007 07:33:25 PM,064XX S PULASKI RD,5011,OTHER OFFENSE,LICENSE VIOLATION,SMALL RETAIL STORE,false,false,0813,008,13,65,26,1150770,1861594,2007,09/22/2007 01:51:05 AM,41.776103835,-87.722842396,"(41.776103835, -87.722842396)"
-5761215,HN565031,09/02/2007 09:00:00 AM,007XX W 76TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0621,006,17,68,18,1172854,1854515,2007,09/09/2007 01:53:39 AM,41.756219476,-87.642092557,"(41.756219476, -87.642092557)"
-6001106,HP107902,09/02/2007 12:00:00 AM,085XX W BRYN MAWR AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,HOTEL/MOTEL,false,false,1614,016,41,76,11,1118573,1936117,2007,01/13/2008 01:05:28 AM,41.981173792,-87.839304814,"(41.981173792, -87.839304814)"
-5756441,HN564338,09/01/2007 09:40:00 PM,013XX E 47TH ST,0560,ASSAULT,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),true,false,2123,002,4,39,08A,1186126,1874149,2007,09/07/2007 01:54:58 AM,41.809793878,-87.592834299,"(41.809793878, -87.592834299)"
-5755163,HN564179,09/01/2007 08:40:00 PM,0000X N LOTUS AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1522,015,28,25,18,1139907,1899914,2007,09/05/2007 01:50:37 AM,41.881464523,-87.761731354,"(41.881464523, -87.761731354)"
-5756474,HN564279,09/01/2007 08:20:00 PM,040XX S DREXEL BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2123,002,4,36,08B,1182625,1878105,2007,09/14/2007 01:49:03 AM,41.8207315,-87.605552379,"(41.8207315, -87.605552379)"
-5756714,HN564024,09/01/2007 06:05:00 PM,039XX W 13TH ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1011,010,24,29,26,1150110,1893788,2007,09/05/2007 01:50:37 AM,41.864461492,-87.724425559,"(41.864461492, -87.724425559)"
-5848884,HN656198,09/01/2007 02:00:00 PM,026XX W ATTRILL ST,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,RESIDENCE,false,false,1431,014,1,22,06,1158063,1914179,2007,10/29/2007 01:03:34 AM,41.920257715,-87.694672919,"(41.920257715, -87.694672919)"
-5756852,HN562967,09/01/2007 02:00:00 AM,081XX S HERMITAGE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,RESIDENCE-GARAGE,false,true,0614,006,18,71,14,1166060,1850510,2007,09/12/2007 01:43:33 AM,41.745376289,-87.667104823,"(41.745376289, -87.667104823)"
-5757648,HN561884,08/31/2007 05:07:00 PM,131XX S ELLIS AVE,0496,BATTERY,AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST,STREET,false,true,0533,005,9,54,04B,1185266,1818407,2007,10/04/2007 01:48:51 AM,41.656852044,-87.597735972,"(41.656852044, -87.597735972)"
-5754951,HN560461,08/30/2007 10:14:01 PM,027XX W 18TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,false,1023,010,28,29,08B,1158500,1891242,2007,09/05/2007 01:50:37 AM,41.857307577,-87.693695502,"(41.857307577, -87.693695502)"
-5749631,HN556373,08/28/2007 10:04:31 PM,074XX S PARNELL AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,0732,007,17,68,14,1173793,1855626,2007,09/08/2007 01:53:39 AM,41.759247441,-87.63861845,"(41.759247441, -87.63861845)"
-5757029,HN567500,08/28/2007 07:00:00 AM,056XX N KIMBALL AVE,1790,OFFENSE INVOLVING CHILDREN,CHILD ABDUCTION,RESIDENCE,false,true,1711,017,39,13,20,1152658,1937199,2007,11/20/2007 01:03:49 AM,41.983534966,-87.713920681,"(41.983534966, -87.713920681)"
-5750075,HN554470,08/27/2007 09:00:00 PM,044XX W JACKSON BLVD,0810,THEFT,OVER $500,APARTMENT,false,false,1131,011,24,26,06,1146503,1898269,2007,12/04/2014 12:43:35 PM,41.876827342,-87.737552762,"(41.876827342, -87.737552762)"
-5763592,HN573034,08/25/2007 07:00:00 AM,048XX W DIVERSEY AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,false,false,2521,025,31,19,11,1143836,1918225,2007,09/16/2007 01:46:17 AM,41.931639191,-87.746844325,"(41.931639191, -87.746844325)"
-5740672,HN549143,08/25/2007 03:00:00 AM,084XX S DREXEL AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0632,006,8,44,26,1183735,1849174,2007,08/30/2007 01:59:27 AM,41.741316381,-87.602382605,"(41.741316381, -87.602382605)"
-5740151,HN547815,08/24/2007 01:30:00 PM,097XX S EMERALD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,2223,022,21,73,14,1172990,1840100,2007,08/28/2007 01:52:53 AM,41.716659779,-87.642018492,"(41.716659779, -87.642018492)"
-5739797,HN548713,08/24/2007 10:00:00 AM,063XX S HALSTED PKWY,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,BANK,false,false,0723,007,20,68,11,1172550,1863067,2007,09/13/2007 01:47:03 AM,41.779693888,-87.642955085,"(41.779693888, -87.642955085)"
-5740177,HN547429,08/23/2007 10:30:00 PM,009XX N HONORE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,DRIVEWAY - RESIDENTIAL,false,false,1322,012,32,24,14,1163854,1906393,2007,08/29/2007 01:49:38 AM,41.898772073,-87.673615773,"(41.898772073, -87.673615773)"
-5740974,HN549815,08/23/2007 06:00:00 PM,055XX S NAGLE AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0811,008,23,56,05,1134293,1866886,2007,09/02/2007 02:22:13 AM,41.790931166,-87.783122937,"(41.790931166, -87.783122937)"
-5736412,HN543461,08/21/2007 09:15:00 PM,046XX W 59TH ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,CTA GARAGE / OTHER PROPERTY,false,false,0813,008,23,62,07,1146422,1865018,2007,09/26/2007 01:50:46 AM,41.785583453,-87.738695271,"(41.785583453, -87.738695271)"
-5733238,HN541950,08/20/2007 10:00:00 PM,022XX W FARWELL AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,2411,024,50,2,07,1159769,1945508,2007,08/29/2007 01:49:38 AM,42.00619107,-87.687537196,"(42.00619107, -87.687537196)"
-5731202,HN540533,08/20/2007 11:00:00 AM,0000X E SUPERIOR ST,0810,THEFT,OVER $500,STREET,false,false,1834,018,42,8,06,1176271,1905324,2007,12/04/2014 12:43:35 PM,41.895567615,-87.62804142,"(41.895567615, -87.62804142)"
-5732218,HN539200,08/20/2007 01:20:00 AM,060XX N KIMBALL AVE,0560,ASSAULT,SIMPLE,RESIDENCE-GARAGE,false,true,1711,017,50,13,08A,1152566,1940199,2007,08/26/2007 01:45:59 AM,41.991768953,-87.714179277,"(41.991768953, -87.714179277)"
-5731048,HN538246,08/19/2007 02:00:00 PM,043XX N SHERIDAN RD,1330,CRIMINAL TRESPASS,TO LAND,GROCERY FOOD STORE,true,false,2322,019,46,3,26,1168852,1929256,2007,08/23/2007 01:50:54 AM,41.961402337,-87.654593687,"(41.961402337, -87.654593687)"
-5732594,HN538120,08/19/2007 11:55:00 AM,005XX E BROWNING AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,0212,002,4,35,18,1180912,1881386,2007,08/29/2007 01:49:38 AM,41.829774451,-87.61173531,"(41.829774451, -87.61173531)"
-5731659,HN537591,08/19/2007 03:00:00 AM,063XX S MARSHFIELD AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0725,007,15,67,08B,1166376,1862532,2007,08/29/2007 01:49:38 AM,41.778359571,-87.665605002,"(41.778359571, -87.665605002)"
-5729356,HN538802,08/19/2007 01:30:00 AM,007XX W WRIGHTWOOD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1933,019,43,7,14,1171086,1917463,2007,08/23/2007 01:50:54 AM,41.928993056,-87.646727651,"(41.928993056, -87.646727651)"
-5727185,HN535800,08/18/2007 02:00:00 AM,022XX N LAWNDALE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,2525,025,35,22,05,1151308,1914458,2007,08/26/2007 01:45:59 AM,41.92115868,-87.719484825,"(41.92115868, -87.719484825)"
-5729140,HN534326,08/17/2007 12:00:00 PM,061XX S MORGAN ST,031A,ROBBERY,ARMED: HANDGUN,RESIDENCE,false,false,0712,007,16,68,03,1170660,1863992,2007,12/11/2007 01:04:21 AM,41.782273627,-87.6498571,"(41.782273627, -87.6498571)"
-5729516,HN534130,08/17/2007 10:18:59 AM,026XX E 79TH ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,0421,004,7,43,06,1195505,1853147,2007,09/27/2007 02:26:25 AM,41.751936174,-87.559127815,"(41.751936174, -87.559127815)"
-5724871,HN533431,08/16/2007 08:30:00 PM,026XX N SPAULDING AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1412,014,35,22,18,1153583,1917546,2007,08/19/2007 02:01:23 AM,41.929587414,-87.711043553,"(41.929587414, -87.711043553)"
-5732099,HN532768,08/16/2007 03:33:02 PM,059XX N CLARK ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,false,false,2013,020,48,77,18,1164654,1939212,2007,08/23/2007 01:50:54 AM,41.988812168,-87.669744224,"(41.988812168, -87.669744224)"
-5726085,HN534128,08/16/2007 10:00:00 AM,036XX S LAKE PARK AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,true,false,2122,002,4,36,26,1182305,1881213,2007,09/26/2007 01:50:46 AM,41.829267505,-87.606629837,"(41.829267505, -87.606629837)"
-5719686,HN528532,08/14/2007 08:50:00 AM,002XX S FRANKLIN ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,STREET,false,false,0112,001,2,32,11,1174329,1899069,2007,09/14/2007 01:49:03 AM,41.878447115,-87.635360718,"(41.878447115, -87.635360718)"
-5721497,HN528389,08/13/2007 09:00:00 PM,019XX N LEAVITT ST,0810,THEFT,OVER $500,STREET,false,false,1434,014,32,22,06,1161423,1913027,2007,12/04/2014 12:43:35 PM,41.917027217,-87.682359789,"(41.917027217, -87.682359789)"
-5717694,HN527540,08/13/2007 05:30:00 PM,064XX N CLARK ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2432,024,40,1,07,1164315,1943026,2007,08/22/2007 01:57:02 AM,41.99928509,-87.670882582,"(41.99928509, -87.670882582)"
-5722406,HN526937,08/13/2007 05:00:00 PM,044XX W ALTGELD ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,2524,025,31,20,08B,1146249,1916269,2007,08/20/2007 01:50:16 AM,41.926226106,-87.738026757,"(41.926226106, -87.738026757)"
-5720784,HN526208,08/13/2007 11:29:33 AM,043XX S COTTAGE GROVE AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,0222,002,4,38,06,1182286,1876479,2007,12/04/2014 12:43:35 PM,41.816277508,-87.606846409,"(41.816277508, -87.606846409)"
-5728880,HN524725,08/12/2007 03:30:00 PM,078XX S WINCHESTER AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,RESIDENCE,false,false,0611,006,18,71,04B,1164679,1852706,2007,09/04/2007 01:42:25 AM,41.751431663,-87.672103227,"(41.751431663, -87.672103227)"
-5716227,HN524372,08/11/2007 07:00:00 PM,073XX S HARVARD AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0731,007,17,69,06,1175186,1856313,2007,12/04/2014 12:43:35 PM,41.761101667,-87.633492695,"(41.761101667, -87.633492695)"
-5714608,HN522969,08/11/2007 04:15:00 PM,110XX S THROOP ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2234,022,34,75,06,1169673,1831406,2007,12/04/2014 12:43:35 PM,41.692874525,-87.654418166,"(41.692874525, -87.654418166)"
-5714952,HN522275,08/11/2007 10:00:00 AM,0000X E 91ST ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0634,006,6,44,08B,1178387,1844707,2007,08/21/2007 01:54:09 AM,41.729181365,-87.622112463,"(41.729181365, -87.622112463)"
-6051544,HN521341,08/10/2007 08:25:19 PM,052XX S HARPER AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,OTHER,true,false,2132,002,4,41,26,1187215,1870524,2007,02/10/2008 01:05:01 AM,41.799820807,-87.588955226,"(41.799820807, -87.588955226)"
-5713234,HN520623,08/10/2007 02:00:00 PM,104XX S STATE ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0512,005,34,49,14,1178063,1835507,2007,08/15/2007 02:43:11 AM,41.70394268,-87.623577176,"(41.70394268, -87.623577176)"
-5711485,HN519684,08/09/2007 11:55:00 PM,096XX S GREENWOOD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0511,005,8,50,08B,1185190,1841440,2007,08/18/2007 02:25:38 AM,41.720059393,-87.597293949,"(41.720059393, -87.597293949)"
-5721581,HN516352,08/08/2007 12:06:16 PM,038XX W ROOSEVELT RD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1011,010,24,29,18,1150936,1894395,2007,08/19/2007 02:01:23 AM,41.866111053,-87.721377418,"(41.866111053, -87.721377418)"
-5704641,HN510463,08/05/2007 01:50:00 PM,003XX E 89TH PL,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0633,006,6,44,14,1179908,1845751,2007,08/10/2007 03:53:50 AM,41.732011607,-87.616508848,"(41.732011607, -87.616508848)"
-5746272,HN555480,08/04/2007 05:00:00 PM,077XX S CONSTANCE AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0414,004,8,43,06,1189916,1854129,2007,08/31/2007 02:09:56 AM,41.754767052,-87.579577076,"(41.754767052, -87.579577076)"
-5700335,HN508532,08/04/2007 12:28:32 PM,072XX S UNION AVE,0810,THEFT,OVER $500,RESIDENCE,false,false,0732,007,17,68,06,1173042,1856858,2007,12/04/2014 12:43:35 PM,41.762644809,-87.641334507,"(41.762644809, -87.641334507)"
-5712533,HN505432,08/02/2007 09:45:00 PM,066XX S HALSTED ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0723,007,6,68,18,1172149,1860942,2007,08/29/2007 01:49:38 AM,41.773871465,-87.644487603,"(41.773871465, -87.644487603)"
-5696566,HN504150,08/02/2007 09:45:00 AM,090XX S COMMERCIAL AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,0424,004,10,46,06,1197759,1845396,2007,08/07/2007 01:56:52 AM,41.730610886,-87.55112629,"(41.730610886, -87.55112629)"
-5705066,HN502701,08/01/2007 04:00:00 PM,013XX N LAVERGNE AVE,0460,BATTERY,SIMPLE,RESIDENCE,true,false,2533,025,37,25,08B,1142827,1908552,2007,08/15/2007 02:43:11 AM,41.905114341,-87.75079374,"(41.905114341, -87.75079374)"
-5692684,HN499798,07/31/2007 07:00:00 AM,045XX N DRAKE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1723,017,33,14,07,1151941,1929981,2007,08/25/2007 01:53:42 AM,41.963742527,-87.716748761,"(41.963742527, -87.716748761)"
-5689399,HN498537,07/30/2007 03:40:00 PM,024XX W ADAMS ST,0460,BATTERY,SIMPLE,ALLEY,false,false,1125,011,2,28,08B,1160370,1899052,2007,08/03/2007 02:38:51 AM,41.878700538,-87.686615546,"(41.878700538, -87.686615546)"
-5692111,HN498304,07/30/2007 02:16:26 PM,117XX S STATE ST,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,true,false,0532,005,9,53,03,1178406,1826881,2007,08/04/2007 03:27:52 AM,41.680263928,-87.622581811,"(41.680263928, -87.622581811)"
-5693309,HN498065,07/30/2007 12:01:00 PM,0000X N HAMLIN BLVD,0810,THEFT,OVER $500,SIDEWALK,false,false,1122,011,28,26,06,1151025,1899824,2007,12/04/2014 12:43:35 PM,41.881007096,-87.720908487,"(41.881007096, -87.720908487)"
-5693889,HN500047,07/30/2007 11:20:00 AM,020XX W 68TH ST,0312,ROBBERY,ARMED:KNIFE/CUTTING INSTRUMENT,STREET,false,false,0726,007,15,67,03,1163691,1859591,2007,08/13/2007 02:45:51 AM,41.770345859,-87.675530854,"(41.770345859, -87.675530854)"
-5700809,HN496946,07/29/2007 07:30:00 PM,021XX S WASHTENAW AVE,0460,BATTERY,SIMPLE,ALLEY,true,false,1023,010,28,30,08B,1158740,1889463,2007,08/09/2007 02:29:33 AM,41.852420903,-87.692863269,"(41.852420903, -87.692863269)"
-5687257,HN495181,07/28/2007 09:54:00 PM,045XX S LAWLER AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0814,008,23,56,04B,1143418,1873950,2007,08/16/2007 02:19:17 AM,41.810150795,-87.749487085,"(41.810150795, -87.749487085)"
-5709792,HN494070,07/28/2007 08:21:41 AM,100XX S MICHIGAN AVE,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,ALLEY,true,false,0511,005,9,49,18,1178972,1838331,2007,08/19/2007 02:01:23 AM,41.711671512,-87.620162962,"(41.711671512, -87.620162962)"
-5964245,HN493281,07/27/2007 10:30:00 PM,049XX W NORTH AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,2533,025,37,25,16,1143198,1910223,2007,12/23/2007 01:04:00 AM,41.909692827,-87.749389137,"(41.909692827, -87.749389137)"
-5693419,HN493225,07/27/2007 10:05:00 PM,007XX W 58TH ST,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0711,007,20,68,08B,1172457,1866453,2007,08/08/2007 02:59:32 AM,41.788987501,-87.643196399,"(41.788987501, -87.643196399)"
-5690181,HN493075,07/27/2007 08:15:00 PM,050XX W BELLE PLAINE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1624,016,45,15,18,1142258,1926834,2007,08/05/2007 01:48:31 AM,41.955292572,-87.752428888,"(41.955292572, -87.752428888)"
-5679417,HN489323,07/26/2007 12:15:00 AM,008XX N LARAMIE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1524,015,37,25,18,1141501,1904898,2007,07/29/2007 01:49:15 AM,41.895111946,-87.755754986,"(41.895111946, -87.755754986)"
-5685402,HN488446,07/25/2007 04:11:42 PM,032XX W ARMITAGE AVE,0560,ASSAULT,SIMPLE,COMMERCIAL / BUSINESS OFFICE,false,false,1421,014,35,22,08A,1154542,1913069,2007,08/05/2007 01:48:31 AM,41.91728301,-87.707639524,"(41.91728301, -87.707639524)"
-5687022,HN487163,07/24/2007 08:27:40 PM,014XX S CHRISTIANA AVE,1345,CRIMINAL DAMAGE,TO CITY OF CHICAGO PROPERTY,"SCHOOL, PUBLIC, BUILDING",false,false,1021,010,24,29,14,1154231,1892646,2007,08/03/2007 02:38:51 AM,41.861246512,-87.709327807,"(41.861246512, -87.709327807)"
-5688272,HN488919,07/24/2007 07:00:00 AM,109XX S WENTWORTH AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0513,005,34,49,07,1176841,1832630,2007,08/02/2007 01:58:25 AM,41.696075329,-87.628138155,"(41.696075329, -87.628138155)"
-5677493,HN486077,07/24/2007 06:40:00 AM,042XX N MOBILE AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1624,016,38,15,05,1133638,1927394,2007,08/31/2010 03:21:15 PM,41.956985235,-87.784105024,"(41.956985235, -87.784105024)"
-5673340,HN483810,07/23/2007 12:05:00 PM,037XX W IRVING PARK RD,0560,ASSAULT,SIMPLE,MEDICAL/DENTAL OFFICE,false,false,1723,017,39,16,08A,1150416,1926360,2007,07/29/2007 01:49:15 AM,41.95383622,-87.722450646,"(41.95383622, -87.722450646)"
-5714881,HN483012,07/22/2007 10:44:47 PM,119XX S LA SALLE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,RESIDENCE PORCH/HALLWAY,true,false,0522,005,9,53,18,1177452,1825573,2007,08/19/2007 02:01:23 AM,41.676696119,-87.626113184,"(41.676696119, -87.626113184)"
-5672782,HN482901,07/22/2007 10:14:43 PM,062XX S WOODLAWN AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,0314,003,20,42,04B,1185337,1863542,2007,08/02/2007 01:58:25 AM,41.780706054,-87.596061852,"(41.780706054, -87.596061852)"
-5671777,HN482789,07/22/2007 08:00:00 AM,113XX S YALE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0522,005,34,49,05,1176673,1829781,2007,08/31/2010 03:21:15 PM,41.688261024,-87.628838576,"(41.688261024, -87.628838576)"
-5689108,HN481228,07/22/2007 12:30:00 AM,003XX E PERSHING RD,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),SIDEWALK,true,false,0211,002,3,35,18,1179212,1879241,2007,08/05/2007 01:48:31 AM,41.82392742,-87.618038053,"(41.82392742, -87.618038053)"
-5676491,HN480229,07/21/2007 02:00:00 PM,050XX W OHIO ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1532,015,28,25,18,1142681,1903566,2007,07/29/2007 01:49:15 AM,41.891434901,-87.751454272,"(41.891434901, -87.751454272)"
-5674653,HN480230,07/21/2007 01:50:00 PM,117XX S PRINCETON AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE,false,false,0522,005,34,53,14,1176437,1826878,2007,07/27/2007 01:58:36 AM,41.680300042,-87.62978934,"(41.680300042, -87.62978934)"
-5670396,HN480883,07/21/2007 05:00:00 AM,055XX S CALIFORNIA AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0824,008,16,63,05,1158620,1867498,2007,07/28/2007 01:56:07 AM,41.792148715,-87.693903679,"(41.792148715, -87.693903679)"
-5669385,HN479381,07/21/2007 02:15:00 AM,009XX N ASHLAND AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,true,false,1323,012,1,24,14,1165600,1906209,2007,07/25/2007 03:43:09 AM,41.898230142,-87.667208067,"(41.898230142, -87.667208067)"
-5669866,HN479865,07/21/2007 01:30:00 AM,039XX W 70TH PL,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0833,008,13,65,07,1151146,1857595,2007,11/09/2007 09:43:39 AM,41.7651226,-87.721568268,"(41.7651226, -87.721568268)"
-5822167,HN478455,07/20/2007 04:25:00 PM,006XX N TRUMBULL AVE,2027,NARCOTICS,POSS: CRACK,VEHICLE NON-COMMERCIAL,true,false,1121,011,27,23,18,1153300,1904215,2007,10/10/2007 01:49:09 AM,41.893011591,-87.712438125,"(41.893011591, -87.712438125)"
-5669528,HN475783,07/19/2007 11:30:00 AM,076XX S VERNON AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,0624,006,6,69,07,1180526,1854487,2007,07/29/2007 01:49:15 AM,41.755970044,-87.61397734,"(41.755970044, -87.61397734)"
-5665691,HN473480,07/18/2007 09:00:00 AM,099XX S WINSTON AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,true,true,2213,022,21,73,26,1169005,1838452,2007,07/22/2007 01:56:08 AM,41.712224293,-87.656661152,"(41.712224293, -87.656661152)"
-5662161,HN472228,07/17/2007 04:50:00 PM,093XX S ASHLAND AVE,0860,THEFT,RETAIL THEFT,OTHER,true,false,2221,022,21,73,06,1167355,1842579,2007,07/21/2007 01:59:35 AM,41.723584846,-87.662586187,"(41.723584846, -87.662586187)"
-5660787,HN469107,07/16/2007 06:30:00 AM,082XX S ELLIS AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0631,006,8,44,05,1184284,1850763,2007,05/09/2008 01:05:13 AM,41.745663952,-87.600321531,"(41.745663952, -87.600321531)"
-5660684,HN468744,07/15/2007 11:00:00 PM,002XX N CENTRAL PARK AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1123,011,28,27,08B,1152295,1901675,2007,07/21/2007 01:59:35 AM,41.88606147,-87.716196223,"(41.88606147, -87.716196223)"
-5665335,HN467538,07/15/2007 10:25:32 AM,068XX S JUSTINE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,0725,007,17,67,08B,1167221,1859421,2007,09/20/2007 02:07:45 AM,41.769804541,-87.662596135,"(41.769804541, -87.662596135)"
-5656965,HN465135,07/13/2007 11:45:00 PM,080XX S PERRY AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,0623,006,17,44,14,1176967,1851605,2007,07/20/2007 02:45:05 AM,41.748142417,-87.627106887,"(41.748142417, -87.627106887)"
-5664162,HN464920,07/13/2007 10:08:10 PM,015XX E 64TH ST,0337,ROBBERY,ATTEMPT: ARMED-OTHER DANG WEAP,STREET,false,false,0314,003,5,42,03,1187928,1862818,2007,07/30/2007 01:43:53 AM,41.778657986,-87.586585953,"(41.778657986, -87.586585953)"
-5654799,HN465001,07/13/2007 10:00:00 PM,056XX S STATE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0233,002,20,40,14,1177271,1867831,2007,07/17/2007 01:59:28 AM,41.792661473,-87.625503607,"(41.792661473, -87.625503607)"
-5656809,HN463004,07/12/2007 03:00:00 PM,090XX S DAUPHIN AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0633,006,8,44,05,1183647,1845092,2007,04/27/2008 01:04:21 AM,41.730116969,-87.602831974,"(41.730116969, -87.602831974)"
-5650939,HN461282,07/12/2007 07:00:00 AM,007XX N RIDGEWAY AVE,0810,THEFT,OVER $500,STREET,false,false,1112,011,27,23,06,1151297,1904554,2007,12/04/2014 12:43:35 PM,41.89398138,-87.71978553,"(41.89398138, -87.71978553)"
-5658007,HN462301,07/12/2007 05:43:00 AM,014XX N LATROBE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,2532,025,37,25,14,1141062,1909028,2007,07/19/2007 02:59:35 AM,41.906453247,-87.757265485,"(41.906453247, -87.757265485)"
-5669125,HN460981,07/12/2007 02:10:00 AM,077XX S WOLCOTT AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0611,006,18,71,18,1164991,1853396,2007,07/25/2007 03:43:09 AM,41.753318536,-87.670940427,"(41.753318536, -87.670940427)"
-5656071,HN459547,07/11/2007 11:05:00 AM,001XX W 87TH ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,0634,006,21,44,06,1176858,1847237,2007,07/19/2007 02:59:35 AM,41.736158541,-87.627637554,"(41.736158541, -87.627637554)"
-5651876,HN459044,07/11/2007 06:15:00 AM,071XX S VINCENNES AVE,041A,BATTERY,AGGRAVATED: HANDGUN,GAS STATION,true,false,0731,007,6,69,04B,1176576,1857697,2007,10/15/2007 01:03:25 AM,41.764868376,-87.628356705,"(41.764868376, -87.628356705)"
-5647835,HN458315,07/10/2007 08:00:00 AM,044XX S ALBANY AVE,0810,THEFT,OVER $500,RESIDENCE,false,false,0912,009,14,58,06,1156401,1875072,2007,12/04/2014 12:43:35 PM,41.812977804,-87.701836415,"(41.812977804, -87.701836415)"
-8160478,HT394971,07/10/2007 12:00:00 AM,049XX S LANGLEY AVE,0842,THEFT,AGG: FINANCIAL ID THEFT,OTHER,false,false,0223,,4,38,06,,,2007,07/18/2011 12:38:42 AM,,,
-5646549,HN455137,07/09/2007 09:00:00 AM,035XX N PITTSBURGH AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1631,016,36,17,05,1120408,1922635,2007,07/15/2007 01:53:52 AM,41.944148308,-87.832845701,"(41.944148308, -87.832845701)"
-5643418,HN454820,07/09/2007 01:00:00 AM,092XX S UNIVERSITY AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,0413,004,8,47,07,1185532,1843906,2007,07/13/2007 02:55:14 AM,41.726818346,-87.595963944,"(41.726818346, -87.595963944)"
-5645434,HN456176,07/08/2007 10:00:00 PM,057XX S DR MARTIN LUTHER KING JR DR,0810,THEFT,OVER $500,RESIDENTIAL YARD (FRONT/BACK),false,false,0234,002,20,40,06,1179867,1866913,2007,12/04/2014 12:43:35 PM,41.790083348,-87.616012584,"(41.790083348, -87.616012584)"
-5643973,HN453482,07/08/2007 11:48:57 AM,065XX S DR MARTIN LUTHER KING JR DR,1310,CRIMINAL DAMAGE,TO PROPERTY,HOTEL/MOTEL,true,false,0312,003,20,42,14,1180088,1861626,2007,07/13/2007 02:55:14 AM,41.775570247,-87.615364116,"(41.775570247, -87.615364116)"
-5655337,HN453344,07/08/2007 10:15:00 AM,011XX W GARFIELD BLVD,1121,DECEPTIVE PRACTICE,COUNTERFEITING DOCUMENT,GROCERY FOOD STORE,false,false,0712,007,16,68,10,1169538,1868147,2007,07/24/2007 02:30:37 AM,41.793699831,-87.653850267,"(41.793699831, -87.653850267)"
-5653345,HN452398,07/07/2007 08:24:05 PM,003XX S COLUMBUS DR,0560,ASSAULT,SIMPLE,STREET,true,false,0124,001,42,32,08A,1178372,1898909,2007,07/18/2007 02:17:00 AM,41.877916883,-87.620520745,"(41.877916883, -87.620520745)"
-5649232,HN451677,07/07/2007 12:20:00 PM,078XX S GREEN ST,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0621,006,17,71,08B,1171954,1852900,2007,07/17/2007 01:59:28 AM,41.751807506,-87.645438185,"(41.751807506, -87.645438185)"
-5648825,HN451308,07/07/2007 03:00:00 AM,040XX W CORTLAND ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2534,025,30,20,14,1149027,1912356,2007,07/15/2007 01:53:52 AM,41.915435094,-87.727920343,"(41.915435094, -87.727920343)"
-5652322,HN450338,07/06/2007 07:10:12 PM,069XX S HARVARD AVE,0560,ASSAULT,SIMPLE,OTHER,false,false,0731,007,6,68,08A,1175110,1859092,2007,07/18/2007 02:17:00 AM,41.768729262,-87.633688393,"(41.768729262, -87.633688393)"
-5641022,HN449447,07/06/2007 11:37:04 AM,022XX S STATE ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,true,false,0134,001,3,33,26,1176629,1889337,2007,07/11/2007 03:46:28 AM,41.851690245,-87.627209622,"(41.851690245, -87.627209622)"
-5666640,HN448925,07/06/2007 01:45:00 AM,017XX N CLYBOURN AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1813,018,43,7,16,1169457,1911795,2007,07/22/2007 01:56:08 AM,41.913475409,-87.652878941,"(41.913475409, -87.652878941)"
-5646386,HN446230,07/04/2007 08:45:00 PM,081XX S LOOMIS BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,0613,006,21,71,08B,1168448,1850958,2007,07/17/2007 01:59:28 AM,41.746554594,-87.658341888,"(41.746554594, -87.658341888)"
-5641194,HN445612,07/04/2007 01:17:02 PM,013XX S LAWNDALE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1011,010,24,29,08B,1151884,1893418,2007,07/16/2007 01:47:01 AM,41.863411457,-87.717922921,"(41.863411457, -87.717922921)"
-5634692,HN444327,07/03/2007 03:00:00 PM,037XX N LINCOLN AVE,0810,THEFT,OVER $500,STREET,false,false,1923,019,47,5,06,1163157,1925031,2007,12/04/2014 12:43:35 PM,41.949930609,-87.675650766,"(41.949930609, -87.675650766)"
-5648619,HN442332,07/02/2007 07:05:59 PM,050XX W MAYPOLE AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,1532,015,28,25,18,1142940,1901037,2007,07/18/2007 02:17:00 AM,41.884490199,-87.750566152,"(41.884490199, -87.750566152)"
-5633742,HN442182,07/02/2007 04:00:00 PM,061XX N NEVA AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1612,016,41,10,08B,1127663,1940543,2007,07/07/2007 02:00:13 AM,41.993170436,-87.805773629,"(41.993170436, -87.805773629)"
-5642550,HN440085,07/01/2007 06:10:00 PM,114XX S YALE AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,0522,005,34,49,08A,1176697,1829042,2007,08/24/2007 01:54:26 AM,41.686232558,-87.628772841,"(41.686232558, -87.628772841)"
-5630198,HN438813,07/01/2007 12:30:00 AM,015XX E 70TH ST,051A,ASSAULT,AGGRAVATED: HANDGUN,SIDEWALK,false,true,0332,003,5,43,04A,1187636,1858841,2007,07/26/2007 03:11:57 AM,41.767751716,-87.587782822,"(41.767751716, -87.587782822)"
-5640478,HN442197,06/30/2007 12:00:00 PM,011XX N RUSH ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,BAR OR TAVERN,false,false,1824,,42,8,11,,,2007,07/22/2007 01:56:08 AM,,,
-5630470,HN437211,06/30/2007 05:47:25 AM,062XX S DR MARTIN LUTHER KING JR DR,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,0311,003,20,40,18,1179947,1863890,2007,07/04/2007 02:03:09 AM,41.781786116,-87.615811751,"(41.781786116, -87.615811751)"
-5626697,HN434891,06/29/2007 02:15:00 AM,033XX W CHICAGO AVE,5011,OTHER OFFENSE,LICENSE VIOLATION,BAR OR TAVERN,true,false,1121,011,27,23,26,1153771,1905090,2007,07/04/2007 02:03:09 AM,41.89540331,-87.710684991,"(41.89540331, -87.710684991)"
-5641094,HN432922,06/28/2007 03:15:00 AM,039XX W 61ST ST,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,false,false,0823,008,13,65,26,1150966,1863896,2007,07/12/2007 02:26:16 AM,41.782417066,-87.722063877,"(41.782417066, -87.722063877)"
-5624649,HN432265,06/27/2007 07:00:00 PM,052XX S DREXEL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2131,002,5,41,08B,1183095,1870366,2007,07/04/2007 02:03:09 AM,41.799484154,-87.60406913,"(41.799484154, -87.60406913)"
-5654760,HN459415,06/27/2007 06:00:00 PM,073XX S EVANS AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0323,003,6,69,14,1182542,1856197,2007,07/17/2007 01:59:28 AM,41.760615965,-87.60653628,"(41.760615965, -87.60653628)"
-5626996,HN430288,06/26/2007 05:24:00 PM,092XX S PERRY AVE,0326,ROBBERY,AGGRAVATED VEHICULAR HIJACKING,STREET,false,false,0634,006,21,49,03,1177187,1843896,2007,07/13/2007 02:55:14 AM,41.726983001,-87.626532734,"(41.726983001, -87.626532734)"
-4130,HN428126,06/25/2007 08:00:00 PM,090XX S COMMERCIAL AVE,0110,HOMICIDE,FIRST DEGREE MURDER,STREET,true,false,0423,004,10,46,01A,1197682,1845294,2007,11/17/2011 12:01:47 PM,41.73033291,-87.551411755,"(41.73033291, -87.551411755)"
-5622051,HN425868,06/24/2007 05:25:20 PM,087XX S BALTIMORE AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,false,false,0424,004,10,46,26,1198376,1847391,2007,07/02/2007 02:00:43 AM,41.736069914,-87.548799449,"(41.736069914, -87.548799449)"
-5619123,HN425128,06/24/2007 08:57:31 AM,055XX S WOOD ST,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,false,false,0715,007,15,67,26,1165330,1868027,2007,06/29/2007 02:43:59 AM,41.793460769,-87.669284117,"(41.793460769, -87.669284117)"
-5619530,HN427986,06/22/2007 06:00:00 PM,074XX S CHAMPLAIN AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0323,003,6,69,03,1181806,1856024,2007,08/17/2007 01:51:00 AM,41.760158268,-87.60923906,"(41.760158268, -87.60923906)"
-5612394,HN420130,06/21/2007 05:50:00 PM,109XX S CAMPBELL AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2212,022,19,75,14,1161612,1832236,2007,06/25/2007 01:50:43 AM,41.695322755,-87.683908255,"(41.695322755, -87.683908255)"
-5612167,HN420039,06/21/2007 05:25:00 PM,055XX N WINTHROP AVE,2022,NARCOTICS,POSS: COCAINE,SIDEWALK,true,false,2023,020,48,77,18,1167904,1937091,2007,06/24/2007 02:00:14 AM,41.98292237,-87.657851909,"(41.98292237, -87.657851909)"
-5715144,HN419918,06/21/2007 04:26:36 PM,109XX S PRINCETON AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0513,005,34,49,18,1176183,1832615,2007,08/19/2007 02:01:23 AM,41.696048925,-87.630547775,"(41.696048925, -87.630547775)"
-5616415,HN419883,06/21/2007 04:00:00 PM,026XX N NEWCASTLE AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,false,false,2512,025,36,18,04A,1130343,1916870,2007,06/30/2007 02:10:43 AM,41.928163478,-87.796460964,"(41.928163478, -87.796460964)"
-5678544,HN419649,06/21/2007 02:00:00 PM,014XX S HOMAN AVE,1661,GAMBLING,GAME/DICE,STREET,true,false,1021,010,24,29,19,1153892,1892889,2007,09/02/2007 02:22:13 AM,41.861920086,-87.710565745,"(41.861920086, -87.710565745)"
-5616360,HN417376,06/20/2007 12:50:00 PM,015XX N LUNA AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,true,true,2532,025,37,25,08B,1139014,1909635,2007,07/26/2007 03:11:57 AM,41.908156421,-87.764773921,"(41.908156421, -87.764773921)"
-5631253,HN437550,06/20/2007 10:40:00 AM,003XX N CENTRAL AVE,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,1523,015,28,25,03,1139020,1901761,2007,07/13/2007 02:55:14 AM,41.886549094,-87.76494351,"(41.886549094, -87.76494351)"
-5610975,HN416946,06/20/2007 02:00:00 AM,036XX N ELSTON AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1733,017,33,16,06,1153652,1924249,2007,12/04/2014 12:43:35 PM,41.947979576,-87.710611068,"(41.947979576, -87.710611068)"
-5607876,HN416736,06/19/2007 10:30:00 PM,052XX N SAWYER AVE,0810,THEFT,OVER $500,STREET,false,false,1712,017,39,13,06,1153723,1934706,2007,12/04/2014 12:43:35 PM,41.976672835,-87.710070499,"(41.976672835, -87.710070499)"
-5612135,HN413736,06/18/2007 05:30:00 PM,001XX W LAKE ST,2170,NARCOTICS,POSSESSION OF DRUG EQUIPMENT,CTA PLATFORM,true,false,0113,001,42,32,18,1175460,1901776,2007,12/26/2007 01:03:36 AM,41.885849966,-87.631126643,"(41.885849966, -87.631126643)"
-5611628,HN415567,06/18/2007 04:00:00 PM,001XX W 87TH ST,1570,SEX OFFENSE,PUBLIC INDECENCY,SMALL RETAIL STORE,false,false,0622,006,21,44,17,1176857,1847317,2007,07/01/2007 02:00:46 AM,41.736378094,-87.627638815,"(41.736378094, -87.627638815)"
-5612697,HN413497,06/18/2007 03:00:00 PM,047XX W BELMONT AVE,0460,BATTERY,SIMPLE,STREET,false,false,1731,017,30,15,08B,1144604,1920904,2007,06/30/2007 02:10:43 AM,41.938976168,-87.743954337,"(41.938976168, -87.743954337)"
-5606039,HN412852,06/18/2007 07:30:00 AM,069XX S PRINCETON AVE,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,RESIDENCE,false,false,0731,007,6,69,05,1175461,1858617,2007,06/29/2007 02:43:59 AM,41.767417965,-87.632415997,"(41.767417965, -87.632415997)"
-5607899,HN410233,06/16/2007 11:45:00 PM,013XX N ROCKWELL ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1423,014,26,24,14,1158792,1908582,2007,08/31/2010 03:21:15 PM,41.904884181,-87.692148169,"(41.904884181, -87.692148169)"
-5601635,HN409499,06/16/2007 04:35:00 PM,035XX S COTTAGE GROVE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,2122,002,4,36,08B,1181324,1881729,2007,06/21/2007 02:21:27 AM,41.83070616,-87.61021311,"(41.83070616, -87.61021311)"
-5600314,HN408052,06/15/2007 10:24:00 PM,036XX W LEXINGTON ST,031A,ROBBERY,ARMED: HANDGUN,STREET,false,false,1133,011,24,27,03,1151954,1896406,2007,07/18/2007 02:17:00 AM,41.871609489,-87.717587277,"(41.871609489, -87.717587277)"
-5700550,HN407635,06/15/2007 06:50:00 PM,040XX W ADAMS ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1115,011,28,26,26,1149528,1898745,2007,08/08/2007 02:59:32 AM,41.878075381,-87.726433432,"(41.878075381, -87.726433432)"
-5600324,HN407131,06/15/2007 03:00:00 PM,041XX N LONG AVE,0890,THEFT,FROM BUILDING,PARK PROPERTY,false,false,1624,016,38,15,06,1139577,1926765,2007,08/17/2007 01:51:00 AM,41.955152675,-87.762286594,"(41.955152675, -87.762286594)"
-5607071,HN407054,06/15/2007 02:05:00 PM,063XX S TALMAN AVE,0810,THEFT,OVER $500,STREET,false,false,0825,008,15,66,06,1159835,1862614,2007,12/04/2014 12:43:35 PM,41.778721462,-87.689582526,"(41.778721462, -87.689582526)"
-5599984,HN406474,06/15/2007 09:15:00 AM,031XX W FRANKLIN BLVD,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1313,012,27,23,08B,1155131,1903199,2007,06/21/2007 02:21:27 AM,41.890187034,-87.705740818,"(41.890187034, -87.705740818)"
-5614679,HN411152,06/14/2007 01:30:00 PM,049XX S DREXEL BLVD,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE PORCH/HALLWAY,false,false,2124,002,4,39,14,1183187,1872520,2007,07/28/2007 01:56:07 AM,41.80539276,-87.603664688,"(41.80539276, -87.603664688)"
-5606659,HN403604,06/13/2007 09:45:00 PM,029XX E 83RD ST,4510,OTHER OFFENSE,PROBATION VIOLATION,GAS STATION,true,false,0424,004,10,46,26,1197383,1850467,2007,06/23/2007 02:27:54 AM,41.744535478,-87.552335092,"(41.744535478, -87.552335092)"
-5588616,HN397262,06/10/2007 09:55:00 PM,035XX W PALMER ST,3760,INTERFERENCE WITH PUBLIC OFFICER,OBSTRUCTING SERVICE,SIDEWALK,true,false,1413,014,26,22,24,1152272,1914350,2007,12/04/2014 12:43:35 PM,41.920843329,-87.715945697,"(41.920843329, -87.715945697)"
-5596297,HN395236,06/09/2007 07:55:00 PM,008XX N LAWLER AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1531,015,37,25,18,1142571,1905295,2007,06/17/2007 03:25:09 AM,41.896181527,-87.751815219,"(41.896181527, -87.751815219)"
-5588848,HN394934,06/09/2007 02:30:00 PM,019XX S TRUMBULL AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,1024,010,24,29,08A,1153765,1890400,2007,07/01/2007 02:00:46 AM,41.855092513,-87.71109816,"(41.855092513, -87.71109816)"
-5591389,HN397706,06/08/2007 11:59:00 PM,019XX S ST LOUIS AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,false,1024,010,24,29,06,1153365,1890230,2007,06/15/2007 03:05:05 AM,41.854633958,-87.712570859,"(41.854633958, -87.712570859)"
-5585609,HN392949,06/08/2007 06:00:00 AM,089XX S BLACKSTONE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,0413,004,8,48,14,1187636,1846031,2007,06/11/2007 01:50:55 AM,41.732599854,-87.588189463,"(41.732599854, -87.588189463)"
-5586477,HN390699,06/07/2007 03:15:00 PM,059XX S HOMAN AVE,0460,BATTERY,SIMPLE,OTHER,false,false,0822,008,16,66,08B,1154695,1865251,2007,06/15/2007 03:05:05 AM,41.786061828,-87.708356048,"(41.786061828, -87.708356048)"
-5590190,HN398208,06/07/2007 12:00:00 PM,031XX N ORIOLE AVE,0810,THEFT,OVER $500,RESIDENCE,false,false,2511,025,36,17,06,1124912,1920153,2007,12/04/2014 12:43:35 PM,41.937264032,-87.816345622,"(41.937264032, -87.816345622)"
-5583890,HN389148,06/06/2007 07:30:04 PM,049XX W ALTGELD ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,2521,025,31,19,15,1143000,1916142,2007,06/11/2007 01:50:55 AM,41.925938882,-87.749968606,"(41.925938882, -87.749968606)"
-5581454,HN389135,06/06/2007 08:00:00 AM,077XX S MARYLAND AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0624,006,6,69,14,1183211,1853575,2007,06/09/2007 01:49:05 AM,41.753405393,-87.604165844,"(41.753405393, -87.604165844)"
-5579896,HN386901,06/05/2007 04:33:07 PM,029XX E 96TH ST,0890,THEFT,FROM BUILDING,APARTMENT,false,false,0431,004,10,51,06,1197310,1841819,2007,06/13/2007 03:40:31 AM,41.720806481,-87.552889891,"(41.720806481, -87.552889891)"
-5576590,HN385365,06/04/2007 09:29:47 PM,006XX N LATROBE AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1524,015,28,25,05,1141214,1903530,2007,07/05/2007 02:15:07 AM,41.891363286,-87.756842835,"(41.891363286, -87.756842835)"
-5575119,HN383733,06/03/2007 11:00:00 PM,044XX N KENTON AVE,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,false,false,1722,017,45,16,26,1144937,1929438,2007,06/11/2007 03:52:33 PM,41.962387907,-87.742514247,"(41.962387907, -87.742514247)"
-5576701,HN378718,06/01/2007 04:05:00 PM,041XX S CALIFORNIA AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,PARK PROPERTY,true,false,0912,009,12,58,24,1158423,1876986,2007,06/07/2007 01:51:10 AM,41.818189047,-87.694367404,"(41.818189047, -87.694367404)"
-5578247,HN378531,06/01/2007 02:50:30 PM,062XX S FRANCISCO AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,true,false,0823,008,15,66,24,1158073,1863328,2007,06/10/2007 02:28:49 AM,41.780716796,-87.696022795,"(41.780716796, -87.696022795)"
-5571706,HN378260,06/01/2007 08:30:00 AM,042XX N AUSTIN AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1624,016,38,15,05,1135626,1927683,2007,07/01/2007 02:00:46 AM,41.957743084,-87.776789528,"(41.957743084, -87.776789528)"
-5571604,HN378777,05/31/2007 10:30:00 PM,058XX W MONTROSE AVE,1121,DECEPTIVE PRACTICE,COUNTERFEITING DOCUMENT,RESTAURANT,false,false,1624,016,38,15,10,1136370,1928613,2007,06/11/2007 03:52:33 PM,41.960281802,-87.774031973,"(41.960281802, -87.774031973)"
-5573116,HN380393,05/31/2007 06:00:00 PM,043XX W 25TH ST,0460,BATTERY,SIMPLE,RESIDENCE,false,false,1013,010,22,30,08B,1148087,1887077,2007,07/08/2007 02:16:25 AM,41.846084794,-87.732024557,"(41.846084794, -87.732024557)"
-5583262,HN375668,05/31/2007 08:46:00 AM,019XX W 51ST ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0915,009,16,61,16,1163917,1870809,2007,10/03/2007 01:46:09 AM,41.801124755,-87.674387282,"(41.801124755, -87.674387282)"
-5567672,HN376028,05/30/2007 10:00:00 PM,084XX S WINCHESTER AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0614,006,18,71,06,1164870,1848396,2007,12/04/2014 12:43:35 PM,41.73960036,-87.671524776,"(41.73960036, -87.671524776)"
-5566919,HN374992,05/30/2007 08:56:53 PM,062XX N CALIFORNIA AVE,0460,BATTERY,SIMPLE,APARTMENT,false,false,2413,024,50,2,08B,1156570,1941510,2007,06/12/2007 01:51:41 AM,41.995286012,-87.69941564,"(41.995286012, -87.69941564)"
-5565228,HN373290,05/30/2007 01:40:00 AM,067XX S LAFAYETTE AVE,0498,BATTERY,AGGRAVATED DOMESTIC BATTERY: HANDS/FIST/FEET SERIOUS INJURY,APARTMENT,false,true,0722,007,6,69,04B,1176961,1860001,2007,06/04/2007 01:38:54 AM,41.771182139,-87.626876218,"(41.771182139, -87.626876218)"
-5565576,HN373056,05/29/2007 07:30:00 PM,066XX S MARYLAND AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0321,003,5,42,14,1183080,1860909,2007,06/02/2007 01:54:14 AM,41.773533662,-87.604418157,"(41.773533662, -87.604418157)"
-5566054,HN374055,05/29/2007 10:30:00 AM,094XX S HALSTED ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,2223,022,21,73,26,1172679,1842193,2007,06/05/2007 01:50:16 AM,41.722410118,-87.643096081,"(41.722410118, -87.643096081)"
-5566879,HN372073,05/28/2007 10:00:00 PM,040XX W 25TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1013,010,22,30,14,1149958,1887125,2007,06/05/2007 01:50:16 AM,41.846180359,-87.725156802,"(41.846180359, -87.725156802)"
-5574955,HN369933,05/28/2007 12:48:42 PM,008XX E 79TH ST,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,0624,006,8,44,18,1183029,1852762,2007,06/05/2007 01:50:16 AM,41.751178666,-87.604858026,"(41.751178666, -87.604858026)"
-5566795,HN375255,05/28/2007 03:30:00 AM,015XX S HARDING AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1014,010,24,29,07,1150339,1892153,2007,07/04/2007 02:03:09 AM,41.859970397,-87.723627522,"(41.859970397, -87.723627522)"
-5559777,HN369539,05/27/2007 10:00:00 PM,048XX W BRYN MAWR AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1621,016,45,12,06,1142890,1936843,2007,12/04/2014 12:43:35 PM,41.982746296,-87.749854915,"(41.982746296, -87.749854915)"
-5560573,HN368469,05/27/2007 04:35:00 PM,003XX E MONROE DR,1220,DECEPTIVE PRACTICE,THEFT OF LOST/MISLAID PROP,PARKING LOT/GARAGE(NON.RESID.),false,false,0124,001,42,32,11,1178787,1900039,2007,06/11/2007 03:52:33 PM,41.881008194,-87.618962451,"(41.881008194, -87.618962451)"
-5559233,HN368229,05/27/2007 01:13:00 PM,001XX E OAK ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,1833,018,42,8,06,1177221,1907200,2007,07/29/2007 01:49:15 AM,41.900693964,-87.624495406,"(41.900693964, -87.624495406)"
-5575594,HN384046,05/27/2007 12:00:00 AM,070XX S WOLCOTT AVE,2850,PUBLIC PEACE VIOLATION,BOMB THREAT,RESIDENCE,false,false,0735,007,17,67,26,1164944,1857950,2007,06/10/2007 02:28:49 AM,41.765816342,-87.67098417,"(41.765816342, -87.67098417)"
-5555055,HN359216,05/22/2007 11:45:00 PM,013XX S CHRISTIANA AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1021,010,24,29,08B,1154289,1893433,2007,06/03/2007 02:49:28 AM,41.863404971,-87.709093894,"(41.863404971, -87.709093894)"
-5548051,HN358674,05/22/2007 05:45:00 PM,087XX S JEFFERY BLVD,0820,THEFT,$500 AND UNDER,STREET,false,false,0412,004,8,48,06,1190987,1847617,2007,12/04/2014 12:43:35 PM,41.736871723,-87.575862317,"(41.736871723, -87.575862317)"
-5666153,HN357928,05/22/2007 12:10:00 PM,030XX W 63RD ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0823,008,15,66,18,1157344,1862670,2007,09/16/2007 01:46:17 AM,41.778925944,-87.69871327,"(41.778925944, -87.69871327)"
-5551505,HN359718,05/21/2007 01:20:00 PM,069XX S EAST END AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0332,003,5,43,08B,1188940,1859159,2007,05/26/2007 01:52:12 AM,41.768593218,-87.582992985,"(41.768593218, -87.582992985)"
-5542755,HN355142,05/21/2007 12:00:00 AM,011XX W COLUMBIA AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,2432,024,49,1,06,1167526,1944964,2007,05/31/2007 02:03:58 AM,42.004534219,-87.659014098,"(42.004534219, -87.659014098)"
-5543017,HN355205,05/20/2007 12:00:00 PM,066XX W WELLINGTON AVE,0810,THEFT,OVER $500,STREET,false,false,2511,025,36,18,06,1131778,1919180,2007,12/04/2014 12:43:35 PM,41.934477614,-87.791134076,"(41.934477614, -87.791134076)"
-5542823,HN351754,05/19/2007 06:00:00 PM,037XX N PARKVIEW TER,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,CONSTRUCTION SITE,false,false,1732,017,39,16,14,1150863,1924616,2007,05/23/2007 02:20:44 AM,41.949041798,-87.720853229,"(41.949041798, -87.720853229)"
-5543411,HN351695,05/19/2007 05:35:00 AM,009XX N RIDGEWAY AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,false,1112,011,27,23,08B,1151251,1906050,2007,05/25/2007 02:27:12 AM,41.898087457,-87.719915192,"(41.898087457, -87.719915192)"
-5540537,HN351703,05/19/2007 03:00:00 AM,084XX S INDIANA AVE,0810,THEFT,OVER $500,STREET,false,false,0632,006,6,44,06,1179026,1849007,2007,12/04/2014 12:43:35 PM,41.740966577,-87.61964104,"(41.740966577, -87.61964104)"
-5538879,HN350766,05/18/2007 06:00:00 PM,053XX S ALBANY AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0911,009,14,63,26,1156583,1868878,2007,05/21/2007 01:50:56 AM,41.795976961,-87.70133589,"(41.795976961, -87.70133589)"
-5554429,HN352700,05/18/2007 05:44:00 PM,017XX N CENTRAL AVE,1121,DECEPTIVE PRACTICE,COUNTERFEITING DOCUMENT,GAS STATION,false,false,2531,025,29,25,10,1138728,1911341,2007,06/11/2007 03:52:33 PM,41.912843081,-87.765783087,"(41.912843081, -87.765783087)"
-5546485,HN350718,05/18/2007 05:20:00 PM,027XX N MASON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2514,025,30,19,08B,1136301,1917436,2007,06/01/2007 02:14:39 AM,41.929612188,-87.774553537,"(41.929612188, -87.774553537)"
-5538174,HN349762,05/18/2007 09:00:00 AM,024XX N ALBANY AVE,2850,PUBLIC PEACE VIOLATION,BOMB THREAT,APARTMENT,true,true,1414,014,35,22,26,1155055,1916254,2007,02/08/2008 01:04:54 AM,41.926012622,-87.705669098,"(41.926012622, -87.705669098)"
-5536501,HN349356,05/18/2007 02:40:00 AM,025XX N HALSTED ST,0870,THEFT,POCKET-PICKING,BAR OR TAVERN,false,false,1933,019,43,7,06,1170462,1917355,2007,06/11/2007 03:52:33 PM,41.928710388,-87.649023805,"(41.928710388, -87.649023805)"
-5536971,HN349382,05/18/2007 02:24:52 AM,019XX W VAN BUREN ST,1460,WEAPONS VIOLATION,POSS FIREARM/AMMO:NO FOID CARD,STREET,true,false,1211,012,2,28,15,1163686,1898216,2007,05/22/2007 02:05:56 AM,41.876337271,-87.674463431,"(41.876337271, -87.674463431)"
-5538483,HN350103,05/18/2007 01:30:00 AM,014XX W WRIGHTWOOD AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1931,019,32,7,14,1166515,1917413,2007,09/12/2007 01:43:33 AM,41.928955068,-87.663525983,"(41.928955068, -87.663525983)"
-5540762,HN348383,05/17/2007 02:50:00 PM,067XX S PRAIRIE AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0322,003,20,69,08B,1179155,1860582,2007,06/11/2007 03:52:33 PM,41.772726728,-87.618816163,"(41.772726728, -87.618816163)"
-5550509,HN354573,05/17/2007 07:00:00 AM,012XX W WINNEMAC AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,2033,020,46,3,06,1166954,1933733,2007,12/04/2014 12:43:35 PM,41.973728424,-87.661442656,"(41.973728424, -87.661442656)"
-5551794,HN345787,05/16/2007 11:40:00 AM,061XX S WHIPPLE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,0823,008,15,66,18,1157065,1863706,2007,08/15/2007 02:43:11 AM,41.781774524,-87.69970813,"(41.781774524, -87.69970813)"
-5533679,HN344893,05/15/2007 09:00:00 PM,063XX S ARTESIAN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,0825,008,15,66,08B,1161082,1862745,2007,05/23/2007 02:20:44 AM,41.779055237,-87.685007285,"(41.779055237, -87.685007285)"
-5728993,HN537984,05/15/2007 10:00:00 AM,033XX N PITTSBURGH AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,false,false,1631,016,36,17,11,1120547,1920991,2007,09/05/2007 01:50:37 AM,41.939634729,-87.8323701,"(41.939634729, -87.8323701)"
-5527938,HN341802,05/14/2007 01:00:00 PM,026XX N ELSTON AVE,0820,THEFT,$500 AND UNDER,GROCERY FOOD STORE,true,false,1432,014,1,22,06,1160776,1917684,2007,12/04/2014 12:43:35 PM,41.929819797,-87.684607426,"(41.929819797, -87.684607426)"
-5524850,HN339142,05/13/2007 12:14:23 AM,057XX W THOMAS ST,0560,ASSAULT,SIMPLE,STREET,false,true,1511,015,29,25,08A,1137785,1906791,2007,11/09/2007 09:43:39 AM,41.900374407,-87.769357396,"(41.900374407, -87.769357396)"
-5557788,HN367114,05/12/2007 09:30:00 PM,012XX N SPRINGFIELD AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2535,025,27,23,26,1150109,1908155,2007,06/11/2007 03:52:33 PM,41.903886118,-87.724054779,"(41.903886118, -87.724054779)"
-5523346,HN337234,05/11/2007 10:45:00 PM,067XX S RHODES AVE,031A,ROBBERY,ARMED: HANDGUN,STREET,true,false,0321,003,20,42,03,1181036,1860229,2007,06/01/2007 02:14:39 AM,41.77171497,-87.61193184,"(41.77171497, -87.61193184)"
-5522595,HN336954,05/11/2007 05:15:00 PM,044XX S CALIFORNIA AVE,0560,ASSAULT,SIMPLE,SIDEWALK,true,false,0912,009,12,58,08A,1158406,1874965,2007,05/16/2007 06:13:58 AM,41.812643516,-87.694484883,"(41.812643516, -87.694484883)"
-5518849,HN334538,05/10/2007 09:00:00 AM,079XX S COLFAX AVE,0890,THEFT,FROM BUILDING,RESIDENCE PORCH/HALLWAY,false,false,0422,004,7,46,06,1194936,1852751,2007,05/12/2007 05:43:52 AM,41.750863551,-87.561225921,"(41.750863551, -87.561225921)"
-5521445,HN331840,05/09/2007 11:40:00 AM,091XX S WILLIAMS AVE,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,RESIDENCE,false,false,0633,006,6,49,11,1179407,1844217,2007,06/01/2007 02:14:39 AM,41.727813556,-87.618390882,"(41.727813556, -87.618390882)"
-5523604,HN329910,05/08/2007 01:18:00 PM,035XX S MICHIGAN AVE,2093,NARCOTICS,FOUND SUSPECT NARCOTICS,POLICE FACILITY/VEH PARKING LOT,true,false,0211,002,3,35,26,1177731,1881697,2007,05/15/2007 06:10:18 AM,41.830700587,-87.623396828,"(41.830700587, -87.623396828)"
-5515047,HN329716,05/08/2007 10:30:00 AM,001XX W 87TH ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0634,006,21,44,06,1176799,1847235,2007,05/12/2007 05:43:52 AM,41.73615438,-87.627853768,"(41.73615438, -87.627853768)"
-5522874,HN329164,05/08/2007 05:15:00 AM,019XX W POTOMAC AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,ALLEY,false,true,1424,014,1,24,08B,1163142,1908704,2007,10/31/2007 01:04:04 AM,41.905128624,-87.676165886,"(41.905128624, -87.676165886)"
-5510675,HN329144,05/08/2007 04:15:00 AM,052XX S WESTERN AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0911,009,14,63,05,1161214,1870068,2007,05/19/2007 02:08:46 AM,41.799147791,-87.684320696,"(41.799147791, -87.684320696)"
-5509571,HN326284,05/06/2007 04:55:00 PM,080XX S MARYLAND AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0631,006,8,44,18,1183334,1851990,2007,05/08/2007 05:38:45 AM,41.749053125,-87.603764352,"(41.749053125, -87.603764352)"
-5591265,HN326853,05/06/2007 10:30:00 AM,122XX S GREEN ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,0524,005,34,53,05,1172835,1823679,2007,06/17/2007 03:25:09 AM,41.671601419,-87.643068152,"(41.671601419, -87.643068152)"
-5506746,HN325157,05/06/2007 12:10:00 AM,004XX E 63RD ST,041A,BATTERY,AGGRAVATED: HANDGUN,RESTAURANT,true,false,0313,003,20,42,04B,1180035,1863367,2007,07/04/2007 02:03:09 AM,41.780348938,-87.61550513,"(41.780348938, -87.61550513)"
-5505322,HN323782,05/05/2007 08:00:00 AM,006XX N TRUMBULL AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENTIAL YARD (FRONT/BACK),false,false,1121,011,27,23,14,1153299,1904272,2007,05/08/2007 05:38:45 AM,41.893168024,-87.712440283,"(41.893168024, -87.712440283)"
-5520488,HN321232,05/03/2007 11:45:00 PM,087XX S STATE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0634,006,21,44,08B,1177767,1847230,2007,05/15/2007 06:10:18 AM,41.73611883,-87.624307529,"(41.73611883, -87.624307529)"
-5506110,HN323941,05/03/2007 09:00:00 PM,001XX W DELAWARE PL,0810,THEFT,OVER $500,STREET,false,false,1832,018,42,8,06,1175092,1906695,2007,12/04/2014 12:43:35 PM,41.899356223,-87.632330423,"(41.899356223, -87.632330423)"
-5506442,HN322420,05/03/2007 03:00:00 PM,103XX S CHARLES ST,1320,CRIMINAL DAMAGE,TO VEHICLE,"SCHOOL, PUBLIC, GROUNDS",false,false,2212,022,19,72,14,1168616,1835901,2007,05/08/2007 05:38:45 AM,41.705232329,-87.658159037,"(41.705232329, -87.658159037)"
-5505742,HN319927,05/03/2007 12:20:00 PM,007XX E 67TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0321,003,20,42,18,1182403,1860792,2007,05/08/2007 05:38:45 AM,41.773228322,-87.606903484,"(41.773228322, -87.606903484)"
-5500895,HN318056,05/02/2007 01:15:00 PM,011XX S MASON AVE,2170,NARCOTICS,POSSESSION OF DRUG EQUIPMENT,SIDEWALK,true,false,1513,015,29,25,18,1136858,1894207,2007,05/05/2007 08:11:46 AM,41.865858911,-87.773064124,"(41.865858911, -87.773064124)"
-5526834,HN315110,04/30/2007 09:45:00 PM,069XX S DORCHESTER AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0321,003,5,43,18,1186701,1859044,2007,08/22/2007 01:57:02 AM,41.768330955,-87.591203547,"(41.768330955, -87.591203547)"
-5493816,HN314606,04/30/2007 01:00:00 PM,040XX W FULLERTON AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2524,025,31,22,06,1149290,1915694,2007,12/04/2014 12:43:35 PM,41.924589776,-87.726867371,"(41.924589776, -87.726867371)"
-5499312,HN313537,04/29/2007 11:15:00 PM,0000X E WACKER DR,0890,THEFT,FROM BUILDING,HOTEL/MOTEL,false,false,0122,001,42,32,06,1176982,1902484,2007,06/15/2007 03:05:05 AM,41.887758444,-87.625516158,"(41.887758444, -87.625516158)"
-5488048,HN308771,04/27/2007 12:00:00 PM,029XX N KOLMAR AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,2521,025,31,20,03,1145436,1919359,2007,05/15/2007 06:10:18 AM,41.934720809,-87.740935727,"(41.934720809, -87.740935727)"
-5486520,HN308036,04/27/2007 06:00:00 AM,009XX N RICHMOND ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1311,012,26,24,14,1156583,1906450,2007,05/01/2007 07:13:54 AM,41.899078808,-87.700320302,"(41.899078808, -87.700320302)"
-5487645,HN309090,04/26/2007 10:30:00 PM,045XX S CHRISTIANA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0821,008,14,58,14,1154763,1874126,2007,05/02/2007 06:09:42 AM,41.810414726,-87.707869954,"(41.810414726, -87.707869954)"
-5487392,HN308673,04/26/2007 07:00:00 PM,018XX W 51ST ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0931,009,16,61,14,1164954,1870909,2007,05/02/2007 06:09:42 AM,41.801377289,-87.670581402,"(41.801377289, -87.670581402)"
-5482455,HN305561,04/25/2007 11:36:00 PM,023XX N SPAULDING AVE,2027,NARCOTICS,POSS: CRACK,RESIDENCE,true,false,1413,014,35,22,18,1153801,1915362,2007,04/28/2007 05:26:07 AM,41.923589997,-87.710300775,"(41.923589997, -87.710300775)"
-5486541,HN305473,04/25/2007 10:13:42 PM,077XX S GREEN ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0621,006,17,71,05,1172022,1853363,2007,08/31/2010 03:21:15 PM,41.753076545,-87.645175426,"(41.753076545, -87.645175426)"
-5497935,HN305141,04/25/2007 06:42:38 PM,062XX W BYRON ST,5008,OTHER OFFENSE,FIREARM REGISTRATION VIOLATION,RESIDENCE,true,false,1633,016,38,17,26,1133729,1925222,2007,05/05/2007 08:11:46 AM,41.95102344,-87.783821675,"(41.95102344, -87.783821675)"
-5476512,HN301606,04/23/2007 10:50:00 PM,076XX S SOUTH CHICAGO AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,GAS STATION,false,false,0411,004,5,45,07,1186253,1854790,2007,04/26/2007 05:54:48 AM,41.756668181,-87.592979864,"(41.756668181, -87.592979864)"
-5509148,HN301507,04/23/2007 10:00:00 PM,010XX W 75TH ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0612,006,17,68,18,1170579,1855054,2007,08/05/2007 01:48:31 AM,41.75774842,-87.650414263,"(41.75774842, -87.650414263)"
-5477365,HN302013,04/23/2007 06:00:00 PM,048XX N WINTHROP AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2033,020,46,3,14,1167956,1932409,2007,04/26/2007 05:54:48 AM,41.970073703,-87.657796457,"(41.970073703, -87.657796457)"
-5481259,HN304126,04/23/2007 06:00:00 PM,041XX N ELSTON AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,1723,017,39,16,26,1150123,1927442,2007,04/29/2007 06:45:31 AM,41.956811034,-87.72349945,"(41.956811034, -87.72349945)"
-5477432,HN301001,04/23/2007 02:45:00 PM,025XX N TRIPP AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,2524,025,31,20,05,1147578,1916708,2007,05/03/2007 07:26:52 AM,41.92740534,-87.733131974,"(41.92740534, -87.733131974)"
-5487650,HN299367,04/22/2007 10:10:00 PM,109XX S WENTWORTH AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0513,005,34,49,18,1176848,1832365,2007,09/30/2007 01:49:21 AM,41.695347973,-87.62812047,"(41.695347973, -87.62812047)"
-5473167,HN298171,04/22/2007 02:58:00 AM,0000X W JACKSON BLVD,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,COMMERCIAL / BUSINESS OFFICE,false,false,0112,001,2,32,14,1175749,1898934,2007,04/24/2007 05:19:41 AM,41.87804486,-87.630150898,"(41.87804486, -87.630150898)"
-5473391,HN299154,04/22/2007 02:00:00 AM,011XX W WELLINGTON AVE,0820,THEFT,$500 AND UNDER,ALLEY,false,false,1932,019,44,6,06,1168171,1920033,2007,12/04/2014 12:43:35 PM,41.936108818,-87.657364832,"(41.936108818, -87.657364832)"
-5472007,HN297360,04/21/2007 09:20:00 PM,080XX S CRANDON AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,HOSPITAL BUILDING/GROUNDS,false,false,0414,004,8,46,14,1192880,1852198,2007,04/23/2007 05:51:22 AM,41.749396455,-87.568777964,"(41.749396455, -87.568777964)"
-5476674,HN295264,04/20/2007 08:00:23 PM,055XX S ASHLAND AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,false,false,0715,007,15,67,04B,1166574,1868056,2007,05/12/2007 05:43:52 AM,41.793513889,-87.664721643,"(41.793513889, -87.664721643)"
-5469268,HN294229,04/20/2007 11:02:28 AM,044XX N SHERIDAN RD,0460,BATTERY,SIMPLE,SMALL RETAIL STORE,false,false,2313,019,46,3,08B,1168841,1929869,2007,06/21/2007 02:21:27 AM,41.963084669,-87.654616277,"(41.963084669, -87.654616277)"
-5502492,HN321483,04/20/2007 09:23:00 AM,0000X W 110TH PL,0810,THEFT,OVER $500,STREET,false,false,0513,005,34,49,06,1178011,1831656,2007,12/04/2014 12:43:35 PM,41.693376166,-87.623883747,"(41.693376166, -87.623883747)"
-5489533,HN305580,04/19/2007 11:40:00 PM,035XX N OAK PARK AVE,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,true,false,1632,016,36,17,26,1130478,1923023,2007,05/02/2007 06:09:42 AM,41.945045714,-87.795823115,"(41.945045714, -87.795823115)"
-5477926,HN288740,04/17/2007 02:40:00 PM,009XX N HUDSON AVE,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,1823,018,27,8,26,1173042,1906702,2007,04/26/2007 05:54:48 AM,41.89942115,-87.639859762,"(41.89942115, -87.639859762)"
-5459426,HN285694,04/15/2007 10:30:00 PM,082XX S DAMEN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0614,006,18,71,08B,1164495,1850137,2007,04/24/2007 05:19:41 AM,41.744385829,-87.672849765,"(41.744385829, -87.672849765)"
-5455731,HN284632,04/15/2007 11:25:00 AM,087XX S ADA ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,true,true,2222,022,21,71,14,1168818,1846710,2007,04/18/2007 05:47:17 AM,41.73488951,-87.657108432,"(41.73488951, -87.657108432)"
-5506615,HN280483,04/13/2007 09:15:00 AM,037XX W GRENSHAW ST,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,SIDEWALK,true,false,1133,011,24,29,18,1151657,1894743,2007,05/08/2007 05:38:45 AM,41.867051871,-87.718721404,"(41.867051871, -87.718721404)"
-5453915,HN281311,04/12/2007 02:10:00 PM,070XX N ASHLAND BLVD,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,2423,024,49,1,08A,1164539,1946810,2007,05/25/2007 02:27:12 AM,42.00966371,-87.669950691,"(42.00966371, -87.669950691)"
-5444983,HN276572,04/11/2007 04:19:00 AM,007XX N CLARK ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,1832,018,42,8,06,1175367,1905240,2007,04/13/2007 05:31:29 AM,41.895357459,-87.631364089,"(41.895357459, -87.631364089)"
-5443560,HN276117,04/10/2007 06:00:00 PM,007XX W 48TH ST,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,0935,009,11,61,08A,1172184,1873079,2007,04/18/2007 05:47:17 AM,41.807175957,-87.644002565,"(41.807175957, -87.644002565)"
-5447966,HN275105,04/10/2007 09:00:00 AM,016XX N WELLS ST,0890,THEFT,FROM BUILDING,GROCERY FOOD STORE,false,false,1814,018,43,7,06,1174430,1911584,2007,04/14/2007 05:42:51 AM,41.912786696,-87.634615644,"(41.912786696, -87.634615644)"
-5482999,HN273278,04/09/2007 12:20:00 PM,019XX W MARQUETTE RD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0726,007,15,67,18,1164297,1860187,2007,07/22/2007 01:56:08 AM,41.771968623,-87.673292729,"(41.771968623, -87.673292729)"
-5448157,HN272293,04/08/2007 06:20:00 PM,014XX S KEDVALE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1011,010,24,29,26,1148999,1892474,2007,04/30/2007 06:19:44 AM,41.860877268,-87.72853804,"(41.860877268, -87.72853804)"
-5436019,HN268304,04/06/2007 09:15:00 AM,007XX E 47TH ST,0560,ASSAULT,SIMPLE,RESTAURANT,false,false,0222,002,4,38,08A,1182169,1874055,2007,04/13/2007 05:31:29 AM,41.809628578,-87.607350702,"(41.809628578, -87.607350702)"
-5433961,HN266195,04/04/2007 10:29:35 PM,048XX W SUPERIOR ST,2017,NARCOTICS,MANU/DELIVER:CRACK,STREET,true,false,1531,015,28,25,18,1144026,1904487,2007,04/07/2007 05:43:19 AM,41.893937096,-87.746491524,"(41.893937096, -87.746491524)"
-5422200,HN260701,04/01/2007 10:30:00 PM,016XX W NORTH AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,1433,014,1,24,03,1165296,1910762,2007,05/18/2007 02:14:47 AM,41.91073036,-87.668194966,"(41.91073036, -87.668194966)"
-5827933,HN638597,04/01/2007 03:30:00 PM,102XX S CALUMET AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,0511,005,9,49,26,1180432,1836962,2007,10/14/2007 01:03:27 AM,41.707881489,-87.61485794,"(41.707881489, -87.61485794)"
-5527255,HN342196,04/01/2007 12:01:00 AM,037XX W GRENSHAW ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1133,011,24,29,07,1151614,1894742,2007,05/16/2007 06:13:58 AM,41.867049971,-87.71887929,"(41.867049971, -87.71887929)"
-5422315,HN258484,03/31/2007 05:00:00 PM,039XX W FULLERTON AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,2525,025,30,22,03,1149948,1915631,2007,04/18/2007 05:47:17 AM,41.924404103,-87.724451219,"(41.924404103, -87.724451219)"
-5420143,HN257221,03/30/2007 10:00:00 PM,082XX S STONY ISLAND AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,CLEANING STORE,false,false,0411,004,8,45,14,1188197,1850713,2007,04/02/2007 06:10:32 AM,41.745434382,-87.585985348,"(41.745434382, -87.585985348)"
-5419751,HN255750,03/30/2007 09:06:42 AM,103XX S EWING AVE,1570,SEX OFFENSE,PUBLIC INDECENCY,RESIDENCE,true,false,0432,004,10,52,17,1202123,1836713,2007,04/03/2007 07:18:56 AM,41.70667413,-87.535434705,"(41.70667413, -87.535434705)"
-5418918,HN255467,03/30/2007 02:30:00 AM,001XX W CERMAK RD,2024,NARCOTICS,POSS: HEROIN(WHITE),CTA PLATFORM,true,false,2111,009,25,34,18,1175599,1889797,2007,04/03/2007 07:18:56 AM,41.852975698,-87.630976134,"(41.852975698, -87.630976134)"
-5431436,HN265493,03/30/2007 12:01:00 AM,021XX S HOMAN AVE,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,APARTMENT,false,true,1024,010,24,30,06,1153981,1889769,2007,05/02/2007 06:09:42 AM,41.853356678,-87.710322143,"(41.853356678, -87.710322143)"
-5416001,HN253613,03/29/2007 06:58:00 AM,051XX S PULASKI RD,0495,BATTERY,AGGRAVATED OF A SENIOR CITIZEN,GAS STATION,false,false,0815,008,23,62,04B,1150529,1870253,2007,04/08/2007 10:39:00 AM,41.799870158,-87.72350074,"(41.799870158, -87.72350074)"
-5416233,HN253383,03/28/2007 11:00:00 PM,020XX W DEVON AVE,0810,THEFT,OVER $500,STREET,false,false,2413,024,50,2,06,1161421,1942448,2007,12/04/2014 12:43:35 PM,41.997759985,-87.681544988,"(41.997759985, -87.681544988)"
-5417652,HN255837,03/28/2007 09:00:00 PM,021XX S THROOP ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1222,012,25,31,14,1167986,1890144,2007,04/03/2007 07:18:56 AM,41.854095373,-87.658908167,"(41.854095373, -87.658908167)"
-5413602,HN252761,03/28/2007 05:09:55 PM,046XX W MONROE ST,0326,ROBBERY,AGGRAVATED VEHICULAR HIJACKING,VACANT LOT/LAND,false,false,1113,011,28,25,03,1145167,1899113,2007,04/07/2007 05:43:19 AM,41.879168723,-87.742436863,"(41.879168723, -87.742436863)"
-5412662,HN252521,03/28/2007 03:24:28 PM,052XX W MADISON ST,0860,THEFT,RETAIL THEFT,DRUG STORE,true,false,1522,015,28,25,06,1141447,1899561,2007,03/31/2007 07:38:54 AM,41.880467561,-87.756085176,"(41.880467561, -87.756085176)"
-5465114,HN251398,03/27/2007 10:45:00 PM,030XX E 79TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0421,004,7,46,18,1198230,1853221,2007,07/25/2007 03:43:09 AM,41.752071514,-87.549139682,"(41.752071514, -87.549139682)"
-5408517,HN250325,03/27/2007 12:52:54 PM,049XX N KENMORE AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,2024,020,46,3,18,1168393,1933306,2007,03/31/2007 07:38:54 AM,41.972525632,-87.656163527,"(41.972525632, -87.656163527)"
-5404284,HN248676,03/26/2007 11:00:00 AM,002XX E 48TH ST,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE PORCH/HALLWAY,false,false,0224,002,3,38,14,1178587,1873210,2007,03/29/2007 08:44:27 AM,41.807392106,-87.62051448,"(41.807392106, -87.62051448)"
-5405804,HN247590,03/26/2007 02:04:00 AM,077XX S EVANS AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,0624,006,6,69,14,1182605,1853869,2007,03/29/2007 08:44:27 AM,41.754226232,-87.606377483,"(41.754226232, -87.606377483)"
-5401120,HN244065,03/24/2007 02:55:00 AM,071XX S ASHLAND AVE,0560,ASSAULT,SIMPLE,STREET,false,false,0735,007,17,67,08A,1166869,1857599,2007,03/29/2007 08:44:27 AM,41.764812258,-87.663938393,"(41.764812258, -87.663938393)"
-5410700,HN252265,03/23/2007 08:00:00 PM,028XX N CLARK ST,0890,THEFT,FROM BUILDING,BAR OR TAVERN,false,false,1932,019,44,6,06,1171504,1918894,2007,04/04/2007 06:21:29 AM,41.932910587,-87.645149438,"(41.932910587, -87.645149438)"
-5404200,HN242863,03/23/2007 03:10:00 PM,031XX W GRAND AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, GROUNDS",true,false,1311,012,26,23,08B,1155450,1905895,2007,03/28/2007 09:12:21 AM,41.897578702,-87.704496733,"(41.897578702, -87.704496733)"
-5476391,HN300176,03/23/2007 08:00:00 AM,016XX W OGDEN AVE,0810,THEFT,OVER $500,RESIDENCE,false,false,1211,012,2,28,06,1165447,1899459,2007,12/04/2014 12:43:35 PM,41.879710887,-87.667962264,"(41.879710887, -87.667962264)"
-5398367,HN240482,03/22/2007 10:55:27 AM,006XX N LAMON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1532,015,37,25,08B,1143532,1903942,2007,03/25/2007 06:52:34 AM,41.89245081,-87.748319495,"(41.89245081, -87.748319495)"
-5397174,HN239008,03/21/2007 02:10:00 PM,066XX S DREXEL AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0321,003,5,42,06,1183403,1861302,2007,03/26/2007 05:46:31 AM,41.774604573,-87.603221894,"(41.774604573, -87.603221894)"
-5393951,HN238827,03/21/2007 01:35:00 PM,048XX S DREXEL BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2124,002,4,39,18,1183175,1873000,2007,03/24/2007 07:32:13 AM,41.806710198,-87.603693754,"(41.806710198, -87.603693754)"
-5400387,HN237915,03/20/2007 01:00:00 AM,086XX S MARYLAND AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,true,0632,006,8,44,14,1183368,1847686,2007,03/30/2007 06:35:15 AM,41.737241691,-87.603773476,"(41.737241691, -87.603773476)"
-5390878,HN233553,03/18/2007 04:10:00 PM,083XX S EXCHANGE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,CTA GARAGE / OTHER PROPERTY,true,false,0423,004,10,46,18,1197247,1850437,2007,04/28/2007 05:26:07 AM,41.744456542,-87.552834399,"(41.744456542, -87.552834399)"
-5382539,HN230715,03/16/2007 10:48:27 PM,030XX W FULLERTON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1414,014,35,22,08B,1155558,1915756,2007,12/04/2014 12:43:35 PM,41.924635952,-87.703834244,"(41.924635952, -87.703834244)"
-5382039,HN230557,03/16/2007 09:09:43 PM,021XX W CULLERTON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1223,012,25,31,18,1162473,1890348,2007,04/04/2007 03:56:04 PM,41.854772209,-87.679137302,"(41.854772209, -87.679137302)"
-5380800,HN229876,03/16/2007 02:30:00 PM,015XX S AVERS AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, GROUNDS",false,false,1014,010,24,29,08B,1151001,1892241,2007,03/23/2007 06:55:06 AM,41.860198954,-87.721195178,"(41.860198954, -87.721195178)"
-5378510,HN228607,03/15/2007 09:49:00 PM,113XX S WENTWORTH AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,false,false,0522,005,34,49,04A,1176918,1829898,2007,03/18/2007 06:37:55 AM,41.688576588,-87.627938145,"(41.688576588, -87.627938145)"
-5373419,HN224243,03/13/2007 05:35:00 PM,062XX S COTTAGE GROVE AVE,0560,ASSAULT,SIMPLE,OTHER,true,false,0313,003,20,42,08A,1182684,1863478,2007,03/23/2007 06:55:06 AM,41.78059244,-87.605790115,"(41.78059244, -87.605790115)"
-5373309,HN223507,03/13/2007 11:08:21 AM,007XX W GARFIELD BLVD,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0711,007,3,68,05,1172076,1868220,2007,04/04/2007 06:21:29 AM,41.793844726,-87.644541483,"(41.793844726, -87.644541483)"
-5373005,HN221827,03/12/2007 02:15:00 PM,013XX W 83RD ST,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,APARTMENT,false,false,0613,006,21,71,04A,1168698,1849761,2007,03/19/2007 05:56:57 AM,41.743264474,-87.657460278,"(41.743264474, -87.657460278)"
-5371397,HN221780,03/12/2007 01:30:00 PM,061XX S INDIANA AVE,1822,NARCOTICS,MANU/DEL:CANNABIS OVER 10 GMS,SIDEWALK,true,false,0311,003,20,40,18,1178686,1864569,2007,03/17/2007 06:01:53 AM,41.783678141,-87.620414212,"(41.783678141, -87.620414212)"
-5371756,HN221275,03/11/2007 11:00:00 AM,048XX N KOSTNER AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1712,017,39,14,14,1146205,1931974,2007,03/16/2007 09:21:57 AM,41.969322798,-87.737787458,"(41.969322798, -87.737787458)"
-5366326,HN218581,03/10/2007 03:50:00 PM,011XX S STATE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,CTA TRAIN,true,false,0132,001,2,32,18,1176557,1895307,2007,12/02/2007 01:04:24 AM,41.868073948,-87.62729369,"(41.868073948, -87.62729369)"
-5369851,HN221283,03/10/2007 01:00:00 PM,021XX N NATCHEZ AVE,0820,THEFT,$500 AND UNDER,FACTORY/MANUFACTURING BUILDING,false,false,2512,025,36,19,06,1132700,1913338,2007,12/04/2014 12:43:35 PM,41.91843042,-87.787882235,"(41.91843042, -87.787882235)"
-5366451,HN218152,03/10/2007 12:09:00 PM,030XX W POPE JOHN PAUL II DR,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,SIDEWALK,true,false,0912,009,14,58,24,1157039,1876024,2007,03/13/2007 05:26:14 AM,41.815577325,-87.699470436,"(41.815577325, -87.699470436)"
-5680568,HN217053,03/09/2007 08:29:26 PM,004XX N LARAMIE AVE,2250,LIQUOR LAW VIOLATION,LIQUOR LICENSE VIOLATION,BAR OR TAVERN,true,false,1532,015,28,25,22,1141665,1902436,2007,08/31/2010 03:21:15 PM,41.888352891,-87.755213572,"(41.888352891, -87.755213572)"
-5361596,HN215336,03/08/2007 06:00:00 PM,011XX S WABASH AVE,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,0132,001,2,32,06,1176902,1895641,2007,12/04/2014 12:43:35 PM,41.868982672,-87.62601704,"(41.868982672, -87.62601704)"
-5362333,HN214572,03/08/2007 02:30:00 PM,075XX N ASHLAND AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,OTHER,false,true,2422,024,49,1,26,1164096,1949974,2007,03/29/2007 08:44:27 AM,42.018355192,-87.671490619,"(42.018355192, -87.671490619)"
-5375652,HN225438,03/07/2007 02:00:00 PM,084XX S SAGINAW AVE,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",false,false,0423,004,7,46,06,1195333,1849722,2007,03/17/2007 06:01:53 AM,41.742541949,-87.559870924,"(41.742541949, -87.559870924)"
-5356378,HN211902,03/07/2007 06:00:00 AM,013XX W FLETCHER ST,1320,CRIMINAL DAMAGE,TO VEHICLE,RESIDENCE-GARAGE,false,false,1932,019,32,6,14,1166945,1921074,2007,03/13/2007 05:26:14 AM,41.938991811,-87.661840508,"(41.938991811, -87.661840508)"
-5355952,HN211770,03/06/2007 10:35:00 PM,006XX N LEAMINGTON AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1532,015,28,25,26,1141954,1903727,2007,03/13/2007 05:26:14 AM,41.891890202,-87.754120245,"(41.891890202, -87.754120245)"
-5354341,HN210046,03/05/2007 11:24:32 PM,014XX W 72ND PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0734,007,17,67,08B,1167951,1856708,2007,03/15/2007 06:47:07 PM,41.762344061,-87.659998119,"(41.762344061, -87.659998119)"
-5356507,HN209977,03/05/2007 09:00:00 PM,094XX S DR MARTIN LUTHER KING JR DR,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,GAS STATION,false,false,0633,006,9,49,07,1180622,1842136,2007,03/13/2007 05:26:14 AM,41.722075256,-87.614003861,"(41.722075256, -87.614003861)"
-5349652,HN204516,03/02/2007 06:20:00 PM,011XX W 112TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2234,022,34,75,08B,1170614,1830483,2007,03/08/2007 09:51:06 PM,41.69032126,-87.650999754,"(41.69032126, -87.650999754)"
-5350421,HN203989,03/02/2007 02:19:00 PM,0000X N STATE ST,0890,THEFT,FROM BUILDING,SMALL RETAIL STORE,false,false,0122,001,42,32,06,1176404,1900511,2007,03/08/2007 09:51:06 PM,41.882357487,-87.627698312,"(41.882357487, -87.627698312)"
-5350682,HN205492,03/02/2007 09:30:00 AM,030XX E 79TH ST,0890,THEFT,FROM BUILDING,PAWN SHOP,false,false,0421,004,7,46,06,1197843,1853210,2007,03/08/2007 09:51:06 PM,41.752051,-87.550558199,"(41.752051, -87.550558199)"
-5348281,HN203614,03/01/2007 09:00:00 PM,043XX W WALTON ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1111,011,37,23,07,1147118,1906011,2007,03/16/2007 09:21:57 AM,41.898060524,-87.735096471,"(41.898060524, -87.735096471)"
-5346411,HN202549,03/01/2007 09:00:00 AM,004XX N GREEN ST,0810,THEFT,OVER $500,STREET,false,false,1323,012,27,24,06,1170677,1903529,2007,12/04/2014 12:43:35 PM,41.89076637,-87.648639318,"(41.89076637, -87.648639318)"
-5351425,HN201222,02/28/2007 11:20:00 PM,045XX W WASHINGTON BLVD,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,1113,011,28,26,18,1146280,1900074,2007,03/08/2007 09:51:06 PM,41.881784719,-87.738325611,"(41.881784719, -87.738325611)"
-5349438,HN203473,02/28/2007 04:00:00 PM,033XX S KEDZIE AVE,0890,THEFT,FROM BUILDING,FEDERAL BUILDING,false,false,1032,010,22,30,06,1155536,1882025,2007,03/15/2007 06:47:07 PM,41.832075125,-87.704822722,"(41.832075125, -87.704822722)"
-5454131,HN283466,02/28/2007 12:00:00 PM,073XX S FAIRFIELD AVE,1195,DECEPTIVE PRACTICE,FINAN EXPLOIT-ELDERLY/DISABLED,RESIDENCE,true,true,0835,008,18,66,11,1159347,1856020,2007,01/03/2008 01:04:43 AM,41.760636573,-87.691551967,"(41.760636573, -87.691551967)"
-5344694,HN199678,02/28/2007 07:05:00 AM,042XX W MAYPOLE AVE,0460,BATTERY,SIMPLE,APARTMENT,true,false,1114,011,28,26,08B,1148007,1901154,2007,03/26/2007 05:46:31 AM,41.884715329,-87.731956255,"(41.884715329, -87.731956255)"
-5342714,HN199351,02/27/2007 09:30:00 PM,008XX W 59TH ST,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,GAS STATION,false,false,0712,007,16,68,07,1171834,1865770,2007,03/03/2007 06:09:01 AM,41.787126973,-87.645500756,"(41.787126973, -87.645500756)"
-5341130,HN197827,02/27/2007 01:40:00 AM,035XX W DOUGLAS BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,false,1021,010,24,29,08B,1152660,1893261,2007,03/15/2007 06:47:07 PM,41.862965331,-87.715078414,"(41.862965331, -87.715078414)"
-5788295,HN197514,02/26/2007 09:40:00 PM,002XX W 117TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0522,005,34,53,18,1176656,1827393,2007,10/14/2007 01:03:27 AM,41.681708371,-87.628972288,"(41.681708371, -87.628972288)"
-5341303,HN197650,02/26/2007 09:30:00 PM,007XX N LA SALLE DR,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,1832,,42,8,06,,,2007,12/04/2014 12:43:35 PM,,,
-5336435,HN194860,02/25/2007 03:20:00 AM,017XX N CLYBOURN AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,RESTAURANT,false,false,1813,018,43,7,04B,1169469,1911784,2007,03/02/2007 05:52:50 AM,41.913444963,-87.652835176,"(41.913444963, -87.652835176)"
-5338476,HN194446,02/24/2007 07:48:43 PM,003XX N WALLER AVE,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),SIDEWALK,true,false,1512,015,29,25,18,1138056,1901487,2007,03/03/2007 06:09:01 AM,41.88581467,-87.768490238,"(41.88581467, -87.768490238)"
-5336789,HN193450,02/24/2007 08:20:00 AM,046XX W MADISON ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,SIDEWALK,true,false,1113,011,28,25,16,1145479,1899576,2007,02/27/2007 06:32:20 AM,41.88043335,-87.741279513,"(41.88043335, -87.741279513)"
-5439206,HN190880,02/22/2007 10:20:00 PM,035XX W MADISON ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1123,011,28,27,16,1152815,1899828,2007,07/04/2007 02:03:09 AM,41.880982832,-87.714335579,"(41.880982832, -87.714335579)"
-5332192,HN190754,02/22/2007 05:30:00 PM,064XX S UNIVERSITY AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0314,003,20,42,03,1184819,1862500,2007,03/13/2007 05:26:14 AM,41.777858886,-87.597993579,"(41.777858886, -87.597993579)"
-5337971,HN190409,02/22/2007 04:10:00 PM,068XX S HERMITAGE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0725,007,17,67,14,1165816,1859442,2007,01/23/2008 02:56:07 PM,41.76989212,-87.667745668,"(41.76989212, -87.667745668)"
-5331453,HN189977,02/22/2007 11:30:00 AM,007XX N CARPENTER ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1323,012,27,24,06,1169288,1904862,2007,12/04/2014 12:43:35 PM,41.894454512,-87.653701601,"(41.894454512, -87.653701601)"
-5432011,HN189598,02/22/2007 07:50:00 AM,039XX S CALUMET AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0214,002,3,38,16,1179039,1878955,2007,07/08/2007 02:16:25 AM,41.823146564,-87.618681452,"(41.823146564, -87.618681452)"
-5331449,HN189037,02/21/2007 08:05:38 PM,066XX S WOODLAWN AVE,0461,BATTERY,AGG PO HANDS ETC SERIOUS INJ,STREET,true,false,0321,003,5,42,04B,1185397,1861179,2007,03/13/2007 05:26:14 AM,41.774220364,-87.595916171,"(41.774220364, -87.595916171)"
-5338608,HN187540,02/21/2007 01:44:00 AM,070XX S JEFFERY BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0332,003,5,43,18,1190716,1858887,2007,02/27/2007 06:32:20 AM,41.767804128,-87.576491949,"(41.767804128, -87.576491949)"
-5332821,HN190081,02/20/2007 02:05:00 PM,008XX E 103RD ST,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0512,005,9,50,08A,1183714,1836812,2007,02/27/2007 06:32:20 AM,41.707394098,-87.602843897,"(41.707394098, -87.602843897)"
-5323382,HN183417,02/18/2007 03:57:59 PM,0000X N STATE ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0122,001,42,32,06,1176328,1900431,2007,02/24/2007 07:01:13 AM,41.882139677,-87.627979796,"(41.882139677, -87.627979796)"
-5355619,HN182950,02/18/2007 09:19:00 AM,028XX W 64TH ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0823,008,15,66,26,1158594,1862120,2007,03/13/2007 05:26:14 AM,41.777391259,-87.694145617,"(41.777391259, -87.694145617)"
diff --git a/work-with-data/dataprep/data/crime_partfiles/part-00004-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv b/work-with-data/dataprep/data/crime_partfiles/part-00004-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv
deleted file mode 100644
index 802f1698..00000000
--- a/work-with-data/dataprep/data/crime_partfiles/part-00004-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv
+++ /dev/null
@@ -1,923 +0,0 @@
-5325593,HN182341,02/17/2007 09:20:00 PM,008XX W 123RD ST,0460,BATTERY,SIMPLE,STREET,false,false,0524,005,34,53,08B,1173187,1823334,2007,02/28/2007 05:34:33 AM,41.670646937,-87.641789973,"(41.670646937, -87.641789973)"
-5327691,HN177982,02/15/2007 02:05:00 PM,024XX W DIVISION ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1423,014,26,24,18,1159780,1907943,2007,08/01/2007 02:03:29 AM,41.903110391,-87.688536587,"(41.903110391, -87.688536587)"
-5317338,HN177372,02/15/2007 12:00:00 AM,012XX E 95TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0511,005,8,50,14,1186051,1842186,2007,02/17/2007 06:14:10 AM,41.722086258,-87.594116913,"(41.722086258, -87.594116913)"
-5324624,HN184178,02/12/2007 07:00:00 PM,021XX W 52ND ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0915,009,16,61,05,1163150,1870036,2007,02/24/2007 07:01:13 AM,41.799019648,-87.677221789,"(41.799019648, -87.677221789)"
-5312361,HN172228,02/11/2007 08:30:00 PM,040XX W WILCOX ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1115,011,28,26,26,1149517,1899070,2007,02/13/2007 08:48:27 AM,41.878967431,-87.726465383,"(41.878967431, -87.726465383)"
-5311722,HN172153,02/11/2007 07:20:00 PM,051XX S LEAMINGTON AVE,0560,ASSAULT,SIMPLE,RESIDENCE,true,false,0814,008,23,56,08A,1142864,1869832,2007,02/14/2007 06:32:53 AM,41.798860653,-87.751621346,"(41.798860653, -87.751621346)"
-5334880,HN193397,02/10/2007 12:00:00 AM,033XX N OSCEOLA AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1631,016,36,17,26,1125874,1921200,2007,02/27/2007 06:32:20 AM,41.940121157,-87.812786672,"(41.940121157, -87.812786672)"
-5307741,HN168042,02/09/2007 03:30:00 AM,033XX W CHICAGO AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1121,011,27,23,06,1153829,1905171,2007,12/04/2014 12:43:35 PM,41.895624426,-87.71046981,"(41.895624426, -87.71046981)"
-5307097,HN167222,02/08/2007 06:10:00 PM,053XX S MARSHFIELD AVE,0496,BATTERY,AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST,APARTMENT,true,false,0932,009,16,61,04B,1166218,1868966,2007,02/12/2007 06:20:13 AM,41.796018629,-87.666001163,"(41.796018629, -87.666001163)"
-5465136,HN292118,02/07/2007 12:00:00 PM,021XX N KENNETH AVE,0820,THEFT,$500 AND UNDER,APARTMENT,false,false,2522,025,31,20,06,1146408,1913948,2007,12/04/2014 12:43:35 PM,41.919854028,-87.737501761,"(41.919854028, -87.737501761)"
-5308078,HN163212,02/06/2007 03:00:00 PM,007XX N LOCKWOOD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1524,015,28,25,08B,1140921,1904660,2007,02/13/2007 08:48:27 AM,41.89446954,-87.757891067,"(41.89446954, -87.757891067)"
-5294662,HN157094,02/02/2007 04:17:31 PM,005XX N ST LOUIS AVE,2017,NARCOTICS,MANU/DELIVER:CRACK,SIDEWALK,true,false,1121,011,27,23,18,1152910,1903463,2007,06/11/2007 03:52:33 PM,41.890955763,-87.713890394,"(41.890955763, -87.713890394)"
-5294860,HN156885,02/02/2007 02:25:00 PM,076XX S EGGLESTON AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,0621,006,17,69,08A,1174654,1854378,2007,02/24/2007 07:01:13 AM,41.755803655,-87.635500031,"(41.755803655, -87.635500031)"
-5294955,HN156811,02/02/2007 02:10:00 PM,052XX S JUSTINE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0932,009,16,61,18,1166861,1869682,2007,02/06/2007 06:23:18 AM,41.797969696,-87.663622786,"(41.797969696, -87.663622786)"
-5288760,HN151410,01/30/2007 12:44:00 PM,001XX N WABASH AVE,0560,ASSAULT,SIMPLE,RESTAURANT,false,false,0122,001,42,32,08A,1176838,1901015,2007,02/03/2007 06:11:22 AM,41.883730687,-87.626089427,"(41.883730687, -87.626089427)"
-5282517,HN150713,01/30/2007 12:50:00 AM,007XX N LEAVITT ST,0560,ASSAULT,SIMPLE,STREET,true,false,1313,012,32,24,08A,1161554,1905248,2007,02/01/2007 06:57:55 AM,41.895678324,-87.682095476,"(41.895678324, -87.682095476)"
-5282556,HN149345,01/29/2007 11:30:00 AM,061XX S KEDZIE AVE,1330,CRIMINAL TRESPASS,TO LAND,LIBRARY,true,false,0823,008,15,66,26,1156066,1863779,2007,02/01/2007 06:57:55 AM,41.781994989,-87.703368783,"(41.781994989, -87.703368783)"
-5280544,HN149222,01/29/2007 10:45:00 AM,042XX S UNION AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0935,009,11,61,26,1172383,1876656,2007,11/23/2007 01:04:36 AM,41.816987217,-87.643167333,"(41.816987217, -87.643167333)"
-5278895,HN148114,01/28/2007 12:00:00 AM,021XX W 110TH PL,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,2212,022,19,75,07,1163884,1831461,2007,02/09/2007 07:49:56 AM,41.69314871,-87.675611338,"(41.69314871, -87.675611338)"
-5279545,HN146958,01/27/2007 06:30:41 PM,034XX W CARROLL AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1123,011,28,27,14,1153202,1902142,2007,02/01/2007 06:57:55 AM,41.887325017,-87.712853098,"(41.887325017, -87.712853098)"
-5280283,HN146593,01/27/2007 04:35:00 AM,060XX S DR MARTIN LUTHER KING JR DR,0820,THEFT,$500 AND UNDER,SIDEWALK,false,true,0313,003,20,42,06,1180005,1864722,2007,12/04/2014 12:43:35 PM,41.784067876,-87.615573648,"(41.784067876, -87.615573648)"
-5279417,HN145645,01/26/2007 11:30:00 PM,027XX W 21ST ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,1023,010,28,30,08B,1158386,1889902,2007,02/06/2007 06:23:18 AM,41.853632804,-87.694150563,"(41.853632804, -87.694150563)"
-5276084,HN145407,01/26/2007 09:00:00 PM,069XX S EGGLESTON AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,0732,007,6,68,26,1174439,1858869,2007,02/02/2007 09:25:46 AM,41.768132276,-87.636154561,"(41.768132276, -87.636154561)"
-5270816,HN140206,01/23/2007 11:06:00 PM,034XX W MADISON ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1123,011,28,27,14,1153201,1899758,2007,01/27/2007 07:34:41 AM,41.880783096,-87.712920063,"(41.880783096, -87.712920063)"
-5285629,HN139457,01/23/2007 02:37:08 PM,064XX S HOYNE AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,0726,007,15,67,26,1163433,1861797,2007,02/03/2007 06:11:22 AM,41.776404834,-87.676414814,"(41.776404834, -87.676414814)"
-5279820,HN139392,01/23/2007 02:00:00 PM,095XX S HALSTED ST,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,2223,022,21,73,18,1172611,1841778,2007,01/30/2007 06:41:08 AM,41.721272795,-87.643357338,"(41.721272795, -87.643357338)"
-5260690,HN136560,01/21/2007 09:10:00 PM,060XX S WASHTENAW AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0825,008,15,66,14,1159372,1864382,2007,01/24/2007 07:03:14 AM,41.783582602,-87.691231529,"(41.783582602, -87.691231529)"
-5261637,HN135877,01/21/2007 11:30:00 AM,004XX E 63RD ST,1330,CRIMINAL TRESPASS,TO LAND,TAVERN/LIQUOR STORE,true,false,0312,003,20,42,26,1180281,1863295,2007,01/25/2007 07:51:08 AM,41.780145724,-87.614605468,"(41.780145724, -87.614605468)"
-5276704,HN135222,01/20/2007 10:18:28 PM,011XX S STATE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,false,false,0132,001,2,32,18,1176467,1895662,2007,06/11/2007 03:52:33 PM,41.869050121,-87.62761338,"(41.869050121, -87.62761338)"
-5258383,HN133494,01/19/2007 10:11:00 PM,007XX W 41ST ST,0460,BATTERY,SIMPLE,DEPARTMENT STORE,false,false,0925,009,11,61,08B,1171862,1877817,2007,01/23/2007 06:13:38 AM,41.820184581,-87.645044374,"(41.820184581, -87.645044374)"
-5257583,HN130630,01/18/2007 01:40:00 PM,063XX S VERNON AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0312,003,20,42,06,1180379,1863108,2007,01/21/2007 07:32:49 AM,41.779630329,-87.614251921,"(41.779630329, -87.614251921)"
-5245311,HN124686,01/15/2007 01:00:00 AM,037XX S HERMITAGE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0922,009,11,59,14,1165267,1879615,2007,01/18/2007 07:10:58 AM,41.82526091,-87.669186757,"(41.82526091, -87.669186757)"
-5246267,HN124809,01/15/2007 12:05:00 AM,030XX W OHIO ST,031A,ROBBERY,ARMED: HANDGUN,FACTORY/MANUFACTURING BUILDING,false,false,1313,012,27,23,03,1155671,1903796,2007,03/08/2007 09:51:06 PM,41.891814404,-87.703741595,"(41.891814404, -87.703741595)"
-5249427,HN124277,01/14/2007 08:35:00 PM,116XX S PRINCETON AVE,0460,BATTERY,SIMPLE,RESIDENCE,true,false,0522,005,34,53,08B,1176413,1827616,2007,01/20/2007 06:07:58 AM,41.682325766,-87.629855134,"(41.682325766, -87.629855134)"
-5252421,HN121291,01/13/2007 01:10:00 AM,016XX S PULASKI RD,0340,ROBBERY,ATTEMPT: STRONGARM-NO WEAPON,SIDEWALK,true,false,1014,010,24,29,03,1150021,1891683,2007,02/02/2007 09:25:46 AM,41.858686852,-87.724807051,"(41.858686852, -87.724807051)"
-5241590,HN120638,01/12/2007 06:10:00 PM,007XX N CHRISTIANA AVE,2830,OTHER OFFENSE,OBSCENE TELEPHONE CALLS,RESIDENCE,false,false,1121,011,27,23,17,1153871,1904696,2007,01/30/2007 06:41:08 AM,41.894320144,-87.710328221,"(41.894320144, -87.710328221)"
-5252170,HN128501,01/12/2007 03:30:00 PM,011XX N WESTERN AVE,0460,BATTERY,SIMPLE,STREET,false,false,1312,012,1,24,08B,1160235,1907765,2007,01/23/2007 06:13:38 AM,41.902612548,-87.686870207,"(41.902612548, -87.686870207)"
-5245725,HN120229,01/11/2007 09:00:00 PM,119XX S WALLACE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0524,005,34,53,14,1174502,1825544,2007,01/18/2007 07:10:58 AM,41.676682475,-87.636911808,"(41.676682475, -87.636911808)"
-5236623,HN117360,01/10/2007 10:20:00 PM,043XX W DICKENS AVE,2022,NARCOTICS,POSS: COCAINE,ALLEY,true,false,2522,025,30,20,18,1147219,1913646,2007,01/13/2007 05:03:08 AM,41.91900981,-87.734529736,"(41.91900981, -87.734529736)"
-5242989,HN113295,01/08/2007 02:30:00 AM,035XX W JACKSON BLVD,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1123,011,28,27,05,1152623,1898495,2007,01/23/2007 06:13:38 AM,41.877328737,-87.715075854,"(41.877328737, -87.715075854)"
-5228417,HN112233,01/08/2007 12:02:00 AM,026XX N MILWAUKEE AVE,0312,ROBBERY,ARMED:KNIFE/CUTTING INSTRUMENT,STREET,false,false,1412,014,35,22,03,1154298,1917637,2007,01/27/2007 07:34:41 AM,41.929822855,-87.708413679,"(41.929822855, -87.708413679)"
-5227253,HN110806,01/07/2007 12:05:00 AM,008XX N GREENVIEW AVE,0460,BATTERY,SIMPLE,CHA APARTMENT,false,false,1323,012,27,24,08B,1166258,1905943,2007,01/11/2007 08:03:48 AM,41.897486175,-87.664798907,"(41.897486175, -87.664798907)"
-5227759,HN110490,01/06/2007 05:30:00 PM,019XX E 71ST ST,0320,ROBBERY,STRONGARM - NO WEAPON,RESTAURANT,false,false,0332,003,5,43,03,1190354,1858350,2007,01/30/2007 06:41:08 AM,41.76633929,-87.577836108,"(41.76633929, -87.577836108)"
-5227733,HN109966,01/06/2007 02:00:00 PM,049XX W WAVELAND AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1634,016,38,15,08B,1142811,1924108,2007,01/10/2007 07:09:41 AM,41.94780188,-87.750464081,"(41.94780188, -87.750464081)"
-5226752,HN109667,01/06/2007 11:00:00 AM,014XX W ELMDALE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2013,020,48,77,14,1165754,1939923,2007,01/10/2007 07:09:41 AM,41.990739712,-87.665677916,"(41.990739712, -87.665677916)"
-5228486,HN108742,01/05/2007 08:15:00 PM,017XX W HURON ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1324,012,1,24,26,1164340,1904762,2007,01/09/2007 06:41:38 AM,41.894286218,-87.671876908,"(41.894286218, -87.671876908)"
-5234745,HN107718,01/05/2007 11:15:00 AM,078XX S LAFLIN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,0612,006,17,71,08B,1167437,1852683,2007,01/13/2007 05:03:08 AM,41.751309939,-87.661997123,"(41.751309939, -87.661997123)"
-5244622,HN107634,01/05/2007 10:35:00 AM,009XX N KARLOV AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1111,011,37,23,18,1148919,1906019,2007,01/16/2007 05:09:07 AM,41.898047824,-87.728481294,"(41.898047824, -87.728481294)"
-5223411,HN106240,01/04/2007 01:50:00 PM,001XX N STATE ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,false,false,0122,001,42,32,06,1176390,1900949,2007,01/09/2007 06:41:38 AM,41.883559699,-87.627736496,"(41.883559699, -87.627736496)"
-5223012,HN107188,01/03/2007 04:00:00 PM,004XX S CLARK ST,0610,BURGLARY,FORCIBLE ENTRY,HOTEL/MOTEL,false,false,0131,001,2,32,05,1175556,1898268,2007,01/24/2007 07:03:14 AM,41.876221652,-87.630879555,"(41.876221652, -87.630879555)"
-5220908,HN100097,01/01/2007 12:29:27 AM,061XX S RACINE AVE,1477,WEAPONS VIOLATION,RECKLESS FIREARM DISCHARGE,RESIDENCE PORCH/HALLWAY,true,false,0712,007,16,68,15,1169412,1864069,2007,01/10/2007 07:09:41 AM,41.782512054,-87.654430379,"(41.782512054, -87.654430379)"
-6727269,HR144314,12/31/2006 12:00:00 PM,0000X S RIVERSIDE PLZ,0840,THEFT,FINANCIAL ID THEFT: OVER $300,FACTORY/MANUFACTURING BUILDING,false,false,0111,,2,28,06,,,2006,02/10/2009 01:05:09 AM,,,
-5217602,HM801427,12/30/2006 12:00:00 PM,018XX S SPRINGFIELD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1014,010,24,29,08B,1150644,1890879,2006,01/11/2007 08:03:48 AM,41.856468439,-87.722541209,"(41.856468439, -87.722541209)"
-5397441,HM799155,12/29/2006 12:55:30 AM,002XX W 106TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0512,005,34,49,18,1176752,1834694,2006,05/26/2007 01:52:12 AM,41.701741242,-87.628402163,"(41.701741242, -87.628402163)"
-5221796,HM797844,12/27/2006 08:00:00 PM,037XX S WALLACE ST,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,0925,009,11,60,06,1172956,1879883,2006,12/04/2014 12:43:35 PM,41.825829758,-87.640970043,"(41.825829758, -87.640970043)"
-5206570,HM796511,12/27/2006 04:30:00 AM,091XX S GREENWOOD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0413,004,8,47,14,1185107,1844449,2006,12/31/2006 07:09:40 AM,41.728318378,-87.597503747,"(41.728318378, -87.597503747)"
-5202463,HM792445,12/24/2006 02:05:00 PM,036XX W 71ST ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,0833,008,13,65,08B,1153507,1857248,2006,12/27/2006 05:16:09 AM,41.764123971,-87.712923572,"(41.764123971, -87.712923572)"
-5212650,HM793429,12/24/2006 11:45:00 AM,041XX W VAN BUREN ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1132,011,24,26,14,1148616,1897735,2006,01/02/2007 06:08:05 AM,41.875321473,-87.729808188,"(41.875321473, -87.729808188)"
-5201373,HM792116,12/23/2006 09:00:00 PM,014XX N RIDGEWAY AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE-GARAGE,false,false,2535,025,26,23,14,1151137,1909610,2006,12/26/2006 05:17:36 AM,41.90785868,-87.720240457,"(41.90785868, -87.720240457)"
-5228118,HM790080,12/22/2006 11:05:00 PM,005XX S KOSTNER AVE,3800,INTERFERENCE WITH PUBLIC OFFICER,INTERFERENCE JUDICIAL PROCESS,PARKING LOT/GARAGE(NON.RESID.),false,false,1131,011,24,26,26,1147099,1897314,2006,12/04/2014 12:43:35 PM,41.874195335,-87.735388839,"(41.874195335, -87.735388839)"
-5198424,HM787587,12/21/2006 05:45:00 PM,048XX W ADAMS ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1533,015,28,25,08B,1144101,1898932,2006,01/22/2007 06:32:08 AM,41.87869211,-87.746355623,"(41.87869211, -87.746355623)"
-5200131,HM784922,12/20/2006 12:20:00 PM,054XX S MICHIGAN AVE,031A,ROBBERY,ARMED: HANDGUN,STREET,false,false,0232,002,3,40,03,1178037,1869153,2006,12/28/2006 06:04:50 AM,41.796271826,-87.622654732,"(41.796271826, -87.622654732)"
-5200098,HM784603,12/20/2006 09:05:00 AM,012XX S LAWNDALE AVE,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,SIDEWALK,true,false,1011,010,24,29,18,1151868,1893990,2006,12/31/2006 07:09:40 AM,41.864981405,-87.717966605,"(41.864981405, -87.717966605)"
-5190939,HM782345,12/19/2006 02:03:00 AM,009XX W 31ST ST,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,STREET,true,false,0923,009,11,60,04A,1170268,1884331,2006,12/24/2006 06:39:17 AM,41.838094507,-87.650701973,"(41.838094507, -87.650701973)"
-5193110,HM784041,12/18/2006 10:00:00 PM,113XX S CHURCH ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,true,2212,022,19,75,26,1166095,1829573,2006,12/31/2006 07:09:40 AM,41.687921101,-87.667569805,"(41.687921101, -87.667569805)"
-5190724,HM780477,12/18/2006 06:00:00 AM,011XX W 15TH ST,0890,THEFT,FROM BUILDING,CONSTRUCTION SITE,false,false,1232,012,2,28,06,1169110,1892861,2006,04/02/2007 06:10:32 AM,41.861526749,-87.654703846,"(41.861526749, -87.654703846)"
-5186588,HM777007,12/16/2006 12:30:48 AM,025XX E 83RD ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0423,004,7,46,08B,1194731,1850395,2006,12/18/2006 06:13:25 AM,41.744403544,-87.562054513,"(41.744403544, -87.562054513)"
-5186082,HM775707,12/15/2006 12:30:00 PM,077XX S LAWNDALE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0833,008,18,70,06,1153227,1853136,2006,12/04/2014 12:43:35 PM,41.752845508,-87.714058346,"(41.752845508, -87.714058346)"
-5906548,HN280982,12/15/2006 10:00:00 AM,030XX N MILWAUKEE AVE,1110,DECEPTIVE PRACTICE,BOGUS CHECK,GAS STATION,false,false,2523,025,30,21,11,1150967,1920214,2006,08/31/2010 03:21:15 PM,41.936960325,-87.720586608,"(41.936960325, -87.720586608)"
-5534921,HN346784,12/15/2006 01:00:00 AM,059XX N OZANAM AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,false,true,1612,016,41,10,26,1123303,1938767,2006,06/02/2007 01:54:14 AM,41.988369494,-87.821850735,"(41.988369494, -87.821850735)"
-5181218,HM773065,12/14/2006 01:18:00 AM,028XX W CORTEZ ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,true,true,1311,012,26,24,08B,1157158,1907007,2006,12/16/2006 05:56:32 AM,41.900595603,-87.698193193,"(41.900595603, -87.698193193)"
-5182729,HM772933,12/13/2006 11:07:00 PM,065XX S MARYLAND AVE,0330,ROBBERY,AGGRAVATED,SIDEWALK,false,false,0321,003,20,42,03,1182973,1862028,2006,12/18/2006 06:13:25 AM,41.776606791,-87.60477564,"(41.776606791, -87.60477564)"
-5183870,HM773645,12/13/2006 10:00:00 PM,028XX W CATALPA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2011,020,40,4,14,1156226,1936446,2006,12/20/2006 06:24:03 AM,41.981397142,-87.700818623,"(41.981397142, -87.700818623)"
-5180989,HM771325,12/12/2006 04:00:00 PM,063XX S INGLESIDE AVE,0610,BURGLARY,FORCIBLE ENTRY,OTHER,false,false,0314,003,20,42,05,1183861,1862896,2006,12/18/2006 06:13:25 AM,41.778967972,-87.601493232,"(41.778967972, -87.601493232)"
-5175864,HM769094,12/12/2006 03:09:00 AM,044XX N SHERIDAN RD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,2313,019,46,3,08B,1168843,1929764,2006,12/14/2006 06:21:01 AM,41.962796502,-87.654611982,"(41.962796502, -87.654611982)"
-5175798,HM765711,12/10/2006 05:20:00 AM,059XX S WESTERN AVE,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0825,008,16,66,03,1161428,1865232,2006,12/16/2006 05:56:32 AM,41.785872737,-87.683669923,"(41.785872737, -87.683669923)"
-5360660,HM765580,12/10/2006 02:00:00 AM,029XX E 91ST ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0423,004,10,46,18,1197115,1845225,2006,07/04/2007 02:03:09 AM,41.730157684,-87.553491099,"(41.730157684, -87.553491099)"
-5173247,HM765464,12/10/2006 12:45:00 AM,011XX W MADISON ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1212,012,27,28,08B,1169028,1900237,2006,12/28/2006 06:04:50 AM,41.88176884,-87.654790817,"(41.88176884, -87.654790817)"
-5172146,HM765480,12/09/2006 11:20:00 PM,021XX N MILWAUKEE AVE,0890,THEFT,FROM BUILDING,BAR OR TAVERN,false,false,1431,014,1,22,06,1158676,1914100,2006,12/16/2006 05:56:32 AM,41.920028383,-87.692422811,"(41.920028383, -87.692422811)"
-5172568,HM764404,12/09/2006 11:24:03 AM,036XX W LEXINGTON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1133,011,24,27,08B,1152155,1896491,2006,12/16/2006 05:56:32 AM,41.87183878,-87.71684708,"(41.87183878, -87.71684708)"
-5169351,HM762117,12/08/2006 12:55:00 AM,033XX W BELMONT AVE,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,1733,017,35,21,06,1153793,1921128,2006,12/12/2006 04:47:45 AM,41.939412513,-87.710176182,"(41.939412513, -87.710176182)"
-5165747,HM759954,12/06/2006 02:15:00 PM,019XX W OGDEN AVE,0890,THEFT,FROM BUILDING,HOSPITAL BUILDING/GROUNDS,false,false,1224,012,2,28,06,1163359,1897070,2006,12/08/2006 06:10:22 AM,41.873199429,-87.675696303,"(41.873199429, -87.675696303)"
-5168137,HM761173,12/05/2006 02:30:00 AM,012XX S MICHIGAN AVE,0890,THEFT,FROM BUILDING,OTHER,false,false,0132,001,2,33,06,1177378,1895029,2006,12/12/2006 04:47:45 AM,41.86729253,-87.624288107,"(41.86729253, -87.624288107)"
-5172196,HM765018,12/04/2006 02:50:00 PM,099XX S PROSPECT AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,2213,022,19,72,05,1167240,1838988,2006,12/20/2006 06:24:03 AM,41.713733043,-87.663109824,"(41.713733043, -87.663109824)"
-5158372,HM755053,12/03/2006 11:00:00 PM,024XX W ARTHINGTON ST,2850,PUBLIC PEACE VIOLATION,BOMB THREAT,RESIDENCE,false,false,1135,011,2,28,26,1160035,1896005,2006,08/31/2010 03:21:15 PM,41.870346204,-87.687929726,"(41.870346204, -87.687929726)"
-5157631,HM753482,12/02/2006 08:50:00 PM,028XX N CLARK ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,2333,019,44,6,06,1171497,1919059,2006,12/05/2006 04:13:46 AM,41.933363508,-87.645170295,"(41.933363508, -87.645170295)"
-5174291,HM767570,12/01/2006 12:00:00 PM,055XX S WOOD ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0715,007,15,67,07,1165250,1868024,2006,12/13/2006 04:48:10 AM,41.793454232,-87.669577556,"(41.793454232, -87.669577556)"
-5161563,HM752038,12/01/2006 11:45:00 AM,111XX S STATE ST,1330,CRIMINAL TRESPASS,TO LAND,GAS STATION,true,false,0522,005,34,49,26,1178187,1831335,2006,12/07/2006 06:24:52 AM,41.692491316,-87.623249065,"(41.692491316, -87.623249065)"
-5348051,HM749508,11/30/2006 02:08:02 PM,005XX E BROWNING AVE,2093,NARCOTICS,FOUND SUSPECT NARCOTICS,CHA PARKING LOT/GROUNDS,true,false,0212,002,4,35,26,1180492,1881375,2006,06/27/2007 02:51:19 AM,41.829753936,-87.613276607,"(41.829753936, -87.613276607)"
-5152217,HM747883,11/29/2006 06:00:00 PM,022XX S DR MARTIN LUTHER KING JR DR,1330,CRIMINAL TRESPASS,TO LAND,OTHER,true,false,0133,001,2,33,26,1178906,1889450,2006,12/09/2006 04:10:59 AM,41.85194864,-87.618849106,"(41.85194864, -87.618849106)"
-5152185,HM747934,11/29/2006 02:30:00 PM,002XX E RANDOLPH ST,0810,THEFT,OVER $500,PARK PROPERTY,false,false,0124,001,42,32,06,1177892,1901182,2006,12/04/2014 12:43:35 PM,41.884165043,-87.622214011,"(41.884165043, -87.622214011)"
-5151154,HM747275,11/29/2006 01:00:00 PM,002XX S LARAMIE AVE,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,OTHER,false,true,1522,015,29,25,26,1141709,1898801,2006,02/05/2007 06:51:54 AM,41.878377185,-87.755141925,"(41.878377185, -87.755141925)"
-5148268,HM744568,11/27/2006 07:00:00 PM,019XX N DRAKE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1422,014,26,22,06,1152549,1912630,2006,12/04/2014 12:43:35 PM,41.916118022,-87.714973501,"(41.916118022, -87.714973501)"
-5167325,HM741321,11/26/2006 01:20:00 PM,025XX W 59TH ST,1121,DECEPTIVE PRACTICE,COUNTERFEITING DOCUMENT,GROCERY FOOD STORE,false,false,0824,008,16,63,10,1160327,1865492,2006,12/11/2006 05:19:46 AM,41.78660897,-87.687699584,"(41.78660897, -87.687699584)"
-5164202,HM740584,11/26/2006 12:50:00 AM,029XX N NEW ENGLAND AVE,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,2511,025,36,18,06,1129946,1918980,2006,12/04/2014 12:43:35 PM,41.933960386,-87.797871401,"(41.933960386, -87.797871401)"
-6299304,HP377521,11/25/2006 07:00:00 PM,075XX S INDIANA AVE,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,COMMERCIAL / BUSINESS OFFICE,false,false,0623,,6,69,11,,,2006,07/02/2008 01:04:10 AM,,,
-5154546,HM739029,11/25/2006 06:28:00 AM,100XX W OHARE ST,1330,CRIMINAL TRESPASS,TO LAND,AIRPORT/AIRCRAFT,true,false,1651,016,41,76,26,1100635,1934208,2006,12/03/2006 06:03:12 AM,41.976200173,-87.905312411,"(41.976200173, -87.905312411)"
-5345324,HM738040,11/24/2006 03:19:24 PM,027XX W AUGUSTA BLVD,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1311,012,26,24,26,1157659,1906491,2006,05/05/2007 08:11:46 AM,41.899169462,-87.69636706,"(41.899169462, -87.69636706)"
-5344808,HM737883,11/24/2006 01:53:23 PM,094XX S HARVARD AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0634,006,21,49,18,1175635,1842523,2006,05/05/2007 08:11:46 AM,41.723250134,-87.632258841,"(41.723250134, -87.632258841)"
-5139589,HM737069,11/23/2006 10:00:00 PM,049XX W GLADYS AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,SIDEWALK,false,false,1533,015,24,25,04A,1143444,1897845,2006,11/29/2006 06:25:08 AM,41.875721552,-87.748795208,"(41.875721552, -87.748795208)"
-5447480,HM735102,11/22/2006 02:49:02 PM,131XX S LANGLEY AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0533,005,9,54,18,1183285,1818309,2006,08/08/2007 02:59:32 AM,41.65662928,-87.604987701,"(41.65662928, -87.604987701)"
-5138928,HM734312,11/22/2006 08:08:14 AM,078XX S YATES BLVD,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0414,004,7,43,14,1193518,1853466,2006,11/25/2006 05:00:04 AM,41.75286037,-87.566398704,"(41.75286037, -87.566398704)"
-5136403,HM733977,11/21/2006 10:35:00 PM,012XX N MILWAUKEE AVE,0312,ROBBERY,ARMED:KNIFE/CUTTING INSTRUMENT,PARK PROPERTY,false,false,1433,014,1,24,03,1165658,1908235,2006,12/04/2006 05:55:00 AM,41.903788392,-87.666937234,"(41.903788392, -87.666937234)"
-5314675,HM733931,11/21/2006 09:30:00 PM,066XX S BELL AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,0832,008,15,66,18,1162504,1860663,2006,06/24/2007 02:00:14 AM,41.773312406,-87.679852078,"(41.773312406, -87.679852078)"
-5137694,HM734435,11/21/2006 09:00:00 PM,063XX W ROSCOE ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1633,016,36,17,07,1133475,1921896,2006,01/01/2007 07:32:02 AM,41.941901002,-87.784833623,"(41.941901002, -87.784833623)"
-5136001,HM729923,11/19/2006 05:45:00 PM,005XX N HOMAN AVE,0497,BATTERY,AGGRAVATED DOMESTIC BATTERY: OTHER DANG WEAPON,SIDEWALK,false,false,1121,011,27,23,04B,1153575,1903454,2006,11/29/2006 06:25:08 AM,41.890917867,-87.711448406,"(41.890917867, -87.711448406)"
-5206249,HM787057,11/19/2006 12:00:00 AM,033XX W WARREN BLVD,1751,OFFENSE INVOLVING CHILDREN,CRIM SEX ABUSE BY FAM MEMBER,RESIDENCE,false,true,1123,011,28,27,20,1154235,1900190,2006,02/14/2007 06:32:53 AM,41.881947974,-87.70911174,"(41.881947974, -87.70911174)"
-5135687,HM728143,11/18/2006 10:00:00 AM,059XX S MICHIGAN AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0233,002,20,40,05,1178218,1865511,2006,11/26/2006 06:54:40 AM,41.786273718,-87.622101485,"(41.786273718, -87.622101485)"
-5342294,HM725401,11/17/2006 10:30:00 AM,100XX W OHARE ST,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,AIRPORT/AIRCRAFT,true,false,1651,016,41,76,18,1100635,1934208,2006,04/21/2007 05:09:41 AM,41.976200173,-87.905312411,"(41.976200173, -87.905312411)"
-5125874,HM724011,11/14/2006 05:00:00 PM,007XX W BARRY AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,APARTMENT,false,false,2332,019,44,6,06,1170720,1920648,2006,12/02/2006 04:50:35 AM,41.93774087,-87.647979016,"(41.93774087, -87.647979016)"
-5337733,HM719076,11/13/2006 11:00:00 PM,103XX S EWING AVE,2230,LIQUOR LAW VIOLATION,ILLEGAL CONSUMPTION BY MINOR,SIDEWALK,true,false,0432,004,10,52,22,1202118,1837208,2006,04/28/2007 05:26:07 AM,41.708032578,-87.535436219,"(41.708032578, -87.535436219)"
-5117853,HM718770,11/13/2006 05:20:00 PM,010XX N WINCHESTER AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1322,012,1,24,26,1163168,1907110,2006,11/28/2006 06:48:34 AM,41.900754023,-87.676115238,"(41.900754023, -87.676115238)"
-5114403,HM715593,11/11/2006 11:03:44 PM,107XX S BENSLEY AVE,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,true,false,0434,004,10,51,26,1194601,1834439,2006,11/14/2006 04:28:54 AM,41.700621997,-87.563053957,"(41.700621997, -87.563053957)"
-5319739,HM714464,11/11/2006 11:18:00 AM,060XX S WINCHESTER AVE,2017,NARCOTICS,MANU/DELIVER:CRACK,VEHICLE NON-COMMERCIAL,true,false,0714,007,15,67,18,1164346,1864803,2006,05/05/2007 08:11:46 AM,41.784634505,-87.672983173,"(41.784634505, -87.672983173)"
-5113797,HM714722,11/10/2006 06:00:00 PM,036XX S MICHIGAN AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,RESIDENCE,false,true,0211,002,3,35,04A,1177828,1881078,2006,06/02/2010 10:34:17 AM,41.828999802,-87.623059711,"(41.828999802, -87.623059711)"
-5113499,HM713193,11/10/2006 05:01:00 PM,052XX S TROY ST,5000,OTHER OFFENSE,OTHER CRIME AGAINST PERSON,RESIDENCE,false,false,0911,009,14,63,26,1156301,1869898,2006,02/01/2007 06:57:55 AM,41.798781667,-87.702342553,"(41.798781667, -87.702342553)"
-5111778,HM711058,11/09/2006 04:15:00 PM,022XX S WESTERN AVE,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,1034,010,25,31,06,1160669,1889301,2006,11/14/2006 04:28:54 AM,41.851936659,-87.685787742,"(41.851936659, -87.685787742)"
-5108824,HM709869,11/09/2006 03:30:00 AM,022XX N KEDZIE BLVD,2820,OTHER OFFENSE,TELEPHONE THREAT,NURSING HOME/RETIREMENT HOME,false,false,1413,014,26,22,26,1154622,1914949,2006,11/12/2006 06:16:17 AM,41.922440283,-87.707295178,"(41.922440283, -87.707295178)"
-5112192,HM709748,11/08/2006 11:30:00 PM,117XX S LOWE AVE,0454,BATTERY,AGG PO HANDS NO/MIN INJURY,STREET,true,false,0524,005,34,53,08B,1174102,1826979,2006,11/12/2006 06:16:17 AM,41.680629208,-87.638333544,"(41.680629208, -87.638333544)"
-5105755,HM707039,11/07/2006 02:30:00 PM,046XX N SHERIDAN RD,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2312,019,46,3,26,1168742,1931179,2006,11/11/2006 07:40:50 AM,41.966681503,-87.654942128,"(41.966681503, -87.654942128)"
-5107676,HM706536,11/07/2006 12:15:00 PM,059XX S JUSTINE ST,0560,ASSAULT,SIMPLE,APARTMENT,false,false,0713,007,15,67,08A,1166991,1865020,2006,11/11/2006 07:40:50 AM,41.785173827,-87.663279301,"(41.785173827, -87.663279301)"
-5118125,HM707161,11/06/2006 11:30:00 PM,025XX W MARQUETTE RD,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,0832,008,15,66,14,1160739,1860186,2006,06/14/2007 02:11:59 AM,41.77204007,-87.686335343,"(41.77204007, -87.686335343)"
-5112204,HM705385,11/06/2006 05:00:00 PM,054XX S BLACKSTONE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2131,002,4,41,06,1186794,1869524,2006,12/04/2014 12:43:35 PM,41.797086727,-87.590530816,"(41.797086727, -87.590530816)"
-5105202,HM704334,11/06/2006 11:25:00 AM,020XX N ORCHARD ST,0320,ROBBERY,STRONGARM - NO WEAPON,PARK PROPERTY,false,false,1812,018,43,7,03,1171321,1913600,2006,11/20/2006 06:27:20 AM,41.918387636,-87.645977893,"(41.918387636, -87.645977893)"
-5101234,HM703132,11/05/2006 05:52:25 PM,085XX S HOUSTON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,0424,004,10,46,08B,1197928,1848764,2006,11/11/2006 07:40:50 AM,41.739848727,-87.550394962,"(41.739848727, -87.550394962)"
-5100708,HM702574,11/05/2006 09:47:00 AM,018XX S ALLPORT ST,0810,THEFT,OVER $500,GROCERY FOOD STORE,false,false,1222,012,25,31,06,1168295,1890948,2006,12/04/2014 12:43:35 PM,41.856294954,-87.657750802,"(41.856294954, -87.657750802)"
-5106152,HM706731,11/03/2006 08:00:00 PM,018XX W ERIE ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1324,012,1,24,06,1163978,1904417,2006,12/04/2014 12:43:35 PM,41.893347162,-87.673216169,"(41.893347162, -87.673216169)"
-5100054,HM699809,11/03/2006 07:20:00 PM,087XX S MICHIGAN AVE,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,ALLEY,false,false,0632,006,6,44,04A,1178716,1847148,2006,11/08/2006 05:36:51 AM,41.735872306,-87.620833239,"(41.735872306, -87.620833239)"
-5097844,HM699485,11/03/2006 02:00:00 PM,034XX S HALSTED ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0924,009,11,60,06,1171483,1882406,2006,12/04/2014 12:43:35 PM,41.83278555,-87.646300086,"(41.83278555, -87.646300086)"
-5191698,HM698575,11/03/2006 08:50:00 AM,005XX E 36TH ST,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,CHA APARTMENT,true,false,0212,002,4,35,18,1180734,1881036,2006,05/29/2007 01:47:17 AM,41.828818126,-87.612399159,"(41.828818126, -87.612399159)"
-5104614,HM701201,11/02/2006 05:00:00 AM,056XX S PRAIRIE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0234,002,20,40,14,1179041,1867700,2006,11/13/2006 05:58:15 AM,41.792261823,-87.619017294,"(41.792261823, -87.619017294)"
-5173686,HM695041,11/01/2006 11:39:11 AM,002XX N LEAMINGTON AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1532,015,28,25,18,1142044,1901046,2006,05/22/2007 02:05:56 AM,41.884531546,-87.753856202,"(41.884531546, -87.753856202)"
-5271512,HM693932,10/31/2006 08:10:46 PM,045XX S HONORE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0914,009,20,61,18,1164732,1874779,2006,02/24/2007 07:01:13 AM,41.812001718,-87.671286231,"(41.812001718, -87.671286231)"
-5085896,HM689735,10/29/2006 08:32:20 PM,029XX E 91ST ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0423,004,10,46,14,1197007,1845222,2006,11/02/2006 05:58:34 AM,41.730152136,-87.553886827,"(41.730152136, -87.553886827)"
-5084772,HM690210,10/29/2006 12:00:00 AM,030XX N LINCOLN AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,false,1931,019,32,6,06,1166130,1920267,2006,11/01/2006 04:25:06 AM,41.936794841,-87.664858959,"(41.936794841, -87.664858959)"
-5268351,HM686456,10/28/2006 12:05:00 AM,063XX S RHODES AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,0312,003,20,42,16,1180946,1863291,2006,03/17/2007 06:01:53 AM,41.780119469,-87.612167622,"(41.780119469, -87.612167622)"
-5090332,HM684278,10/26/2006 11:10:10 PM,045XX S LAMON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0814,008,23,56,08B,1144409,1874287,2006,12/06/2006 05:02:18 AM,41.811057043,-87.745843719,"(41.811057043, -87.745843719)"
-5266164,HM684140,10/26/2006 09:45:00 PM,061XX S RICHMOND ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0823,008,15,66,18,1157803,1863947,2006,06/17/2007 03:25:09 AM,41.782420908,-87.696995879,"(41.782420908, -87.696995879)"
-5267490,HM683344,10/26/2006 02:27:00 PM,071XX S VERNON AVE,1822,NARCOTICS,MANU/DEL:CANNABIS OVER 10 GMS,STREET,true,false,0323,003,6,69,18,1180435,1857958,2006,06/17/2007 03:25:09 AM,41.765496925,-87.614204492,"(41.765496925, -87.614204492)"
-5078569,HM683137,10/26/2006 01:03:01 PM,011XX N CENTRAL AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1524,015,37,25,05,1138835,1907256,2006,10/31/2006 04:43:16 AM,41.901631417,-87.765489337,"(41.901631417, -87.765489337)"
-5078885,HM681905,10/25/2006 01:00:00 PM,042XX S WESTERN AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,SIDEWALK,false,false,0914,009,12,58,04A,1161034,1876454,2006,11/15/2006 04:46:56 AM,41.816675497,-87.684804083,"(41.816675497, -87.684804083)"
-5274165,HM680969,10/25/2006 09:00:54 AM,085XX S MUSKEGON AVE,2027,NARCOTICS,POSS: CRACK,RESIDENCE,true,false,0423,004,10,46,18,1196677,1848803,2006,06/13/2007 03:40:31 AM,41.739986886,-87.554977055,"(41.739986886, -87.554977055)"
-5074058,HM680471,10/25/2006 01:37:42 AM,081XX S BURNHAM AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,0422,004,7,46,04B,1196203,1851734,2006,06/11/2007 03:52:33 PM,41.748041532,-87.55661676,"(41.748041532, -87.55661676)"
-5073543,HM679386,10/24/2006 09:40:00 AM,025XX N AUSTIN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2512,025,29,19,26,1135926,1916314,2006,10/28/2006 04:53:45 AM,41.926539992,-87.775958382,"(41.926539992, -87.775958382)"
-5076900,HM678802,10/24/2006 12:30:00 AM,003XX W 51ST ST,0865,THEFT,DELIVERY CONTAINER THEFT,OTHER RAILROAD PROP / TRAIN DEPOT,false,false,0935,009,3,37,06,1174794,1871176,2006,11/17/2006 04:48:43 AM,41.801896096,-87.634486673,"(41.801896096, -87.634486673)"
-5071880,HM677449,10/23/2006 03:10:00 PM,077XX S WOOD ST,0560,ASSAULT,SIMPLE,STREET,false,false,0611,006,17,71,08A,1165647,1853558,2006,11/26/2006 06:54:40 AM,41.753749203,-87.668531838,"(41.753749203, -87.668531838)"
-5262160,HM674352,10/21/2006 07:06:00 PM,049XX W THOMAS ST,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1531,015,37,25,18,1143245,1906826,2006,03/17/2007 06:01:53 AM,41.900370203,-87.74930144,"(41.900370203, -87.74930144)"
-5122840,HM720270,10/21/2006 09:00:00 AM,030XX N HARLEM AVE,1110,DECEPTIVE PRACTICE,BOGUS CHECK,BANK,false,false,2511,025,36,17,11,1127507,1919637,2006,11/24/2006 05:10:29 AM,41.935804726,-87.806820017,"(41.935804726, -87.806820017)"
-5078825,HM675714,10/20/2006 07:30:00 PM,076XX S LANGLEY AVE,0925,MOTOR VEHICLE THEFT,"ATT: TRUCK, BUS, MOTOR HOME",STREET,false,false,0624,006,6,69,07,1182261,1854359,2006,10/29/2006 05:43:02 AM,41.755578813,-87.607622972,"(41.755578813, -87.607622972)"
-5064995,HM671271,10/19/2006 11:00:00 PM,036XX W 55TH ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,0822,008,23,62,26,1152854,1867925,2006,05/29/2007 01:47:17 AM,41.793436206,-87.715035624,"(41.793436206, -87.715035624)"
-5064700,HM669496,10/19/2006 01:20:00 PM,030XX S DR MARTIN LUTHER KING JR DR,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",true,false,2112,001,2,35,08B,1179263,1885133,2006,10/22/2006 06:20:26 AM,41.840094342,-87.617670874,"(41.840094342, -87.617670874)"
-5325602,HM668643,10/18/2006 11:00:00 PM,018XX N CICERO AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,2533,025,37,25,16,1144075,1911684,2006,04/21/2007 05:09:41 AM,41.913685537,-87.746130628,"(41.913685537, -87.746130628)"
-5061251,HM668531,10/18/2006 10:00:00 PM,023XX S STATE ST,0560,ASSAULT,SIMPLE,CHA APARTMENT,false,false,0134,001,3,33,08A,1176644,1888767,2006,08/31/2010 03:21:15 PM,41.850125786,-87.627171772,"(41.850125786, -87.627171772)"
-5065620,HM671956,10/17/2006 02:00:00 PM,076XX S WESTERN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,COMMERCIAL / BUSINESS OFFICE,false,false,0835,008,18,70,26,1161657,1854128,2006,08/31/2010 03:21:15 PM,41.755397042,-87.683138077,"(41.755397042, -87.683138077)"
-5056133,HM663220,10/16/2006 11:00:00 AM,067XX S ARTESIAN AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0832,008,15,66,05,1161251,1859581,2006,08/31/2010 03:21:15 PM,41.770369279,-87.68447523,"(41.770369279, -87.68447523)"
-5053453,HM661977,10/15/2006 03:30:00 PM,022XX W 95TH ST,0890,THEFT,FROM BUILDING,SMALL RETAIL STORE,false,false,2213,022,19,72,06,1162781,1841554,2006,10/17/2006 05:43:21 AM,41.720868657,-87.679369043,"(41.720868657, -87.679369043)"
-5064943,HM661670,10/15/2006 11:30:00 AM,076XX S EGGLESTON AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,STREET,true,true,0621,006,17,69,26,1174579,1854210,2006,10/23/2006 03:43:52 AM,41.755344311,-87.63577988,"(41.755344311, -87.63577988)"
-5052703,HM660494,10/14/2006 09:45:00 PM,016XX W MONTEREY AVE,0326,ROBBERY,AGGRAVATED VEHICULAR HIJACKING,PARKING LOT/GARAGE(NON.RESID.),false,false,2234,022,34,75,03,1167410,1830187,2006,11/02/2006 05:58:34 AM,41.689578032,-87.662738199,"(41.689578032, -87.662738199)"
-5058390,HM666593,10/13/2006 09:00:00 PM,064XX N MAGNOLIA AVE,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,RESIDENCE,false,false,2432,024,40,1,11,1166720,1943049,2006,11/11/2006 07:40:50 AM,41.999296804,-87.662034619,"(41.999296804, -87.662034619)"
-5119250,HM718861,10/13/2006 10:30:00 AM,033XX N AVERS AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1732,017,30,21,05,1150090,1922134,2006,11/20/2006 06:27:20 AM,41.942246118,-87.723759555,"(41.942246118, -87.723759555)"
-5052293,HM658496,10/13/2006 07:00:00 AM,037XX N LAWNDALE AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,1732,017,39,16,05,1151014,1924831,2006,10/21/2006 04:17:24 AM,41.949628812,-87.720292522,"(41.949628812, -87.720292522)"
-5059308,HM656490,10/12/2006 08:10:00 PM,038XX S VERNON AVE,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,true,false,0212,002,4,35,26,1179866,1879394,2006,10/20/2006 06:42:46 AM,41.824332298,-87.615634088,"(41.824332298, -87.615634088)"
-5258285,HM655851,10/12/2006 12:20:08 PM,062XX S ARTESIAN AVE,2094,NARCOTICS,ATTEMPT POSSESSION CANNABIS,STREET,true,false,0825,008,15,66,18,1161074,1863043,2006,04/07/2007 05:43:19 AM,41.779873155,-87.685028378,"(41.779873155, -87.685028378)"
-5253284,HM654939,10/11/2006 10:49:48 PM,054XX S NARRAGANSETT AVE,2022,NARCOTICS,POSS: COCAINE,STREET,true,false,0811,008,23,56,18,1134673,1867910,2006,03/17/2007 06:01:53 AM,41.793734525,-87.781705439,"(41.793734525, -87.781705439)"
-5046751,HM653465,10/11/2006 09:45:00 AM,007XX W 31ST ST,1330,CRIMINAL TRESPASS,TO LAND,SMALL RETAIL STORE,true,false,0924,009,11,60,26,1171507,1884283,2006,10/14/2006 06:13:36 AM,41.83793568,-87.64615693,"(41.83793568, -87.64615693)"
-5041487,HM649052,10/09/2006 01:11:00 AM,067XX S PULASKI RD,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,0833,008,13,65,14,1150830,1859315,2006,10/12/2006 05:28:35 AM,41.769848723,-87.722681759,"(41.769848723, -87.722681759)"
-5041017,HM648701,10/08/2006 09:30:00 PM,023XX S TROY ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,false,1033,010,24,30,08B,1155766,1888305,2006,10/12/2006 05:28:35 AM,41.84930357,-87.70380993,"(41.84930357, -87.70380993)"
-5039808,HM648506,10/08/2006 06:30:00 PM,100XX S PEORIA ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,2232,022,34,73,14,1172044,1838263,2006,10/17/2006 05:43:21 AM,41.711639572,-87.645536995,"(41.711639572, -87.645536995)"
-5044347,HM646305,10/07/2006 03:31:28 PM,003XX E PERSHING RD,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,0213,002,3,38,06,1179295,1879163,2006,11/27/2006 06:32:11 AM,41.823711486,-87.617735941,"(41.823711486, -87.617735941)"
-5037172,HM645404,10/07/2006 05:10:00 AM,011XX S JEFFERSON ST,0820,THEFT,$500 AND UNDER,GAS STATION,false,false,0131,001,2,28,06,1172528,1895381,2006,12/04/2014 12:43:35 PM,41.868366989,-87.642082601,"(41.868366989, -87.642082601)"
-5224579,HM644514,10/06/2006 06:11:00 PM,063XX N CLARK ST,2021,NARCOTICS,POSS: BARBITUATES,STREET,true,false,2433,024,40,77,18,1164501,1942283,2006,06/05/2007 01:50:16 AM,41.997242329,-87.670219509,"(41.997242329, -87.670219509)"
-5035387,HM641523,10/05/2006 10:30:00 AM,059XX S LA SALLE ST,0810,THEFT,OVER $500,RESIDENCE,false,false,0233,002,20,68,06,1176358,1865603,2006,12/04/2014 12:43:35 PM,41.786568195,-87.628918374,"(41.786568195, -87.628918374)"
-5034632,HM641142,10/05/2006 05:28:00 AM,020XX E 75TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,false,0414,004,8,43,08B,1191314,1855606,2006,10/12/2006 05:28:35 AM,41.758786339,-87.574406154,"(41.758786339, -87.574406154)"
-5033245,HM640986,10/04/2006 11:15:00 PM,043XX W MADISON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1115,011,28,26,08B,1147658,1899705,2006,10/17/2006 05:43:21 AM,41.880745813,-87.733275051,"(41.880745813, -87.733275051)"
-5352562,HM639919,10/04/2006 01:46:08 PM,029XX E 92ND ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,0423,004,7,46,06,1197291,1844562,2006,03/08/2007 09:51:06 PM,41.728333979,-87.552868393,"(41.728333979, -87.552868393)"
-5030362,HM638241,10/03/2006 05:00:00 PM,057XX W BELDEN AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,2515,025,37,19,03,1137971,1914755,2006,11/03/2006 06:27:43 AM,41.922225199,-87.768481556,"(41.922225199, -87.768481556)"
-5030867,HM637435,10/03/2006 12:00:00 AM,042XX S CALIFORNIA AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0912,009,12,58,05,1158442,1876399,2006,10/16/2006 05:23:36 AM,41.816577858,-87.694313719,"(41.816577858, -87.694313719)"
-5026385,HM634387,10/01/2006 06:05:00 PM,032XX W WALNUT ST,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,RESIDENCE-GARAGE,true,false,1123,011,28,27,04A,1154820,1901407,2006,10/06/2006 04:52:45 AM,41.885275847,-87.706930998,"(41.885275847, -87.706930998)"
-5024566,HM634406,10/01/2006 06:00:00 AM,021XX W 18TH PL,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,1223,012,25,31,07,1162560,1891016,2006,10/06/2006 04:52:45 AM,41.856603447,-87.678799284,"(41.856603447, -87.678799284)"
-5336698,HM631655,09/30/2006 08:59:05 AM,096XX S BALTIMORE AVE,0810,THEFT,OVER $500,APARTMENT,false,false,0431,004,10,51,06,1198529,1841623,2006,12/04/2014 12:43:35 PM,41.720238229,-87.548431615,"(41.720238229, -87.548431615)"
-5023498,HM632841,09/30/2006 12:00:00 AM,018XX N ALBANY AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1421,014,26,22,26,1155252,1912199,2006,10/06/2006 04:52:45 AM,41.914881413,-87.70505438,"(41.914881413, -87.70505438)"
-5218932,HM630165,09/29/2006 02:30:00 PM,052XX S WOOD ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0932,009,16,61,18,1165189,1870144,2006,02/24/2007 07:01:13 AM,41.799273062,-87.669741228,"(41.799273062, -87.669741228)"
-5048339,HM655824,09/29/2006 12:00:00 PM,077XX S CLAREMONT AVE,1110,DECEPTIVE PRACTICE,BOGUS CHECK,SMALL RETAIL STORE,false,false,0835,008,18,70,11,1162039,1852930,2006,01/08/2007 09:05:18 AM,41.752101621,-87.681771386,"(41.752101621, -87.681771386)"
-5234139,HM626998,09/27/2006 08:02:00 PM,001XX N FRANKLIN ST,2210,LIQUOR LAW VIOLATION,SELL/GIVE/DEL LIQUOR TO MINOR,CONVENIENCE STORE,true,false,0111,001,42,32,22,1174257,1901580,2006,03/20/2007 05:41:42 AM,41.885339058,-87.63555012,"(41.885339058, -87.63555012)"
-5023108,HM626291,09/27/2006 02:20:00 PM,007XX N MICHIGAN AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,1834,018,42,8,06,1177338,1905320,2006,10/03/2006 05:10:58 AM,41.895532503,-87.624122746,"(41.895532503, -87.624122746)"
-5197593,HM625128,09/26/2006 10:20:00 PM,079XX S DR MARTIN LUTHER KING JR DR,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0623,006,6,44,18,1180234,1852648,2006,05/22/2007 02:05:56 AM,41.75093032,-87.615103708,"(41.75093032, -87.615103708)"
-5015893,HM623252,09/26/2006 04:20:00 AM,028XX W BELLE PLAINE AVE,0917,MOTOR VEHICLE THEFT,"CYCLE, SCOOTER, BIKE W-VIN",STREET,false,false,1724,017,33,16,07,1156405,1927149,2006,09/30/2006 05:45:54 AM,41.955882029,-87.7004129,"(41.955882029, -87.7004129)"
-5012884,HM622766,09/24/2006 07:55:00 PM,026XX W 61ST ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,0825,008,15,66,08B,1160028,1864153,2006,10/01/2006 07:25:21 AM,41.782940726,-87.688832681,"(41.782940726, -87.688832681)"
-5009739,HM618402,09/23/2006 01:00:00 PM,0000X E CHICAGO AVE,0560,ASSAULT,SIMPLE,STREET,true,false,1833,018,42,8,08A,1176470,1905774,2006,09/26/2006 04:49:47 AM,41.896797946,-87.627296947,"(41.896797946, -87.627296947)"
-5009515,HM617140,09/22/2006 06:30:00 PM,008XX N KILDARE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1111,011,37,23,14,1147511,1905472,2006,10/10/2006 11:20:40 AM,41.896573921,-87.733666841,"(41.896573921, -87.733666841)"
-5010804,HM621370,09/22/2006 06:00:00 AM,038XX S IRON ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0922,009,11,59,06,1167858,1879153,2006,12/04/2014 12:43:35 PM,41.823937779,-87.659694366,"(41.823937779, -87.659694366)"
-5007013,HM616520,09/22/2006 05:45:00 AM,002XX S MICHIGAN AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0123,001,42,32,06,1177288,1899300,2006,12/04/2014 12:43:35 PM,41.879014445,-87.624489022,"(41.879014445, -87.624489022)"
-5007976,HM616328,09/22/2006 01:00:00 AM,041XX W 28TH ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,1031,010,22,30,07,1148871,1885090,2006,10/10/2006 11:20:40 AM,41.840617107,-87.729198597,"(41.840617107, -87.729198597)"
-5026994,HM635947,09/20/2006 11:26:00 AM,111XX S MICHIGAN AVE,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,ATM (AUTOMATIC TELLER MACHINE),false,false,0531,005,9,49,11,1178836,1830869,2006,10/20/2006 06:42:46 AM,41.691197839,-87.620887093,"(41.691197839, -87.620887093)"
-5012825,HM622349,09/19/2006 12:00:00 PM,026XX W SUPERIOR ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1313,012,26,24,06,1158843,1904937,2006,12/04/2014 12:43:35 PM,41.894880948,-87.692060913,"(41.894880948, -87.692060913)"
-5174897,HM609806,09/19/2006 02:00:00 AM,045XX N RAVENSWOOD AVE,2170,NARCOTICS,POSSESSION OF DRUG EQUIPMENT,STREET,true,false,1922,019,47,4,18,1163363,1930259,2006,05/22/2007 02:05:56 AM,41.964272148,-87.674745843,"(41.964272148, -87.674745843)"
-5174325,HM609583,09/18/2006 10:05:50 PM,019XX N HARDING AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,2535,025,30,20,16,1149689,1912893,2006,05/22/2007 02:05:56 AM,41.916895823,-87.725474214,"(41.916895823, -87.725474214)"
-4995625,HM606761,09/17/2006 12:00:00 AM,061XX S EBERHART AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0313,003,20,42,14,1180680,1864244,2006,09/20/2006 04:13:37 AM,41.78274071,-87.613113545,"(41.78274071, -87.613113545)"
-5001215,HM604755,09/16/2006 11:18:00 AM,010XX N SPRINGFIELD AVE,0497,BATTERY,AGGRAVATED DOMESTIC BATTERY: OTHER DANG WEAPON,RESIDENCE,false,true,1112,011,27,23,04B,1150224,1906990,2006,10/12/2006 05:28:35 AM,41.900687,-87.723662765,"(41.900687, -87.723662765)"
-4994136,HM602712,09/15/2006 12:50:00 PM,038XX W NORTH AVE,1330,CRIMINAL TRESPASS,TO LAND,SMALL RETAIL STORE,true,false,2535,025,30,23,26,1150266,1910314,2006,09/18/2006 04:34:02 AM,41.909807562,-87.723421686,"(41.909807562, -87.723421686)"
-4991695,HM602020,09/15/2006 01:10:00 AM,079XX S WINCHESTER AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,0611,006,18,71,08A,1164700,1851950,2006,05/13/2009 01:04:22 AM,41.749356647,-87.672047564,"(41.749356647, -87.672047564)"
-5103768,HM600770,09/14/2006 01:55:00 PM,070XX S EGGLESTON AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0732,007,6,68,18,1174538,1858172,2006,11/18/2006 05:07:44 AM,41.766217424,-87.6358124,"(41.766217424, -87.6358124)"
-4987512,HM597453,09/12/2006 07:02:44 PM,050XX W MADISON ST,1330,CRIMINAL TRESPASS,TO LAND,RESTAURANT,true,false,1533,015,28,25,26,1142881,1899515,2006,09/16/2006 04:37:42 AM,41.880314743,-87.750820746,"(41.880314743, -87.750820746)"
-5147789,HM597066,09/12/2006 11:13:19 AM,080XX S EBERHART AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0631,006,6,44,18,1181019,1851472,2006,05/29/2007 01:47:17 AM,41.747685228,-87.612263224,"(41.747685228, -87.612263224)"
-4982954,HM594578,09/11/2006 12:15:00 PM,024XX S STATE ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,true,false,0134,001,3,33,26,1176654,1888386,2006,09/14/2006 04:31:21 AM,41.849080069,-87.62714657,"(41.849080069, -87.62714657)"
-5148191,HM593317,09/10/2006 04:40:00 PM,119XX S PERRY AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0522,005,9,53,18,1177683,1825981,2006,01/23/2007 06:13:38 AM,41.677810527,-87.625255393,"(41.677810527, -87.625255393)"
-4979649,HM593029,09/10/2006 01:10:00 PM,038XX W GEORGE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,2523,025,30,21,18,1150523,1918973,2006,06/11/2007 03:52:33 PM,41.933563615,-87.722250892,"(41.933563615, -87.722250892)"
-5162252,HM589983,09/08/2006 08:25:00 PM,064XX S DR MARTIN LUTHER KING JR DR,2017,NARCOTICS,MANU/DELIVER:CRACK,SIDEWALK,true,false,0312,003,20,69,18,1179983,1862561,2006,05/15/2007 06:10:18 AM,41.778138385,-87.615720429,"(41.778138385, -87.615720429)"
-4984869,HM587739,09/07/2006 07:15:00 AM,019XX W LARCHMONT AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1923,019,47,5,05,1162724,1926195,2006,10/12/2006 05:28:35 AM,41.953133792,-87.677209666,"(41.953133792, -87.677209666)"
-4972805,HM586416,09/07/2006 04:00:00 AM,056XX S MC VICKER AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0811,008,23,56,06,1137049,1866434,2006,12/04/2014 12:43:35 PM,41.789641952,-87.773027898,"(41.789641952, -87.773027898)"
-4972886,HM586132,09/06/2006 11:45:00 PM,029XX W WARREN BLVD,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1331,012,2,27,08B,1156753,1900171,2006,09/27/2006 04:44:36 AM,41.881845206,-87.699866157,"(41.881845206, -87.699866157)"
-5171088,HM585943,09/06/2006 09:46:33 PM,031XX N MONTICELLO AVE,2093,NARCOTICS,FOUND SUSPECT NARCOTICS,STREET,true,false,2523,025,35,21,26,1151482,1920562,2006,05/22/2007 02:05:56 AM,41.937905149,-87.718684728,"(41.937905149, -87.718684728)"
-5160228,HM583864,09/05/2006 09:09:30 PM,006XX W DIVISION ST,2027,NARCOTICS,POSS: CRACK,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,1822,018,27,8,18,1171482,1908281,2006,05/15/2007 06:10:18 AM,41.903788476,-87.645543079,"(41.903788476, -87.645543079)"
-4969802,HM582846,09/05/2006 01:00:00 PM,079XX S ASHLAND AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0611,006,21,71,08B,1167009,1852265,2006,09/12/2006 04:52:48 AM,41.750172042,-87.66357746,"(41.750172042, -87.66357746)"
-4967769,HM580881,09/04/2006 12:06:22 PM,023XX S STATE ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,0134,001,3,33,26,1176644,1888767,2006,09/13/2006 04:50:05 AM,41.850125786,-87.627171772,"(41.850125786, -87.627171772)"
-4969084,HM580769,09/04/2006 10:50:00 AM,027XX W 25TH ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,1034,010,12,30,26,1158598,1887335,2006,09/09/2006 04:19:56 AM,41.846584344,-87.693442651,"(41.846584344, -87.693442651)"
-4965340,HM576838,09/02/2006 05:06:06 AM,0000X W 69TH ST,0460,BATTERY,SIMPLE,CTA PLATFORM,true,false,0731,007,6,69,08B,1177312,1859236,2006,09/09/2006 04:19:56 AM,41.769074977,-87.62561266,"(41.769074977, -87.62561266)"
-4968561,HM576509,09/02/2006 12:20:00 AM,111XX S ASHLAND AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,2234,022,34,75,14,1167273,1830397,2006,05/18/2007 06:17:05 PM,41.690157233,-87.663233777,"(41.690157233, -87.663233777)"
-5159427,HM576021,09/01/2006 06:30:00 PM,041XX S WENTWORTH AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0935,009,3,37,18,1175586,1877131,2006,05/15/2007 06:10:18 AM,41.818219464,-87.631403736,"(41.818219464, -87.631403736)"
-4963505,HM575803,09/01/2006 07:00:00 AM,054XX S NORDICA AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE,false,false,0811,008,23,56,14,1130342,1867463,2006,09/18/2006 04:34:02 AM,41.792583037,-87.797597522,"(41.792583037, -87.797597522)"
-4961558,HM574629,09/01/2006 01:30:00 AM,021XX N LAMON AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2522,025,31,19,26,1143328,1914109,2006,09/12/2006 04:52:48 AM,41.920353993,-87.748814273,"(41.920353993, -87.748814273)"
-4959805,HM572188,08/30/2006 08:30:00 PM,019XX W MONTEREY AVE,4510,OTHER OFFENSE,PROBATION VIOLATION,POLICE FACILITY/VEH PARKING LOT,true,false,2212,022,19,75,26,1165808,1830858,2006,09/02/2006 04:14:01 AM,41.691453444,-87.668584188,"(41.691453444, -87.668584188)"
-4959036,HM572112,08/30/2006 07:40:00 PM,012XX N SPRINGFIELD AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,2535,025,27,23,18,1150112,1908067,2006,06/11/2007 03:52:33 PM,41.903644579,-87.724046055,"(41.903644579, -87.724046055)"
-4977031,HM571652,08/30/2006 03:35:00 PM,085XX S COTTAGE GROVE AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,false,false,0632,006,6,44,06,1183014,1848447,2006,09/11/2006 04:23:45 AM,41.739338188,-87.605046822,"(41.739338188, -87.605046822)"
-4959838,HM571126,08/30/2006 11:12:00 AM,107XX S HALSTED ST,0460,BATTERY,SIMPLE,GROCERY FOOD STORE,true,false,2233,022,34,75,08B,1172840,1833829,2006,09/02/2006 04:14:01 AM,41.699454527,-87.642752065,"(41.699454527, -87.642752065)"
-4964231,HM575212,08/29/2006 11:30:00 PM,022XX E 71ST ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0331,003,5,43,06,1192410,1858392,2006,12/04/2014 12:43:35 PM,41.766404738,-87.570298886,"(41.766404738, -87.570298886)"
-4971760,HM584932,08/28/2006 09:00:00 AM,103XX S DR MARTIN LUTHER KING JR DR,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,RESIDENCE,false,false,0512,005,9,49,11,1180782,1836344,2006,09/12/2006 04:52:48 AM,41.706177595,-87.613595152,"(41.706177595, -87.613595152)"
-4962793,HM566413,08/28/2006 02:00:00 AM,0000X N HAMLIN BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1122,011,28,26,08B,1151025,1899824,2006,09/12/2006 04:52:48 AM,41.881007096,-87.720908487,"(41.881007096, -87.720908487)"
-4950226,HM564388,08/26/2006 07:00:00 PM,025XX W DEVON AVE,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,2412,024,50,2,06,1158456,1942438,2006,12/04/2014 12:43:35 PM,41.997793958,-87.692452435,"(41.997793958, -87.692452435)"
-4965020,HM576678,08/26/2006 05:00:00 AM,127XX S HALSTED ST,0810,THEFT,OVER $500,SIDEWALK,false,false,0523,005,34,53,06,1173359,1820326,2006,12/04/2014 12:43:35 PM,41.662388692,-87.641248875,"(41.662388692, -87.641248875)"
-5125270,HM562869,08/26/2006 03:33:00 AM,034XX W 26TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1032,010,22,30,18,1153818,1886476,2006,02/10/2007 06:09:10 AM,41.84432354,-87.711008002,"(41.84432354, -87.711008002)"
-4950544,HM563788,08/25/2006 09:00:00 PM,056XX S FRANCISCO AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,0824,008,16,63,07,1158048,1867018,2006,11/22/2006 05:48:53 AM,41.790843184,-87.696014174,"(41.790843184, -87.696014174)"
-4947130,HM560656,08/24/2006 10:30:00 PM,101XX S MORGAN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2232,022,34,73,08B,1171475,1837703,2006,08/31/2006 05:15:08 AM,41.710115302,-87.647637148,"(41.710115302, -87.647637148)"
-4944200,HM556039,08/22/2006 01:00:00 PM,085XX S KILBOURN AVE,1790,OFFENSE INVOLVING CHILDREN,CHILD ABDUCTION,RESIDENCE,false,true,0834,008,18,70,20,1147963,1847295,2006,06/02/2010 10:34:17 AM,41.736919108,-87.73349828,"(41.736919108, -87.73349828)"
-4939886,HM553500,08/21/2006 02:30:00 PM,001XX W CERMAK RD,0454,BATTERY,AGG PO HANDS NO/MIN INJURY,SIDEWALK,true,false,2111,009,25,34,08B,1175439,1889713,2006,08/27/2006 04:05:44 AM,41.852748786,-87.6315659,"(41.852748786, -87.6315659)"
-4950002,HM561343,08/20/2006 11:00:00 PM,131XX S CORLISS AVE,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,CHA APARTMENT,false,false,0533,005,9,54,11,1184051,1818196,2006,08/31/2006 05:15:08 AM,41.656301396,-87.602188339,"(41.656301396, -87.602188339)"
-4935961,HM551529,08/19/2006 09:00:00 PM,086XX S BENNETT AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0412,004,8,45,05,1190393,1848080,2006,09/11/2006 04:23:45 AM,41.738156565,-87.578023625,"(41.738156565, -87.578023625)"
-4936974,HM550167,08/19/2006 02:30:00 AM,095XX S GREEN ST,1320,CRIMINAL DAMAGE,TO VEHICLE,RESIDENCE,false,false,2223,022,21,73,14,1172295,1841209,2006,08/24/2006 04:55:25 AM,41.719718321,-87.644531457,"(41.719718321, -87.644531457)"
-5102050,HM547804,08/18/2006 03:19:11 PM,039XX W JACKSON BLVD,2017,NARCOTICS,MANU/DELIVER:CRACK,SIDEWALK,true,false,1122,011,28,26,18,1149855,1898428,2006,01/13/2007 05:03:08 AM,41.877199146,-87.725241001,"(41.877199146, -87.725241001)"
-4932814,HM547411,08/17/2006 05:00:00 PM,019XX W NORWOOD ST,0810,THEFT,OVER $500,STREET,false,false,2413,024,40,2,06,1162389,1940365,2006,12/04/2014 12:43:35 PM,41.992023894,-87.678042707,"(41.992023894, -87.678042707)"
-4941836,HM551784,08/17/2006 02:30:00 PM,034XX W 84TH ST,0810,THEFT,OVER $500,RESIDENCE-GARAGE,false,false,0834,008,18,70,06,1154692,1848709,2006,12/04/2014 12:43:35 PM,41.740668021,-87.708807398,"(41.740668021, -87.708807398)"
-5096332,HM542313,08/15/2006 06:18:03 PM,019XX S TROY ST,1661,GAMBLING,GAME/DICE,STREET,true,false,1022,010,24,29,19,1155625,1890286,2006,01/13/2007 05:03:08 AM,41.854742494,-87.704274157,"(41.854742494, -87.704274157)"
-4928771,HM542131,08/15/2006 02:45:00 PM,058XX S WOOD ST,0460,BATTERY,SIMPLE,PARK PROPERTY,false,false,0715,007,15,67,08B,1165393,1865758,2006,08/26/2006 05:22:07 AM,41.787233016,-87.669117382,"(41.787233016, -87.669117382)"
-4926374,HM540892,08/15/2006 12:30:00 AM,002XX W MARQUETTE RD,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,true,0722,007,6,68,14,1175474,1860573,2006,06/01/2007 02:14:39 AM,41.772785159,-87.632309909,"(41.772785159, -87.632309909)"
-4925396,HM541348,08/14/2006 10:00:00 PM,036XX N MAGNOLIA AVE,0810,THEFT,OVER $500,STREET,false,false,1923,019,44,6,06,1167273,1924180,2006,12/04/2014 12:43:35 PM,41.947507762,-87.660545431,"(41.947507762, -87.660545431)"
-4986390,HM598590,08/14/2006 01:50:00 PM,016XX E 71ST ST,1206,DECEPTIVE PRACTICE,"THEFT BY LESSEE,MOTOR VEH",OTHER,false,false,0332,003,5,43,11,1188678,1858311,2006,09/23/2006 04:42:43 AM,41.766272502,-87.58398041,"(41.766272502, -87.58398041)"
-4921756,HM538937,08/14/2006 02:30:00 AM,019XX W RACE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,false,true,1324,012,1,24,26,1163422,1903806,2006,08/22/2006 04:57:03 AM,41.89168225,-87.675275363,"(41.89168225, -87.675275363)"
-4922117,HM538003,08/13/2006 03:45:00 PM,026XX N NARRAGANSETT AVE,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,2512,025,36,19,06,1133254,1916911,2006,12/04/2014 12:43:35 PM,41.928225472,-87.785762931,"(41.928225472, -87.785762931)"
-4920082,HM535370,08/11/2006 07:30:00 PM,082XX S SOUTH SHORE DR,0820,THEFT,$500 AND UNDER,STREET,false,false,0424,004,7,46,06,1198538,1851061,2006,12/04/2014 12:43:35 PM,41.746136613,-87.548083265,"(41.746136613, -87.548083265)"
-5094552,HM534305,08/11/2006 06:02:29 PM,049XX S JUSTINE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0931,009,20,61,18,1166887,1871649,2006,12/02/2006 04:50:35 AM,41.803366816,-87.663471225,"(41.803366816, -87.663471225)"
-4924082,HM532898,08/11/2006 12:50:00 AM,068XX S RIDGELAND AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0332,003,5,43,26,1189089,1859840,2006,08/18/2006 04:56:43 AM,41.77045837,-87.582425041,"(41.77045837, -87.582425041)"
-5148599,HM531738,08/10/2006 01:00:00 PM,112XX S MICHIGAN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0531,005,9,49,18,1178839,1830689,2006,12/20/2006 06:24:03 AM,41.690703825,-87.62088156,"(41.690703825, -87.62088156)"
-4919317,HM531391,08/10/2006 05:00:00 AM,080XX S DR MARTIN LUTHER KING JR DR,0820,THEFT,$500 AND UNDER,STREET,false,false,0623,006,6,44,06,1180250,1851983,2006,12/04/2014 12:43:35 PM,41.749105119,-87.615065419,"(41.749105119, -87.615065419)"
-4914630,HM531052,08/10/2006 12:00:00 AM,013XX W ARGYLE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2033,020,48,3,14,1166321,1933360,2006,08/12/2006 04:03:08 AM,41.972718499,-87.663781069,"(41.972718499, -87.663781069)"
-4915431,HM530439,08/09/2006 07:25:00 PM,017XX W DIVISION ST,1570,SEX OFFENSE,PUBLIC INDECENCY,RESIDENCE,false,false,1322,012,1,24,17,1164750,1908010,2006,08/16/2006 04:03:39 AM,41.903190283,-87.670278913,"(41.903190283, -87.670278913)"
-4915908,HM530118,08/09/2006 05:00:00 PM,085XX S VINCENNES AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0622,006,21,71,14,1173071,1848113,2006,08/13/2006 04:49:32 AM,41.738646776,-87.641485946,"(41.738646776, -87.641485946)"
-4953340,HM528814,08/09/2006 12:19:00 AM,0000X N PULASKI RD,1330,CRIMINAL TRESPASS,TO LAND,GAS STATION,true,false,1122,011,28,26,26,1149764,1899891,2006,08/30/2006 04:14:32 AM,41.88121555,-87.725537088,"(41.88121555, -87.725537088)"
-4914309,HM527803,08/08/2006 03:20:00 PM,057XX S ADA ST,0820,THEFT,$500 AND UNDER,RESIDENCE PORCH/HALLWAY,false,true,0713,007,16,67,06,1168272,1866491,2006,12/04/2014 12:43:35 PM,41.789182937,-87.658540259,"(41.789182937, -87.658540259)"
-5063598,HM669693,08/08/2006 12:00:00 PM,038XX W 60TH PL,1120,DECEPTIVE PRACTICE,FORGERY,WAREHOUSE,false,false,0823,008,13,65,10,1151568,1864164,2006,10/25/2006 05:02:56 AM,41.783140734,-87.719849737,"(41.783140734, -87.719849737)"
-4919986,HM527213,08/08/2006 09:45:28 AM,126XX S YALE AVE,0820,THEFT,$500 AND UNDER,RESIDENCE,false,false,0523,005,9,53,06,1176960,1820928,2006,12/04/2014 12:43:35 PM,41.663960572,-87.628053177,"(41.663960572, -87.628053177)"
-4910053,HM526812,08/08/2006 02:42:51 AM,015XX W WASHBURNE AVE,041A,BATTERY,AGGRAVATED: HANDGUN,SIDEWALK,false,false,1231,012,2,28,04B,1166321,1894517,2006,11/15/2006 04:46:56 AM,41.866130995,-87.664894384,"(41.866130995, -87.664894384)"
-5089300,HM526500,08/07/2006 10:20:48 PM,050XX S ASHLAND AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0931,009,16,61,18,1166480,1871496,2006,11/21/2006 05:02:11 AM,41.802955661,-87.664968254,"(41.802955661, -87.664968254)"
-4908710,HM524149,08/06/2006 08:00:00 PM,056XX S ADA ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,true,0713,007,16,67,14,1168250,1867338,2006,05/25/2007 02:27:12 AM,41.791507679,-87.658596552,"(41.791507679, -87.658596552)"
-4907387,HM523702,08/06/2006 04:10:00 PM,023XX W LAWRENCE AVE,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,true,false,1911,019,47,4,26,1159753,1931780,2006,08/12/2006 04:03:08 AM,41.968521222,-87.687976647,"(41.968521222, -87.687976647)"
-5075798,HM521365,08/05/2006 12:10:00 PM,118XX S UNION AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,0524,005,34,53,18,1173742,1826268,2006,12/02/2006 04:50:35 AM,41.678686071,-87.639672273,"(41.678686071, -87.639672273)"
-5080750,HM520850,08/05/2006 04:06:45 AM,054XX S ALBANY AVE,2022,NARCOTICS,POSS: COCAINE,ALLEY,true,false,0911,009,14,63,18,1156590,1868627,2006,12/24/2006 06:39:17 AM,41.795288041,-87.701316987,"(41.795288041, -87.701316987)"
-4993211,HM603193,08/04/2006 04:39:00 PM,002XX W 87TH ST,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,OTHER,true,false,0622,006,21,44,11,1176365,1847246,2006,09/18/2006 04:34:02 AM,41.736194317,-87.629443454,"(41.736194317, -87.629443454)"
-5146628,HM744431,08/04/2006 01:00:00 PM,016XX S HAMLIN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,SIDEWALK,false,false,1014,010,24,29,14,1151278,1891710,2006,11/29/2006 06:25:08 AM,41.858736403,-87.720192295,"(41.858736403, -87.720192295)"
-4908864,HM519314,08/04/2006 12:57:00 PM,043XX S COTTAGE GROVE AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,0222,002,4,38,15,1182283,1876607,2006,08/12/2006 04:03:08 AM,41.816628819,-87.606853443,"(41.816628819, -87.606853443)"
-4905566,HM521396,08/03/2006 09:30:00 PM,013XX W PRATT BLVD,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,2432,024,49,1,05,1165980,1945257,2006,08/16/2006 04:03:39 AM,42.005371488,-87.664693432,"(42.005371488, -87.664693432)"
-5119780,HM516451,08/03/2006 10:27:10 AM,027XX S DEARBORN ST,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,CHA PARKING LOT/GROUNDS,false,false,2113,001,3,35,18,1176354,1886614,2006,12/07/2006 06:24:52 AM,41.844224335,-87.62830098,"(41.844224335, -87.62830098)"
-4903851,HM519093,08/03/2006 10:00:00 AM,077XX S EGGLESTON AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0621,006,17,69,26,1174592,1853718,2006,08/12/2006 04:03:08 AM,41.753993914,-87.635746858,"(41.753993914, -87.635746858)"
-4912234,HM517019,08/03/2006 01:00:00 AM,006XX W MARQUETTE RD,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0723,007,6,68,05,1172706,1860489,2006,08/27/2006 04:05:44 AM,41.772616119,-87.6424591,"(41.772616119, -87.6424591)"
-4946265,HM559701,08/03/2006 12:00:00 AM,089XX S ADA ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2222,022,21,73,06,1168850,1845638,2006,12/04/2014 12:43:35 PM,41.731947097,-87.657022058,"(41.731947097, -87.657022058)"
-5014044,HM515011,08/02/2006 02:17:19 PM,085XX S OGLESBY AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0412,004,8,46,18,1193276,1848988,2006,12/02/2006 04:50:35 AM,41.740578278,-87.567431585,"(41.740578278, -87.567431585)"
-4902843,HM514115,08/02/2006 01:14:04 AM,073XX S HERMITAGE AVE,031A,ROBBERY,ARMED: HANDGUN,PARK PROPERTY,false,false,0735,007,17,67,03,1165907,1856219,2006,08/20/2006 04:35:28 AM,41.761045839,-87.667503552,"(41.761045839, -87.667503552)"
-4901016,HM513926,08/01/2006 09:20:00 PM,077XX S WINCHESTER AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,false,false,0611,006,18,71,26,1164662,1853314,2006,08/05/2006 04:48:25 AM,41.753100461,-87.672148402,"(41.753100461, -87.672148402)"
-5092943,HM513867,08/01/2006 09:05:24 PM,007XX W 59TH ST,2017,NARCOTICS,MANU/DELIVER:CRACK,APARTMENT,true,false,0711,007,20,68,18,1172454,1865708,2006,01/27/2007 07:34:41 AM,41.786943204,-87.643229324,"(41.786943204, -87.643229324)"
-4899757,HM513793,08/01/2006 08:45:00 PM,021XX W CULLERTON ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1223,012,25,31,14,1162563,1890350,2006,08/05/2006 04:48:25 AM,41.854775815,-87.678806907,"(41.854775815, -87.678806907)"
-4896142,HM509780,07/31/2006 01:00:00 AM,045XX N KENNETH AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1722,017,45,16,14,1145604,1929800,2006,08/05/2006 04:48:25 AM,41.963368618,-87.740052711,"(41.963368618, -87.740052711)"
-5080492,HM509621,07/30/2006 09:38:16 PM,010XX W HOLLYWOOD AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,2022,020,48,77,16,1168044,1938069,2006,01/23/2007 06:13:38 AM,41.985602995,-87.657308632,"(41.985602995, -87.657308632)"
-5077801,HM508953,07/30/2006 03:28:20 PM,045XX W GLADYS AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1131,011,24,26,18,1146223,1897930,2006,12/24/2006 06:39:17 AM,41.875902414,-87.738589472,"(41.875902414, -87.738589472)"
-4892584,HM507685,07/29/2006 02:00:00 PM,001XX N ASHLAND AVE,0937,MOTOR VEHICLE THEFT,"THEFT/RECOVERY: CYCLE, SCOOTER, BIKE W-VIN",STREET,true,false,1333,012,27,28,07,1165688,1901005,2006,08/02/2006 04:53:53 AM,41.883948103,-87.667033276,"(41.883948103, -87.667033276)"
-4891305,HM504818,07/28/2006 09:30:00 AM,058XX N SHERIDAN RD,0890,THEFT,FROM BUILDING,RESIDENCE PORCH/HALLWAY,true,false,2022,020,48,77,06,1168618,1938903,2006,08/05/2006 04:48:25 AM,41.987879058,-87.655173219,"(41.987879058, -87.655173219)"
-4890951,HM505352,07/28/2006 07:00:00 AM,0000X E GRAND AVE,0890,THEFT,FROM BUILDING,PARKING LOT/GARAGE(NON.RESID.),false,false,1834,018,42,8,06,1176869,1903879,2006,08/02/2006 04:53:53 AM,41.891588953,-87.62588889,"(41.891588953, -87.62588889)"
-5074971,HM501457,07/26/2006 09:20:00 PM,042XX W MADISON ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1115,011,28,26,16,1148308,1899719,2006,12/24/2006 06:39:17 AM,41.880771734,-87.730887921,"(41.880771734, -87.730887921)"
-4887612,HM500884,07/26/2006 03:00:00 PM,007XX S STATE ST,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,0132,001,2,32,06,1176431,1897043,2006,12/04/2014 12:43:35 PM,41.872840483,-87.627703866,"(41.872840483, -87.627703866)"
-4897046,HM499381,07/25/2006 11:30:00 PM,058XX S BISHOP ST,051A,ASSAULT,AGGRAVATED: HANDGUN,SIDEWALK,true,false,0713,007,16,67,04A,1167616,1866249,2006,08/16/2006 04:03:39 AM,41.788532964,-87.660952535,"(41.788532964, -87.660952535)"
-4889939,HM497224,07/25/2006 01:20:00 AM,052XX W LE MOYNE ST,0460,BATTERY,SIMPLE,STREET,false,false,2532,025,37,25,08B,1141251,1909436,2006,08/05/2006 04:48:25 AM,41.90756936,-87.756561127,"(41.90756936, -87.756561127)"
-4882390,HM497233,07/25/2006 01:00:00 AM,033XX W OGDEN AVE,1360,CRIMINAL TRESPASS,TO VEHICLE,POLICE FACILITY/VEH PARKING LOT,true,false,1024,010,24,29,26,1154500,1890985,2006,07/27/2006 05:41:42 AM,41.856683172,-87.708384737,"(41.856683172, -87.708384737)"
-4883275,HM497254,07/24/2006 11:00:00 PM,057XX S NORDICA AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,0811,008,23,56,08B,1130411,1865475,2006,08/02/2006 04:53:53 AM,41.78712641,-87.797389945,"(41.78712641, -87.797389945)"
-4882198,HM496824,07/24/2006 08:34:05 PM,029XX W BRYN MAWR AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,ALLEY,false,false,2011,020,40,2,24,1155573,1937102,2006,07/27/2006 05:41:42 AM,41.983210446,-87.703202418,"(41.983210446, -87.703202418)"
-4879930,HM493593,07/23/2006 10:40:00 AM,113XX S EGGLESTON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2233,022,34,49,08B,1175357,1829813,2006,07/27/2006 05:41:42 AM,41.688378269,-87.633655387,"(41.688378269, -87.633655387)"
-4879680,HM494610,07/22/2006 08:00:00 AM,036XX W DIVERSEY AVE,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,2523,025,35,21,06,1151460,1918409,2006,12/04/2014 12:43:35 PM,41.931997574,-87.7188223,"(41.931997574, -87.7188223)"
-4881654,HM495542,07/21/2006 07:00:00 PM,079XX S KEDZIE AVE,0880,THEFT,PURSE-SNATCHING,SIDEWALK,false,false,0834,008,18,70,06,1156378,1852016,2006,08/02/2006 04:53:53 AM,41.749709229,-87.70254117,"(41.749709229, -87.70254117)"
-4880903,HM489871,07/21/2006 02:26:43 PM,080XX S HERMITAGE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,ALLEY,false,false,0611,006,21,71,14,1166123,1851157,2006,07/27/2006 05:41:42 AM,41.747150411,-87.666855617,"(41.747150411, -87.666855617)"
-4929773,HM486593,07/19/2006 08:00:00 PM,011XX N LAWNDALE AVE,0460,BATTERY,SIMPLE,STREET,false,false,1112,011,27,23,08B,1151468,1907145,2006,09/07/2006 04:29:35 AM,41.901087984,-87.719089369,"(41.901087984, -87.719089369)"
-4871064,HM484644,07/18/2006 11:40:00 PM,011XX E 47TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,2123,002,4,39,08B,1184969,1874122,2006,07/29/2006 04:46:09 AM,41.809747066,-87.597078786,"(41.809747066, -87.597078786)"
-4882963,HM487490,07/18/2006 10:00:00 PM,081XX S SOUTH SHORE DR,0820,THEFT,$500 AND UNDER,ALLEY,false,false,0422,004,7,46,06,1198607,1851506,2006,12/04/2014 12:43:35 PM,41.747355996,-87.547815554,"(41.747355996, -87.547815554)"
-5022089,HM483177,07/18/2006 12:28:00 PM,033XX W MAYPOLE AVE,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,SIDEWALK,true,false,1123,011,28,27,18,1154048,1900822,2006,10/24/2006 05:50:48 AM,41.883685977,-87.709781543,"(41.883685977, -87.709781543)"
-4916991,HM531503,07/18/2006 11:50:00 AM,053XX N MILWAUKEE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),false,false,1622,016,45,11,07,1137344,1935315,2006,08/13/2006 04:49:32 AM,41.978655194,-87.770289091,"(41.978655194, -87.770289091)"
-4877690,HM491495,07/16/2006 01:30:00 PM,006XX E 32ND ST,0810,THEFT,OVER $500,OTHER,false,false,2122,002,4,35,06,1180891,1883645,2006,12/04/2014 12:43:35 PM,41.83597379,-87.611742735,"(41.83597379, -87.611742735)"
-4864295,HM477987,07/15/2006 08:40:00 PM,048XX N MARINE DR,0460,BATTERY,SIMPLE,SIDEWALK,false,false,2024,020,48,3,08B,1170055,1932458,2006,07/19/2006 05:15:59 AM,41.970162486,-87.650076954,"(41.970162486, -87.650076954)"
-4862433,HM476432,07/14/2006 08:00:00 PM,067XX N WESTERN AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2412,024,50,2,07,1159059,1944474,2006,07/19/2006 05:15:59 AM,42.003368407,-87.690177954,"(42.003368407, -87.690177954)"
-4857804,HM471848,07/12/2006 10:35:43 PM,013XX W 15TH ST,1340,CRIMINAL DAMAGE,TO STATE SUP PROP,CHA APARTMENT,false,false,1231,012,2,28,14,1167750,1892902,2006,07/26/2006 05:48:20 AM,41.861668647,-87.659694953,"(41.861668647, -87.659694953)"
-4861997,HM475788,07/12/2006 10:00:00 AM,113XX S FOREST AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0531,005,9,49,06,1180233,1829651,2006,12/04/2014 12:43:35 PM,41.687823654,-87.615809723,"(41.687823654, -87.615809723)"
-4855089,HM469173,07/11/2006 09:00:00 AM,0000X E 25TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2113,001,2,33,14,1177345,1887735,2006,07/24/2006 04:51:03 AM,41.847278058,-87.624630274,"(41.847278058, -87.624630274)"
-5013969,HM467237,07/10/2006 07:15:00 PM,030XX W MADISON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1331,012,2,27,18,1156366,1899909,2006,04/06/2007 02:27:04 PM,41.881134082,-87.701294304,"(41.881134082, -87.701294304)"
-4857602,HM471637,07/10/2006 04:00:00 PM,114XX S LOOMIS ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2234,022,34,75,14,1169086,1828923,2006,07/15/2006 04:07:00 AM,41.686073439,-87.656638699,"(41.686073439, -87.656638699)"
-5140368,HM464235,07/09/2006 11:03:00 AM,028XX W FLOURNOY ST,2017,NARCOTICS,MANU/DELIVER:CRACK,STREET,true,false,1135,011,2,27,18,1157262,1896940,2006,01/08/2007 09:05:18 AM,41.8729687,-87.698084925,"(41.8729687, -87.698084925)"
-4850037,HM464724,07/09/2006 04:00:00 AM,077XX S MARSHFIELD AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,0611,006,17,71,26,1166725,1853456,2006,09/30/2006 05:45:54 AM,41.753446374,-87.664584242,"(41.753446374, -87.664584242)"
-4848983,HM462918,07/08/2006 03:00:00 PM,030XX W PALMER BLVD,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,1414,014,35,22,08A,1155504,1914647,2006,07/13/2006 04:51:06 AM,41.921593856,-87.704062568,"(41.921593856, -87.704062568)"
-4849130,HM462378,07/08/2006 11:50:00 AM,024XX S STATE ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,0134,001,3,33,26,1176677,1887948,2006,07/11/2006 03:41:17 AM,41.847877647,-87.627075379,"(41.847877647, -87.627075379)"
-5488510,HN308130,07/07/2006 10:00:00 AM,085XX S PULASKI RD,1205,DECEPTIVE PRACTICE,"THEFT BY LESSEE,NON-VEH",OTHER,false,false,0834,008,18,70,11,1151185,1847657,2006,11/27/2007 04:21:33 PM,41.73785027,-87.721684272,"(41.73785027, -87.721684272)"
-4856280,HM468118,07/07/2006 12:30:00 AM,007XX E 89TH ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0632,006,6,44,06,1182578,1846162,2006,07/14/2006 05:11:24 AM,41.733078002,-87.606714917,"(41.733078002, -87.606714917)"
-4846869,HM459417,07/06/2006 10:16:09 PM,058XX W WASHINGTON BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1512,015,29,25,08B,1137356,1900190,2006,07/10/2006 03:52:16 AM,41.882268146,-87.77109203,"(41.882268146, -87.77109203)"
-4866055,HM459598,07/06/2006 09:00:00 PM,043XX W ADAMS ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1115,011,28,26,08B,1147454,1898617,2006,12/16/2009 01:04:58 AM,41.877764126,-87.734052025,"(41.877764126, -87.734052025)"
-4862052,HM475721,07/06/2006 08:30:00 PM,009XX W LELAND AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,2312,019,46,3,08B,1169104,1931446,2006,08/14/2006 05:12:46 AM,41.967406286,-87.653603329,"(41.967406286, -87.653603329)"
-4845112,HM455056,07/05/2006 12:01:00 AM,039XX W ROOSEVELT RD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,1011,010,24,29,08B,1150195,1894377,2006,07/14/2006 05:11:24 AM,41.866076122,-87.724098178,"(41.866076122, -87.724098178)"
-5001373,HM454459,07/04/2006 05:30:33 PM,003XX E RANDOLPH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,PARK PROPERTY,true,false,0124,001,42,32,18,1178413,1901198,2006,10/24/2006 05:50:48 AM,41.884197085,-87.620300377,"(41.884197085, -87.620300377)"
-5001284,HM454392,07/04/2006 04:03:53 PM,013XX S INDEPENDENCE BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1011,010,24,29,18,1151260,1893725,2006,10/24/2006 05:50:48 AM,41.864266152,-87.720205546,"(41.864266152, -87.720205546)"
-4840539,HM453139,07/03/2006 10:15:00 PM,005XX W 95TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2223,022,21,73,08B,1174446,1841853,2006,07/09/2006 04:34:06 AM,41.721438051,-87.636633893,"(41.721438051, -87.636633893)"
-4839282,HM451798,07/03/2006 10:00:00 AM,009XX W BUENA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2322,019,46,3,14,1168967,1928134,2006,07/05/2006 04:01:36 AM,41.95832103,-87.65420358,"(41.95832103, -87.65420358)"
-4839531,HM451920,07/03/2006 04:00:00 AM,0000X W DIVISION ST,0870,THEFT,POCKET-PICKING,BAR OR TAVERN,false,false,1824,018,42,8,06,1175792,1908406,2006,07/06/2006 04:48:17 AM,41.904035566,-87.629707817,"(41.904035566, -87.629707817)"
-4839489,HM452152,07/03/2006 12:30:00 AM,078XX S ESSEX AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0421,004,7,43,14,1194184,1853283,2006,07/06/2006 04:48:17 AM,41.752341886,-87.563964126,"(41.752341886, -87.563964126)"
-4839786,HM451526,07/02/2006 10:30:00 PM,017XX N ALBANY AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1421,014,26,23,05,1155429,1911199,2006,07/22/2006 04:38:54 AM,41.912133769,-87.704431041,"(41.912133769, -87.704431041)"
-4838459,HM450003,07/02/2006 11:14:00 AM,015XX W 54TH ST,051A,ASSAULT,AGGRAVATED: HANDGUN,SIDEWALK,false,false,0932,009,16,61,04A,1166956,1868877,2006,08/05/2006 04:48:25 AM,41.795758649,-87.663297414,"(41.795758649, -87.663297414)"
-4885310,HM498148,07/01/2006 09:00:00 AM,041XX W FULLERTON AVE,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,2524,025,31,20,06,1148615,1915680,2006,12/04/2014 12:43:35 PM,41.924564432,-87.729347996,"(41.924564432, -87.729347996)"
-5098922,HM444126,06/29/2006 12:32:38 PM,013XX S HOMAN AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,1021,010,24,29,18,1153958,1893413,2006,12/02/2006 04:50:35 AM,41.863356687,-87.71030951,"(41.863356687, -87.71030951)"
-4830590,HM443120,06/28/2006 08:20:00 PM,036XX W SHAKESPEARE AVE,1661,GAMBLING,GAME/DICE,SIDEWALK,true,false,2525,025,26,22,19,1151612,1914001,2006,06/11/2007 03:52:33 PM,41.919898655,-87.718379891,"(41.919898655, -87.718379891)"
-4831364,HM443638,06/28/2006 07:00:00 PM,024XX N KILBOURN AVE,0890,THEFT,FROM BUILDING,RESIDENCE PORCH/HALLWAY,false,false,2521,025,31,20,06,1145933,1916085,2006,07/01/2006 04:02:09 AM,41.925727207,-87.739192604,"(41.925727207, -87.739192604)"
-4977256,HM442587,06/28/2006 04:14:11 PM,049XX W IOWA ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1531,015,37,25,16,1143405,1905490,2006,10/14/2006 06:13:36 AM,41.896701077,-87.748747184,"(41.896701077, -87.748747184)"
-4972103,HM440336,06/27/2006 03:30:01 PM,050XX W IOWA ST,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),SIDEWALK,true,false,1531,015,37,25,18,1142714,1905556,2006,10/07/2006 09:43:17 AM,41.89689508,-87.751283498,"(41.89689508, -87.751283498)"
-4828773,HM440404,06/27/2006 02:45:00 PM,101XX S WINSTON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,2213,022,21,73,08B,1169285,1837379,2006,07/06/2006 04:48:17 AM,41.709273772,-87.655666637,"(41.709273772, -87.655666637)"
-4826600,HM439189,06/26/2006 07:10:00 PM,075XX S STONY ISLAND AVE,0460,BATTERY,SIMPLE,HOSPITAL BUILDING/GROUNDS,false,false,0411,004,8,43,08B,1188271,1855208,2006,07/01/2006 04:02:09 AM,41.757767317,-87.585571058,"(41.757767317, -87.585571058)"
-4873951,HM439722,06/26/2006 07:00:00 PM,010XX N CALIFORNIA AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,1311,012,26,24,06,1157498,1906724,2006,12/04/2014 12:43:35 PM,41.899812113,-87.696952062,"(41.899812113, -87.696952062)"
-4824516,HM436988,06/25/2006 03:00:00 PM,064XX S SANGAMON ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,0723,007,16,68,04B,1171046,1861978,2006,06/30/2006 04:03:48 AM,41.776738546,-87.648500726,"(41.776738546, -87.648500726)"
-4819858,HM431146,06/22/2006 08:00:00 PM,053XX S WOODLAWN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,ALLEY,false,false,2131,002,4,41,14,1185166,1870125,2006,06/26/2006 04:02:24 AM,41.798774366,-87.596481915,"(41.798774366, -87.596481915)"
-4833811,HM438269,06/22/2006 05:00:00 PM,034XX W 87TH ST,0810,THEFT,OVER $500,CEMETARY,false,false,0834,008,18,70,06,1155156,1846726,2006,12/04/2014 12:43:35 PM,41.73521708,-87.707160191,"(41.73521708, -87.707160191)"
-4818390,HM430894,06/22/2006 12:00:00 PM,040XX S KEDZIE AVE,0460,BATTERY,SIMPLE,APARTMENT,false,false,0912,009,14,58,08B,1155715,1877563,2006,07/27/2006 05:41:42 AM,41.819827239,-87.704285815,"(41.819827239, -87.704285815)"
-4957909,HM427101,06/20/2006 11:59:57 PM,042XX W MAYPOLE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1114,011,28,26,18,1147750,1901068,2006,09/23/2006 04:42:43 AM,41.884484274,-87.732902215,"(41.884484274, -87.732902215)"
-4830276,HM424798,06/19/2006 09:18:16 PM,003XX E 47TH ST,0560,ASSAULT,SIMPLE,CTA PLATFORM,true,false,0222,002,3,38,08A,1179017,1873965,2006,07/02/2006 04:30:55 AM,41.809454094,-87.618914359,"(41.809454094, -87.618914359)"
-4811190,HM424657,06/19/2006 08:11:07 PM,013XX W 108TH PL,0460,BATTERY,SIMPLE,RESIDENCE,false,true,2234,022,34,75,08B,1169311,1832774,2006,06/24/2006 03:34:29 AM,41.696636361,-87.655704116,"(41.696636361, -87.655704116)"
-4821282,HM423892,06/19/2006 02:20:20 PM,055XX W GLADYS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1522,015,29,25,08B,1139195,1897825,2006,08/07/2006 05:46:13 AM,41.875745017,-87.764396677,"(41.875745017, -87.764396677)"
-4825948,HM428832,06/19/2006 12:01:00 AM,033XX W DIVISION ST,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,ALLEY,false,false,1422,014,26,23,07,1153851,1907824,2006,06/29/2006 04:05:20 AM,41.902904069,-87.710318249,"(41.902904069, -87.710318249)"
-4808622,HM422851,06/19/2006 12:00:00 AM,037XX W DIVISION ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1112,011,27,23,08B,1151080,1907697,2006,06/29/2006 04:05:20 AM,41.902610341,-87.720500051,"(41.902610341, -87.720500051)"
-4810040,HM424068,06/19/2006 12:00:00 AM,057XX S THROOP ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0713,007,16,67,07,1168593,1866903,2006,06/21/2006 04:12:51 AM,41.790306594,-87.65735138,"(41.790306594, -87.65735138)"
-4808672,HM422945,06/18/2006 01:00:00 PM,023XX N KENNETH AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2522,025,31,20,05,1146285,1915390,2006,10/26/2007 01:04:16 AM,41.92381336,-87.737916901,"(41.92381336, -87.737916901)"
-4831808,HM421367,06/18/2006 03:00:00 AM,036XX S INDIANA AVE,0326,ROBBERY,AGGRAVATED VEHICULAR HIJACKING,STREET,true,false,0211,002,3,35,03,1178158,1880860,2006,07/07/2006 04:46:09 AM,41.8283941,-87.621855594,"(41.8283941, -87.621855594)"
-4807259,HM421065,06/18/2006 01:15:00 AM,034XX W 73RD ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0831,008,18,66,08B,1154645,1856029,2006,06/20/2006 03:50:51 AM,41.760756242,-87.708784923,"(41.760756242, -87.708784923)"
-4827510,HM423964,06/17/2006 07:00:00 PM,036XX S PRAIRIE AVE,0810,THEFT,OVER $500,OTHER,false,false,0211,002,2,35,06,1178576,1880609,2006,12/04/2014 12:43:35 PM,41.827695827,-87.620329646,"(41.827695827, -87.620329646)"
-4807308,HM420308,06/17/2006 05:20:00 PM,019XX W GARFIELD BLVD,0326,ROBBERY,AGGRAVATED VEHICULAR HIJACKING,STREET,false,false,0715,007,15,67,03,1164353,1868017,2006,06/29/2006 04:05:20 AM,41.793453981,-87.672866984,"(41.793453981, -87.672866984)"
-4808589,HM419341,06/17/2006 04:30:00 AM,004XX E RANDOLPH ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0124,001,42,32,04B,1179262,1901369,2006,07/18/2006 04:10:42 AM,41.884646917,-87.617177553,"(41.884646917, -87.617177553)"
-4810135,HM424167,06/16/2006 09:00:00 PM,050XX W 50TH ST,1345,CRIMINAL DAMAGE,TO CITY OF CHICAGO PROPERTY,PARK PROPERTY,false,false,0814,008,23,56,14,1143734,1871049,2006,06/21/2006 04:12:51 AM,41.802184096,-87.748400437,"(41.802184096, -87.748400437)"
-5109514,HM418592,06/16/2006 07:35:36 PM,028XX N BROADWAY,0820,THEFT,$500 AND UNDER,STREET,false,false,2333,019,44,6,06,1171609,1919192,2006,12/04/2014 12:43:35 PM,41.933725998,-87.64475478,"(41.933725998, -87.64475478)"
-4804980,HM418588,06/16/2006 03:30:00 PM,010XX W LAWRENCE AVE,0560,ASSAULT,SIMPLE,RESIDENTIAL YARD (FRONT/BACK),false,false,2024,020,46,3,08A,1168525,1932090,2006,07/10/2006 03:52:16 AM,41.969186027,-87.655713508,"(41.969186027, -87.655713508)"
-4809440,HM417904,06/16/2006 01:30:00 PM,101XX S LAFAYETTE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0511,005,9,49,14,1177759,1837440,2006,06/22/2006 03:55:52 AM,41.70925397,-87.624632109,"(41.70925397, -87.624632109)"
-4805171,HM417761,06/16/2006 10:05:00 AM,035XX N JANSSEN AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,true,false,1923,019,44,6,05,1166046,1923753,2006,08/12/2006 04:03:08 AM,41.946362394,-87.665067792,"(41.946362394, -87.665067792)"
-4805286,HM419400,06/15/2006 06:00:00 PM,014XX W ROSEMONT AVE,0810,THEFT,OVER $500,RESIDENCE PORCH/HALLWAY,false,false,2433,024,40,77,06,1165523,1942034,2006,12/04/2014 12:43:35 PM,41.996537288,-87.666467117,"(41.996537288, -87.666467117)"
-4809399,HM414806,06/14/2006 11:50:54 PM,016XX E 67TH ST,1360,CRIMINAL TRESPASS,TO VEHICLE,STREET,false,false,0332,003,5,43,26,1188389,1860846,2006,06/24/2006 03:34:29 AM,41.77323566,-87.584958835,"(41.77323566, -87.584958835)"
-4800324,HM413997,06/14/2006 05:00:00 PM,033XX W CRYSTAL ST,1570,SEX OFFENSE,PUBLIC INDECENCY,ALLEY,true,false,1422,014,26,23,17,1153738,1908158,2006,06/16/2006 04:00:38 AM,41.903822848,-87.710724417,"(41.903822848, -87.710724417)"
-4799157,HM412933,06/14/2006 06:30:00 AM,020XX N LAMON AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,2522,025,31,19,26,1143425,1913273,2006,01/30/2007 06:41:08 AM,41.918058107,-87.748478821,"(41.918058107, -87.748478821)"
-4803885,HM412607,06/13/2006 10:54:00 PM,071XX S HONORE ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0735,007,17,67,08B,1165298,1857149,2006,06/25/2006 04:27:37 AM,41.763610801,-87.669709288,"(41.763610801, -87.669709288)"
-4947579,HM412545,06/13/2006 10:43:27 PM,004XX W 116TH ST,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,0522,005,34,53,18,1175261,1828024,2006,09/23/2006 04:42:43 AM,41.683471117,-87.634059997,"(41.683471117, -87.634059997)"
-4805468,HM412121,06/13/2006 06:07:17 PM,027XX N AVERS AVE,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,PARK PROPERTY,true,false,2524,025,30,22,26,1150207,1918041,2006,06/19/2006 03:39:37 AM,41.931012303,-87.723436549,"(41.931012303, -87.723436549)"
-4933670,HM411442,06/13/2006 02:04:08 PM,025XX W 71ST ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0832,008,18,66,18,1160569,1857446,2006,09/12/2006 04:52:48 AM,41.764524622,-87.687033988,"(41.764524622, -87.687033988)"
-4796590,HM410309,06/12/2006 03:00:00 AM,003XX E 68TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0322,003,20,69,14,1179491,1859957,2006,06/15/2006 04:27:30 AM,41.771003996,-87.617603554,"(41.771003996, -87.617603554)"
-4792942,HM407169,06/11/2006 10:00:00 AM,011XX N SPRINGFIELD AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,false,1112,011,27,23,08A,1150137,1907285,2006,06/16/2006 04:00:38 AM,41.901498205,-87.723974627,"(41.901498205, -87.723974627)"
-4936403,HM406734,06/11/2006 01:39:50 AM,039XX N CLARENDON AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,2323,019,46,6,18,1170250,1926516,2006,08/26/2006 05:22:07 AM,41.953853186,-87.649534294,"(41.953853186, -87.649534294)"
-4792502,HM406072,06/10/2006 06:10:00 PM,034XX W OHIO ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1121,011,27,23,14,1153130,1903742,2006,06/13/2006 03:45:29 AM,41.891717006,-87.713075032,"(41.891717006, -87.713075032)"
-6233877,HM404147,06/09/2006 06:54:58 PM,081XX S ASHLAND AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,STREET,true,false,0614,,21,71,26,,,2006,05/18/2008 01:04:57 AM,,,
-4792606,HM403740,06/09/2006 01:30:00 PM,001XX W 79TH ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,false,false,0623,006,17,69,06,1176736,1852645,2006,06/13/2006 03:45:29 AM,41.751001502,-87.6279221,"(41.751001502, -87.6279221)"
-4790499,HM403387,06/09/2006 08:30:00 AM,033XX W BELMONT AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENTIAL YARD (FRONT/BACK),false,false,1412,014,35,21,14,1153584,1921044,2006,06/14/2006 04:14:16 AM,41.939186177,-87.710946562,"(41.939186177, -87.710946562)"
-4813544,HM422152,06/09/2006 12:01:00 AM,006XX S KARLOV AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE PORCH/HALLWAY,false,false,1132,011,24,26,14,1149114,1896802,2006,06/23/2006 03:55:49 AM,41.87275159,-87.72800387,"(41.87275159, -87.72800387)"
-4947509,HM402261,06/08/2006 09:00:00 PM,046XX W MADISON ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1113,011,28,25,16,1145566,1899578,2006,09/09/2006 04:19:56 AM,41.880437191,-87.740960003,"(41.880437191, -87.740960003)"
-4788410,HM401563,06/08/2006 03:30:00 AM,130XX S BRANDON AVE,2830,OTHER OFFENSE,OBSCENE TELEPHONE CALLS,RESIDENCE,false,false,0433,004,10,55,17,1199386,1819041,2006,06/10/2006 03:42:52 AM,41.658249536,-87.546048638,"(41.658249536, -87.546048638)"
-4792172,HM398922,06/07/2006 11:10:00 AM,011XX N NOBLE ST,1330,CRIMINAL TRESPASS,TO LAND,OTHER,true,false,1323,012,27,24,26,1166883,1907620,2006,06/13/2006 03:45:29 AM,41.90207459,-87.662455202,"(41.90207459, -87.662455202)"
-4786682,HM399347,06/07/2006 02:00:00 AM,086XX S COLFAX AVE,0496,BATTERY,AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST,RESIDENCE,true,true,0423,004,7,46,04B,1194950,1848315,2006,06/12/2006 03:33:56 AM,41.738690462,-87.561320459,"(41.738690462, -87.561320459)"
-4780073,HM393338,06/04/2006 04:30:00 PM,071XX N SHERIDAN RD,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,2423,024,49,1,04B,1166468,1947618,2006,06/13/2006 03:45:29 AM,42.011839637,-87.662830019,"(42.011839637, -87.662830019)"
-4777701,HM391821,06/03/2006 06:45:00 PM,098XX S PRAIRIE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0511,005,6,49,06,1179695,1839725,2006,12/04/2014 12:43:35 PM,41.715480368,-87.617472725,"(41.715480368, -87.617472725)"
-5271613,HM391414,06/03/2006 03:28:00 PM,063XX S MAY ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0724,007,16,68,26,1169704,1862569,2006,02/01/2007 06:57:55 AM,41.778389543,-87.653403308,"(41.778389543, -87.653403308)"
-4778003,HM390439,06/03/2006 12:01:00 AM,024XX S SPAULDING AVE,0810,THEFT,OVER $500,STREET,false,false,1024,010,22,30,06,1154716,1887357,2006,12/04/2014 12:43:35 PM,41.846723205,-87.707688921,"(41.846723205, -87.707688921)"
-4776690,HM387647,06/01/2006 06:50:00 PM,051XX S SACRAMENTO AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0911,009,14,63,08B,1157202,1870519,2006,04/26/2010 01:11:05 AM,41.800467579,-87.699021557,"(41.800467579, -87.699021557)"
-6155530,HP241499,06/01/2006 12:01:00 AM,022XX S HOMAN AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,1024,,22,30,06,,,2006,03/30/2008 01:04:35 AM,,,
-4865008,HM384245,05/31/2006 09:39:39 AM,062XX S STEWART AVE,2027,NARCOTICS,POSS: CRACK,"SCHOOL, PUBLIC, BUILDING",true,false,0711,007,20,68,18,1174730,1863727,2006,07/25/2006 04:21:28 AM,41.781456694,-87.634943307,"(41.781456694, -87.634943307)"
-5855576,HM382444,05/30/2006 01:30:00 PM,002XX N LARAMIE AVE,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,STREET,true,false,1523,015,28,25,18,1141634,1901013,2006,11/11/2007 01:03:25 AM,41.884448577,-87.755362614,"(41.884448577, -87.755362614)"
-4782392,HM396605,05/30/2006 08:00:00 AM,013XX W FULLERTON AVE,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,1933,019,32,7,06,1167103,1916106,2006,12/04/2014 12:43:35 PM,41.925355965,-87.661402916,"(41.925355965, -87.661402916)"
-4768761,HM381423,05/29/2006 11:55:00 PM,003XX W OAK ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,1823,018,27,8,08B,1173434,1907132,2006,06/06/2006 03:45:55 AM,41.900592389,-87.638407171,"(41.900592389, -87.638407171)"
-4770252,HM381308,05/29/2006 10:30:00 PM,063XX S WASHTENAW AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0825,008,15,66,08B,1159511,1862326,2006,06/07/2006 04:08:49 AM,41.777937799,-87.690778231,"(41.777937799, -87.690778231)"
-4768884,HM379509,05/28/2006 11:04:22 PM,010XX N LATROBE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,VEHICLE NON-COMMERCIAL,false,false,1524,015,37,25,14,1141120,1906757,2006,06/02/2006 03:40:19 AM,41.900220291,-87.757108462,"(41.900220291, -87.757108462)"
-4769441,HM378462,05/28/2006 11:24:24 AM,078XX S WABASH AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0623,006,6,69,06,1178113,1852879,2006,12/04/2014 12:43:35 PM,41.751612534,-87.622869044,"(41.751612534, -87.622869044)"
-4765962,HM378139,05/28/2006 05:00:00 AM,060XX S LAFAYETTE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0311,003,20,40,14,1177056,1864899,2006,05/30/2006 03:34:25 AM,41.784620626,-87.626380383,"(41.784620626, -87.626380383)"
-4766134,HM378069,05/28/2006 03:30:00 AM,028XX N ROCKWELL ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,RESIDENCE,false,true,1411,014,1,21,04A,1158501,1918847,2006,06/02/2006 03:40:19 AM,41.93305808,-87.69293553,"(41.93305808, -87.69293553)"
-4957952,HM377680,05/27/2006 10:45:00 PM,040XX W VAN BUREN ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1132,011,24,26,18,1149670,1897761,2006,09/23/2006 04:42:43 AM,41.875372418,-87.725937605,"(41.875372418, -87.725937605)"
-4771644,HM382996,05/26/2006 06:00:00 PM,038XX W HARRISON ST,0610,BURGLARY,FORCIBLE ENTRY,CHURCH/SYNAGOGUE/PLACE OF WORSHIP,false,false,1133,011,24,26,05,1150532,1897031,2006,06/23/2006 03:55:49 AM,41.873352433,-87.722791718,"(41.873352433, -87.722791718)"
-4763390,HM375541,05/26/2006 07:00:00 AM,054XX N KENMORE AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,2023,020,48,77,06,1168221,1936545,2006,06/02/2006 03:40:19 AM,41.981417266,-87.656701921,"(41.981417266, -87.656701921)"
-4757238,HM368530,05/23/2006 04:20:00 PM,053XX W WINONA ST,1792,KIDNAPPING,CHILD ABDUCTION/STRANGER,STREET,false,false,1623,016,45,11,20,1140021,1933778,2006,06/07/2006 04:08:49 AM,41.974388833,-87.760481976,"(41.974388833, -87.760481976)"
-4756084,HM365290,05/22/2006 03:21:00 AM,011XX N CLARK ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,true,false,1824,018,42,8,15,1175285,1908141,2006,06/11/2007 03:52:33 PM,41.903319789,-87.631578099,"(41.903319789, -87.631578099)"
-4753520,HM364173,05/21/2006 11:45:00 AM,088XX S DORCHESTER AVE,0460,BATTERY,SIMPLE,STREET,false,false,0412,004,8,48,08B,1186962,1846420,2006,05/24/2006 03:44:11 AM,41.733683296,-87.590646295,"(41.733683296, -87.590646295)"
-4759908,HM368356,05/20/2006 11:00:00 AM,003XX W 35TH ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,SPORTS ARENA/STADIUM,false,false,0925,009,11,34,11,1174472,1881700,2006,06/05/2006 03:42:54 AM,41.830782103,-87.635354068,"(41.830782103, -87.635354068)"
-4752025,HM362167,05/20/2006 10:49:04 AM,121XX S EGGLESTON AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0523,005,34,53,05,1175442,1824498,2006,05/29/2006 03:30:35 AM,41.673791185,-87.633502259,"(41.673791185, -87.633502259)"
-4784861,HM389295,05/19/2006 06:30:00 PM,059XX W RACE AVE,1751,OFFENSE INVOLVING CHILDREN,CRIM SEX ABUSE BY FAM MEMBER,RESIDENCE,false,false,1512,015,29,25,20,1136814,1902977,2006,07/11/2006 03:41:17 AM,41.889925767,-87.773015467,"(41.889925767, -87.773015467)"
-4750830,HM362002,05/19/2006 05:30:00 PM,073XX N RIDGE BLVD,0810,THEFT,OVER $500,OTHER,false,false,2411,024,49,2,06,1160624,1948855,2006,12/04/2014 12:43:35 PM,42.015357587,-87.684298183,"(42.015357587, -87.684298183)"
-4748245,HM359622,05/19/2006 02:23:00 AM,067XX N WESTERN AVE,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,STREET,false,false,2412,024,50,2,11,1159047,1944938,2006,05/29/2006 03:30:35 AM,42.004641886,-87.690209279,"(42.004641886, -87.690209279)"
-4749554,HM360208,05/18/2006 06:00:00 PM,098XX S WESTERN AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,2213,022,19,72,06,1162156,1839249,2006,12/04/2014 12:43:35 PM,41.71455636,-87.681722192,"(41.71455636, -87.681722192)"
-4750497,HM358467,05/18/2006 02:00:00 PM,099XX S EMERALD AVE,0842,THEFT,AGG: FINANCIAL ID THEFT,RESIDENCE,false,false,2232,022,34,73,06,1173021,1838924,2006,05/25/2006 03:41:58 AM,41.713431982,-87.641939541,"(41.713431982, -87.641939541)"
-4751609,HM358204,05/18/2006 12:15:34 PM,061XX S ADA ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0713,007,16,67,07,1168330,1863760,2006,05/23/2006 03:30:06 AM,41.781687495,-87.658406196,"(41.781687495, -87.658406196)"
-4750992,HM357218,05/17/2006 05:00:00 PM,080XX S RHODES AVE,1752,OFFENSE INVOLVING CHILDREN,AGG CRIM SEX ABUSE FAM MEMBER,APARTMENT,false,true,0631,006,6,44,20,1181339,1851851,2006,08/12/2006 04:03:08 AM,41.748717881,-87.611079,"(41.748717881, -87.611079)"
-4749895,HM356324,05/17/2006 01:30:00 PM,122XX S RACINE AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0524,005,34,53,26,1170506,1823651,2006,05/23/2006 03:30:06 AM,41.671575461,-87.651593063,"(41.671575461, -87.651593063)"
-4746282,HM355096,05/16/2006 08:00:00 PM,036XX W WRIGHTWOOD AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,2524,025,35,22,08A,1151792,1917084,2006,05/21/2006 03:52:27 AM,41.928355128,-87.717637205,"(41.928355128, -87.717637205)"
-4754276,HM357882,05/16/2006 06:00:00 PM,003XX E GARFIELD BLVD,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,OTHER,false,false,0234,002,3,40,14,1179192,1868453,2006,05/26/2006 03:55:36 AM,41.794324681,-87.618440644,"(41.794324681, -87.618440644)"
-4748891,HM356413,05/15/2006 04:00:00 PM,056XX S ELLIS AVE,0890,THEFT,FROM BUILDING,COMMERCIAL / BUSINESS OFFICE,false,false,2133,002,5,41,06,1183829,1867630,2006,05/24/2006 03:44:11 AM,41.791959226,-87.601462818,"(41.791959226, -87.601462818)"
-4742046,HM351228,05/15/2006 01:30:00 AM,056XX S EMERALD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0711,007,20,68,08B,1172221,1867530,2006,05/25/2006 03:41:58 AM,41.791948102,-87.644030066,"(41.791948102, -87.644030066)"
-4740523,HM350430,05/14/2006 11:00:00 AM,001XX W POLK ST,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,0131,001,2,32,06,1175424,1896835,2006,12/04/2014 12:43:35 PM,41.872292374,-87.631407236,"(41.872292374, -87.631407236)"
-4737801,HM347775,05/12/2006 09:00:00 PM,022XX S KIRKLAND AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1013,010,22,29,14,1147740,1888819,2006,05/16/2006 03:27:27 AM,41.850871729,-87.733253363,"(41.850871729, -87.733253363)"
-4740351,HM347313,05/12/2006 07:47:00 PM,063XX S ARTESIAN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,true,true,0825,008,15,66,08B,1161084,1862690,2006,05/26/2006 03:55:36 AM,41.778904268,-87.685001473,"(41.778904268, -87.685001473)"
-4738766,HM346584,05/11/2006 09:00:00 PM,055XX W WINDSOR AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1623,016,45,15,26,1138452,1929761,2006,01/30/2007 06:41:08 AM,41.963394474,-87.766349492,"(41.963394474, -87.766349492)"
-4736403,HM344607,05/11/2006 03:40:00 PM,114XX S HALSTED ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,2234,022,34,75,06,1172991,1829061,2006,05/14/2006 03:39:19 AM,41.68636707,-87.642339221,"(41.68636707, -87.642339221)"
-4744734,HM344264,05/11/2006 02:10:02 PM,074XX N GREENVIEW AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE PORCH/HALLWAY,true,false,2422,024,49,1,26,1165033,1949722,2006,05/19/2006 03:39:57 AM,42.017643768,-87.668049841,"(42.017643768, -87.668049841)"
-4849367,HM460224,05/11/2006 12:00:00 PM,015XX W 17TH ST,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,RESIDENCE,false,false,1222,012,25,31,11,1166452,1891861,2006,08/07/2006 05:46:13 AM,41.8588399,-87.664489436,"(41.8588399, -87.664489436)"
-4738915,HM347329,05/10/2006 09:45:00 PM,040XX W JACKSON BLVD,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,true,false,1132,011,28,26,04B,1149264,1898334,2006,01/30/2008 01:05:27 AM,41.876952669,-87.72741344,"(41.876952669, -87.72741344)"
-4855737,HM342875,05/10/2006 09:30:00 PM,023XX W WALNUT ST,2170,NARCOTICS,POSSESSION OF DRUG EQUIPMENT,SIDEWALK,true,false,1332,012,27,28,18,1160582,1901571,2006,09/02/2006 04:14:01 AM,41.885608513,-87.685767332,"(41.885608513, -87.685767332)"
-4735415,HM342595,05/10/2006 06:20:00 PM,026XX W 25TH ST,0460,BATTERY,SIMPLE,OTHER,false,false,1034,010,12,30,08B,1159161,1887269,2006,05/14/2006 03:39:19 AM,41.846391703,-87.691378269,"(41.846391703, -87.691378269)"
-4733423,HM341990,05/10/2006 09:00:00 AM,080XX S LAFLIN ST,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,RESIDENCE,false,false,0612,006,21,71,11,1167696,1851308,2006,05/21/2006 03:52:27 AM,41.747531198,-87.661087381,"(41.747531198, -87.661087381)"
-4732319,HM341047,05/10/2006 12:05:00 AM,027XX W MAYPOLE AVE,0326,ROBBERY,AGGRAVATED VEHICULAR HIJACKING,VACANT LOT/LAND,false,false,1331,012,27,27,03,1158051,1900882,2006,06/04/2006 04:24:23 AM,41.883769867,-87.695080496,"(41.883769867, -87.695080496)"
-4734764,HM339668,05/09/2006 11:15:00 AM,020XX N BURLING ST,0320,ROBBERY,STRONGARM - NO WEAPON,PARK PROPERTY,true,false,1812,018,43,7,03,1170991,1913808,2006,06/14/2006 04:14:16 AM,41.91896565,-87.647184215,"(41.91896565, -87.647184215)"
-4734058,HM338333,05/08/2006 02:40:00 PM,008XX E 101ST ST,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,true,false,0511,005,8,50,03,1183471,1838136,2006,05/13/2006 03:25:14 AM,41.711032972,-87.603692674,"(41.711032972, -87.603692674)"
-4736888,HM336521,05/07/2006 08:15:00 PM,058XX S MICHIGAN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE PORCH/HALLWAY,false,false,0233,002,20,40,14,1178119,1866176,2006,05/14/2006 03:39:19 AM,41.788100791,-87.622444306,"(41.788100791, -87.622444306)"
-4728638,HM335523,05/07/2006 07:00:00 AM,041XX S DREXEL BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,CONSTRUCTION SITE,false,false,2123,002,4,36,14,1182789,1877620,2006,05/10/2006 03:31:10 AM,41.819396811,-87.604965839,"(41.819396811, -87.604965839)"
-4727062,HM334715,05/06/2006 07:05:00 PM,079XX S VERNON AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0624,006,6,44,08B,1180574,1852659,2006,05/10/2006 03:31:10 AM,41.75095271,-87.613857455,"(41.75095271, -87.613857455)"
-4726349,HM333520,05/06/2006 04:30:00 AM,041XX S ARTESIAN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0914,009,12,58,08B,1160674,1876921,2006,05/15/2006 03:25:18 AM,41.817964449,-87.686111753,"(41.817964449, -87.686111753)"
-4725616,HM333587,05/05/2006 09:00:00 PM,016XX N PARKSIDE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,2531,025,29,25,05,1138498,1910597,2006,05/17/2006 03:39:53 AM,41.910805632,-87.766646123,"(41.910805632, -87.766646123)"
-4728214,HM331601,05/05/2006 08:05:00 AM,009XX N MAYFIELD AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1511,015,29,25,26,1137011,1905611,2006,05/11/2006 03:38:51 AM,41.897150267,-87.772228724,"(41.897150267, -87.772228724)"
-4724613,HM330707,05/04/2006 07:31:00 PM,068XX S PERRY AVE,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,false,false,0722,007,6,69,04B,1176532,1859826,2006,05/17/2006 03:39:53 AM,41.770711579,-87.628454027,"(41.770711579, -87.628454027)"
-4727590,HM330258,05/04/2006 03:31:54 PM,031XX N NEENAH AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,2511,025,36,19,14,1132164,1920036,2006,05/10/2006 03:31:10 AM,41.936819869,-87.789695551,"(41.936819869, -87.789695551)"
-4727376,HM335794,05/04/2006 01:00:00 PM,034XX W FULLERTON AVE,0890,THEFT,FROM BUILDING,CLEANING STORE,false,false,1413,014,26,22,06,1153272,1915787,2006,05/09/2006 03:25:05 AM,41.924766762,-87.712233218,"(41.924766762, -87.712233218)"
-4725746,HM331937,05/03/2006 12:50:00 PM,065XX S RICHMOND ST,141C,WEAPONS VIOLATION,UNLAWFUL USE OTHER DANG WEAPON,STREET,false,false,0831,008,15,66,15,1157805,1860868,2006,05/08/2006 03:22:32 AM,41.773971644,-87.697072069,"(41.773971644, -87.697072069)"
-4722207,HM327135,05/03/2006 02:00:00 AM,082XX S GREEN ST,0920,MOTOR VEHICLE THEFT,ATT: AUTOMOBILE,STREET,false,false,0613,006,21,71,07,1172118,1850082,2006,05/06/2006 03:25:41 AM,41.744070946,-87.644919822,"(41.744070946, -87.644919822)"
-4721105,HM327002,05/02/2006 11:30:00 PM,015XX S RIDGEWAY AVE,0460,BATTERY,SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,1014,010,24,29,08B,1151675,1891906,2006,05/05/2006 03:33:59 AM,41.859266461,-87.718729883,"(41.859266461, -87.718729883)"
-4862065,HM326852,05/02/2006 09:40:00 PM,001XX W 103RD ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,SIDEWALK,true,false,0512,005,34,49,16,1177348,1836622,2006,08/05/2006 04:48:25 AM,41.707018537,-87.626161833,"(41.707018537, -87.626161833)"
-4726705,HM333664,05/02/2006 05:00:00 PM,033XX N SHEFFIELD AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1924,019,44,6,05,1169057,1922344,2006,05/24/2006 03:44:11 AM,41.942431088,-87.654041419,"(41.942431088, -87.654041419)"
-4719409,HM326024,05/01/2006 04:00:00 PM,061XX S ARTESIAN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,STREET,false,false,0825,008,15,66,14,1161125,1864078,2006,05/04/2006 03:35:01 AM,41.782712282,-87.684812786,"(41.782712282, -87.684812786)"
-4718676,HM323053,05/01/2006 06:22:59 AM,019XX W DEVON AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,2412,024,50,2,05,1162106,1942550,2006,05/22/2006 03:27:56 AM,41.998025541,-87.679022256,"(41.998025541, -87.679022256)"
-4718386,HM321690,04/30/2006 11:10:00 AM,063XX S ASHLAND AVE,1330,CRIMINAL TRESPASS,TO LAND,TAVERN/LIQUOR STORE,false,false,0725,007,16,67,26,1166716,1862724,2006,05/07/2006 03:57:33 AM,41.778879192,-87.664353067,"(41.778879192, -87.664353067)"
-4719252,HM323483,04/30/2006 08:50:00 AM,0000X E 91ST ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,0634,006,6,44,08B,1178536,1844712,2006,05/09/2006 03:25:05 AM,41.729191706,-87.621566491,"(41.729191706, -87.621566491)"
-4715156,HM320994,04/29/2006 10:40:00 PM,023XX W GARFIELD BLVD,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0915,009,16,63,03,1162027,1868179,2006,05/11/2006 03:38:51 AM,41.793947251,-87.681391755,"(41.793947251, -87.681391755)"
-4715132,HM319177,04/29/2006 01:00:00 AM,023XX S TRUMBULL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,false,1024,010,22,30,08B,1153769,1888433,2006,05/02/2006 03:29:06 AM,41.849694756,-87.711135792,"(41.849694756, -87.711135792)"
-4714019,HM319207,04/28/2006 11:00:00 PM,062XX S ARTESIAN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,true,0825,008,15,66,14,1161075,1863025,2006,05/31/2007 02:03:58 AM,41.77982374,-87.685025209,"(41.77982374, -87.685025209)"
-4739155,HM349450,04/28/2006 03:00:00 PM,009XX N TRUMBULL AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1121,011,27,23,26,1153245,1906034,2006,05/23/2006 03:30:06 AM,41.898004195,-87.712591787,"(41.898004195, -87.712591787)"
-4714094,HM317583,04/28/2006 09:40:00 AM,006XX N HOMAN AVE,0460,BATTERY,SIMPLE,STREET,false,false,1121,011,27,23,08B,1153627,1904393,2006,05/06/2006 03:25:41 AM,41.893493541,-87.711232432,"(41.893493541, -87.711232432)"
-4715511,HM318084,04/28/2006 12:01:00 AM,075XX S VERNON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,0624,006,6,69,14,1180512,1855041,2006,05/02/2006 03:29:06 AM,41.757490601,-87.614011671,"(41.757490601, -87.614011671)"
-4713709,HM316655,04/27/2006 08:00:00 AM,019XX N LA CROSSE AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,2533,025,31,19,05,1143778,1912771,2006,05/10/2006 03:31:10 AM,41.916673954,-87.747194469,"(41.916673954, -87.747194469)"
-4820228,HM312127,04/25/2006 03:45:00 PM,028XX W ROOSEVELT RD,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),PARK PROPERTY,true,false,1022,010,24,29,18,1157241,1894536,2006,07/22/2006 04:38:54 AM,41.866372306,-87.698227328,"(41.866372306, -87.698227328)"
-4713585,HM311529,04/25/2006 10:30:00 AM,027XX E 81ST ST,0545,ASSAULT,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,0422,004,7,46,08A,1195714,1851827,2006,05/01/2006 04:24:48 AM,41.74830883,-87.558405507,"(41.74830883, -87.558405507)"
-4708531,HM310911,04/24/2006 10:33:24 PM,081XX S LUELLA AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,SIDEWALK,false,false,0414,004,8,46,04A,1192563,1851381,2006,05/03/2006 03:43:36 AM,41.747162262,-87.569966119,"(41.747162262, -87.569966119)"
-4705722,HM311289,04/24/2006 06:00:00 PM,016XX N LA SALLE DR,0810,THEFT,OVER $500,STREET,false,false,1814,018,43,7,06,1174784,1911570,2006,12/04/2014 12:43:35 PM,41.912740359,-87.633315568,"(41.912740359, -87.633315568)"
-4706798,HM310310,04/24/2006 04:20:00 PM,038XX W GRENSHAW ST,0820,THEFT,$500 AND UNDER,RESIDENCE,true,false,1133,011,24,29,06,1150791,1894801,2006,12/04/2014 12:43:35 PM,41.867227999,-87.721899113,"(41.867227999, -87.721899113)"
-4830351,HM308180,04/23/2006 02:43:52 PM,063XX S CHAMPLAIN AVE,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),SIDEWALK,true,false,0312,003,20,42,18,1181612,1863312,2006,07/01/2006 04:02:09 AM,41.780161742,-87.60972534,"(41.780161742, -87.60972534)"
-4706055,HM309490,04/22/2006 05:00:00 PM,051XX S KILDARE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,RESIDENCE,false,false,0815,008,23,57,14,1148526,1870178,2006,05/05/2006 03:33:59 AM,41.799703107,-87.730848329,"(41.799703107, -87.730848329)"
-4699025,HM303427,04/21/2006 12:01:00 AM,086XX S HERMITAGE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0614,006,18,71,06,1166228,1847213,2006,12/04/2014 12:43:35 PM,41.736325267,-87.66658284,"(41.736325267, -87.66658284)"
-4699071,HM303597,04/20/2006 10:00:00 PM,098XX S CALHOUN AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0431,004,7,51,26,1194802,1840130,2006,04/23/2006 03:51:29 AM,41.716233729,-87.562131359,"(41.716233729, -87.562131359)"
-4704328,HM302826,04/20/2006 08:00:00 PM,036XX S FEDERAL ST,0560,ASSAULT,SIMPLE,CHA PARKING LOT/GROUNDS,false,false,0211,002,3,35,08A,1176167,1880544,2006,04/28/2006 03:43:43 AM,41.827571984,-87.629169842,"(41.827571984, -87.629169842)"
-4699423,HM301908,04/20/2006 11:45:00 AM,025XX S KARLOV AVE,0460,BATTERY,SIMPLE,STREET,false,false,1013,010,22,30,08B,1149721,1886590,2006,06/11/2007 03:52:33 PM,41.844716853,-87.726040469,"(41.844716853, -87.726040469)"
-4699902,HM301017,04/19/2006 10:25:00 PM,006XX S SACRAMENTO BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,1134,011,28,27,08B,1156423,1897182,2006,05/04/2006 03:35:01 AM,41.87364977,-87.701158757,"(41.87364977, -87.701158757)"
-4697855,HM300633,04/19/2006 06:55:00 PM,107XX S MACKINAW AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,0432,004,10,52,15,1200236,1834543,2006,05/04/2006 03:35:01 AM,41.70076723,-87.542417655,"(41.70076723, -87.542417655)"
-4697461,HM300565,04/19/2006 06:32:07 PM,060XX S INDIANA AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0311,003,20,40,08B,1178587,1865207,2006,05/06/2006 03:25:41 AM,41.785431128,-87.620757793,"(41.785431128, -87.620757793)"
-4693664,HM297765,04/18/2006 12:00:00 PM,0000X W RANDOLPH ST,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,0122,001,42,32,06,1175995,1901321,2006,04/20/2006 03:40:12 AM,41.884589391,-87.629175742,"(41.884589391, -87.629175742)"
-4731495,HM297028,04/17/2006 11:59:35 PM,013XX N NORTH BRANCH ST,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1822,018,32,8,18,1168526,1908656,2006,05/16/2006 03:27:27 AM,41.904882022,-87.656390235,"(41.904882022, -87.656390235)"
-4693071,HM295609,04/17/2006 11:20:00 AM,035XX W OHIO ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1121,011,27,23,08B,1152464,1903808,2006,04/27/2006 04:04:19 AM,41.891911302,-87.715519218,"(41.891911302, -87.715519218)"
-4690488,HM294462,04/16/2006 12:20:00 PM,053XX S HYDE PARK BLVD,0880,THEFT,PURSE-SNATCHING,STREET,false,false,2132,002,5,41,06,1188512,1870406,2006,04/18/2006 03:38:47 AM,41.799466087,-87.584202615,"(41.799466087, -87.584202615)"
-4692154,HM295143,04/16/2006 03:00:00 AM,065XX S EVANS AVE,1562,SEX OFFENSE,AGG CRIMINAL SEXUAL ABUSE,APARTMENT,false,false,0321,003,20,42,17,1182378,1861975,2006,06/03/2006 03:59:47 AM,41.776475166,-87.606958499,"(41.776475166, -87.606958499)"
-4689827,HM293664,04/16/2006 02:05:00 AM,072XX S WESTERN AVE,033A,ROBBERY,ATTEMPT: ARMED-HANDGUN,SIDEWALK,false,false,0832,008,18,66,03,1161593,1856419,2006,05/16/2006 03:27:27 AM,41.761685211,-87.683309181,"(41.761685211, -87.683309181)"
-4690263,HM293197,04/15/2006 07:30:00 PM,054XX S WENTWORTH AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0232,002,3,37,06,1175924,1869100,2006,04/20/2006 03:40:12 AM,41.796174063,-87.6304048,"(41.796174063, -87.6304048)"
-4691393,HM293175,04/15/2006 06:35:00 PM,058XX S CALIFORNIA AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,SIDEWALK,true,false,0824,008,16,63,24,1158754,1865601,2006,06/11/2007 03:52:33 PM,41.78694035,-87.693464089,"(41.78694035, -87.693464089)"
-4690233,HM292346,04/15/2006 11:25:00 AM,050XX S INDIANA AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,0224,002,3,38,06,1178492,1871668,2006,12/04/2014 12:43:35 PM,41.803162883,-87.620909788,"(41.803162883, -87.620909788)"
-4695609,HM289514,04/13/2006 09:03:00 PM,073XX S EMERALD AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,RESIDENCE,true,false,0732,007,17,68,15,1172607,1856347,2006,06/11/2007 03:52:33 PM,41.761252154,-87.642943877,"(41.761252154, -87.642943877)"
-4686703,HM288992,04/13/2006 05:34:13 PM,001XX N STATE ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0122,001,42,32,06,1176390,1900949,2006,04/18/2006 03:38:47 AM,41.883559699,-87.627736496,"(41.883559699, -87.627736496)"
-4687681,HM288422,04/13/2006 12:25:00 PM,072XX S UNION AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,false,0732,007,17,68,08A,1172958,1856984,2006,04/22/2006 04:02:27 AM,41.762992423,-87.641638666,"(41.762992423, -87.641638666)"
-4686938,HM288340,04/13/2006 11:49:27 AM,075XX N HOYNE AVE,1330,CRIMINAL TRESPASS,TO LAND,APARTMENT,true,false,2424,024,49,1,26,1160930,1950059,2006,04/18/2006 03:38:47 AM,42.018655019,-87.683138557,"(42.018655019, -87.683138557)"
-4685993,HM285960,04/12/2006 07:15:00 AM,001XX S HAMLIN BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,1122,011,28,26,08B,1151058,1898883,2006,04/15/2006 04:32:33 AM,41.878424243,-87.720811972,"(41.878424243, -87.720811972)"
-4714194,HM285526,04/11/2006 10:15:00 PM,035XX W 63RD ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0823,008,15,66,16,1154091,1862578,2006,05/20/2006 03:31:18 AM,41.778738741,-87.710641605,"(41.778738741, -87.710641605)"
-4684431,HM284548,04/11/2006 01:30:00 PM,118XX S STATE ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0522,005,34,53,08B,1178331,1826695,2006,04/15/2006 04:32:33 AM,41.679755213,-87.622861959,"(41.679755213, -87.622861959)"
-4696521,HM284457,04/11/2006 11:40:00 AM,005XX E BROWNING AVE,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,0212,002,4,35,26,1180772,1881382,2006,04/24/2006 03:36:24 AM,41.8297667,-87.612249086,"(41.8297667, -87.612249086)"
-4686134,HM289095,04/10/2006 04:00:00 PM,023XX N DAMEN AVE,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,1432,014,32,22,06,1162608,1915094,2006,12/04/2014 12:43:35 PM,41.922674439,-87.677948069,"(41.922674439, -87.677948069)"
-4680709,HM282765,04/10/2006 03:07:22 PM,001XX N GREEN ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,1212,012,27,28,07,1170659,1901384,2006,04/14/2006 04:02:25 AM,41.884880743,-87.648768266,"(41.884880743, -87.648768266)"
-4678790,HM282008,04/08/2006 12:00:00 AM,097XX S INDIANA AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0511,005,6,49,05,1179260,1840322,2006,04/15/2006 04:32:33 AM,41.717128528,-87.619047749,"(41.717128528, -87.619047749)"
-4676358,HM276791,04/07/2006 09:00:00 AM,029XX S FEDERAL ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA HALLWAY/STAIRWELL/ELEVATOR,false,true,2113,001,3,35,08B,1176013,1885806,2006,04/11/2006 03:34:47 AM,41.842014797,-87.629576675,"(41.842014797, -87.629576675)"
-4677654,HM276401,04/07/2006 12:01:00 AM,076XX S ASHLAND AVE,0498,BATTERY,AGGRAVATED DOMESTIC BATTERY: HANDS/FIST/FEET SERIOUS INJURY,STREET,false,true,0611,006,17,71,04B,1166956,1854252,2006,05/18/2007 06:17:05 PM,41.755625778,-87.663715006,"(41.755625778, -87.663715006)"
-4673604,HM274869,04/05/2006 11:00:00 PM,025XX W HURON ST,0810,THEFT,OVER $500,STREET,false,false,1313,012,26,24,06,1159321,1904626,2006,12/04/2014 12:43:35 PM,41.89401772,-87.690313907,"(41.89401772, -87.690313907)"
-4672376,HM274376,04/05/2006 10:50:00 PM,027XX N ASHLAND AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,1931,019,32,7,26,1165147,1918454,2006,04/21/2006 04:05:47 AM,41.931840846,-87.668523269,"(41.931840846, -87.668523269)"
-4672429,HM271419,04/04/2006 04:00:00 AM,027XX N KENMORE AVE,1563,SEX OFFENSE,CRIMINAL SEXUAL ABUSE,RESIDENCE,false,false,1933,019,32,7,17,1168753,1918193,2006,05/01/2006 04:24:48 AM,41.931047158,-87.655279421,"(41.931047158, -87.655279421)"
-4790844,HM270047,04/03/2006 08:25:00 PM,089XX S COMMERCIAL AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0423,004,10,46,18,1197650,1846464,2006,08/05/2006 04:48:25 AM,41.733544282,-87.551490045,"(41.733544282, -87.551490045)"
-4782421,HM270012,04/03/2006 07:51:19 PM,091XX S CREGIER AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0413,004,8,48,18,1189820,1844590,2006,06/24/2006 03:34:29 AM,41.728593442,-87.580234808,"(41.728593442, -87.580234808)"
-4668723,HM269658,04/03/2006 05:09:00 PM,013XX W HASTINGS ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA PARKING LOT/GROUNDS,false,true,1231,012,2,28,08B,1167850,1893896,2006,04/08/2006 03:34:53 AM,41.864394113,-87.659299225,"(41.864394113, -87.659299225)"
-4668159,HM269442,04/03/2006 10:00:00 AM,117XX S MAPLEWOOD AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,2212,022,19,75,06,1161547,1826491,2006,04/06/2006 03:47:15 AM,41.679558775,-87.684304716,"(41.679558775, -87.684304716)"
-4692016,HM295841,04/03/2006 12:00:00 AM,022XX E 79TH ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,ATM (AUTOMATIC TELLER MACHINE),false,false,0414,004,7,43,11,1192269,1853061,2006,05/06/2006 03:25:41 AM,41.751779477,-87.570988853,"(41.751779477, -87.570988853)"
-4668031,HM268776,04/02/2006 06:00:00 PM,092XX S WOODLAWN AVE,0460,BATTERY,SIMPLE,STREET,false,false,0413,004,8,47,08B,1185857,1844153,2006,04/07/2006 03:45:50 AM,41.727488495,-87.594765682,"(41.727488495, -87.594765682)"
-4676247,HM268708,04/02/2006 03:00:00 AM,002XX E 103RD ST,0610,BURGLARY,FORCIBLE ENTRY,COMMERCIAL / BUSINESS OFFICE,false,false,0512,005,9,49,05,1179787,1836690,2006,04/28/2006 03:43:43 AM,41.707149828,-87.617228223,"(41.707149828, -87.617228223)"
-4771496,HM266362,04/01/2006 07:32:26 PM,001XX N LA CROSSE AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,1532,015,28,25,18,1144050,1900913,2006,06/10/2006 03:42:52 AM,41.884129171,-87.746493149,"(41.884129171, -87.746493149)"
-4664939,HM263258,03/31/2006 09:05:00 AM,047XX N KENNETH AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1722,017,45,16,26,1145637,1931343,2006,04/06/2006 03:47:15 AM,41.967602102,-87.739892087,"(41.967602102, -87.739892087)"
-4667225,HM263137,03/31/2006 08:00:00 AM,020XX N BURLING ST,0460,BATTERY,SIMPLE,ALLEY,false,false,1812,018,43,7,08B,1170914,1913752,2006,04/19/2006 03:48:12 AM,41.918813673,-87.647468764,"(41.918813673, -87.647468764)"
-4790839,HM262702,03/30/2006 09:55:00 PM,003XX S HOMAN AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1134,011,28,27,16,1153813,1898231,2006,06/20/2006 03:50:51 AM,41.876580687,-87.710713511,"(41.876580687, -87.710713511)"
-4662313,HM262046,03/30/2006 04:33:52 PM,106XX S AVENUE O,0935,MOTOR VEHICLE THEFT,"THEFT/RECOVERY: TRUCK,BUS,MHOME",OTHER,false,false,0432,004,10,52,07,1200903,1835207,2006,04/02/2006 04:37:42 AM,41.702572469,-87.539953006,"(41.702572469, -87.539953006)"
-4668215,HM264719,03/29/2006 10:30:00 PM,079XX S PRAIRIE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,0623,006,6,44,08B,1179363,1852621,2006,04/11/2006 03:34:47 AM,41.750876137,-87.618296275,"(41.750876137, -87.618296275)"
-4661789,HM261614,03/29/2006 05:00:00 PM,077XX N PAULINA ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2422,024,49,1,14,1163697,1951317,2006,04/01/2006 03:33:39 AM,42.022048862,-87.672920733,"(42.022048862, -87.672920733)"
-4659557,HM259010,03/28/2006 06:00:00 PM,031XX W MARQUETTE RD,0810,THEFT,OVER $500,STREET,false,false,0831,008,15,66,06,1156662,1860072,2006,12/04/2014 12:43:35 PM,41.77181043,-87.701283563,"(41.77181043, -87.701283563)"
-4659678,HM256568,03/27/2006 09:09:00 PM,043XX W CORTEZ ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1111,011,37,23,08B,1146908,1906673,2006,03/31/2006 03:37:39 AM,41.899881138,-87.735850857,"(41.899881138, -87.735850857)"
-4656649,HM255977,03/27/2006 02:50:00 PM,029XX W 65TH ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0823,008,15,66,08B,1157778,1861432,2006,04/04/2006 03:37:55 AM,41.775519891,-87.697155753,"(41.775519891, -87.697155753)"
-4729088,HM255478,03/27/2006 12:01:00 PM,100XX W OHARE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,AIRPORT/AIRCRAFT,true,false,1651,016,41,76,18,1100635,1934208,2006,05/13/2006 03:25:14 AM,41.976200173,-87.905312411,"(41.976200173, -87.905312411)"
-4655024,HM253446,03/26/2006 08:30:00 AM,075XX S COLES AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,0421,004,7,43,08A,1195768,1855900,2006,04/04/2006 03:37:55 AM,41.759484111,-87.558073149,"(41.759484111, -87.558073149)"
-4654985,HM252699,03/25/2006 07:30:00 PM,011XX W 104TH PL,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2232,022,34,73,14,1170768,1835550,2006,03/30/2006 03:33:32 AM,41.70422256,-87.65028884,"(41.70422256, -87.65028884)"
-4656202,HM252141,03/25/2006 01:10:00 PM,021XX N LECLAIRE AVE,0460,BATTERY,SIMPLE,STREET,false,false,2522,025,37,19,08B,1142005,1913549,2006,04/04/2006 03:37:55 AM,41.918841936,-87.753689194,"(41.918841936, -87.753689194)"
-4654074,HM251989,03/25/2006 09:00:00 AM,040XX W ADAMS ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1115,011,28,26,05,1149511,1898665,2006,04/03/2006 03:33:36 AM,41.877856182,-87.726497929,"(41.877856182, -87.726497929)"
-4724645,HM251357,03/24/2006 10:39:05 PM,019XX N ST LOUIS AVE,2093,NARCOTICS,FOUND SUSPECT NARCOTICS,APARTMENT,true,false,1422,014,35,22,26,1152984,1912842,2006,05/09/2006 03:25:05 AM,41.916691151,-87.713369695,"(41.916691151, -87.713369695)"
-4682792,HM284306,03/24/2006 08:30:00 AM,078XX S DAMEN AVE,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,OTHER,true,false,0611,006,18,71,07,1164438,1852334,2006,04/15/2006 04:32:33 AM,41.75041592,-87.672996842,"(41.75041592, -87.672996842)"
-4650458,HM249306,03/24/2006 12:03:00 AM,104XX S AVENUE F,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0432,004,10,52,14,1203446,1836368,2006,03/28/2006 03:57:36 AM,41.705693682,-87.530601823,"(41.705693682, -87.530601823)"
-4650886,HM248236,03/23/2006 01:30:00 PM,065XX S WINCHESTER AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,false,0726,007,15,67,08B,1164525,1861162,2006,03/26/2006 04:17:13 AM,41.774639349,-87.672429483,"(41.774639349, -87.672429483)"
-4652175,HM251106,03/22/2006 06:00:00 PM,050XX N MENARD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,VEHICLE NON-COMMERCIAL,false,false,1622,016,45,11,14,1136475,1933180,2006,03/27/2006 03:56:29 AM,41.972812194,-87.773536233,"(41.972812194, -87.773536233)"
-4644410,HM243573,03/20/2006 08:53:00 PM,029XX S BONFIELD ST,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE PORCH/HALLWAY,true,false,0923,009,11,60,26,1169089,1885419,2006,03/25/2006 04:20:22 AM,41.84110571,-87.654996732,"(41.84110571, -87.654996732)"
-4651044,HM242951,03/20/2006 02:43:00 PM,001XX E 47TH ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,0224,002,3,38,06,1178259,1873864,2006,03/26/2006 04:17:13 AM,41.809194199,-87.621697617,"(41.809194199, -87.621697617)"
-4645526,HM241695,03/19/2006 06:40:00 PM,065XX S HALSTED ST,0880,THEFT,PURSE-SNATCHING,SIDEWALK,false,false,0723,007,20,68,06,1172032,1861738,2006,03/25/2006 04:20:22 AM,41.776058354,-87.644893141,"(41.776058354, -87.644893141)"
-4644864,HM240745,03/19/2006 01:00:00 AM,009XX N LAWNDALE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1112,011,27,23,08B,1151494,1906347,2006,03/27/2006 03:56:29 AM,41.898897683,-87.719014857,"(41.898897683, -87.719014857)"
-4765926,HM239520,03/18/2006 01:23:24 PM,0000X N KARLOV AVE,0454,BATTERY,AGG PO HANDS NO/MIN INJURY,OTHER,false,false,1115,011,28,26,08B,1149020,1899743,2006,06/11/2007 03:52:33 PM,41.880823848,-87.72827287,"(41.880823848, -87.72827287)"
-4639633,HM238909,03/18/2006 03:35:00 AM,028XX N WESTERN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,1411,014,1,22,14,1159837,1918470,2006,03/19/2006 04:41:00 AM,41.931996078,-87.688036262,"(41.931996078, -87.688036262)"
-4639441,HM237689,03/17/2006 06:30:00 AM,026XX N PAULINA ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1931,019,32,7,07,1164515,1917508,2006,03/20/2006 03:53:54 AM,41.929258392,-87.670872627,"(41.929258392, -87.670872627)"
-4721125,HM234444,03/15/2006 09:00:00 PM,013XX W TAYLOR ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,STREET,true,false,1213,012,2,28,26,1167837,1895751,2006,05/13/2006 03:25:14 AM,41.869484665,-87.65929348,"(41.869484665, -87.65929348)"
-4637191,HM233984,03/15/2006 03:00:00 PM,024XX W CONGRESS PKWY,0810,THEFT,OVER $500,"SCHOOL, PRIVATE, BUILDING",false,false,1135,011,2,28,06,1160449,1897662,2006,12/04/2014 12:43:35 PM,41.874884616,-87.686363945,"(41.874884616, -87.686363945)"
-4637779,HM233374,03/15/2006 10:37:00 AM,021XX S LAFLIN ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",true,false,1222,012,25,31,08B,1166665,1889621,2006,03/22/2006 04:29:24 AM,41.852688583,-87.663771704,"(41.852688583, -87.663771704)"
-4638234,HM233949,03/15/2006 10:00:00 AM,056XX N ST LOUIS AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1711,017,39,13,05,1152070,1937294,2006,08/12/2006 04:03:08 AM,41.9838073,-87.716080734,"(41.9838073, -87.716080734)"
-4637459,HM233210,03/15/2006 09:30:00 AM,030XX N MOBILE AVE,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",true,false,2511,025,36,19,08B,1133838,1919612,2006,03/19/2006 04:41:00 AM,41.935627071,-87.783553257,"(41.935627071, -87.783553257)"
-4640907,HM232439,03/14/2006 07:50:00 PM,065XX S HALSTED ST,1570,SEX OFFENSE,PUBLIC INDECENCY,COMMERCIAL / BUSINESS OFFICE,false,false,0723,007,20,68,17,1172036,1861575,2006,04/14/2006 04:02:25 AM,41.775610975,-87.644883261,"(41.775610975, -87.644883261)"
-4643231,HM231003,03/14/2006 03:05:00 AM,059XX S WABASH AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0233,002,20,40,08B,1177775,1865516,2006,09/22/2006 04:36:35 AM,41.786297483,-87.623725583,"(41.786297483, -87.623725583)"
-4630496,HM228772,03/12/2006 08:55:00 PM,035XX W ADAMS ST,0454,BATTERY,AGG PO HANDS NO/MIN INJURY,APARTMENT,true,true,1123,011,28,27,08B,1152962,1898808,2006,03/15/2006 05:07:15 AM,41.878180934,-87.713822837,"(41.878180934, -87.713822837)"
-4629626,HM227561,03/12/2006 03:16:54 AM,024XX W TAYLOR ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,false,1135,011,28,28,08B,1160010,1895591,2006,03/14/2006 03:54:43 AM,41.869210664,-87.688032936,"(41.869210664, -87.688032936)"
-4629851,HM228108,03/12/2006 02:00:00 AM,024XX N LINCOLN AVE,0810,THEFT,OVER $500,MOVIE HOUSE/THEATER,false,false,1933,019,43,7,06,1170293,1916345,2006,12/04/2014 12:43:35 PM,41.9259426,-87.649674411,"(41.9259426, -87.649674411)"
-4639509,HM227357,03/12/2006 12:05:00 AM,067XX S ADA ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0724,007,17,67,08B,1168448,1860064,2006,05/26/2006 03:55:36 AM,41.771542669,-87.658079979,"(41.771542669, -87.658079979)"
-4633556,HM225837,03/11/2006 09:11:00 AM,017XX E 71ST PL,0460,BATTERY,SIMPLE,APARTMENT,false,true,0324,003,5,43,08B,1189233,1857880,2006,03/17/2006 04:34:02 AM,41.765076516,-87.581959966,"(41.765076516, -87.581959966)"
-4625376,HM222871,03/09/2006 06:00:00 PM,082XX S CHAPPEL AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,0414,004,8,46,07,1191244,1850939,2006,07/10/2006 03:52:16 AM,41.745981384,-87.574813489,"(41.745981384, -87.574813489)"
-4626947,HM222374,03/09/2006 01:33:13 PM,085XX S MARSHFIELD AVE,1790,OFFENSE INVOLVING CHILDREN,CHILD ABDUCTION,ALLEY,false,false,0614,006,18,71,20,1166874,1847895,2006,03/17/2006 04:34:02 AM,41.738183018,-87.664196685,"(41.738183018, -87.664196685)"
-4624044,HM220151,03/07/2006 03:00:00 PM,024XX W NORTH AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1434,014,1,24,08B,1160084,1910612,2006,03/14/2006 03:54:43 AM,41.910428061,-87.687346102,"(41.910428061, -87.687346102)"
-4626998,HM218889,03/06/2006 09:15:00 PM,022XX N KEYSTONE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,2525,025,31,20,03,1148982,1914379,2006,03/19/2006 04:41:00 AM,41.920987267,-87.728033212,"(41.920987267, -87.728033212)"
-4707434,HM216429,03/06/2006 12:57:23 PM,005XX E BROWNING AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),CHA APARTMENT,true,false,0212,002,4,35,18,1180710,1881381,2006,05/02/2006 03:29:06 AM,41.829765384,-87.612476591,"(41.829765384, -87.612476591)"
-4620350,HM215094,03/05/2006 03:16:39 PM,032XX N MILWAUKEE AVE,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,false,false,1732,017,30,21,06,1149882,1921190,2006,03/09/2006 03:43:37 AM,41.939659761,-87.724548694,"(41.939659761, -87.724548694)"
-4620196,HM214587,03/04/2006 04:00:00 PM,042XX N KEELER AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,1722,017,38,16,14,1147599,1927959,2006,03/09/2006 03:43:37 AM,41.958278634,-87.732765147,"(41.958278634, -87.732765147)"
-4614095,HM207782,03/01/2006 03:34:33 PM,002XX N CENTRAL AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1512,015,29,25,08B,1138960,1901158,2006,03/09/2006 03:43:37 AM,41.884895475,-87.765178509,"(41.884895475, -87.765178509)"
-4618232,HM205640,02/28/2006 08:50:00 AM,007XX E 75TH ST,0810,THEFT,OVER $500,STREET,false,false,0624,006,6,69,06,1182226,1855397,2006,12/04/2014 12:43:35 PM,41.758428003,-87.607719152,"(41.758428003, -87.607719152)"
-4648368,HM245138,02/28/2006 12:00:00 AM,060XX N PAULINA ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2433,024,40,77,06,1164040,1940461,2006,12/04/2014 12:43:35 PM,41.992252494,-87.671967108,"(41.992252494, -87.671967108)"
-4608697,HM203502,02/27/2006 08:45:00 AM,002XX W WACKER DR,0820,THEFT,$500 AND UNDER,STREET,false,false,0113,001,42,32,06,1174617,1902081,2006,12/04/2014 12:43:35 PM,41.886705791,-87.63421316,"(41.886705791, -87.63421316)"
-4610130,HM202576,02/26/2006 04:45:00 PM,070XX N CLARK ST,0440,BATTERY,AGG: HANDS/FIST/FEET NO/MINOR INJURY,SIDEWALK,false,false,2424,024,49,1,08B,1163327,1947034,2006,03/05/2006 04:52:17 AM,42.010304056,-87.674403678,"(42.010304056, -87.674403678)"
-4700413,HM201291,02/25/2006 08:20:00 PM,027XX W CERMAK RD,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1034,010,28,30,16,1158284,1889234,2006,05/02/2006 03:29:06 AM,41.851801823,-87.694543181,"(41.851801823, -87.694543181)"
-4691824,HM199728,02/25/2006 12:50:00 AM,024XX W WALTON ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,1312,012,1,24,18,1159742,1906284,2006,04/29/2006 03:51:07 AM,41.898558743,-87.688721957,"(41.898558743, -87.688721957)"
-4609699,HM197651,02/23/2006 11:00:00 PM,003XX E 131ST PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0533,005,9,54,08B,1180520,1818348,2006,03/09/2006 03:43:37 AM,41.65679997,-87.61510394,"(41.65679997, -87.61510394)"
-4603263,HM197910,02/23/2006 08:00:00 PM,105XX S VERNON AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0512,005,9,49,14,1181072,1835086,2006,02/25/2006 03:52:18 AM,41.70271882,-87.612571752,"(41.70271882, -87.612571752)"
-4739002,HM348736,02/23/2006 12:00:00 PM,072XX S PHILLIPS AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,APARTMENT,false,false,0334,003,7,43,06,1193863,1857225,2006,05/17/2006 03:39:53 AM,41.76316691,-87.565011414,"(41.76316691, -87.565011414)"
-4607147,HM194238,02/22/2006 09:23:48 AM,044XX W CARROLL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,1113,011,28,26,08B,1146720,1901888,2006,03/02/2006 04:44:02 AM,41.886754161,-87.736663612,"(41.886754161, -87.736663612)"
-4602658,HM194456,02/22/2006 08:50:00 AM,047XX S BISHOP ST,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,0933,009,20,61,08B,1167511,1873093,2006,02/27/2006 04:31:37 AM,41.807315947,-87.661141299,"(41.807315947, -87.661141299)"
-4714038,HM191651,02/20/2006 08:42:13 PM,032XX N CICERO AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1634,016,30,15,16,1143769,1920876,2006,05/06/2006 03:25:41 AM,41.938915045,-87.747023917,"(41.938915045, -87.747023917)"
-4706584,HM191316,02/20/2006 05:23:12 PM,007XX N LOCKWOOD AVE,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),STREET,true,false,1524,015,37,25,18,1140858,1904186,2006,05/02/2006 03:29:06 AM,41.893169986,-87.758134122,"(41.893169986, -87.758134122)"
-4706162,HM191258,02/20/2006 04:36:21 PM,0000X E 23RD ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0134,001,2,33,18,1176882,1889075,2006,05/02/2006 03:29:06 AM,41.850965586,-87.626288975,"(41.850965586, -87.626288975)"
-4685801,HM189790,02/19/2006 05:20:00 PM,015XX S KEDZIE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1022,010,24,29,18,1155321,1892135,2006,04/29/2006 03:51:07 AM,41.859822463,-87.705340335,"(41.859822463, -87.705340335)"
-4593317,HM185031,02/16/2006 06:10:00 PM,031XX W LAWRENCE AVE,1330,CRIMINAL TRESPASS,TO LAND,SMALL RETAIL STORE,true,false,1713,017,33,14,26,1154326,1931678,2006,02/19/2006 04:13:23 AM,41.968351768,-87.707934291,"(41.968351768, -87.707934291)"
-4592716,HM184974,02/16/2006 07:15:00 AM,035XX N ROCKWELL ST,0810,THEFT,OVER $500,STREET,false,false,1913,019,47,5,06,1158110,1923324,2006,12/04/2014 12:43:35 PM,41.94535127,-87.69424977,"(41.94535127, -87.69424977)"
-4591306,HM183787,02/16/2006 02:00:00 AM,015XX S KEDZIE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,false,1022,010,24,29,08B,1155230,1892551,2006,02/25/2006 03:52:18 AM,41.860965839,-87.705663209,"(41.860965839, -87.705663209)"
-4593838,HM181880,02/15/2006 01:05:00 AM,124XX S EMERALD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,0523,005,34,53,08B,1173627,1822189,2006,02/20/2006 03:28:46 AM,41.667495167,-87.640213312,"(41.667495167, -87.640213312)"
-4597769,HM179658,02/13/2006 07:50:00 PM,063XX S HALSTED ST,1345,CRIMINAL DAMAGE,TO CITY OF CHICAGO PROPERTY,STREET,false,false,0723,007,20,68,14,1172016,1862832,2006,02/25/2006 03:52:18 AM,41.779060769,-87.64491969,"(41.779060769, -87.64491969)"
-4591887,HM178264,02/13/2006 12:50:00 PM,019XX N SEDGWICK ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESTAURANT,false,false,1814,018,43,7,14,1173319,1913308,2006,02/18/2006 03:56:51 AM,41.917542197,-87.638645845,"(41.917542197, -87.638645845)"
-4588726,HM178595,02/13/2006 10:05:00 AM,067XX S THROOP ST,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,false,false,0724,007,17,67,26,1168781,1860000,2006,02/17/2006 03:48:57 AM,41.771359864,-87.656861158,"(41.771359864, -87.656861158)"
-4687479,HM178005,02/12/2006 08:00:00 PM,027XX W LAWRENCE AVE,2027,NARCOTICS,POSS: CRACK,OTHER,true,false,1911,019,47,4,18,1157282,1931730,2006,04/29/2006 03:51:07 AM,41.968434735,-87.697063787,"(41.968434735, -87.697063787)"
-4691333,HM176479,02/11/2006 09:05:00 PM,012XX W GUNNISON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,2033,020,48,3,18,1167398,1932504,2006,04/29/2006 03:51:07 AM,41.970346442,-87.659845491,"(41.970346442, -87.659845491)"
-4585541,HM175483,02/11/2006 11:11:20 AM,014XX S ALBANY AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,"SCHOOL, PUBLIC, GROUNDS",false,false,1022,010,24,29,14,1155903,1892942,2006,02/15/2006 03:40:56 AM,41.862025257,-87.703182225,"(41.862025257, -87.703182225)"
-4582904,HM172749,02/09/2006 08:47:00 PM,047XX S EVANS AVE,031A,ROBBERY,ARMED: HANDGUN,STREET,false,false,0223,002,4,38,03,1182011,1873817,2006,03/22/2006 04:29:24 AM,41.808979148,-87.607937582,"(41.808979148, -87.607937582)"
-4582942,HM171329,02/09/2006 07:45:00 AM,003XX W 111TH PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,0522,005,34,49,08B,1176119,1831027,2006,03/25/2006 04:20:22 AM,41.69169265,-87.630829521,"(41.69169265, -87.630829521)"
-4580982,HM169927,02/08/2006 12:25:00 PM,050XX S LAFLIN ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0933,009,16,61,08B,1167224,1871441,2006,02/13/2006 03:49:20 AM,41.802788826,-87.66224124,"(41.802788826, -87.66224124)"
-4579583,HM169879,02/08/2006 12:00:00 PM,016XX W LE MOYNE ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,OTHER,false,false,1433,014,1,24,04A,1164966,1910121,2006,02/12/2006 04:11:43 AM,41.908978428,-87.669425489,"(41.908978428, -87.669425489)"
-4660010,HM168826,02/07/2006 07:53:48 PM,021XX S KOSTNER AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1012,010,24,29,18,1147352,1889220,2006,04/01/2006 03:33:39 AM,41.851979557,-87.734667158,"(41.851979557, -87.734667158)"
-4578679,HM168725,02/07/2006 07:05:00 PM,113XX S LONGWOOD DR,4651,OTHER OFFENSE,SEX OFFENDER: FAIL REG NEW ADD,RESIDENCE,true,false,2212,022,19,75,26,1164726,1829223,2006,06/11/2007 03:52:33 PM,41.686989564,-87.672591438,"(41.686989564, -87.672591438)"
-4578101,HM168467,02/07/2006 05:03:34 PM,090XX S COMMERCIAL AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,SMALL RETAIL STORE,true,false,0424,004,10,46,14,1197763,1845268,2006,02/10/2006 03:41:04 AM,41.730259544,-87.551115898,"(41.730259544, -87.551115898)"
-4578162,HM168946,02/07/2006 08:00:00 AM,053XX N WASHTENAW AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2011,020,40,4,05,1157430,1935311,2006,02/15/2006 03:40:56 AM,41.978258165,-87.696421703,"(41.978258165, -87.696421703)"
-4577824,HM166487,02/06/2006 03:00:00 PM,023XX N MOODY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2512,025,29,19,08B,1134973,1915063,2006,02/20/2006 03:28:46 AM,41.923124048,-87.779489974,"(41.923124048, -87.779489974)"
-4575564,HM164679,02/05/2006 12:20:00 PM,005XX N DEARBORN ST,1780,OFFENSE INVOLVING CHILDREN,OTHER OFFENSE,RESIDENCE,false,true,1831,018,42,8,26,1175878,1904085,2006,02/15/2006 03:40:56 AM,41.892176591,-87.629522129,"(41.892176591, -87.629522129)"
-4580071,HM170713,02/05/2006 09:00:00 AM,059XX W DIVERSEY AVE,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,2514,025,30,19,06,1136298,1918039,2006,12/04/2014 12:43:35 PM,41.931266941,-87.77455012,"(41.931266941, -87.77455012)"
-4573498,HM164165,02/05/2006 03:45:00 AM,066XX S STATE ST,1310,CRIMINAL DAMAGE,TO PROPERTY,GAS STATION,true,false,0322,003,20,69,14,1177469,1860721,2006,02/07/2006 03:28:20 AM,41.77314643,-87.624992346,"(41.77314643, -87.624992346)"
-4574360,HM163139,02/04/2006 03:30:00 PM,053XX N BROADWAY,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,2013,020,48,77,06,1167314,1935410,2006,02/09/2006 03:37:51 AM,41.978322407,-87.660070384,"(41.978322407, -87.660070384)"
-4575977,HM162932,02/04/2006 01:39:00 PM,0000X N PULASKI RD,1330,CRIMINAL TRESPASS,TO LAND,GAS STATION,true,false,1122,011,28,26,26,1149764,1899891,2006,02/09/2006 03:37:51 AM,41.88121555,-87.725537088,"(41.88121555, -87.725537088)"
-4573425,HM162767,02/04/2006 12:10:00 PM,052XX N LINCOLN AVE,1330,CRIMINAL TRESPASS,TO LAND,RESTAURANT,true,false,2011,020,40,4,26,1158504,1934480,2006,02/07/2006 03:28:20 AM,41.975955881,-87.692494907,"(41.975955881, -87.692494907)"
-4573638,HM162609,02/04/2006 10:36:37 AM,121XX S EMERALD AVE,1780,OFFENSE INVOLVING CHILDREN,OTHER OFFENSE,RESIDENCE,false,false,0523,005,34,53,26,1173558,1824234,2006,02/14/2006 03:22:09 AM,41.673108507,-87.640405655,"(41.673108507, -87.640405655)"
-4571591,HM160832,02/03/2006 12:00:00 PM,074XX S YATES BLVD,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0334,003,7,43,14,1193457,1856019,2006,02/09/2006 03:37:51 AM,41.759867495,-87.566538852,"(41.759867495, -87.566538852)"
-4578638,HM160265,02/03/2006 05:45:00 AM,023XX W ADDISON ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1912,019,47,5,14,1160032,1923887,2006,02/10/2006 03:41:04 AM,41.946856635,-87.687169614,"(41.946856635, -87.687169614)"
-4567016,HM154871,01/31/2006 09:07:02 AM,002XX N LAVERGNE AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, GROUNDS",false,false,1532,015,28,25,08B,1142961,1901186,2006,06/11/2007 03:52:33 PM,41.884898681,-87.75048532,"(41.884898681, -87.75048532)"
-4563955,HM154280,01/30/2006 08:55:00 PM,005XX N LAWLER AVE,0560,ASSAULT,SIMPLE,RESIDENCE PORCH/HALLWAY,false,true,1532,015,28,25,08A,1142568,1902910,2006,02/03/2006 03:39:58 AM,41.889636861,-87.751885598,"(41.889636861, -87.751885598)"
-4561630,HM151390,01/28/2006 10:00:00 PM,067XX S PAXTON AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0331,003,5,43,14,1192083,1860488,2006,01/31/2006 03:37:56 AM,41.772164275,-87.571429384,"(41.772164275, -87.571429384)"
-4560308,HM149455,01/27/2006 10:45:00 PM,079XX S ELLIS AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0624,006,8,44,07,1184311,1852507,2006,04/24/2006 03:36:24 AM,41.750449038,-87.600168148,"(41.750449038, -87.600168148)"
-4560011,HM148391,01/27/2006 05:35:00 PM,015XX E 71ST PL,0460,BATTERY,SIMPLE,STREET,false,false,0324,003,5,43,08B,1187500,1857844,2006,01/31/2006 03:37:56 AM,41.765019094,-87.588312969,"(41.765019094, -87.588312969)"
-4561300,HM147668,01/26/2006 11:00:00 PM,010XX N MENARD AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1511,015,29,25,07,1137540,1906385,2006,06/11/2007 03:52:33 PM,41.899264709,-87.770267093,"(41.899264709, -87.770267093)"
-4564191,HM146503,01/26/2006 05:50:00 PM,0000X W 47TH ST,0820,THEFT,$500 AND UNDER,PARK PROPERTY,false,false,0231,002,3,38,06,1176619,1873814,2006,12/04/2014 12:43:35 PM,41.809094102,-87.627714291,"(41.809094102, -87.627714291)"
-4567004,HM146484,01/26/2006 05:25:00 PM,012XX N BURLING ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,1822,018,27,8,26,1171043,1908530,2006,02/03/2006 03:39:58 AM,41.904481394,-87.647148303,"(41.904481394, -87.647148303)"
-4555428,HM145149,01/26/2006 12:20:00 AM,035XX W HURON ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1121,011,27,23,26,1152867,1904404,2006,02/07/2006 03:28:20 AM,41.893538813,-87.714023369,"(41.893538813, -87.714023369)"
-4578230,HM145168,01/26/2006 12:05:00 AM,046XX W 59TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,0813,008,13,65,14,1146615,1865053,2006,02/11/2006 03:54:44 AM,41.785675833,-87.737986743,"(41.785675833, -87.737986743)"
-4558164,HM144944,01/25/2006 09:20:27 PM,050XX N KENNETH AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1712,017,39,14,08B,1145743,1933199,2006,01/31/2006 03:37:56 AM,41.97269309,-87.739455026,"(41.97269309, -87.739455026)"
-4627080,HM143626,01/25/2006 09:00:00 AM,063XX W 56TH ST,2022,NARCOTICS,POSS: COCAINE,"SCHOOL, PUBLIC, BUILDING",true,false,0811,008,23,56,18,1135058,1866707,2006,03/18/2006 04:28:50 AM,41.790426489,-87.780322015,"(41.790426489, -87.780322015)"
-4556084,HM144069,01/25/2006 02:00:00 AM,040XX N ELSTON AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,1723,017,39,16,14,1150903,1926632,2006,01/30/2006 03:44:08 AM,41.954573068,-87.720653219,"(41.954573068, -87.720653219)"
-4642723,HM141940,01/24/2006 01:20:00 PM,039XX W GRENSHAW ST,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,STREET,true,false,1132,011,24,29,18,1150488,1894794,2006,03/28/2006 03:57:36 AM,41.867214707,-87.723011661,"(41.867214707, -87.723011661)"
-4563934,HM141290,01/23/2006 11:00:00 AM,005XX E 74TH ST,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,true,0323,003,6,69,05,1181301,1856034,2006,02/16/2006 03:38:57 AM,41.760197358,-87.611089567,"(41.760197358, -87.611089567)"
-4557294,HM146305,01/22/2006 11:55:00 PM,014XX W TAYLOR ST,0810,THEFT,OVER $500,RESTAURANT,false,false,1231,012,2,28,06,1166992,1895647,2006,12/04/2014 12:43:35 PM,41.869217445,-87.662398676,"(41.869217445, -87.662398676)"
-4558153,HM144502,01/22/2006 08:00:00 PM,021XX N LINCOLN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,1812,018,43,7,26,1172374,1914493,2006,02/06/2006 03:16:34 AM,41.920814845,-87.642082678,"(41.920814845, -87.642082678)"
-4546278,HM134420,01/20/2006 02:30:00 AM,001XX W HUBBARD ST,0460,BATTERY,SIMPLE,BAR OR TAVERN,false,false,1831,018,42,8,08B,1175314,1903252,2006,02/15/2006 03:40:56 AM,41.88990347,-87.631618456,"(41.88990347, -87.631618456)"
-4545063,HM133508,01/19/2006 04:00:00 PM,115XX S OAKLEY AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2212,022,19,75,26,1162992,1828077,2006,05/18/2007 06:17:05 PM,41.683881055,-87.678971263,"(41.683881055, -87.678971263)"
-4550229,HM132837,01/19/2006 11:40:00 AM,029XX S STATE ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,true,false,2113,001,3,35,26,1176716,1885520,2006,01/26/2006 07:28:55 PM,41.841214155,-87.627005535,"(41.841214155, -87.627005535)"
-4540120,HM128455,01/16/2006 09:00:00 PM,087XX S COMMERCIAL AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0424,004,10,46,06,1197700,1847611,2006,12/04/2014 12:43:35 PM,41.736690495,-87.551268691,"(41.736690495, -87.551268691)"
-4567704,HM157386,01/14/2006 10:40:00 AM,012XX S ASHLAND AVE,1120,DECEPTIVE PRACTICE,FORGERY,BANK,true,false,1224,012,2,28,10,1165874,1894639,2006,03/25/2006 04:20:22 AM,41.866475313,-87.666531874,"(41.866475313, -87.666531874)"
-4543673,HM123389,01/14/2006 01:06:00 AM,035XX S CALUMET AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0211,002,4,35,14,1179136,1881574,2006,05/18/2007 06:17:05 PM,41.830331088,-87.618245628,"(41.830331088, -87.618245628)"
-4540706,HM128718,01/13/2006 04:15:00 PM,069XX N HIAWATHA AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,"SCHOOL, PUBLIC, BUILDING",false,false,1621,016,41,12,14,1135081,1945795,2006,01/26/2006 07:28:55 PM,42.007453697,-87.778361731,"(42.007453697, -87.778361731)"
-4537372,HM123298,01/13/2006 01:00:00 PM,022XX N LAWLER AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,2522,025,31,19,14,1142400,1914280,2006,01/26/2006 07:28:55 PM,41.920840542,-87.752219706,"(41.920840542, -87.752219706)"
-4610750,HM119169,01/11/2006 07:10:00 PM,039XX W IRVING PARK RD,1670,GAMBLING,GAME/AMUSEMENT DEVICE,RESTAURANT,true,false,1732,017,39,16,19,1149063,1926242,2006,09/19/2010 12:45:18 AM,41.953538775,-87.727427531,"(41.953538775, -87.727427531)"
-4534035,HM118401,01/11/2006 01:28:13 PM,079XX S GREENWOOD AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,false,0624,006,8,44,06,1184891,1852521,2006,01/26/2006 07:28:55 PM,41.750473874,-87.598042348,"(41.750473874, -87.598042348)"
-4604592,HM118073,01/11/2006 12:05:00 PM,039XX W GRENSHAW ST,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,STREET,true,false,1132,011,24,29,18,1150488,1894794,2006,03/05/2006 04:52:17 AM,41.867214707,-87.723011661,"(41.867214707, -87.723011661)"
-4535708,HM118099,01/11/2006 10:31:00 AM,027XX W FULTON ST,0820,THEFT,$500 AND UNDER,SMALL RETAIL STORE,false,false,1331,012,27,27,06,1157934,1901933,2006,12/04/2014 12:43:35 PM,41.886656296,-87.695481445,"(41.886656296, -87.695481445)"
-4534052,HM116831,01/10/2006 07:00:00 AM,114XX S ADA ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,2234,022,34,75,05,1169419,1828937,2006,05/18/2007 06:17:05 PM,41.686104673,-87.655419247,"(41.686104673, -87.655419247)"
-4525138,HM113126,01/08/2006 01:45:00 PM,018XX W MONROE ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,CHA APARTMENT,false,false,1211,012,2,28,26,1164255,1899544,2006,01/26/2006 07:28:55 PM,41.87996941,-87.672336705,"(41.87996941, -87.672336705)"
-4536448,HM124161,01/08/2006 12:05:00 AM,005XX W OAKDALE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2333,019,44,6,07,1172115,1919898,2006,05/18/2007 06:17:05 PM,41.935652123,-87.642874373,"(41.935652123, -87.642874373)"
-4524963,HM112140,01/07/2006 09:45:00 PM,016XX S HOMAN AVE,0460,BATTERY,SIMPLE,STREET,false,false,1021,010,24,29,08B,1154013,1891476,2006,05/18/2007 06:17:05 PM,41.858040245,-87.710159219,"(41.858040245, -87.710159219)"
-4525151,HM112050,01/07/2006 08:00:00 PM,026XX N ELSTON AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,1432,014,1,22,06,1160776,1917684,2006,05/18/2007 06:17:05 PM,41.929819797,-87.684607426,"(41.929819797, -87.684607426)"
-4536325,HM111776,01/07/2006 04:50:00 PM,008XX E 53RD ST,0560,ASSAULT,SIMPLE,APARTMENT,true,false,2131,002,5,41,08A,1182955,1870097,2006,05/18/2007 06:17:05 PM,41.798749254,-87.604590903,"(41.798749254, -87.604590903)"
-4524768,HM110153,01/06/2006 06:45:00 PM,022XX S MILLARD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1013,010,22,30,14,1152343,1888986,2006,05/18/2007 06:17:05 PM,41.851240488,-87.716354864,"(41.851240488, -87.716354864)"
-4530450,HM109566,01/06/2006 12:20:00 PM,001XX E 47TH ST,1330,CRIMINAL TRESPASS,TO LAND,RESTAURANT,false,false,0221,002,3,38,26,1178102,1873939,2006,01/26/2006 07:28:55 PM,41.809403571,-87.622271183,"(41.809403571, -87.622271183)"
-4522298,HM109473,01/06/2006 02:30:00 AM,051XX S ADA ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,0933,009,16,61,14,1168238,1870712,2006,02/03/2006 03:39:58 AM,41.800766583,-87.658543438,"(41.800766583, -87.658543438)"
-4516206,HM105144,01/03/2006 10:15:00 PM,075XX S COLFAX AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0421,004,7,43,26,1194799,1855229,2006,05/18/2007 06:17:05 PM,41.757666746,-87.561646497,"(41.757666746, -87.561646497)"
-4516622,HM102695,01/02/2006 04:30:10 PM,013XX W 13TH ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,false,false,1231,012,2,28,04A,1167785,1894147,2006,05/18/2007 06:17:05 PM,41.865084278,-87.659530606,"(41.865084278, -87.659530606)"
-4515683,HM101750,01/01/2006 11:00:00 PM,0000X W ONTARIO ST,0890,THEFT,FROM BUILDING,APARTMENT,false,false,1832,018,42,8,06,1175891,1904437,2006,01/26/2006 07:28:55 PM,41.893142205,-87.629463781,"(41.893142205, -87.629463781)"
-4581671,HM172459,01/01/2006 09:00:00 PM,048XX W MONTANA ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2521,025,31,19,26,1143701,1915829,2006,02/16/2006 03:38:57 AM,41.925066863,-87.747400603,"(41.925066863, -87.747400603)"
-9398072,HW541230,01/01/2006 09:00:00 AM,049XX N AVERS AVE,1752,OFFENSE INVOLVING CHILDREN,AGG CRIM SEX ABUSE FAM MEMBER,RESIDENCE,false,false,1712,,39,14,20,,,2006,01/17/2014 12:40:17 AM,,,
-4582633,HL818109,12/30/2005 09:44:39 PM,047XX W ERIE ST,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,1111,011,28,25,18,1144671,1903933,2005,02/14/2006 03:22:09 AM,41.892404727,-87.744136594,"(41.892404727, -87.744136594)"
-4511361,HL817693,12/30/2005 04:20:00 PM,003XX W 23RD PL,0320,ROBBERY,STRONGARM - NO WEAPON,DRIVEWAY - RESIDENTIAL,false,false,2111,009,25,34,03,1174361,1888668,2005,01/30/2006 03:44:08 AM,41.849905334,-87.635553628,"(41.849905334, -87.635553628)"
-4512098,HL816710,12/30/2005 02:15:00 AM,033XX W BELMONT AVE,0460,BATTERY,SIMPLE,STREET,false,false,1733,017,35,21,08B,1153422,1921125,2005,01/26/2006 03:51:08 AM,41.939411673,-87.711539799,"(41.939411673, -87.711539799)"
-4515277,HL815699,12/29/2005 03:00:00 PM,008XX N MICHIGAN AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,1833,018,42,8,06,1177376,1906134,2005,01/26/2006 03:51:08 AM,41.897765295,-87.623958464,"(41.897765295, -87.623958464)"
-4507572,HL814762,12/29/2005 01:50:00 AM,073XX N WESTERN AVE,0870,THEFT,POCKET-PICKING,BAR OR TAVERN,false,false,2411,024,49,2,06,1159055,1948443,2005,01/26/2006 03:51:08 AM,42.014259548,-87.690082954,"(42.014259548, -87.690082954)"
-4534369,HL005521,12/27/2005 04:00:00 PM,002XX E ILLINOIS ST,0610,BURGLARY,FORCIBLE ENTRY,FACTORY/MANUFACTURING BUILDING,false,false,1834,018,42,8,05,1178214,1903711,2005,03/05/2006 04:52:17 AM,41.891097416,-87.62095451,"(41.891097416, -87.62095451)"
-4521384,HL812254,12/27/2005 03:30:00 PM,019XX N BURLING ST,1320,CRIMINAL DAMAGE,TO VEHICLE,DRIVEWAY - RESIDENTIAL,false,false,1813,018,43,7,14,1170936,1912992,2005,01/26/2006 03:51:08 AM,41.916727712,-87.647410268,"(41.916727712, -87.647410268)"
-4504759,HL811324,12/27/2005 09:00:00 AM,016XX W GREENLEAF AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,APARTMENT,false,false,2423,024,49,1,04B,1164432,1947070,2005,01/30/2006 03:44:08 AM,42.01037943,-87.670336969,"(42.01037943, -87.670336969)"
-4506878,HL813307,12/26/2005 08:00:00 PM,026XX N CENTRAL AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,COMMERCIAL / BUSINESS OFFICE,false,false,2514,025,30,19,14,1138623,1917266,2005,01/26/2006 03:51:08 AM,41.929103848,-87.766024838,"(41.929103848, -87.766024838)"
-4503414,HL809696,12/25/2005 09:00:00 PM,016XX N PAULINA ST,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,1433,014,32,24,06,1164722,1910921,2005,12/04/2014 12:43:35 PM,41.911178861,-87.670299112,"(41.911178861, -87.670299112)"
-4502652,HL808226,12/24/2005 07:30:00 PM,019XX S LOOMIS ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,1222,012,25,31,08B,1167382,1890660,2005,01/26/2006 03:51:08 AM,41.85552432,-87.661110247,"(41.85552432, -87.661110247)"
-4499434,HL803048,12/22/2005 12:10:00 AM,062XX S KILDARE AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE PORCH/HALLWAY,true,false,0813,008,13,65,26,1148739,1862800,2005,01/26/2006 03:51:08 AM,41.779452619,-87.730256985,"(41.779452619, -87.730256985)"
-4502501,HL802318,12/21/2005 10:30:00 AM,001XX E 83RD ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0631,006,6,44,05,1178749,1850046,2005,01/26/2006 03:51:08 AM,41.743824017,-87.620624411,"(41.743824017, -87.620624411)"
-4501030,HL800779,12/20/2005 09:26:00 PM,023XX S STATE ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,0134,001,3,33,26,1176640,1888950,2005,01/26/2006 03:51:08 AM,41.850628042,-87.62718093,"(41.850628042, -87.62718093)"
-4497354,HL800397,12/20/2005 08:00:00 AM,055XX N NEENAH AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1613,016,41,10,05,1131967,1936416,2005,01/26/2006 03:51:08 AM,41.981771723,-87.790037894,"(41.981771723, -87.790037894)"
-4496250,HL798708,12/19/2005 07:05:00 PM,003XX E ILLINOIS ST,1121,DECEPTIVE PRACTICE,COUNTERFEITING DOCUMENT,BAR OR TAVERN,false,false,1834,018,42,8,10,1178703,1903728,2005,01/26/2006 03:51:08 AM,41.891132911,-87.619158156,"(41.891132911, -87.619158156)"
-4494779,HL797824,12/19/2005 11:45:00 AM,013XX S KOSTNER AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,1011,010,24,29,04B,1147228,1893239,2005,01/26/2006 03:51:08 AM,41.863010573,-87.735019486,"(41.863010573, -87.735019486)"
-4498679,HL797219,12/19/2005 12:45:00 AM,059XX S MAY ST,031A,ROBBERY,ARMED: HANDGUN,APARTMENT,false,false,0712,007,16,68,03,1169710,1865275,2005,01/30/2006 03:44:08 AM,41.785814999,-87.653302857,"(41.785814999, -87.653302857)"
-4491843,HL794404,12/17/2005 08:00:00 AM,083XX S MACKINAW AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0424,004,10,46,26,1199881,1850294,2005,01/26/2006 03:51:08 AM,41.743998175,-87.543188149,"(41.743998175, -87.543188149)"
-4491043,HL792389,12/16/2005 09:50:00 AM,014XX N MILWAUKEE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESTAURANT,false,false,1424,014,1,24,14,1163971,1909552,2005,01/26/2006 03:51:08 AM,41.907438128,-87.673096738,"(41.907438128, -87.673096738)"
-4490285,HL791666,12/15/2005 09:10:00 PM,035XX W FULLERTON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,SMALL RETAIL STORE,false,false,1413,014,26,22,14,1152599,1915692,2005,01/26/2006 03:51:08 AM,41.924519424,-87.71470865,"(41.924519424, -87.71470865)"
-4489108,HL790838,12/14/2005 08:00:00 PM,003XX W HILL ST,0810,THEFT,OVER $500,STREET,false,false,1823,018,27,8,06,1173732,1907631,2005,12/04/2014 12:43:35 PM,41.901955042,-87.63729774,"(41.901955042, -87.63729774)"
-4487424,HL788987,12/13/2005 11:00:00 AM,038XX W MONTROSE AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,SMALL RETAIL STORE,false,false,1723,017,39,16,14,1150111,1928928,2005,01/26/2006 03:51:08 AM,41.960888959,-87.723504688,"(41.960888959, -87.723504688)"
-4486551,HL786216,12/12/2005 10:40:00 PM,131XX S CORLISS AVE,031A,ROBBERY,ARMED: HANDGUN,CHA PARKING LOT/GROUNDS,false,false,0533,,9,54,03,,,2005,01/26/2006 03:51:08 AM,,,
-4484233,HL785794,12/11/2005 05:30:00 PM,027XX N HAMPDEN CT,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,false,false,2333,019,43,7,11,1172252,1918565,2005,01/26/2006 03:51:08 AM,41.931991289,-87.642410372,"(41.931991289, -87.642410372)"
-4480434,HL781114,12/09/2005 11:15:00 PM,009XX W AGATITE AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,2313,019,46,3,05,1169283,1929774,2005,01/26/2006 03:51:08 AM,41.962814367,-87.652993984,"(41.962814367, -87.652993984)"
-4486017,HL788058,12/09/2005 04:00:00 PM,017XX E 70TH ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0332,003,5,43,26,1189261,1858957,2005,01/26/2006 03:51:08 AM,41.768031223,-87.581822849,"(41.768031223, -87.581822849)"
-4479921,HL778736,12/08/2005 05:00:00 PM,007XX W NORTH AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,1822,018,27,8,06,1170822,1910836,2005,01/26/2006 03:51:08 AM,41.910814035,-87.647892403,"(41.910814035, -87.647892403)"
-4482941,HL779761,12/08/2005 11:50:00 AM,087XX S ASHLAND AVE,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,STREET,false,false,2221,022,21,71,11,1167151,1846958,2005,01/26/2006 03:51:08 AM,41.735605838,-87.66320855,"(41.735605838, -87.66320855)"
-4480878,HL778790,12/07/2005 10:30:00 PM,052XX S BERKELEY AVE,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,RESIDENCE,true,true,2131,002,4,41,20,1184026,1870653,2005,01/26/2006 03:51:08 AM,41.80024998,-87.600645984,"(41.80024998, -87.600645984)"
-4479513,HL776648,12/07/2005 01:30:00 PM,087XX S ESCANABA AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0423,004,10,46,14,1196866,1847537,2005,01/26/2006 03:51:08 AM,41.736508189,-87.554326586,"(41.736508189, -87.554326586)"
-4577333,HL773721,12/05/2005 07:05:00 PM,052XX S HOMAN AVE,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,0822,008,14,63,18,1154635,1869799,2005,02/14/2006 03:22:09 AM,41.798543394,-87.708454834,"(41.798543394, -87.708454834)"
-4474699,HL772573,12/05/2005 09:05:32 AM,050XX W MAYPOLE AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,1532,015,28,25,08A,1142922,1901037,2005,06/11/2007 03:52:33 PM,41.884490534,-87.750632251,"(41.884490534, -87.750632251)"
-4471670,HL771522,12/04/2005 03:30:00 AM,017XX S MORGAN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1233,012,25,31,08B,1170194,1891742,2005,01/26/2006 03:51:08 AM,41.858432542,-87.650757361,"(41.858432542, -87.650757361)"
-4472006,HL771277,12/03/2005 11:45:00 PM,030XX W CERMAK RD,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,true,false,1022,010,24,30,07,1156436,1889267,2005,01/26/2006 03:51:08 AM,41.851929902,-87.701324956,"(41.851929902, -87.701324956)"
-4562994,HL768571,12/02/2005 11:24:11 PM,025XX E 79TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,RESIDENCE PORCH/HALLWAY,true,false,0421,004,7,43,18,1194402,1853117,2005,02/15/2006 03:40:56 AM,41.751881017,-87.563170711,"(41.751881017, -87.563170711)"
-4469011,HL766191,12/01/2005 04:44:31 PM,046XX W LAKE ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,PARKING LOT/GARAGE(NON.RESID.),false,false,1113,011,28,25,11,1145224,1901709,2005,01/26/2006 03:51:08 AM,41.886291377,-87.742161904,"(41.886291377, -87.742161904)"
-4468734,HL764994,12/01/2005 07:30:00 AM,064XX W BELMONT AVE,0560,ASSAULT,SIMPLE,SMALL RETAIL STORE,false,false,2511,025,36,19,08A,1133128,1920547,2005,01/26/2006 03:51:08 AM,41.938205281,-87.786140668,"(41.938205281, -87.786140668)"
-4468101,HL765972,11/30/2005 07:30:00 PM,002XX E CULLERTON ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,0134,001,2,33,06,1178193,1890707,2005,01/26/2006 03:51:08 AM,41.855414181,-87.621427725,"(41.855414181, -87.621427725)"
-4466629,HL763417,11/30/2005 06:15:00 AM,037XX S DR MARTIN LUTHER KING JR DR,0340,ROBBERY,ATTEMPT: STRONGARM-NO WEAPON,CHA APARTMENT,false,false,0212,002,3,35,03,1179507,1880415,2005,01/26/2006 03:51:08 AM,41.827142222,-87.616919896,"(41.827142222, -87.616919896)"
-4463674,HL761627,11/29/2005 10:38:39 AM,020XX E 75TH ST,0610,BURGLARY,FORCIBLE ENTRY,BARBERSHOP,false,false,0414,004,8,43,05,1191252,1855604,2005,01/26/2006 03:51:08 AM,41.758782351,-87.57463344,"(41.758782351, -87.57463344)"
-4471305,HL767531,11/29/2005 10:00:00 AM,061XX S FAIRFIELD AVE,0810,THEFT,OVER $500,STREET,false,false,0825,008,15,66,06,1159062,1863579,2005,12/04/2014 12:43:35 PM,41.781385404,-87.692390051,"(41.781385404, -87.692390051)"
-4459772,HL759089,11/27/2005 09:45:00 PM,034XX W 85TH ST,031A,ROBBERY,ARMED: HANDGUN,STREET,false,false,0834,008,18,70,03,1155049,1847978,2005,03/18/2006 04:28:50 AM,41.738654917,-87.707518847,"(41.738654917, -87.707518847)"
-4462783,HL758808,11/27/2005 12:00:00 AM,006XX S JEFFERSON ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,0131,001,2,28,07,1172382,1897418,2005,01/26/2006 03:51:08 AM,41.873959886,-87.642558413,"(41.873959886, -87.642558413)"
-4463950,HL756533,11/26/2005 01:45:00 PM,016XX N HUMBOLDT BLVD,0440,BATTERY,AGG: HANDS/FIST/FEET NO/MINOR INJURY,SIDEWALK,false,false,1421,014,35,23,08B,1156076,1910999,2005,01/26/2006 03:51:08 AM,41.91157191,-87.702059545,"(41.91157191, -87.702059545)"
-4461208,HL759350,11/26/2005 12:00:00 PM,001XX S MICHIGAN AVE,0870,THEFT,POCKET-PICKING,GOVERNMENT BUILDING/PROPERTY,false,false,0124,001,42,32,06,1177362,1899912,2005,01/26/2006 03:51:08 AM,41.880692129,-87.624198743,"(41.880692129, -87.624198743)"
-4458057,HL756069,11/25/2005 10:00:00 PM,054XX S NORDICA AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0811,008,23,56,14,1130330,1867909,2005,01/26/2006 03:51:08 AM,41.79380715,-87.797631334,"(41.79380715, -87.797631334)"
-4457864,HL755226,11/24/2005 08:00:00 PM,018XX W 34TH ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0922,009,11,59,06,1164435,1882116,2005,12/04/2014 12:43:35 PM,41.832141531,-87.672168531,"(41.832141531, -87.672168531)"
-4455852,HL753008,11/23/2005 10:33:16 PM,003XX N CENTRAL AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1523,015,28,25,08B,1139015,1901951,2005,01/26/2006 03:51:08 AM,41.887070569,-87.76495725,"(41.887070569, -87.76495725)"
-4454950,HL753113,11/23/2005 04:00:00 PM,034XX N KENTON AVE,2830,OTHER OFFENSE,OBSCENE TELEPHONE CALLS,RESIDENCE,false,false,1731,017,30,16,17,1145146,1922793,2005,01/26/2006 03:51:08 AM,41.944149511,-87.741914426,"(41.944149511, -87.741914426)"
-4451919,HL748813,11/21/2005 08:30:00 PM,014XX N CLYBOURN AVE,0560,ASSAULT,SIMPLE,STREET,false,false,1822,018,27,8,08A,1171348,1910006,2005,01/26/2006 03:51:08 AM,41.908524919,-87.645984517,"(41.908524919, -87.645984517)"
-4457204,HL747742,11/20/2005 10:30:00 PM,041XX N ASHLAND AVE,0810,THEFT,OVER $500,STREET,false,false,1923,019,47,6,06,1164877,1927433,2005,12/04/2014 12:43:35 PM,41.956485416,-87.669259841,"(41.956485416, -87.669259841)"
-4567564,HL746827,11/20/2005 07:40:08 PM,012XX N BURLING ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,CHA PARKING LOT/GROUNDS,true,false,1822,018,27,8,18,1171043,1908530,2005,02/11/2006 03:54:44 AM,41.904481394,-87.647148303,"(41.904481394, -87.647148303)"
-4455345,HL752312,11/20/2005 05:00:00 PM,118XX S ARTESIAN AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,true,false,2212,022,19,75,26,1162155,1825897,2005,01/26/2006 03:51:08 AM,41.67791614,-87.682095592,"(41.67791614, -87.682095592)"
-4448014,HL745411,11/19/2005 11:50:57 PM,024XX N AVERS AVE,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,RESIDENCE,false,false,2524,025,30,22,05,1150264,1916021,2005,01/26/2006 03:51:08 AM,41.925468134,-87.723279898,"(41.925468134, -87.723279898)"
-4448154,HL745370,11/19/2005 11:15:00 PM,047XX W NORTH AVE,0860,THEFT,RETAIL THEFT,DRUG STORE,true,false,2533,025,37,25,06,1144223,1910247,2005,01/26/2006 03:51:08 AM,41.909739475,-87.745623072,"(41.909739475, -87.745623072)"
-4458774,HL746332,11/19/2005 01:00:00 AM,025XX N HALSTED ST,0890,THEFT,FROM BUILDING,BAR OR TAVERN,false,false,1933,019,43,7,06,1170462,1917355,2005,02/01/2006 03:54:18 AM,41.928710388,-87.649023805,"(41.928710388, -87.649023805)"
-4448274,HL742977,11/18/2005 05:45:00 PM,085XX S COTTAGE GROVE AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0632,006,6,44,06,1183016,1848397,2005,01/26/2006 03:51:08 AM,41.739200936,-87.605041045,"(41.739200936, -87.605041045)"
-4479761,HL741431,11/17/2005 10:05:03 PM,049XX W QUINCY ST,2024,NARCOTICS,POSS: HEROIN(WHITE),OTHER,true,false,1533,015,28,25,18,1143552,1898588,2005,01/26/2006 03:51:08 AM,41.877758417,-87.748380075,"(41.877758417, -87.748380075)"
-4446992,HL739741,11/16/2005 10:10:00 PM,011XX E 65TH ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,0314,003,20,42,15,1185152,1862183,2005,06/11/2007 03:52:33 PM,41.77698119,-87.596782762,"(41.77698119, -87.596782762)"
-4440314,HL736253,11/15/2005 09:00:00 AM,027XX W FOSTER AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,2031,020,40,4,08B,1157354,1934387,2005,01/26/2006 03:51:08 AM,41.975724213,-87.696726448,"(41.975724213, -87.696726448)"
-4436413,HL733939,11/14/2005 12:40:00 AM,060XX W MIAMI AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,1611,016,45,10,03,1134828,1939954,2005,01/26/2006 03:51:08 AM,41.991430022,-87.779431604,"(41.991430022, -87.779431604)"
-4438709,HL727230,11/10/2005 01:15:00 PM,013XX N HOMAN AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,true,false,1422,014,26,23,26,1153419,1908474,2005,06/11/2007 03:52:33 PM,41.904696328,-87.711887778,"(41.904696328, -87.711887778)"
-4430196,HL726553,11/10/2005 12:10:00 AM,049XX N KENMORE AVE,0890,THEFT,FROM BUILDING,HOTEL/MOTEL,false,false,2024,020,46,3,06,1168397,1933162,2005,01/26/2006 03:51:08 AM,41.972130405,-87.656153004,"(41.972130405, -87.656153004)"
-4442347,HL739382,11/10/2005 12:01:00 AM,056XX N BROADWAY,0890,THEFT,FROM BUILDING,OTHER,false,false,2022,020,48,77,06,1167325,1937797,2005,01/26/2006 03:51:08 AM,41.984872163,-87.659960921,"(41.984872163, -87.659960921)"
-4428262,HL724088,11/08/2005 07:30:00 PM,034XX N KEDVALE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,false,1731,017,30,16,08B,1148015,1922826,2005,01/26/2006 03:51:08 AM,41.944185262,-87.731368308,"(41.944185262, -87.731368308)"
-4428957,HL723813,11/08/2005 05:10:00 PM,048XX S LAVERGNE AVE,0880,THEFT,PURSE-SNATCHING,ALLEY,false,false,0814,008,23,56,06,1143809,1871835,2005,01/26/2006 03:51:08 AM,41.804339604,-87.748105749,"(41.804339604, -87.748105749)"
-4446539,HL722409,11/07/2005 10:25:00 PM,020XX N KIMBALL AVE,0460,BATTERY,SIMPLE,ALLEY,false,false,1413,014,35,22,08B,1153421,1913210,2005,01/26/2006 03:51:08 AM,41.917692294,-87.711754358,"(41.917692294, -87.711754358)"
-4427019,HL721991,11/07/2005 07:45:00 PM,076XX S SOUTH CHICAGO AVE,031A,ROBBERY,ARMED: HANDGUN,GAS STATION,false,false,0411,004,5,43,03,1186321,1854836,2005,06/04/2012 01:47:36 PM,41.756792803,-87.592729208,"(41.756792803, -87.592729208)"
-4433240,HL727481,11/07/2005 06:00:00 PM,009XX E 52ND ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,2131,002,4,41,08B,1183250,1870823,2005,01/26/2006 03:51:08 AM,41.800734589,-87.603486482,"(41.800734589, -87.603486482)"
-4431926,HL723209,11/07/2005 02:00:00 PM,003XX E 133RD ST,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",false,false,0533,005,9,54,06,1180722,1817241,2005,01/26/2006 03:51:08 AM,41.653757567,-87.614398582,"(41.653757567, -87.614398582)"
-4426888,HL721022,11/07/2005 12:25:00 PM,071XX S CARPENTER ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0733,007,17,68,14,1170508,1857486,2005,01/26/2006 03:51:08 AM,41.764423685,-87.65060373,"(41.764423685, -87.65060373)"
-4520272,HL719988,11/06/2005 09:00:00 PM,041XX W MADISON ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1115,011,28,26,26,1148477,1899723,2005,01/26/2006 03:51:08 AM,41.880779454,-87.730267258,"(41.880779454, -87.730267258)"
-4426139,HL717859,11/05/2005 06:20:55 PM,002XX W 107TH PL,0330,ROBBERY,AGGRAVATED,SIDEWALK,false,false,0513,005,34,49,03,1176456,1833691,2005,01/26/2006 03:51:08 AM,41.698995509,-87.629516032,"(41.698995509, -87.629516032)"
-4422238,HL718595,11/05/2005 05:00:00 PM,009XX N MICHIGAN AVE,0890,THEFT,FROM BUILDING,OTHER,false,false,1833,018,42,8,06,1177362,1906783,2005,01/26/2006 03:51:08 AM,41.899546499,-87.623990175,"(41.899546499, -87.623990175)"
-4637076,HM235473,11/05/2005 07:30:00 AM,004XX N NOBLE ST,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,COMMERCIAL / BUSINESS OFFICE,false,false,1324,012,27,24,11,1166945,1902891,2005,05/09/2006 03:25:05 AM,41.889096553,-87.662363311,"(41.889096553, -87.662363311)"
-4420782,HL715983,11/04/2005 07:41:00 PM,053XX S CICERO AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0815,008,23,56,14,1145300,1869006,2005,01/26/2006 03:51:08 AM,41.796548392,-87.742708658,"(41.796548392, -87.742708658)"
-4509013,HL716354,11/04/2005 01:04:00 AM,010XX W NORTH AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,1811,018,32,7,16,1169059,1910864,2005,01/26/2006 03:51:08 AM,41.910929347,-87.654368177,"(41.910929347, -87.654368177)"
-4421753,HL715243,11/03/2005 08:00:00 PM,042XX W MADISON ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1115,011,28,26,08B,1148326,1899720,2005,01/26/2006 03:51:08 AM,41.880774132,-87.7308218,"(41.880774132, -87.7308218)"
-4418330,HL713014,11/03/2005 08:30:00 AM,027XX W 68TH ST,0890,THEFT,FROM BUILDING,HOSPITAL BUILDING/GROUNDS,false,false,0831,008,15,66,06,1159482,1859406,2005,01/26/2006 03:51:08 AM,41.769925494,-87.690964504,"(41.769925494, -87.690964504)"
-4419299,HL713552,11/03/2005 03:00:00 AM,045XX S ROCKWELL ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,0912,009,12,58,14,1159752,1874521,2005,01/26/2006 03:51:08 AM,41.81139756,-87.689559912,"(41.81139756, -87.689559912)"
-4420095,HL709907,11/01/2005 08:05:00 PM,043XX W JACKSON BLVD,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,RESIDENCE,true,false,1131,011,28,26,15,1147575,1898295,2005,06/11/2007 03:52:33 PM,41.876878202,-87.733616001,"(41.876878202, -87.733616001)"
-4417114,HL710866,11/01/2005 05:00:00 PM,073XX S STEWART AVE,0810,THEFT,OVER $500,RESIDENCE-GARAGE,false,false,0731,007,17,69,06,1174937,1856213,2005,12/04/2014 12:43:35 PM,41.76083281,-87.634408271,"(41.76083281, -87.634408271)"
-4411724,HL707572,10/30/2005 08:15:00 PM,108XX S RACINE AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,false,false,2234,022,34,75,26,1170293,1832832,2005,01/26/2006 03:51:08 AM,41.696774265,-87.652106937,"(41.696774265, -87.652106937)"
-4478143,HL704698,10/30/2005 01:09:51 PM,043XX W KAMERLING AVE,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,2534,025,37,23,18,1146881,1908583,2005,01/26/2006 03:51:08 AM,41.905122896,-87.735901172,"(41.905122896, -87.735901172)"
-4420883,HL715554,10/29/2005 02:30:00 PM,021XX N LARAMIE AVE,2851,PUBLIC PEACE VIOLATION,ARSON THREAT,APARTMENT,false,false,2515,025,37,19,26,1141329,1913794,2005,01/26/2006 03:51:08 AM,41.919526753,-87.756166862,"(41.919526753, -87.756166862)"
-4408880,HL702544,10/29/2005 11:30:00 AM,002XX N SANGAMON ST,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,false,false,1212,012,27,28,26,1170011,1901809,2005,01/26/2006 03:51:08 AM,41.886061132,-87.6511354,"(41.886061132, -87.6511354)"
-4410740,HL702348,10/29/2005 09:40:00 AM,005XX E 33RD ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,false,2122,002,4,35,08B,1180495,1882843,2005,01/26/2006 03:51:08 AM,41.833782165,-87.61322045,"(41.833782165, -87.61322045)"
-4479400,HL702026,10/29/2005 03:01:00 AM,073XX N CLARK ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2423,024,49,1,18,1163193,1948666,2005,01/26/2006 03:51:08 AM,42.014785127,-87.674850521,"(42.014785127, -87.674850521)"
-4408168,HL702672,10/29/2005 02:00:00 AM,117XX S CAMPBELL AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,2212,022,19,75,05,1161794,1826744,2005,01/26/2006 03:51:08 AM,41.680247945,-87.683393597,"(41.680247945, -87.683393597)"
-4406636,HL701012,10/28/2005 01:00:00 PM,027XX W AINSLIE ST,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,2031,020,40,4,08A,1157311,1932608,2005,01/26/2006 03:51:08 AM,41.970843423,-87.696933174,"(41.970843423, -87.696933174)"
-4405274,HL698739,10/27/2005 01:10:00 PM,097XX S PRAIRIE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0511,005,6,49,08B,1179599,1840332,2005,01/26/2006 03:51:08 AM,41.717148245,-87.617805844,"(41.717148245, -87.617805844)"
-4404987,HL698991,10/27/2005 09:00:00 AM,046XX S FRANCISCO AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0912,009,14,58,05,1157777,1873657,2005,01/26/2006 03:51:08 AM,41.809067003,-87.696827612,"(41.809067003, -87.696827612)"
-4411425,HL698650,10/27/2005 12:45:00 AM,067XX S STONY ISLAND AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0332,003,5,43,14,1187984,1860846,2005,01/26/2006 03:51:08 AM,41.773245321,-87.586443445,"(41.773245321, -87.586443445)"
-4435486,HL729008,10/26/2005 06:00:00 PM,078XX S THROOP ST,1562,SEX OFFENSE,AGG CRIMINAL SEXUAL ABUSE,"SCHOOL, PRIVATE, BUILDING",false,false,0612,006,17,71,17,1169197,1853020,2005,01/26/2006 03:51:08 AM,41.752196846,-87.655537851,"(41.752196846, -87.655537851)"
-4407768,HL694301,10/24/2005 03:00:00 PM,041XX S CALIFORNIA AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0912,009,14,58,08B,1158338,1877159,2005,01/26/2006 03:51:08 AM,41.818665515,-87.694674497,"(41.818665515, -87.694674497)"
-4398887,HL692291,10/24/2005 10:14:49 AM,035XX W LAWRENCE AVE,1330,CRIMINAL TRESPASS,TO LAND,COMMERCIAL / BUSINESS OFFICE,true,false,1723,017,33,14,26,1152085,1931629,2005,01/26/2006 03:51:08 AM,41.968261902,-87.716175671,"(41.968261902, -87.716175671)"
-4396563,HL691167,10/23/2005 03:50:00 PM,025XX S MICHIGAN AVE,0460,BATTERY,SIMPLE,HOSPITAL BUILDING/GROUNDS,false,false,2112,001,2,33,08B,1177690,1887529,2005,01/26/2006 03:51:08 AM,41.84670496,-87.623370388,"(41.84670496, -87.623370388)"
-4399082,HL691216,10/23/2005 12:30:00 PM,004XX W 28TH ST,0820,THEFT,$500 AND UNDER,RESIDENCE PORCH/HALLWAY,false,false,0923,009,11,60,06,1173473,1886325,2005,12/04/2014 12:43:35 PM,41.843495709,-87.638882231,"(41.843495709, -87.638882231)"
-4449747,HL688358,10/22/2005 02:29:31 AM,003XX S HAMLIN BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1133,011,28,26,18,1151056,1898404,2005,01/26/2006 03:51:08 AM,41.877109853,-87.720831867,"(41.877109853, -87.720831867)"
-4410314,HL706005,10/21/2005 06:00:00 PM,003XX S CANAL ST,0890,THEFT,FROM BUILDING,PARKING LOT/GARAGE(NON.RESID.),false,false,0111,001,2,28,06,1173152,1898669,2005,01/26/2006 03:51:08 AM,41.877375673,-87.639694241,"(41.877375673, -87.639694241)"
-4398386,HL692438,10/21/2005 04:15:00 PM,036XX S WENTWORTH AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0925,009,3,34,07,1175592,1880521,2005,07/29/2006 04:46:09 AM,41.827521784,-87.631280113,"(41.827521784, -87.631280113)"
-4394141,HL687017,10/21/2005 01:00:00 PM,080XX S INGLESIDE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0631,006,8,44,08B,1184007,1851613,2005,01/26/2006 03:51:08 AM,41.748002912,-87.601310003,"(41.748002912, -87.601310003)"
-4400489,HL694581,10/20/2005 04:45:00 PM,029XX W 59TH ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0824,008,14,63,04B,1157585,1865413,2005,01/26/2006 03:51:08 AM,41.786448245,-87.697755405,"(41.786448245, -87.697755405)"
-4388322,HL681828,10/18/2005 11:53:21 PM,015XX W 62ND ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE PORCH/HALLWAY,true,true,0713,007,15,67,08B,1166832,1863646,2005,01/26/2006 03:51:08 AM,41.781406799,-87.663901487,"(41.781406799, -87.663901487)"
-4385235,HL680361,10/18/2005 10:50:00 AM,071XX S MARSHFIELD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0735,007,17,67,14,1166535,1857468,2005,01/26/2006 03:51:08 AM,41.764459902,-87.665166329,"(41.764459902, -87.665166329)"
-4384585,HL680080,10/18/2005 12:30:00 AM,008XX W SUPERIOR ST,0820,THEFT,$500 AND UNDER,RESIDENCE-GARAGE,false,false,1323,012,27,24,06,1170614,1905312,2005,12/04/2014 12:43:35 PM,41.895660415,-87.648818444,"(41.895660415, -87.648818444)"
-4381654,HL676409,10/16/2005 11:05:00 AM,0000X N LA SALLE ST,0810,THEFT,OVER $500,SIDEWALK,false,false,0113,001,42,32,06,1175165,1900665,2005,12/04/2014 12:43:35 PM,41.882807937,-87.632243264,"(41.882807937, -87.632243264)"
-4380806,HL673436,10/14/2005 07:15:00 PM,111XX S WENTWORTH AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,RESIDENCE,false,true,0522,005,34,49,04A,1176887,1830891,2005,01/26/2006 03:51:08 AM,41.691302224,-87.628021869,"(41.691302224, -87.628021869)"
-4379283,HL672581,10/14/2005 12:15:00 PM,115XX S EWING AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,RESIDENCE-GARAGE,false,false,0433,004,10,52,07,1202198,1829221,2005,01/26/2006 03:51:08 AM,41.686113522,-87.535414224,"(41.686113522, -87.535414224)"
-4384406,HL676582,10/14/2005 03:00:00 AM,044XX S ROCKWELL ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0912,009,12,58,08B,1159739,1875003,2005,01/26/2006 03:51:08 AM,41.812720495,-87.689594352,"(41.812720495, -87.689594352)"
-4379258,HL670028,10/13/2005 07:40:00 AM,121XX S HARVARD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,false,0523,005,34,53,08B,1176105,1824311,2005,01/26/2006 03:51:08 AM,41.673263225,-87.631081195,"(41.673263225, -87.631081195)"
-4375855,HL668822,10/12/2005 03:35:00 PM,041XX S DR MARTIN LUTHER KING JR DR,033B,ROBBERY,ATTEMPT: ARMED-OTHER FIREARM,STREET,false,false,0213,002,3,38,03,1179502,1877636,2005,03/05/2006 04:52:17 AM,41.819516544,-87.617023243,"(41.819516544, -87.617023243)"
-4377902,HL670882,10/11/2005 03:00:00 PM,081XX S COLES AVE,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,RESIDENCE,false,false,0422,004,7,46,20,1198198,1851422,2005,01/26/2006 03:51:08 AM,41.74713573,-87.549317016,"(41.74713573, -87.549317016)"
-4373995,HL668644,10/10/2005 09:00:00 PM,049XX N WHIPPLE ST,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,1713,017,33,14,06,1155181,1932882,2005,12/04/2014 12:43:35 PM,41.971638444,-87.704758009,"(41.971638444, -87.704758009)"
-4374615,HL662184,10/09/2005 01:20:00 AM,035XX W 64TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0823,008,15,66,14,1153601,1861978,2005,01/26/2006 03:51:08 AM,41.777101972,-87.712453882,"(41.777101972, -87.712453882)"
-4369166,HL661634,10/08/2005 06:15:00 PM,077XX S MICHIGAN AVE,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,true,true,0623,006,6,69,04A,1178534,1853703,2005,01/26/2006 03:51:08 AM,41.753864134,-87.621301298,"(41.753864134, -87.621301298)"
-4367937,HL661257,10/08/2005 02:17:20 PM,052XX W LAKE ST,1220,DECEPTIVE PRACTICE,THEFT OF LOST/MISLAID PROP,OTHER,false,false,1523,015,28,25,11,1141361,1901993,2005,01/26/2006 03:51:08 AM,41.887142859,-87.756340922,"(41.887142859, -87.756340922)"
-4449146,HL660941,10/08/2005 11:51:28 AM,056XX S BISHOP ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0713,007,16,67,18,1167593,1867100,2005,01/26/2006 03:51:08 AM,41.790868703,-87.661012467,"(41.790868703, -87.661012467)"
-4444660,HL660037,10/07/2005 08:39:24 PM,004XX W CHICAGO AVE,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,CHA PARKING LOT/GROUNDS,true,false,1823,018,27,8,18,1173213,1905687,2005,01/26/2006 03:51:08 AM,41.896632139,-87.639261847,"(41.896632139, -87.639261847)"
-4363306,HL658322,10/06/2005 12:00:00 PM,064XX S RACINE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,0724,007,16,68,07,1169475,1861808,2005,01/26/2006 03:51:08 AM,41.776306232,-87.654264865,"(41.776306232, -87.654264865)"
-3372,HL655808,10/05/2005 11:00:00 PM,015XX W 91ST ST,0110,HOMICIDE,FIRST DEGREE MURDER,STREET,true,false,2221,022,21,73,01A,1167359,1844338,2005,11/17/2011 12:01:46 PM,41.728411722,-87.662521326,"(41.728411722, -87.662521326)"
-4363919,HL656028,10/05/2005 09:45:00 PM,072XX S LOOMIS BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0734,007,17,67,08B,1168203,1856974,2005,02/24/2006 04:04:33 AM,41.763068584,-87.659066862,"(41.763068584, -87.659066862)"
-4363802,HL655502,10/05/2005 08:45:00 AM,082XX S INGLESIDE AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0631,006,8,44,05,1183958,1850573,2005,01/26/2006 03:51:08 AM,41.745150186,-87.601521964,"(41.745150186, -87.601521964)"
-4360137,HL654090,10/04/2005 11:50:12 PM,027XX W LAWRENCE AVE,0610,BURGLARY,FORCIBLE ENTRY,COMMERCIAL / BUSINESS OFFICE,true,false,2031,020,40,4,05,1157318,1931810,2005,01/26/2006 03:51:08 AM,41.968653525,-87.696929231,"(41.968653525, -87.696929231)"
-4356795,HL653143,10/04/2005 03:10:00 PM,080XX S MERRILL AVE,0560,ASSAULT,SIMPLE,MEDICAL/DENTAL OFFICE,false,false,0414,004,8,46,08A,1191886,1852026,2005,01/26/2006 03:51:08 AM,41.748948652,-87.572425887,"(41.748948652, -87.572425887)"
-4358551,HL651163,10/03/2005 10:00:00 AM,043XX N LINDER AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,"SCHOOL, PUBLIC, BUILDING",false,false,1624,016,38,15,06,1138928,1928643,2005,01/26/2006 03:51:08 AM,41.960317924,-87.764626657,"(41.960317924, -87.764626657)"
-4353143,HL651749,10/03/2005 12:00:00 AM,006XX S DEARBORN ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0132,001,2,32,08B,1175976,1897615,2005,01/26/2006 03:51:08 AM,41.874420338,-87.62935714,"(41.874420338, -87.62935714)"
-4354007,HL648821,10/01/2005 10:30:00 PM,049XX N CALIFORNIA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2031,020,40,4,14,1156759,1933018,2005,01/26/2006 03:51:08 AM,41.971979718,-87.698951769,"(41.971979718, -87.698951769)"
-4369713,HL650012,10/01/2005 01:15:00 AM,061XX S WABASH AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0311,003,20,40,06,1177728,1864380,2005,12/04/2014 12:43:35 PM,41.783181249,-87.623932272,"(41.783181249, -87.623932272)"
-4347488,HL644977,09/30/2005 03:28:32 PM,093XX S MERRILL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0413,004,7,48,08B,1192183,1843322,2005,01/26/2006 03:51:08 AM,41.725056869,-87.571619875,"(41.725056869, -87.571619875)"
-4398485,HL644583,09/30/2005 10:17:24 AM,024XX S STATE ST,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,0134,001,3,33,18,1176662,1888124,2005,01/26/2006 03:51:08 AM,41.848360942,-87.627125117,"(41.848360942, -87.627125117)"
-5621419,HN430228,09/30/2005 09:00:00 AM,013XX N LAKE SHORE DR,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,1824,,43,8,06,,,2005,07/06/2007 02:26:39 AM,,,
-4345453,HL644147,09/29/2005 05:00:00 PM,007XX W VAN BUREN ST,1310,CRIMINAL DAMAGE,TO PROPERTY,COMMERCIAL / BUSINESS OFFICE,false,false,0111,001,2,28,14,1171976,1898458,2005,01/26/2006 03:51:08 AM,41.876822674,-87.644018376,"(41.876822674, -87.644018376)"
-4343485,HL643035,09/29/2005 03:30:00 PM,002XX S CLARK ST,0890,THEFT,FROM BUILDING,SMALL RETAIL STORE,false,false,0112,001,42,32,06,1175531,1899116,2005,01/26/2006 03:51:08 AM,41.878549177,-87.630945865,"(41.878549177, -87.630945865)"
-4342419,HL641180,09/28/2005 03:50:00 PM,071XX S ASHLAND AVE,1330,CRIMINAL TRESPASS,TO LAND,HOTEL/MOTEL,true,false,0735,007,17,67,26,1166888,1857001,2005,01/26/2006 03:51:08 AM,41.76317086,-87.663885811,"(41.76317086, -87.663885811)"
-4338346,HL639962,09/27/2005 11:19:00 PM,047XX N CUMBERLAND AVE,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,1614,016,36,76,06,1119277,1930590,2005,01/26/2006 03:51:08 AM,41.965995882,-87.836833463,"(41.965995882, -87.836833463)"
-4334319,HL636642,09/26/2005 12:00:00 PM,056XX N SPAULDING AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,1711,017,39,13,07,1153324,1937187,2005,01/26/2006 03:51:08 AM,41.983488795,-87.711471561,"(41.983488795, -87.711471561)"
-4335358,HL636774,09/26/2005 03:00:00 AM,013XX N CLEVELAND AVE,0610,BURGLARY,FORCIBLE ENTRY,PARKING LOT/GARAGE(NON.RESID.),false,false,1821,018,43,8,05,1172707,1909186,2005,01/26/2006 03:51:08 AM,41.906244805,-87.641016571,"(41.906244805, -87.641016571)"
-4349928,HL635347,09/25/2005 05:00:00 PM,067XX S MAPLEWOOD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0832,008,15,66,14,1160496,1859816,2005,01/26/2006 03:51:08 AM,41.77102975,-87.687236301,"(41.77102975, -87.687236301)"
-4397276,HL634561,09/25/2005 12:48:18 PM,001XX N LAMON AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,1532,015,28,25,18,1143665,1900102,2005,01/26/2006 03:51:08 AM,41.881910905,-87.747927244,"(41.881910905, -87.747927244)"
-4329102,HL633952,09/25/2005 02:30:00 AM,002XX W LAKE ST,0460,BATTERY,SIMPLE,BAR OR TAVERN,true,false,0113,001,42,32,08B,1174600,1901685,2005,01/26/2006 03:51:08 AM,41.885619524,-87.634287434,"(41.885619524, -87.634287434)"
-4328882,HL632346,09/23/2005 05:30:00 PM,111XX S HOMEWOOD AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2212,022,19,75,14,1165397,1830334,2005,01/26/2006 03:51:08 AM,41.690024192,-87.67010368,"(41.690024192, -87.67010368)"
-4327359,HL630853,09/23/2005 01:00:00 PM,010XX W VERNON PARK PL,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1213,012,25,28,14,1169352,1897091,2005,01/26/2006 03:51:08 AM,41.873128949,-87.65369258,"(41.873128949, -87.65369258)"
-4325724,HL630200,09/22/2005 11:00:00 PM,064XX S MOZART ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0823,008,15,66,05,1158530,1861681,2005,01/26/2006 03:51:08 AM,41.776187885,-87.6943922,"(41.776187885, -87.6943922)"
-4317710,HL625542,09/21/2005 04:15:00 AM,073XX S MOZART ST,031A,ROBBERY,ARMED: HANDGUN,STREET,false,false,0835,008,18,66,03,1158599,1856034,2005,01/26/2006 03:51:08 AM,41.760690272,-87.694293058,"(41.760690272, -87.694293058)"
-4318628,HL624784,09/20/2005 06:55:00 PM,007XX S KEDVALE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,ALLEY,false,false,1132,011,24,26,03,1148802,1896176,2005,01/26/2006 03:51:08 AM,41.871039805,-87.729165553,"(41.871039805, -87.729165553)"
-4322374,HL625848,09/20/2005 06:30:00 PM,062XX S EVANS AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0313,003,20,42,14,1182328,1863999,2005,01/26/2006 03:51:08 AM,41.78203037,-87.607079129,"(41.78203037, -87.607079129)"
-4315979,HL624304,09/20/2005 09:00:00 AM,013XX W HASTINGS ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1231,012,2,28,06,1167850,1893896,2005,12/04/2014 12:43:35 PM,41.864394113,-87.659299225,"(41.864394113, -87.659299225)"
-4314495,HL621385,09/19/2005 05:30:00 AM,051XX S MARSHFIELD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0932,009,16,61,08B,1166178,1870384,2005,01/26/2006 03:51:08 AM,41.799910641,-87.666107484,"(41.799910641, -87.666107484)"
-4317443,HL620910,09/18/2005 11:30:00 PM,026XX S TRUMBULL AVE,0460,BATTERY,SIMPLE,SIDEWALK,true,false,1032,010,22,30,08B,1153761,1885915,2005,01/26/2006 03:51:08 AM,41.842785221,-87.711232097,"(41.842785221, -87.711232097)"
-4381816,HL614821,09/15/2005 09:00:10 PM,066XX N MOZART ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2412,024,50,2,18,1156085,1944032,2005,01/26/2006 03:51:08 AM,42.002216323,-87.70113122,"(42.002216323, -87.70113122)"
-4391313,HL614783,09/15/2005 08:40:34 PM,050XX S ELIZABETH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0933,009,16,61,18,1168883,1871393,2005,01/26/2006 03:51:08 AM,41.802621403,-87.656158335,"(41.802621403, -87.656158335)"
-4308490,HL613446,09/15/2005 08:30:00 AM,019XX N HUMBOLDT BLVD,2851,PUBLIC PEACE VIOLATION,ARSON THREAT,RESIDENCE,false,true,1421,014,35,22,26,1156109,1912542,2005,01/26/2006 03:51:08 AM,41.915805363,-87.70189658,"(41.915805363, -87.70189658)"
-4310846,HL613286,09/15/2005 07:46:28 AM,033XX W GRAND AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,false,1121,011,27,23,08B,1153953,1906587,2005,01/26/2006 03:51:08 AM,41.899507593,-87.709976598,"(41.899507593, -87.709976598)"
-4306801,HL613112,09/14/2005 11:45:00 PM,063XX S STATE ST,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0312,003,20,69,03,1177383,1863200,2005,01/26/2006 03:51:08 AM,41.779951011,-87.625232772,"(41.779951011, -87.625232772)"
-4415483,HL584961,09/14/2005 05:00:00 AM,041XX W GLADYS AVE,2050,NARCOTICS,CRIMINAL DRUG CONSPIRACY,STREET,true,false,1132,011,28,26,18,1149047,1898077,2005,01/26/2006 03:51:08 AM,41.876251633,-87.728216858,"(41.876251633, -87.728216858)"
-4381833,HL607717,09/12/2005 04:00:00 PM,063XX S DR MARTIN LUTHER KING JR DR,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,CTA PLATFORM,true,false,0312,003,20,69,18,1179963,1863306,2005,01/26/2006 03:51:08 AM,41.780183197,-87.615770958,"(41.780183197, -87.615770958)"
-4295484,HL607679,09/12/2005 02:23:00 PM,028XX E 91ST ST,0560,ASSAULT,SIMPLE,APARTMENT,false,false,0423,004,10,46,08A,1196649,1845213,2005,01/26/2006 03:51:08 AM,41.730136328,-87.555198561,"(41.730136328, -87.555198561)"
-3345,HL607066,09/12/2005 11:17:00 AM,074XX S RACINE AVE,0110,HOMICIDE,FIRST DEGREE MURDER,RETAIL STORE,false,false,0734,007,17,67,01A,1169563,1855701,2005,11/17/2011 12:01:46 PM,41.759545945,-87.654119056,"(41.759545945, -87.654119056)"
-4293450,HL607638,09/12/2005 02:00:00 AM,048XX W ARMITAGE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2522,025,31,19,26,1143221,1912886,2005,01/26/2006 03:51:08 AM,41.916999954,-87.749238021,"(41.916999954, -87.749238021)"
-4293321,HL605429,09/11/2005 01:39:09 PM,001XX W 87TH ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,0622,006,21,44,06,1176857,1847317,2005,01/26/2006 03:51:08 AM,41.736378094,-87.627638815,"(41.736378094, -87.627638815)"
-4388555,HL605209,09/11/2005 12:55:00 PM,050XX W WASHINGTON BLVD,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,SIDEWALK,true,false,1533,015,28,25,18,1142442,1899985,2005,01/26/2006 03:51:08 AM,41.881612647,-87.752421047,"(41.881612647, -87.752421047)"
-4291251,HL605470,09/11/2005 04:15:00 AM,022XX W 111TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,OTHER,false,false,2212,022,19,75,14,1163140,1831009,2005,01/26/2006 03:51:08 AM,41.6919239,-87.678347873,"(41.6919239, -87.678347873)"
-4295738,HL604154,09/10/2005 07:00:00 PM,008XX E 40TH ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE PORCH/HALLWAY,false,false,2122,002,4,36,05,1182966,1878533,2005,01/26/2006 03:51:08 AM,41.82189803,-87.604288123,"(41.82189803, -87.604288123)"
-4289335,HL603255,09/10/2005 12:00:00 PM,026XX N ELSTON AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,1432,014,1,22,06,1160776,1917684,2005,01/26/2006 03:51:08 AM,41.929819797,-87.684607426,"(41.929819797, -87.684607426)"
-4290404,HL602519,09/10/2005 01:07:00 AM,012XX S INDEPENDENCE BLVD,0560,ASSAULT,SIMPLE,SIDEWALK,true,false,1011,010,24,29,08A,1151249,1894060,2005,01/26/2006 03:51:08 AM,41.865185646,-87.720237144,"(41.865185646, -87.720237144)"
-4376505,HL598084,09/10/2005 12:20:00 AM,010XX N CICERO AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,1531,015,37,25,16,1144136,1906250,2005,01/26/2006 03:51:08 AM,41.898772903,-87.746043203,"(41.898772903, -87.746043203)"
-4288591,HL601694,09/09/2005 09:10:00 AM,004XX E 82ND ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0631,006,6,44,05,1180947,1850698,2005,01/26/2006 03:51:08 AM,41.74556294,-87.612550809,"(41.74556294, -87.612550809)"
-4376269,HL598047,09/08/2005 11:25:00 AM,004XX S SPRINGFIELD AVE,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,STREET,true,false,1132,011,24,26,18,1150429,1897464,2005,01/26/2006 03:51:08 AM,41.874542644,-87.723158583,"(41.874542644, -87.723158583)"
-4302961,HL597635,09/07/2005 06:00:00 PM,0000X E ROOSEVELT RD,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,true,false,0132,001,2,32,11,1176812,1895113,2005,01/26/2006 03:51:08 AM,41.867535841,-87.626363413,"(41.867535841, -87.626363413)"
-4345976,HL644450,09/07/2005 12:00:00 AM,081XX S ST LAWRENCE AVE,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,STREET,false,false,0631,006,6,44,11,1181697,1850919,2005,01/26/2006 03:51:08 AM,41.746152116,-87.609795888,"(41.746152116, -87.609795888)"
-4358406,HL594178,09/06/2005 06:56:54 AM,051XX W NORTH AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,SIDEWALK,true,false,2533,025,37,25,16,1142124,1910197,2005,01/26/2006 03:51:08 AM,41.909641476,-87.753335256,"(41.909641476, -87.753335256)"
-4278272,HL594092,09/06/2005 02:50:00 AM,019XX S TRUMBULL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1024,010,24,29,08B,1153689,1890225,2005,01/26/2006 03:51:08 AM,41.854613804,-87.711381768,"(41.854613804, -87.711381768)"
-4330686,HL636036,09/05/2005 12:00:00 PM,010XX N MONITOR AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1511,015,29,25,26,1137136,1906135,2005,01/26/2006 03:51:08 AM,41.898585944,-87.771757012,"(41.898585944, -87.771757012)"
-4276177,HL590738,09/04/2005 04:00:00 AM,076XX S STONY ISLAND AVE,0460,BATTERY,SIMPLE,STREET,false,false,0411,004,5,43,08B,1188093,1854872,2005,01/26/2006 03:51:08 AM,41.756849546,-87.58623409,"(41.756849546, -87.58623409)"
-4273653,HL590015,09/03/2005 04:00:00 PM,026XX W HURON ST,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,false,true,1313,012,26,24,26,1158799,1904606,2005,01/26/2006 03:51:08 AM,41.893973558,-87.692231597,"(41.893973558, -87.692231597)"
-4273233,HL588584,09/02/2005 11:30:00 PM,078XX S PHILLIPS AVE,0460,BATTERY,SIMPLE,STREET,false,false,0421,004,7,43,08B,1193843,1853657,2005,01/26/2006 03:51:08 AM,41.753376533,-87.565201483,"(41.753376533, -87.565201483)"
-4364689,HL586854,09/02/2005 09:50:00 AM,050XX W WASHINGTON BLVD,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,SIDEWALK,true,false,1533,015,28,25,18,1142442,1899985,2005,01/26/2006 03:51:08 AM,41.881612647,-87.752421047,"(41.881612647, -87.752421047)"
-5596732,HN362355,09/01/2005 12:01:00 AM,117XX S ELIZABETH ST,1753,OFFENSE INVOLVING CHILDREN,SEX ASSLT OF CHILD BY FAM MBR,RESIDENCE,false,false,0524,,34,53,02,,,2005,08/12/2007 02:23:40 AM,,,
-4266633,HL581587,08/30/2005 06:20:34 PM,017XX N KEELER AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE PORCH/HALLWAY,true,false,2534,025,30,23,26,1148077,1911194,2005,01/26/2006 03:51:08 AM,41.912264801,-87.731440545,"(41.912264801, -87.731440545)"
-4342253,HL581242,08/30/2005 02:45:23 PM,038XX S DR MARTIN LUTHER KING JR DR,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,0212,002,3,35,18,1179533,1879312,2005,01/26/2006 03:51:08 AM,41.82411491,-87.616858255,"(41.82411491, -87.616858255)"
-4267038,HL583911,08/29/2005 06:20:00 PM,009XX N PINE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1524,015,37,25,26,1139306,1905645,2005,01/26/2006 03:51:08 AM,41.89720207,-87.763798557,"(41.89720207, -87.763798557)"
-4261936,HL576427,08/28/2005 04:30:00 AM,0000X E HUBBARD ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,false,1834,018,42,8,08B,1176540,1903366,2005,01/26/2006 03:51:08 AM,41.890188691,-87.627112647,"(41.890188691, -87.627112647)"
-4256486,HL575780,08/27/2005 05:00:00 PM,049XX S CAMPBELL AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0911,009,14,63,05,1160495,1871844,2005,01/26/2006 03:51:08 AM,41.80403623,-87.686908473,"(41.80403623, -87.686908473)"
-4256813,HL574363,08/27/2005 04:45:00 AM,005XX E 79TH ST,031A,ROBBERY,ARMED: HANDGUN,RESTAURANT,false,false,0624,006,6,69,03,1181198,1852785,2005,01/26/2006 03:51:08 AM,41.751284126,-87.61156696,"(41.751284126, -87.61156696)"
-4255366,HL574345,08/27/2005 04:34:14 AM,055XX W VAN BUREN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,1522,015,29,25,08B,1139281,1897417,2005,01/26/2006 03:51:08 AM,41.874623846,-87.764090847,"(41.874623846, -87.764090847)"
-4268627,HL573998,08/26/2005 11:10:00 PM,005XX W DIVISION ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1821,018,27,8,14,1172140,1908300,2005,01/26/2006 03:51:08 AM,41.903826111,-87.643125547,"(41.903826111, -87.643125547)"
-4254771,HL573714,08/26/2005 07:30:00 PM,062XX S CALIFORNIA AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0825,008,15,66,06,1158828,1862964,2005,12/04/2014 12:43:35 PM,41.77970254,-87.693264737,"(41.77970254, -87.693264737)"
-4253262,HL572268,08/26/2005 04:30:00 AM,027XX E 81ST ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,0422,004,7,46,08B,1196085,1851837,2005,01/26/2006 03:51:08 AM,41.748327094,-87.557045736,"(41.748327094, -87.557045736)"
-4267533,HL571850,08/25/2005 09:55:13 PM,061XX N WINTHROP AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE PORCH/HALLWAY,true,false,2433,024,48,77,26,1167699,1941166,2005,01/26/2006 03:51:08 AM,41.994108698,-87.658487756,"(41.994108698, -87.658487756)"
-4257416,HL570106,08/25/2005 01:02:01 AM,041XX W POTOMAC AVE,1330,CRIMINAL TRESPASS,TO LAND,CONSTRUCTION SITE,true,false,2534,025,37,23,26,1148210,1908289,2005,01/26/2006 03:51:08 AM,41.904290627,-87.731026873,"(41.904290627, -87.731026873)"
-4256021,HL569070,08/24/2005 02:05:54 PM,075XX S CARPENTER ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,RESIDENCE,false,false,0612,006,17,71,15,1170581,1854742,2005,01/26/2006 03:51:08 AM,41.756892208,-87.650416011,"(41.756892208, -87.650416011)"
-4245238,HL566498,08/23/2005 02:40:00 PM,008XX W 78TH ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,STREET,false,false,0621,006,17,71,26,1171893,1853169,2005,01/26/2006 03:51:08 AM,41.752547015,-87.645653842,"(41.752547015, -87.645653842)"
-4247733,HL565179,08/22/2005 05:00:00 PM,005XX E 44TH PL,0810,THEFT,OVER $500,CONSTRUCTION SITE,false,false,0222,002,3,38,06,1180723,1875590,2005,12/04/2014 12:43:35 PM,41.813874139,-87.612607121,"(41.813874139, -87.612607121)"
-4253349,HL563309,08/21/2005 07:25:53 PM,011XX W 47TH ST,1330,CRIMINAL TRESPASS,TO LAND,OTHER,true,false,0921,009,11,61,26,1169709,1873676,2005,01/26/2006 03:51:08 AM,41.808868304,-87.653062766,"(41.808868304, -87.653062766)"
-4240175,HL562527,08/21/2005 12:40:00 PM,102XX S STATE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,0511,005,9,49,08B,1178022,1836920,2005,01/26/2006 03:51:08 AM,41.707821075,-87.623684671,"(41.707821075, -87.623684671)"
-4258087,HL562355,08/21/2005 11:02:17 AM,017XX E 87TH ST,0610,BURGLARY,FORCIBLE ENTRY,RESTAURANT,false,false,0412,004,8,45,05,1189183,1847672,2005,12/01/2007 01:05:44 AM,41.737066029,-87.582469749,"(41.737066029, -87.582469749)"
-4239879,HL563228,08/21/2005 05:00:00 AM,061XX N ELSTON AVE,0890,THEFT,FROM BUILDING,HOTEL/MOTEL,false,false,1611,016,45,10,06,1133994,1940330,2005,01/26/2006 03:51:08 AM,41.992476559,-87.782490442,"(41.992476559, -87.782490442)"
-4241043,HL561797,08/21/2005 12:10:57 AM,012XX N MONTICELLO AVE,0610,BURGLARY,FORCIBLE ENTRY,"SCHOOL, PUBLIC, BUILDING",false,false,2535,025,26,23,05,1151767,1908135,2005,01/26/2006 03:51:08 AM,41.90379876,-87.717965025,"(41.90379876, -87.717965025)"
-4238445,HL562118,08/20/2005 11:30:00 PM,015XX N KINGSBURY ST,0810,THEFT,OVER $500,STREET,false,false,1822,018,32,8,06,1169533,1910280,2005,12/04/2014 12:43:35 PM,41.909316508,-87.652643905,"(41.909316508, -87.652643905)"
-4241709,HL560109,08/20/2005 04:08:33 AM,080XX S DREXEL AVE,0496,BATTERY,AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST,APARTMENT,false,false,0631,006,8,44,04B,1183673,1851771,2005,01/26/2006 03:51:08 AM,41.748444272,-87.602528956,"(41.748444272, -87.602528956)"
-4237043,HL559296,08/17/2005 09:45:00 PM,073XX N BELL AVE,0810,THEFT,OVER $500,STREET,false,false,2411,024,49,2,06,1160072,1948434,2005,12/04/2014 12:43:35 PM,42.01421382,-87.686341061,"(42.01421382, -87.686341061)"
-4255489,HL573413,08/17/2005 05:40:00 PM,024XX W DEVON AVE,1120,DECEPTIVE PRACTICE,FORGERY,CURRENCY EXCHANGE,false,false,2412,024,50,2,10,1159119,1942458,2005,01/26/2006 03:51:08 AM,41.997835196,-87.690012941,"(41.997835196, -87.690012941)"
-4230823,HL551031,08/15/2005 05:30:00 PM,0000X W 87TH ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",PARKING LOT/GARAGE(NON.RESID.),false,false,0634,006,21,44,07,1176974,1847236,2005,01/26/2006 03:51:08 AM,41.736153186,-87.627212603,"(41.736153186, -87.627212603)"
-4224154,HL550214,08/15/2005 11:10:00 AM,014XX N HARDING AVE,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,true,false,2535,025,30,23,26,1149815,1909582,2005,01/26/2006 03:51:08 AM,41.907807671,-87.725097554,"(41.907807671, -87.725097554)"
-4225546,HL548958,08/14/2005 04:43:01 PM,052XX W RACE AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1523,015,28,25,26,1141316,1903090,2005,01/26/2006 03:51:08 AM,41.890153991,-87.756479095,"(41.890153991, -87.756479095)"
-4439986,HL737149,08/13/2005 11:30:00 PM,016XX W FULLERTON AVE,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,1811,018,32,7,06,1164611,1915960,2005,01/26/2006 03:51:08 AM,41.925008552,-87.670563826,"(41.925008552, -87.670563826)"
-4220235,HL546369,08/12/2005 04:30:00 PM,051XX N AUSTIN AVE,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,1622,016,45,11,06,1135240,1933676,2005,12/04/2014 12:43:35 PM,41.974195319,-87.778065832,"(41.974195319, -87.778065832)"
-4218402,HL544414,08/11/2005 04:30:00 AM,009XX N ROCKWELL ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1312,012,26,24,06,1158936,1906107,2005,12/04/2014 12:43:35 PM,41.89808962,-87.691687212,"(41.89808962, -87.691687212)"
-4250654,HL570747,08/10/2005 12:00:00 PM,020XX N CLYBOURN AVE,1140,DECEPTIVE PRACTICE,EMBEZZLEMENT,BANK,true,false,1811,018,43,7,12,1167422,1913722,2005,01/26/2006 03:51:08 AM,41.91880727,-87.660299516,"(41.91880727, -87.660299516)"
-4211983,HL540060,08/10/2005 03:04:00 AM,005XX S CLINTON ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0131,001,2,28,26,1172768,1897874,2005,01/26/2006 03:51:08 AM,41.875202647,-87.641127713,"(41.875202647, -87.641127713)"
-4210233,HL539261,08/09/2005 06:00:00 PM,075XX S PULASKI RD,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),false,false,0833,008,13,65,07,1150961,1854579,2005,01/26/2006 03:51:08 AM,41.756849802,-87.722324877,"(41.756849802, -87.722324877)"
-4216744,HL537642,08/09/2005 08:57:04 AM,029XX N MC VICKER AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,"SCHOOL, PUBLIC, BUILDING",false,false,2511,025,29,19,26,1135587,1919083,2005,01/26/2006 03:51:08 AM,41.934144479,-87.777138069,"(41.934144479, -87.777138069)"
-4206512,HL536576,08/07/2005 02:00:00 AM,039XX W 67TH PL,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0833,008,13,65,14,1151124,1859586,2005,01/26/2006 03:51:08 AM,41.770586658,-87.721597003,"(41.770586658, -87.721597003)"
-4204345,HL532948,08/06/2005 10:11:51 PM,058XX W RACE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,1512,015,29,25,05,1137358,1903068,2005,06/11/2007 03:52:33 PM,41.890165717,-87.771015435,"(41.890165717, -87.771015435)"
-4296516,HL531043,08/05/2005 11:09:43 PM,006XX N CENTRAL PARK AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,1122,011,27,23,18,1152231,1903962,2005,01/26/2006 03:51:08 AM,41.892338494,-87.716370862,"(41.892338494, -87.716370862)"
-4295817,HL530727,08/05/2005 07:50:16 PM,105XX S KEDZIE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,2211,022,19,74,18,1156923,1834305,2005,01/26/2006 03:51:08 AM,41.70109621,-87.701020766,"(41.70109621, -87.701020766)"
-4204764,HL535565,08/05/2005 05:35:00 PM,049XX S LAKE PARK AVE,0890,THEFT,FROM BUILDING,LIBRARY,false,false,2132,002,4,39,06,1187129,1872564,2005,01/26/2006 03:51:08 AM,41.805420755,-87.589205839,"(41.805420755, -87.589205839)"
-4211904,HL530188,08/05/2005 03:30:00 PM,0000X W 47TH ST,0460,BATTERY,SIMPLE,PARK PROPERTY,false,false,0231,002,3,38,08B,1176619,1873814,2005,01/26/2006 03:51:08 AM,41.809094102,-87.627714291,"(41.809094102, -87.627714291)"
-4205717,HL529336,08/05/2005 06:30:00 AM,002XX W 111TH PL,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0522,005,34,49,14,1176381,1830954,2005,01/26/2006 03:51:08 AM,41.691486459,-87.629872494,"(41.691486459, -87.629872494)"
-4199078,HL527529,08/03/2005 11:35:00 AM,035XX W 60TH ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0822,008,16,66,04B,1153812,1864642,2005,01/26/2006 03:51:08 AM,41.784408209,-87.71160974,"(41.784408209, -87.71160974)"
-4288560,HL524864,08/03/2005 12:58:00 AM,047XX S CICERO AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0814,008,23,56,16,1145112,1872973,2005,01/26/2006 03:51:08 AM,41.807438008,-87.74329822,"(41.807438008, -87.74329822)"
-4191589,HL523247,08/02/2005 10:52:17 AM,055XX N CLARK ST,0810,THEFT,OVER $500,STREET,false,false,2012,020,40,77,06,1164988,1936640,2005,12/04/2014 12:43:35 PM,41.981747414,-87.668589158,"(41.981747414, -87.668589158)"
-4187771,HL520595,08/01/2005 04:54:00 AM,074XX S BENNETT AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0333,003,8,43,14,1190043,1855700,2005,01/26/2006 03:51:08 AM,41.759074958,-87.579061189,"(41.759074958, -87.579061189)"
-4195602,HL520052,07/31/2005 07:50:00 PM,041XX W 21ST PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1012,010,24,29,08B,1148875,1889389,2005,01/26/2006 03:51:08 AM,41.852414041,-87.729072917,"(41.852414041, -87.729072917)"
-4354842,HL519727,07/31/2005 05:08:07 PM,079XX S KINGSTON AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0422,004,7,46,18,1194602,1852942,2005,01/26/2006 03:51:08 AM,41.751395888,-87.562443563,"(41.751395888, -87.562443563)"
-4184537,HL516918,07/30/2005 06:30:00 AM,046XX N BROADWAY,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,2311,019,46,3,04B,1167840,1931061,2005,01/26/2006 03:51:08 AM,41.966377257,-87.658262047,"(41.966377257, -87.658262047)"
-4201951,HL514725,07/29/2005 07:55:00 AM,047XX W CONGRESS PKWY,0460,BATTERY,SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,1131,011,24,25,08B,1144685,1897229,2005,01/26/2006 03:51:08 AM,41.87400789,-87.744254184,"(41.87400789, -87.744254184)"
-4201973,HL512190,07/27/2005 10:15:00 PM,007XX S INDEPENDENCE BLVD,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,1133,011,24,26,08A,1151166,1896683,2005,01/26/2006 03:51:08 AM,41.872385083,-87.720473096,"(41.872385083, -87.720473096)"
-4189158,HL511650,07/27/2005 05:00:00 PM,041XX S DR MARTIN LUTHER KING JR DR,0460,BATTERY,SIMPLE,CTA GARAGE / OTHER PROPERTY,false,false,0213,002,3,38,08B,1179576,1877821,2005,01/26/2006 03:51:08 AM,41.820022505,-87.616746124,"(41.820022505, -87.616746124)"
-4179847,HL511408,07/27/2005 04:05:02 PM,091XX S CRANDON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0413,004,7,48,14,1193135,1844786,2005,01/26/2006 03:51:08 AM,41.729051053,-87.568085073,"(41.729051053, -87.568085073)"
-4268808,HL505406,07/24/2005 07:20:00 PM,054XX S NORMAL AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0934,009,3,61,18,1173842,1868869,2005,01/26/2006 03:51:08 AM,41.795586639,-87.638046451,"(41.795586639, -87.638046451)"
-4172739,HL504893,07/23/2005 09:40:00 PM,027XX W FRANCIS PL,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1431,014,1,22,07,1157690,1913514,2005,01/26/2006 03:51:08 AM,41.918440518,-87.696061549,"(41.918440518, -87.696061549)"
-4172607,HL503601,07/23/2005 08:12:57 PM,054XX W HARRISON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,false,1522,015,29,25,08B,1140191,1896825,2005,01/26/2006 03:51:08 AM,41.87298271,-87.760764171,"(41.87298271, -87.760764171)"
-4171424,HL503760,07/23/2005 01:50:00 PM,060XX S CALIFORNIA AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0825,008,15,66,26,1158794,1864144,2005,01/26/2006 03:51:08 AM,41.782941321,-87.693357189,"(41.782941321, -87.693357189)"
-5833313,HL505011,07/23/2005 12:00:00 PM,009XX N RACINE AVE,0810,THEFT,OVER $500,OTHER,false,false,1323,,27,24,06,,,2005,12/04/2014 12:43:35 PM,,,
-4172616,HL503881,07/22/2005 11:00:00 PM,053XX W MONROE ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1522,015,29,25,06,1140704,1899101,2005,12/04/2014 12:43:35 PM,41.879218943,-87.758824744,"(41.879218943, -87.758824744)"
-4281611,HL501680,07/22/2005 09:10:45 PM,062XX W GRAND AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,2512,025,29,19,18,1134056,1914568,2005,01/26/2006 03:51:08 AM,41.921781913,-87.782871086,"(41.921781913, -87.782871086)"
-4170750,HL500505,07/21/2005 10:00:00 PM,044XX W CHICAGO AVE,1330,CRIMINAL TRESPASS,TO LAND,WAREHOUSE,false,false,1111,011,37,23,26,1146417,1904986,2005,01/26/2006 03:51:08 AM,41.895261196,-87.737697344,"(41.895261196, -87.737697344)"
-4310781,HL499226,07/21/2005 06:30:00 PM,0000X W 95TH ST,2027,NARCOTICS,POSS: CRACK,CTA PLATFORM,true,false,0634,006,21,49,18,1177743,1841988,2005,01/26/2006 03:51:08 AM,41.721734655,-87.624553594,"(41.721734655, -87.624553594)"
-4165740,HL498165,07/19/2005 09:30:00 PM,091XX S EUCLID AVE,0820,THEFT,$500 AND UNDER,DRIVEWAY - RESIDENTIAL,false,false,0413,004,8,48,06,1190822,1844421,2005,12/04/2014 12:43:35 PM,41.728105574,-87.576569766,"(41.728105574, -87.576569766)"
-4259891,HL495170,07/19/2005 09:10:00 PM,033XX W HURON ST,2024,NARCOTICS,POSS: HEROIN(WHITE),ALLEY,true,false,1121,011,27,23,18,1154216,1904512,2005,01/26/2006 03:51:08 AM,41.893808349,-87.709066051,"(41.893808349, -87.709066051)"
-4161928,HL493107,07/18/2005 09:55:00 PM,065XX N HARLEM AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,false,false,1611,016,41,10,14,1127417,1943198,2005,01/26/2006 03:51:08 AM,42.00046016,-87.806618456,"(42.00046016, -87.806618456)"
-4159396,HL492720,07/18/2005 04:00:00 PM,050XX N BROADWAY,0460,BATTERY,SIMPLE,SIDEWALK,false,false,2033,020,48,3,08B,1167371,1933372,2005,01/26/2006 03:51:08 AM,41.972728844,-87.659919683,"(41.972728844, -87.659919683)"
-4253908,HL491783,07/18/2005 11:50:44 AM,029XX W MADISON ST,2024,NARCOTICS,POSS: HEROIN(WHITE),ALLEY,true,false,1331,012,2,27,18,1156829,1899919,2005,01/26/2006 03:51:08 AM,41.881152154,-87.699593918,"(41.881152154, -87.699593918)"
-4157192,HL491142,07/18/2005 02:13:00 AM,027XX W LEXINGTON ST,041A,BATTERY,AGGRAVATED: HANDGUN,RESIDENCE PORCH/HALLWAY,true,false,1135,011,2,27,04B,1158364,1896552,2005,04/12/2006 04:09:54 AM,41.871881539,-87.694049562,"(41.871881539, -87.694049562)"
-4160066,HL490692,07/17/2005 09:09:06 PM,013XX W 51ST ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0933,009,16,61,14,1167895,1870893,2005,01/26/2006 03:51:08 AM,41.80127065,-87.659796131,"(41.80127065, -87.659796131)"
-4155617,HL490260,07/17/2005 01:00:00 PM,013XX E 87TH ST,0460,BATTERY,SIMPLE,ALLEY,true,false,0412,004,8,45,08B,1186377,1847597,2005,01/26/2006 03:51:08 AM,41.736926941,-87.592752315,"(41.736926941, -87.592752315)"
-4155877,HL489531,07/17/2005 04:00:00 AM,075XX S EMERALD AVE,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,0621,006,17,68,06,1172574,1854602,2005,12/04/2014 12:43:35 PM,41.756464384,-87.643116138,"(41.756464384, -87.643116138)"
-4155307,HL487873,07/16/2005 12:15:00 PM,021XX E 71ST ST,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,true,false,0333,003,5,43,03,1192064,1858245,2005,11/10/2013 12:45:55 AM,41.766009775,-87.571571852,"(41.766009775, -87.571571852)"
-4151399,HL482705,07/14/2005 06:50:00 AM,047XX W IRVING PARK RD,1310,CRIMINAL DAMAGE,TO PROPERTY,SMALL RETAIL STORE,false,false,1722,017,45,15,14,1144038,1926211,2005,01/26/2006 03:51:08 AM,41.953549711,-87.745900882,"(41.953549711, -87.745900882)"
-4148103,HL481724,07/13/2005 09:00:00 AM,063XX N GLENWOOD AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2433,024,40,77,06,1165788,1942048,2005,12/04/2014 12:43:35 PM,41.996570037,-87.665491899,"(41.996570037, -87.665491899)"
-4258948,HL479834,07/12/2005 09:14:52 PM,059XX S LOOMIS BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0713,007,16,67,18,1167972,1865376,2005,01/26/2006 03:51:08 AM,41.786129699,-87.659672294,"(41.786129699, -87.659672294)"
-4146224,HL478877,07/12/2005 01:10:00 PM,068XX S ST LAWRENCE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,DRIVEWAY - RESIDENTIAL,false,false,0321,003,6,42,14,1181459,1859735,2005,01/26/2006 03:51:08 AM,41.770349638,-87.610396496,"(41.770349638, -87.610396496)"
-4142506,HL478343,07/12/2005 08:20:00 AM,0000X E ROOSEVELT RD,0890,THEFT,FROM BUILDING,SMALL RETAIL STORE,false,false,0132,001,2,33,06,1176915,1895035,2005,01/26/2006 03:51:08 AM,41.867319476,-87.625987647,"(41.867319476, -87.625987647)"
-4154137,HL488570,07/12/2005 12:00:00 AM,014XX N CAMPBELL AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,false,false,1423,014,26,24,11,1159442,1909311,2005,01/26/2006 03:51:08 AM,41.906871258,-87.689740429,"(41.906871258, -87.689740429)"
-4138085,HL475730,07/11/2005 12:20:00 AM,012XX N ASHLAND AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1424,014,1,24,08B,1165466,1908095,2005,01/26/2006 03:51:08 AM,41.903408313,-87.667646484,"(41.903408313, -87.667646484)"
-4142320,HL476214,07/10/2005 09:00:00 PM,020XX W SUMMERDALE AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,false,2012,020,40,4,06,1161507,1935554,2005,01/26/2006 03:51:08 AM,41.978840805,-87.681421669,"(41.978840805, -87.681421669)"
-4137995,HL470827,07/08/2005 04:10:00 PM,072XX S JEFFERY BLVD,0890,THEFT,FROM BUILDING,APARTMENT,false,false,0333,003,5,43,06,1190756,1857366,2005,01/26/2006 03:51:08 AM,41.763629415,-87.576394395,"(41.763629415, -87.576394395)"
-4143031,HL470945,07/08/2005 03:30:00 PM,021XX S CLARK ST,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,2111,001,3,34,06,1175793,1889963,2005,12/04/2014 12:43:35 PM,41.853426858,-87.63025911,"(41.853426858, -87.63025911)"
-4136831,HL468525,07/07/2005 02:58:00 PM,110XX S MICHIGAN AVE,0860,THEFT,RETAIL THEFT,DRUG STORE,true,false,0513,005,9,49,06,1178748,1831610,2005,01/26/2006 03:51:08 AM,41.693233246,-87.621186839,"(41.693233246, -87.621186839)"
-4134012,HL468733,07/07/2005 11:00:00 AM,041XX W NELSON ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,2523,025,31,21,05,1147774,1919985,2005,01/26/2006 03:51:08 AM,41.936393955,-87.732327336,"(41.936393955, -87.732327336)"
-4138475,HL467624,07/07/2005 02:30:00 AM,056XX S KOLIN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0813,008,13,62,14,1148296,1866520,2005,01/26/2006 03:51:08 AM,41.789669408,-87.731785684,"(41.789669408, -87.731785684)"
-4250973,HL466253,07/06/2005 01:25:36 PM,052XX W FERDINAND ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1523,015,28,25,18,1141348,1902685,2005,01/26/2006 03:51:08 AM,41.889042032,-87.756371576,"(41.889042032, -87.756371576)"
-4249217,HL466247,07/06/2005 12:55:00 PM,050XX W LE MOYNE ST,2017,NARCOTICS,MANU/DELIVER:CRACK,STREET,true,false,2533,025,37,25,18,1142465,1909464,2005,01/26/2006 03:51:08 AM,41.907623709,-87.752100797,"(41.907623709, -87.752100797)"
-4128459,HL463851,07/05/2005 12:45:00 PM,051XX N MILWAUKEE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1623,016,45,11,06,1138491,1933502,2005,12/04/2014 12:43:35 PM,41.973659402,-87.766115038,"(41.973659402, -87.766115038)"
-4135940,HL464116,07/05/2005 09:30:00 AM,0000X S KARLOV AVE,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,1115,011,28,26,03,1149028,1899496,2005,01/26/2006 03:51:08 AM,41.880145898,-87.728249889,"(41.880145898, -87.728249889)"
-4124850,HL463104,07/05/2005 02:50:00 AM,032XX W CERMAK RD,0820,THEFT,$500 AND UNDER,VACANT LOT/LAND,true,false,1024,010,24,30,06,1155134,1889154,2005,12/04/2014 12:43:35 PM,41.851646014,-87.706106696,"(41.851646014, -87.706106696)"
-4129134,HL461749,07/04/2005 11:30:00 AM,010XX N ST LOUIS AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1121,011,27,23,06,1152894,1906618,2005,12/04/2014 12:43:35 PM,41.899613709,-87.713865497,"(41.899613709, -87.713865497)"
-4123262,HL461024,07/03/2005 11:25:00 PM,041XX N WESTERN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1912,019,47,5,08B,1159592,1927416,2005,01/26/2006 03:51:08 AM,41.956549505,-87.688689359,"(41.956549505, -87.688689359)"
-4218882,HL459218,07/03/2005 11:45:00 AM,006XX N LAWNDALE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1122,011,27,23,18,1151570,1903833,2005,01/26/2006 03:51:08 AM,41.891997521,-87.718801848,"(41.891997521, -87.718801848)"
-4122458,HL459434,07/03/2005 02:50:09 AM,048XX W CHICAGO AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,true,true,1531,015,37,25,08B,1144142,1904915,2005,06/11/2007 03:52:33 PM,41.895109399,-87.74605473,"(41.895109399, -87.74605473)"
-4168039,HL461832,07/01/2005 08:00:00 PM,077XX S MARYLAND AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0624,006,6,69,06,1183208,1853703,2005,01/26/2006 03:51:08 AM,41.753756708,-87.604172862,"(41.753756708, -87.604172862)"
-4123171,HL456733,07/01/2005 06:30:00 PM,010XX N LA SALLE DR,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1824,018,42,8,05,1174986,1907310,2005,01/26/2006 03:51:08 AM,41.901046191,-87.632701308,"(41.901046191, -87.632701308)"
-4129140,HL459808,07/01/2005 02:30:00 PM,055XX S HALSTED ST,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,0712,007,20,68,06,1171871,1868200,2005,12/04/2014 12:43:35 PM,41.793794349,-87.645293789,"(41.793794349, -87.645293789)"
-4123293,HL455564,07/01/2005 07:04:08 AM,058XX S TROY ST,3730,INTERFERENCE WITH PUBLIC OFFICER,OBSTRUCTING JUSTICE,APARTMENT,true,false,0824,008,14,63,24,1156351,1865421,2005,12/04/2014 12:43:35 PM,41.786495141,-87.702279707,"(41.786495141, -87.702279707)"
-4244512,HL566391,06/30/2005 05:00:00 PM,050XX S LAKE SHORE DR SB,1242,DECEPTIVE PRACTICE,COMPUTER FRAUD,OTHER,false,false,2132,002,4,39,11,1189144,1871655,2005,01/26/2006 03:51:08 AM,41.802878289,-87.581844902,"(41.802878289, -87.581844902)"
-4116178,HL450605,06/28/2005 10:15:00 PM,038XX S COTTAGE GROVE AVE,1345,CRIMINAL DAMAGE,TO CITY OF CHICAGO PROPERTY,STREET,false,false,0212,002,4,36,14,1181923,1879997,2005,01/26/2006 03:51:08 AM,41.825939573,-87.608069027,"(41.825939573, -87.608069027)"
-4115098,HL448320,06/27/2005 07:00:00 PM,053XX S MARSHFIELD AVE,0460,BATTERY,SIMPLE,STREET,false,false,0932,009,16,61,08B,1166287,1869371,2005,01/26/2006 03:51:08 AM,41.797128527,-87.665736602,"(41.797128527, -87.665736602)"
-4188916,HL447892,06/27/2005 06:38:00 PM,010XX N LARRABEE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,CHA PARKING LOT/GROUNDS,true,false,1823,018,27,8,18,1172192,1907242,2005,01/26/2006 03:51:08 AM,41.900921752,-87.642965815,"(41.900921752, -87.642965815)"
-4110691,HL447605,06/27/2005 04:15:00 PM,045XX N HAZEL ST,0880,THEFT,PURSE-SNATCHING,SIDEWALK,false,false,2313,019,46,3,06,1169402,1930496,2005,01/26/2006 03:51:08 AM,41.964792964,-87.652535377,"(41.964792964, -87.652535377)"
-4103878,HL445990,06/26/2005 09:00:00 PM,038XX W 46TH PL,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0821,008,14,57,26,1151550,1873566,2005,01/26/2006 03:51:08 AM,41.80894157,-87.719669656,"(41.80894157, -87.719669656)"
-4113028,HL451755,06/25/2005 10:00:00 PM,030XX W WILSON AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,1713,017,33,14,05,1155288,1930406,2005,01/26/2006 03:51:08 AM,41.964842002,-87.704431386,"(41.964842002, -87.704431386)"
-4822378,HM435859,06/25/2005 03:00:00 AM,064XX W WABANSIA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,2513,,36,25,14,,,2005,06/27/2006 03:48:36 AM,,,
-4207673,HL442125,06/24/2005 09:04:53 PM,033XX S OAKLEY AVE,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,RESIDENCE,true,false,0913,009,12,59,18,1161604,1882485,2005,01/26/2006 03:51:08 AM,41.83321343,-87.682545599,"(41.83321343, -87.682545599)"
-4104535,HL441637,06/24/2005 05:28:00 PM,054XX S FAIRFIELD AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,true,false,0911,009,14,63,04B,1158929,1868337,2005,01/26/2006 03:51:08 AM,41.794444733,-87.692747694,"(41.794444733, -87.692747694)"
-4101679,HL440056,06/23/2005 10:00:00 PM,009XX W CERMAK RD,0820,THEFT,$500 AND UNDER,CONSTRUCTION SITE,true,false,1233,012,25,31,06,1170606,1889577,2005,12/04/2014 12:43:35 PM,41.852482607,-87.649308375,"(41.852482607, -87.649308375)"
-4200990,HL438842,06/23/2005 11:30:00 AM,032XX W DOUGLAS BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1022,010,24,29,18,1155178,1893072,2005,01/26/2006 03:51:08 AM,41.862396563,-87.705840112,"(41.862396563, -87.705840112)"
-4098848,HL439151,06/22/2005 09:30:00 PM,015XX N LEAMINGTON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,2533,025,37,25,14,1141721,1909553,2005,01/26/2006 03:51:08 AM,41.907881736,-87.754831681,"(41.907881736, -87.754831681)"
-4212028,HL437998,06/22/2005 09:18:00 PM,015XX S KEDZIE AVE,2027,NARCOTICS,POSS: CRACK,APARTMENT,true,false,1022,010,24,29,18,1155310,1892547,2005,01/26/2006 03:51:08 AM,41.860953257,-87.705369652,"(41.860953257, -87.705369652)"
-4099577,HL436550,06/22/2005 10:36:58 AM,082XX S EVANS AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0631,006,6,44,06,1182619,1850575,2005,01/26/2006 03:51:08 AM,41.745186819,-87.606428161,"(41.745186819, -87.606428161)"
diff --git a/work-with-data/dataprep/data/crime_partfiles/part-00005-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv b/work-with-data/dataprep/data/crime_partfiles/part-00005-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv
deleted file mode 100644
index f56545d9..00000000
--- a/work-with-data/dataprep/data/crime_partfiles/part-00005-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv
+++ /dev/null
@@ -1,887 +0,0 @@
-4090568,HL434740,06/21/2005 08:20:00 AM,064XX N WESTERN AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,2412,024,50,2,06,1159181,1942888,2005,12/04/2014 12:43:35 PM,41.999013853,-87.689772976,"(41.999013853, -87.689772976)"
-4094219,HL437167,06/21/2005 08:00:00 AM,010XX W HURON ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",PARKING LOT/GARAGE(NON.RESID.),false,false,1323,012,27,24,07,1169533,1904803,2005,04/21/2006 04:05:47 AM,41.894287283,-87.652803513,"(41.894287283, -87.652803513)"
-4087693,HL431778,06/20/2005 01:30:00 AM,036XX N CALIFORNIA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,1733,017,33,16,14,1157035,1923866,2005,01/26/2006 03:51:08 AM,41.94686048,-87.698186313,"(41.94686048, -87.698186313)"
-4280908,HL592493,06/20/2005 12:00:00 AM,023XX N MILWAUKEE AVE,1120,DECEPTIVE PRACTICE,FORGERY,OTHER,false,false,1414,014,35,22,10,1156508,1915749,2005,01/26/2006 03:51:08 AM,41.924597553,-87.70034371,"(41.924597553, -87.70034371)"
-4166607,HL427845,06/18/2005 12:25:00 AM,103XX S INDIANA AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0512,005,9,49,18,1179368,1836421,2005,01/26/2006 03:51:08 AM,41.706421207,-87.618770764,"(41.706421207, -87.618770764)"
-4085010,HL427805,06/17/2005 11:10:00 PM,080XX S UNION AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,SIDEWALK,true,false,0621,006,21,71,04B,1172994,1851472,2005,01/26/2006 03:51:08 AM,41.74786601,-87.64166914,"(41.74786601, -87.64166914)"
-4083770,HL427522,06/17/2005 08:45:00 PM,017XX W CULLERTON ST,0460,BATTERY,SIMPLE,STREET,false,false,1223,012,25,31,08B,1165330,1890500,2005,01/26/2006 03:51:08 AM,41.855129102,-87.668646583,"(41.855129102, -87.668646583)"
-4078120,HL423563,06/15/2005 10:35:00 PM,075XX S WENTWORTH AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,0623,006,17,69,14,1176209,1855143,2005,01/26/2006 03:51:08 AM,41.757868151,-87.62977842,"(41.757868151, -87.62977842)"
-4163503,HL423532,06/15/2005 10:15:00 PM,040XX W VAN BUREN ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1132,011,24,26,18,1149725,1897762,2005,01/26/2006 03:51:08 AM,41.875374094,-87.725735639,"(41.875374094, -87.725735639)"
-4095087,HL436485,06/15/2005 02:43:00 PM,031XX W 111TH ST,1205,DECEPTIVE PRACTICE,"THEFT BY LESSEE,NON-VEH",SMALL RETAIL STORE,false,false,2211,022,19,74,11,1157531,1830805,2005,06/09/2008 01:03:25 AM,41.69147929,-87.698888859,"(41.69147929, -87.698888859)"
-4160139,HL421383,06/14/2005 10:40:00 PM,030XX W LYNDALE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1414,014,35,22,18,1155470,1914967,2005,01/26/2006 03:51:08 AM,41.922472646,-87.704178867,"(41.922472646, -87.704178867)"
-4073639,HL420590,06/14/2005 04:30:00 PM,034XX N WESTERN AVE,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,1913,019,47,5,06,1159733,1922580,2005,01/26/2006 03:51:08 AM,41.943276328,-87.688304805,"(41.943276328, -87.688304805)"
-4076704,HL420290,06/14/2005 02:23:43 PM,043XX W DRUMMOND PL,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,RESIDENCE,false,false,2524,025,31,20,11,1146809,1917221,2005,01/26/2006 03:51:08 AM,41.928827796,-87.73594463,"(41.928827796, -87.73594463)"
-4070733,HL419133,06/13/2005 11:40:00 PM,029XX N CENTRAL AVE,1220,DECEPTIVE PRACTICE,THEFT OF LOST/MISLAID PROP,SIDEWALK,true,false,2514,025,31,19,11,1138487,1919007,2005,01/26/2006 03:51:08 AM,41.933883805,-87.766482305,"(41.933883805, -87.766482305)"
-4078210,HL415330,06/12/2005 06:00:00 AM,090XX S BISHOP ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,RESIDENCE,false,false,2222,022,21,73,26,1168289,1844907,2005,01/26/2006 03:51:08 AM,41.729953203,-87.65909821,"(41.729953203, -87.65909821)"
-4068386,HL415499,06/12/2005 04:00:00 AM,082XX S WABASH AVE,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,false,false,0631,006,6,44,07,1178102,1850457,2005,01/26/2006 03:51:08 AM,41.74496653,-87.622982626,"(41.74496653, -87.622982626)"
-4208028,HL413656,06/11/2005 12:20:00 PM,067XX S WINCHESTER AVE,1822,NARCOTICS,MANU/DEL:CANNABIS OVER 10 GMS,VEHICLE NON-COMMERCIAL,true,false,0726,007,15,67,18,1164477,1859982,2005,01/26/2006 03:51:08 AM,41.771402283,-87.672638675,"(41.771402283, -87.672638675)"
-4064467,HL412693,06/11/2005 12:00:00 AM,006XX N LAKE SHORE DR,0890,THEFT,FROM BUILDING,HOTEL/MOTEL,false,false,1834,018,42,8,06,1179752,1904733,2005,01/26/2006 03:51:08 AM,41.893866651,-87.615274856,"(41.893866651, -87.615274856)"
-4071396,HL412387,06/10/2005 09:53:00 PM,010XX W 61ST ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0712,007,16,68,08B,1170258,1864399,2005,01/26/2006 03:51:08 AM,41.783399242,-87.651319106,"(41.783399242, -87.651319106)"
-4064153,HL410670,06/10/2005 06:10:00 AM,070XX S DR MARTIN LUTHER KING JR DR,0460,BATTERY,SIMPLE,APARTMENT,false,false,0322,003,6,69,08B,1180176,1858392,2005,01/26/2006 03:51:08 AM,41.766693805,-87.61514052,"(41.766693805, -87.61514052)"
-4062453,HL409452,06/09/2005 02:30:00 PM,016XX W 35TH ST,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,STREET,true,false,0922,009,11,59,26,1166051,1881487,2005,01/26/2006 03:51:08 AM,41.830381207,-87.666257142,"(41.830381207, -87.666257142)"
-4152959,HL409369,06/09/2005 02:23:00 PM,047XX W OHIO ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1111,011,28,25,26,1144429,1903601,2005,01/26/2006 03:51:08 AM,41.891498237,-87.745033731,"(41.891498237, -87.745033731)"
-4344799,HL644155,06/09/2005 12:30:00 PM,079XX S RICHMOND ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0835,008,18,70,26,1158055,1851654,2005,01/26/2006 03:51:08 AM,41.748681941,-87.696405703,"(41.748681941, -87.696405703)"
-4060885,HL407287,06/08/2005 12:00:00 PM,007XX N RIDGEWAY AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1112,011,27,23,14,1151212,1904744,2005,01/26/2006 03:51:08 AM,41.894504427,-87.720092722,"(41.894504427, -87.720092722)"
-4056297,HL405422,06/07/2005 05:37:00 PM,032XX S LITUANICA AVE,0560,ASSAULT,SIMPLE,CHA APARTMENT,false,false,0924,009,11,60,08A,1170787,1883697,2005,01/26/2006 03:51:08 AM,41.836343419,-87.648816066,"(41.836343419, -87.648816066)"
-4055793,HL405876,06/07/2005 06:00:00 AM,028XX S KARLOV AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1031,010,22,30,05,1149556,1884555,2005,01/26/2006 03:51:08 AM,41.839135748,-87.72669875,"(41.839135748, -87.72669875)"
-4053696,HL404402,06/06/2005 05:52:00 PM,048XX N RAVENSWOOD AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,2032,020,47,3,06,1163542,1932027,2005,12/04/2014 12:43:35 PM,41.969119838,-87.674037693,"(41.969119838, -87.674037693)"
-4049161,HL401436,06/05/2005 07:00:00 PM,033XX W WELLINGTON AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,1412,014,35,21,06,1153616,1919802,2005,12/04/2014 12:43:35 PM,41.935777397,-87.710862097,"(41.935777397, -87.710862097)"
-4049944,HL400504,06/05/2005 11:45:16 AM,001XX W 79TH ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,GROCERY FOOD STORE,true,false,0623,006,17,69,11,1176736,1852645,2005,01/26/2006 03:51:08 AM,41.751001502,-87.6279221,"(41.751001502, -87.6279221)"
-4145889,HL399744,06/04/2005 11:50:31 PM,010XX N LAVERGNE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1531,015,37,25,18,1142878,1906420,2005,01/26/2006 03:51:08 AM,41.899262941,-87.750659592,"(41.899262941, -87.750659592)"
-4142098,HL398016,06/04/2005 05:23:02 AM,072XX S HALSTED ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0732,007,17,68,16,1172254,1857055,2005,01/26/2006 03:51:08 AM,41.763202761,-87.644216859,"(41.763202761, -87.644216859)"
-4193650,HL526077,06/03/2005 07:30:00 PM,047XX N WESTERN AVE,0890,THEFT,FROM BUILDING,SMALL RETAIL STORE,false,false,1911,019,47,4,06,1159547,1931583,2005,01/26/2006 03:51:08 AM,41.9679849,-87.68873955,"(41.9679849, -87.68873955)"
-4044622,HL395657,06/02/2005 11:45:00 PM,061XX S GREENWOOD AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,false,0314,003,20,42,06,1184437,1864184,2005,01/26/2006 03:51:08 AM,41.782488886,-87.599341286,"(41.782488886, -87.599341286)"
-4043039,HL394482,06/02/2005 10:00:00 AM,032XX W ROOSEVELT RD,2820,OTHER OFFENSE,TELEPHONE THREAT,RESTAURANT,false,true,1134,011,24,29,26,1155179,1894572,2005,01/26/2006 03:51:08 AM,41.866512706,-87.705796192,"(41.866512706, -87.705796192)"
-4040984,HL393521,06/02/2005 12:50:00 AM,019XX W RACE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,1324,012,1,24,08B,1163129,1903798,2005,01/26/2006 03:51:08 AM,41.891666459,-87.676351642,"(41.891666459, -87.676351642)"
-4081180,HL426436,06/01/2005 12:00:00 PM,045XX W 56TH ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,0813,008,13,62,06,1146751,1867047,2005,01/26/2006 03:51:08 AM,41.791145105,-87.737437424,"(41.791145105, -87.737437424)"
-4065104,HL392022,06/01/2005 11:56:18 AM,038XX N ELSTON AVE,0810,THEFT,OVER $500,STREET,true,false,1732,017,35,16,06,1152191,1925573,2005,12/04/2014 12:43:35 PM,41.951641731,-87.715946348,"(41.951641731, -87.715946348)"
-4039458,HL390657,05/31/2005 04:30:00 PM,001XX W 115TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0522,005,34,49,14,1177026,1828731,2005,01/26/2006 03:51:08 AM,41.685371737,-87.627577766,"(41.685371737, -87.627577766)"
-4042686,HL389827,05/30/2005 06:00:00 PM,031XX W POLK ST,031A,ROBBERY,ARMED: HANDGUN,RESIDENCE,false,false,1134,011,24,27,03,1155390,1896235,2005,01/26/2006 03:51:08 AM,41.871071919,-87.704976897,"(41.871071919, -87.704976897)"
-4121260,HL387558,05/30/2005 08:29:00 AM,046XX S WASHTENAW AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,0912,009,12,58,16,1159196,1873497,2005,01/26/2006 03:51:08 AM,41.808598985,-87.691627344,"(41.808598985, -87.691627344)"
-4033856,HL388335,05/30/2005 12:00:00 AM,063XX W WARWICK AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1633,016,38,17,06,1133585,1924309,2005,12/04/2014 12:43:35 PM,41.948520603,-87.784372518,"(41.948520603, -87.784372518)"
-4031852,HL385781,05/29/2005 12:00:00 AM,046XX S LARAMIE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0814,008,23,56,06,1142517,1873281,2005,12/04/2014 12:43:35 PM,41.808331702,-87.752808467,"(41.808331702, -87.752808467)"
-4032642,HL385880,05/28/2005 07:00:00 PM,036XX N PAULINA ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,1923,019,47,6,05,1164303,1924223,2005,01/26/2006 03:51:08 AM,41.947689209,-87.671461146,"(41.947689209, -87.671461146)"
-4033025,HL383943,05/28/2005 06:39:00 AM,045XX S CICERO AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,HOTEL/MOTEL,true,false,0815,008,23,56,14,1145156,1874305,2005,01/26/2006 03:51:08 AM,41.811092392,-87.743103286,"(41.811092392, -87.743103286)"
-4031242,HL382791,05/27/2005 10:00:00 AM,030XX W FILLMORE ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1134,011,28,29,05,1156047,1895175,2005,01/26/2006 03:51:08 AM,41.868149945,-87.702593396,"(41.868149945, -87.702593396)"
-4034263,HL387642,05/27/2005 07:30:00 AM,065XX N CALIFORNIA AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,CHURCH/SYNAGOGUE/PLACE OF WORSHIP,false,false,2412,024,50,2,14,1156439,1943123,2005,01/26/2006 03:51:08 AM,41.999714811,-87.699853629,"(41.999714811, -87.699853629)"
-4027386,HL380759,05/26/2005 05:45:00 PM,0000X N WESTERN AVE,0460,BATTERY,SIMPLE,RESTAURANT,true,false,1332,012,2,28,08B,1160446,1900212,2005,01/26/2006 03:51:08 AM,41.881882111,-87.686304377,"(41.881882111, -87.686304377)"
-4023741,HL378632,05/25/2005 12:00:00 PM,052XX S MORGAN ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0934,009,16,61,06,1170490,1870272,2005,01/26/2006 03:51:08 AM,41.799510364,-87.650297443,"(41.799510364, -87.650297443)"
-4027890,HL376705,05/24/2005 06:40:00 PM,040XX W ROOSEVELT RD,0820,THEFT,$500 AND UNDER,SMALL RETAIL STORE,true,false,1132,011,24,29,06,1149433,1894437,2005,12/04/2014 12:43:35 PM,41.866255574,-87.726894007,"(41.866255574, -87.726894007)"
-4020960,HL375588,05/24/2005 09:22:00 AM,076XX S CONSTANCE AVE,0545,ASSAULT,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,0414,004,8,43,08A,1189903,1854626,2005,01/26/2006 03:51:08 AM,41.756131176,-87.57960876,"(41.756131176, -87.57960876)"
-4102365,HL375246,05/24/2005 01:15:00 AM,045XX W 16TH ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,1012,010,24,29,18,1146514,1891697,2005,01/26/2006 03:51:08 AM,41.858792755,-87.73767981,"(41.858792755, -87.73767981)"
-4097315,HL374502,05/23/2005 05:55:53 PM,001XX S CICERO AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),VACANT LOT/LAND,true,false,1533,015,28,25,18,1144372,1898942,2005,01/26/2006 03:51:08 AM,41.878714461,-87.745360302,"(41.878714461, -87.745360302)"
-4018221,HL373442,05/23/2005 09:30:00 AM,072XX N ROCKWELL ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2411,024,50,2,14,1157688,1947792,2005,01/26/2006 03:51:08 AM,42.012501261,-87.69513082,"(42.012501261, -87.69513082)"
-4101902,HL372452,05/22/2005 06:55:00 PM,062XX S CALUMET AVE,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,CHA PARKING LOT/GROUNDS,true,false,0311,003,20,40,18,1179607,1863744,2005,01/26/2006 03:51:08 AM,41.781393256,-87.617062723,"(41.781393256, -87.617062723)"
-4138812,HL371730,05/22/2005 12:13:17 PM,005XX W OAK ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,1823,018,27,8,18,1172690,1907113,2005,01/26/2006 03:51:08 AM,41.900556757,-87.641140463,"(41.900556757, -87.641140463)"
-4012575,HL370181,05/21/2005 03:00:00 PM,011XX W BRYN MAWR AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,true,false,2023,020,48,77,24,1167746,1937315,2005,01/26/2006 03:51:08 AM,41.98354045,-87.658426504,"(41.98354045, -87.658426504)"
-4109365,HL370033,05/21/2005 01:26:41 PM,047XX S KNOX AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0815,008,23,56,16,1146111,1872996,2005,01/26/2006 03:51:08 AM,41.807482244,-87.739633536,"(41.807482244, -87.739633536)"
-4025363,HL378384,05/20/2005 12:00:00 AM,096XX S YATES AVE,0820,THEFT,$500 AND UNDER,DRIVEWAY - RESIDENTIAL,false,false,0431,004,7,51,06,1194055,1841464,2005,12/04/2014 12:43:35 PM,41.719912682,-87.564823593,"(41.719912682, -87.564823593)"
-4009774,HL366889,05/19/2005 11:00:00 PM,072XX S SOUTH SHORE DR,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0334,003,7,43,05,1194953,1857770,2005,01/26/2006 03:51:08 AM,41.764635643,-87.560998509,"(41.764635643, -87.560998509)"
-4011610,HL367825,05/19/2005 06:00:00 PM,021XX N SAYRE AVE,0810,THEFT,OVER $500,ALLEY,false,false,2512,025,36,25,06,1129194,1913638,2005,12/04/2014 12:43:35 PM,41.919314126,-87.800756972,"(41.919314126, -87.800756972)"
-4007993,HL365188,05/19/2005 04:28:42 AM,061XX S COTTAGE GROVE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0313,003,20,42,14,1182575,1864672,2005,01/26/2006 03:51:08 AM,41.783871415,-87.606152705,"(41.783871415, -87.606152705)"
-4021766,HL364795,05/18/2005 09:54:04 PM,015XX E 69TH PL,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0332,003,5,43,05,1187792,1859256,2005,01/26/2006 03:51:08 AM,41.7688868,-87.587197825,"(41.7688868, -87.587197825)"
-4024633,HL363790,05/18/2005 01:30:00 PM,003XX E PERSHING RD,0820,THEFT,$500 AND UNDER,DELIVERY TRUCK,false,false,0211,002,3,35,06,1179235,1879242,2005,12/04/2014 12:43:35 PM,41.823929639,-87.617953645,"(41.823929639, -87.617953645)"
-4003625,HL360730,05/16/2005 10:30:00 PM,006XX W 115TH ST,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,0524,005,34,53,08A,1173775,1828571,2005,01/26/2006 03:51:08 AM,41.685005141,-87.639483591,"(41.685005141, -87.639483591)"
-4013449,HL360541,05/16/2005 06:23:18 PM,041XX W 19TH ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,true,false,1012,010,24,29,24,1149017,1890363,2005,01/26/2006 03:51:08 AM,41.855084076,-87.728526551,"(41.855084076, -87.728526551)"
-4506407,HL359772,05/16/2005 03:07:00 PM,029XX W SHAKESPEARE AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,OTHER,false,false,1414,014,35,22,26,1156349,1914254,2005,06/11/2007 03:52:33 PM,41.920498376,-87.700968456,"(41.920498376, -87.700968456)"
-3995826,HL356914,05/14/2005 10:50:00 PM,033XX W MAYPOLE AVE,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,false,false,1123,011,28,27,04B,1154256,1900827,2005,01/26/2006 03:51:08 AM,41.883695547,-87.709017611,"(41.883695547, -87.709017611)"
-3993901,HL354033,05/13/2005 12:45:00 PM,052XX N LINCOLN AVE,1121,DECEPTIVE PRACTICE,COUNTERFEITING DOCUMENT,OTHER,false,false,2011,020,40,4,10,1158503,1934520,2005,01/26/2006 03:51:08 AM,41.976065663,-87.692497484,"(41.976065663, -87.692497484)"
-3995571,HL356081,05/13/2005 12:00:00 PM,034XX S WALLACE ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0924,009,11,60,26,1172898,1882129,2005,01/26/2006 03:51:08 AM,41.831994266,-87.641116435,"(41.831994266, -87.641116435)"
-4025001,HL377978,05/13/2005 08:30:00 AM,036XX W SCHOOL ST,1330,CRIMINAL TRESPASS,TO LAND,VACANT LOT/LAND,false,false,1732,017,35,21,26,1151250,1921732,2005,01/26/2006 03:51:08 AM,41.941120283,-87.719506589,"(41.941120283, -87.719506589)"
-3997312,HL352195,05/12/2005 02:30:00 PM,010XX E 83RD ST,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,RESTAURANT,false,false,0631,006,8,44,11,1184520,1850213,2005,01/26/2006 03:51:08 AM,41.744149174,-87.59947398,"(41.744149174, -87.59947398)"
-4081092,HL349730,05/11/2005 01:45:00 PM,079XX S KINGSTON AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,0422,004,7,46,26,1194520,1853010,2005,01/26/2006 03:51:08 AM,41.751584501,-87.562741816,"(41.751584501, -87.562741816)"
-3989778,HL349751,05/11/2005 12:25:00 PM,037XX W POLK ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,1133,011,24,27,08B,1151655,1896077,2005,01/26/2006 03:51:08 AM,41.870712557,-87.718693681,"(41.870712557, -87.718693681)"
-4047247,HL398154,05/11/2005 05:35:00 AM,014XX N STATE PKWY,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,RESIDENCE,false,false,1824,018,42,8,11,1175983,1910119,2005,01/26/2006 03:51:08 AM,41.908731814,-87.628954565,"(41.908731814, -87.628954565)"
-3991700,HL349586,05/09/2005 02:00:00 AM,033XX W OHIO ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,true,1121,011,27,23,07,1153792,1903835,2005,01/26/2006 03:51:08 AM,41.89195905,-87.710641314,"(41.89195905, -87.710641314)"
-4060008,HL343662,05/08/2005 08:03:00 PM,040XX W WILCOX ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,1115,011,28,26,18,1149370,1899067,2005,01/26/2006 03:51:08 AM,41.87896205,-87.727005223,"(41.87896205, -87.727005223)"
-3982702,HL342175,05/07/2005 09:30:00 PM,030XX W 26TH ST,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,STREET,false,false,1033,010,12,30,11,1156740,1886640,2005,01/26/2006 03:51:08 AM,41.844714972,-87.700280266,"(41.844714972, -87.700280266)"
-3983923,HL340795,05/07/2005 08:45:22 AM,004XX W 101ST ST,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,false,false,2232,022,9,73,07,1174929,1837974,2005,01/26/2006 03:51:08 AM,41.710782791,-87.634980004,"(41.710782791, -87.634980004)"
-3977819,HL340540,05/07/2005 03:40:00 AM,024XX W BELMONT AVE,1340,CRIMINAL DAMAGE,TO STATE SUP PROP,JAIL / LOCK-UP FACILITY,true,false,1913,019,47,5,14,1159318,1921219,2005,01/26/2006 03:51:08 AM,41.939550218,-87.689867691,"(41.939550218, -87.689867691)"
-3979719,HL340533,05/07/2005 03:20:00 AM,004XX N LA SALLE DR,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,true,false,1831,018,42,8,14,1175020,1903314,2005,01/26/2006 03:51:08 AM,41.890080194,-87.632696284,"(41.890080194, -87.632696284)"
-3977602,HL340364,05/07/2005 12:45:00 AM,023XX N KARLOV AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,2525,025,31,20,26,1148704,1915151,2005,01/26/2006 03:51:08 AM,41.923111087,-87.729034667,"(41.923111087, -87.729034667)"
-3974796,HL336932,05/05/2005 12:40:00 PM,021XX W TOUHY AVE,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,STREET,true,false,2411,024,50,2,08B,1160370,1947673,2005,01/26/2006 03:51:08 AM,42.01211943,-87.685265743,"(42.01211943, -87.685265743)"
-3971626,HL335441,05/04/2005 05:53:19 PM,062XX N WESTERN AVE,1330,CRIMINAL TRESPASS,TO LAND,DRUG STORE,true,false,2413,024,50,2,26,1159144,1941488,2005,01/26/2006 03:51:08 AM,41.995172965,-87.689947786,"(41.995172965, -87.689947786)"
-3971797,HL334258,05/04/2005 08:36:49 AM,080XX S WINCHESTER AVE,0810,THEFT,OVER $500,OTHER,false,false,0611,006,18,71,06,1164790,1851545,2005,12/04/2014 12:43:35 PM,41.748243369,-87.671729179,"(41.748243369, -87.671729179)"
-3961762,HL329560,05/01/2005 07:30:00 PM,107XX S STATE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0513,005,9,49,08B,1178199,1833611,2005,01/26/2006 03:51:08 AM,41.698736714,-87.623136424,"(41.698736714, -87.623136424)"
-3964448,HL329343,05/01/2005 07:53:00 AM,0000X E OHIO ST,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,1834,018,42,8,06,1176754,1904172,2005,12/04/2014 12:43:35 PM,41.892395561,-87.62630236,"(41.892395561, -87.62630236)"
-4046776,HL327863,04/30/2005 06:50:00 PM,078XX S EAST END AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0414,004,8,43,18,1188939,1853258,2005,01/26/2006 03:51:08 AM,41.752400382,-87.58318527,"(41.752400382, -87.58318527)"
-4077172,HL327238,04/30/2005 10:55:00 AM,037XX S DR MARTIN LUTHER KING JR DR,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),CHA PARKING LOT/GROUNDS,true,false,0212,002,3,35,18,1179518,1879937,2005,01/26/2006 03:51:08 AM,41.825830302,-87.616894164,"(41.825830302, -87.616894164)"
-3966587,HL326225,04/29/2005 07:00:00 PM,003XX E PERSHING RD,1780,OFFENSE INVOLVING CHILDREN,OTHER OFFENSE,RESIDENCE PORCH/HALLWAY,false,false,0211,002,3,35,26,1179117,1879239,2005,01/26/2006 03:51:08 AM,41.823924102,-87.618386633,"(41.823924102, -87.618386633)"
-4051431,HL325863,04/29/2005 06:19:34 PM,055XX S TROY ST,2027,NARCOTICS,POSS: CRACK,VEHICLE NON-COMMERCIAL,true,false,0824,008,14,63,18,1156278,1867919,2005,01/26/2006 03:51:08 AM,41.793351479,-87.702480159,"(41.793351479, -87.702480159)"
-3966077,HL332405,04/29/2005 06:00:00 PM,003XX W KINZIE ST,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE,false,false,1831,018,42,8,14,1174111,1903005,2005,01/26/2006 03:51:08 AM,41.889252599,-87.636043732,"(41.889252599, -87.636043732)"
-3957960,HL325434,04/28/2005 09:00:00 PM,044XX S CALIFORNIA AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,0912,009,12,58,14,1158485,1874986,2005,01/26/2006 03:51:08 AM,41.81269953,-87.694194535,"(41.81269953, -87.694194535)"
-3957507,HL324572,04/28/2005 07:00:00 PM,017XX N CICERO AVE,0810,THEFT,OVER $500,OTHER,false,false,2533,025,37,25,06,1144100,1910903,2005,12/04/2014 12:43:35 PM,41.911541921,-87.746058428,"(41.911541921, -87.746058428)"
-3954756,HL323089,04/28/2005 10:00:00 AM,085XX S OGLESBY AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0412,004,8,46,14,1193279,1848828,2005,01/26/2006 03:51:08 AM,41.740139151,-87.567425811,"(41.740139151, -87.567425811)"
-3968030,HL321204,04/27/2005 01:51:51 PM,057XX S HOYNE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,DRIVEWAY - RESIDENTIAL,false,false,0715,007,15,67,14,1163309,1866295,2005,01/26/2006 03:51:08 AM,41.788750539,-87.67674348,"(41.788750539, -87.67674348)"
-3950420,HL320224,04/26/2005 10:40:00 PM,025XX W JACKSON BLVD,0560,ASSAULT,SIMPLE,STREET,false,false,1125,011,2,28,08A,1159586,1898573,2005,01/26/2006 03:51:08 AM,41.877402293,-87.689507435,"(41.877402293, -87.689507435)"
-3965349,HL319400,04/26/2005 04:24:04 PM,062XX S JUSTINE ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0713,007,16,67,05,1167032,1863307,2005,01/26/2006 03:51:08 AM,41.780472265,-87.663177927,"(41.780472265, -87.663177927)"
-4030812,HL318915,04/26/2005 12:01:56 PM,066XX S RHODES AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0321,003,20,42,18,1180998,1861286,2005,01/26/2006 03:51:08 AM,41.774616358,-87.612038638,"(41.774616358, -87.612038638)"
-3962920,HL330724,04/25/2005 08:00:00 PM,062XX N AVERS AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,false,false,1711,017,39,13,14,1149531,1941038,2005,01/26/2006 03:51:08 AM,41.994130886,-87.725321066,"(41.994130886, -87.725321066)"
-3956929,HL320056,04/25/2005 05:00:00 AM,002XX N CLARK ST,0820,THEFT,$500 AND UNDER,CTA TRAIN,false,false,0113,001,42,32,06,1175460,1901755,2005,12/04/2014 12:43:35 PM,41.885792341,-87.631127274,"(41.885792341, -87.631127274)"
-3946895,HL316057,04/25/2005 01:09:29 AM,082XX S INGLESIDE AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,RESIDENCE,false,false,0631,006,8,44,04B,1183966,1850243,2005,01/26/2006 03:51:08 AM,41.744244444,-87.601502935,"(41.744244444, -87.601502935)"
-3946732,HL317859,04/25/2005 01:00:00 AM,031XX N MEADE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2511,025,36,19,07,1135128,1920602,2005,01/26/2006 03:51:08 AM,41.938320941,-87.778788804,"(41.938320941, -87.778788804)"
-4110078,HL441947,04/24/2005 06:00:00 PM,017XX N HONORE ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,1434,014,32,24,26,1163785,1911652,2005,01/26/2006 03:51:08 AM,41.9132046,-87.673720673,"(41.9132046, -87.673720673)"
-3952424,HL321490,04/24/2005 12:00:00 PM,079XX S PAULINA ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0611,006,21,71,07,1166357,1851827,2005,01/26/2006 03:51:08 AM,41.74898401,-87.665979133,"(41.74898401, -87.665979133)"
-4029479,HL313221,04/23/2005 11:54:00 AM,002XX E 42ND ST,2017,NARCOTICS,MANU/DELIVER:CRACK,VEHICLE NON-COMMERCIAL,true,false,0214,002,3,38,18,1178408,1877169,2005,01/26/2006 03:51:08 AM,41.818260014,-87.621050641,"(41.818260014, -87.621050641)"
-3942228,HL312849,04/22/2005 07:30:00 PM,019XX S MICHIGAN AVE,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,0132,001,3,33,06,1177480,1890933,2005,12/04/2014 12:43:35 PM,41.856050532,-87.624037885,"(41.856050532, -87.624037885)"
-3934913,HL307933,04/20/2005 05:30:00 AM,102XX S COMMERCIAL AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0431,004,10,51,05,1197539,1837814,2005,01/26/2006 03:51:08 AM,41.709810704,-87.552184224,"(41.709810704, -87.552184224)"
-3933508,HL306210,04/20/2005 12:05:00 AM,001XX E 70TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,0322,003,6,69,08B,1178637,1858682,2005,01/26/2006 03:51:08 AM,41.76752472,-87.620772701,"(41.76752472, -87.620772701)"
-3929801,HL298056,04/16/2005 02:45:00 AM,005XX E BROWNING AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA APARTMENT,true,true,0212,002,4,35,08B,1180405,1881373,2005,01/26/2006 03:51:08 AM,41.829750449,-87.613595867,"(41.829750449, -87.613595867)"
-3923716,HL297187,04/15/2005 07:40:00 AM,063XX S ASHLAND AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,CTA GARAGE / OTHER PROPERTY,false,false,0725,007,16,67,14,1166795,1862745,2005,01/26/2006 03:51:08 AM,41.778935131,-87.664062847,"(41.778935131, -87.664062847)"
-3919218,HL294014,04/13/2005 11:00:00 PM,067XX S RIDGELAND AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0332,003,5,43,14,1189073,1860333,2005,01/26/2006 03:51:08 AM,41.771811586,-87.582467913,"(41.771811586, -87.582467913)"
-3944202,HL297847,04/13/2005 06:00:00 PM,041XX W OAKDALE AVE,1562,SEX OFFENSE,AGG CRIMINAL SEXUAL ABUSE,RESIDENCE,true,false,2523,025,31,21,17,1148176,1919330,2005,06/02/2010 10:34:17 AM,41.934588835,-87.730866846,"(41.934588835, -87.730866846)"
-4004393,HL291831,04/13/2005 07:10:00 AM,027XX W MADISON ST,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,1125,011,2,27,16,1158307,1899866,2005,01/26/2006 03:51:08 AM,41.880976639,-87.694168225,"(41.880976639, -87.694168225)"
-3919140,HL292244,04/13/2005 06:30:00 AM,058XX N CLARK ST,0460,BATTERY,SIMPLE,RESIDENCE,false,false,2012,020,40,77,08B,1164665,1938480,2005,01/26/2006 03:51:08 AM,41.986803305,-87.669724627,"(41.986803305, -87.669724627)"
-3957360,HL325054,04/10/2005 09:30:00 AM,034XX W VAN BUREN ST,0820,THEFT,$500 AND UNDER,HOSPITAL BUILDING/GROUNDS,false,false,1133,011,28,27,06,1153420,1897772,2005,12/04/2014 12:43:35 PM,41.875328956,-87.712168686,"(41.875328956, -87.712168686)"
-3983190,HL344497,04/07/2005 11:50:00 PM,014XX W CATALPA AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,2013,020,48,77,08B,1165372,1936581,2005,01/26/2006 03:51:08 AM,41.98157733,-87.667178608,"(41.98157733, -87.667178608)"
-4000310,HL278921,04/06/2005 07:30:00 PM,001XX E RANDOLPH ST,1505,PROSTITUTION,CALL OPERATION,HOTEL/MOTEL,true,false,0124,001,42,32,16,1177728,1901183,2005,01/26/2006 03:51:08 AM,41.884171514,-87.6228162,"(41.884171514, -87.6228162)"
-3992868,HL277634,04/06/2005 10:25:00 AM,044XX W MADISON ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1113,011,28,26,16,1146569,1899600,2005,01/26/2006 03:51:08 AM,41.880478505,-87.737276487,"(41.880478505, -87.737276487)"
-3906398,HL276807,04/05/2005 09:25:00 PM,062XX S RACINE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0712,007,16,68,03,1169424,1863608,2005,01/26/2006 03:51:08 AM,41.781246755,-87.654399729,"(41.781246755, -87.654399729)"
-3907033,HL282281,04/05/2005 03:00:00 PM,116XX S PARNELL AVE,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,0524,005,34,53,06,1174772,1827503,2005,12/04/2014 12:43:35 PM,41.682052289,-87.635865492,"(41.682052289, -87.635865492)"
-3897838,HL275273,04/05/2005 02:00:00 AM,040XX W CHICAGO AVE,0890,THEFT,FROM BUILDING,GAS STATION,false,false,1111,011,37,23,06,1149526,1905085,2005,01/26/2006 03:51:08 AM,41.895473067,-87.726276093,"(41.895473067, -87.726276093)"
-3895819,HL272929,04/04/2005 03:16:00 AM,031XX N ELSTON AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,true,1411,014,1,21,08A,1157677,1920491,2005,01/26/2006 03:51:08 AM,41.937586179,-87.695918749,"(41.937586179, -87.695918749)"
-4000194,HL272493,04/03/2005 08:15:00 PM,022XX S STATE ST,2027,NARCOTICS,POSS: CRACK,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,0134,001,3,33,18,1176629,1889337,2005,01/26/2006 03:51:08 AM,41.851690245,-87.627209622,"(41.851690245, -87.627209622)"
-3894611,HL271700,04/03/2005 08:00:00 AM,045XX N KENTON AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1722,017,45,16,14,1144845,1929820,2005,01/26/2006 03:51:08 AM,41.963437886,-87.742842819,"(41.963437886, -87.742842819)"
-3894119,HL271325,04/03/2005 03:50:00 AM,008XX N KEDVALE AVE,0560,ASSAULT,SIMPLE,STREET,true,false,1111,011,37,23,08A,1148526,1905215,2005,01/26/2006 03:51:08 AM,41.895849158,-87.729945529,"(41.895849158, -87.729945529)"
-3897709,HL270163,04/01/2005 05:00:00 PM,0000X E GARFIELD BLVD,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,RESTAURANT,true,false,0232,002,3,40,11,1177959,1868613,2005,01/26/2006 03:51:08 AM,41.794791784,-87.622957126,"(41.794791784, -87.622957126)"
-4174840,HL506585,04/01/2005 12:00:00 PM,024XX E 73RD ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0334,003,7,43,06,1193715,1856987,2005,01/26/2006 03:51:08 AM,41.762517446,-87.565561637,"(41.762517446, -87.565561637)"
-4000289,HL357840,04/01/2005 12:00:00 AM,039XX S LAKE PARK AVE,1562,SEX OFFENSE,AGG CRIMINAL SEXUAL ABUSE,APARTMENT,false,false,2122,002,4,36,17,1183460,1878926,2005,01/26/2006 03:51:08 AM,41.822964933,-87.602463624,"(41.822964933, -87.602463624)"
-3893335,HL267101,03/31/2005 02:30:00 PM,030XX W WARREN BLVD,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,OTHER,false,false,1331,012,2,27,07,1155853,1900152,2005,01/26/2006 03:51:08 AM,41.88181125,-87.703171464,"(41.88181125, -87.703171464)"
-3986278,HL265968,03/31/2005 01:00:00 PM,056XX S ABERDEEN ST,2094,NARCOTICS,ATTEMPT POSSESSION CANNABIS,STREET,true,false,0712,007,16,68,18,1169981,1867531,2005,01/26/2006 03:51:08 AM,41.791999837,-87.652243716,"(41.791999837, -87.652243716)"
-3891092,HL264885,03/30/2005 08:17:46 PM,037XX N SOUTHPORT AVE,0460,BATTERY,SIMPLE,RESIDENCE PORCH/HALLWAY,true,false,1923,019,44,6,08B,1166264,1924905,2005,01/26/2006 03:51:08 AM,41.949518868,-87.664233441,"(41.949518868, -87.664233441)"
-3894596,HL264445,03/30/2005 04:45:00 PM,068XX S RIDGELAND AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0332,003,5,43,05,1189002,1860092,2005,01/26/2006 03:51:08 AM,41.771151963,-87.582735882,"(41.771151963, -87.582735882)"
-3906302,HL277796,03/30/2005 04:30:00 PM,060XX W MONTROSE AVE,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,STREET,false,false,1624,016,38,15,11,1135524,1928592,2005,01/26/2006 03:51:08 AM,41.960239286,-87.77714283,"(41.960239286, -87.77714283)"
-3889964,HL264211,03/30/2005 02:05:00 PM,131XX S CARVER DR,0560,ASSAULT,SIMPLE,STREET,false,false,0533,005,9,54,08A,1186819,1818582,2005,01/26/2006 03:51:08 AM,41.65729576,-87.592047878,"(41.65729576, -87.592047878)"
-3914164,HL288023,03/30/2005 01:15:00 PM,014XX W WEBSTER AVE,0860,THEFT,RETAIL THEFT,OTHER,false,false,1811,018,32,7,06,1166165,1914693,2005,01/26/2006 03:51:08 AM,41.921498729,-87.664890033,"(41.921498729, -87.664890033)"
-3895235,HL263741,03/30/2005 11:00:00 AM,106XX S PARNELL AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,2233,022,34,49,05,1174561,1834223,2005,01/26/2006 03:51:08 AM,41.700497682,-87.636438858,"(41.700497682, -87.636438858)"
-3887333,HL263442,03/29/2005 08:00:00 PM,025XX W 117TH ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,2212,022,19,75,05,1161332,1827001,2005,01/26/2006 03:51:08 AM,41.680962752,-87.685077669,"(41.680962752, -87.685077669)"
-4100711,HL261866,03/29/2005 12:30:00 PM,013XX W 88TH ST,1822,NARCOTICS,MANU/DEL:CANNABIS OVER 10 GMS,VEHICLE NON-COMMERCIAL,true,false,2222,022,21,71,18,1168665,1846361,2005,01/26/2006 03:51:08 AM,41.733935101,-87.657678997,"(41.733935101, -87.657678997)"
-3886862,HL260119,03/28/2005 02:55:00 PM,060XX S THROOP ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, GROUNDS",false,false,0713,007,16,67,08B,1168649,1864822,2005,01/26/2006 03:51:08 AM,41.784594874,-87.657206049,"(41.784594874, -87.657206049)"
-3882444,HL258913,03/27/2005 10:25:00 PM,053XX W CHICAGO AVE,141C,WEAPONS VIOLATION,UNLAWFUL USE OTHER DANG WEAPON,STREET,true,false,1524,015,37,25,15,1140842,1904779,2005,06/11/2007 03:52:33 PM,41.894797544,-87.758178285,"(41.894797544, -87.758178285)"
-3890999,HL254878,03/24/2005 06:30:00 PM,050XX N MANGO AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1622,016,45,11,06,1136885,1933269,2005,12/04/2014 12:43:35 PM,41.973049056,-87.772026411,"(41.973049056, -87.772026411)"
-3879278,HL253118,03/24/2005 04:00:00 PM,088XX S LOWE AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,false,2223,022,21,71,08B,1173479,1846259,2005,01/26/2006 03:51:08 AM,41.733550142,-87.640045842,"(41.733550142, -87.640045842)"
-3881851,HL253780,03/24/2005 02:00:00 PM,001XX W 87TH ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",PARKING LOT/GARAGE(NON.RESID.),false,false,0622,006,21,44,07,1176920,1847317,2005,01/26/2006 03:51:08 AM,41.736376676,-87.627408005,"(41.736376676, -87.627408005)"
-3975299,HL251752,03/23/2005 08:30:00 PM,010XX N LARRABEE ST,2027,NARCOTICS,POSS: CRACK,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,1823,018,27,8,18,1172192,1907242,2005,01/26/2006 03:51:08 AM,41.900921752,-87.642965815,"(41.900921752, -87.642965815)"
-3878255,HL253425,03/23/2005 07:30:00 PM,004XX W GOETHE ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,1821,018,27,8,26,1173123,1908967,2005,01/26/2006 03:51:08 AM,41.905634634,-87.639494966,"(41.905634634, -87.639494966)"
-3876312,HL250217,03/23/2005 03:07:00 AM,008XX W JACKSON BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,RESTAURANT,false,false,1213,012,27,28,14,1170955,1898896,2005,01/26/2006 03:51:08 AM,41.878047017,-87.647754301,"(41.878047017, -87.647754301)"
-3972526,HL248256,03/21/2005 10:36:41 PM,031XX W DOUGLAS BLVD,2017,NARCOTICS,MANU/DELIVER:CRACK,SIDEWALK,true,false,1022,010,24,29,18,1155832,1893333,2005,01/26/2006 03:51:08 AM,41.863099633,-87.703432325,"(41.863099633, -87.703432325)"
-3868814,HL243891,03/19/2005 12:40:00 PM,093XX S KENWOOD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0413,004,8,47,08B,1186794,1843214,2005,01/26/2006 03:51:08 AM,41.724889664,-87.591362999,"(41.724889664, -87.591362999)"
-3867609,HL243100,03/19/2005 01:30:00 AM,018XX W 23RD ST,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,STREET,true,false,1034,010,25,31,04A,1164355,1888918,2005,01/26/2006 03:51:08 AM,41.850808598,-87.672269968,"(41.850808598, -87.672269968)"
-3876547,HL242890,03/18/2005 11:03:49 PM,015XX W HOWARD ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,true,false,2422,024,49,1,24,1164499,1950305,2005,06/11/2007 03:52:33 PM,42.019254901,-87.669998214,"(42.019254901, -87.669998214)"
-3864930,HL240417,03/17/2005 06:00:00 PM,062XX N GREENVIEW AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2433,024,40,77,26,1165134,1941704,2005,01/26/2006 03:51:08 AM,41.995640064,-87.667907509,"(41.995640064, -87.667907509)"
-3865520,HL239622,03/17/2005 12:15:00 PM,085XX S CONSTANCE AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0412,004,8,45,08B,1189965,1848842,2005,01/26/2006 03:51:08 AM,41.740257863,-87.579567238,"(41.740257863, -87.579567238)"
-3864863,HL238517,03/16/2005 07:56:41 PM,0000X N PARKSIDE AVE,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,false,false,1513,015,29,25,04B,1138640,1899751,2005,02/25/2006 03:52:18 AM,41.881040292,-87.766387751,"(41.881040292, -87.766387751)"
-3867969,HL241222,03/16/2005 03:05:00 PM,062XX S STEWART AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0711,007,20,68,08B,1174730,1863727,2005,01/26/2006 03:51:08 AM,41.781456694,-87.634943307,"(41.781456694, -87.634943307)"
-3863503,HL237584,03/16/2005 11:40:00 AM,049XX S VINCENNES AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0223,002,4,38,14,1180444,1872536,2005,01/26/2006 03:51:08 AM,41.805500128,-87.613724306,"(41.805500128, -87.613724306)"
-3934844,HL307617,03/15/2005 08:00:00 PM,079XX S OGLESBY AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0414,004,7,46,05,1193200,1852723,2005,01/26/2006 03:51:08 AM,41.75082929,-87.567588257,"(41.75082929, -87.567588257)"
-6008307,HP114494,03/15/2005 12:00:00 PM,052XX N HARLEM AVE,0810,THEFT,OVER $500,COMMERCIAL / BUSINESS OFFICE,true,false,1613,,41,10,06,,,2005,12/04/2014 12:43:35 PM,,,
-3859069,HL234497,03/14/2005 06:42:00 PM,128XX S HALSTED ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0523,005,34,53,08B,1173293,1819919,2005,01/26/2006 03:51:08 AM,41.661273269,-87.641502354,"(41.661273269, -87.641502354)"
-3860301,HL234204,03/14/2005 02:30:00 PM,076XX S CICERO AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0833,008,13,65,06,1145766,1853738,2005,01/26/2006 03:51:08 AM,41.75464162,-87.741385158,"(41.75464162, -87.741385158)"
-3858642,HL233208,03/14/2005 08:00:00 AM,016XX E 67TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0332,003,5,43,08B,1188532,1860850,2005,01/26/2006 03:51:08 AM,41.77324322,-87.584434511,"(41.77324322, -87.584434511)"
-3858653,HL232844,03/13/2005 10:50:22 PM,026XX W 47TH ST,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0912,009,12,58,03,1159273,1873438,2005,01/26/2006 03:51:08 AM,41.808435504,-87.69134654,"(41.808435504, -87.69134654)"
-3856876,HL232689,03/13/2005 07:45:00 PM,033XX N KOSTNER AVE,0460,BATTERY,SIMPLE,STREET,true,false,1731,017,31,16,08B,1146499,1921846,2005,12/04/2014 12:43:35 PM,41.941525136,-87.736965566,"(41.941525136, -87.736965566)"
-3858883,HL234039,03/13/2005 04:30:00 PM,015XX S KOLIN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1012,010,24,29,08B,1147679,1892031,2005,01/26/2006 03:51:08 AM,41.859687035,-87.73339488,"(41.859687035, -87.73339488)"
-3859424,HL229248,03/11/2005 08:30:00 PM,003XX W OAK ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,false,false,1823,018,27,8,26,1173434,1907132,2005,01/26/2006 03:51:08 AM,41.900592389,-87.638407171,"(41.900592389, -87.638407171)"
-3853800,HL229305,03/11/2005 06:45:00 PM,082XX S COMMERCIAL AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,true,0424,004,7,46,26,1197577,1851090,2005,01/26/2006 03:51:08 AM,41.746240202,-87.55160353,"(41.746240202, -87.55160353)"
-3866732,HL226278,03/10/2005 12:40:00 PM,026XX W HIRSCH ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,1423,014,26,24,08B,1158652,1909252,2005,01/26/2006 03:51:08 AM,41.906725586,-87.69264405,"(41.906725586, -87.69264405)"
-3852066,HL225836,03/10/2005 09:30:00 AM,062XX S KEDZIE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0823,008,15,66,03,1156096,1862711,2005,01/26/2006 03:51:08 AM,41.779063637,-87.703287484,"(41.779063637, -87.703287484)"
-3997098,HL224456,03/09/2005 02:12:00 PM,059XX S PEORIA ST,2017,NARCOTICS,MANU/DELIVER:CRACK,STREET,true,false,0712,007,16,68,18,1171277,1865648,2005,01/26/2006 03:51:08 AM,41.786804401,-87.647546589,"(41.786804401, -87.647546589)"
-3858632,HL233307,03/09/2005 08:00:00 AM,005XX W CERMAK RD,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,false,false,1233,012,25,31,14,1172969,1889736,2005,01/26/2006 03:51:08 AM,41.852866935,-87.640630817,"(41.852866935, -87.640630817)"
-3847210,HL221890,03/08/2005 07:11:53 AM,028XX W 64TH ST,1365,CRIMINAL TRESPASS,TO RESIDENCE,ALLEY,false,true,0823,008,15,66,26,1158363,1862113,2005,01/26/2006 03:51:08 AM,41.777376758,-87.694992659,"(41.777376758, -87.694992659)"
-3846647,HL221675,03/07/2005 11:45:00 PM,056XX W FULLERTON AVE,0820,THEFT,$500 AND UNDER,DRUG STORE,true,false,2515,025,30,19,06,1138610,1915438,2005,12/04/2014 12:43:35 PM,41.924087853,-87.766117045,"(41.924087853, -87.766117045)"
-3846500,HL221681,03/07/2005 11:38:00 PM,063XX S FAIRFIELD AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0825,008,15,66,26,1159178,1862349,2005,01/26/2006 03:51:08 AM,41.778007736,-87.691998398,"(41.778007736, -87.691998398)"
-3847709,HL221377,03/07/2005 07:20:00 PM,028XX S STATE ST,0460,BATTERY,SIMPLE,CHA PARKING LOT/GROUNDS,false,false,2113,001,3,35,08B,1176702,1886077,2005,01/26/2006 03:51:08 AM,41.842742921,-87.627040101,"(41.842742921, -87.627040101)"
-3846799,HL220117,03/07/2005 09:01:09 AM,023XX S DR MARTIN LUTHER KING JR DR,1330,CRIMINAL TRESPASS,TO LAND,HOTEL/MOTEL,true,false,0133,001,2,33,26,1178919,1889126,2005,01/26/2006 03:51:08 AM,41.851059266,-87.618811288,"(41.851059266, -87.618811288)"
-3940387,HL219647,03/06/2005 11:38:23 PM,036XX W ROOSEVELT RD,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,1133,011,24,29,18,1152068,1894503,2005,01/26/2006 03:51:08 AM,41.866385199,-87.717218881,"(41.866385199, -87.717218881)"
-3849187,HL217960,03/05/2005 07:00:00 PM,063XX S WOLCOTT AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0726,007,15,67,05,1164739,1862381,2005,01/26/2006 03:51:08 AM,41.777979931,-87.671610606,"(41.777979931, -87.671610606)"
-3845611,HL217284,03/05/2005 02:45:00 PM,045XX W WASHINGTON BLVD,0820,THEFT,$500 AND UNDER,STREET,false,false,1113,011,28,26,06,1146318,1900075,2005,12/04/2014 12:43:35 PM,41.88178674,-87.738186049,"(41.88178674, -87.738186049)"
-3945261,HL216215,03/05/2005 12:53:25 AM,016XX W KINZIE ST,2250,LIQUOR LAW VIOLATION,LIQUOR LICENSE VIOLATION,OTHER,true,false,1324,012,27,24,22,1165125,1902824,2005,01/26/2006 03:51:08 AM,41.888951557,-87.669048955,"(41.888951557, -87.669048955)"
-3851751,HL215727,03/04/2005 08:20:00 PM,066XX S PERRY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,false,0722,007,6,69,08B,1176584,1860777,2005,01/26/2006 03:51:08 AM,41.773320057,-87.628234836,"(41.773320057, -87.628234836)"
-3839186,HL210289,03/02/2005 05:30:00 AM,012XX S WABASH AVE,1330,CRIMINAL TRESPASS,TO LAND,RESTAURANT,true,false,0132,001,2,33,26,1177005,1894764,2005,01/26/2006 03:51:08 AM,41.8665738,-87.625665449,"(41.8665738, -87.625665449)"
-3835163,HL207324,02/28/2005 09:00:00 AM,001XX N SANGAMON ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),false,false,1212,012,27,28,07,1170039,1900839,2005,01/26/2006 03:51:08 AM,41.883398775,-87.651060898,"(41.883398775, -87.651060898)"
-3903379,HL204296,02/26/2005 11:00:00 PM,029XX S STATE ST,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,CHA PARKING LOT/GROUNDS,true,false,2113,001,3,35,18,1176716,1885520,2005,01/26/2006 03:51:08 AM,41.841214155,-87.627005535,"(41.841214155, -87.627005535)"
-3832748,HL204081,02/26/2005 08:15:00 PM,0000X E 102ND PL,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0511,005,9,49,04B,1178421,1836982,2005,01/26/2006 03:51:08 AM,41.707982179,-87.622221652,"(41.707982179, -87.622221652)"
-3832024,HL203540,02/26/2005 02:00:00 PM,032XX W 66TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0831,008,15,66,14,1155594,1860631,2005,01/26/2006 03:51:08 AM,41.773365882,-87.705183579,"(41.773365882, -87.705183579)"
-3831851,HL201807,02/25/2005 02:00:00 PM,036XX W CERMAK RD,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,1013,010,22,30,08B,1152104,1889088,2005,01/26/2006 03:51:08 AM,41.851525097,-87.717229369,"(41.851525097, -87.717229369)"
-3831253,HL199726,02/24/2005 02:15:00 PM,051XX N DAMEN AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,"SCHOOL, PUBLIC, BUILDING",true,false,2032,020,47,4,04A,1162033,1934198,2005,01/26/2006 03:51:08 AM,41.975108885,-87.679525341,"(41.975108885, -87.679525341)"
-3834017,HL201198,02/24/2005 10:15:00 AM,036XX E 114TH ST,1900,OTHER NARCOTIC VIOLATION,INTOXICATING COMPOUNDS,"SCHOOL, PUBLIC, BUILDING",false,false,0433,004,10,52,18,1202032,1829944,2005,01/26/2006 03:51:08 AM,41.688101718,-87.535997405,"(41.688101718, -87.535997405)"
-3833102,HL198579,02/23/2005 09:19:00 PM,068XX S HALSTED ST,1330,CRIMINAL TRESPASS,TO LAND,TAVERN/LIQUOR STORE,true,false,0723,007,6,68,26,1172116,1859172,2005,01/26/2006 03:51:08 AM,41.7690151,-87.644660529,"(41.7690151, -87.644660529)"
-3903602,HL198556,02/23/2005 08:15:00 PM,014XX S ASHLAND AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1224,012,2,28,16,1165908,1893436,2005,01/26/2006 03:51:08 AM,41.863173451,-87.666441364,"(41.863173451, -87.666441364)"
-3825915,HL196605,02/22/2005 09:14:00 PM,005XX S LA SALLE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CTA PLATFORM,true,true,0131,001,2,32,08B,1175288,1897997,2005,01/26/2006 03:51:08 AM,41.875484026,-87.631871681,"(41.875484026, -87.631871681)"
-3823531,HL194440,02/21/2005 07:13:06 PM,006XX E 88TH ST,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,false,false,0632,006,6,44,26,1182135,1846815,2005,01/26/2006 03:51:08 AM,41.734880161,-87.608317668,"(41.734880161, -87.608317668)"
-3829195,HL194352,02/21/2005 06:06:00 PM,010XX W NORTH AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,1811,018,32,7,06,1169374,1910873,2005,01/26/2006 03:51:08 AM,41.910947195,-87.653210723,"(41.910947195, -87.653210723)"
-3827141,HL198636,02/21/2005 04:00:00 PM,060XX N WINTHROP AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2433,024,48,77,05,1167805,1940314,2005,01/26/2006 03:51:08 AM,41.9917685,-87.658122556,"(41.9917685, -87.658122556)"
-3906585,HL193008,02/20/2005 11:38:41 PM,047XX N LAPORTE AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),ALLEY,true,false,1623,016,45,15,18,1142470,1930986,2005,01/26/2006 03:51:08 AM,41.966682068,-87.751545891,"(41.966682068, -87.751545891)"
-3827065,HL192248,02/20/2005 02:35:00 PM,014XX W 61ST ST,0890,THEFT,FROM BUILDING,APARTMENT,false,false,0713,007,16,67,06,1167554,1864327,2005,01/26/2006 03:51:08 AM,41.783260092,-87.661234954,"(41.783260092, -87.661234954)"
-3821979,HL192180,02/20/2005 01:15:00 PM,074XX S BENNETT AVE,0496,BATTERY,AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST,APARTMENT,true,true,0333,003,8,43,04B,1190043,1855700,2005,01/26/2006 03:51:08 AM,41.759074958,-87.579061189,"(41.759074958, -87.579061189)"
-3822283,HL191274,02/19/2005 10:40:00 PM,056XX N WASHTENAW AVE,1330,CRIMINAL TRESPASS,TO LAND,APARTMENT,true,false,2011,020,40,2,26,1157322,1937569,2005,01/26/2006 03:51:08 AM,41.984456427,-87.696757163,"(41.984456427, -87.696757163)"
-3819546,HL189022,02/18/2005 05:29:13 PM,015XX W 21ST ST,0326,ROBBERY,AGGRAVATED VEHICULAR HIJACKING,VEHICLE NON-COMMERCIAL,false,false,1222,012,25,31,03,1166296,1890205,2005,01/26/2006 03:51:08 AM,41.85429902,-87.665109364,"(41.85429902, -87.665109364)"
-3825009,HL195457,02/18/2005 04:00:00 PM,013XX W ESTES AVE,0890,THEFT,FROM BUILDING,OTHER,false,false,2423,024,49,1,06,1166019,1947562,2005,01/26/2006 03:51:08 AM,42.011695611,-87.664483694,"(42.011695611, -87.664483694)"
-3820003,HL188773,02/18/2005 03:20:00 PM,063XX S LANGLEY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0312,003,20,42,08B,1182025,1863051,2005,01/26/2006 03:51:08 AM,41.779435987,-87.608219303,"(41.779435987, -87.608219303)"
-3932867,HL304969,02/14/2005 09:00:00 PM,025XX W 116TH PL,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2212,022,19,75,26,1161435,1827256,2005,01/26/2006 03:51:08 AM,41.681660392,-87.684693606,"(41.681660392, -87.684693606)"
-3806000,HL174319,02/10/2005 08:14:48 PM,032XX N WESTERN AVE,0554,ASSAULT,AGG PO HANDS NO/MIN INJURY,CTA BUS,true,false,1913,019,47,5,08A,1159739,1921242,2005,01/26/2006 03:51:08 AM,41.939604649,-87.688319756,"(41.939604649, -87.688319756)"
-3805949,HL173464,02/10/2005 09:45:00 AM,021XX N LONG AVE,0820,THEFT,$500 AND UNDER,"SCHOOL, PUBLIC, BUILDING",false,false,2515,025,37,19,06,1139988,1913987,2005,12/04/2014 12:43:35 PM,41.920081027,-87.761089223,"(41.920081027, -87.761089223)"
-3803850,HL171895,02/09/2005 04:15:00 PM,030XX W BELMONT AVE,0560,ASSAULT,SIMPLE,SMALL RETAIL STORE,false,false,1733,017,33,21,08A,1155812,1921164,2005,01/26/2006 03:51:08 AM,41.939470786,-87.702754784,"(41.939470786, -87.702754784)"
-3806509,HL171707,02/09/2005 12:00:00 PM,043XX S DREXEL BLVD,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,2123,002,4,39,08B,1183099,1876120,2005,01/26/2006 03:51:08 AM,41.815273485,-87.603875363,"(41.815273485, -87.603875363)"
-3802809,HL169768,02/08/2005 03:43:05 PM,075XX S RACINE AVE,1260,DECEPTIVE PRACTICE,LIBRARY THEFT,LIBRARY,true,false,0612,006,17,71,11,1169583,1854922,2005,06/11/2007 03:52:33 PM,41.757407834,-87.6540683,"(41.757407834, -87.6540683)"
-3823848,HL167930,02/07/2005 03:55:00 PM,013XX W WILSON AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,true,false,2311,019,46,3,26,1166426,1930623,2005,01/26/2006 03:51:08 AM,41.965205822,-87.663473655,"(41.965205822, -87.663473655)"
-3801043,HL166478,02/06/2005 08:10:00 PM,051XX S HARPER AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,RESIDENCE-GARAGE,false,false,2132,002,4,41,14,1187121,1871107,2005,01/26/2006 03:51:08 AM,41.801422834,-87.589281438,"(41.801422834, -87.589281438)"
-3801728,HL165614,02/06/2005 09:39:00 AM,024XX W MONROE ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,true,false,1125,011,2,28,26,1159849,1899642,2005,01/26/2006 03:51:08 AM,41.88033031,-87.688512279,"(41.88033031, -87.688512279)"
-3796957,HL165461,02/05/2005 10:00:00 PM,055XX W EDMUNDS ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),true,false,1623,016,45,11,07,1138295,1932440,2005,01/26/2006 03:51:08 AM,41.97074874,-87.766861613,"(41.97074874, -87.766861613)"
-3796231,HL162874,02/04/2005 07:00:00 PM,024XX S MILLARD AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,1013,010,22,30,26,1152380,1887692,2005,01/26/2006 03:51:08 AM,41.847688865,-87.716253192,"(41.847688865, -87.716253192)"
-3795739,HL162444,02/04/2005 04:13:30 PM,110XX S INDIANA AVE,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,LIBRARY,true,false,0513,005,9,49,26,1179575,1832059,2005,01/26/2006 03:51:08 AM,41.694446562,-87.618145389,"(41.694446562, -87.618145389)"
-3799975,HL165869,02/04/2005 04:00:00 PM,005XX W BARRY AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,false,false,2332,019,44,6,26,1171962,1920672,2005,01/26/2006 03:51:08 AM,41.93777939,-87.643413761,"(41.93777939, -87.643413761)"
-3795299,HL162475,02/04/2005 01:30:00 PM,021XX W 32ND ST,0810,THEFT,OVER $500,STREET,false,false,0913,009,12,59,06,1162855,1883480,2005,12/04/2014 12:43:35 PM,41.835917718,-87.677927578,"(41.835917718, -87.677927578)"
-3851652,HL159564,02/03/2005 08:55:00 AM,005XX E BROWNING AVE,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,CHA APARTMENT,true,false,0212,002,4,35,18,1180912,1881386,2005,01/26/2006 03:51:08 AM,41.829774451,-87.61173531,"(41.829774451, -87.61173531)"
-3783875,HL153294,01/30/2005 09:24:22 PM,029XX E 78TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0421,004,7,43,14,1196990,1853870,2005,01/26/2006 03:51:08 AM,41.753883341,-87.553662074,"(41.753883341, -87.553662074)"
-3839780,HL151498,01/29/2005 07:25:00 PM,077XX S SAWYER AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0835,008,18,70,18,1156020,1853020,2005,01/26/2006 03:51:08 AM,41.752471562,-87.703826127,"(41.752471562, -87.703826127)"
-3777629,HL148547,01/28/2005 04:20:00 AM,062XX S NASHVILLE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,0812,008,23,64,14,1133438,1862620,2005,01/26/2006 03:51:08 AM,41.779239441,-87.786357609,"(41.779239441, -87.786357609)"
-3779357,HL147235,01/27/2005 12:15:00 PM,013XX S TROY ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1022,010,24,29,08B,1155540,1893401,2005,01/26/2006 03:51:08 AM,41.863292107,-87.704502406,"(41.863292107, -87.704502406)"
-3775918,HL146518,01/26/2005 11:25:00 PM,032XX W FULTON BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1123,011,28,27,08B,1154330,1901847,2005,01/26/2006 03:51:08 AM,41.886493052,-87.708718613,"(41.886493052, -87.708718613)"
-3776475,HL146495,01/26/2005 10:45:00 PM,010XX W 74TH ST,031A,ROBBERY,ARMED: HANDGUN,STREET,false,false,0733,007,17,68,03,1170920,1855796,2005,01/26/2006 03:51:08 AM,41.759777128,-87.649142914,"(41.759777128, -87.649142914)"
-3772766,HL144026,01/25/2005 05:00:00 PM,044XX W WEST END AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,1113,011,28,26,04B,1147026,1900651,2005,01/26/2006 03:51:08 AM,41.883353848,-87.735571531,"(41.883353848, -87.735571531)"
-3771769,HL143917,01/25/2005 02:30:00 PM,045XX S HALSTED ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0935,009,11,61,26,1171781,1874407,2005,01/26/2006 03:51:08 AM,41.810828985,-87.645441657,"(41.810828985, -87.645441657)"
-3775550,HL143114,01/25/2005 11:38:37 AM,0000X N PINE AVE,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,APARTMENT,false,false,1522,015,28,25,11,1139549,1899588,2005,01/26/2006 03:51:08 AM,41.880576472,-87.763053889,"(41.880576472, -87.763053889)"
-3776911,HL141595,01/24/2005 03:50:00 PM,032XX N LAKE SHORE DR,0460,BATTERY,SIMPLE,STREET,false,false,2332,019,44,6,08B,1173053,1921821,2005,01/26/2006 03:51:08 AM,41.940908123,-87.639369982,"(41.940908123, -87.639369982)"
-3773904,HL142029,01/24/2005 12:00:00 PM,042XX S UNION AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,0935,009,11,61,06,1172384,1876620,2005,01/26/2006 03:51:08 AM,41.816888408,-87.643164726,"(41.816888408, -87.643164726)"
-3770854,HL140920,01/24/2005 11:00:00 AM,047XX W IRVING PARK RD,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,1722,017,45,15,06,1143749,1926204,2005,01/26/2006 03:51:08 AM,41.953535933,-87.746963462,"(41.953535933, -87.746963462)"
-3769669,HL141376,01/23/2005 11:00:00 PM,050XX W CONCORD PL,0820,THEFT,$500 AND UNDER,STREET,false,false,2533,025,37,25,06,1142516,1910460,2005,12/04/2014 12:43:35 PM,41.910355894,-87.751888647,"(41.910355894, -87.751888647)"
-3771070,HL139266,01/23/2005 12:01:00 AM,016XX W 73RD ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,0735,007,17,67,08B,1166710,1856344,2005,01/26/2006 03:51:08 AM,41.761371763,-87.664556937,"(41.761371763, -87.664556937)"
-3782181,HL152914,01/23/2005 12:00:00 AM,0000X E 118TH PL,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0532,005,9,53,26,1178615,1826363,2005,01/26/2006 03:51:08 AM,41.678837724,-87.621832434,"(41.678837724, -87.621832434)"
-3808793,HL175942,01/21/2005 11:00:00 AM,121XX S NORMAL AVE,1120,DECEPTIVE PRACTICE,FORGERY,RESIDENCE,false,false,0523,005,34,53,10,1175191,1824555,2005,01/26/2006 03:51:08 AM,41.673953192,-87.634419251,"(41.673953192, -87.634419251)"
-3764608,HL136073,01/21/2005 01:30:00 AM,024XX N ARTESIAN AVE,0810,THEFT,OVER $500,STREET,false,false,1431,014,1,22,06,1159571,1916452,2005,12/04/2014 12:43:35 PM,41.926464037,-87.689069498,"(41.926464037, -87.689069498)"
-3763918,HL134895,01/20/2005 02:30:00 PM,012XX N WASHTENAW AVE,2170,NARCOTICS,POSSESSION OF DRUG EQUIPMENT,SIDEWALK,true,false,1423,014,26,24,18,1158138,1907899,2005,06/11/2007 03:52:33 PM,41.903023361,-87.694569195,"(41.903023361, -87.694569195)"
-3785730,HL154721,01/20/2005 11:00:00 AM,048XX S DR MARTIN LUTHER KING JR DR,0810,THEFT,OVER $500,CONSTRUCTION SITE,false,false,0224,002,3,38,06,1179635,1873067,2005,12/04/2014 12:43:35 PM,41.806975786,-87.616675128,"(41.806975786, -87.616675128)"
-3764243,HL134342,01/20/2005 08:56:11 AM,090XX S EXCHANGE AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, GROUNDS",false,false,0423,004,10,46,08B,1197290,1845844,2005,01/26/2006 03:51:08 AM,41.731851918,-87.552829473,"(41.731851918, -87.552829473)"
-3764922,HL134756,01/19/2005 09:30:00 PM,070XX S MERRILL AVE,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,false,false,0331,003,5,43,07,1191813,1858347,2005,01/26/2006 03:51:08 AM,41.766295767,-87.572488531,"(41.766295767, -87.572488531)"
-3839583,HL133661,01/19/2005 07:26:51 PM,012XX W 72ND ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,0734,007,17,67,26,1169019,1856990,2005,01/26/2006 03:51:08 AM,41.763094899,-87.656075612,"(41.763094899, -87.656075612)"
-3763863,HL133478,01/19/2005 03:00:00 PM,024XX N NEVA AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,RESIDENCE-GARAGE,false,false,2512,025,36,18,07,1128229,1915519,2005,01/26/2006 03:51:08 AM,41.924492223,-87.804259943,"(41.924492223, -87.804259943)"
-3761305,HL128590,01/16/2005 10:30:00 PM,071XX S YALE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,true,0731,007,6,69,14,1175908,1857250,2005,01/26/2006 03:51:08 AM,41.763656754,-87.630818482,"(41.763656754, -87.630818482)"
-3757043,HL128261,01/16/2005 05:59:00 PM,034XX W 13TH PL,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,ALLEY,true,false,1021,010,24,29,15,1153778,1893653,2005,06/11/2007 03:52:33 PM,41.864018856,-87.710963892,"(41.864018856, -87.710963892)"
-3768435,HL127786,01/15/2005 05:30:00 PM,058XX S NARRAGANSETT AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0811,008,23,56,14,1134698,1864733,2005,01/26/2006 03:51:08 AM,41.785015819,-87.781688514,"(41.785015819, -87.781688514)"
-3756452,HL126057,01/15/2005 11:20:00 AM,071XX S MARSHFIELD AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,true,0735,007,17,67,08A,1166547,1856966,2005,01/26/2006 03:51:08 AM,41.763082091,-87.665136638,"(41.763082091, -87.665136638)"
-3756094,HL125702,01/15/2005 12:00:00 AM,037XX W ALTGELD ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2524,025,35,22,07,1150841,1916392,2005,01/26/2006 03:51:08 AM,41.926474906,-87.721149974,"(41.926474906, -87.721149974)"
-3755934,HL125278,01/14/2005 08:29:03 PM,086XX S COTTAGE GROVE AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0632,006,6,44,06,1183033,1847810,2005,01/26/2006 03:51:08 AM,41.737589747,-87.604996961,"(41.737589747, -87.604996961)"
-3756656,HL124127,01/14/2005 10:30:00 AM,071XX S WESTERN AVE,0860,THEFT,RETAIL THEFT,DRUG STORE,true,false,0832,008,18,66,06,1161645,1857408,2005,01/26/2006 03:51:08 AM,41.764398091,-87.683091194,"(41.764398091, -87.683091194)"
-3750520,HL121555,01/12/2005 10:20:00 PM,021XX S SPAULDING AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1024,010,24,30,08B,1154646,1889730,2005,01/26/2006 03:51:08 AM,41.853236392,-87.70788239,"(41.853236392, -87.70788239)"
-3771571,HL119903,01/12/2005 08:10:00 AM,062XX S STEWART AVE,0545,ASSAULT,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",true,false,0711,007,20,68,08A,1174729,1863752,2005,01/26/2006 03:51:08 AM,41.781525319,-87.634946229,"(41.781525319, -87.634946229)"
-3768381,HL137169,01/12/2005 07:15:00 AM,044XX N KENNETH AVE,0820,THEFT,$500 AND UNDER,DRIVEWAY - RESIDENTIAL,false,false,1722,017,45,16,06,1145624,1929167,2005,12/04/2014 12:43:35 PM,41.961631236,-87.739995293,"(41.961631236, -87.739995293)"
-3811169,HL116792,01/10/2005 02:35:00 PM,062XX S DR MARTIN LUTHER KING JR DR,2091,NARCOTICS,FORFEIT PROPERTY,STREET,true,false,0311,003,20,40,26,1179958,1863484,2005,01/26/2006 03:51:08 AM,41.780671761,-87.615783843,"(41.780671761, -87.615783843)"
-3745543,HL116057,01/10/2005 09:30:00 AM,060XX S DR MARTIN LUTHER KING JR DR,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0313,003,20,42,05,1180002,1864838,2005,01/26/2006 03:51:08 AM,41.78438626,-87.615581097,"(41.78438626, -87.615581097)"
-3742276,HL112641,01/08/2005 03:16:01 AM,022XX E 103RD ST,0454,BATTERY,AGG PO HANDS NO/MIN INJURY,POLICE FACILITY/VEH PARKING LOT,true,false,0434,004,10,51,08B,1193141,1837090,2005,01/26/2006 03:51:08 AM,41.707932328,-87.568313606,"(41.707932328, -87.568313606)"
-3826252,HL112111,01/07/2005 07:45:07 PM,0000X N OAKLEY BLVD,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1332,012,2,28,18,1161032,1900467,2005,01/26/2006 03:51:08 AM,41.88256971,-87.684145512,"(41.88256971, -87.684145512)"
-3738484,HL108311,01/05/2005 03:30:00 PM,047XX W NORTH AVE,0860,THEFT,RETAIL THEFT,DRUG STORE,true,false,2533,025,37,25,06,1144212,1910247,2005,01/26/2006 03:51:08 AM,41.909739682,-87.745663482,"(41.909739682, -87.745663482)"
-3739217,HL108315,01/05/2005 10:00:00 AM,063XX S CHAMPLAIN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0312,003,20,42,14,1181695,1863217,2005,01/26/2006 03:51:08 AM,41.779899136,-87.609423985,"(41.779899136, -87.609423985)"
-3736514,HL107476,01/04/2005 06:00:00 PM,030XX W ARTHINGTON ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,1134,011,28,27,26,1156098,1895839,2005,01/26/2006 03:51:08 AM,41.869971002,-87.702388247,"(41.869971002, -87.702388247)"
-3739239,HL105176,01/03/2005 08:50:00 PM,111XX S LONGWOOD DR,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,2212,022,19,75,08B,1164794,1830539,2005,01/26/2006 03:51:08 AM,41.690599467,-87.67230553,"(41.690599467, -87.67230553)"
-3741321,HL103303,01/02/2005 09:00:00 PM,005XX W 45TH PL,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,true,0935,009,11,61,26,1173352,1874782,2005,01/26/2006 03:51:08 AM,41.81182337,-87.639668298,"(41.81182337, -87.639668298)"
-9016183,HW163548,01/01/2005 12:00:00 AM,044XX S STATE ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,0215,,3,38,06,,,2005,02/23/2013 12:41:52 AM,,,
-3739313,HK835104,12/30/2004 09:40:00 PM,044XX W THOMAS ST,0460,BATTERY,SIMPLE,OTHER,false,false,1111,011,37,23,08B,1146235,1906989,2004,02/25/2006 12:14:30 AM,41.900761111,-87.73831477,"(41.900761111, -87.73831477)"
-3786396,HK833773,12/29/2004 11:10:00 AM,031XX W FIFTH AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1124,011,28,27,18,1155650,1899150,2004,02/25/2006 12:14:30 AM,41.87906575,-87.703943862,"(41.87906575, -87.703943862)"
-3739943,HK831388,12/27/2004 11:30:00 PM,031XX W LEXINGTON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,1134,011,24,27,08B,1155428,1896488,2004,02/25/2006 12:14:30 AM,41.871765414,-87.704830583,"(41.871765414, -87.704830583)"
-3730522,HK830987,12/27/2004 07:02:20 PM,047XX N WESTERN AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1911,019,47,4,05,1159480,1931139,2004,02/25/2006 12:14:30 AM,41.966767925,-87.688998182,"(41.966767925, -87.688998182)"
-3786840,HK831105,12/27/2004 06:35:00 PM,005XX N ST LOUIS AVE,2017,NARCOTICS,MANU/DELIVER:CRACK,SIDEWALK,true,false,1121,011,27,23,18,1152913,1903391,2004,02/25/2006 12:14:30 AM,41.890758129,-87.713881286,"(41.890758129, -87.713881286)"
-3731374,HL101794,12/27/2004 08:00:00 AM,020XX W 47TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,SIDEWALK,false,false,0914,009,12,61,14,1163599,1873535,2004,02/25/2006 12:14:30 AM,41.808611912,-87.675476985,"(41.808611912, -87.675476985)"
-3722260,HK829176,12/26/2004 06:56:00 PM,044XX N PULASKI RD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,true,true,1722,017,39,14,08B,1148885,1929310,2004,02/25/2006 12:14:30 AM,41.961961049,-87.728002212,"(41.961961049, -87.728002212)"
-3736482,HK828590,12/26/2004 11:10:00 AM,026XX W GRAND AVE,0820,THEFT,$500 AND UNDER,GOVERNMENT BUILDING/PROPERTY,true,false,1313,012,27,23,06,1158930,1903977,2004,12/04/2014 12:43:35 PM,41.89224484,-87.69176775,"(41.89224484, -87.69176775)"
-3721608,HK828458,12/25/2004 01:30:00 AM,062XX S ABERDEEN ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0712,007,16,68,14,1170008,1863585,2004,02/25/2006 12:14:30 AM,41.781170967,-87.652259328,"(41.781170967, -87.652259328)"
-3788981,HK826966,12/24/2004 09:10:00 PM,060XX S COTTAGE GROVE AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,0313,003,20,42,18,1182560,1865396,2004,02/25/2006 12:14:30 AM,41.785858484,-87.606185252,"(41.785858484, -87.606185252)"
-3739777,HK824423,12/23/2004 11:45:00 AM,025XX W JACKSON BLVD,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,1125,011,2,28,26,1159587,1898573,2004,02/25/2006 12:14:30 AM,41.877402272,-87.689503763,"(41.877402272, -87.689503763)"
-3718657,HK823424,12/22/2004 07:20:00 PM,010XX W 14TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1232,012,25,28,08B,1169742,1893610,2004,02/25/2006 12:14:30 AM,41.863568333,-87.652362095,"(41.863568333, -87.652362095)"
-3718735,HK823036,12/21/2004 02:20:00 PM,053XX S NAGLE AVE,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,RESIDENCE,false,false,0811,008,23,56,11,1134316,1868742,2004,02/25/2006 12:14:30 AM,41.796023961,-87.782995032,"(41.796023961, -87.782995032)"
-3721365,HK820691,12/21/2004 11:00:00 AM,060XX S TALMAN AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,true,false,0825,008,15,66,26,1159705,1864382,2004,02/25/2006 12:14:30 AM,41.783575771,-87.690010626,"(41.783575771, -87.690010626)"
-8125432,HT358359,12/20/2004 12:00:00 PM,014XX S KARLOV AVE,0843,THEFT,ATTEMPT FINANCIAL IDENTITY THEFT,RESIDENCE,false,false,1011,,24,29,06,,,2004,06/25/2011 12:39:13 AM,,,
-3712477,HK818152,12/19/2004 07:45:00 PM,001XX E WACKER DR,0890,THEFT,FROM BUILDING,HOTEL/MOTEL,false,false,0124,001,42,32,06,1177809,1902574,2004,02/25/2006 12:14:30 AM,41.887986649,-87.62247645,"(41.887986649, -87.62247645)"
-3713862,HK818451,12/19/2004 04:30:00 PM,009XX E 40TH ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2123,002,4,36,05,1183200,1878556,2004,02/25/2006 12:14:30 AM,41.821955692,-87.603428979,"(41.821955692, -87.603428979)"
-3716104,HK821530,12/17/2004 08:00:00 AM,109XX S MACKINAW AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0432,004,10,52,07,1200169,1833069,2004,02/25/2006 12:14:30 AM,41.696724128,-87.542712523,"(41.696724128, -87.542712523)"
-3712680,HK817779,12/16/2004 02:30:00 PM,019XX W GEORGE ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,ATM (AUTOMATIC TELLER MACHINE),false,false,1931,019,32,5,11,1163060,1919232,2004,02/25/2006 12:14:30 AM,41.934019875,-87.676170747,"(41.934019875, -87.676170747)"
-3718678,HK822485,12/16/2004 07:00:00 AM,022XX W MONROE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1211,012,2,28,14,1161466,1899542,2004,02/25/2006 12:14:30 AM,41.880022409,-87.682577609,"(41.880022409, -87.682577609)"
-3705399,HK809650,12/15/2004 02:00:00 AM,019XX S WELLS ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2111,009,25,34,14,1175045,1891012,2004,02/25/2006 12:14:30 AM,41.856322163,-87.632973102,"(41.856322163, -87.632973102)"
-3706152,HK809766,12/14/2004 09:00:00 PM,026XX N AVERS AVE,0810,THEFT,OVER $500,STREET,false,false,2524,025,30,22,06,1150230,1917193,2004,12/04/2014 12:43:35 PM,41.928684869,-87.723374197,"(41.928684869, -87.723374197)"
-3704179,HK803795,12/11/2004 08:30:00 PM,009XX N PULASKI RD,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1112,011,27,23,08B,1149598,1905719,2004,02/25/2006 12:14:30 AM,41.89721143,-87.725995168,"(41.89721143, -87.725995168)"
-3700208,HK804903,12/11/2004 06:00:00 PM,064XX S KIMBARK AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0314,003,20,42,06,1185786,1862526,2004,12/04/2014 12:43:35 PM,41.777907487,-87.594447762,"(41.777907487, -87.594447762)"
-3699211,HK803938,12/11/2004 05:30:00 PM,008XX N MICHIGAN AVE,0890,THEFT,FROM BUILDING,DEPARTMENT STORE,false,false,1833,018,42,8,06,1177262,1905766,2004,02/25/2006 12:14:30 AM,41.896758072,-87.624388338,"(41.896758072, -87.624388338)"
-3698591,HK802644,12/11/2004 12:00:00 AM,073XX S BELL AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0835,008,18,66,14,1162628,1856209,2004,02/25/2006 12:14:30 AM,41.761087416,-87.679521631,"(41.761087416, -87.679521631)"
-3698644,HK800596,12/10/2004 09:30:00 AM,019XX N LINCOLN AVE,1330,CRIMINAL TRESPASS,TO LAND,RESTAURANT,true,false,1814,018,43,7,26,1173300,1913510,2004,02/25/2006 12:14:30 AM,41.918096916,-87.63870964,"(41.918096916, -87.63870964)"
-3727616,HK834739,12/10/2004 06:00:00 AM,054XX S RIDGEWOOD CT,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,2131,002,4,41,05,1186189,1869591,2004,02/25/2006 12:14:30 AM,41.797284904,-87.592747276,"(41.797284904, -87.592747276)"
-3698161,HK800350,12/10/2004 04:42:23 AM,055XX S MICHIGAN AVE,031A,ROBBERY,ARMED: HANDGUN,STREET,false,false,0233,002,20,40,03,1178064,1868155,2004,02/25/2006 12:14:30 AM,41.793532607,-87.62258598,"(41.793532607, -87.62258598)"
-3708058,HK797942,12/08/2004 08:00:00 PM,051XX W HURON ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1531,015,28,25,18,1142228,1904180,2004,02/25/2006 12:14:30 AM,41.893128208,-87.753102701,"(41.893128208, -87.753102701)"
-3690968,HK794203,12/07/2004 01:25:32 AM,003XX N LOCKWOOD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,VACANT LOT/LAND,false,false,1523,015,28,25,08B,1140933,1902012,2004,02/25/2006 12:14:30 AM,41.887202884,-87.757912214,"(41.887202884, -87.757912214)"
-3690629,HK793928,12/06/2004 08:41:56 PM,094XX S ASHLAND AVE,1330,CRIMINAL TRESPASS,TO LAND,GROCERY FOOD STORE,true,false,2221,022,21,73,26,1167280,1842373,2004,02/25/2006 12:14:30 AM,41.723021154,-87.662866785,"(41.723021154, -87.662866785)"
-3687672,HK790701,12/05/2004 07:05:23 AM,010XX N LAMON AVE,0560,ASSAULT,SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,1531,015,37,25,08A,1143540,1906666,2004,02/25/2006 12:14:30 AM,41.899925629,-87.748221887,"(41.899925629, -87.748221887)"
-3686828,HK790508,12/05/2004 02:30:00 AM,009XX W ADDISON ST,0460,BATTERY,SIMPLE,STREET,true,false,2324,019,44,6,08B,1169040,1924139,2004,02/25/2006 12:14:30 AM,41.947357012,-87.654051617,"(41.947357012, -87.654051617)"
-3694076,HK789802,12/04/2004 05:55:27 PM,012XX S WABASH AVE,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,false,false,0132,001,2,33,06,1176923,1894821,2004,02/25/2006 12:14:30 AM,41.866732066,-87.625964752,"(41.866732066, -87.625964752)"
-3683716,HK785986,12/02/2004 07:00:00 PM,030XX W 64TH ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,APARTMENT,false,false,0823,008,15,66,04B,1157026,1862077,2004,02/25/2006 12:14:30 AM,41.777305099,-87.699895109,"(41.777305099, -87.699895109)"
-3687054,HK785865,12/02/2004 04:40:00 PM,031XX S RHODES AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,2122,002,4,35,08B,1180364,1884547,2004,02/25/2006 12:14:30 AM,41.838461073,-87.613648727,"(41.838461073, -87.613648727)"
-3682838,HK784457,12/01/2004 11:45:00 PM,068XX S UNION AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0723,007,6,68,08B,1172872,1859744,2004,02/25/2006 12:14:30 AM,41.770568091,-87.641872548,"(41.770568091, -87.641872548)"
-3724163,HK781391,11/30/2004 02:20:00 PM,032XX W ADAMS ST,0545,ASSAULT,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",true,false,1124,011,28,27,08A,1154515,1898924,2004,02/25/2006 12:14:30 AM,41.878468348,-87.708117444,"(41.878468348, -87.708117444)"
-3759483,HK780526,11/30/2004 01:25:00 AM,034XX N HOYNE AVE,2012,NARCOTICS,MANU/DELIVER:COCAINE,STREET,true,false,1913,019,32,5,18,1161780,1923025,2004,02/25/2006 12:14:30 AM,41.944454918,-87.680768598,"(41.944454918, -87.680768598)"
-3675505,HK777823,11/28/2004 03:00:00 PM,035XX N CICERO AVE,1780,OFFENSE INVOLVING CHILDREN,OTHER OFFENSE,RESTAURANT,false,true,1634,016,38,15,26,1143705,1923317,2004,02/25/2006 12:14:30 AM,41.945614575,-87.747197791,"(41.945614575, -87.747197791)"
-3674799,HK776038,11/27/2004 04:37:36 PM,079XX S RHODES AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0624,006,6,44,14,1181248,1852216,2004,02/25/2006 12:14:30 AM,41.749721577,-87.611401232,"(41.749721577, -87.611401232)"
-3674945,HK775256,11/27/2004 08:00:00 AM,100XX W OHARE ST,1330,CRIMINAL TRESPASS,TO LAND,AIRPORT/AIRCRAFT,false,false,1651,016,41,76,26,1100629,1934213,2004,02/25/2006 12:14:30 AM,41.976213976,-87.905334384,"(41.976213976, -87.905334384)"
-3683253,HK774923,11/27/2004 12:05:00 AM,130XX S ELLIS AVE,1710,OFFENSE INVOLVING CHILDREN,ENDANGERING LIFE/HEALTH CHILD,CHA APARTMENT,false,true,0533,005,9,54,26,1185319,1818677,2004,02/25/2006 12:14:30 AM,41.657591722,-87.597533606,"(41.657591722, -87.597533606)"
-3673904,HK774544,11/26/2004 08:05:00 PM,056XX N ARTESIAN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2011,020,40,2,26,1159010,1937527,2004,02/25/2006 12:14:30 AM,41.984306583,-87.690550064,"(41.984306583, -87.690550064)"
-3680931,HK777928,11/26/2004 01:00:00 AM,071XX S WENTWORTH AVE,0820,THEFT,$500 AND UNDER,RESIDENCE,false,true,0731,007,6,69,06,1176161,1857397,2004,12/04/2014 12:43:35 PM,41.764054465,-87.629886782,"(41.764054465, -87.629886782)"
-3670900,HK771771,11/24/2004 09:00:00 PM,102XX S RHODES AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,0511,005,9,49,26,1181755,1837278,2004,02/25/2006 12:14:30 AM,41.70871824,-87.610003381,"(41.70871824, -87.610003381)"
-3669033,HK769285,11/23/2004 05:30:00 PM,046XX S PULASKI RD,0810,THEFT,OVER $500,STREET,false,false,0815,008,14,57,06,1150422,1873827,2004,12/04/2014 12:43:35 PM,41.80967982,-87.723800193,"(41.80967982, -87.723800193)"
-3667137,HK767131,11/22/2004 01:30:00 PM,051XX W FULLERTON AVE,0810,THEFT,OVER $500,STREET,false,false,2522,025,31,19,06,1141391,1915425,2004,12/04/2014 12:43:35 PM,41.924001245,-87.755898711,"(41.924001245, -87.755898711)"
-3725148,HK760443,11/19/2004 11:11:00 AM,071XX S ROCKWELL ST,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,STREET,true,false,0832,008,18,66,18,1160321,1857015,2004,02/25/2006 12:14:30 AM,41.763347004,-87.687954831,"(41.763347004, -87.687954831)"
-3666573,HK762512,11/19/2004 12:01:00 AM,030XX W 26TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1033,010,12,30,14,1156716,1886639,2004,02/25/2006 12:14:30 AM,41.844712713,-87.70036837,"(41.844712713, -87.70036837)"
-3666180,HK757649,11/18/2004 12:30:00 AM,057XX S LAFLIN ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,RESIDENCE,false,false,0713,007,16,67,04B,1167356,1866688,2004,02/25/2006 12:14:30 AM,41.789743208,-87.661893289,"(41.789743208, -87.661893289)"
-3659600,HK756849,11/17/2004 05:35:00 PM,003XX E 69TH ST,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,0322,003,6,69,04A,1179465,1859294,2004,02/25/2006 12:14:30 AM,41.769185249,-87.617719081,"(41.769185249, -87.617719081)"
-3673061,HK756750,11/17/2004 04:45:00 PM,011XX S MOZART ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,1135,011,28,29,08B,1157573,1894890,2004,02/25/2006 12:14:30 AM,41.867336972,-87.696998888,"(41.867336972, -87.696998888)"
-3659682,HK756615,11/17/2004 12:30:00 PM,026XX W ALTGELD ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1431,014,1,22,05,1158163,1916472,2004,02/25/2006 12:14:30 AM,41.926547832,-87.694242711,"(41.926547832, -87.694242711)"
-3659853,HK756223,11/17/2004 12:15:00 PM,043XX S WASHTENAW AVE,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0912,009,12,58,05,1159049,1875768,2004,02/25/2006 12:14:30 AM,41.814833907,-87.692104359,"(41.814833907, -87.692104359)"
-3957324,HL325027,11/16/2004 11:00:00 AM,016XX W WALNUT ST,2820,OTHER OFFENSE,TELEPHONE THREAT,COMMERCIAL / BUSINESS OFFICE,false,false,1333,012,27,28,26,1165231,1901835,2004,02/25/2006 12:14:30 AM,41.886235412,-87.668687815,"(41.886235412, -87.668687815)"
-3659289,HK752394,11/15/2004 04:05:00 PM,006XX N AVERS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,1122,011,27,23,08B,1150569,1903996,2004,02/25/2006 12:14:30 AM,41.892464425,-87.722473849,"(41.892464425, -87.722473849)"
-3653951,HK751736,11/15/2004 02:00:00 AM,0000X E 9TH ST,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,0132,001,2,32,06,1176893,1896253,2004,12/04/2014 12:43:35 PM,41.870662241,-87.626031568,"(41.870662241, -87.626031568)"
-3655393,HK751436,11/15/2004 12:01:00 AM,078XX S LAFLIN ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0612,006,17,71,07,1167431,1852929,2004,02/25/2006 12:14:30 AM,41.751985125,-87.662012076,"(41.751985125, -87.662012076)"
-3653449,HK750716,11/14/2004 05:32:43 PM,073XX S COLES AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0334,003,7,43,08B,1194808,1857092,2004,02/25/2006 12:14:30 AM,41.762778734,-87.561552258,"(41.762778734, -87.561552258)"
-3749588,HK747510,11/12/2004 08:45:00 PM,014XX W 69TH ST,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,0725,007,17,67,18,1168183,1859037,2004,02/25/2006 12:14:30 AM,41.768730154,-87.659080894,"(41.768730154, -87.659080894)"
-3656077,HK748110,11/12/2004 06:00:00 PM,074XX S VINCENNES AVE,0610,BURGLARY,FORCIBLE ENTRY,OTHER,false,false,0731,007,17,69,05,1175883,1855682,2004,02/25/2006 12:14:30 AM,41.75935454,-87.630957038,"(41.75935454, -87.630957038)"
-3650560,HK746929,11/12/2004 04:30:10 PM,021XX S RACINE AVE,0460,BATTERY,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1233,012,25,31,08B,1168742,1889867,2004,02/25/2006 12:14:30 AM,41.853318934,-87.656141382,"(41.853318934, -87.656141382)"
-3655295,HK745529,11/11/2004 09:00:00 PM,056XX S PRINCETON AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,STREET,true,false,0711,007,3,68,04A,1175208,1867555,2004,02/25/2006 12:14:30 AM,41.791950463,-87.633076556,"(41.791950463, -87.633076556)"
-3649738,HK744914,11/11/2004 03:30:00 PM,023XX S WENTWORTH AVE,0460,BATTERY,SIMPLE,COMMERCIAL / BUSINESS OFFICE,false,false,2111,009,25,34,08B,1175294,1888994,2004,02/25/2006 12:14:30 AM,41.850779048,-87.63211964,"(41.850779048, -87.63211964)"
-3652462,HK744609,11/11/2004 12:30:00 PM,015XX E 72ND ST,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,false,false,0324,003,5,43,07,1187687,1857515,2004,02/25/2006 12:14:30 AM,41.764111843,-87.587638024,"(41.764111843, -87.587638024)"
-3648976,HK744153,11/10/2004 08:20:00 AM,020XX N DAYTON ST,0610,BURGLARY,FORCIBLE ENTRY,OTHER,false,false,1812,018,43,7,05,1170241,1914074,2004,02/25/2006 12:14:30 AM,41.919712001,-87.649931989,"(41.919712001, -87.649931989)"
-3645104,HK739880,11/09/2004 01:52:00 PM,021XX E 87TH ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",true,false,0412,004,8,45,08B,1191686,1847743,2004,02/25/2006 12:14:30 AM,41.737200568,-87.573297366,"(41.737200568, -87.573297366)"
-3643559,HK737880,11/08/2004 09:47:46 AM,0000X N PINE AVE,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,true,false,1522,015,28,25,26,1139539,1899901,2004,06/11/2007 03:52:33 PM,41.881435567,-87.763082972,"(41.881435567, -87.763082972)"
-3639296,HK735753,11/06/2004 06:59:00 PM,033XX W DICKENS AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,ALLEY,true,false,1413,014,26,22,15,1153704,1913795,2004,06/11/2007 03:52:33 PM,41.919291951,-87.710698996,"(41.919291951, -87.710698996)"
-3639300,HK733735,11/05/2004 09:35:00 PM,013XX W LELAND AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,2311,019,46,3,03,1166637,1931374,2004,02/25/2006 12:14:30 AM,41.967262064,-87.662676247,"(41.967262064, -87.662676247)"
-3637119,HK728392,11/03/2004 12:19:52 PM,027XX W ADAMS ST,0810,THEFT,OVER $500,RESIDENCE,false,false,1125,011,2,27,06,1157824,1898864,2004,12/04/2014 12:43:35 PM,41.878236913,-87.695969096,"(41.878236913, -87.695969096)"
-3636205,HK727534,11/02/2004 10:48:00 PM,007XX W MELROSE ST,0330,ROBBERY,AGGRAVATED,SIDEWALK,false,false,2332,019,44,6,03,1170939,1921831,2004,02/25/2006 12:14:30 AM,41.940982262,-87.647139354,"(41.940982262, -87.647139354)"
-3627641,HK723383,10/31/2004 11:00:00 PM,006XX N LOCKWOOD AVE,0454,BATTERY,AGG PO HANDS NO/MIN INJURY,RESIDENCE,true,false,1524,015,37,25,08B,1140866,1903936,2004,06/11/2007 03:52:33 PM,41.892483808,-87.758110896,"(41.892483808, -87.758110896)"
-3630621,HK725963,10/31/2004 10:00:00 PM,052XX S TROY ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0911,009,14,63,06,1156219,1869979,2004,12/04/2014 12:43:35 PM,41.799005594,-87.702641088,"(41.799005594, -87.702641088)"
-3744220,HK721323,10/31/2004 12:50:00 PM,018XX S HALSTED ST,2022,NARCOTICS,POSS: COCAINE,SIDEWALK,true,false,1233,012,25,31,18,1171281,1891374,2004,02/25/2006 12:14:30 AM,41.857398938,-87.646778204,"(41.857398938, -87.646778204)"
-3632913,HK722125,10/31/2004 11:30:00 AM,070XX W WRIGHTWOOD AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,2512,025,36,18,14,1129124,1916545,2004,02/25/2006 12:14:30 AM,41.927292495,-87.800947855,"(41.927292495, -87.800947855)"
-3626698,HK721173,10/30/2004 11:09:07 PM,100XX S COMMERCIAL AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,false,0431,004,10,51,06,1197904,1839118,2004,02/25/2006 12:14:30 AM,41.713379903,-87.55080417,"(41.713379903, -87.55080417)"
-3628115,HK721751,10/30/2004 07:30:00 PM,020XX W GRANVILLE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2413,024,50,2,14,1161860,1941216,2004,02/25/2006 12:14:30 AM,41.994370157,-87.679964647,"(41.994370157, -87.679964647)"
-3629505,HK721017,10/30/2004 06:00:00 PM,001XX E 120TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0532,005,9,53,14,1179600,1825393,2004,02/25/2006 12:14:30 AM,41.676153522,-87.618256437,"(41.676153522, -87.618256437)"
-3629149,HK724029,10/29/2004 10:30:00 PM,011XX N SACRAMENTO AVE,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,1311,012,26,24,06,1156210,1907640,2004,12/04/2014 12:43:35 PM,41.902351818,-87.701658141,"(41.902351818, -87.701658141)"
-3628518,HK720270,10/29/2004 10:00:00 PM,031XX W 54TH PL,0820,THEFT,$500 AND UNDER,STREET,false,false,0911,009,14,63,06,1156375,1868361,2004,12/04/2014 12:43:35 PM,41.794562435,-87.702112565,"(41.794562435, -87.702112565)"
-3626091,HK718983,10/29/2004 09:30:00 PM,001XX E MARQUETTE RD,0650,BURGLARY,HOME INVASION,APARTMENT,true,false,0322,003,20,69,05,1178463,1860585,2004,06/02/2010 10:34:17 AM,41.772750709,-87.621352739,"(41.772750709, -87.621352739)"
-3673994,HK773541,10/29/2004 05:50:00 PM,001XX S LA SALLE ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,BANK,false,false,0112,001,42,32,06,1175197,1899581,2004,02/25/2006 12:14:30 AM,41.879832659,-87.632158282,"(41.879832659, -87.632158282)"
-3623236,HK715624,10/27/2004 08:30:00 PM,002XX N LAFLIN ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1333,012,27,28,07,1166464,1901781,2004,02/25/2006 12:14:30 AM,41.886060937,-87.664161515,"(41.886060937, -87.664161515)"
-3624847,HK717947,10/27/2004 04:00:00 PM,005XX W 31ST ST,0810,THEFT,OVER $500,GAS STATION,false,false,0924,009,11,60,06,1173462,1884332,2004,12/04/2014 12:43:35 PM,41.838026996,-87.638981725,"(41.838026996, -87.638981725)"
-3621112,HK712997,10/27/2004 09:28:33 AM,013XX E 87TH ST,0810,THEFT,OVER $500,STREET,false,false,0412,004,8,48,06,1186671,1847525,2004,12/04/2014 12:43:35 PM,41.736722418,-87.591677477,"(41.736722418, -87.591677477)"
-3628458,HK718578,10/26/2004 11:59:00 PM,012XX N LA SALLE DR,1242,DECEPTIVE PRACTICE,COMPUTER FRAUD,OTHER,false,false,1821,018,43,8,11,1174870,1908551,2004,02/25/2006 12:14:30 AM,41.904454155,-87.633090172,"(41.904454155, -87.633090172)"
-3623663,HK713464,10/26/2004 02:45:00 PM,014XX W 119TH ST,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",false,false,0524,005,34,53,06,1168712,1825787,2004,02/25/2006 12:14:30 AM,41.677475775,-87.658097817,"(41.677475775, -87.658097817)"
-3617554,HK709724,10/25/2004 06:05:00 PM,003XX W 101ST PL,0560,ASSAULT,SIMPLE,RESIDENCE,true,true,0511,005,9,49,08A,1175566,1837578,2004,02/25/2006 12:14:30 AM,41.70968191,-87.632658989,"(41.70968191, -87.632658989)"
-3622039,HK712590,10/25/2004 04:30:00 PM,014XX N CLAREMONT AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,1424,014,1,24,08B,1160516,1909594,2004,02/25/2006 12:14:30 AM,41.907625652,-87.685787331,"(41.907625652, -87.685787331)"
-3616084,HK708943,10/25/2004 12:15:00 PM,027XX N MILWAUKEE AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,1412,014,35,22,06,1153459,1918208,2004,02/25/2006 12:14:30 AM,41.931406463,-87.711481577,"(41.931406463, -87.711481577)"
-3615973,HK708898,10/25/2004 11:45:00 AM,061XX N WOLCOTT AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2413,024,40,2,05,1162661,1941098,2004,02/25/2006 12:14:30 AM,41.994029554,-87.677021545,"(41.994029554, -87.677021545)"
-3612927,HK707921,10/24/2004 07:00:00 PM,053XX S HAMLIN AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0822,008,23,62,07,1151906,1868917,2004,02/25/2006 12:14:30 AM,41.796177059,-87.718485881,"(41.796177059, -87.718485881)"
-3616726,HK707588,10/24/2004 06:15:27 PM,090XX S LANGLEY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0633,006,6,44,08B,1182442,1845181,2004,02/25/2006 12:14:30 AM,41.730389173,-87.607243461,"(41.730389173, -87.607243461)"
-3614645,HK707101,10/24/2004 01:46:00 PM,034XX W EVERGREEN AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,false,1422,014,26,23,08A,1152905,1908722,2004,02/25/2006 12:14:30 AM,41.905387066,-87.71376928,"(41.905387066, -87.71376928)"
-3611365,HK703610,10/22/2004 08:46:00 PM,051XX W CHICAGO AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,GAS STATION,false,false,1531,015,37,25,14,1141640,1904877,2004,02/25/2006 12:14:30 AM,41.89505175,-87.755244987,"(41.89505175, -87.755244987)"
-3623057,HK715772,10/22/2004 08:30:00 PM,026XX W HIRSCH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1423,014,26,24,08B,1158461,1909248,2004,02/25/2006 12:14:30 AM,41.906718522,-87.693345782,"(41.906718522, -87.693345782)"
-3617295,HK703594,10/22/2004 08:10:00 PM,048XX W POLK ST,2022,NARCOTICS,POSS: COCAINE,ALLEY,true,false,1533,015,24,25,18,1144049,1895813,2004,02/25/2006 12:14:30 AM,41.870134162,-87.746624842,"(41.870134162, -87.746624842)"
-3617645,HK701851,10/22/2004 01:24:39 AM,058XX S INDIANA AVE,2230,LIQUOR LAW VIOLATION,ILLEGAL CONSUMPTION BY MINOR,SIDEWALK,true,false,0233,002,20,40,22,1178640,1866222,2004,02/25/2006 12:14:30 AM,41.788215182,-87.62053262,"(41.788215182, -87.62053262)"
-3618747,HK711544,10/22/2004 12:00:00 AM,057XX S MARYLAND AVE,0890,THEFT,FROM BUILDING,HOSPITAL BUILDING/GROUNDS,false,false,2133,002,5,41,06,1182843,1867373,2004,02/25/2006 12:14:30 AM,41.791276973,-87.605086252,"(41.791276973, -87.605086252)"
-3610326,HK701375,10/21/2004 07:40:00 PM,046XX N CLIFTON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,OTHER,false,true,2311,019,46,3,08B,1167717,1930873,2004,02/25/2006 12:14:30 AM,41.965864037,-87.658719738,"(41.965864037, -87.658719738)"
-3608455,HK701213,10/21/2004 04:00:00 PM,067XX N CALIFORNIA AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2412,024,50,2,08B,1156397,1944740,2004,02/25/2006 12:14:30 AM,42.004152775,-87.699964133,"(42.004152775, -87.699964133)"
-3672680,HK699320,10/20/2004 08:15:00 PM,044XX W MADISON ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1113,011,28,26,16,1147053,1899691,2004,02/25/2006 12:14:30 AM,41.880718981,-87.735496941,"(41.880718981, -87.735496941)"
-3630078,HK721894,10/20/2004 08:00:00 PM,068XX W ARDMORE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1612,016,41,10,14,1129837,1938139,2004,02/25/2006 12:14:30 AM,41.986536606,-87.797831972,"(41.986536606, -87.797831972)"
-3605776,HK698300,10/20/2004 01:11:17 PM,038XX W NORTH AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,2535,025,30,23,06,1150264,1910314,2004,02/25/2006 12:14:30 AM,41.909807601,-87.723429033,"(41.909807601, -87.723429033)"
-3635772,HK726303,10/20/2004 12:00:00 PM,064XX S YALE AVE,0810,THEFT,OVER $500,STREET,false,false,0722,007,20,68,06,1175637,1862524,2004,12/04/2014 12:43:35 PM,41.77813527,-87.631654044,"(41.77813527, -87.631654044)"
-3605428,HK695826,10/19/2004 12:30:00 AM,007XX N CLARK ST,0610,BURGLARY,FORCIBLE ENTRY,RESTAURANT,false,false,1832,018,42,8,05,1175437,1905603,2004,02/25/2006 12:14:30 AM,41.896351977,-87.631096087,"(41.896351977, -87.631096087)"
-3601459,HK694628,10/18/2004 05:35:00 PM,095XX S BENNETT AVE,0560,ASSAULT,SIMPLE,RESIDENCE,true,false,0431,004,7,51,08A,1190552,1842161,2004,02/25/2006 12:14:30 AM,41.721910415,-87.577631489,"(41.721910415, -87.577631489)"
-3606514,HK694398,10/18/2004 02:00:00 PM,007XX E 107TH ST,0890,THEFT,FROM BUILDING,WAREHOUSE,true,false,0513,005,9,50,06,1182820,1833927,2004,02/25/2006 12:14:30 AM,41.69949805,-87.606206907,"(41.69949805, -87.606206907)"
-3634089,HK722867,10/18/2004 01:00:00 PM,026XX W WARREN BLVD,1563,SEX OFFENSE,CRIMINAL SEXUAL ABUSE,RESIDENCE,true,false,1331,012,2,27,17,1158507,1900207,2004,12/04/2014 12:43:35 PM,41.881908286,-87.693424501,"(41.881908286, -87.693424501)"
-3599599,HK693439,10/17/2004 10:00:00 PM,081XX S KINGSTON AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,0422,004,7,46,07,1194548,1851738,2004,02/25/2006 12:14:30 AM,41.748093344,-87.562680961,"(41.748093344, -87.562680961)"
-3607060,HK690759,10/16/2004 04:10:10 PM,063XX S COTTAGE GROVE AVE,1330,CRIMINAL TRESPASS,TO LAND,CURRENCY EXCHANGE,true,false,0312,003,20,42,26,1182686,1863410,2004,02/25/2006 12:14:30 AM,41.780405795,-87.605784892,"(41.780405795, -87.605784892)"
-3624498,HK690560,10/16/2004 02:00:00 PM,054XX S WENTWORTH AVE,0820,THEFT,$500 AND UNDER,SMALL RETAIL STORE,false,false,0232,002,3,37,06,1175923,1869140,2004,12/04/2014 12:43:35 PM,41.79628385,-87.630407267,"(41.79628385, -87.630407267)"
-3598006,HK689706,10/16/2004 01:17:25 AM,030XX W LAWRENCE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,true,true,1713,017,33,14,08B,1155496,1931698,2004,02/25/2006 12:14:30 AM,41.968383133,-87.703631707,"(41.968383133, -87.703631707)"
-4278997,HL595256,10/15/2004 02:30:00 PM,015XX W FARWELL AVE,0890,THEFT,FROM BUILDING,OTHER,false,false,2431,024,49,1,06,1164651,1945743,2004,02/25/2006 12:14:30 AM,42.006733459,-87.669569046,"(42.006733459, -87.669569046)"
-3594694,HK687223,10/14/2004 08:00:00 PM,061XX S COTTAGE GROVE AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,0313,003,20,42,26,1182656,1864625,2004,02/25/2006 12:14:30 AM,41.783740562,-87.605857191,"(41.783740562, -87.605857191)"
-3596447,HK686786,10/14/2004 01:40:00 PM,081XX S ELIZABETH ST,0281,CRIM SEXUAL ASSAULT,NON-AGGRAVATED,APARTMENT,false,false,0613,006,21,71,02,1169447,1850805,2004,02/25/2006 12:14:30 AM,41.746113178,-87.654685733,"(41.746113178, -87.654685733)"
-3600377,HK690155,10/14/2004 01:00:00 AM,028XX W 39TH PL,0460,BATTERY,SIMPLE,RESIDENCE,false,true,0912,009,14,58,08B,1158067,1878288,2004,02/25/2006 12:14:30 AM,41.821769154,-87.69563789,"(41.821769154, -87.69563789)"
-3661402,HK685270,10/13/2004 09:10:00 PM,030XX W MADISON ST,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,1331,012,2,27,16,1156036,1899902,2004,02/25/2006 12:14:30 AM,41.881121536,-87.702506237,"(41.881121536, -87.702506237)"
-3598370,HK686343,10/13/2004 01:30:00 PM,037XX S ARCHER AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0913,009,12,59,06,1160938,1879845,2004,12/04/2014 12:43:35 PM,41.825982789,-87.685062384,"(41.825982789, -87.685062384)"
-3607299,HK683485,10/13/2004 03:30:00 AM,037XX S GILES AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0211,002,3,35,08B,1178829,1880511,2004,02/25/2006 12:14:30 AM,41.827421141,-87.619404418,"(41.827421141, -87.619404418)"
-3593551,HK684114,10/12/2004 12:15:00 PM,048XX W IRVING PARK RD,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,1624,016,45,15,06,1143366,1926193,2004,02/25/2006 12:14:30 AM,41.95351293,-87.748371698,"(41.95351293, -87.748371698)"
-3588249,HK681479,10/12/2004 07:00:00 AM,040XX N MEADE AVE,0890,THEFT,FROM BUILDING,RESIDENCE-GARAGE,false,false,1624,016,38,15,06,1135002,1926360,2004,02/25/2006 12:14:30 AM,41.954123736,-87.779115056,"(41.954123736, -87.779115056)"
-3586756,HK678646,10/10/2004 04:15:00 PM,015XX W 87TH ST,0820,THEFT,$500 AND UNDER,OTHER,false,false,0614,006,21,71,06,1167644,1847078,2004,12/04/2014 12:43:35 PM,41.735924587,-87.661398952,"(41.735924587, -87.661398952)"
-3583269,HK672253,10/07/2004 04:50:00 PM,045XX W NORTH AVE,0810,THEFT,OVER $500,VACANT LOT/LAND,true,false,2533,025,37,23,06,1145553,1910199,2004,12/04/2014 12:43:35 PM,41.909582647,-87.740738376,"(41.909582647, -87.740738376)"
-3591113,HK672162,10/07/2004 03:55:00 PM,0000X N STATE ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0122,001,42,32,06,1176405,1900487,2004,02/25/2006 12:14:30 AM,41.882291607,-87.627695365,"(41.882291607, -87.627695365)"
-3598082,HK690084,10/07/2004 11:50:00 AM,038XX N KEDZIE AVE,0820,THEFT,$500 AND UNDER,OTHER,true,false,1733,017,33,16,06,1154329,1925436,2004,12/04/2014 12:43:35 PM,41.951223262,-87.708090727,"(41.951223262, -87.708090727)"
-3581335,HK670750,10/06/2004 10:07:00 PM,031XX S WELLS ST,0560,ASSAULT,SIMPLE,STREET,false,false,0924,009,11,34,08A,1175083,1884409,2004,02/25/2006 12:14:30 AM,41.838202178,-87.633031262,"(41.838202178, -87.633031262)"
-3580548,HK670390,10/06/2004 05:00:00 PM,072XX S MAPLEWOOD AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,0832,008,18,66,26,1160585,1856634,2004,02/25/2006 12:14:30 AM,41.762296045,-87.686997706,"(41.762296045, -87.686997706)"
-3585135,HK668652,10/05/2004 10:52:00 PM,005XX W 71ST ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0732,007,6,68,14,1173760,1857868,2004,02/25/2006 12:14:30 AM,41.76540049,-87.638673038,"(41.76540049, -87.638673038)"
-3623821,HK666113,10/04/2004 05:40:00 PM,040XX S CALUMET AVE,2091,NARCOTICS,FORFEIT PROPERTY,VEHICLE NON-COMMERCIAL,true,false,0214,002,3,38,26,1179054,1878421,2004,02/25/2006 12:14:30 AM,41.821680884,-87.618642719,"(41.821680884, -87.618642719)"
-3582465,HK665382,10/04/2004 12:56:00 PM,017XX N PULASKI RD,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,STREET,false,true,2534,025,30,23,04A,1149398,1911493,2004,02/25/2006 12:14:30 AM,41.913059745,-87.726579743,"(41.913059745, -87.726579743)"
-3574839,HK664731,10/04/2004 04:41:07 AM,087XX S MARQUETTE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,true,false,0423,004,7,46,14,1195735,1847422,2004,06/11/2007 03:52:33 PM,41.736220637,-87.5584739,"(41.736220637, -87.5584739)"
-3576719,HK663756,10/03/2004 02:04:42 PM,020XX S PAULINA ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,true,false,1222,012,25,31,14,1165395,1890262,2004,02/25/2006 12:14:30 AM,41.854474627,-87.668414769,"(41.854474627, -87.668414769)"
-3653983,HK746992,10/03/2004 12:00:00 PM,033XX W WARREN BLVD,0820,THEFT,$500 AND UNDER,STREET,false,false,1123,011,28,27,06,1153900,1900181,2004,12/04/2014 12:43:35 PM,41.881929957,-87.710342102,"(41.881929957, -87.710342102)"
-3593397,HK659854,10/01/2004 03:25:00 PM,007XX E 50TH PL,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE PORCH/HALLWAY,false,false,0223,002,4,38,14,1182082,1871733,2004,02/25/2006 12:14:30 AM,41.803258839,-87.607741702,"(41.803258839, -87.607741702)"
-3593353,HK658238,09/30/2004 08:15:36 PM,068XX S RACINE AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0724,007,17,67,18,1169452,1859693,2004,02/25/2006 12:14:30 AM,41.770502908,-87.654410389,"(41.770502908, -87.654410389)"
-3577799,HK654752,09/29/2004 08:36:08 AM,039XX S INDIANA AVE,502R,OTHER OFFENSE,VEHICLE TITLE/REG OFFENSE,STREET,true,false,0214,002,3,38,26,1178203,1879070,2004,02/25/2006 12:14:30 AM,41.823481174,-87.621744889,"(41.823481174, -87.621744889)"
-3565833,HK654179,09/28/2004 07:30:00 PM,029XX W FULLERTON AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,1414,014,35,22,26,1156036,1915846,2004,02/25/2006 12:14:30 AM,41.924873277,-87.702075424,"(41.924873277, -87.702075424)"
-3569259,HK653659,09/28/2004 12:00:00 PM,007XX E 61ST ST,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0313,003,20,42,05,1182245,1864767,2004,02/25/2006 12:14:30 AM,41.784139756,-87.607359653,"(41.784139756, -87.607359653)"
-3564478,HK652424,09/28/2004 02:30:00 AM,006XX E 71ST ST,0890,THEFT,FROM BUILDING,APARTMENT,false,false,0322,003,6,69,06,1181674,1858121,2004,02/25/2006 12:14:30 AM,41.765915699,-87.609658179,"(41.765915699, -87.609658179)"
-3633339,HK726615,09/28/2004 12:00:00 AM,053XX N CLARK ST,1110,DECEPTIVE PRACTICE,BOGUS CHECK,CURRENCY EXCHANGE,false,false,2012,020,40,77,11,1165039,1935919,2004,02/25/2006 12:14:30 AM,41.979767881,-87.668422179,"(41.979767881, -87.668422179)"
-3602950,HK652229,09/27/2004 10:35:00 PM,059XX S ASHLAND AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0713,007,15,67,16,1166722,1865588,2004,02/25/2006 12:14:30 AM,41.786738238,-87.664249369,"(41.786738238, -87.664249369)"
-3561938,HK648582,09/26/2004 04:57:38 AM,076XX N ROGERS AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,false,2422,024,49,1,06,1165462,1950617,2004,02/25/2006 12:14:30 AM,42.020090499,-87.666445559,"(42.020090499, -87.666445559)"
-3563816,HK648490,09/26/2004 03:36:31 AM,052XX W HARRISON ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,true,1522,015,29,25,14,1141447,1896855,2004,06/11/2007 03:52:33 PM,41.873041951,-87.756151995,"(41.873041951, -87.756151995)"
-3561761,HK648255,09/25/2004 11:32:00 PM,035XX N ASHLAND AVE,0820,THEFT,$500 AND UNDER,GAS STATION,false,false,1923,019,32,6,06,1164973,1923966,2004,12/04/2014 12:43:35 PM,41.946969765,-87.669005717,"(41.946969765, -87.669005717)"
-3560884,HK647303,09/25/2004 02:35:00 PM,012XX S ASHLAND AVE,0820,THEFT,$500 AND UNDER,GROCERY FOOD STORE,true,false,1224,012,2,28,06,1165876,1894550,2004,12/04/2014 12:43:35 PM,41.866231046,-87.666527069,"(41.866231046, -87.666527069)"
-3567981,HK645992,09/24/2004 09:25:00 PM,061XX W FULLERTON AVE,0560,ASSAULT,SIMPLE,STREET,true,false,2512,025,29,19,08A,1134939,1915257,2004,02/25/2006 12:14:30 AM,41.92365701,-87.779610301,"(41.92365701, -87.779610301)"
-3560393,HK645809,09/24/2004 08:07:00 PM,067XX S BLACKSTONE AVE,0560,ASSAULT,SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,0332,003,5,43,08A,1187397,1860687,2004,02/25/2006 12:14:30 AM,41.772822981,-87.588600261,"(41.772822981, -87.588600261)"
-3559551,HK645679,09/24/2004 07:11:00 PM,011XX W BRYN MAWR AVE,1330,CRIMINAL TRESPASS,TO LAND,CTA PLATFORM,true,false,2023,020,48,77,26,1167641,1937312,2004,02/25/2006 12:14:30 AM,41.983534487,-87.65881276,"(41.983534487, -87.65881276)"
-3565345,HK645066,09/24/2004 02:30:00 PM,013XX E 93RD ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,VEHICLE NON-COMMERCIAL,true,false,0413,004,8,47,04B,1186773,1843543,2004,02/25/2006 12:14:30 AM,41.725792973,-87.591429534,"(41.725792973, -87.591429534)"
-3559817,HK644409,09/24/2004 09:51:08 AM,114XX S INDIANA AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,0531,005,9,49,08A,1179574,1829161,2004,02/25/2006 12:14:30 AM,41.68649406,-87.618237132,"(41.68649406, -87.618237132)"
-3558292,HK643745,09/23/2004 09:50:00 PM,075XX S COLFAX AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0421,004,7,43,08B,1194802,1855075,2004,02/25/2006 12:14:30 AM,41.757244084,-87.561640565,"(41.757244084, -87.561640565)"
-3558159,HK643660,09/23/2004 08:30:00 PM,054XX S DAMEN AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0915,009,16,61,07,1163992,1868468,2004,02/25/2006 12:14:30 AM,41.794699184,-87.674178063,"(41.794699184, -87.674178063)"
-3559059,HK643590,09/22/2004 08:03:00 PM,131XX S DANIEL DR,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0533,005,9,54,08B,1180242,1818478,2004,02/25/2006 12:14:30 AM,41.657163062,-87.616117217,"(41.657163062, -87.616117217)"
-3556276,HK640574,09/22/2004 02:00:00 PM,022XX S STATE ST,0460,BATTERY,SIMPLE,SIDEWALK,true,false,0134,001,3,33,08B,1176624,1889521,2004,02/25/2006 12:14:30 AM,41.852195267,-87.62722242,"(41.852195267, -87.62722242)"
-3557831,HK639810,09/21/2004 05:00:00 PM,018XX N WOLCOTT AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1434,014,32,22,05,1163359,1912171,2004,02/25/2006 12:14:30 AM,41.91463775,-87.675271071,"(41.91463775, -87.675271071)"
-3556452,HK638865,09/21/2004 01:00:00 PM,112XX S HALSTED ST,0820,THEFT,$500 AND UNDER,SMALL RETAIL STORE,false,false,2233,022,34,75,06,1172953,1830284,2004,12/04/2014 12:43:35 PM,41.689724012,-87.642442424,"(41.689724012, -87.642442424)"
-3558610,HK641442,09/19/2004 11:30:00 PM,034XX S WOOD ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,STREET,false,false,0922,009,11,59,26,1164956,1881721,2004,02/25/2006 12:14:30 AM,41.83104659,-87.670268101,"(41.83104659, -87.670268101)"
-3550486,HK633313,09/19/2004 04:00:21 AM,011XX S MONITOR AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1513,015,29,25,14,1137601,1894423,2004,06/11/2007 03:52:33 PM,41.866438307,-87.770331278,"(41.866438307, -87.770331278)"
-3552606,HK633306,09/19/2004 03:45:00 AM,028XX N MENARD AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2514,025,30,19,14,1137257,1918580,2004,02/25/2006 12:14:30 AM,41.932734299,-87.771012876,"(41.932734299, -87.771012876)"
-3570094,HK650589,09/18/2004 04:45:00 PM,018XX N LINDER AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,false,2532,025,37,25,08A,1139487,1911474,2004,02/25/2006 12:14:30 AM,41.913194232,-87.762991414,"(41.913194232, -87.762991414)"
-3611493,HK701522,09/17/2004 12:00:00 AM,014XX E 53RD ST,1120,DECEPTIVE PRACTICE,FORGERY,DRUG STORE,false,false,2131,002,4,41,10,1186705,1870450,2004,02/25/2006 12:14:30 AM,41.79962985,-87.590827854,"(41.79962985, -87.590827854)"
-3545306,HK627125,09/15/2004 11:00:00 AM,052XX N MARMORA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1622,016,45,11,14,1135831,1934747,2004,02/25/2006 12:14:30 AM,41.977123707,-87.775866893,"(41.977123707, -87.775866893)"
-3582178,HK624150,09/14/2004 09:40:00 PM,032XX N LARAMIE AVE,2022,NARCOTICS,POSS: COCAINE,STREET,true,false,1634,016,30,15,18,1141092,1920982,2004,02/25/2006 12:14:30 AM,41.93925574,-87.756860081,"(41.93925574, -87.756860081)"
-3541257,HK624756,09/13/2004 10:00:00 PM,008XX W ALDINE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2331,019,44,6,06,1169747,1922151,2004,12/04/2014 12:43:35 PM,41.941886455,-87.651511013,"(41.941886455, -87.651511013)"
-3536643,HK619341,09/12/2004 06:55:00 PM,060XX W ROSCOE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,ALLEY,false,false,1633,016,36,17,14,1135775,1921947,2004,03/28/2012 09:57:58 AM,41.942000255,-87.77637878,"(41.942000255, -87.77637878)"
-3538461,HK622072,09/11/2004 07:00:00 PM,002XX W 112TH ST,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,RESIDENCE,false,true,0522,005,34,49,20,1176343,1830702,2004,02/25/2006 12:14:30 AM,41.690795785,-87.630019148,"(41.690795785, -87.630019148)"
-3535713,HK616017,09/11/2004 03:40:00 AM,030XX S KEELEY ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,true,true,0923,009,11,60,08B,1169711,1884816,2004,02/25/2006 12:14:30 AM,41.83943752,-87.652731757,"(41.83943752, -87.652731757)"
-3535612,HK616134,09/11/2004 02:40:00 AM,067XX N OLMSTED AVE,0610,BURGLARY,FORCIBLE ENTRY,BAR OR TAVERN,false,false,1612,016,41,9,05,1124515,1943924,2004,02/25/2006 12:14:30 AM,42.002500864,-87.817278464,"(42.002500864, -87.817278464)"
-3556993,HK615912,09/11/2004 01:35:00 AM,021XX N DAMEN AVE,2250,LIQUOR LAW VIOLATION,LIQUOR LICENSE VIOLATION,TAVERN/LIQUOR STORE,true,false,1432,014,32,22,22,1162711,1914438,2004,02/25/2006 12:14:30 AM,41.920872171,-87.677588046,"(41.920872171, -87.677588046)"
-3536160,HK615352,09/10/2004 08:00:00 PM,062XX S WOOD ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0714,007,15,67,08B,1165376,1863429,2004,02/25/2006 12:14:30 AM,41.780842304,-87.669245667,"(41.780842304, -87.669245667)"
-3535553,HK614499,09/10/2004 12:20:00 PM,035XX W ROOSEVELT RD,0460,BATTERY,SIMPLE,STREET,false,false,1021,010,24,29,08B,1152606,1894432,2004,02/25/2006 12:14:30 AM,41.866179755,-87.715245695,"(41.866179755, -87.715245695)"
-3538065,HK610686,09/08/2004 02:40:00 PM,012XX N LARRABEE ST,0460,BATTERY,SIMPLE,CHA PARKING LOT/GROUNDS,false,false,1822,018,27,8,08B,1172063,1908621,2004,02/25/2006 12:14:30 AM,41.904708652,-87.6433989,"(41.904708652, -87.6433989)"
-3530896,HK609821,09/08/2004 09:45:00 AM,023XX W DIVERSEY AVE,0460,BATTERY,SIMPLE,STREET,false,false,1432,014,1,21,08B,1159884,1918576,2004,02/25/2006 12:14:30 AM,41.932285978,-87.687860611,"(41.932285978, -87.687860611)"
-3528173,HK609573,09/08/2004 12:00:00 AM,009XX N WINCHESTER AVE,0810,THEFT,OVER $500,STREET,false,false,1322,012,32,24,06,1163267,1906454,2004,12/04/2014 12:43:35 PM,41.898951828,-87.675770074,"(41.898951828, -87.675770074)"
-3529204,HK609929,09/07/2004 07:00:00 PM,061XX N MILWAUKEE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,BARBERSHOP,false,false,1611,016,45,10,14,1133550,1940587,2004,02/25/2006 12:14:30 AM,41.993189614,-87.784117589,"(41.993189614, -87.784117589)"
-3525751,HK606922,09/06/2004 08:59:52 PM,051XX W WASHINGTON BLVD,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1533,015,28,25,05,1142180,1899979,2004,02/25/2006 12:14:30 AM,41.881601045,-87.753383264,"(41.881601045, -87.753383264)"
-3529330,HK605334,09/06/2004 03:05:00 AM,042XX W GLADYS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1132,011,28,26,08B,1147782,1898047,2004,02/25/2006 12:14:30 AM,41.876193689,-87.732862323,"(41.876193689, -87.732862323)"
-3603104,HK604485,09/05/2004 05:07:00 PM,074XX S DORCHESTER AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0324,003,5,43,18,1186801,1855547,2004,02/25/2006 12:14:30 AM,41.758732501,-87.590947632,"(41.758732501, -87.590947632)"
-3528892,HK607783,09/05/2004 10:00:00 AM,013XX S CALIFORNIA BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1023,010,28,29,14,1157933,1893883,2004,02/25/2006 12:14:30 AM,41.86456633,-87.695704729,"(41.86456633, -87.695704729)"
-3525970,HK603504,09/04/2004 06:00:00 PM,012XX N NOBLE ST,0820,THEFT,$500 AND UNDER,OTHER COMMERCIAL TRANSPORTATION,false,false,1433,014,32,24,06,1166864,1908503,2004,12/04/2014 12:43:35 PM,41.904498008,-87.662499628,"(41.904498008, -87.662499628)"
-3522701,HK601469,09/04/2004 03:53:17 AM,098XX S MANISTEE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0431,004,10,51,14,1196380,1840047,2004,02/25/2006 12:14:30 AM,41.715967037,-87.556354773,"(41.715967037, -87.556354773)"
-3523835,HK601444,09/04/2004 02:58:15 AM,003XX E 130TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0533,005,9,54,14,1180471,1818843,2004,02/25/2006 12:14:30 AM,41.658159448,-87.615268148,"(41.658159448, -87.615268148)"
-3541450,HK623485,09/03/2004 12:00:00 PM,093XX S BISHOP ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2222,022,21,73,08B,1168258,1843054,2004,02/25/2006 12:14:30 AM,41.724868962,-87.659264929,"(41.724868962, -87.659264929)"
-4887254,HK597418,09/02/2004 09:32:00 AM,061XX S NORMAL BLVD,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0711,,20,68,07,,,2004,06/11/2007 03:52:33 PM,,,
-3518119,HK596813,09/01/2004 02:30:00 PM,031XX S WENTWORTH AVE,0820,THEFT,$500 AND UNDER,CHA PARKING LOT/GROUNDS,false,false,0924,009,11,34,06,1175427,1883894,2004,12/04/2014 12:43:35 PM,41.836781273,-87.631784407,"(41.836781273, -87.631784407)"
-3617119,HK711060,09/01/2004 03:00:00 AM,024XX W FLOURNOY ST,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,true,1135,011,2,28,26,1160289,1897003,2004,02/25/2006 12:14:30 AM,41.873079567,-87.68696962,"(41.873079567, -87.68696962)"
-3517926,HK593721,08/31/2004 03:40:00 PM,124XX S PERRY AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0523,005,9,53,08B,1177899,1822263,2004,02/25/2006 12:14:30 AM,41.667602886,-87.6245767,"(41.667602886, -87.6245767)"
-3516293,HK593850,08/31/2004 03:00:00 PM,024XX E 78TH ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0421,004,7,43,08B,1193729,1853680,2004,02/25/2006 12:14:30 AM,41.753442439,-87.565618491,"(41.753442439, -87.565618491)"
-3517979,HK592099,08/30/2004 07:45:00 PM,040XX W BARRY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2523,025,31,21,08B,1148539,1920337,2004,02/25/2006 12:14:30 AM,41.937345122,-87.729506741,"(41.937345122, -87.729506741)"
-3513686,HK591502,08/30/2004 02:35:00 PM,026XX S PULASKI RD,0880,THEFT,PURSE-SNATCHING,STREET,false,false,1031,010,22,30,06,1150087,1886420,2004,02/25/2006 12:14:30 AM,41.844243241,-87.724701712,"(41.844243241, -87.724701712)"
-3511425,HK590158,08/29/2004 10:00:00 PM,053XX W WARWICK AVE,0560,ASSAULT,SIMPLE,RESIDENTIAL YARD (FRONT/BACK),false,false,1634,016,38,15,08A,1139867,1924371,2004,02/25/2006 12:14:30 AM,41.948578005,-87.761279229,"(41.948578005, -87.761279229)"
-3513346,HK589215,08/29/2004 01:55:00 PM,039XX W VAN BUREN ST,0820,THEFT,$500 AND UNDER,RESIDENCE PORCH/HALLWAY,false,false,1132,011,24,26,06,1150092,1897771,2004,12/04/2014 12:43:35 PM,41.875391654,-87.724387913,"(41.875391654, -87.724387913)"
-3516967,HK587354,08/28/2004 02:45:00 PM,075XX S COLES AVE,0560,ASSAULT,SIMPLE,STREET,false,false,0421,004,7,43,08A,1195590,1856007,2004,02/25/2006 12:14:30 AM,41.759782127,-87.55872197,"(41.759782127, -87.55872197)"
-3702746,HK587005,08/28/2004 11:45:00 AM,046XX N RACINE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,2311,019,46,3,18,1167501,1931267,2004,02/25/2006 12:14:30 AM,41.96694985,-87.65950253,"(41.96694985, -87.65950253)"
-3556105,HK586248,08/28/2004 12:36:52 AM,131XX S BALTIMORE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0433,004,10,55,18,1199055,1818129,2004,02/25/2006 12:14:30 AM,41.655755194,-87.547290246,"(41.655755194, -87.547290246)"
-3513682,HK585077,08/27/2004 02:37:35 PM,060XX W WABANSIA AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,2513,025,29,25,08B,1135670,1910711,2004,02/25/2006 12:14:30 AM,41.911169263,-87.777032636,"(41.911169263, -87.777032636)"
-3510558,HK581750,08/25/2004 11:44:16 PM,003XX W 64TH ST,0460,BATTERY,SIMPLE,STREET,false,false,0722,007,20,68,08B,1175160,1862549,2004,02/25/2006 12:14:30 AM,41.77821454,-87.63340199,"(41.77821454, -87.63340199)"
-3507132,HK583068,08/25/2004 10:00:00 PM,029XX W SCHUBERT AVE,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,1411,014,35,22,06,1156170,1917764,2004,12/04/2014 12:43:35 PM,41.930133706,-87.701531116,"(41.930133706, -87.701531116)"
-3507189,HK583438,08/25/2004 05:25:00 PM,040XX N SAWYER AVE,0820,THEFT,$500 AND UNDER,RESIDENCE-GARAGE,false,false,1724,017,33,16,06,1154034,1926888,2004,12/04/2014 12:43:35 PM,41.955213548,-87.709136265,"(41.955213548, -87.709136265)"
-3542182,HK580810,08/25/2004 03:45:00 PM,024XX S HOMAN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1024,010,22,30,18,1154058,1887187,2004,02/25/2006 12:14:30 AM,41.846269833,-87.710108302,"(41.846269833, -87.710108302)"
-3563306,HK650737,08/25/2004 09:00:00 AM,027XX N MC VICKER AVE,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,2512,025,29,19,06,1135639,1917476,2004,12/04/2014 12:43:35 PM,41.929733766,-87.776985289,"(41.929733766, -87.776985289)"
-3505792,HK579859,08/25/2004 12:15:00 AM,073XX N CLAREMONT AVE,0810,THEFT,OVER $500,STREET,false,false,2411,024,49,2,06,1159340,1948859,2004,12/04/2014 12:43:35 PM,42.015395181,-87.689022749,"(42.015395181, -87.689022749)"
-3497595,HK573008,08/21/2004 11:29:00 PM,029XX N RIDGEWAY AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,2523,025,35,21,03,1151098,1919355,2004,02/25/2006 12:14:30 AM,41.934600589,-87.720127742,"(41.934600589, -87.720127742)"
-3498443,HK574172,08/21/2004 07:40:00 PM,080XX W FOREST PRESERVE AVE,0820,THEFT,$500 AND UNDER,PARK PROPERTY,false,false,1631,016,36,17,06,1121872,1922596,2004,12/04/2014 12:43:35 PM,41.944017705,-87.827465398,"(41.944017705, -87.827465398)"
-3497733,HK569371,08/20/2004 09:30:13 AM,017XX W 79TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,SMALL RETAIL STORE,false,false,0611,006,21,71,14,1166027,1852267,2004,02/25/2006 12:14:30 AM,41.75019845,-87.667175898,"(41.75019845, -87.667175898)"
-3494103,HK568107,08/19/2004 03:00:00 PM,036XX W DEVON AVE,0810,THEFT,OVER $500,STREET,false,false,1711,017,50,13,06,1150916,1942135,2004,12/04/2014 12:43:35 PM,41.997114015,-87.720197512,"(41.997114015, -87.720197512)"
-3493947,HK562339,08/16/2004 09:45:00 PM,077XX S MORGAN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0612,006,17,71,08B,1170952,1853237,2004,02/25/2006 12:14:30 AM,41.752754203,-87.649100227,"(41.752754203, -87.649100227)"
-3486774,HK559871,08/15/2004 07:55:00 PM,077XX S ESSEX AVE,0460,BATTERY,SIMPLE,STREET,false,false,0421,004,7,43,08B,1194238,1854378,2004,02/25/2006 12:14:30 AM,41.755345327,-87.563730349,"(41.755345327, -87.563730349)"
-3491852,HK559414,08/15/2004 01:30:00 AM,014XX W 85TH ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0614,006,21,71,06,1168044,1848416,2004,12/04/2014 12:43:35 PM,41.739587673,-87.659895137,"(41.739587673, -87.659895137)"
-3493580,HK554144,08/12/2004 10:04:07 PM,029XX S STATE ST,2024,NARCOTICS,POSS: HEROIN(WHITE),CHA PARKING LOT/GROUNDS,true,false,2113,001,3,35,18,1176728,1885065,2004,02/25/2006 12:14:30 AM,41.83996533,-87.626975231,"(41.83996533, -87.626975231)"
-3484859,HK553720,08/12/2004 05:30:00 PM,008XX E 63RD ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,0313,003,20,42,06,1183084,1863463,2004,02/25/2006 12:14:30 AM,41.780541982,-87.604324126,"(41.780541982, -87.604324126)"
-3481967,HK554024,08/11/2004 08:00:00 PM,020XX N KEDZIE AVE,0890,THEFT,FROM BUILDING,RESIDENCE-GARAGE,false,false,1414,014,35,22,06,1154756,1913339,2004,02/25/2006 12:14:30 AM,41.918019625,-87.706846035,"(41.918019625, -87.706846035)"
-3479334,HK551450,08/10/2004 11:00:00 PM,016XX W JUNEWAY TER,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2422,024,49,1,07,1163919,1951532,2004,02/25/2006 12:14:30 AM,42.022634122,-87.672097653,"(42.022634122, -87.672097653)"
-3478051,HK549810,08/10/2004 07:30:00 PM,048XX W ARMITAGE AVE,0820,THEFT,$500 AND UNDER,RESIDENCE,false,true,2533,025,31,19,06,1143325,1912809,2004,12/04/2014 12:43:35 PM,41.916786714,-87.748857849,"(41.916786714, -87.748857849)"
-3475693,HK547491,08/09/2004 07:36:22 PM,073XX N ASHLAND BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,true,false,2423,024,49,1,14,1164475,1948727,2004,02/25/2006 12:14:30 AM,42.014925354,-87.670131523,"(42.014925354, -87.670131523)"
-3521656,HK600285,08/09/2004 12:00:00 PM,023XX S TRUMBULL AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,1024,010,22,30,05,1153769,1888430,2004,02/25/2006 12:14:30 AM,41.849686523,-87.711135871,"(41.849686523, -87.711135871)"
-3475020,HK545909,08/09/2004 04:12:41 AM,124XX S COTTAGE GROVE AVE,0610,BURGLARY,FORCIBLE ENTRY,SIDEWALK,false,false,0532,005,9,54,05,1183663,1823193,2004,02/25/2006 12:14:30 AM,41.670022919,-87.603453219,"(41.670022919, -87.603453219)"
-3658510,HK546420,08/09/2004 12:01:00 AM,069XX S WOLCOTT AVE,1120,DECEPTIVE PRACTICE,FORGERY,RESIDENCE,false,false,0735,007,17,67,10,1164929,1858500,2004,02/25/2006 12:14:30 AM,41.767325934,-87.671023627,"(41.767325934, -87.671023627)"
-3502602,HK543459,08/07/2004 08:15:00 PM,052XX W CHICAGO AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1524,015,37,25,18,1141269,1904867,2004,02/25/2006 12:14:30 AM,41.895031161,-87.756607838,"(41.895031161, -87.756607838)"
-3492640,HK567129,08/07/2004 07:00:00 AM,064XX S LANGLEY AVE,2830,OTHER OFFENSE,OBSCENE TELEPHONE CALLS,RESIDENCE,false,false,0312,003,20,42,17,1182039,1862479,2004,02/25/2006 12:14:30 AM,41.777866041,-87.608185661,"(41.777866041, -87.608185661)"
-3470763,HK540379,08/06/2004 11:30:00 AM,054XX W DIVISION ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,false,false,2532,025,37,25,06,1139873,1907504,2004,02/25/2006 12:14:30 AM,41.902293041,-87.761670517,"(41.902293041, -87.761670517)"
-3468812,HK539075,08/05/2004 04:50:00 PM,087XX S STONY ISLAND AVE,0460,BATTERY,SIMPLE,SMALL RETAIL STORE,false,false,0412,004,8,48,08B,1188279,1847492,2004,02/25/2006 12:14:30 AM,41.736593686,-87.585787413,"(41.736593686, -87.585787413)"
-3462356,HK533417,08/03/2004 08:10:00 AM,0000X N CLARK ST,0460,BATTERY,SIMPLE,STREET,false,false,0113,001,42,32,08B,1175577,1900369,2004,02/25/2006 12:14:30 AM,41.88198645,-87.6307393,"(41.88198645, -87.6307393)"
-3466303,HK533157,08/03/2004 02:37:34 AM,048XX N TROY ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,true,false,1713,017,33,14,15,1154451,1932377,2004,06/11/2007 03:52:33 PM,41.970267361,-87.707455895,"(41.970267361, -87.707455895)"
-3468953,HK528682,07/31/2004 11:30:00 PM,087XX S GREENWOOD AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0412,004,8,47,03,1185027,1847519,2004,02/25/2006 12:14:30 AM,41.736744672,-87.597700681,"(41.736744672, -87.597700681)"
-3464202,HK528085,07/31/2004 03:45:00 PM,0000X E GRAND AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,1834,018,42,8,06,1176882,1903879,2004,02/25/2006 12:14:30 AM,41.891588659,-87.625841147,"(41.891588659, -87.625841147)"
-3457373,HK526093,07/30/2004 06:30:00 PM,036XX N SAWYER AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENTIAL YARD (FRONT/BACK),false,false,1733,017,33,16,26,1154115,1923959,2004,02/25/2006 12:14:30 AM,41.947174553,-87.708916941,"(41.947174553, -87.708916941)"
-3477895,HK526086,07/30/2004 05:55:00 PM,009XX N HUDSON AVE,2027,NARCOTICS,POSS: CRACK,CHA PARKING LOT/GROUNDS,true,false,1823,018,27,8,18,1173036,1906859,2004,02/25/2006 12:14:30 AM,41.8998521,-87.639877139,"(41.8998521, -87.639877139)"
-3477823,HK524597,07/30/2004 01:49:00 AM,025XX W JACKSON BLVD,2027,NARCOTICS,POSS: CRACK,STREET,true,false,1125,011,2,28,18,1159587,1898573,2004,02/25/2006 12:14:30 AM,41.877402272,-87.689503763,"(41.877402272, -87.689503763)"
-3456744,HK524058,07/29/2004 08:05:00 PM,014XX W 95TH ST,1330,CRIMINAL TRESPASS,TO LAND,PARKING LOT/GARAGE(NON.RESID.),true,false,2222,022,21,73,26,1168235,1841776,2004,02/25/2006 12:14:30 AM,41.721362432,-87.659385827,"(41.721362432, -87.659385827)"
-3455066,HK523247,07/29/2004 12:00:00 PM,038XX W 43RD ST,0810,THEFT,OVER $500,ALLEY,false,false,0821,008,14,57,06,1151264,1875817,2004,12/04/2014 12:43:35 PM,41.815124232,-87.720659797,"(41.815124232, -87.720659797)"
-3464372,HK523040,07/29/2004 09:45:00 AM,016XX W HOWARD ST,0820,THEFT,$500 AND UNDER,CTA TRAIN,false,false,2422,024,49,1,06,1163722,1950306,2004,12/04/2014 12:43:35 PM,42.019274133,-87.672857442,"(42.019274133, -87.672857442)"
-3453617,HK522517,07/29/2004 06:07:00 AM,023XX W WASHINGTON BLVD,0580,STALKING,SIMPLE,CHA APARTMENT,false,true,1332,012,2,28,08A,1160601,1900581,2004,02/25/2006 12:14:30 AM,41.882891472,-87.685724995,"(41.882891472, -87.685724995)"
-3453473,HK521604,07/28/2004 06:30:00 AM,082XX S EXCHANGE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0423,004,7,46,05,1197222,1850688,2004,02/25/2006 12:14:30 AM,41.745145927,-87.552917659,"(41.745145927, -87.552917659)"
-3481194,HK518973,07/27/2004 03:20:00 PM,010XX N RIDGEWAY AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1112,011,27,23,18,1151219,1907052,2004,02/25/2006 12:14:30 AM,41.900837672,-87.720006415,"(41.900837672, -87.720006415)"
-3452962,HK518347,07/26/2004 12:00:00 PM,049XX W GRAND AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2533,025,37,19,06,1142930,1912337,2004,12/04/2014 12:43:35 PM,41.915498873,-87.750320879,"(41.915498873, -87.750320879)"
-3456556,HK515275,07/25/2004 09:28:21 PM,013XX N LARRABEE ST,2093,NARCOTICS,FOUND SUSPECT NARCOTICS,CHA PARKING LOT/GROUNDS,true,false,1822,018,27,8,26,1172047,1909172,2004,02/25/2006 12:14:30 AM,41.906220979,-87.643441395,"(41.906220979, -87.643441395)"
-3451527,HK513447,07/24/2004 10:20:00 PM,024XX E 73RD ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0334,003,7,43,14,1193683,1856987,2004,02/25/2006 12:14:30 AM,41.76251823,-87.565678919,"(41.76251823, -87.565678919)"
-3469980,HK003582,07/24/2004 10:20:00 AM,035XX W 63RD ST,0560,ASSAULT,SIMPLE,POLICE FACILITY/VEH PARKING LOT,true,false,0823,008,15,66,08A,1154004,1862576,2004,02/25/2006 12:14:30 AM,41.778734981,-87.71096061,"(41.778734981, -87.71096061)"
-3458358,HK070132,07/23/2004 08:00:00 AM,007XX E 61ST ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0313,003,20,42,14,1182137,1864763,2004,02/25/2006 12:14:30 AM,41.784131281,-87.607755741,"(41.784131281, -87.607755741)"
-3448690,HK003479,07/22/2004 03:00:00 AM,113XX S LOWE AVE,0820,THEFT,$500 AND UNDER,DRIVEWAY - RESIDENTIAL,false,false,2233,022,34,49,06,1174047,1829580,2004,12/04/2014 12:43:35 PM,41.687767979,-87.638458078,"(41.687767979, -87.638458078)"
-3444117,HK512008,07/21/2004 11:30:00 PM,075XX N WESTERN AVE,0890,THEFT,FROM BUILDING,DRUG STORE,false,false,2411,024,50,2,06,1158975,1949766,2004,02/25/2006 12:14:30 AM,42.017891545,-87.690340753,"(42.017891545, -87.690340753)"
-3443863,HK510707,07/21/2004 12:45:00 PM,047XX W NORTH AVE,0820,THEFT,$500 AND UNDER,SMALL RETAIL STORE,false,false,2533,025,37,25,06,1144212,1910247,2004,12/04/2014 12:43:35 PM,41.909739682,-87.745663482,"(41.909739682, -87.745663482)"
-3457478,HK509743,07/20/2004 11:40:00 PM,018XX W IOWA ST,0920,MOTOR VEHICLE THEFT,ATT: AUTOMOBILE,STREET,true,false,1322,012,32,24,07,1163575,1906065,2004,02/25/2006 12:14:30 AM,41.897877899,-87.674649773,"(41.897877899, -87.674649773)"
-3450874,HK001018,07/20/2004 11:00:00 AM,014XX N LINDER AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,false,2532,025,37,25,08B,1139453,1909003,2004,02/25/2006 12:14:30 AM,41.906414143,-87.763176668,"(41.906414143, -87.763176668)"
-3439896,HK507771,07/20/2004 06:30:00 AM,088XX S LANGLEY AVE,0498,BATTERY,AGGRAVATED DOMESTIC BATTERY: HANDS/FIST/FEET SERIOUS INJURY,APARTMENT,false,true,0632,006,6,44,04B,1182413,1846246,2004,02/25/2006 12:14:30 AM,41.733312328,-87.607316788,"(41.733312328, -87.607316788)"
-3456091,HK521371,07/19/2004 03:00:00 PM,054XX S WELLS ST,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0934,009,3,37,06,1175593,1868501,2004,12/04/2014 12:43:35 PM,41.794537768,-87.631636522,"(41.794537768, -87.631636522)"
-3436997,HK505203,07/19/2004 04:12:00 AM,031XX S ASHLAND AVE,0610,BURGLARY,FORCIBLE ENTRY,SMALL RETAIL STORE,false,false,0922,009,11,59,05,1166215,1883940,2004,02/25/2006 12:14:30 AM,41.837108991,-87.66558546,"(41.837108991, -87.66558546)"
-3436897,HK505175,07/19/2004 03:12:03 AM,088XX S BURLEY AVE,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,STREET,false,false,0424,004,7,46,11,1199163,1847026,2004,02/25/2006 12:14:30 AM,41.735048604,-87.545928465,"(41.735048604, -87.545928465)"
-3444669,HK508432,07/18/2004 02:30:00 PM,099XX S LOWE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,2232,022,9,73,05,1173764,1838914,2004,02/25/2006 12:14:30 AM,41.713388136,-87.639218712,"(41.713388136, -87.639218712)"
-3437416,HK504082,07/18/2004 09:00:00 AM,054XX W DIVISION ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,2532,025,37,25,14,1139833,1907503,2004,02/25/2006 12:14:30 AM,41.902291028,-87.76181747,"(41.902291028, -87.76181747)"
-3435872,HK504133,07/18/2004 04:30:00 AM,047XX S DAMEN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0915,009,12,61,14,1163780,1873312,2004,02/25/2006 12:14:30 AM,41.807996169,-87.674819382,"(41.807996169, -87.674819382)"
-3452475,HK503060,07/17/2004 11:45:00 PM,052XX W ADDISON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1634,016,38,15,18,1140572,1923468,2004,02/25/2006 12:14:30 AM,41.946087146,-87.75871,"(41.946087146, -87.75871)"
-4081509,HK502690,07/17/2004 08:26:23 PM,008XX E 48TH ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,2124,002,4,39,26,1182689,1873327,2004,02/25/2006 12:14:30 AM,41.807618819,-87.605466064,"(41.807618819, -87.605466064)"
-3452935,HK500819,07/16/2004 10:45:00 PM,047XX W FIFTH AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,1131,011,24,25,16,1144568,1895270,2004,02/25/2006 12:14:30 AM,41.868634352,-87.744733079,"(41.868634352, -87.744733079)"
-3439827,HK499702,07/16/2004 01:30:00 PM,004XX E 72ND ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,0323,003,6,69,15,1180595,1857422,2004,02/25/2006 12:14:30 AM,41.764022413,-87.613634485,"(41.764022413, -87.613634485)"
-3432603,HK497368,07/15/2004 08:30:00 AM,020XX W SHAKESPEARE AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1432,014,32,22,05,1162403,1914362,2004,02/25/2006 12:14:30 AM,41.920670079,-87.678721836,"(41.920670079, -87.678721836)"
-3431314,HK497024,07/14/2004 09:00:00 PM,017XX W PIERCE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1433,014,1,24,06,1164614,1910467,2004,12/04/2014 12:43:35 PM,41.909935345,-87.670708751,"(41.909935345, -87.670708751)"
-3433294,HK495212,07/14/2004 02:15:00 PM,007XX S KARLOV AVE,0810,THEFT,OVER $500,VEHICLE-COMMERCIAL,false,false,1132,011,24,26,06,1149199,1896642,2004,12/04/2014 12:43:35 PM,41.872310886,-87.727695938,"(41.872310886, -87.727695938)"
-3428071,HK494041,07/13/2004 11:02:00 PM,031XX W HOMER ST,051A,ASSAULT,AGGRAVATED: HANDGUN,RESIDENCE,true,false,1421,014,35,22,04A,1154964,1912751,2004,02/25/2006 12:14:30 AM,41.916401932,-87.706097629,"(41.916401932, -87.706097629)"
-3429837,HK491736,07/12/2004 09:40:00 PM,050XX W DICKENS AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,true,false,2522,025,31,19,24,1142310,1913530,2004,02/25/2006 12:14:30 AM,41.918784135,-87.752569051,"(41.918784135, -87.752569051)"
-3428760,HK489447,07/11/2004 10:08:00 PM,031XX W FOSTER AVE,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,1713,017,33,13,06,1154343,1934335,2004,12/04/2014 12:43:35 PM,41.97564239,-87.707800448,"(41.97564239, -87.707800448)"
-3424360,HK488010,07/11/2004 06:15:52 AM,031XX W 46TH ST,0810,THEFT,OVER $500,STREET,true,false,0912,009,14,58,06,1156095,1873928,2004,12/04/2014 12:43:35 PM,41.809844681,-87.702989619,"(41.809844681, -87.702989619)"
-3424716,HK489085,07/10/2004 11:30:00 PM,016XX W ARTHUR AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,ALLEY,false,false,2432,024,40,1,04B,1164121,1943538,2004,02/25/2006 12:14:30 AM,42.000694147,-87.671581696,"(42.000694147, -87.671581696)"
-3690634,HK490277,07/10/2004 01:00:00 PM,022XX E 75TH ST,0890,THEFT,FROM BUILDING,COMMERCIAL / BUSINESS OFFICE,false,false,0414,004,7,43,06,1192789,1855642,2004,02/25/2006 12:14:30 AM,41.758849291,-87.5689993,"(41.758849291, -87.5689993)"
-3425866,HK490872,07/09/2004 03:45:00 AM,028XX N LOCKWOOD AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2514,025,31,19,26,1140519,1918346,2004,02/25/2006 12:14:30 AM,41.932032844,-87.759030941,"(41.932032844, -87.759030941)"
-3462280,HK529429,07/08/2004 08:00:00 AM,001XX N LA SALLE ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,false,false,0113,001,42,32,11,1175070,1901133,2004,02/25/2006 12:14:30 AM,41.884094285,-87.632578071,"(41.884094285, -87.632578071)"
-3419952,HK481857,07/07/2004 10:00:00 PM,043XX W WILCOX ST,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,1115,011,28,26,06,1147519,1898944,2004,12/04/2014 12:43:35 PM,41.878660207,-87.733804972,"(41.878660207, -87.733804972)"
-3433470,HK479777,07/07/2004 12:00:00 PM,072XX S ASHLAND AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0735,007,17,67,07,1166900,1856520,2004,02/25/2006 12:14:30 AM,41.761850675,-87.663855549,"(41.761850675, -87.663855549)"
-3420388,HK483730,07/07/2004 09:00:00 AM,090XX S KINGSTON AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0423,004,7,48,26,1194769,1845565,2004,02/25/2006 12:14:30 AM,41.731148679,-87.56207388,"(41.731148679, -87.56207388)"
-3419431,HK479505,07/06/2004 06:30:00 PM,047XX N LINDER AVE,0810,THEFT,OVER $500,STREET,false,false,1623,016,45,15,06,1138769,1931133,2004,12/04/2014 12:43:35 PM,41.967153604,-87.765150522,"(41.967153604, -87.765150522)"
-3439524,HK474702,07/05/2004 04:05:00 AM,0000X E CHICAGO AVE,1563,SEX OFFENSE,CRIMINAL SEXUAL ABUSE,SIDEWALK,true,false,1833,018,42,8,17,1176232,1905688,2004,02/25/2006 12:14:30 AM,41.896567329,-87.628173669,"(41.896567329, -87.628173669)"
-3420515,HK474006,07/04/2004 08:20:00 PM,007XX E ADMINISTRATION DR,0460,BATTERY,SIMPLE,RESTAURANT,false,true,0234,002,20,40,08B,1182357,1867807,2004,02/25/2006 12:14:30 AM,41.79247919,-87.606854844,"(41.79247919, -87.606854844)"
-3413667,HK473238,07/04/2004 11:00:00 AM,047XX W IRVING PARK RD,0820,THEFT,$500 AND UNDER,DEPARTMENT STORE,false,false,1722,017,45,15,06,1143749,1926204,2004,12/04/2014 12:43:35 PM,41.953535933,-87.746963462,"(41.953535933, -87.746963462)"
-3419543,HK472681,07/04/2004 01:53:42 AM,049XX S FEDERAL ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA APARTMENT,false,false,0231,002,3,38,08B,1176577,1872007,2004,02/25/2006 12:14:30 AM,41.804136472,-87.627922736,"(41.804136472, -87.627922736)"
-3436827,HK472434,07/03/2004 11:01:36 PM,050XX S WOLCOTT AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,VEHICLE NON-COMMERCIAL,true,false,0915,009,16,61,18,1164493,1871497,2004,02/25/2006 12:14:30 AM,41.803000573,-87.672255476,"(41.803000573, -87.672255476)"
-3427221,HK472255,07/03/2004 09:15:00 PM,024XX S STATE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0134,001,3,33,18,1176662,1888115,2004,02/25/2006 12:14:30 AM,41.848336245,-87.627125389,"(41.848336245, -87.627125389)"
-3412531,HK471939,07/03/2004 12:00:00 PM,003XX S CLARK ST,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0112,001,2,32,06,1175629,1898511,2004,12/04/2014 12:43:35 PM,41.876886819,-87.630604222,"(41.876886819, -87.630604222)"
-3410796,HK470037,07/02/2004 01:00:00 PM,057XX S MARYLAND AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,HOSPITAL BUILDING/GROUNDS,false,false,2133,002,5,41,11,1182924,1867064,2004,02/25/2006 12:14:30 AM,41.790427167,-87.604798846,"(41.790427167, -87.604798846)"
-3421147,HK468507,07/02/2004 03:30:00 AM,009XX N CLEVELAND AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA PARKING LOT/GROUNDS,false,true,1823,018,27,8,08B,1172857,1906778,2004,02/25/2006 12:14:30 AM,41.8996338,-87.640537005,"(41.8996338, -87.640537005)"
-3409514,HK468312,07/02/2004 12:09:45 AM,022XX S UNION AVE,0453,BATTERY,AGGRAVATED PO: OTHER DANG WEAP,STREET,true,false,1233,012,25,31,04B,1171924,1889041,2004,02/25/2006 12:14:30 AM,41.850982867,-87.644486736,"(41.850982867, -87.644486736)"
-3410960,HK467394,07/01/2004 02:00:00 AM,021XX W GRACE ST,0920,MOTOR VEHICLE THEFT,ATT: AUTOMOBILE,STREET,false,false,1912,019,47,5,07,1161563,1925176,2004,02/25/2006 12:14:30 AM,41.950361913,-87.68150607,"(41.950361913, -87.68150607)"
-3407571,HK466455,06/30/2004 10:00:00 PM,081XX S ELLIS AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,true,0631,006,8,44,08B,1184343,1851425,2004,02/25/2006 12:14:30 AM,41.74747917,-87.600084676,"(41.74747917, -87.600084676)"
-3410219,HK464983,06/30/2004 02:23:10 PM,030XX N CENTRAL AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,ALLEY,false,false,2514,025,31,19,14,1138464,1919678,2004,02/25/2006 12:14:30 AM,41.935725517,-87.766550528,"(41.935725517, -87.766550528)"
-3410334,HK464379,06/29/2004 05:45:00 PM,083XX S DREXEL AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0632,006,8,44,08B,1183644,1849547,2004,02/25/2006 12:14:30 AM,41.742342054,-87.602704417,"(41.742342054, -87.602704417)"
-3409783,HK468733,06/27/2004 07:40:00 PM,048XX N SHERIDAN RD,1790,OFFENSE INVOLVING CHILDREN,CHILD ABDUCTION,APARTMENT,false,true,2024,020,48,3,20,1168798,1932398,2004,02/25/2006 12:14:30 AM,41.970025259,-87.654700724,"(41.970025259, -87.654700724)"
-3404852,HK456977,06/26/2004 06:45:00 PM,058XX S CARPENTER ST,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,true,false,0712,007,16,68,26,1170274,1866013,2004,02/25/2006 12:14:30 AM,41.787827897,-87.651213499,"(41.787827897, -87.651213499)"
-3397476,HK453325,06/24/2004 08:00:00 PM,031XX N KEATING AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2521,025,31,19,26,1144194,1920784,2004,02/25/2006 12:14:30 AM,41.938654602,-87.745464234,"(41.938654602, -87.745464234)"
-3413502,HK451667,06/24/2004 08:15:00 AM,027XX S DEARBORN ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,true,false,2113,001,3,35,26,1176354,1886634,2004,06/11/2007 03:52:33 PM,41.844279217,-87.628300377,"(41.844279217, -87.628300377)"
-3396064,HK450576,06/23/2004 05:22:33 PM,084XX S BURNHAM AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0423,004,7,46,14,1196328,1849682,2004,02/25/2006 12:14:30 AM,41.742407587,-87.556226634,"(41.742407587, -87.556226634)"
-3394393,HK446410,06/21/2004 12:30:00 PM,0000X N MICHIGAN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,0122,001,42,32,14,1177259,1900830,2004,02/25/2006 12:14:30 AM,41.883213506,-87.624549106,"(41.883213506, -87.624549106)"
-3391382,HK446966,06/21/2004 12:00:00 AM,068XX S ST LAWRENCE AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,false,0321,003,20,42,06,1181374,1859915,2004,02/25/2006 12:14:30 AM,41.770845536,-87.610702525,"(41.770845536, -87.610702525)"
-3395399,HK442850,06/20/2004 04:55:00 AM,124XX S UNION AVE,4388,OTHER OFFENSE,VIO BAIL BOND: DOM VIOLENCE,RESIDENCE PORCH/HALLWAY,false,false,0523,005,34,53,26,1173872,1822277,2004,02/25/2006 12:14:30 AM,41.667731242,-87.639314082,"(41.667731242, -87.639314082)"
-3388632,HK441405,06/19/2004 01:05:50 PM,016XX S CHRISTIANA AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1021,010,24,29,08B,1154337,1891784,2004,02/25/2006 12:14:30 AM,41.858878973,-87.708961711,"(41.858878973, -87.708961711)"
-3385179,HK437805,06/17/2004 10:30:00 AM,027XX N ELSTON AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1432,014,1,22,05,1160286,1918237,2004,02/25/2006 12:14:30 AM,41.931347425,-87.686392712,"(41.931347425, -87.686392712)"
-3396524,HK435667,06/16/2004 07:30:00 PM,050XX S ASHLAND AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0931,009,16,61,16,1166558,1871562,2004,02/25/2006 12:14:30 AM,41.803135108,-87.664680309,"(41.803135108, -87.664680309)"
-3384038,HK434245,06/16/2004 06:00:00 AM,017XX N NAGLE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,2513,025,36,25,05,1132983,1911247,2004,02/25/2006 12:14:30 AM,41.912687513,-87.786891401,"(41.912687513, -87.786891401)"
-3383601,HK432850,06/15/2004 01:00:00 PM,047XX S ASHLAND AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0931,009,20,61,06,1166512,1873209,2004,12/04/2014 12:43:35 PM,41.807655646,-87.664802029,"(41.807655646, -87.664802029)"
-3391869,HK431751,06/15/2004 01:58:55 AM,051XX S CALUMET AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0232,002,3,40,18,1179317,1871270,2004,02/25/2006 12:14:30 AM,41.802051931,-87.617896303,"(41.802051931, -87.617896303)"
-3380871,HK432280,06/14/2004 10:30:00 PM,055XX S STATE ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),false,false,0233,002,3,40,07,1177174,1868423,2004,02/25/2006 12:14:30 AM,41.79428817,-87.625841426,"(41.79428817, -87.625841426)"
-3389124,HK432631,06/14/2004 10:20:00 PM,061XX N WINCHESTER AVE,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,ALLEY,false,false,2413,024,40,2,20,1162295,1941049,2004,02/25/2006 12:14:30 AM,41.993902786,-87.678369225,"(41.993902786, -87.678369225)"
-3396269,HK431364,06/14/2004 08:35:00 PM,002XX S SPRINGFIELD AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1122,011,28,26,26,1150390,1898595,2004,02/25/2006 12:14:30 AM,41.877646995,-87.72327226,"(41.877646995, -87.72327226)"
-3390157,HK429010,06/13/2004 02:00:00 AM,056XX W 63RD ST,0890,THEFT,FROM BUILDING,APARTMENT,false,false,0812,008,13,64,06,1140110,1862269,2004,02/25/2006 12:14:30 AM,41.778157172,-87.761905407,"(41.778157172, -87.761905407)"
-3382427,HK428705,06/12/2004 11:00:00 PM,026XX W OGDEN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1023,010,28,29,26,1158742,1893135,2004,02/25/2006 12:14:30 AM,41.862497215,-87.692755379,"(41.862497215, -87.692755379)"
-3391062,HK426041,06/12/2004 12:46:27 PM,068XX S CAMPBELL AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0832,008,15,66,18,1160938,1858849,2004,02/25/2006 12:14:30 AM,41.768367041,-87.685642774,"(41.768367041, -87.685642774)"
-3376378,HK425928,06/12/2004 11:30:42 AM,054XX W CONGRESS PKWY,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1522,015,29,25,08B,1140054,1897186,2004,02/25/2006 12:14:30 AM,41.873975849,-87.761258336,"(41.873975849, -87.761258336)"
-3392855,HK423832,06/11/2004 01:20:00 PM,042XX W CARROLL AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1114,011,28,26,18,1148227,1901924,2004,02/25/2006 12:14:30 AM,41.886824062,-87.731128533,"(41.886824062, -87.731128533)"
-3376997,HK428607,06/11/2004 01:00:00 PM,063XX S WOODLAWN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0314,003,20,42,26,1185350,1863036,2004,02/25/2006 12:14:30 AM,41.77931724,-87.596030098,"(41.77931724, -87.596030098)"
-3373830,HK422157,06/10/2004 04:40:00 PM,085XX S ST LAWRENCE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0632,006,6,44,05,1181777,1848216,2004,02/25/2006 12:14:30 AM,41.738732934,-87.609586053,"(41.738732934, -87.609586053)"
-3374566,HK421476,06/10/2004 01:24:06 PM,055XX S LOWE AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",true,false,0711,007,3,68,08B,1172947,1868139,2004,02/25/2006 12:14:30 AM,41.793603259,-87.641349983,"(41.793603259, -87.641349983)"
-3370731,HK420534,06/10/2004 12:35:26 AM,117XX S MICHIGAN AVE,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,false,false,0532,005,9,53,04B,1178896,1827354,2004,02/25/2006 12:14:30 AM,41.681550804,-87.62077387,"(41.681550804, -87.62077387)"
-3383133,HK431040,06/09/2004 04:00:00 PM,036XX S RHODES AVE,0810,THEFT,OVER $500,APARTMENT,false,false,0212,002,4,35,06,1180140,1880966,2004,12/04/2014 12:43:35 PM,41.828639699,-87.614580632,"(41.828639699, -87.614580632)"
-3370243,HK419162,06/08/2004 08:30:00 PM,010XX N RUSH ST,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,1824,018,42,8,06,1176359,1907211,2004,02/25/2006 12:14:30 AM,41.900743649,-87.627661215,"(41.900743649, -87.627661215)"
-3371315,HK417477,06/08/2004 06:00:00 PM,044XX W THOMAS ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1111,011,37,23,06,1146675,1907000,2004,12/04/2014 12:43:35 PM,41.900782911,-87.736698324,"(41.900782911, -87.736698324)"
-3370570,HK419806,06/08/2004 08:30:00 AM,0000X W 35TH ST,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,2113,002,3,35,06,1176748,1881839,2004,12/04/2014 12:43:35 PM,41.831112482,-87.626999184,"(41.831112482, -87.626999184)"
-3372224,HK418922,06/07/2004 10:30:00 PM,116XX S STEWART AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0522,005,34,53,08B,1175667,1827991,2004,02/25/2006 12:14:30 AM,41.683371507,-87.632574759,"(41.683371507, -87.632574759)"
-3369282,HK415409,06/07/2004 07:05:00 PM,004XX W BELDEN AVE,0820,THEFT,$500 AND UNDER,ALLEY,false,false,1812,018,43,7,06,1172878,1915529,2004,12/04/2014 12:43:35 PM,41.923646512,-87.640200122,"(41.923646512, -87.640200122)"
-3368180,HK412189,06/06/2004 10:35:00 PM,030XX S DRAKE AVE,4510,OTHER OFFENSE,PROBATION VIOLATION,SIDEWALK,true,false,1032,010,22,30,26,1153140,1884268,2004,02/25/2006 12:14:30 AM,41.83827797,-87.713554637,"(41.83827797, -87.713554637)"
-3369151,HK413444,06/06/2004 09:48:00 PM,010XX W MARQUETTE RD,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,true,false,0724,007,17,68,26,1170793,1860436,2004,02/25/2006 12:14:30 AM,41.772512637,-87.649473163,"(41.772512637, -87.649473163)"
-3365989,HK413234,06/06/2004 07:00:00 PM,016XX W 69TH ST,0810,THEFT,OVER $500,STREET,false,false,0725,007,17,67,06,1166446,1858991,2004,12/04/2014 12:43:35 PM,41.768641114,-87.665449192,"(41.768641114, -87.665449192)"
-3367291,HK413066,06/06/2004 05:55:00 PM,066XX S STEWART AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,true,false,0722,007,6,68,08B,1174728,1860917,2004,02/25/2006 12:14:30 AM,41.773745788,-87.635034295,"(41.773745788, -87.635034295)"
-3364722,HK412824,06/06/2004 02:35:00 PM,015XX W FULLERTON AVE,0870,THEFT,POCKET-PICKING,SMALL RETAIL STORE,false,false,1931,019,32,7,06,1165799,1916071,2004,02/25/2006 12:14:30 AM,41.925287862,-87.666195414,"(41.925287862, -87.666195414)"
-3368668,HK416495,06/06/2004 07:30:00 AM,054XX S TALMAN AVE,0890,THEFT,FROM BUILDING,OTHER,false,false,0911,009,14,63,06,1159595,1868298,2004,02/25/2006 12:14:30 AM,41.794324067,-87.690306545,"(41.794324067, -87.690306545)"
-3380791,HK409539,06/04/2004 10:48:00 PM,057XX S ASHLAND AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,0715,007,15,67,16,1166606,1866913,2004,02/25/2006 12:14:30 AM,41.790376676,-87.664636902,"(41.790376676, -87.664636902)"
-3379589,HK409462,06/04/2004 09:53:00 PM,012XX N CLARK ST,2027,NARCOTICS,POSS: CRACK,CTA PLATFORM,true,false,1821,018,42,8,18,1175279,1908359,2004,02/25/2006 12:14:30 AM,41.903918127,-87.631593588,"(41.903918127, -87.631593588)"
-3361054,HK407492,06/04/2004 04:00:00 AM,051XX W CHICAGO AVE,0460,BATTERY,SIMPLE,GAS STATION,false,false,1531,015,37,25,08B,1141661,1904878,2004,02/25/2006 12:14:30 AM,41.895054106,-87.755167834,"(41.895054106, -87.755167834)"
-3386647,HK406084,06/03/2004 03:40:00 PM,034XX W OHIO ST,2094,NARCOTICS,ATTEMPT POSSESSION CANNABIS,STREET,true,false,1121,011,27,23,18,1153409,1903827,2004,02/25/2006 12:14:30 AM,41.891944716,-87.712048126,"(41.891944716, -87.712048126)"
-3373381,HK406143,06/03/2004 12:30:00 PM,049XX S FEDERAL ST,0560,ASSAULT,SIMPLE,CHA PARKING LOT/GROUNDS,false,false,0231,002,3,38,08A,1176577,1872007,2004,02/25/2006 12:14:30 AM,41.804136472,-87.627922736,"(41.804136472, -87.627922736)"
-3373279,HK405246,06/03/2004 12:35:00 AM,056XX W WASHINGTON BLVD,2017,NARCOTICS,MANU/DELIVER:CRACK,SIDEWALK,true,false,1513,015,29,25,18,1138665,1900141,2004,02/25/2006 12:14:30 AM,41.88211005,-87.766286489,"(41.88211005, -87.766286489)"
-3380952,HK405089,06/02/2004 10:15:00 PM,050XX N SHERIDAN RD,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,2024,020,46,3,16,1168686,1933596,2004,02/25/2006 12:14:30 AM,41.97331504,-87.655077675,"(41.97331504, -87.655077675)"
-3357255,HK403727,06/02/2004 09:00:00 AM,008XX W MARQUETTE RD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,ALLEY,false,true,0723,007,16,68,08B,1172119,1860471,2004,02/25/2006 12:14:30 AM,41.772579645,-87.644611403,"(41.772579645, -87.644611403)"
-3371272,HK401434,06/01/2004 12:10:00 PM,008XX E 63RD ST,1821,NARCOTICS,MANU/DEL:CANNABIS 10GM OR LESS,SIDEWALK,true,false,0313,003,20,42,18,1182792,1863455,2004,02/25/2006 12:14:30 AM,41.780526818,-87.605394886,"(41.780526818, -87.605394886)"
-3355703,HK401972,06/01/2004 08:00:00 AM,019XX W 52ND ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0915,009,16,61,07,1164222,1870152,2004,02/25/2006 12:14:30 AM,41.799315445,-87.673287239,"(41.799315445, -87.673287239)"
-4511069,HL816981,06/01/2004 12:01:00 AM,022XX S DRAKE AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,1024,010,22,30,06,1153019,1888618,2004,02/25/2006 12:14:30 AM,41.850217297,-87.713883516,"(41.850217297, -87.713883516)"
-3354108,HK400436,05/31/2004 07:00:00 PM,070XX W IRVING PARK RD,0820,THEFT,$500 AND UNDER,STREET,false,false,1632,016,38,17,06,1128506,1925778,2004,12/04/2014 12:43:35 PM,41.952639456,-87.803008905,"(41.952639456, -87.803008905)"
-3360639,HK399073,05/31/2004 09:52:22 AM,015XX W HASTINGS ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,1231,012,2,28,26,1166344,1893854,2004,02/25/2006 12:14:30 AM,41.864311174,-87.664828903,"(41.864311174, -87.664828903)"
-3358150,HK398395,05/30/2004 09:07:31 PM,007XX W BITTERSWEET PL,0890,THEFT,FROM BUILDING,OTHER,false,false,2323,019,46,3,06,1170240,1927133,2004,02/25/2006 12:14:30 AM,41.955546474,-87.649552958,"(41.955546474, -87.649552958)"
-3354969,HK397433,05/30/2004 10:38:00 AM,024XX W BERWYN AVE,0460,BATTERY,SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,2011,020,40,4,08B,1159288,1935081,2004,02/25/2006 12:14:30 AM,41.977588922,-87.689595227,"(41.977588922, -87.689595227)"
-3351987,HK396972,05/30/2004 02:05:00 AM,045XX N SHERIDAN RD,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,2313,019,46,3,06,1168753,1930362,2004,12/04/2014 12:43:35 PM,41.96443939,-87.654925468,"(41.96443939, -87.654925468)"
-3372557,HK388354,05/28/2004 11:55:00 PM,065XX S DR MARTIN LUTHER KING JR DR,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0312,003,20,69,16,1179998,1861992,2004,02/25/2006 12:14:30 AM,41.776576649,-87.615682846,"(41.776576649, -87.615682846)"
-3368962,HK394816,05/28/2004 11:20:00 PM,032XX N CLARK ST,2027,NARCOTICS,POSS: CRACK,PARKING LOT/GARAGE(NON.RESID.),true,false,1924,019,44,6,18,1169940,1921434,2004,02/25/2006 12:14:30 AM,41.939914763,-87.650822643,"(41.939914763, -87.650822643)"
-3370334,HK392749,05/28/2004 01:04:55 AM,064XX S DR MARTIN LUTHER KING JR DR,1513,PROSTITUTION,SOLICIT FOR BUSINESS,SIDEWALK,true,false,0312,003,20,42,16,1180073,1862159,2004,02/25/2006 12:14:30 AM,41.777033196,-87.615402792,"(41.777033196, -87.615402792)"
-3349711,HK392433,05/27/2004 09:00:00 PM,060XX S HALSTED ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE PORCH/HALLWAY,false,true,0711,007,16,68,08B,1172051,1864544,2004,02/25/2006 12:14:30 AM,41.783757924,-87.644741116,"(41.783757924, -87.644741116)"
-3348547,HK392315,05/27/2004 08:00:00 AM,017XX W 75TH PL,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,0611,006,17,71,14,1165985,1854729,2004,02/25/2006 12:14:30 AM,41.756955417,-87.667259959,"(41.756955417, -87.667259959)"
-3348984,HK388988,05/26/2004 11:17:38 AM,050XX S INDIANA AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,ABANDONED BUILDING,false,false,0224,002,3,38,14,1178486,1871871,2004,02/25/2006 12:14:30 AM,41.803720069,-87.620925622,"(41.803720069, -87.620925622)"
-3344580,HK388474,05/26/2004 01:00:00 AM,064XX S LONG AVE,0265,CRIM SEXUAL ASSAULT,AGGRAVATED: OTHER,RESIDENCE,true,true,0813,008,13,64,02,1141556,1861341,2004,02/25/2006 12:14:30 AM,41.775584087,-87.756627025,"(41.775584087, -87.756627025)"
-3345606,HK387352,05/25/2004 03:45:00 PM,0000X E 111TH PL,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0531,005,9,49,06,1178586,1831007,2004,12/04/2014 12:43:35 PM,41.691582201,-87.621798187,"(41.691582201, -87.621798187)"
-3345334,HK385591,05/24/2004 06:00:00 PM,004XX W 57TH ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0711,007,20,68,08B,1174225,1867092,2004,02/25/2006 12:14:30 AM,41.790701857,-87.636694788,"(41.790701857, -87.636694788)"
-3342313,HK385569,05/23/2004 11:00:00 PM,070XX N SHERIDAN RD,0820,THEFT,$500 AND UNDER,STREET,false,false,2423,024,49,1,06,1166611,1946747,2004,12/04/2014 12:43:35 PM,42.009446526,-87.66232898,"(42.009446526, -87.66232898)"
-3340403,HK383535,05/23/2004 06:00:00 PM,017XX N WELLS ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1814,018,43,7,26,1174423,1911793,2004,02/25/2006 12:14:30 AM,41.913360358,-87.634635105,"(41.913360358, -87.634635105)"
-3339308,HK381934,05/22/2004 11:35:00 PM,025XX W WABANSIA AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1434,014,1,24,18,1159318,1911258,2004,06/11/2007 03:52:33 PM,41.912216531,-87.690142301,"(41.912216531, -87.690142301)"
-3339789,HK380021,05/22/2004 12:40:00 AM,025XX S SAWYER AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1024,010,22,30,08B,1155053,1887203,2004,02/25/2006 12:14:30 AM,41.846293867,-87.70645626,"(41.846293867, -87.70645626)"
-3337749,HK375855,05/19/2004 08:00:00 PM,071XX S STATE ST,1310,CRIMINAL DAMAGE,TO PROPERTY,GROCERY FOOD STORE,false,false,0323,003,6,69,14,1177565,1857496,2004,02/25/2006 12:14:30 AM,41.764294509,-87.624737832,"(41.764294509, -87.624737832)"
-3349101,HK375008,05/19/2004 07:00:00 PM,016XX W JONQUIL TER,1310,CRIMINAL DAMAGE,TO PROPERTY,PARK PROPERTY,false,false,2422,024,49,1,14,1163973,1950959,2004,02/25/2006 12:14:30 AM,42.021060657,-87.671915226,"(42.021060657, -87.671915226)"
-3338583,HK379470,05/19/2004 02:43:00 PM,057XX W CORNELIA AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,1633,016,38,15,06,1137257,1922726,2004,02/25/2006 12:14:30 AM,41.94411136,-87.770912916,"(41.94411136, -87.770912916)"
-3340088,HK374315,05/19/2004 11:45:00 AM,001XX S HALSTED ST,1151,DECEPTIVE PRACTICE,ILLEGAL POSSESSION CASH CARD,SMALL RETAIL STORE,true,false,1213,012,27,28,11,1171109,1899664,2004,02/25/2006 12:14:30 AM,41.880151085,-87.647166309,"(41.880151085, -87.647166309)"
-3330838,HK371755,05/17/2004 05:00:00 PM,017XX N MERRIMAC AVE,0810,THEFT,OVER $500,DRIVEWAY - RESIDENTIAL,false,false,2513,025,29,25,06,1134497,1911270,2004,12/04/2014 12:43:35 PM,41.912724024,-87.7813287,"(41.912724024, -87.7813287)"
-3332848,HK369289,05/17/2004 08:50:00 AM,105XX S WABASH AVE,0460,BATTERY,SIMPLE,STREET,false,false,0512,005,9,49,08B,1178392,1835331,2004,02/25/2006 12:14:30 AM,41.703452265,-87.622377765,"(41.703452265, -87.622377765)"
-3337233,HK369792,05/17/2004 02:00:00 AM,047XX W ERIE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1111,011,28,25,08B,1144681,1903853,2004,02/25/2006 12:14:30 AM,41.892185009,-87.744101886,"(41.892185009, -87.744101886)"
-3336877,HK368985,05/17/2004 01:47:23 AM,026XX S TROY ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,true,false,1033,010,12,30,14,1155757,1885906,2004,02/25/2006 12:14:30 AM,41.842720616,-87.703907491,"(41.842720616, -87.703907491)"
-3327864,HK368367,05/16/2004 06:30:25 PM,087XX S COMMERCIAL AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,true,false,0423,004,10,46,14,1197619,1847626,2004,02/25/2006 12:14:30 AM,41.736733675,-87.551564943,"(41.736733675, -87.551564943)"
-3359130,HK367739,05/16/2004 01:05:00 PM,0000X N LATROBE AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1522,015,28,25,18,1141331,1900172,2004,02/25/2006 12:14:30 AM,41.882146362,-87.756496043,"(41.882146362, -87.756496043)"
-3336530,HK374754,05/15/2004 07:00:00 AM,001XX E WACKER DR,0810,THEFT,OVER $500,STREET,false,false,0124,001,42,32,06,1177258,1902722,2004,12/04/2014 12:43:35 PM,41.888405276,-87.624495391,"(41.888405276, -87.624495391)"
-3330626,HK367449,05/14/2004 09:00:00 PM,051XX N WINTHROP AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2033,020,48,3,07,1167897,1934499,2004,02/25/2006 12:14:30 AM,41.975810003,-87.657952816,"(41.975810003, -87.657952816)"
-3323631,HK362836,05/14/2004 02:51:50 AM,010XX W 14TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1232,012,25,28,08B,1169994,1893539,2004,02/25/2006 12:14:30 AM,41.863368015,-87.651439096,"(41.863368015, -87.651439096)"
-3338909,HK358808,05/12/2004 12:01:00 AM,060XX S KOLMAR AVE,0810,THEFT,OVER $500,ALLEY,false,false,0813,008,13,65,06,1147025,1864389,2004,12/04/2014 12:43:35 PM,41.78384591,-87.736500367,"(41.78384591, -87.736500367)"
-3323172,HK355855,05/10/2004 04:45:00 PM,070XX S INDIANA AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0322,003,6,69,26,1178777,1858192,2004,02/25/2006 12:14:30 AM,41.766176924,-87.620274433,"(41.766176924, -87.620274433)"
-3313825,HK349403,05/07/2004 06:35:00 PM,006XX E 89TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0632,006,6,44,14,1181866,1846142,2004,02/25/2006 12:14:30 AM,41.733039585,-87.609323901,"(41.733039585, -87.609323901)"
-3333522,HK349192,05/07/2004 04:42:40 PM,072XX S COTTAGE GROVE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0323,003,6,69,18,1182777,1856985,2004,02/25/2006 12:14:30 AM,41.762772865,-87.605650573,"(41.762772865, -87.605650573)"
-3323518,HK361804,05/07/2004 02:30:00 PM,031XX W AUGUSTA BLVD,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,1311,012,26,23,06,1154936,1906441,2004,02/25/2006 12:14:30 AM,41.899087299,-87.706369948,"(41.899087299, -87.706369948)"
-3311978,HK347539,05/06/2004 10:50:00 PM,062XX W BELMONT AVE,1570,SEX OFFENSE,PUBLIC INDECENCY,STREET,true,false,1633,016,36,17,17,1134344,1920657,2004,02/25/2006 12:14:30 AM,41.938485747,-87.781668927,"(41.938485747, -87.781668927)"
-3311503,HK347450,05/06/2004 08:45:00 PM,051XX W 64TH PL,0460,BATTERY,SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,0813,008,13,64,08B,1142896,1861236,2004,02/25/2006 12:14:30 AM,41.775271184,-87.75171724,"(41.775271184, -87.75171724)"
-3311063,HK346750,05/06/2004 02:00:00 PM,011XX E 62ND ST,0560,ASSAULT,SIMPLE,STREET,false,true,0314,003,20,42,08A,1184840,1864167,2004,02/25/2006 12:14:30 AM,41.782432787,-87.597864324,"(41.782432787, -87.597864324)"
-3310334,HK344067,05/05/2004 09:55:00 AM,009XX W 88TH ST,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,2222,022,21,71,08A,1171640,1846443,2004,02/25/2006 12:14:30 AM,41.734095515,-87.64677763,"(41.734095515, -87.64677763)"
-3307617,HK342724,05/04/2004 05:18:00 PM,043XX N SHERIDAN RD,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,2322,019,46,3,06,1168852,1929250,2004,02/25/2006 12:14:30 AM,41.961385873,-87.654593862,"(41.961385873, -87.654593862)"
-3309634,HK343783,05/04/2004 02:00:00 PM,020XX S LUMBER ST,1310,CRIMINAL DAMAGE,TO PROPERTY,WAREHOUSE,false,false,1233,012,25,31,14,1173384,1890679,2004,02/25/2006 12:14:30 AM,41.855445402,-87.639079663,"(41.855445402, -87.639079663)"
-3339942,HK342065,05/04/2004 11:50:00 AM,068XX S MAY ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0724,007,17,68,08B,1169794,1859307,2004,02/25/2006 12:14:30 AM,41.769436261,-87.653167943,"(41.769436261, -87.653167943)"
-3333352,HK341892,05/04/2004 10:30:00 AM,059XX N GLENWOOD AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,"SCHOOL, PUBLIC, BUILDING",true,false,2013,020,48,77,18,1165872,1939292,2004,02/25/2006 12:14:30 AM,41.989005706,-87.665261991,"(41.989005706, -87.665261991)"
-3306194,HK341616,05/04/2004 12:00:00 AM,056XX S PRINCETON AVE,0920,MOTOR VEHICLE THEFT,ATT: AUTOMOBILE,STREET,false,false,0711,007,3,68,07,1175295,1867293,2004,02/25/2006 12:14:30 AM,41.791229563,-87.632765373,"(41.791229563, -87.632765373)"
-3303367,HK338067,05/02/2004 09:30:00 AM,107XX S AVENUE C,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0432,004,10,52,26,1204414,1834415,2004,02/25/2006 12:14:30 AM,41.700309681,-87.527124136,"(41.700309681, -87.527124136)"
-3306779,HK337966,05/02/2004 08:30:00 AM,035XX S WESTERN BLVD,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0913,009,11,59,14,1161131,1881384,2004,02/25/2006 12:14:30 AM,41.830201982,-87.684311652,"(41.830201982, -87.684311652)"
-3304854,HK334981,04/30/2004 05:10:00 PM,013XX N HUDSON AVE,0460,BATTERY,SIMPLE,STREET,false,false,1821,018,27,8,08B,1173105,1909581,2004,02/25/2006 12:14:30 AM,41.907319881,-87.639542844,"(41.907319881, -87.639542844)"
-3306419,HK339518,04/30/2004 07:30:00 AM,040XX W 59TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CONVENIENCE STORE,false,true,0813,008,13,65,08B,1150659,1865135,2004,02/25/2006 12:14:30 AM,41.785823058,-87.723157205,"(41.785823058, -87.723157205)"
-3308421,HK333667,04/30/2004 03:15:00 AM,012XX N BURLING ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA HALLWAY/STAIRWELL/ELEVATOR,false,true,1822,018,27,8,08B,1171043,1908538,2004,02/25/2006 12:14:30 AM,41.904503346,-87.647148068,"(41.904503346, -87.647148068)"
-3332789,HK332194,04/29/2004 01:20:00 PM,013XX E 76TH ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0411,004,5,43,18,1186628,1854835,2004,02/25/2006 12:14:30 AM,41.756782801,-87.591604155,"(41.756782801, -87.591604155)"
-3297594,HK331463,04/28/2004 09:30:00 PM,039XX W FLOURNOY ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1132,011,24,26,07,1150030,1896773,2004,02/25/2006 12:14:30 AM,41.872654235,-87.72464154,"(41.872654235, -87.72464154)"
-3300577,HK330622,04/28/2004 06:10:00 PM,011XX N LAKE SHORE DR,4651,OTHER OFFENSE,SEX OFFENDER: FAIL REG NEW ADD,RESIDENCE,true,false,1824,018,42,8,26,1177039,1908369,2004,02/25/2006 12:14:30 AM,41.903905877,-87.625128448,"(41.903905877, -87.625128448)"
-3298506,HK330928,04/28/2004 01:00:00 PM,047XX N SACRAMENTO AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1713,017,33,14,05,1155471,1931622,2004,02/25/2006 12:14:30 AM,41.968175089,-87.703725685,"(41.968175089, -87.703725685)"
-3300946,HK329258,04/28/2004 12:30:00 AM,025XX W JACKSON BLVD,0460,BATTERY,SIMPLE,CHA PARKING LOT/GROUNDS,false,false,1125,011,2,28,08B,1159587,1898573,2004,02/25/2006 12:14:30 AM,41.877402272,-87.689503763,"(41.877402272, -87.689503763)"
-3295206,HK328800,04/27/2004 03:00:00 AM,004XX W HARRISON ST,0890,THEFT,FROM BUILDING,GOVERNMENT BUILDING/PROPERTY,false,false,0131,001,2,28,06,1173318,1897549,2004,02/25/2006 12:14:30 AM,41.874298636,-87.639118002,"(41.874298636, -87.639118002)"
-3293681,HK327437,04/26/2004 06:00:00 PM,002XX S SANGAMON ST,0810,THEFT,OVER $500,STREET,false,false,1213,012,2,28,06,1170168,1899268,2004,12/04/2014 12:43:35 PM,41.879085026,-87.650633095,"(41.879085026, -87.650633095)"
-3294136,HK324801,04/25/2004 08:00:00 PM,031XX S ASHLAND AVE,0460,BATTERY,SIMPLE,GROCERY FOOD STORE,false,false,0922,009,11,59,08B,1166221,1883706,2004,02/25/2006 12:14:30 AM,41.836466744,-87.665570118,"(41.836466744, -87.665570118)"
-3335622,HK323914,04/25/2004 11:05:00 AM,041XX S INDIANA AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0214,002,3,38,18,1178327,1877541,2004,02/25/2006 12:14:30 AM,41.819282654,-87.621336465,"(41.819282654, -87.621336465)"
-3287454,HK317897,04/22/2004 01:30:00 PM,060XX N WESTERN AVE,0820,THEFT,$500 AND UNDER,GAS STATION,false,false,2413,024,40,2,06,1159191,1939791,2004,12/04/2014 12:43:35 PM,41.990515363,-87.689821806,"(41.990515363, -87.689821806)"
-3285750,HK316100,04/21/2004 04:47:00 PM,024XX E 78TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,0421,004,7,43,08B,1193627,1853758,2004,02/25/2006 12:14:30 AM,41.753658974,-87.565989726,"(41.753658974, -87.565989726)"
-3283734,HK314835,04/20/2004 03:30:00 PM,037XX W 61ST PL,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0823,008,13,65,05,1152757,1863615,2004,02/25/2006 12:14:30 AM,41.781610832,-87.715504888,"(41.781610832, -87.715504888)"
-3280222,HK310858,04/18/2004 07:00:00 PM,013XX N WOLCOTT AVE,0810,THEFT,OVER $500,SIDEWALK,false,false,1424,014,1,24,06,1163899,1909344,2004,12/04/2014 12:43:35 PM,41.906868882,-87.673367104,"(41.906868882, -87.673367104)"
-3309224,HK308977,04/18/2004 10:20:00 AM,019XX W WASHINGTON BLVD,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1333,012,27,28,16,1163721,1900751,2004,02/25/2006 12:14:30 AM,41.883292787,-87.674263446,"(41.883292787, -87.674263446)"
-3282517,HK309169,04/18/2004 03:30:00 AM,005XX S CANAL ST,0460,BATTERY,SIMPLE,GOVERNMENT BUILDING/PROPERTY,false,false,0131,001,2,28,08B,1173136,1897988,2004,02/25/2006 12:14:30 AM,41.875507319,-87.639773193,"(41.875507319, -87.639773193)"
-3278723,HK308888,04/17/2004 05:00:00 PM,064XX N RICHMOND ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENTIAL YARD (FRONT/BACK),false,false,2412,024,50,2,14,1155535,1942650,2004,02/25/2006 12:14:30 AM,41.998435183,-87.703192036,"(41.998435183, -87.703192036)"
-3316333,HK299216,04/17/2004 11:30:00 AM,047XX W VAN BUREN ST,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,STREET,true,false,1131,011,24,25,18,1144965,1897568,2004,02/25/2006 12:14:30 AM,41.874932869,-87.743217586,"(41.874932869, -87.743217586)"
-3277593,HK306399,04/16/2004 11:45:00 PM,045XX W WELLINGTON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,false,2521,025,31,20,08B,1145828,1919604,2004,02/25/2006 12:14:30 AM,41.93538567,-87.73948887,"(41.93538567, -87.73948887)"
-3276357,HK305103,04/16/2004 08:00:00 AM,038XX S WOLCOTT AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,OTHER,false,false,0922,009,11,59,14,1164273,1879494,2004,02/25/2006 12:14:30 AM,41.8249499,-87.672836895,"(41.8249499, -87.672836895)"
-3277486,HK304805,04/16/2004 02:40:00 AM,016XX W 63RD ST,0610,BURGLARY,FORCIBLE ENTRY,RESTAURANT,true,false,0714,007,16,67,05,1166414,1862964,2004,02/25/2006 12:14:30 AM,41.779544224,-87.665453392,"(41.779544224, -87.665453392)"
-3275409,HK302585,04/15/2004 03:21:12 AM,015XX W JUNEWAY TER,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,2422,024,49,1,26,1164943,1951438,2004,02/25/2006 12:14:30 AM,42.022354414,-87.66833196,"(42.022354414, -87.66833196)"
-3268605,HK296636,04/11/2004 02:00:00 PM,020XX W PIERCE AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,true,false,1424,014,1,24,05,1162579,1910201,2004,02/25/2006 12:14:30 AM,41.909248318,-87.678191961,"(41.909248318, -87.678191961)"
-3268827,HK295392,04/11/2004 03:00:00 AM,020XX N MILWAUKEE AVE,0460,BATTERY,SIMPLE,STREET,true,false,1431,014,1,22,08B,1159357,1913553,2004,02/25/2006 12:14:30 AM,41.918513382,-87.689935776,"(41.918513382, -87.689935776)"
-3265766,HK293644,04/10/2004 02:00:00 AM,073XX W BELMONT AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1631,016,36,17,14,1126309,1920463,2004,02/25/2006 12:14:30 AM,41.938091478,-87.811204368,"(41.938091478, -87.811204368)"
-3269688,HK292416,04/09/2004 03:00:00 PM,067XX S MICHIGAN AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0322,003,20,69,08B,1178289,1860160,2004,02/25/2006 12:14:30 AM,41.771588415,-87.622003457,"(41.771588415, -87.622003457)"
-3268806,HK294217,04/09/2004 06:00:00 AM,057XX S KIMBARK AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,2133,002,5,41,05,1185682,1867257,2004,02/25/2006 12:14:30 AM,41.790892196,-87.594680028,"(41.790892196, -87.594680028)"
-3268487,HK290010,04/08/2004 01:00:00 PM,064XX S MAPLEWOOD AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0825,008,15,66,08B,1160441,1861877,2004,02/25/2006 12:14:30 AM,41.776686561,-87.687381167,"(41.776686561, -87.687381167)"
-3266420,HK289560,04/08/2004 09:04:24 AM,002XX N LAVERGNE AVE,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",true,false,1532,015,28,25,08A,1142962,1901172,2004,02/25/2006 12:14:30 AM,41.884860245,-87.750481997,"(41.884860245, -87.750481997)"
-3261672,HK288828,04/07/2004 07:30:00 PM,025XX N NORMANDY AVE,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,2512,025,36,18,06,1131354,1916450,2004,12/04/2014 12:43:35 PM,41.926993516,-87.792755563,"(41.926993516, -87.792755563)"
-3268386,HK288271,04/07/2004 03:49:00 PM,081XX S ARTESIAN AVE,0460,BATTERY,SIMPLE,OTHER,false,false,0835,008,18,70,08B,1161407,1850403,2004,02/25/2006 12:14:30 AM,41.745180259,-87.684157267,"(41.745180259, -87.684157267)"
-3266607,HK284726,04/05/2004 10:15:00 PM,053XX S PULASKI RD,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,0815,008,23,62,06,1150567,1868924,2004,02/25/2006 12:14:30 AM,41.796222444,-87.723395965,"(41.796222444, -87.723395965)"
-3278222,HK305989,04/05/2004 09:00:00 AM,035XX N LAKE SHORE DR,0810,THEFT,OVER $500,OTHER,false,false,2331,019,46,6,06,1172082,1924158,2004,12/04/2014 12:43:35 PM,41.947342449,-87.642869545,"(41.947342449, -87.642869545)"
-3255295,HK280484,04/03/2004 05:15:00 PM,040XX W CULLERTON ST,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,1012,010,24,29,08A,1149499,1890150,2004,02/25/2006 12:14:30 AM,41.854490247,-87.726762902,"(41.854490247, -87.726762902)"
-3255117,HK280375,04/03/2004 04:45:00 PM,004XX S STATE ST,0870,THEFT,POCKET-PICKING,CTA BUS,false,false,0132,001,2,32,06,1176392,1898531,2004,02/25/2006 12:14:30 AM,41.876924523,-87.627802145,"(41.876924523, -87.627802145)"
-3253536,HK279201,04/02/2004 12:00:00 AM,101XX S AVENUE M,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,RESIDENCE-GARAGE,false,false,0432,004,10,52,07,1201448,1838538,2004,02/25/2006 12:14:30 AM,41.711699217,-87.537844734,"(41.711699217, -87.537844734)"
-3252581,HK276529,04/01/2004 06:55:00 PM,071XX S BLACKSTONE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0324,003,5,43,03,1187391,1858174,2004,02/25/2006 12:14:30 AM,41.765927234,-87.588702009,"(41.765927234, -87.588702009)"
-3294439,HK327717,04/01/2004 12:01:00 AM,120XX S WALLACE ST,1752,OFFENSE INVOLVING CHILDREN,AGG CRIM SEX ABUSE FAM MEMBER,OTHER,false,false,0523,005,34,53,20,1174524,1824881,2004,02/25/2006 12:14:30 AM,41.674862609,-87.636850897,"(41.674862609, -87.636850897)"
-3256130,HK274520,03/31/2004 06:30:00 PM,017XX W 64TH ST,0460,BATTERY,SIMPLE,STREET,false,false,0725,007,15,67,08B,1165664,1862289,2004,02/25/2006 12:14:30 AM,41.777707889,-87.668222128,"(41.777707889, -87.668222128)"
-3265164,HK273367,03/31/2004 08:59:53 AM,043XX W GLADYS AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1131,011,28,26,26,1147368,1898038,2004,02/25/2006 12:14:30 AM,41.87617693,-87.734382634,"(41.87617693, -87.734382634)"
-3256357,HK272892,03/30/2004 08:58:00 PM,057XX S MICHIGAN AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE PORCH/HALLWAY,true,false,0233,002,20,40,26,1178089,1867250,2004,02/25/2006 12:14:30 AM,41.791048632,-87.622521746,"(41.791048632, -87.622521746)"
-3247857,HK272764,03/29/2004 05:00:00 PM,007XX N CENTRAL PARK AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1112,011,27,23,26,1152215,1904506,2004,02/25/2006 12:14:30 AM,41.8938316,-87.716415259,"(41.8938316, -87.716415259)"
-3296497,HK267731,03/28/2004 01:00:00 PM,055XX W VAN BUREN ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,1522,015,29,25,18,1139134,1897414,2004,02/25/2006 12:14:30 AM,41.874618288,-87.764630649,"(41.874618288, -87.764630649)"
-3244833,HK267920,03/28/2004 12:01:00 AM,0000X E DIVISION ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1824,018,42,8,05,1176872,1908358,2004,02/25/2006 12:14:30 AM,41.903879475,-87.625742206,"(41.903879475, -87.625742206)"
-3309007,HK263023,03/26/2004 03:15:00 AM,055XX N WINTHROP AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,2023,020,48,77,26,1167826,1937035,2004,02/25/2006 12:14:30 AM,41.982770392,-87.658140398,"(41.982770392, -87.658140398)"
-3241314,HK262929,03/26/2004 01:17:07 AM,079XX S LAFAYETTE AVE,031A,ROBBERY,ARMED: HANDGUN,GAS STATION,false,false,0623,006,17,44,03,1177268,1852617,2004,02/25/2006 12:14:30 AM,41.750912681,-87.625973444,"(41.750912681, -87.625973444)"
-3239530,HK261275,03/25/2004 10:45:00 AM,012XX W NORTH AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,1433,014,32,24,06,1167978,1910835,2004,12/04/2014 12:43:35 PM,41.910873182,-87.658340208,"(41.910873182, -87.658340208)"
-3249331,HK260460,03/24/2004 10:20:00 PM,002XX N HOMAN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,1123,011,28,27,08B,1153630,1901547,2004,12/04/2014 12:43:35 PM,41.885683774,-87.711297186,"(41.885683774, -87.711297186)"
-3241466,HK259826,03/24/2004 04:00:00 PM,040XX W GLADYS AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,ALLEY,false,false,1132,011,28,26,04A,1149436,1898087,2004,02/25/2006 12:14:30 AM,41.87627154,-87.726788311,"(41.87627154, -87.726788311)"
-3240595,HK259514,03/24/2004 01:45:00 PM,078XX S HALSTED ST,0860,THEFT,RETAIL THEFT,DRUG STORE,false,false,0621,006,17,71,06,1172297,1852478,2004,02/25/2006 12:14:30 AM,41.750641951,-87.644193637,"(41.750641951, -87.644193637)"
-3241711,HK258770,03/24/2004 08:54:00 AM,011XX N SPRINGFIELD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1112,011,27,23,14,1150126,1907625,2004,02/25/2006 12:14:30 AM,41.902431414,-87.724006161,"(41.902431414, -87.724006161)"
-3239569,HK261158,03/24/2004 08:00:00 AM,018XX N HERMITAGE AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,1433,014,32,22,07,1164342,1912689,2004,02/25/2006 12:14:30 AM,41.916038423,-87.671644991,"(41.916038423, -87.671644991)"
-3276005,HK256379,03/22/2004 11:00:00 PM,044XX S FEDERAL ST,2024,NARCOTICS,POSS: HEROIN(WHITE),CHA PARKING LOT/GROUNDS,true,false,0221,002,3,38,18,1176474,1875514,2004,02/25/2006 12:14:30 AM,41.813762321,-87.628194958,"(41.813762321, -87.628194958)"
-3238638,HK259207,03/22/2004 02:00:00 PM,094XX S NORMAL AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,2223,022,21,73,14,1174580,1842487,2004,02/25/2006 12:14:30 AM,41.723174857,-87.63612427,"(41.723174857, -87.63612427)"
-3289226,HK254086,03/21/2004 09:00:00 PM,033XX W ADAMS ST,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1124,011,28,27,18,1153991,1898912,2004,02/25/2006 12:14:30 AM,41.878445877,-87.710041791,"(41.878445877, -87.710041791)"
-3232881,HK253730,03/21/2004 11:00:00 AM,042XX S ASHLAND AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0914,009,12,61,06,1166338,1876869,2004,12/04/2014 12:43:35 PM,41.817702806,-87.665335867,"(41.817702806, -87.665335867)"
-3238611,HK259585,03/21/2004 09:30:00 AM,001XX W NORTH AVE,0890,THEFT,FROM BUILDING,ATHLETIC CLUB,false,false,1821,018,43,8,06,1174800,1910955,2004,02/25/2006 12:14:30 AM,41.911052413,-87.63327523,"(41.911052413, -87.63327523)"
-3231452,HK250912,03/20/2004 03:55:00 AM,111XX S COTTAGE GROVE AVE,0312,ROBBERY,ARMED:KNIFE/CUTTING INSTRUMENT,STREET,false,false,0531,005,9,50,03,1181881,1831457,2004,02/25/2006 12:14:30 AM,41.69274174,-87.609721067,"(41.69274174, -87.609721067)"
-3228801,HK248015,03/18/2004 06:45:00 PM,056XX S FAIRFIELD AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0824,008,16,63,26,1159054,1866749,2004,02/25/2006 12:14:30 AM,41.790084488,-87.692332737,"(41.790084488, -87.692332737)"
-3227029,HK246069,03/17/2004 07:00:00 PM,056XX W MELROSE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1633,016,38,15,14,1138455,1921089,2004,02/25/2006 12:14:30 AM,41.939597611,-87.76654932,"(41.939597611, -87.76654932)"
-3228266,HK243028,03/16/2004 07:00:00 AM,042XX W ADAMS ST,0890,THEFT,FROM BUILDING,APARTMENT,false,false,1115,011,28,26,06,1148103,1898712,2004,02/25/2006 12:14:30 AM,41.878012357,-87.731666593,"(41.878012357, -87.731666593)"
-3223733,HK242440,03/15/2004 11:00:00 PM,083XX S BUFFALO AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,0424,004,10,46,26,1199632,1850240,2004,02/25/2006 12:14:30 AM,41.743856265,-87.544102301,"(41.743856265, -87.544102301)"
-3224061,HK242560,03/15/2004 09:45:00 PM,012XX W DICKENS AVE,0810,THEFT,OVER $500,STREET,false,false,1811,018,32,7,06,1167632,1914138,2004,12/04/2014 12:43:35 PM,41.919944273,-87.65951595,"(41.919944273, -87.65951595)"
-3223876,HK241685,03/15/2004 06:05:00 PM,012XX W WINNEMAC AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,false,2033,020,48,3,08B,1167328,1933663,2004,02/25/2006 12:14:30 AM,41.973528284,-87.660069393,"(41.973528284, -87.660069393)"
-3227201,HK246587,03/15/2004 03:00:00 PM,004XX N STATE ST,1310,CRIMINAL DAMAGE,TO PROPERTY,GROCERY FOOD STORE,false,false,1831,018,42,8,14,1176256,1903033,2004,02/25/2006 12:14:30 AM,41.889281331,-87.628165664,"(41.889281331, -87.628165664)"
-3222969,HK241089,03/15/2004 12:30:00 PM,030XX N MENARD AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,2514,025,30,19,06,1137219,1919748,2004,02/25/2006 12:14:30 AM,41.9359401,-87.771124377,"(41.9359401, -87.771124377)"
-3224519,HK240798,03/15/2004 11:00:00 AM,003XX E 115TH ST,0560,ASSAULT,SIMPLE,STREET,false,false,0531,005,9,49,08A,1180761,1828818,2004,02/25/2006 12:14:30 AM,41.685525699,-87.613902248,"(41.685525699, -87.613902248)"
-3220617,HK238614,03/14/2004 02:31:04 AM,003XX N LARAMIE AVE,041A,BATTERY,AGGRAVATED: HANDGUN,OTHER,false,false,1523,015,28,25,04B,1141598,1902099,2004,02/25/2006 12:14:30 AM,41.88742936,-87.755467958,"(41.88742936, -87.755467958)"
-3220411,HK238011,03/13/2004 06:25:00 PM,016XX N HARLEM AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),false,false,2513,025,36,25,07,1127899,1909823,2004,02/25/2006 12:14:30 AM,41.90886723,-87.805601319,"(41.90886723, -87.805601319)"
-3224334,HK240924,03/12/2004 02:30:00 PM,014XX E 70TH ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0332,003,5,43,08B,1186922,1858826,2004,02/25/2006 12:14:30 AM,41.767727508,-87.590400389,"(41.767727508, -87.590400389)"
-3232025,HK232791,03/11/2004 08:00:00 AM,002XX S WABASH AVE,0551,ASSAULT,AGGRAVATED PO: OTHER FIREARM,CTA TRAIN,true,false,0123,001,42,32,04A,1176810,1899198,2004,02/25/2006 12:14:30 AM,41.878745371,-87.626247216,"(41.878745371, -87.626247216)"
-3215317,HK231778,03/10/2004 11:00:00 AM,129XX S MARQUETTE AVE,1170,DECEPTIVE PRACTICE,IMPERSONATION,RESIDENCE,false,false,0433,004,10,55,11,1196467,1819891,2004,02/25/2006 12:14:30 AM,41.660654704,-87.556701668,"(41.660654704, -87.556701668)"
-3209891,HK223334,03/06/2004 12:01:00 AM,032XX N CICERO AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,false,false,1731,017,30,15,14,1143835,1921461,2004,02/25/2006 12:14:30 AM,41.940519101,-87.746766635,"(41.940519101, -87.746766635)"
-3213461,HK225856,03/05/2004 02:30:00 PM,106XX S EBERHART AVE,0820,THEFT,$500 AND UNDER,"SCHOOL, PUBLIC, BUILDING",false,false,0512,005,9,49,06,1181432,1834201,2004,12/04/2014 12:43:35 PM,41.700281987,-87.611280704,"(41.700281987, -87.611280704)"
-3210851,HK221745,03/05/2004 01:55:00 PM,065XX S YALE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0722,007,20,68,14,1175593,1861499,2004,02/25/2006 12:14:30 AM,41.775323543,-87.631846001,"(41.775323543, -87.631846001)"
-3249186,HK221540,03/05/2004 11:40:00 AM,027XX W OGDEN AVE,2094,NARCOTICS,ATTEMPT POSSESSION CANNABIS,CHA PARKING LOT/GROUNDS,true,false,1023,010,28,29,18,1158453,1892996,2004,02/25/2006 12:14:30 AM,41.862121697,-87.693820064,"(41.862121697, -87.693820064)"
-3216303,HK221307,03/05/2004 08:00:00 AM,068XX S NORMAL BLVD,0545,ASSAULT,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",true,false,0722,007,6,68,08A,1174172,1859434,2004,02/25/2006 12:14:30 AM,41.769688636,-87.637116474,"(41.769688636, -87.637116474)"
-3296812,HK218773,03/03/2004 11:20:00 PM,112XX S HALSTED ST,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,2233,022,34,75,16,1172943,1830574,2004,02/25/2006 12:14:30 AM,41.690520037,-87.64247052,"(41.690520037, -87.64247052)"
-3269042,HK218278,03/03/2004 07:13:16 PM,001XX W KINZIE ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1831,018,42,8,16,1175362,1902958,2004,02/25/2006 12:14:30 AM,41.88909564,-87.631451013,"(41.88909564, -87.631451013)"
-3204339,HK217490,03/03/2004 08:30:00 AM,046XX S MOZART ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0912,009,12,58,07,1158185,1873868,2004,02/25/2006 12:14:30 AM,41.809637712,-87.695325391,"(41.809637712, -87.695325391)"
-3231740,HK251850,02/29/2004 08:00:00 AM,069XX N KEDZIE AVE,1121,DECEPTIVE PRACTICE,COUNTERFEITING DOCUMENT,APARTMENT,false,false,2411,024,50,2,10,1153898,1945902,2004,02/25/2006 12:14:30 AM,42.007391713,-87.70912684,"(42.007391713, -87.70912684)"
-3239810,HK210310,02/28/2004 09:02:00 PM,047XX N RACINE AVE,1512,PROSTITUTION,SOLICIT FOR PROSTITUTE,STREET,true,false,2311,019,48,3,16,1167500,1931360,2004,02/25/2006 12:14:30 AM,41.967205067,-87.659503517,"(41.967205067, -87.659503517)"
-3201523,HK209622,02/28/2004 12:00:00 PM,020XX W GARFIELD BLVD,0810,THEFT,OVER $500,APARTMENT,false,false,0715,007,15,67,06,1163882,1868011,2004,12/04/2014 12:43:35 PM,41.793447433,-87.674594274,"(41.793447433, -87.674594274)"
-3198080,HK208741,02/27/2004 09:03:00 PM,042XX W NORTH AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,ALLEY,true,false,2534,025,30,23,24,1147757,1910334,2004,06/11/2007 03:52:33 PM,41.909911031,-87.732638274,"(41.909911031, -87.732638274)"
-3203164,HK216321,02/27/2004 05:00:00 PM,016XX N LECLAIRE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2533,025,37,25,06,1142346,1910322,2004,12/04/2014 12:43:35 PM,41.909980368,-87.752516602,"(41.909980368, -87.752516602)"
-3200964,HK207679,02/27/2004 12:01:00 PM,0000X W WALTON ST,1790,OFFENSE INVOLVING CHILDREN,CHILD ABDUCTION,"SCHOOL, PUBLIC, GROUNDS",true,false,1832,018,42,8,20,1175909,1906947,2004,06/02/2010 10:34:17 AM,41.900029366,-87.629322033,"(41.900029366, -87.629322033)"
-3197425,HK207217,02/27/2004 08:00:00 AM,088XX S PAXTON AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0412,004,8,48,14,1192408,1847009,2004,02/25/2006 12:14:30 AM,41.735168878,-87.570676047,"(41.735168878, -87.570676047)"
-3201660,HK207852,02/27/2004 07:30:00 AM,072XX S RHODES AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,0323,003,6,69,07,1181204,1856894,2004,02/25/2006 12:14:30 AM,41.762559522,-87.611418619,"(41.762559522, -87.611418619)"
-3197827,HK206903,02/27/2004 12:05:00 AM,003XX W 105TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0512,005,34,49,08B,1175666,1835252,2004,02/25/2006 12:14:30 AM,41.7032968,-87.632362114,"(41.7032968, -87.632362114)"
-3198741,HK209388,02/26/2004 04:00:00 PM,060XX S VERNON AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,true,false,0313,003,20,42,26,1180352,1864963,2004,02/25/2006 12:14:30 AM,41.784721247,-87.614294037,"(41.784721247, -87.614294037)"
-3194253,HK200736,02/23/2004 08:50:00 PM,018XX W 51ST ST,0810,THEFT,OVER $500,STREET,false,false,0931,009,16,61,06,1165198,1870913,2004,12/04/2014 12:43:35 PM,41.801383099,-87.669686449,"(41.801383099, -87.669686449)"
-3193509,HK200451,02/23/2004 06:45:00 PM,065XX N SHERIDAN RD,1330,CRIMINAL TRESPASS,TO LAND,COLLEGE/UNIVERSITY GROUNDS,true,false,2432,024,49,1,26,1167110,1943634,2004,02/25/2006 12:14:30 AM,42.000893656,-87.660583014,"(42.000893656, -87.660583014)"
-3192906,HK200608,02/23/2004 04:35:00 PM,012XX E 95TH ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0511,005,8,50,06,1186053,1842186,2004,12/04/2014 12:43:35 PM,41.722086211,-87.594109588,"(41.722086211, -87.594109588)"
-3200880,HK211318,02/23/2004 01:00:00 PM,049XX N WHIPPLE ST,1365,CRIMINAL TRESPASS,TO RESIDENCE,APARTMENT,true,false,1713,017,33,14,26,1155104,1932800,2004,02/25/2006 12:14:30 AM,41.971414982,-87.705043361,"(41.971414982, -87.705043361)"
-3193759,HK198855,02/22/2004 11:45:00 PM,092XX S PRINCETON AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,0634,006,21,49,08A,1176331,1843776,2004,02/25/2006 12:14:30 AM,41.72667295,-87.629671948,"(41.72667295, -87.629671948)"
-3195137,HK201207,02/22/2004 09:00:00 PM,070XX N WOLCOTT AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,2424,024,49,1,05,1162291,1946688,2004,02/25/2006 12:14:30 AM,42.009376443,-87.678225232,"(42.009376443, -87.678225232)"
-3216261,HK217632,02/21/2004 03:00:00 AM,012XX W 74TH PL,0820,THEFT,$500 AND UNDER,STREET,false,false,0734,007,17,67,06,1169304,1855419,2004,12/04/2014 12:43:35 PM,41.758777708,-87.655076435,"(41.758777708, -87.655076435)"
-3190327,HK195439,02/21/2004 02:05:00 AM,027XX W BELDEN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,false,1431,014,1,22,08B,1157569,1915199,2004,02/25/2006 12:14:30 AM,41.923066754,-87.696460137,"(41.923066754, -87.696460137)"
-3188580,HK191683,02/19/2004 08:57:00 AM,017XX E 73RD ST,2860,PUBLIC PEACE VIOLATION,FALSE POLICE REPORT,VEHICLE NON-COMMERCIAL,true,false,0324,003,8,43,24,1189407,1856967,2004,02/25/2006 12:14:30 AM,41.762566994,-87.581351469,"(41.762566994, -87.581351469)"
-3184616,HK190601,02/18/2004 06:00:00 PM,019XX N CICERO AVE,0890,THEFT,FROM BUILDING,DEPARTMENT STORE,true,false,2533,025,31,19,06,1144131,1912403,2004,02/25/2006 12:14:30 AM,41.915657496,-87.7459068,"(41.915657496, -87.7459068)"
-3184815,HK189632,02/17/2004 02:15:00 PM,053XX S JUSTINE ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,false,false,0932,009,16,61,04A,1166863,1869578,2004,02/25/2006 12:14:30 AM,41.797684265,-87.663618423,"(41.797684265, -87.663618423)"
-3207716,HK221658,02/17/2004 09:00:00 AM,031XX W 63RD ST,2830,OTHER OFFENSE,OBSCENE TELEPHONE CALLS,COMMERCIAL / BUSINESS OFFICE,false,false,0823,008,15,66,17,1156418,1862645,2004,02/25/2006 12:14:30 AM,41.778876043,-87.702108767,"(41.778876043, -87.702108767)"
-3181799,HK186257,02/16/2004 01:30:00 AM,051XX S HONORE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0932,009,16,61,14,1164933,1870318,2004,02/25/2006 12:14:30 AM,41.799755958,-87.670675126,"(41.799755958, -87.670675126)"
-3228395,HK185471,02/16/2004 12:12:59 AM,033XX N CICERO AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1634,016,38,15,16,1143753,1921514,2004,02/25/2006 12:14:30 AM,41.940666077,-87.747066685,"(41.940666077, -87.747066685)"
-3179843,HK183169,02/14/2004 03:30:00 PM,0000X N HALSTED ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,1212,012,27,28,06,1171091,1900261,2004,02/25/2006 12:14:30 AM,41.881789688,-87.647214877,"(41.881789688, -87.647214877)"
-3226794,HK181670,02/13/2004 06:30:28 PM,007XX S ST LOUIS AVE,2091,NARCOTICS,FORFEIT PROPERTY,STREET,true,false,1133,011,24,27,26,1153126,1896298,2004,02/25/2006 12:14:30 AM,41.871289975,-87.71328724,"(41.871289975, -87.71328724)"
-3176184,HK179735,02/12/2004 06:35:00 PM,015XX W CHICAGO AVE,031A,ROBBERY,ARMED: HANDGUN,BARBERSHOP,false,false,1323,012,1,24,03,1166057,1905476,2004,02/25/2006 12:14:30 AM,41.896208989,-87.665550503,"(41.896208989, -87.665550503)"
-3176060,HK179710,02/12/2004 06:20:00 PM,001XX W RANDOLPH ST,0890,THEFT,FROM BUILDING,HOTEL/MOTEL,false,false,0113,001,42,32,06,1174907,1901229,2004,02/25/2006 12:14:30 AM,41.884361366,-87.633173743,"(41.884361366, -87.633173743)"
-3176652,HK178660,02/11/2004 07:00:00 PM,014XX E 47TH DR,0610,BURGLARY,FORCIBLE ENTRY,OTHER,false,false,2123,002,4,39,05,1186238,1874150,2004,02/25/2006 12:14:30 AM,41.809793973,-87.592423474,"(41.809793973, -87.592423474)"
-3175596,HK178765,02/11/2004 02:00:00 PM,018XX S HOMAN AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1024,010,24,29,26,1153961,1890476,2004,02/25/2006 12:14:30 AM,41.855297166,-87.710376724,"(41.855297166, -87.710376724)"
-3183023,HK188267,02/10/2004 04:00:00 PM,047XX S DREXEL BLVD,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2124,002,4,39,05,1182953,1873507,2004,02/25/2006 12:14:30 AM,41.808106614,-87.604492198,"(41.808106614, -87.604492198)"
-3217938,HK172970,02/09/2004 08:27:48 AM,001XX W 109TH PL,2094,NARCOTICS,ATTEMPT POSSESSION CANNABIS,STREET,true,false,0513,005,34,49,18,1177126,1832298,2004,02/25/2006 12:14:30 AM,41.695157864,-87.627104636,"(41.695157864, -87.627104636)"
-3171404,HK173605,02/07/2004 07:00:00 PM,133XX S BURLEY AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,false,0433,004,10,55,06,1199803,1817281,2004,02/25/2006 12:14:30 AM,41.653409436,-87.544581706,"(41.653409436, -87.544581706)"
-3183652,HK168204,02/06/2004 11:45:00 AM,027XX W OGDEN AVE,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,CHA PARKING LOT/GROUNDS,true,false,1023,010,28,29,18,1158453,1892996,2004,02/25/2006 12:14:30 AM,41.862121697,-87.693820064,"(41.862121697, -87.693820064)"
-3164995,HK165541,02/04/2004 11:00:00 PM,090XX S UNION AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,2223,022,21,71,26,1173191,1844695,2004,02/25/2006 12:14:30 AM,41.729264673,-87.641147005,"(41.729264673, -87.641147005)"
-3165220,HK164289,02/04/2004 01:53:30 PM,081XX S HOUSTON AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,false,0422,004,7,46,08A,1197941,1851647,2004,02/25/2006 12:14:30 AM,41.747759569,-87.55025121,"(41.747759569, -87.55025121)"
-3164992,HK163825,02/04/2004 12:01:00 AM,052XX N KENMORE AVE,0610,BURGLARY,FORCIBLE ENTRY,CHURCH/SYNAGOGUE/PLACE OF WORSHIP,false,false,2023,020,48,77,05,1168337,1935302,2004,02/25/2006 12:14:30 AM,41.978003927,-87.656311442,"(41.978003927, -87.656311442)"
-3160217,HK159089,02/01/2004 05:57:00 PM,078XX S SAGINAW AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,true,0421,004,7,43,04B,1195332,1853111,2004,02/25/2006 12:14:30 AM,41.751841658,-87.559762954,"(41.751841658, -87.559762954)"
-3160031,HK158648,02/01/2004 01:10:00 PM,026XX W POTOMAC AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,STREET,true,false,1423,014,26,24,26,1158385,1908581,2004,06/11/2007 03:52:33 PM,41.904889775,-87.693643233,"(41.904889775, -87.693643233)"
-3163032,HK157927,02/01/2004 01:12:54 AM,012XX S CHRISTIANA AVE,0460,BATTERY,SIMPLE,STREET,false,false,1021,010,24,29,08B,1154197,1893866,2004,02/25/2006 12:14:30 AM,41.864595006,-87.709420069,"(41.864595006, -87.709420069)"
-3160137,HK158072,02/01/2004 12:15:00 AM,059XX S UNION AVE,0498,BATTERY,AGGRAVATED DOMESTIC BATTERY: HANDS/FIST/FEET SERIOUS INJURY,STREET,true,true,0711,007,20,68,04B,1172603,1865748,2004,02/25/2006 12:14:30 AM,41.787049686,-87.642681833,"(41.787049686, -87.642681833)"
-3167303,HK157770,01/31/2004 11:02:48 PM,011XX N LARRABEE ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,1823,018,27,8,26,1172160,1908064,2004,02/25/2006 12:14:30 AM,41.903178073,-87.643059058,"(41.903178073, -87.643059058)"
-3159489,HK157499,01/31/2004 08:17:07 PM,051XX S PULASKI RD,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,0822,008,23,62,06,1150609,1870257,2004,02/25/2006 12:14:30 AM,41.799879577,-87.72320725,"(41.799879577, -87.72320725)"
-3157915,HK154767,01/30/2004 03:08:43 PM,031XX N BROADWAY,1310,CRIMINAL DAMAGE,TO PROPERTY,GROCERY FOOD STORE,false,false,2332,019,44,6,14,1171755,1920630,2004,02/25/2006 12:14:30 AM,41.937668709,-87.64417576,"(41.937668709, -87.64417576)"
-3158414,HK154713,01/30/2004 02:30:00 PM,028XX W 45TH ST,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0912,009,14,58,03,1157915,1874722,2004,02/25/2006 12:14:30 AM,41.811986695,-87.696292495,"(41.811986695, -87.696292495)"
-3157233,HK149887,01/28/2004 09:05:00 PM,018XX S KOMENSKY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1012,010,24,29,08B,1149718,1890681,2004,02/25/2006 12:14:30 AM,41.855943129,-87.725945287,"(41.855943129, -87.725945287)"
-3153466,HK150018,01/27/2004 08:00:00 PM,090XX S BISHOP ST,0810,THEFT,OVER $500,STREET,false,false,2222,022,21,73,06,1168218,1844583,2004,12/04/2014 12:43:35 PM,41.729065625,-87.659367598,"(41.729065625, -87.659367598)"
-3161007,HK145958,01/25/2004 08:12:27 PM,007XX W DIVISION ST,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,STREET,true,false,1822,018,27,8,26,1171193,1908272,2004,02/25/2006 12:14:30 AM,41.903770133,-87.646604902,"(41.903770133, -87.646604902)"
-3189633,HK198176,01/25/2004 02:00:00 PM,012XX N CLARK ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,CONVENIENCE STORE,false,false,1821,018,42,8,11,1175279,1908359,2004,02/25/2006 12:14:30 AM,41.903918127,-87.631593588,"(41.903918127, -87.631593588)"
-3149083,HK144701,01/24/2004 11:45:00 PM,023XX N CLARK ST,0890,THEFT,FROM BUILDING,BAR OR TAVERN,false,false,1812,018,43,7,06,1172847,1916039,2004,02/25/2006 12:14:30 AM,41.925046662,-87.640298882,"(41.925046662, -87.640298882)"
-3196203,HK142844,01/24/2004 08:10:00 AM,044XX S FEDERAL ST,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,CHA PARKING LOT/GROUNDS,true,false,0221,002,3,38,18,1176474,1875514,2004,02/25/2006 12:14:30 AM,41.813762321,-87.628194958,"(41.813762321, -87.628194958)"
-3194573,HK142325,01/23/2004 09:15:00 PM,027XX W 24TH PL,2022,NARCOTICS,POSS: COCAINE,SIDEWALK,true,false,1034,010,12,30,18,1158475,1887662,2004,02/25/2006 12:14:30 AM,41.847484182,-87.693885122,"(41.847484182, -87.693885122)"
-3148540,HK141411,01/23/2004 01:19:14 PM,003XX N CENTRAL AVE,0560,ASSAULT,SIMPLE,HOTEL/MOTEL,true,false,1512,015,29,25,08A,1138946,1901599,2004,02/25/2006 12:14:30 AM,41.88610589,-87.765219199,"(41.88610589, -87.765219199)"
-3151752,HK141077,01/23/2004 11:04:20 AM,051XX W HARRISON ST,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,1533,015,24,25,08A,1142226,1896793,2004,02/25/2006 12:14:30 AM,41.872857405,-87.753293415,"(41.872857405, -87.753293415)"
-3146671,HK140331,01/22/2004 08:33:00 PM,060XX W IRVING PARK RD,0460,BATTERY,SIMPLE,STREET,true,false,1624,016,38,15,08B,1135310,1926004,2004,02/25/2006 12:14:30 AM,41.953141365,-87.77799127,"(41.953141365, -87.77799127)"
-3157654,HK139307,01/22/2004 11:30:00 AM,062XX S CALUMET AVE,1340,CRIMINAL DAMAGE,TO STATE SUP PROP,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,0311,003,20,40,14,1179606,1863772,2004,02/25/2006 12:14:30 AM,41.781470114,-87.617065534,"(41.781470114, -87.617065534)"
-3145208,HK139069,01/21/2004 10:00:00 PM,026XX N HAMPDEN CT,0810,THEFT,OVER $500,STREET,false,false,2333,019,43,7,06,1172703,1917559,2004,12/04/2014 12:43:35 PM,41.929220799,-87.640782888,"(41.929220799, -87.640782888)"
-3192322,HK138648,01/21/2004 09:46:27 PM,079XX S WESTERN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,0835,008,18,70,18,1161714,1852078,2004,02/25/2006 12:14:30 AM,41.749770353,-87.682985974,"(41.749770353, -87.682985974)"
-3143725,HK136387,01/20/2004 06:25:00 PM,048XX N ELSTON AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,1712,017,39,14,06,1145056,1931887,2004,12/04/2014 12:43:35 PM,41.969105903,-87.742014585,"(41.969105903, -87.742014585)"
-3142754,HK134781,01/19/2004 07:00:00 AM,037XX W GEORGE ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2523,025,30,21,05,1150640,1918976,2004,02/25/2006 12:14:30 AM,41.933569558,-87.721820842,"(41.933569558, -87.721820842)"
-3438281,HK505708,01/19/2004 12:01:00 AM,050XX N OTTAWA AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,1613,016,41,10,06,1124207,1932755,2004,02/25/2006 12:14:30 AM,41.97185706,-87.818658523,"(41.97185706, -87.818658523)"
-3140432,HK131856,01/18/2004 03:18:37 AM,134XX S INDIANA AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0533,005,9,54,08B,1179913,1816521,2004,02/25/2006 12:14:30 AM,41.651800249,-87.61738055,"(41.651800249, -87.61738055)"
-3139904,HK132996,01/17/2004 10:00:00 PM,031XX S LAWNDALE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1031,010,22,30,06,1152170,1883274,2004,12/04/2014 12:43:35 PM,41.835569458,-87.71714023,"(41.835569458, -87.71714023)"
-3142676,HK130199,01/17/2004 09:15:56 AM,023XX S DR MARTIN LUTHER KING JR DR,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,true,false,0133,001,2,33,26,1178918,1889132,2004,02/25/2006 12:14:30 AM,41.851075753,-87.618814775,"(41.851075753, -87.618814775)"
-3138665,HK127915,01/16/2004 12:42:58 AM,003XX W 116TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0522,005,34,53,08B,1175955,1828041,2004,02/25/2006 12:14:30 AM,41.683502281,-87.631519004,"(41.683502281, -87.631519004)"
-3131294,HK121598,01/12/2004 09:58:00 AM,026XX N WHIPPLE ST,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,1411,014,35,22,05,1155540,1917767,2004,02/25/2006 12:14:30 AM,41.930154654,-87.703846136,"(41.930154654, -87.703846136)"
-3129204,HK120151,01/12/2004 04:30:00 AM,059XX S EMERALD AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0711,007,20,68,26,1172358,1865275,2004,02/25/2006 12:14:30 AM,41.785757118,-87.643594047,"(41.785757118, -87.643594047)"
-3168491,HK120074,01/12/2004 01:20:00 AM,052XX S HONORE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0932,009,16,61,18,1164938,1870137,2004,02/25/2006 12:14:30 AM,41.799259167,-87.670661907,"(41.799259167, -87.670661907)"
-3149794,HK145096,01/10/2004 03:00:00 PM,031XX W MONTROSE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,OTHER,false,false,1724,017,33,14,26,1154711,1929112,2004,02/25/2006 12:14:30 AM,41.961302788,-87.706587676,"(41.961302788, -87.706587676)"
-3127683,HK116163,01/09/2004 08:45:00 PM,079XX S LANGLEY AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,true,0624,006,6,44,26,1182227,1852684,2004,02/25/2006 12:14:30 AM,41.750983222,-87.607799338,"(41.750983222, -87.607799338)"
-3126820,HK115829,01/09/2004 05:10:00 PM,073XX S SOUTH SHORE DR,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,0334,003,7,43,26,1195030,1857536,2004,02/25/2006 12:14:30 AM,41.763991633,-87.560723992,"(41.763991633, -87.560723992)"
-3126480,HK114352,01/08/2004 11:45:00 PM,130XX S DREXEL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA APARTMENT,false,true,0533,005,9,54,08B,1184514,1818772,2004,02/25/2006 12:14:30 AM,41.657871236,-87.600476266,"(41.657871236, -87.600476266)"
-3126210,HK114343,01/08/2004 11:15:00 PM,047XX W NORTH AVE,0820,THEFT,$500 AND UNDER,DEPARTMENT STORE,true,false,2533,025,37,25,06,1144212,1910247,2004,12/04/2014 12:43:35 PM,41.909739682,-87.745663482,"(41.909739682, -87.745663482)"
-3127474,HK113817,01/08/2004 08:33:00 AM,022XX E 69TH ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0331,003,5,43,05,1192189,1859608,2004,02/25/2006 12:14:30 AM,41.769746912,-87.571069417,"(41.769746912, -87.571069417)"
-3124277,HK112300,01/07/2004 09:16:46 PM,015XX E 62ND ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,true,0314,003,5,42,14,1187696,1864402,2004,02/25/2006 12:14:30 AM,41.783010134,-87.587386093,"(41.783010134, -87.587386093)"
-3123828,HK112200,01/07/2004 08:00:00 PM,062XX N WINTHROP AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,2433,024,48,77,14,1167762,1941706,2004,02/25/2006 12:14:30 AM,41.995589106,-87.658240357,"(41.995589106, -87.658240357)"
-3124173,HK112101,01/07/2004 06:30:00 PM,017XX N MOZART ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,1421,014,1,24,08B,1157192,1911471,2004,02/25/2006 12:14:30 AM,41.912844504,-87.697946855,"(41.912844504, -87.697946855)"
-3803237,HL111553,01/07/2004 02:10:00 PM,016XX E 69TH ST,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,STREET,true,false,0332,003,5,43,18,1188393,1859601,2004,02/25/2006 12:14:30 AM,41.769819181,-87.584983883,"(41.769819181, -87.584983883)"
-3122382,HK110888,01/06/2004 04:00:00 PM,044XX S FAIRFIELD AVE,0610,BURGLARY,FORCIBLE ENTRY,"SCHOOL, PUBLIC, BUILDING",false,false,0912,009,12,58,05,1158734,1875141,2004,02/25/2006 12:14:30 AM,41.813119784,-87.693276959,"(41.813119784, -87.693276959)"
-3121857,HK108998,01/05/2004 09:40:00 PM,100XX W OHARE ST,0460,BATTERY,SIMPLE,RESTAURANT,false,false,1651,016,41,76,08B,1100629,1934213,2004,02/25/2006 12:14:30 AM,41.976213976,-87.905334384,"(41.976213976, -87.905334384)"
-3117967,HK106689,01/04/2004 10:00:00 AM,018XX N PAULINA ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,1433,014,32,22,26,1164762,1912384,2004,02/25/2006 12:14:30 AM,41.915192581,-87.670110606,"(41.915192581, -87.670110606)"
-3117956,HK106632,01/04/2004 09:00:00 AM,054XX S MAY ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0934,009,16,61,26,1169617,1868715,2004,02/25/2006 12:14:30 AM,41.795256774,-87.65354411,"(41.795256774, -87.65354411)"
-3157710,HK154877,01/03/2004 12:00:00 PM,068XX W ARCHER AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,COMMERCIAL / BUSINESS OFFICE,false,false,0811,008,23,56,26,1131658,1867267,2004,02/25/2006 12:14:30 AM,41.792022571,-87.792776327,"(41.792022571, -87.792776327)"
-3117075,HK104427,01/03/2004 02:00:00 AM,013XX W OHIO ST,0880,THEFT,PURSE-SNATCHING,OTHER,false,false,1324,012,27,24,06,1167578,1904182,2004,02/25/2006 12:14:30 AM,41.892625542,-87.660001491,"(41.892625542, -87.660001491)"
-3121008,HK101714,01/01/2004 09:15:30 PM,0000X N HAMLIN BLVD,0340,ROBBERY,ATTEMPT: STRONGARM-NO WEAPON,SIDEWALK,false,false,1122,011,28,26,03,1151026,1899791,2004,02/25/2006 12:14:30 AM,41.88091652,-87.720905679,"(41.88091652, -87.720905679)"
-4210993,HL539831,01/01/2004 12:00:00 PM,007XX W BRIAR PL,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,2332,019,44,6,06,1170623,1921101,2004,02/25/2006 12:14:30 AM,41.938986048,-87.648322203,"(41.938986048, -87.648322203)"
-3114467,HK100746,01/01/2004 10:00:00 AM,012XX S LAFLIN ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,1231,012,2,28,14,1166525,1894570,2004,02/25/2006 12:14:30 AM,41.86627207,-87.66414397,"(41.86627207, -87.66414397)"
-6011605,HP115597,01/01/2004 12:00:00 AM,028XX S UNION AVE,0266,CRIM SEXUAL ASSAULT,PREDATORY,RESIDENCE,false,true,0914,,11,60,02,,,2004,08/26/2013 12:39:43 AM,,,
-7128645,HR475388,01/01/2004 12:00:00 AM,079XX S SANGAMON ST,1751,OFFENSE INVOLVING CHILDREN,CRIM SEX ABUSE BY FAM MEMBER,RESIDENCE,false,false,0621,,17,71,20,,,2004,12/05/2009 01:04:41 AM,,,
-3113221,HJ848031,12/31/2003 04:00:00 PM,068XX S CARPENTER ST,0560,ASSAULT,SIMPLE,STREET,false,false,0724,007,17,68,08A,1170449,1859609,2003,03/22/2006 09:58:07 PM,41.770250747,-87.650758229,"(41.770250747, -87.650758229)"
-3108632,HJ842520,12/28/2003 05:45:00 PM,019XX W MADISON ST,1330,CRIMINAL TRESPASS,TO LAND,SPORTS ARENA/STADIUM,true,false,1211,012,27,28,26,1163736,1900003,2003,03/22/2006 09:58:07 PM,41.881239897,-87.674229461,"(41.881239897, -87.674229461)"
-3122309,HJ841770,12/28/2003 09:15:00 AM,036XX S FEDERAL ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,true,false,0211,002,3,35,26,1176248,1880524,2003,03/22/2006 09:58:07 PM,41.827515281,-87.628873268,"(41.827515281, -87.628873268)"
-3144204,HJ839541,12/26/2003 10:25:00 PM,008XX N PINE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1524,015,37,25,18,1139398,1905308,2003,03/22/2006 09:58:07 PM,41.896275624,-87.76346887,"(41.896275624, -87.76346887)"
-3109598,HJ843146,12/26/2003 09:00:00 PM,053XX N WASHTENAW AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2011,020,40,4,26,1157338,1935664,2003,03/22/2006 09:58:07 PM,41.979228691,-87.696750388,"(41.979228691, -87.696750388)"
-3151329,HK147281,12/25/2003 12:00:00 AM,077XX S ABERDEEN ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0612,006,17,71,26,1170279,1853704,2003,03/22/2006 09:58:07 PM,41.754050374,-87.651552935,"(41.754050374, -87.651552935)"
-3104084,HJ834909,12/23/2003 05:30:00 PM,063XX S INGLESIDE AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,APARTMENT,true,false,0314,003,20,42,04B,1183779,1862977,2003,03/22/2006 09:58:07 PM,41.779192158,-87.601791321,"(41.779192158, -87.601791321)"
-3158399,HJ834406,12/23/2003 03:00:00 PM,013XX W 98TH PL,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,2213,022,21,73,18,1169137,1839496,2003,03/22/2006 09:58:07 PM,41.715086342,-87.656147659,"(41.715086342, -87.656147659)"
-3103377,HJ834306,12/23/2003 01:28:05 PM,091XX S COMMERCIAL AVE,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,SIDEWALK,false,false,0423,004,10,46,11,1197702,1844538,2003,03/22/2006 09:58:07 PM,41.728257885,-87.551363649,"(41.728257885, -87.551363649)"
-3104544,HJ834178,12/23/2003 11:20:00 AM,049XX W NORTH AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,2533,025,37,25,06,1143008,1910138,2003,03/22/2006 09:58:07 PM,41.909463125,-87.750089249,"(41.909463125, -87.750089249)"
-3102234,HJ832452,12/22/2003 01:48:00 PM,062XX S KILPATRICK AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,STREET,true,false,0813,008,13,64,04A,1146071,1862877,2003,03/22/2006 09:58:07 PM,41.779714849,-87.740036384,"(41.779714849, -87.740036384)"
-3100619,HJ829469,12/20/2003 05:30:00 PM,077XX S EMERALD AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0621,006,17,71,05,1172691,1853191,2003,03/22/2006 09:58:07 PM,41.752589845,-87.642728866,"(41.752589845, -87.642728866)"
-3099238,HJ829083,12/20/2003 03:48:00 PM,023XX W MADISON ST,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,1332,012,2,28,06,1160622,1899995,2003,12/04/2014 12:43:35 PM,41.881283001,-87.685664122,"(41.881283001, -87.685664122)"
-3099873,HJ830525,12/20/2003 02:00:00 PM,051XX S DORCHESTER AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2124,002,4,41,07,1186376,1871459,2003,03/22/2006 09:58:07 PM,41.802406412,-87.592002453,"(41.802406412, -87.592002453)"
-3099453,HJ828897,12/20/2003 01:13:00 PM,021XX W HOWARD ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,true,true,2424,024,49,1,08B,1160601,1950317,2003,03/22/2006 09:58:07 PM,42.019369828,-87.684342026,"(42.019369828, -87.684342026)"
-3099903,HJ829793,12/19/2003 07:00:00 PM,046XX W WRIGHTWOOD AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,2521,025,31,19,05,1144841,1917156,2003,03/22/2006 09:58:07 PM,41.928686828,-87.743178112,"(41.928686828, -87.743178112)"
-3100798,HJ827025,12/19/2003 11:14:59 AM,004XX S JEFFERSON ST,0261,CRIM SEXUAL ASSAULT,AGGRAVATED: HANDGUN,RESIDENCE,false,false,0131,001,2,28,02,1172435,1898247,2003,06/02/2010 10:34:17 AM,41.876233548,-87.64233932,"(41.876233548, -87.64233932)"
-3096748,HJ825628,12/18/2003 09:00:00 AM,018XX W CUYLER AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,1923,019,47,5,05,1163155,1926890,2003,03/22/2006 09:58:07 PM,41.95503184,-87.675605674,"(41.95503184, -87.675605674)"
-3095952,HJ824093,12/17/2003 11:30:00 PM,100XX S EMERALD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,2232,022,34,73,14,1173033,1838503,2003,03/22/2006 09:58:07 PM,41.712276432,-87.641907974,"(41.712276432, -87.641907974)"
-3110664,HJ823083,12/17/2003 02:30:00 PM,002XX N PINE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,"SCHOOL, PUBLIC, BUILDING",true,false,1523,015,28,25,14,1139494,1901281,2003,03/22/2006 09:58:07 PM,41.885223285,-87.763214548,"(41.885223285, -87.763214548)"
-3097328,HJ821897,12/16/2003 08:31:38 PM,073XX S KIMBARK AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0324,003,5,43,08B,1186191,1856473,2003,03/22/2006 09:58:07 PM,41.761287956,-87.59315401,"(41.761287956, -87.59315401)"
-3094135,HJ821312,12/16/2003 03:12:00 PM,083XX S COMMERCIAL AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, GROUNDS",false,false,0423,004,10,46,08B,1197504,1850185,2003,03/22/2006 09:58:07 PM,41.743758634,-87.551901126,"(41.743758634, -87.551901126)"
-3113019,HJ816289,12/13/2003 07:30:00 PM,121XX S WALLACE ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,true,0523,005,34,53,06,1174537,1824458,2003,03/22/2006 09:58:07 PM,41.67370154,-87.63681583,"(41.67370154, -87.63681583)"
-3089756,HJ815796,12/12/2003 11:00:00 PM,009XX W FULTON MARKET,5001,OTHER OFFENSE,OTHER CRIME INVOLVING PROPERTY,BAR OR TAVERN,false,false,1212,012,27,28,26,1169755,1902014,2003,03/22/2006 09:58:07 PM,41.886629247,-87.652069506,"(41.886629247, -87.652069506)"
-3089126,HJ814458,12/12/2003 09:29:00 PM,046XX S DREXEL BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,2123,002,4,39,08B,1183141,1874431,2003,03/22/2006 09:58:07 PM,41.810637764,-87.6037739,"(41.810637764, -87.6037739)"
-3089439,HJ816412,12/12/2003 04:00:00 PM,054XX S INGLESIDE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,false,false,2131,002,4,41,14,1183491,1869617,2003,03/22/2006 09:58:07 PM,41.797419609,-87.602640262,"(41.797419609, -87.602640262)"
-3098643,HJ826089,12/11/2003 11:00:00 PM,049XX N WHIPPLE ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1713,017,33,14,05,1155099,1932943,2003,03/22/2006 09:58:07 PM,41.971807482,-87.70505789,"(41.971807482, -87.70505789)"
-3090487,HJ812565,12/11/2003 09:00:00 PM,061XX S WESTERN AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,false,false,0825,008,15,66,06,1161473,1863548,2003,03/22/2006 09:58:07 PM,41.78125068,-87.68355159,"(41.78125068, -87.68355159)"
-3099614,HJ830227,12/11/2003 04:40:00 PM,010XX N LAKE SHORE DR,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,1824,018,42,8,06,1177294,1907219,2003,03/22/2006 09:58:07 PM,41.900744446,-87.624226699,"(41.900744446, -87.624226699)"
-3085851,HJ810579,12/10/2003 07:30:00 PM,024XX W BALMORAL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2011,020,40,4,08B,1159316,1935745,2003,03/22/2006 09:58:07 PM,41.979410391,-87.689473903,"(41.979410391, -87.689473903)"
-3086439,HJ811429,12/10/2003 06:20:00 PM,023XX N WESTERN AVE,0560,ASSAULT,SIMPLE,GAS STATION,false,false,1432,014,32,22,08A,1160008,1915715,2003,03/22/2006 09:58:07 PM,41.924432637,-87.687484121,"(41.924432637, -87.687484121)"
-3086749,HJ808614,12/09/2003 09:15:00 PM,055XX W GRAND AVE,1345,CRIMINAL DAMAGE,TO CITY OF CHICAGO PROPERTY,POLICE FACILITY/VEH PARKING LOT,false,false,2515,025,29,19,14,1138772,1913442,2003,03/22/2006 09:58:07 PM,41.918607665,-87.765570341,"(41.918607665, -87.765570341)"
-3084888,HJ807700,12/09/2003 01:20:00 PM,132XX S LANGLEY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA APARTMENT,false,true,0533,005,9,54,08B,1183295,1817732,2003,03/22/2006 09:58:07 PM,41.655045673,-87.60496895,"(41.655045673, -87.60496895)"
-3079620,HJ804904,12/07/2003 07:30:00 PM,054XX S LAFLIN ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0932,009,16,61,14,1167216,1868831,2003,03/22/2006 09:58:07 PM,41.795626855,-87.662345298,"(41.795626855, -87.662345298)"
-3089015,HJ801155,12/06/2003 12:36:00 AM,064XX S MARYLAND AVE,033A,ROBBERY,ATTEMPT: ARMED-HANDGUN,RESIDENCE PORCH/HALLWAY,false,false,0312,003,20,42,03,1183046,1862401,2003,03/22/2006 09:58:07 PM,41.777628641,-87.604496439,"(41.777628641, -87.604496439)"
-3130346,HK114596,12/05/2003 10:00:00 AM,046XX W JACKSON BLVD,0460,BATTERY,SIMPLE,NURSING HOME/RETIREMENT HOME,false,false,1113,011,28,25,08B,1145163,1898318,2003,03/22/2006 09:58:07 PM,41.876987223,-87.742471647,"(41.876987223, -87.742471647)"
-3077774,HJ799187,12/05/2003 06:30:00 AM,002XX E 93RD ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0633,006,6,49,04B,1179231,1843400,2003,03/22/2006 09:58:07 PM,41.725575615,-87.619060429,"(41.725575615, -87.619060429)"
-3126666,HJ797513,12/04/2003 11:09:00 AM,054XX S HALSTED ST,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,0934,009,3,61,16,1171937,1868690,2003,03/22/2006 09:58:07 PM,41.795137512,-87.645037386,"(41.795137512, -87.645037386)"
-3072621,HJ794459,12/02/2003 06:54:00 PM,031XX S ASHLAND AVE,0460,BATTERY,SIMPLE,POLICE FACILITY/VEH PARKING LOT,false,false,0922,009,11,59,08B,1166149,1883443,2003,03/22/2006 09:58:07 PM,41.835746581,-87.665841812,"(41.835746581, -87.665841812)"
-3073273,HJ794056,12/02/2003 03:05:00 PM,065XX S ASHLAND AVE,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0725,007,15,67,03,1166766,1860952,2003,03/22/2006 09:58:07 PM,41.774015529,-87.664220308,"(41.774015529, -87.664220308)"
-3071356,HJ792343,12/01/2003 05:30:00 PM,049XX S COTTAGE GROVE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,2124,002,4,39,08B,1182469,1872152,2003,03/22/2006 09:58:07 PM,41.804399635,-87.606309408,"(41.804399635, -87.606309408)"
-3088371,HJ813973,12/01/2003 12:00:00 PM,042XX S FAIRFIELD AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,true,0912,009,12,58,26,1158695,1876420,2003,03/22/2006 09:58:07 PM,41.816630318,-87.693385076,"(41.816630318, -87.693385076)"
-3073316,HJ790496,11/30/2003 05:00:00 PM,038XX W CHICAGO AVE,0460,BATTERY,SIMPLE,STREET,false,false,1112,011,27,23,08B,1150906,1905112,2003,03/22/2006 09:58:07 PM,41.895520252,-87.721206936,"(41.895520252, -87.721206936)"
-3069547,HJ789940,11/27/2003 10:45:00 AM,013XX N HUDSON AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,false,1821,018,27,8,06,1173105,1909581,2003,03/22/2006 09:58:07 PM,41.907319881,-87.639542844,"(41.907319881, -87.639542844)"
-3064361,HJ784066,11/26/2003 09:54:41 PM,021XX E 75TH ST,1330,CRIMINAL TRESPASS,TO LAND,GROCERY FOOD STORE,true,false,0414,004,8,43,26,1191894,1855620,2003,03/22/2006 09:58:07 PM,41.758810695,-87.572280075,"(41.758810695, -87.572280075)"
-3059529,HJ778359,11/24/2003 05:26:00 AM,010XX W 35TH ST,0610,BURGLARY,FORCIBLE ENTRY,RESTAURANT,false,false,0924,009,11,60,05,1170212,1881670,2003,03/22/2006 09:58:07 PM,41.830793697,-87.650985027,"(41.830793697, -87.650985027)"
-3059470,HJ778304,11/24/2003 03:15:10 AM,012XX W HASTINGS ST,0460,BATTERY,SIMPLE,CHA APARTMENT,true,false,1231,012,2,28,08B,1168170,1893828,2003,03/22/2006 09:58:07 PM,41.864200615,-87.65812648,"(41.864200615, -87.65812648)"
-3066213,HJ778021,11/23/2003 07:30:00 PM,021XX W 52ND PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,0915,009,16,61,08B,1162888,1869697,2003,03/22/2006 09:58:07 PM,41.798094873,-87.678192083,"(41.798094873, -87.678192083)"
-3058924,HJ777029,11/23/2003 03:30:00 AM,059XX S SACRAMENTO AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,0824,008,16,66,07,1157433,1865273,2003,03/22/2006 09:58:07 PM,41.786067147,-87.698316511,"(41.786067147, -87.698316511)"
-3058947,HJ777147,11/22/2003 07:00:00 PM,045XX N KILDARE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1722,017,45,16,14,1146744,1929673,2003,03/22/2006 09:58:07 PM,41.962998383,-87.735864534,"(41.962998383, -87.735864534)"
-3058879,HJ777022,11/22/2003 03:00:00 PM,020XX W FLETCHER ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,1913,019,32,5,05,1162258,1920873,2003,03/22/2006 09:58:07 PM,41.938539708,-87.679072047,"(41.938539708, -87.679072047)"
-3057068,HJ774115,11/21/2003 09:55:00 PM,048XX W NORTH AVE,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,2533,025,37,25,06,1143880,1910159,2003,03/22/2006 09:58:07 PM,41.909504436,-87.746885335,"(41.909504436, -87.746885335)"
-3056851,HJ774667,11/21/2003 09:00:00 PM,108XX S VERNON AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0513,005,9,49,06,1181217,1832846,2003,12/04/2014 12:43:35 PM,41.696568627,-87.61210949,"(41.696568627, -87.61210949)"
-3056778,HJ774015,11/21/2003 08:33:00 PM,008XX W HUTCHINSON ST,0330,ROBBERY,AGGRAVATED,SIDEWALK,false,false,2322,019,46,3,03,1169857,1928451,2003,03/22/2006 09:58:07 PM,41.959171487,-87.650922352,"(41.959171487, -87.650922352)"
-3057084,HJ773746,11/21/2003 05:00:00 PM,027XX N ST LOUIS AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,1412,014,35,22,03,1152546,1917734,2003,03/22/2006 09:58:07 PM,41.930123891,-87.714849274,"(41.930123891, -87.714849274)"
-3055168,HJ772307,11/20/2003 12:00:00 PM,068XX S DR MARTIN LUTHER KING JR DR,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0322,003,20,69,26,1180144,1859530,2003,03/22/2006 09:58:07 PM,41.769817328,-87.615222985,"(41.769817328, -87.615222985)"
-3056151,HJ770510,11/20/2003 09:35:41 AM,081XX S MARYLAND AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,0631,006,8,44,08B,1183290,1850809,2003,03/22/2006 09:58:07 PM,41.745813359,-87.603962267,"(41.745813359, -87.603962267)"
-3056154,HJ770174,11/20/2003 02:00:00 AM,006XX W DIVISION ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA HALLWAY/STAIRWELL/ELEVATOR,false,false,1822,018,27,8,08B,1171458,1908280,2003,03/22/2006 09:58:07 PM,41.90378626,-87.645631266,"(41.90378626, -87.645631266)"
-3054179,HJ770558,11/19/2003 11:25:00 PM,065XX S DREXEL AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0321,003,20,42,14,1183317,1861534,2003,03/22/2006 09:58:07 PM,41.775243205,-87.603529935,"(41.775243205, -87.603529935)"
-3114765,HJ766109,11/18/2003 11:50:00 PM,071XX S YATES BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0334,003,7,43,18,1193492,1857923,2003,03/22/2006 09:58:07 PM,41.765091359,-87.56634836,"(41.765091359, -87.56634836)"
-3053664,HJ766869,11/18/2003 02:00:00 PM,046XX N CLARK ST,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,true,false,1922,019,47,3,26,1165499,1930635,2003,06/11/2007 03:52:33 PM,41.965258587,-87.66688166,"(41.965258587, -87.66688166)"
-3051021,HJ766771,11/18/2003 07:30:00 AM,054XX N EAST RIVER RD,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1614,016,41,76,26,1116672,1934794,2003,03/22/2006 09:58:07 PM,41.977573197,-87.846323947,"(41.977573197, -87.846323947)"
-3069375,HJ791370,11/17/2003 08:00:00 PM,004XX E 42ND ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0213,002,3,38,26,1179688,1877285,2003,03/22/2006 09:58:07 PM,41.818549115,-87.616351671,"(41.818549115, -87.616351671)"
-3057049,HJ773062,11/16/2003 09:00:00 PM,014XX W ARTHUR AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,2432,024,40,1,07,1165244,1943313,2003,03/22/2006 09:58:07 PM,42.000052851,-87.667456846,"(42.000052851, -87.667456846)"
-3048817,HJ763326,11/16/2003 03:38:39 PM,077XX S MARSHFIELD AVE,2900,WEAPONS VIOLATION,UNLAWFUL USE/SALE AIR RIFLE,SIDEWALK,true,false,0611,006,17,71,15,1166724,1853493,2003,06/11/2007 03:52:33 PM,41.753547929,-87.664586853,"(41.753547929, -87.664586853)"
-3047258,HJ761947,11/15/2003 10:15:00 PM,062XX N WINTHROP AVE,0454,BATTERY,AGG PO HANDS NO/MIN INJURY,SIDEWALK,true,false,2433,024,48,77,08B,1167691,1941391,2003,03/22/2006 09:58:07 PM,41.994726276,-87.658510661,"(41.994726276, -87.658510661)"
-3045959,HJ760282,11/15/2003 04:30:00 AM,060XX N WINTHROP AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,true,false,2433,024,48,77,14,1167810,1940135,2003,03/22/2006 09:58:07 PM,41.991277212,-87.658109356,"(41.991277212, -87.658109356)"
-3112358,HJ759713,11/14/2003 08:30:00 PM,109XX S AVENUE H,1822,NARCOTICS,MANU/DEL:CANNABIS OVER 10 GMS,STREET,true,false,0432,004,10,52,18,1202820,1832751,2003,03/22/2006 09:58:07 PM,41.695784322,-87.533017202,"(41.695784322, -87.533017202)"
-3044910,HJ757320,11/13/2003 06:10:00 PM,036XX W DOUGLAS BLVD,1320,CRIMINAL DAMAGE,TO VEHICLE,ALLEY,false,true,1011,010,24,29,14,1152195,1893249,2003,03/22/2006 09:58:07 PM,41.862941578,-87.71678571,"(41.862941578, -87.71678571)"
-3042492,HJ756266,11/12/2003 06:00:00 PM,042XX N SAWYER AVE,0810,THEFT,OVER $500,STREET,false,false,1724,017,33,16,06,1154001,1927960,2003,12/04/2014 12:43:35 PM,41.958155847,-87.709228879,"(41.958155847, -87.709228879)"
-3056172,HJ752437,11/11/2003 11:45:00 AM,034XX W CONGRESS PKWY,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,STREET,true,false,1133,011,28,27,26,1153235,1897513,2003,03/22/2006 09:58:07 PM,41.874621905,-87.712854812,"(41.874621905, -87.712854812)"
-3040996,HJ751828,11/10/2003 08:45:00 PM,015XX W 79TH ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0612,006,17,71,06,1167703,1852390,2003,12/04/2014 12:43:35 PM,41.750500206,-87.661030752,"(41.750500206, -87.661030752)"
-3045446,HJ749375,11/09/2003 10:00:00 PM,028XX W WILCOX ST,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1124,011,2,27,18,1157192,1899262,2003,03/22/2006 09:58:07 PM,41.879341919,-87.698278851,"(41.879341919, -87.698278851)"
diff --git a/work-with-data/dataprep/data/crime_partfiles/part-00006-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv b/work-with-data/dataprep/data/crime_partfiles/part-00006-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv
deleted file mode 100644
index b9b3b6c8..00000000
--- a/work-with-data/dataprep/data/crime_partfiles/part-00006-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv
+++ /dev/null
@@ -1,971 +0,0 @@
-3036924,HJ743735,11/06/2003 09:00:00 PM,021XX N MENARD AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,DRIVEWAY - RESIDENTIAL,false,false,2515,025,29,19,14,1137341,1913578,2003,03/22/2006 09:58:07 PM,41.919006741,-87.770824767,"(41.919006741, -87.770824767)"
-3033313,HJ743098,11/06/2003 04:16:00 PM,072XX S ASHLAND AVE,0820,THEFT,$500 AND UNDER,CTA BUS,false,false,0735,007,17,67,06,1166889,1856966,2003,12/04/2014 12:43:35 PM,41.763074794,-87.663883144,"(41.763074794, -87.663883144)"
-3038481,HJ751044,11/06/2003 12:00:00 PM,064XX S CICERO AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0813,008,13,64,06,1145536,1861346,2003,12/04/2014 12:43:35 PM,41.77552364,-87.742036389,"(41.77552364, -87.742036389)"
-3031339,HJ741747,11/05/2003 10:20:00 PM,052XX W ALTGELD ST,1020,ARSON,BY FIRE,VEHICLE NON-COMMERCIAL,false,false,2515,025,31,19,09,1140658,1916082,2003,03/22/2006 09:58:07 PM,41.925817636,-87.7585759,"(41.925817636, -87.7585759)"
-3058833,HJ741638,11/05/2003 10:00:16 PM,053XX W CHICAGO AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,APARTMENT,false,false,1524,015,37,25,15,1140875,1904780,2003,06/11/2007 03:52:33 PM,41.894799681,-87.758057059,"(41.894799681, -87.758057059)"
-3031396,HJ741466,11/05/2003 07:20:00 PM,027XX W FRANCIS PL,051A,ASSAULT,AGGRAVATED: HANDGUN,APARTMENT,true,true,1431,014,1,22,04A,1157995,1913691,2003,03/22/2006 09:58:07 PM,41.918919995,-87.694936112,"(41.918919995, -87.694936112)"
-3121280,HJ740219,11/05/2003 12:38:00 PM,001XX N LARAMIE AVE,2094,NARCOTICS,ATTEMPT POSSESSION CANNABIS,STREET,true,false,1532,015,28,25,18,1141716,1900943,2003,03/22/2006 09:58:07 PM,41.884254972,-87.755063227,"(41.884254972, -87.755063227)"
-3029145,HJ737645,11/04/2003 05:30:00 AM,061XX S RACINE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,POLICE FACILITY/VEH PARKING LOT,false,false,0713,007,16,67,07,1169331,1864110,2003,03/22/2006 09:58:07 PM,41.782626318,-87.654726162,"(41.782626318, -87.654726162)"
-3025222,HJ734928,11/02/2003 09:00:00 PM,059XX W FULTON ST,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,1512,015,29,25,26,1136719,1901439,2003,03/22/2006 09:58:07 PM,41.885706996,-87.773401211,"(41.885706996, -87.773401211)"
-3028674,HJ734567,11/02/2003 05:00:00 PM,065XX S LOWE AVE,0460,BATTERY,SIMPLE,CHA APARTMENT,true,true,0723,007,20,68,08B,1173195,1861783,2003,03/22/2006 09:58:07 PM,41.776156212,-87.640628359,"(41.776156212, -87.640628359)"
-3024325,HJ733593,11/01/2003 09:00:00 PM,051XX N OAKLEY AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2031,020,47,4,07,1160117,1934304,2003,03/22/2006 09:58:07 PM,41.975439664,-87.68656817,"(41.975439664, -87.68656817)"
-3034175,HJ732932,11/01/2003 08:25:00 PM,011XX W LAWRENCE AVE,2025,NARCOTICS,POSS: HALLUCINOGENS,OTHER,true,false,2033,020,46,3,18,1167933,1932075,2003,03/22/2006 09:58:07 PM,41.969157694,-87.657890711,"(41.969157694, -87.657890711)"
-3143445,HK137177,11/01/2003 09:00:00 AM,011XX S FRANCISCO AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,1135,011,28,29,06,1157154,1895097,2003,03/22/2006 09:58:07 PM,41.867913513,-87.698531485,"(41.867913513, -87.698531485)"
-3023609,HJ730948,10/31/2003 09:00:00 PM,119XX S PRINCETON AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0522,005,34,53,04B,1176398,1825491,2003,03/22/2006 09:58:07 PM,41.676494773,-87.629973543,"(41.676494773, -87.629973543)"
-3028145,HJ730793,10/31/2003 07:15:00 PM,063XX S ALBANY AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0823,008,15,66,08B,1156841,1862694,2003,03/22/2006 09:58:07 PM,41.779001975,-87.700556679,"(41.779001975, -87.700556679)"
-3074956,HJ729537,10/31/2003 11:00:00 AM,023XX S STATE ST,2024,NARCOTICS,POSS: HEROIN(WHITE),CHA APARTMENT,true,false,0134,001,3,33,18,1176650,1888540,2003,03/22/2006 09:58:07 PM,41.849502747,-87.627156603,"(41.849502747, -87.627156603)"
-3024003,HJ732588,10/30/2003 11:00:00 PM,010XX N RUSH ST,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,1824,018,42,8,06,1176261,1907708,2003,03/22/2006 09:58:07 PM,41.902109652,-87.628006162,"(41.902109652, -87.628006162)"
-3019778,HJ727115,10/30/2003 08:25:00 AM,026XX W 63RD ST,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,GROCERY FOOD STORE,false,false,0825,008,15,66,04A,1159823,1862738,2003,03/22/2006 09:58:07 PM,41.779061982,-87.689623116,"(41.779061982, -87.689623116)"
-3019820,HJ725960,10/29/2003 02:00:00 PM,009XX E 104TH ST,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0512,005,9,50,08B,1184181,1836149,2003,03/22/2006 09:58:07 PM,41.705563859,-87.601154398,"(41.705563859, -87.601154398)"
-3019339,HJ725462,10/28/2003 03:15:00 PM,040XX N GREENVIEW AVE,0460,BATTERY,SIMPLE,STREET,false,false,1923,019,47,6,08B,1165314,1926668,2003,03/22/2006 09:58:07 PM,41.954376919,-87.667675164,"(41.954376919, -87.667675164)"
-3017115,HJ723892,10/28/2003 07:30:00 AM,074XX N ROGERS AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2422,024,49,1,05,1163370,1949260,2003,03/22/2006 09:58:07 PM,42.016411337,-87.674182398,"(42.016411337, -87.674182398)"
-3015098,HJ722453,10/28/2003 12:51:29 AM,096XX S MERRION AVE,0271,CRIM SEXUAL ASSAULT,ATTEMPT AGG: HANDGUN,RESIDENCE,true,false,0431,004,7,51,02,1192729,1841233,2003,03/22/2006 09:58:07 PM,41.719311174,-87.569687782,"(41.719311174, -87.569687782)"
-3015360,HJ722677,10/27/2003 10:00:00 PM,003XX W 24TH ST,0810,THEFT,OVER $500,STREET,false,false,2111,009,25,34,06,1174090,1888411,2003,12/04/2014 12:43:35 PM,41.849206145,-87.636555884,"(41.849206145, -87.636555884)"
-3022322,HJ725031,10/27/2003 03:15:00 PM,068XX S CREGIER AVE,1792,KIDNAPPING,CHILD ABDUCTION/STRANGER,STREET,false,false,0332,003,5,43,20,1189333,1860240,2003,03/22/2006 09:58:07 PM,41.771550153,-87.581517832,"(41.771550153, -87.581517832)"
-3013422,HJ720554,10/26/2003 02:00:00 PM,017XX W BELMONT AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,COMMERCIAL / BUSINESS OFFICE,false,false,1924,019,32,6,14,1164309,1921336,2003,03/22/2006 09:58:07 PM,41.939767004,-87.671521025,"(41.939767004, -87.671521025)"
-3013082,HJ718791,10/26/2003 05:40:48 AM,028XX S SPAULDING AVE,0820,THEFT,$500 AND UNDER,APARTMENT,false,true,1032,010,22,30,06,1154877,1884553,2003,12/04/2014 12:43:35 PM,41.839025473,-87.707173078,"(41.839025473, -87.707173078)"
-3011269,HJ717524,10/25/2003 02:30:00 PM,031XX W 26TH ST,0810,THEFT,OVER $500,STREET,false,false,1033,010,12,30,06,1156116,1886536,2003,12/04/2014 12:43:35 PM,41.844442183,-87.702573069,"(41.844442183, -87.702573069)"
-3009951,HJ716645,10/25/2003 06:05:00 AM,107XX S LANGLEY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0513,005,9,50,08B,1182769,1834147,2003,03/22/2006 09:58:07 PM,41.70010294,-87.606386846,"(41.70010294, -87.606386846)"
-3013019,HJ720032,10/24/2003 08:00:00 AM,018XX W OHIO ST,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, GROUNDS",false,false,1324,012,1,24,08A,1163830,1904082,2003,03/22/2006 09:58:07 PM,41.89243102,-87.673769176,"(41.89243102, -87.673769176)"
-3040299,HJ712766,10/23/2003 12:50:00 PM,033XX N MENARD AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1633,016,38,15,18,1137077,1921630,2003,03/22/2006 09:58:07 PM,41.941107061,-87.771600925,"(41.941107061, -87.771600925)"
-3009929,HJ711930,10/23/2003 12:25:00 AM,071XX S KEDZIE AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,GAS STATION,false,false,0831,008,18,66,04B,1156224,1857359,2003,03/22/2006 09:58:07 PM,41.764374364,-87.702962032,"(41.764374364, -87.702962032)"
-3011805,HJ712765,10/22/2003 09:00:00 PM,079XX S MARQUETTE AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,0422,004,7,46,26,1195598,1852810,2003,03/22/2006 09:58:07 PM,41.751009124,-87.558798137,"(41.751009124, -87.558798137)"
-3005822,HJ710801,10/22/2003 09:00:00 AM,003XX N ORLEANS ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1831,018,42,8,07,1173830,1902814,2003,03/22/2006 09:58:07 PM,41.888734745,-87.637081359,"(41.888734745, -87.637081359)"
-3003959,HJ709745,10/22/2003 12:00:00 AM,061XX N ST LOUIS AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1711,017,50,13,26,1151892,1940609,2003,03/22/2006 09:58:07 PM,41.992907359,-87.716647602,"(41.992907359, -87.716647602)"
-3005866,HJ710139,10/21/2003 02:30:00 PM,011XX W 66TH ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0724,007,17,68,08B,1169683,1861069,2003,03/22/2006 09:58:07 PM,41.774273815,-87.653523766,"(41.774273815, -87.653523766)"
-3004936,HJ710098,10/21/2003 07:00:00 AM,035XX W MEDILL AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1413,014,26,22,06,1152075,1915347,2003,12/04/2014 12:43:35 PM,41.923583072,-87.716643179,"(41.923583072, -87.716643179)"
-3002590,HJ707543,10/21/2003 12:59:00 AM,064XX S DR MARTIN LUTHER KING JR DR,051A,ASSAULT,AGGRAVATED: HANDGUN,STREET,true,false,0312,003,20,69,04A,1179988,1862346,2003,03/22/2006 09:58:07 PM,41.777548289,-87.615708676,"(41.777548289, -87.615708676)"
-3014189,HJ717331,10/20/2003 06:00:00 PM,010XX N FRANCISCO AVE,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,1311,012,26,24,06,1156820,1906978,2003,12/04/2014 12:43:35 PM,41.900522885,-87.699435477,"(41.900522885, -87.699435477)"
-3005658,HJ710499,10/20/2003 08:00:00 AM,001XX W ELM ST,0620,BURGLARY,UNLAWFUL ENTRY,CHA APARTMENT,false,false,1824,018,42,8,05,1175108,1908108,2003,03/22/2006 09:58:07 PM,41.903233207,-87.632229241,"(41.903233207, -87.632229241)"
-3004681,HJ702646,10/18/2003 04:45:00 PM,072XX S ROCKWELL ST,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE PORCH/HALLWAY,false,false,0831,008,18,66,26,1160257,1856403,2003,03/22/2006 09:58:07 PM,41.761668903,-87.688206228,"(41.761668903, -87.688206228)"
-2997859,HJ700260,10/17/2003 02:00:00 PM,062XX N ARTESIAN AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2413,024,50,2,06,1158802,1941722,2003,12/04/2014 12:43:35 PM,41.995822114,-87.691199376,"(41.995822114, -87.691199376)"
-2998123,HJ698853,10/17/2003 08:36:26 AM,083XX S MACKINAW AVE,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0424,004,10,46,08A,1199962,1850229,2003,06/11/2007 03:52:33 PM,41.743817769,-87.542893553,"(41.743817769, -87.542893553)"
-2996459,HJ698179,10/16/2003 08:35:00 PM,042XX W LAWRENCE AVE,031A,ROBBERY,ARMED: HANDGUN,RESTAURANT,false,false,1722,017,39,14,03,1147518,1931528,2003,03/22/2006 09:58:07 PM,41.968073796,-87.732971001,"(41.968073796, -87.732971001)"
-2995772,HJ698077,10/16/2003 12:00:00 PM,026XX W BELMONT AVE,0890,THEFT,FROM BUILDING,RESIDENCE PORCH/HALLWAY,false,false,1411,014,1,21,06,1158147,1921120,2003,03/22/2006 09:58:07 PM,41.939302594,-87.694174176,"(41.939302594, -87.694174176)"
-2995156,HJ696114,10/16/2003 02:40:38 AM,052XX S ASHLAND AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,0932,009,16,61,08B,1166529,1869729,2003,03/22/2006 09:58:07 PM,41.798105761,-87.664838947,"(41.798105761, -87.664838947)"
-2994355,HJ696323,10/15/2003 07:00:00 PM,061XX N WOLCOTT AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2413,024,40,2,14,1162659,1941154,2003,03/22/2006 09:58:07 PM,41.994183262,-87.677027324,"(41.994183262, -87.677027324)"
-3230042,HJ693315,10/14/2003 05:48:10 PM,083XX S DR MARTIN LUTHER KING JR DR,0610,BURGLARY,FORCIBLE ENTRY,OTHER,false,false,0632,006,6,44,05,1180313,1850048,2003,06/11/2007 03:52:33 PM,41.743793816,-87.614893767,"(41.743793816, -87.614893767)"
-2991728,HJ691257,10/13/2003 07:56:20 PM,074XX S RHODES AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0323,003,6,69,03,1181146,1856066,2003,03/22/2006 09:58:07 PM,41.760288739,-87.611656655,"(41.760288739, -87.611656655)"
-2998010,HJ691993,10/13/2003 05:00:00 PM,079XX S ST LAWRENCE AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,false,0624,006,6,44,06,1181574,1852408,2003,03/22/2006 09:58:07 PM,41.750240934,-87.610200729,"(41.750240934, -87.610200729)"
-2990311,HJ691917,10/12/2003 10:00:00 PM,104XX S GREEN BAY AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0432,004,10,52,14,1200556,1836057,2003,03/22/2006 09:58:07 PM,41.704913709,-87.541194969,"(41.704913709, -87.541194969)"
-2994180,HJ689522,10/12/2003 09:00:00 PM,032XX W EASTWOOD AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1713,017,33,14,08B,1154206,1930628,2003,03/22/2006 09:58:07 PM,41.965472908,-87.708403687,"(41.965472908, -87.708403687)"
-2997130,HJ689363,10/12/2003 03:15:00 PM,031XX N ELSTON AVE,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,false,false,1411,014,1,21,26,1157452,1920696,2003,03/22/2006 09:58:07 PM,41.938153301,-87.696740071,"(41.938153301, -87.696740071)"
-2998815,HJ687209,10/11/2003 07:27:14 PM,050XX W CRYSTAL ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,2533,025,37,25,08B,1142251,1907885,2003,03/22/2006 09:58:07 PM,41.903294731,-87.752926174,"(41.903294731, -87.752926174)"
-2987821,HJ687001,10/11/2003 05:21:00 PM,026XX S KEELER AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,false,false,1031,010,22,30,04A,1148777,1885857,2003,03/22/2006 09:58:07 PM,41.84272367,-87.729523757,"(41.84272367, -87.729523757)"
-2986905,HJ685583,10/11/2003 12:33:01 AM,052XX S WOOD ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0932,009,16,61,14,1165267,1870211,2003,03/22/2006 09:58:07 PM,41.799455265,-87.669453285,"(41.799455265, -87.669453285)"
-2986295,HJ685553,10/10/2003 06:30:00 PM,021XX W 83RD ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,0614,006,18,71,07,1163673,1849550,2003,03/22/2006 09:58:07 PM,41.742792287,-87.675878115,"(41.742792287, -87.675878115)"
-2988700,HJ685045,10/10/2003 04:00:00 PM,024XX N WASHTENAW AVE,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,false,false,1431,014,1,22,26,1158003,1916028,2003,03/22/2006 09:58:07 PM,41.925332735,-87.694842787,"(41.925332735, -87.694842787)"
-3030013,HJ736333,10/10/2003 12:00:00 AM,078XX S HERMITAGE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0611,006,17,71,06,1166077,1852876,2003,12/04/2014 12:43:35 PM,41.75186857,-87.666975392,"(41.75186857, -87.666975392)"
-2984720,HJ683202,10/09/2003 09:45:00 PM,011XX N MAYFIELD AVE,0550,ASSAULT,AGGRAVATED PO: HANDGUN,VACANT LOT/LAND,true,false,1511,015,29,25,04A,1136850,1907250,2003,03/22/2006 09:58:07 PM,41.901650775,-87.772780731,"(41.901650775, -87.772780731)"
-2984657,HJ682215,10/09/2003 09:00:00 AM,007XX W 15TH PL,0810,THEFT,OVER $500,STREET,false,false,1232,012,25,28,06,1171442,1892769,2003,12/04/2014 12:43:35 PM,41.861223393,-87.646146262,"(41.861223393, -87.646146262)"
-2984908,HJ680347,10/08/2003 04:52:07 PM,037XX S WELLS ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0925,009,3,34,08B,1175209,1880313,2003,03/22/2006 09:58:07 PM,41.826959594,-87.632691501,"(41.826959594, -87.632691501)"
-3010516,HJ677312,10/08/2003 04:49:00 PM,034XX W MONROE ST,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,STREET,true,false,1123,011,28,27,18,1153735,1899287,2003,03/22/2006 09:58:07 PM,41.879480014,-87.710971788,"(41.879480014, -87.710971788)"
-2982128,HJ680336,10/08/2003 04:00:00 PM,076XX S CICERO AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0833,008,13,65,06,1145766,1853744,2003,12/04/2014 12:43:35 PM,41.754658085,-87.741385006,"(41.754658085, -87.741385006)"
-2983188,HJ679970,10/08/2003 02:10:00 PM,084XX S VINCENNES AVE,1330,CRIMINAL TRESPASS,TO LAND,APARTMENT,true,false,0622,006,21,71,26,1173661,1848602,2003,03/22/2006 09:58:07 PM,41.73997562,-87.639309868,"(41.73997562, -87.639309868)"
-3050711,HJ765276,10/08/2003 10:00:00 AM,115XX S MAY ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,0524,005,34,53,06,1170768,1828330,2003,03/22/2006 09:58:07 PM,41.684409728,-87.650498431,"(41.684409728, -87.650498431)"
-2983727,HJ677145,10/07/2003 10:00:00 AM,047XX W LAKE ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,true,false,1113,011,28,25,11,1144325,1901863,2003,03/22/2006 09:58:07 PM,41.886730919,-87.745459408,"(41.886730919, -87.745459408)"
-3019413,HJ723148,10/07/2003 06:00:00 AM,035XX W 47TH PL,1310,CRIMINAL DAMAGE,TO PROPERTY,"SCHOOL, PUBLIC, GROUNDS",false,false,0821,008,14,58,14,1153485,1872807,2003,03/22/2006 09:58:07 PM,41.806820635,-87.712592498,"(41.806820635, -87.712592498)"
-3006475,HJ674300,10/05/2003 10:45:00 PM,060XX W GRAND AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2512,025,37,19,18,1136034,1914176,2003,03/22/2006 09:58:07 PM,41.920671147,-87.775612621,"(41.920671147, -87.775612621)"
-3003608,HJ674142,10/05/2003 09:00:00 PM,097XX S BRENNAN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0431,004,7,51,18,1193195,1841004,2003,03/22/2006 09:58:07 PM,41.718671421,-87.567988455,"(41.718671421, -87.567988455)"
-2979226,HJ673410,10/05/2003 01:35:32 PM,0000X W 95TH ST,1345,CRIMINAL DAMAGE,TO CITY OF CHICAGO PROPERTY,PARKING LOT/GARAGE(NON.RESID.),false,false,0634,006,21,49,14,1177568,1842013,2003,03/22/2006 09:58:07 PM,41.72180721,-87.625193832,"(41.72180721, -87.625193832)"
-2975976,HJ672194,10/04/2003 08:00:00 PM,016XX E 53RD ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,true,2132,002,5,41,05,1188182,1870409,2003,03/22/2006 09:58:07 PM,41.799482205,-87.585412695,"(41.799482205, -87.585412695)"
-3010714,HJ670224,10/04/2003 04:30:00 PM,055XX W CONGRESS PKWY,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,STREET,true,false,1522,015,29,25,18,1139939,1897183,2003,03/22/2006 09:58:07 PM,41.873969719,-87.761680642,"(41.873969719, -87.761680642)"
-2973276,HJ667230,10/02/2003 02:00:51 PM,128XX S GREEN ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,true,0523,005,34,53,06,1172974,1819480,2003,03/22/2006 09:58:07 PM,41.660075598,-87.642682573,"(41.660075598, -87.642682573)"
-3004478,HJ666001,10/01/2003 08:08:00 PM,076XX S EXCHANGE AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,0421,004,7,43,16,1195669,1855067,2003,03/22/2006 09:58:07 PM,41.757200746,-87.558463473,"(41.757200746, -87.558463473)"
-2971150,HJ665319,10/01/2003 04:00:05 PM,082XX S SAGINAW AVE,143B,WEAPONS VIOLATION,UNLAWFUL POSS OTHER FIREARM,RESIDENCE,false,false,0423,004,7,46,15,1195231,1850748,2003,03/22/2006 09:58:07 PM,41.745359891,-87.560210871,"(41.745359891, -87.560210871)"
-2974369,HJ670183,10/01/2003 01:00:00 PM,023XX W HURON ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,1313,012,1,24,06,1160828,1904657,2003,03/22/2006 09:58:07 PM,41.894071662,-87.684778317,"(41.894071662, -87.684778317)"
-2971811,HJ665117,10/01/2003 12:45:00 PM,079XX S HERMITAGE AVE,1330,CRIMINAL TRESPASS,TO LAND,SIDEWALK,true,false,0611,006,21,71,26,1166095,1852216,2003,03/22/2006 09:58:07 PM,41.750057054,-87.666928163,"(41.750057054, -87.666928163)"
-2974068,HJ665839,10/01/2003 12:04:00 PM,020XX N ORCHARD ST,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, GROUNDS",false,false,1812,018,43,7,06,1171322,1913569,2003,03/22/2006 09:58:07 PM,41.918302548,-87.645975132,"(41.918302548, -87.645975132)"
-3486614,HK554700,10/01/2003 09:00:00 AM,062XX S MARSHFIELD AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,false,false,0714,007,15,67,11,1166363,1863592,2003,03/22/2006 09:58:07 PM,41.781268622,-87.665622487,"(41.781268622, -87.665622487)"
-3016671,HJ659634,09/28/2003 07:55:00 PM,021XX S KEDZIE AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1022,010,24,30,16,1155396,1889517,2003,03/22/2006 09:58:07 PM,41.852636872,-87.705135339,"(41.852636872, -87.705135339)"
-2966103,HJ659720,09/28/2003 06:00:00 PM,002XX S WOOD ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1211,012,2,28,06,1164489,1898665,2003,12/04/2014 12:43:35 PM,41.877552412,-87.671502378,"(41.877552412, -87.671502378)"
-2984947,HJ658493,09/28/2003 08:30:00 AM,005XX E BROWNING AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA APARTMENT,true,true,0212,002,4,35,08B,1180799,1881383,2003,03/22/2006 09:58:07 PM,41.829768822,-87.612149993,"(41.829768822, -87.612149993)"
-2965440,HJ658355,09/28/2003 04:38:00 AM,043XX S CALIFORNIA AVE,0560,ASSAULT,SIMPLE,STREET,false,false,0912,009,12,58,08A,1158466,1875617,2003,03/22/2006 09:58:07 PM,41.814431462,-87.694247015,"(41.814431462, -87.694247015)"
-2993438,HJ658073,09/27/2003 11:30:00 PM,012XX N CAMPBELL AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1423,014,26,24,18,1159471,1908257,2003,03/22/2006 09:58:07 PM,41.903978401,-87.689662952,"(41.903978401, -87.689662952)"
-2965420,HJ657798,09/27/2003 08:30:00 PM,017XX W EDMAIRE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,false,2234,022,34,75,08B,1166853,1828882,2003,03/22/2006 09:58:07 PM,41.68600877,-87.664814459,"(41.68600877, -87.664814459)"
-2975614,HJ657861,09/27/2003 07:15:00 PM,039XX W COLUMBUS AVE,0920,MOTOR VEHICLE THEFT,ATT: AUTOMOBILE,SIDEWALK,false,false,0834,008,18,70,07,1151955,1847528,2003,03/22/2006 09:58:07 PM,41.737481219,-87.718866526,"(41.737481219, -87.718866526)"
-2966330,HJ660252,09/27/2003 03:00:00 PM,044XX N CLIFTON AVE,0810,THEFT,OVER $500,CTA GARAGE / OTHER PROPERTY,false,false,2311,019,46,3,06,1167946,1929378,2003,12/04/2014 12:43:35 PM,41.961756754,-87.657921084,"(41.961756754, -87.657921084)"
-2969386,HJ657265,09/27/2003 02:30:00 PM,055XX S PRAIRIE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,true,false,0233,002,20,40,14,1178948,1868168,2003,03/22/2006 09:58:07 PM,41.793548179,-87.619344055,"(41.793548179, -87.619344055)"
-2965915,HJ655580,09/27/2003 08:00:00 AM,035XX N WILTON AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,2331,019,44,6,05,1169274,1923481,2003,03/22/2006 09:58:07 PM,41.945546341,-87.653210692,"(41.945546341, -87.653210692)"
-2965812,HJ657216,09/26/2003 11:30:00 PM,025XX W ALTGELD ST,0810,THEFT,OVER $500,STREET,false,false,1431,014,35,22,06,1159272,1916573,2003,12/04/2014 12:43:35 PM,41.926802229,-87.690164852,"(41.926802229, -87.690164852)"
-2968344,HJ655844,09/26/2003 07:35:00 PM,017XX W HOWARD ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,2422,024,49,1,06,1163121,1950306,2003,03/22/2006 09:58:07 PM,42.019286838,-87.675069044,"(42.019286838, -87.675069044)"
-2964281,HJ655954,09/26/2003 07:00:00 PM,007XX N PINE AVE,0460,BATTERY,SIMPLE,SIDEWALK,true,false,1524,015,37,25,08B,1139417,1904701,2003,03/22/2006 09:58:07 PM,41.894609596,-87.763413895,"(41.894609596, -87.763413895)"
-2964173,HJ655646,09/26/2003 06:21:58 PM,100XX S LAFAYETTE AVE,0880,THEFT,PURSE-SNATCHING,STREET,false,false,0511,005,9,49,06,1177664,1838047,2003,03/22/2006 09:58:07 PM,41.710921807,-87.624961722,"(41.710921807, -87.624961722)"
-3020440,HJ727733,09/25/2003 10:00:00 PM,100XX W OHARE ST,1206,DECEPTIVE PRACTICE,"THEFT BY LESSEE,MOTOR VEH",OTHER,false,false,1651,016,41,76,11,1100629,1934213,2003,03/22/2006 09:58:07 PM,41.976213976,-87.905334384,"(41.976213976, -87.905334384)"
-2991475,HJ649656,09/23/2003 10:40:00 PM,034XX W HURON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1121,011,27,23,18,1153252,1904412,2003,03/22/2006 09:58:07 PM,41.893553131,-87.712609178,"(41.893553131, -87.712609178)"
-2984007,HJ648497,09/23/2003 05:25:00 PM,016XX W 78TH ST,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,STREET,true,false,0611,006,17,71,18,1166616,1853024,2003,03/22/2006 09:58:07 PM,41.75226323,-87.664995986,"(41.75226323, -87.664995986)"
-2957803,HJ647319,09/22/2003 09:00:00 PM,007XX S WOOD ST,0460,BATTERY,SIMPLE,STREET,false,false,1224,012,2,28,08B,1164456,1896915,2003,03/22/2006 09:58:07 PM,41.872750962,-87.671673078,"(41.872750962, -87.671673078)"
-2961166,HJ646854,09/22/2003 06:43:00 PM,048XX N WINTHROP AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,PARKING LOT/GARAGE(NON.RESID.),true,false,2024,020,46,3,08B,1168044,1932174,2003,03/22/2006 09:58:07 PM,41.969426952,-87.657479696,"(41.969426952, -87.657479696)"
-2956646,HJ647819,09/22/2003 03:00:00 PM,049XX W BYRON ST,0560,ASSAULT,SIMPLE,STREET,false,false,1634,016,45,15,08A,1142888,1925509,2003,03/22/2006 09:58:07 PM,41.951644913,-87.750146003,"(41.951644913, -87.750146003)"
-2958229,HJ647903,09/22/2003 02:30:00 PM,038XX W LELAND AVE,0460,BATTERY,SIMPLE,STREET,false,false,1723,017,39,14,08B,1150202,1931003,2003,03/22/2006 09:58:07 PM,41.966581127,-87.723115792,"(41.966581127, -87.723115792)"
-2956261,HJ646337,09/22/2003 02:10:00 PM,049XX N MARINE DR,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,PARK PROPERTY,false,false,2024,020,48,3,04B,1169933,1933241,2003,03/22/2006 09:58:07 PM,41.972313728,-87.650502603,"(41.972313728, -87.650502603)"
-2961473,HJ645668,09/22/2003 09:57:40 AM,024XX N RUTHERFORD AVE,0810,THEFT,OVER $500,STREET,true,false,2512,025,36,18,06,1131040,1915813,2003,12/04/2014 12:43:35 PM,41.925250937,-87.793924125,"(41.925250937, -87.793924125)"
-2974280,HJ670349,09/22/2003 09:20:00 AM,083XX S SOUTH CHICAGO AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0412,004,8,45,07,1191382,1849948,2003,03/22/2006 09:58:07 PM,41.743258652,-87.574339865,"(41.743258652, -87.574339865)"
-2961928,HJ645077,09/21/2003 11:10:00 PM,069XX S MAPLEWOOD AVE,0460,BATTERY,SIMPLE,STREET,true,false,0832,008,18,66,08B,1160524,1858805,2003,03/22/2006 09:58:07 PM,41.768254843,-87.687161505,"(41.768254843, -87.687161505)"
-2956042,HJ646237,09/21/2003 07:00:00 PM,093XX S ELIZABETH ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,2222,022,21,73,06,1169589,1842964,2003,03/22/2006 09:58:07 PM,41.724593285,-87.654392064,"(41.724593285, -87.654392064)"
-2956441,HJ644679,09/21/2003 01:30:00 PM,054XX W VAN BUREN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,1522,015,29,25,08B,1140257,1897520,2003,03/22/2006 09:58:07 PM,41.874888673,-87.760504821,"(41.874888673, -87.760504821)"
-2957826,HJ644455,09/21/2003 05:30:00 AM,021XX W HUBBARD ST,0460,BATTERY,SIMPLE,WAREHOUSE,false,false,1313,012,26,24,08B,1162316,1903108,2003,03/22/2006 09:58:07 PM,41.889790086,-87.679356731,"(41.889790086, -87.679356731)"
-2952401,HJ642620,09/20/2003 04:30:00 PM,014XX E 53RD ST,0460,BATTERY,SIMPLE,SMALL RETAIL STORE,false,false,2132,002,4,41,08B,1187131,1870381,2003,03/22/2006 09:58:07 PM,41.7994304,-87.589267812,"(41.7994304, -87.589267812)"
-2969573,HJ663441,09/20/2003 09:00:00 AM,008XX S WELLS ST,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,COMMERCIAL / BUSINESS OFFICE,false,false,0131,001,2,32,11,1174794,1896769,2003,03/22/2006 09:58:07 PM,41.87212538,-87.633722191,"(41.87212538, -87.633722191)"
-2953187,HJ641131,09/19/2003 11:40:00 PM,083XX S BRANDON AVE,0560,ASSAULT,SIMPLE,STREET,false,false,0424,004,10,46,08A,1198978,1849961,2003,03/22/2006 09:58:07 PM,41.743107104,-87.546507903,"(41.743107104, -87.546507903)"
-3033465,HJ638420,09/18/2003 06:00:00 PM,085XX S HERMITAGE AVE,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,STREET,true,false,0614,006,18,71,18,1166127,1847991,2003,03/22/2006 09:58:07 PM,41.738462361,-87.6669308,"(41.738462361, -87.6669308)"
-2949876,HJ633280,09/16/2003 12:46:48 PM,002XX N PINE AVE,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",true,false,1523,015,28,25,08B,1139494,1901281,2003,03/22/2006 09:58:07 PM,41.885223285,-87.763214548,"(41.885223285, -87.763214548)"
-2944484,HJ631651,09/15/2003 04:40:00 PM,035XX N ELSTON AVE,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,1733,017,35,21,06,1154312,1923546,2003,03/22/2006 09:58:07 PM,41.946037311,-87.708203891,"(41.946037311, -87.708203891)"
-2946268,HJ630802,09/15/2003 09:00:00 AM,0000X E 99TH PL,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,RESIDENCE,false,false,0511,005,9,49,06,1178338,1839052,2003,03/22/2006 09:58:07 PM,41.71366442,-87.622463018,"(41.71366442, -87.622463018)"
-2978017,HJ628757,09/14/2003 07:45:00 AM,036XX W MADISON ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1122,011,28,27,16,1152271,1899735,2003,03/22/2006 09:58:07 PM,41.88073838,-87.716335577,"(41.88073838, -87.716335577)"
-2941703,HJ629085,09/14/2003 04:00:00 AM,043XX W LE MOYNE ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2534,025,37,23,07,1146781,1909455,2003,03/22/2006 09:58:07 PM,41.907517665,-87.73624621,"(41.907517665, -87.73624621)"
-2972135,HJ625956,09/12/2003 08:07:39 PM,056XX W CHICAGO AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1511,015,29,25,18,1138747,1904822,2003,03/22/2006 09:58:07 PM,41.894953821,-87.76587171,"(41.894953821, -87.76587171)"
-2946735,HJ634352,09/12/2003 05:00:00 PM,021XX E 69TH ST,1365,CRIMINAL TRESPASS,TO RESIDENCE,APARTMENT,false,false,0331,003,5,43,26,1191938,1859683,2003,03/22/2006 09:58:07 PM,41.769958818,-87.571987022,"(41.769958818, -87.571987022)"
-2943361,HJ623863,09/11/2003 09:30:00 PM,032XX W ROOSEVELT RD,0460,BATTERY,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),false,true,1022,010,24,29,08B,1154747,1894483,2003,03/22/2006 09:58:07 PM,41.866277135,-87.707384498,"(41.866277135, -87.707384498)"
-2939477,HJ625917,09/11/2003 09:00:00 PM,004XX E ONTARIO ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,1834,018,42,8,06,1179087,1904534,2003,03/22/2006 09:58:07 PM,41.893335832,-87.617723243,"(41.893335832, -87.617723243)"
-2951577,HJ637705,09/11/2003 08:30:00 PM,046XX W PATTERSON AVE,0460,BATTERY,SIMPLE,RESIDENCE,true,false,1731,017,38,15,08B,1144442,1923809,2003,03/22/2006 09:58:07 PM,41.946950805,-87.74447639,"(41.946950805, -87.74447639)"
-2936904,HJ622021,09/11/2003 02:50:00 AM,122XX S STATE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0523,005,9,53,08B,1178422,1823742,2003,03/22/2006 09:58:07 PM,41.671649672,-87.622618017,"(41.671649672, -87.622618017)"
-2931267,HJ615846,09/08/2003 09:15:00 AM,015XX E 53RD ST,2851,PUBLIC PEACE VIOLATION,ARSON THREAT,NURSING HOME/RETIREMENT HOME,false,false,2132,002,4,41,26,1187460,1870388,2003,03/22/2006 09:58:07 PM,41.799441788,-87.588061081,"(41.799441788, -87.588061081)"
-2931759,HJ614204,09/07/2003 08:11:10 PM,050XX W SUPERIOR ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1531,015,28,25,08B,1142472,1904460,2003,03/22/2006 09:58:07 PM,41.89389203,-87.752199603,"(41.89389203, -87.752199603)"
-2930366,HJ613734,09/07/2003 03:40:00 PM,011XX E 67TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,0321,003,5,42,14,1185034,1860852,2003,03/22/2006 09:58:07 PM,41.77333158,-87.597257105,"(41.77333158, -87.597257105)"
-2929363,HJ614200,09/07/2003 03:00:00 PM,065XX S PULASKI RD,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,0833,008,13,65,06,1150792,1860766,2003,03/22/2006 09:58:07 PM,41.773831242,-87.722783294,"(41.773831242, -87.722783294)"
-2979446,HJ670771,09/07/2003 12:01:00 AM,082XX S LANGLEY AVE,1754,OFFENSE INVOLVING CHILDREN,AGG SEX ASSLT OF CHILD FAM MBR,RESIDENCE,false,false,0631,006,6,44,02,1182362,1850779,2003,03/22/2006 09:58:07 PM,41.745752571,-87.607363535,"(41.745752571, -87.607363535)"
-2974418,HJ664840,09/06/2003 09:00:00 PM,030XX W 21ST PL,0820,THEFT,$500 AND UNDER,STREET,false,false,1022,010,24,30,06,1156483,1889520,2003,12/04/2014 12:43:35 PM,41.852623213,-87.701145616,"(41.852623213, -87.701145616)"
-2949405,HJ638460,09/06/2003 06:00:00 PM,021XX N HAMLIN AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2525,025,26,22,06,1150647,1914256,2003,12/04/2014 12:43:35 PM,41.920617332,-87.721918803,"(41.920617332, -87.721918803)"
-2928490,HJ611783,09/06/2003 04:32:53 PM,093XX S MICHIGAN AVE,0497,BATTERY,AGGRAVATED DOMESTIC BATTERY: OTHER DANG WEAPON,STREET,false,true,0634,006,6,49,04B,1178759,1842881,2003,03/22/2006 09:58:07 PM,41.724162147,-87.620805111,"(41.724162147, -87.620805111)"
-2932225,HJ610463,09/05/2003 11:35:00 PM,012XX N BURLING ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,true,false,1822,018,27,8,26,1171043,1908538,2003,03/22/2006 09:58:07 PM,41.904503346,-87.647148068,"(41.904503346, -87.647148068)"
-2926752,HJ610784,09/05/2003 11:00:00 PM,035XX N CLARK ST,0890,THEFT,FROM BUILDING,BAR OR TAVERN,false,false,1923,019,44,6,06,1168534,1923731,2003,03/22/2006 09:58:07 PM,41.946248432,-87.655923383,"(41.946248432, -87.655923383)"
-2945548,HJ609242,09/05/2003 01:02:53 PM,016XX W 78TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0611,006,17,71,18,1166463,1853020,2003,03/22/2006 09:58:07 PM,41.752255514,-87.665556779,"(41.752255514, -87.665556779)"
-2924758,HJ606510,09/03/2003 10:36:00 PM,127XX S HALSTED ST,1790,OFFENSE INVOLVING CHILDREN,CHILD ABDUCTION,STREET,false,true,0523,,34,53,20,,,2003,06/11/2007 03:52:33 PM,,,
-2933964,HJ605059,09/03/2003 01:01:00 PM,039XX W WILCOX ST,141A,WEAPONS VIOLATION,UNLAWFUL USE HANDGUN,SIDEWALK,false,false,1122,011,28,26,15,1150371,1899011,2003,03/22/2006 09:58:07 PM,41.878788915,-87.723331167,"(41.878788915, -87.723331167)"
-2921625,HJ603225,09/02/2003 04:20:00 PM,026XX S AVERS AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,1031,010,22,30,06,1151084,1886444,2003,12/04/2014 12:43:35 PM,41.844289653,-87.721042236,"(41.844289653, -87.721042236)"
-2931062,HJ609281,09/02/2003 07:30:00 AM,050XX N MOZART ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2031,020,40,4,14,1156419,1933285,2003,03/22/2006 09:58:07 PM,41.972719283,-87.700194749,"(41.972719283, -87.700194749)"
-2921506,HJ602119,09/02/2003 07:00:00 AM,063XX S PARNELL AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,0722,007,20,68,05,1173753,1862624,2003,03/22/2006 09:58:07 PM,41.778451658,-87.638557871,"(41.778451658, -87.638557871)"
-2920250,HJ599048,08/31/2003 12:00:00 PM,055XX W IOWA ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1524,015,37,25,05,1139106,1905494,2003,03/22/2006 09:58:07 PM,41.896791348,-87.764536817,"(41.896791348, -87.764536817)"
-3015620,HJ589066,08/29/2003 05:10:00 PM,010XX N LAWNDALE AVE,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,STREET,true,false,1112,011,27,23,18,1151474,1906939,2003,03/22/2006 09:58:07 PM,41.900522582,-87.719072748,"(41.900522582, -87.719072748)"
-2915376,HJ594321,08/28/2003 03:00:00 PM,078XX S EMERALD AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,0621,006,17,71,07,1172620,1852874,2003,03/22/2006 09:58:07 PM,41.75172152,-87.642998372,"(41.75172152, -87.642998372)"
-2916696,HJ592214,08/28/2003 09:42:01 AM,023XX S STATE ST,1330,CRIMINAL TRESPASS,TO LAND,CTA PLATFORM,true,false,0134,001,3,33,26,1176732,1888450,2003,03/22/2006 09:58:07 PM,41.849253929,-87.626858373,"(41.849253929, -87.626858373)"
-2915079,HJ591995,08/27/2003 10:00:00 PM,051XX W AINSLIE ST,0917,MOTOR VEHICLE THEFT,"CYCLE, SCOOTER, BIKE W-VIN",RESIDENCE,false,false,1623,016,45,11,07,1141183,1932135,2003,03/22/2006 09:58:07 PM,41.969858904,-87.756249603,"(41.969858904, -87.756249603)"
-2911245,HJ588892,08/26/2003 07:30:00 PM,029XX W LAWRENCE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,false,1713,017,33,14,08B,1155672,1931781,2003,03/22/2006 09:58:07 PM,41.968607339,-87.702982317,"(41.968607339, -87.702982317)"
-2934055,HJ584712,08/24/2003 08:40:00 PM,083XX W CATHERINE AVE,2220,LIQUOR LAW VIOLATION,ILLEGAL POSSESSION BY MINOR,PARK PROPERTY,true,false,1614,016,41,10,22,1119999,1934739,2003,03/22/2006 09:58:07 PM,41.977369714,-87.834089837,"(41.977369714, -87.834089837)"
-2907592,HJ583396,08/24/2003 09:27:32 AM,0000X E 111TH ST,0560,ASSAULT,SIMPLE,OTHER,true,false,0531,005,9,49,08A,1177617,1831314,2003,03/22/2006 09:58:07 PM,41.692446564,-87.625336551,"(41.692446564, -87.625336551)"
-2910339,HJ583776,08/24/2003 06:00:00 AM,035XX W WRIGHTWOOD AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1413,014,26,22,14,1152130,1917013,2003,03/22/2006 09:58:07 PM,41.928153631,-87.716397043,"(41.928153631, -87.716397043)"
-2906394,HJ581665,08/23/2003 10:00:00 AM,065XX S BISHOP ST,1790,OFFENSE INVOLVING CHILDREN,CHILD ABDUCTION,RESIDENCE,false,false,0725,007,17,67,20,1167752,1861301,2003,03/22/2006 09:58:07 PM,41.774952121,-87.660595813,"(41.774952121, -87.660595813)"
-2912762,HJ590773,08/23/2003 02:00:00 AM,070XX S PAXTON AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,true,0331,003,5,43,26,1192060,1858602,2003,03/22/2006 09:58:07 PM,41.766989507,-87.571574925,"(41.766989507, -87.571574925)"
-2907859,HJ580830,08/23/2003 12:05:00 AM,030XX S HAMLIN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,true,false,1031,010,22,30,14,1151473,1884443,2003,03/22/2006 09:58:07 PM,41.838791037,-87.719667129,"(41.838791037, -87.719667129)"
-2904350,HJ581298,08/22/2003 06:00:00 PM,010XX N LAWLER AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1531,015,37,25,14,1142545,1906385,2003,03/22/2006 09:58:07 PM,41.899173097,-87.751883581,"(41.899173097, -87.751883581)"
-2903845,HJ579777,08/22/2003 12:30:00 PM,073XX S YALE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0731,007,17,69,06,1175852,1856336,2003,12/04/2014 12:43:35 PM,41.761149887,-87.631051083,"(41.761149887, -87.631051083)"
-2930960,HJ576185,08/20/2003 11:47:00 PM,103XX S PRINCETON AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,0512,005,34,49,16,1176068,1836625,2003,03/22/2006 09:58:07 PM,41.707055519,-87.630849072,"(41.707055519, -87.630849072)"
-2901008,HJ576545,08/20/2003 10:30:00 PM,021XX N LINCOLN PARK WEST,0917,MOTOR VEHICLE THEFT,"CYCLE, SCOOTER, BIKE W-VIN",STREET,false,false,1814,018,43,7,07,1173864,1914841,2003,03/22/2006 09:58:07 PM,41.921736684,-87.63659776,"(41.921736684, -87.63659776)"
-2901871,HJ576208,08/20/2003 10:00:00 PM,054XX W WASHINGTON BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1522,015,28,25,08B,1139839,1900171,2003,03/22/2006 09:58:07 PM,41.882171007,-87.761974768,"(41.882171007, -87.761974768)"
-2901733,HJ573916,08/19/2003 11:00:00 PM,012XX W LAWRENCE AVE,0460,BATTERY,SIMPLE,SIDEWALK,true,false,2311,019,46,3,08B,1166879,1931967,2003,03/22/2006 09:58:07 PM,41.968884075,-87.661769355,"(41.968884075, -87.661769355)"
-2902597,HJ572180,08/19/2003 09:29:53 AM,036XX S STATE ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,true,false,0211,002,3,35,26,1176849,1880942,2003,03/22/2006 09:58:07 PM,41.828648762,-87.626655684,"(41.828648762, -87.626655684)"
-2900787,HJ571285,08/18/2003 05:58:41 PM,047XX S COTTAGE GROVE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,false,0223,002,4,38,08B,1182343,1874014,2003,03/22/2006 09:58:07 PM,41.809512036,-87.606713778,"(41.809512036, -87.606713778)"
-2894615,HJ566529,08/16/2003 03:30:00 PM,064XX S DR MARTIN LUTHER KING JR DR,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,0312,003,20,69,08B,1179989,1862315,2003,03/22/2006 09:58:07 PM,41.777463199,-87.615705959,"(41.777463199, -87.615705959)"
-2894119,HJ564426,08/15/2003 05:10:00 PM,028XX W DEVON AVE,0890,THEFT,FROM BUILDING,SMALL RETAIL STORE,false,false,2412,024,50,2,06,1156401,1942375,2003,03/22/2006 09:58:07 PM,41.997663039,-87.700013774,"(41.997663039, -87.700013774)"
-2908315,HJ563226,08/15/2003 04:25:00 AM,055XX W WASHINGTON BLVD,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1522,015,29,25,18,1139188,1900156,2003,03/22/2006 09:58:07 PM,41.882141713,-87.76436564,"(41.882141713, -87.76436564)"
-2890689,HJ562116,08/14/2003 04:54:50 PM,010XX W MONTROSE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESTAURANT,true,false,2311,019,46,3,14,1168507,1929424,2003,03/22/2006 09:58:07 PM,41.961870827,-87.655857204,"(41.961870827, -87.655857204)"
-2891264,HJ561376,08/14/2003 10:50:00 AM,032XX S WELLS ST,0810,THEFT,OVER $500,VEHICLE-COMMERCIAL,false,false,0924,009,11,34,06,1175108,1883442,2003,12/04/2014 12:43:35 PM,41.835548092,-87.632968458,"(41.835548092, -87.632968458)"
-2889624,HJ559983,08/13/2003 08:00:00 AM,051XX S HYDE PARK BLVD,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2132,002,4,41,05,1188257,1871311,2003,03/22/2006 09:58:07 PM,41.801955564,-87.585108861,"(41.801955564, -87.585108861)"
-2890169,HJ558659,08/13/2003 03:27:00 AM,025XX N ST LOUIS AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,SIDEWALK,true,false,1413,014,26,22,26,1152576,1916693,2003,03/22/2006 09:58:07 PM,41.927266707,-87.714766629,"(41.927266707, -87.714766629)"
-2885083,HJ556590,08/11/2003 07:30:00 PM,006XX N NOBLE ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,RESIDENCE-GARAGE,false,false,1324,012,27,24,07,1166986,1904326,2003,03/22/2006 09:58:07 PM,41.893033416,-87.662171523,"(41.893033416, -87.662171523)"
-2886712,HJ554329,08/11/2003 07:43:22 AM,050XX S TRIPP AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,0815,008,23,57,08B,1148924,1870764,2003,03/22/2006 09:58:07 PM,41.801303516,-87.729373636,"(41.801303516, -87.729373636)"
-2889240,HJ552511,08/10/2003 07:56:25 AM,080XX S EVANS AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,false,false,0631,006,6,44,26,1182659,1851917,2003,03/22/2006 09:58:07 PM,41.748868485,-87.606240045,"(41.748868485, -87.606240045)"
-2881720,HJ550930,08/08/2003 11:30:00 PM,009XX W DIVERSEY PKWY,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1932,019,44,6,05,1169598,1918829,2003,03/22/2006 09:58:07 PM,41.932773992,-87.652155664,"(41.932773992, -87.652155664)"
-2891486,HJ548112,08/08/2003 06:55:00 AM,035XX W NORTH AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1422,014,26,23,16,1152341,1910360,2003,03/22/2006 09:58:07 PM,41.909893049,-87.715797734,"(41.909893049, -87.715797734)"
-2887565,HJ548010,08/08/2003 03:12:18 AM,011XX N LARRABEE ST,041A,BATTERY,AGGRAVATED: HANDGUN,CHA PARKING LOT/GROUNDS,true,false,1823,018,27,8,04B,1172160,1908064,2003,03/22/2006 09:58:07 PM,41.903178073,-87.643059058,"(41.903178073, -87.643059058)"
-2879815,HJ546914,08/07/2003 10:00:00 AM,069XX S ADA ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0734,007,17,67,14,1168576,1858368,2003,03/22/2006 09:58:07 PM,41.766885869,-87.657659614,"(41.766885869, -87.657659614)"
-2878526,HJ545169,08/06/2003 07:30:00 PM,047XX N SHERIDAN RD,0460,BATTERY,SIMPLE,SIDEWALK,true,false,2312,019,46,3,08B,1168730,1931931,2003,03/22/2006 09:58:07 PM,41.968745275,-87.654964358,"(41.968745275, -87.654964358)"
-2877187,HJ543563,08/06/2003 02:10:00 AM,093XX S MERRILL AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0413,004,7,48,08B,1192103,1843328,2003,03/22/2006 09:58:07 PM,41.725075276,-87.571912717,"(41.725075276, -87.571912717)"
-2876565,HJ545089,08/06/2003 12:00:00 AM,003XX N LARAMIE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,1523,015,28,25,07,1141619,1901449,2003,03/22/2006 09:58:07 PM,41.885645292,-87.755406914,"(41.885645292, -87.755406914)"
-2878658,HJ542950,08/05/2003 05:58:00 PM,065XX S PAULINA ST,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,RESIDENCE,false,true,0725,007,15,67,20,1166090,1861030,2003,03/22/2006 09:58:07 PM,41.774243977,-87.666696181,"(41.774243977, -87.666696181)"
-2875689,HJ541387,08/05/2003 08:49:06 AM,008XX N LAVERGNE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1531,015,37,25,08B,1142824,1905299,2003,03/22/2006 09:58:07 PM,41.896187794,-87.750885888,"(41.896187794, -87.750885888)"
-2876114,HJ541280,08/05/2003 04:00:00 AM,065XX S DAMEN AVE,0320,ROBBERY,STRONGARM - NO WEAPON,ALLEY,true,true,0726,007,15,67,03,1164113,1861288,2003,03/22/2006 09:58:07 PM,41.774993789,-87.673936273,"(41.774993789, -87.673936273)"
-2873875,HJ538520,08/03/2003 09:40:58 PM,077XX S NORMAL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,0621,006,17,69,08B,1174256,1853855,2003,03/22/2006 09:58:07 PM,41.754377328,-87.636974117,"(41.754377328, -87.636974117)"
-2872666,HJ536779,08/03/2003 12:41:03 AM,036XX S CALUMET AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0211,,4,35,14,,,2003,03/22/2006 09:58:07 PM,,,
-2871375,HJ536363,08/02/2003 08:15:00 PM,045XX S DAMEN AVE,0820,THEFT,$500 AND UNDER,DEPARTMENT STORE,true,false,0914,009,12,61,06,1163722,1874119,2003,12/04/2014 12:43:35 PM,41.810211892,-87.675009439,"(41.810211892, -87.675009439)"
-3010103,HJ715484,08/01/2003 10:00:00 AM,065XX N RICHMOND ST,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,2412,024,50,2,05,1155521,1943184,2003,03/22/2006 09:58:07 PM,41.999900784,-87.703229082,"(41.999900784, -87.703229082)"
-2866263,HJ531123,07/31/2003 01:13:42 PM,030XX E 79TH ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,true,false,0421,004,7,46,15,1197863,1853211,2003,03/22/2006 09:58:07 PM,41.752053244,-87.550484876,"(41.752053244, -87.550484876)"
-3402803,HJ530935,07/31/2003 11:50:00 AM,040XX W GLADYS AVE,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),ALLEY,true,false,1132,011,24,26,18,1149438,1898007,2003,08/21/2009 09:11:30 AM,41.876051972,-87.726783043,"(41.876051972, -87.726783043)"
-2875579,HJ530439,07/31/2003 04:40:00 AM,075XX S EGGLESTON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0621,006,17,69,08B,1174564,1854734,2003,03/22/2006 09:58:07 PM,41.756782565,-87.635819281,"(41.756782565, -87.635819281)"
-2866751,HJ530147,07/30/2003 11:44:16 PM,112XX S MICHIGAN AVE,0334,ROBBERY,ATTEMPT: ARMED-KNIFE/CUT INSTR,SIDEWALK,false,false,0531,005,9,49,03,1178758,1830717,2003,03/22/2006 09:58:07 PM,41.690782499,-87.621177257,"(41.690782499, -87.621177257)"
-2865660,HJ529692,07/30/2003 07:33:42 PM,006XX E 51ST ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0223,002,4,38,06,1181460,1871377,2003,12/04/2014 12:43:35 PM,41.802296332,-87.610033833,"(41.802296332, -87.610033833)"
-2869952,HJ527028,07/29/2003 02:50:00 PM,080XX S WESTERN AVE,0890,THEFT,FROM BUILDING,NURSING HOME/RETIREMENT HOME,false,false,0835,008,18,70,06,1161807,1851535,2003,03/22/2006 09:58:07 PM,41.748278349,-87.682660228,"(41.748278349, -87.682660228)"
-2875808,HJ526580,07/29/2003 01:15:00 PM,028XX W WILCOX ST,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),SIDEWALK,true,false,1124,011,2,27,18,1157142,1899260,2003,03/22/2006 09:58:07 PM,41.879337446,-87.698462498,"(41.879337446, -87.698462498)"
-2865228,HJ524436,07/28/2003 02:10:00 PM,021XX S ALBANY AVE,501A,OTHER OFFENSE,ANIMAL ABUSE/NEGLECT,OTHER,false,false,1022,010,24,30,26,1155976,1889628,2003,03/22/2006 09:58:07 PM,41.852929805,-87.70300356,"(41.852929805, -87.70300356)"
-2864022,HJ524729,07/28/2003 12:03:00 AM,003XX E 134TH ST,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,false,true,0533,005,9,54,26,1181010,1816583,2003,03/22/2006 09:58:07 PM,41.651945315,-87.613364914,"(41.651945315, -87.613364914)"
-2858393,HJ522313,07/27/2003 01:35:33 PM,026XX W GRAND AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1313,012,26,24,07,1158967,1904048,2003,06/11/2007 03:52:33 PM,41.892438911,-87.691629914,"(41.892438911, -87.691629914)"
-2862973,HJ522070,07/27/2003 10:42:41 AM,035XX W 63RD ST,1320,CRIMINAL DAMAGE,TO VEHICLE,POLICE FACILITY/VEH PARKING LOT,false,false,0823,008,15,66,14,1154004,1862576,2003,03/22/2006 09:58:07 PM,41.778734981,-87.71096061,"(41.778734981, -87.71096061)"
-2859484,HJ521943,07/27/2003 10:00:00 AM,100XX S STATE ST,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0511,005,9,49,08B,1177989,1838334,2003,03/22/2006 09:58:07 PM,41.711702031,-87.623762848,"(41.711702031, -87.623762848)"
-2857602,HJ521890,07/27/2003 02:30:00 AM,034XX W BELLE PLAINE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1723,017,33,16,14,1152585,1927078,2003,03/22/2006 09:58:07 PM,41.955763759,-87.71445805,"(41.955763759, -87.71445805)"
-2864469,HJ521380,07/27/2003 12:30:00 AM,060XX S RICHMOND ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,true,false,0823,008,15,66,08B,1157796,1864165,2003,03/22/2006 09:58:07 PM,41.783019273,-87.697015629,"(41.783019273, -87.697015629)"
-2868886,HJ535536,07/27/2003 12:00:00 AM,038XX N CLAREMONT AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE-GARAGE,false,false,1912,019,47,5,14,1159973,1925682,2003,03/22/2006 09:58:07 PM,41.951783439,-87.687336751,"(41.951783439, -87.687336751)"
-2861000,HJ516999,07/25/2003 12:45:00 PM,014XX S DRAKE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1021,010,24,29,08B,1152899,1892797,2003,03/22/2006 09:58:07 PM,41.861687336,-87.714213347,"(41.861687336, -87.714213347)"
-3159379,HJ516160,07/24/2003 04:49:00 PM,079XX S LAFAYETTE AVE,0810,THEFT,OVER $500,OTHER,false,false,0623,006,17,44,06,1177268,1852617,2003,12/04/2014 12:43:35 PM,41.750912681,-87.625973444,"(41.750912681, -87.625973444)"
-2863103,HJ526942,07/24/2003 10:00:00 AM,053XX N NEENAH AVE,0620,BURGLARY,UNLAWFUL ENTRY,PARKING LOT/GARAGE(NON.RESID.),false,false,1613,016,41,10,05,1131876,1935042,2003,03/22/2006 09:58:07 PM,41.978002921,-87.790404602,"(41.978002921, -87.790404602)"
-2850982,HJ512818,07/23/2003 12:30:00 AM,022XX S HOMAN AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,1024,010,22,30,07,1154018,1888506,2003,11/21/2006 05:02:11 AM,41.849890122,-87.710219977,"(41.849890122, -87.710219977)"
-2850944,HJ511737,07/22/2003 04:50:00 PM,008XX N LATROBE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1524,015,37,25,08B,1141169,1904829,2003,03/22/2006 09:58:07 PM,41.894928728,-87.756976055,"(41.894928728, -87.756976055)"
-2851676,HJ513318,07/22/2003 10:00:00 AM,122XX S LOOMIS ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0524,005,34,53,05,1169174,1823635,2003,03/22/2006 09:58:07 PM,41.671560368,-87.656468624,"(41.671560368, -87.656468624)"
-2907852,HJ552404,07/20/2003 04:00:00 AM,039XX W FLOURNOY ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,true,1132,011,24,26,14,1150486,1896784,2003,03/22/2006 09:58:07 PM,41.872675535,-87.722967056,"(41.872675535, -87.722967056)"
-2859640,HJ507350,07/20/2003 01:30:00 AM,011XX N PULASKI RD,0460,BATTERY,SIMPLE,RESTAURANT,false,false,1112,011,27,23,08B,1149540,1907707,2003,03/22/2006 09:58:07 PM,41.902667832,-87.726156519,"(41.902667832, -87.726156519)"
-2856151,HJ504148,07/19/2003 02:30:00 AM,048XX N KEDZIE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,VEHICLE NON-COMMERCIAL,false,false,1713,017,39,14,03,1154139,1931720,2003,03/22/2006 09:58:07 PM,41.968470763,-87.708620757,"(41.968470763, -87.708620757)"
-2838216,HJ501920,07/17/2003 09:30:00 PM,004XX W 103RD PL,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,2232,022,34,49,06,1175120,1836312,2003,12/04/2014 12:43:35 PM,41.706217772,-87.634329933,"(41.706217772, -87.634329933)"
-2837227,HJ499136,07/16/2003 09:50:00 PM,077XX S CICERO AVE,031A,ROBBERY,ARMED: HANDGUN,HOTEL/MOTEL,false,false,0834,008,13,70,03,1145778,1853078,2003,03/22/2006 09:58:07 PM,41.752830235,-87.741357823,"(41.752830235, -87.741357823)"
-2837059,HJ499975,07/16/2003 09:00:00 PM,071XX S WASHTENAW AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0831,008,18,66,07,1159654,1856958,2003,03/22/2006 09:58:07 PM,41.763204295,-87.690401096,"(41.763204295, -87.690401096)"
-2835767,HJ496423,07/15/2003 06:42:00 PM,017XX W 66TH ST,051B,ASSAULT,AGGRAVATED: OTHER FIREARM,RESIDENCE,false,false,0725,007,15,67,04A,1165829,1860966,2003,03/22/2006 09:58:07 PM,41.774073901,-87.667654777,"(41.774073901, -87.667654777)"
-3039099,HJ752013,07/15/2003 12:00:00 PM,046XX W ADAMS ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1113,011,28,25,06,1145622,1898765,2003,12/04/2014 12:43:35 PM,41.878205161,-87.740774986,"(41.878205161, -87.740774986)"
-2835780,HJ494154,07/14/2003 06:38:07 PM,037XX S INDIANA AVE,4650,OTHER OFFENSE,SEX OFFENDER: FAIL TO REGISTER,OTHER,true,false,0211,002,3,35,26,1178175,1880239,2003,03/22/2006 09:58:07 PM,41.82668964,-87.621812093,"(41.82668964, -87.621812093)"
-2832936,HJ491353,07/13/2003 01:30:00 PM,014XX N HUMBOLDT DR,0820,THEFT,$500 AND UNDER,OTHER,false,false,1423,014,26,24,06,1156234,1909164,2003,12/04/2014 12:43:35 PM,41.906533322,-87.701528756,"(41.906533322, -87.701528756)"
-2828480,HJ489251,07/12/2003 12:00:00 PM,019XX W HARRISON ST,0890,THEFT,FROM BUILDING,HOSPITAL BUILDING/GROUNDS,false,false,1224,012,2,28,06,1163812,1897354,2003,03/22/2006 09:58:07 PM,41.873969214,-87.674025114,"(41.873969214, -87.674025114)"
-2875811,HJ528328,07/11/2003 05:30:00 PM,075XX S CALUMET AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0623,006,6,69,14,1179815,1855308,2003,03/22/2006 09:58:07 PM,41.758239243,-87.616557906,"(41.758239243, -87.616557906)"
-2827614,HJ486624,07/11/2003 11:36:51 AM,058XX S WABASH AVE,0810,THEFT,OVER $500,RESIDENCE PORCH/HALLWAY,false,false,0233,002,20,40,06,1177749,1866437,2003,12/04/2014 12:43:35 PM,41.788825388,-87.623793044,"(41.788825388, -87.623793044)"
-2830289,HJ488785,07/11/2003 11:30:00 AM,007XX N SACRAMENTO BLVD,0820,THEFT,$500 AND UNDER,OTHER,false,false,1313,012,27,23,06,1156139,1904505,2003,12/04/2014 12:43:35 PM,41.893750529,-87.702003675,"(41.893750529, -87.702003675)"
-2839570,HJ486015,07/11/2003 12:20:00 AM,054XX W CORTLAND ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,false,2532,025,37,25,08B,1139738,1911957,2003,03/22/2006 09:58:07 PM,41.914515055,-87.762057463,"(41.914515055, -87.762057463)"
-2828302,HJ485741,07/10/2003 04:35:00 PM,056XX S WOOD ST,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0715,007,15,67,03,1165264,1867548,2003,03/22/2006 09:58:07 PM,41.792147733,-87.669539696,"(41.792147733, -87.669539696)"
-2864078,HJ529527,07/09/2003 02:00:00 PM,066XX S WABASH AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0322,003,20,69,07,1177797,1860729,2003,03/22/2006 09:58:07 PM,41.773160964,-87.623789744,"(41.773160964, -87.623789744)"
-2823283,HJ480284,07/08/2003 11:22:34 AM,0000X N STATE ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0122,001,42,32,06,1176335,1900391,2003,03/22/2006 09:58:07 PM,41.882029756,-87.6279553,"(41.882029756, -87.6279553)"
-2822205,HJ479975,07/08/2003 08:35:32 AM,045XX S MICHIGAN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,COMMERCIAL / BUSINESS OFFICE,false,false,0221,002,3,38,14,1177872,1875172,2003,03/22/2006 09:58:07 PM,41.81279225,-87.623077399,"(41.81279225, -87.623077399)"
-2823443,HJ479080,07/07/2003 07:40:00 PM,006XX N LA SALLE DR,1330,CRIMINAL TRESPASS,TO LAND,GAS STATION,true,false,1832,018,42,8,26,1175066,1904462,2003,03/22/2006 09:58:07 PM,41.893229338,-87.632492917,"(41.893229338, -87.632492917)"
-2819285,HJ477421,07/07/2003 03:00:00 AM,011XX N NORTH BRANCH ST,0810,THEFT,OVER $500,OTHER,false,false,1822,018,32,8,06,1169019,1907862,2003,12/04/2014 12:43:35 PM,41.902692547,-87.654602385,"(41.902692547, -87.654602385)"
-2819546,HJ476197,07/06/2003 02:30:09 PM,012XX S WABASH AVE,1330,CRIMINAL TRESPASS,TO LAND,GAS STATION,true,false,0132,001,2,33,26,1177002,1894869,2003,03/22/2006 09:58:07 PM,41.866861994,-87.625673284,"(41.866861994, -87.625673284)"
-2817745,HJ474286,07/05/2003 02:15:00 PM,097XX S CALUMET AVE,0560,ASSAULT,SIMPLE,RESIDENCE,true,true,0511,005,6,49,08A,1180255,1840545,2003,03/22/2006 09:58:07 PM,41.717717758,-87.615396712,"(41.717717758, -87.615396712)"
-2817826,HJ473247,07/05/2003 01:19:27 AM,050XX W LAKE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,false,1532,015,28,25,08B,1142976,1901959,2003,03/22/2006 09:58:07 PM,41.887019607,-87.750410954,"(41.887019607, -87.750410954)"
-2828556,HJ487268,07/03/2003 05:00:00 PM,021XX E 71ST ST,1120,DECEPTIVE PRACTICE,FORGERY,OTHER,false,false,0333,003,5,43,10,1191447,1858232,2003,03/22/2006 09:58:07 PM,41.765989074,-87.573833752,"(41.765989074, -87.573833752)"
-2814770,HJ470954,07/03/2003 03:00:00 PM,025XX S MICHIGAN AVE,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),false,false,2112,001,2,33,07,1177689,1887544,2003,03/22/2006 09:58:07 PM,41.846746144,-87.623373603,"(41.846746144, -87.623373603)"
-2814742,HJ469340,07/02/2003 03:15:00 AM,058XX N WINTHROP AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,2022,020,48,77,08B,1167775,1938696,2003,03/22/2006 09:58:07 PM,41.98732932,-87.658279816,"(41.98732932, -87.658279816)"
-2808608,HJ464943,07/01/2003 12:01:00 AM,065XX S PULASKI RD,0610,BURGLARY,FORCIBLE ENTRY,GROCERY FOOD STORE,false,false,0833,008,13,65,05,1150790,1860818,2003,03/22/2006 09:58:07 PM,41.773973977,-87.722789273,"(41.773973977, -87.722789273)"
-2814025,HJ467751,07/01/2003 12:01:00 AM,016XX N KILDARE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,2534,025,30,23,05,1147432,1910570,2003,03/22/2006 09:58:07 PM,41.910564878,-87.733826142,"(41.910564878, -87.733826142)"
-2810240,HJ464059,06/30/2003 05:44:46 PM,002XX S STATE ST,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,true,false,0123,001,42,32,26,1176460,1899032,2003,03/22/2006 09:58:07 PM,41.878297763,-87.627537345,"(41.878297763, -87.627537345)"
-2807800,HJ460881,06/29/2003 02:00:00 AM,019XX N KEDZIE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1421,014,35,22,14,1154696,1912714,2003,03/22/2006 09:58:07 PM,41.916305775,-87.707083247,"(41.916305775, -87.707083247)"
-2825502,HJ460047,06/28/2003 06:55:57 PM,024XX N LAVERGNE AVE,5001,OTHER OFFENSE,OTHER CRIME INVOLVING PROPERTY,STREET,true,false,2521,025,31,19,26,1142701,1915504,2003,03/22/2006 09:58:07 PM,41.924193722,-87.751083217,"(41.924193722, -87.751083217)"
-2806423,HJ458377,06/28/2003 12:39:02 AM,072XX S HALSTED ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,0732,007,17,68,15,1172268,1856553,2003,03/22/2006 09:58:07 PM,41.761824903,-87.644180287,"(41.761824903, -87.644180287)"
-2804858,HJ458342,06/28/2003 12:15:00 AM,015XX N WELLS ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1821,018,43,8,08B,1174455,1910790,2003,03/22/2006 09:58:07 PM,41.910607365,-87.634547566,"(41.910607365, -87.634547566)"
-2820446,HJ478932,06/27/2003 10:00:00 PM,028XX W SHAKESPEARE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1414,014,35,22,06,1156749,1914262,2003,12/04/2014 12:43:35 PM,41.920512225,-87.699498555,"(41.920512225, -87.699498555)"
-2804167,HJ457871,06/27/2003 07:00:00 PM,077XX N HERMITAGE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2422,024,49,1,08B,1163377,1951256,2003,03/22/2006 09:58:07 PM,42.021888247,-87.674100071,"(42.021888247, -87.674100071)"
-2803099,HJ456252,06/27/2003 12:45:00 AM,061XX S LANGLEY AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0313,003,20,42,14,1181988,1864543,2003,03/22/2006 09:58:07 PM,41.78353103,-87.608308828,"(41.78353103, -87.608308828)"
-2802788,HJ455475,06/26/2003 05:05:00 PM,029XX N LEAVITT ST,1330,CRIMINAL TRESPASS,TO LAND,SIDEWALK,true,false,1913,019,1,5,26,1161228,1919302,2003,03/22/2006 09:58:07 PM,41.934250296,-87.682901341,"(41.934250296, -87.682901341)"
-2802055,HJ453750,06/25/2003 09:20:00 PM,001XX W 87TH ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,0622,006,21,44,06,1176932,1847317,2003,03/22/2006 09:58:07 PM,41.736376406,-87.627364041,"(41.736376406, -87.627364041)"
-2804532,HJ455051,06/25/2003 04:00:00 PM,009XX E 104TH ST,0460,BATTERY,SIMPLE,RESIDENCE,false,true,0512,005,9,50,08B,1184181,1836149,2003,03/22/2006 09:58:07 PM,41.705563859,-87.601154398,"(41.705563859, -87.601154398)"
-2824987,HJ452621,06/25/2003 12:45:00 PM,039XX W GRENSHAW ST,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),STREET,true,false,1132,011,24,29,18,1150418,1894713,2003,03/22/2006 09:58:07 PM,41.866993799,-87.723270756,"(41.866993799, -87.723270756)"
-2804205,HJ452938,06/25/2003 09:00:00 AM,016XX W SHERWIN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE PORCH/HALLWAY,false,false,2423,024,49,1,14,1163943,1948670,2003,03/22/2006 09:58:07 PM,42.014780239,-87.672090699,"(42.014780239, -87.672090699)"
-2809059,HJ451882,06/25/2003 03:49:41 AM,037XX S VINCENNES AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA APARTMENT,false,false,0212,002,4,35,08B,1180841,1880237,2003,03/22/2006 09:58:07 PM,41.826623147,-87.6120312,"(41.826623147, -87.6120312)"
-2801436,HJ451230,06/24/2003 07:35:00 PM,065XX W FULLERTON AVE,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,2512,025,36,19,06,1132458,1915278,2003,12/04/2014 12:43:35 PM,41.923758237,-87.788726112,"(41.923758237, -87.788726112)"
-2801724,HJ451245,06/24/2003 06:40:00 PM,001XX S KILDARE AVE,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,false,false,1115,011,28,26,04B,1147714,1898798,2003,03/22/2006 09:58:07 PM,41.878255826,-87.733092714,"(41.878255826, -87.733092714)"
-2799616,HJ450372,06/24/2003 12:20:00 PM,026XX S CENTRAL PARK AVE,1565,SEX OFFENSE,INDECENT SOLICITATION/CHILD,STREET,false,false,1032,010,22,30,17,1152748,1886486,2003,03/22/2006 09:58:07 PM,41.84437219,-87.714934485,"(41.84437219, -87.714934485)"
-2801094,HJ449678,06/24/2003 06:50:00 AM,069XX W GEORGE ST,0850,THEFT,ATTEMPT THEFT,STREET,false,false,2511,025,36,18,06,1129555,1918541,2003,03/22/2006 09:58:07 PM,41.932762409,-87.799318393,"(41.932762409, -87.799318393)"
-2797776,HJ448915,06/23/2003 07:58:27 PM,001XX N STATE ST,0820,THEFT,$500 AND UNDER,SMALL RETAIL STORE,false,false,0122,001,42,32,06,1176299,1901295,2003,12/04/2014 12:43:35 PM,41.884511195,-87.62806021,"(41.884511195, -87.62806021)"
-2799702,HJ448302,06/23/2003 03:37:56 PM,062XX S COTTAGE GROVE AVE,1330,CRIMINAL TRESPASS,TO LAND,PARKING LOT/GARAGE(NON.RESID.),true,false,0313,003,20,42,26,1182592,1863962,2003,03/22/2006 09:58:07 PM,41.781922716,-87.606112394,"(41.781922716, -87.606112394)"
-2795753,HJ447662,06/23/2003 09:00:00 AM,0000X E 8TH ST,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,0132,001,2,32,26,1176482,1896706,2003,03/22/2006 09:58:07 PM,41.871914584,-87.627526797,"(41.871914584, -87.627526797)"
-2794810,HJ446532,06/22/2003 07:33:59 PM,050XX S PAULINA ST,0460,BATTERY,SIMPLE,STREET,false,false,0931,,16,61,08B,1165815,1871547,2003,03/22/2006 09:58:07 PM,41.803109775,-87.667405667,"(41.803109775, -87.667405667)"
-2794951,HJ445698,06/21/2003 07:00:00 PM,043XX N KEYSTONE AVE,0460,BATTERY,SIMPLE,NURSING HOME/RETIREMENT HOME,false,false,1722,017,39,16,08B,1148467,1928709,2003,03/22/2006 09:58:07 PM,41.960319954,-87.729554605,"(41.960319954, -87.729554605)"
-2793702,HJ443234,06/21/2003 02:54:00 AM,006XX W BARRY AVE,0291,CRIM SEXUAL ASSAULT,ATTEMPT NON-AGGRAVATED,SIDEWALK,true,false,2332,019,44,6,02,1171641,1920673,2003,03/22/2006 09:58:07 PM,41.937789216,-87.644593458,"(41.937789216, -87.644593458)"
-2792895,HJ442622,06/20/2003 07:40:00 PM,002XX W 119TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,COMMERCIAL / BUSINESS OFFICE,false,false,0522,,34,53,14,1176941,1826072,2003,03/22/2006 09:58:07 PM,41.678076946,-87.627968622,"(41.678076946, -87.627968622)"
-2813381,HJ441188,06/20/2003 08:05:00 AM,005XX W DIVISION ST,2024,NARCOTICS,POSS: HEROIN(WHITE),CHA PARKING LOT/GROUNDS,true,false,1821,018,27,8,18,1172311,1908305,2003,03/22/2006 09:58:07 PM,41.903836054,-87.64249728,"(41.903836054, -87.64249728)"
-2794409,HJ440860,06/20/2003 12:05:00 AM,071XX S CORNELL AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,STREET,false,false,0324,,8,43,04A,1188517,1857704,2003,03/22/2006 09:58:07 PM,41.76461069,-87.584589891,"(41.76461069, -87.584589891)"
-2790031,HJ439961,06/19/2003 03:54:36 PM,008XX E 103RD ST,2850,PUBLIC PEACE VIOLATION,BOMB THREAT,"SCHOOL, PUBLIC, BUILDING",false,false,0512,,9,50,26,1183529,1836806,2003,03/22/2006 09:58:07 PM,41.707381937,-87.603521549,"(41.707381937, -87.603521549)"
-2788903,HJ438572,06/18/2003 11:39:00 PM,033XX S LOWE AVE,0810,THEFT,OVER $500,STREET,true,false,0924,,11,60,06,1172464,1882994,2003,12/04/2014 12:43:35 PM,41.834377488,-87.642683307,"(41.834377488, -87.642683307)"
-2791499,HJ438772,06/18/2003 12:00:00 PM,020XX N ORLEANS ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1814,018,43,7,07,1173535,1913606,2003,03/22/2006 09:58:07 PM,41.918355118,-87.637843384,"(41.918355118, -87.637843384)"
-2786955,HJ436223,06/17/2003 11:42:25 PM,043XX S DR MARTIN LUTHER KING JR DR,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,false,0222,,3,38,08B,1179623,1876246,2003,03/22/2006 09:58:07 PM,41.815699503,-87.616621901,"(41.815699503, -87.616621901)"
-2800085,HJ434896,06/17/2003 01:37:58 PM,078XX S SAGINAW AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0421,004,7,43,18,1195251,1853128,2003,03/22/2006 09:58:07 PM,41.751890305,-87.560059216,"(41.751890305, -87.560059216)"
-2795842,HJ446366,06/16/2003 09:00:00 PM,029XX S DR MARTIN LUTHER KING JR DR,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2122,001,4,35,05,1179425,1885823,2003,03/22/2006 09:58:07 PM,41.841984044,-87.617055292,"(41.841984044, -87.617055292)"
-2785556,HJ433502,06/16/2003 07:20:00 PM,001XX W 113TH PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0522,,34,49,08B,1177202,1829729,2003,03/22/2006 09:58:07 PM,41.68810644,-87.626903516,"(41.68810644, -87.626903516)"
-2889358,HJ524560,06/16/2003 05:53:00 PM,062XX S WESTERN AVE,1120,DECEPTIVE PRACTICE,FORGERY,OTHER,false,false,0825,008,15,66,10,1161493,1862851,2003,03/22/2006 09:58:07 PM,41.779337602,-87.683497577,"(41.779337602, -87.683497577)"
-2808088,HJ432451,06/16/2003 03:00:00 AM,030XX S HAMLIN AVE,0460,BATTERY,SIMPLE,RESIDENCE,true,true,1031,010,22,30,08B,1151489,1883889,2003,06/02/2010 10:34:17 AM,41.837270476,-87.719622941,"(41.837270476, -87.719622941)"
-2781435,HJ429520,06/14/2003 09:40:00 PM,023XX W ADAMS ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1211,,2,28,14,1161041,1898987,2003,03/22/2006 09:58:07 PM,41.878508272,-87.684153572,"(41.878508272, -87.684153572)"
-2781390,HJ428085,06/14/2003 08:15:00 AM,087XX S ESCANABA AVE,0560,ASSAULT,SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,0423,,10,46,08A,1196869,1847446,2003,03/22/2006 09:58:07 PM,41.736258403,-87.554318613,"(41.736258403, -87.554318613)"
-2780504,HJ427170,06/13/2003 07:50:00 PM,015XX S CALIFORNIA BLVD,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,1022,,12,29,06,1157889,1892642,2003,03/22/2006 09:58:07 PM,41.861161794,-87.695900078,"(41.861161794, -87.695900078)"
-2784040,HJ426673,06/13/2003 03:45:00 PM,060XX S PRAIRIE AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0311,,20,40,05,1179041,1864806,2003,03/22/2006 09:58:07 PM,41.784320408,-87.619105448,"(41.784320408, -87.619105448)"
-2784643,HJ425169,06/12/2003 08:30:00 PM,030XX W POLK ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1134,,28,27,14,1155966,1896168,2003,03/22/2006 09:58:07 PM,41.870876474,-87.702863989,"(41.870876474, -87.702863989)"
-2778096,HJ425014,06/12/2003 06:50:00 PM,054XX N CLARK ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2012,,40,77,06,1165023,1936158,2003,12/04/2014 12:43:35 PM,41.980424045,-87.668474198,"(41.980424045, -87.668474198)"
-2801290,HJ423973,06/12/2003 10:20:38 AM,050XX W ERIE ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1532,015,28,25,26,1142908,1903815,2003,03/22/2006 09:58:07 PM,41.892113959,-87.750614386,"(41.892113959, -87.750614386)"
-2777154,HJ423283,06/11/2003 11:02:40 PM,003XX N CENTRAL AVE,0460,BATTERY,SIMPLE,STREET,false,false,1523,,28,25,08B,1139029,1901505,2003,03/22/2006 09:58:07 PM,41.885846434,-87.764916687,"(41.885846434, -87.764916687)"
-2792942,HJ443548,06/11/2003 09:50:00 PM,048XX N DAMEN AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,STREET,false,false,2032,,47,4,04A,1162102,1931886,2003,03/22/2006 09:58:07 PM,41.968763213,-87.679336508,"(41.968763213, -87.679336508)"
-2776590,HJ422220,06/11/2003 02:38:12 PM,031XX S RACINE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0924,,11,60,06,1168788,1883916,2003,12/04/2014 12:43:35 PM,41.836987856,-87.656144776,"(41.836987856, -87.656144776)"
-2776143,HJ423012,06/11/2003 01:30:00 PM,002XX W ADAMS ST,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,0112,,2,32,06,1174679,1899372,2003,12/04/2014 12:43:35 PM,41.879270748,-87.634066541,"(41.879270748, -87.634066541)"
-2774846,HJ420999,06/10/2003 10:17:57 PM,001XX N CARPENTER ST,0880,THEFT,PURSE-SNATCHING,SIDEWALK,false,false,1212,,27,28,06,1169484,1900823,2003,03/22/2006 09:58:07 PM,41.88336696,-87.65309935,"(41.88336696, -87.65309935)"
-2774598,HJ419923,06/10/2003 01:45:00 PM,049XX N LINCOLN AVE,0460,BATTERY,SIMPLE,OTHER,false,false,2031,,47,4,08B,1159133,1932853,2003,03/22/2006 09:58:07 PM,41.97147838,-87.690226746,"(41.97147838, -87.690226746)"
-2776723,HJ418525,06/09/2003 04:10:00 PM,054XX S BISHOP ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,RESIDENCE PORCH/HALLWAY,false,false,0933,,16,61,04B,1167552,1868632,2003,03/22/2006 09:58:07 PM,41.795073572,-87.661118879,"(41.795073572, -87.661118879)"
-2772755,HJ416939,06/09/2003 03:00:00 AM,004XX W 79TH ST,0281,CRIM SEXUAL ASSAULT,NON-AGGRAVATED,HOTEL/MOTEL,false,false,0621,,17,44,02,1174715,1852503,2003,06/02/2010 10:34:17 AM,41.750657067,-87.635332229,"(41.750657067, -87.635332229)"
-2771288,HJ416675,06/09/2003 01:05:00 AM,081XX S EUCLID AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0414,,8,46,08B,1190565,1851601,2003,03/22/2006 09:58:07 PM,41.747814369,-87.57728013,"(41.747814369, -87.57728013)"
-2780975,HJ420632,06/08/2003 02:00:00 PM,031XX W HARRISON ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,1134,,24,27,06,1155221,1897148,2003,03/22/2006 09:58:07 PM,41.873580679,-87.705572846,"(41.873580679, -87.705572846)"
-2786981,HJ415332,06/08/2003 01:00:00 PM,008XX E 63RD ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0312,,20,42,18,1182652,1863371,2003,03/22/2006 09:58:07 PM,41.780299565,-87.60591075,"(41.780299565, -87.60591075)"
-2774519,HJ413255,06/07/2003 01:14:34 PM,053XX W HARRISON ST,0560,ASSAULT,SIMPLE,ALLEY,false,false,1522,,29,25,08A,1140796,1896839,2003,03/22/2006 09:58:07 PM,41.873010032,-87.758542553,"(41.873010032, -87.758542553)"
-2770700,HJ416502,06/07/2003 01:00:00 PM,016XX S MILLER ST,0890,THEFT,FROM BUILDING,APARTMENT,false,true,1233,,25,31,06,1169831,1892033,2003,03/22/2006 09:58:07 PM,41.859238981,-87.65208132,"(41.859238981, -87.65208132)"
-2768672,HJ409666,06/06/2003 07:35:00 PM,032XX W 26TH ST,0460,BATTERY,SIMPLE,SMALL RETAIL STORE,false,false,1024,,22,30,08B,1155109,1886590,2003,03/22/2006 09:58:07 PM,41.844610601,-87.706267168,"(41.844610601, -87.706267168)"
-2768751,HJ412734,06/06/2003 07:00:00 PM,058XX S STATE ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0233,,20,40,06,1177320,1866043,2003,12/04/2014 12:43:35 PM,41.787753917,-87.625377921,"(41.787753917, -87.625377921)"
-2773907,HJ410562,06/06/2003 04:40:00 PM,049XX W WELLINGTON AVE,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,"SCHOOL, PUBLIC, GROUNDS",true,false,2521,,31,19,04A,1143177,1919539,2003,03/22/2006 09:58:07 PM,41.935257279,-87.749233173,"(41.935257279, -87.749233173)"
-2770531,HJ410013,06/05/2003 11:10:00 PM,018XX N MONITOR AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,2531,,29,25,08B,1137091,1911853,2003,03/22/2006 09:58:07 PM,41.914277637,-87.771784808,"(41.914277637, -87.771784808)"
-2773478,HJ410975,06/05/2003 08:00:00 PM,068XX S MORGAN ST,0460,BATTERY,SIMPLE,STREET,false,false,0724,,17,68,08B,1170776,1859726,2003,03/22/2006 09:58:07 PM,41.770564681,-87.649556173,"(41.770564681, -87.649556173)"
-2788811,HJ000041,06/05/2003 02:30:00 AM,130XX S DR MARTIN LUTHER KING JR DR,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0533,,9,54,18,1180990,1819139,2003,03/22/2006 09:58:07 PM,41.658959837,-87.613359979,"(41.658959837, -87.613359979)"
-2764599,HJ407575,06/04/2003 05:00:00 AM,065XX S CAMPBELL AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0832,,15,66,05,1160874,1861160,2003,03/22/2006 09:58:07 PM,41.774710076,-87.68581359,"(41.774710076, -87.68581359)"
-2763515,HJ405870,06/03/2003 09:42:49 PM,008XX W 71ST ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,true,0733,,17,68,14,1171834,1857812,2003,03/22/2006 09:58:07 PM,41.765289285,-87.645734055,"(41.765289285, -87.645734055)"
-2763904,HJ403715,06/02/2003 09:18:52 PM,030XX S KEELEY ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0923,,11,60,14,1170072,1884411,2003,03/22/2006 09:58:07 PM,41.838318306,-87.651418855,"(41.838318306, -87.651418855)"
-2798954,HJ451204,06/02/2003 07:00:00 AM,016XX W WOLFRAM ST,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,1931,019,32,6,26,1164921,1918941,2003,03/22/2006 09:58:07 PM,41.933182007,-87.669339922,"(41.933182007, -87.669339922)"
-2758768,HJ401987,06/02/2003 12:00:00 AM,047XX W CORNELIA AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,1731,,30,15,05,1144348,1923025,2003,03/22/2006 09:58:07 PM,41.944801211,-87.744841697,"(41.944801211, -87.744841697)"
-2757829,HJ400230,06/01/2003 02:30:00 AM,020XX N SEMINARY AVE,0810,THEFT,OVER $500,RESIDENTIAL YARD (FRONT/BACK),false,false,1811,,43,7,06,1168591,1914056,2003,12/04/2014 12:43:35 PM,41.919698529,-87.655994825,"(41.919698529, -87.655994825)"
-2757523,HJ400150,06/01/2003 01:00:00 AM,049XX S WESTERN AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,CTA TRAIN,false,false,0915,,12,63,14,1161225,1872072,2003,03/22/2006 09:58:07 PM,41.804646794,-87.684224857,"(41.804646794, -87.684224857)"
-2765831,HJ398612,05/31/2003 12:00:48 PM,057XX S THROOP ST,2017,NARCOTICS,MANU/DELIVER:CRACK,SIDEWALK,true,false,0713,,16,67,18,1168678,1866716,2003,03/22/2006 09:58:07 PM,41.78979161,-87.657045102,"(41.78979161, -87.657045102)"
-2789621,HJ439266,05/30/2003 04:00:00 PM,011XX S WABASH AVE,0890,THEFT,FROM BUILDING,COLLEGE/UNIVERSITY GROUNDS,false,false,0132,,2,32,06,1176901,1895697,2003,03/22/2006 09:58:07 PM,41.869136362,-87.626019018,"(41.869136362, -87.626019018)"
-2757112,HJ399264,05/29/2003 10:30:00 PM,050XX W ERIE ST,5001,OTHER OFFENSE,OTHER CRIME INVOLVING PROPERTY,STREET,true,false,1532,015,28,25,26,1142906,1903895,2003,03/22/2006 09:58:07 PM,41.892333526,-87.750619736,"(41.892333526, -87.750619736)"
-2754350,HJ395098,05/29/2003 06:59:21 PM,014XX S BLUE ISLAND AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,true,true,1231,,2,28,14,1168439,1893306,2003,03/22/2006 09:58:07 PM,41.862762394,-87.657154087,"(41.862762394, -87.657154087)"
-2755595,HJ394133,05/29/2003 12:30:00 PM,065XX S STONY ISLAND AVE,0560,ASSAULT,SIMPLE,RESTAURANT,true,false,0321,,5,42,08A,1187979,1861565,2003,03/22/2006 09:58:07 PM,41.775218436,-87.586438883,"(41.775218436, -87.586438883)"
-2763870,HJ392344,05/28/2003 02:25:00 PM,047XX S UNION AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,"SCHOOL, PUBLIC, BUILDING",false,false,0935,,11,61,14,1172475,1873200,2003,03/22/2006 09:58:07 PM,41.807501584,-87.642931704,"(41.807501584, -87.642931704)"
-2764492,HJ407985,05/27/2003 05:30:00 PM,041XX N LEAVITT ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,1912,,47,5,05,1160905,1927668,2003,03/22/2006 09:58:07 PM,41.957213801,-87.683855398,"(41.957213801, -87.683855398)"
-2788215,HJ437233,05/26/2003 05:30:00 PM,035XX N WILTON AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,false,false,2331,,44,6,11,,,2003,03/22/2006 09:58:07 PM,,,
-2748594,HJ387869,05/25/2003 11:00:00 PM,052XX S NORMANDY AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0811,,23,56,14,1132562,1869080,2003,03/22/2006 09:58:07 PM,41.796982129,-87.789419367,"(41.796982129, -87.789419367)"
-2767259,HJ386134,05/25/2003 01:50:00 PM,052XX W NORTH AVE,1110,DECEPTIVE PRACTICE,BOGUS CHECK,CURRENCY EXCHANGE,false,false,2532,,37,25,11,1141443,1910181,2003,03/22/2006 09:58:07 PM,41.909610179,-87.755837389,"(41.909610179, -87.755837389)"
-2752497,HJ386709,05/25/2003 08:45:00 AM,022XX S SPAULDING AVE,0820,THEFT,$500 AND UNDER,RESIDENCE PORCH/HALLWAY,false,false,1024,,22,30,06,1154748,1889010,2003,12/04/2014 12:43:35 PM,41.85125859,-87.707527272,"(41.85125859, -87.707527272)"
-2746347,HJ385265,05/24/2003 07:00:00 PM,006XX W FULLERTON PKWY,0820,THEFT,$500 AND UNDER,ALLEY,false,false,1812,,43,7,06,1171501,1916157,2003,12/04/2014 12:43:35 PM,41.925400204,-87.645241182,"(41.925400204, -87.645241182)"
-2885741,HJ382661,05/23/2003 06:40:28 PM,036XX S FEDERAL ST,2024,NARCOTICS,POSS: HEROIN(WHITE),CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,0211,002,3,35,18,1176248,1880524,2003,03/22/2006 09:58:07 PM,41.827515281,-87.628873268,"(41.827515281, -87.628873268)"
-2745307,HJ381391,05/22/2003 10:00:00 PM,024XX W BRYN MAWR AVE,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,2011,,40,2,06,1159159,1937152,2003,12/04/2014 12:43:35 PM,41.983274498,-87.690012421,"(41.983274498, -87.690012421)"
-2806403,HJ448897,05/22/2003 07:45:00 PM,029XX N LAWNDALE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2523,025,30,21,08B,1150788,1919457,2003,06/11/2007 03:52:33 PM,41.934886564,-87.721264326,"(41.934886564, -87.721264326)"
-2744658,HJ379539,05/22/2003 11:15:00 AM,001XX N KILDARE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1114,,28,26,08B,1147662,1900449,2003,03/22/2006 09:58:07 PM,41.882787357,-87.73324126,"(41.882787357, -87.73324126)"
-2750694,HJ391100,05/21/2003 10:00:00 PM,018XX E 93RD ST,0560,ASSAULT,SIMPLE,RESIDENCE PORCH/HALLWAY,true,false,0413,,8,48,08A,1189972,1843627,2003,03/22/2006 09:58:07 PM,41.725947222,-87.579708892,"(41.725947222, -87.579708892)"
-2741254,HJ377511,05/21/2003 12:22:00 PM,004XX N CLARK ST,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,1831,,42,8,06,1175501,1903229,2003,12/04/2014 12:43:35 PM,41.889836158,-87.630932408,"(41.889836158, -87.630932408)"
-2742309,HJ377016,05/21/2003 08:38:03 AM,011XX E 132ND ST,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,false,false,0533,,9,54,26,1186088,1817988,2003,03/22/2006 09:58:07 PM,41.655682958,-87.594741325,"(41.655682958, -87.594741325)"
-2732394,HJ364902,05/15/2003 12:15:00 PM,080XX S CHAPPEL AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0414,,8,46,08B,1191225,1851783,2003,03/22/2006 09:58:07 PM,41.748297854,-87.574855847,"(41.748297854, -87.574855847)"
-2731529,HJ363997,05/14/2003 11:04:11 PM,036XX S FEDERAL ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,STREET,true,false,0211,,3,35,26,1176261,1880543,2003,03/22/2006 09:58:07 PM,41.827567126,-87.628825001,"(41.827567126, -87.628825001)"
-2729923,HJ363535,05/14/2003 04:00:00 PM,046XX N CENTRAL PARK AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1723,,33,14,05,1151585,1930797,2003,03/22/2006 09:58:07 PM,41.965988708,-87.718036122,"(41.965988708, -87.718036122)"
-2726766,HJ359444,05/12/2003 06:45:00 PM,065XX S MARYLAND AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0321,,20,42,05,1182982,1861691,2003,03/22/2006 09:58:07 PM,41.775681823,-87.604753113,"(41.775681823, -87.604753113)"
-2726169,HJ358117,05/12/2003 12:07:59 PM,026XX W PERSHING RD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,true,false,0912,,12,58,08B,1159650,1878663,2003,03/22/2006 09:58:07 PM,41.822765812,-87.689820267,"(41.822765812, -87.689820267)"
-2726125,HJ357703,05/12/2003 11:00:00 AM,033XX W WARNER AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1724,017,33,16,07,1153598,1927446,2003,03/22/2006 09:58:07 PM,41.956753441,-87.710724189,"(41.956753441, -87.710724189)"
-2726843,HJ357663,05/11/2003 10:30:00 PM,009XX W 32ND PL,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,0924,,11,60,06,1170205,1883382,2003,12/04/2014 12:43:35 PM,41.835491737,-87.650960813,"(41.835491737, -87.650960813)"
-2722301,HJ353912,05/10/2003 09:15:00 AM,012XX N MILWAUKEE AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,DEPARTMENT STORE,false,false,1433,,1,24,26,1165098,1908710,2003,03/22/2006 09:58:07 PM,41.905103744,-87.668980729,"(41.905103744, -87.668980729)"
-2721644,HJ352983,05/09/2003 08:20:00 PM,076XX S CICERO AVE,0890,THEFT,FROM BUILDING,DEPARTMENT STORE,false,false,0833,,13,65,06,1145766,1853744,2003,03/22/2006 09:58:07 PM,41.754658085,-87.741385006,"(41.754658085, -87.741385006)"
-2721905,HJ352769,05/09/2003 07:15:00 PM,027XX N MILWAUKEE AVE,0820,THEFT,$500 AND UNDER,SMALL RETAIL STORE,true,false,1412,,35,22,06,1153493,1918181,2003,12/04/2014 12:43:35 PM,41.931331697,-87.711357352,"(41.931331697, -87.711357352)"
-2723552,HJ351202,05/09/2003 03:14:00 AM,052XX W GRAND AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,GROCERY FOOD STORE,false,false,2515,,37,19,14,1141369,1912898,2003,03/22/2006 09:58:07 PM,41.917067294,-87.756042055,"(41.917067294, -87.756042055)"
-2732465,HJ350468,05/08/2003 06:16:25 PM,010XX N ST LOUIS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1121,,27,23,08B,1152812,1906658,2003,03/22/2006 09:58:07 PM,41.899725097,-87.714165624,"(41.899725097, -87.714165624)"
-2736686,HJ350474,05/08/2003 06:02:57 PM,006XX E 131ST ST,1661,GAMBLING,GAME/DICE,STREET,true,false,0533,,9,54,19,1182781,1818517,2003,03/22/2006 09:58:07 PM,41.657211735,-87.606825473,"(41.657211735, -87.606825473)"
-2720303,HJ350211,05/08/2003 02:36:00 PM,098XX S CARPENTER ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,2223,,21,73,26,1171094,1839486,2003,03/22/2006 09:58:07 PM,41.715016439,-87.648980535,"(41.715016439, -87.648980535)"
-2719030,HJ348125,05/07/2003 01:30:00 PM,067XX S LANGLEY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0321,,6,42,08B,1182016,1860399,2003,03/22/2006 09:58:07 PM,41.772158853,-87.608334263,"(41.772158853, -87.608334263)"
-2720093,HJ351113,05/07/2003 12:00:00 PM,018XX W 21ST ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,1223,012,25,31,07,1164493,1890146,2003,03/22/2006 09:58:07 PM,41.854175431,-87.671728755,"(41.854175431, -87.671728755)"
-2717743,HJ347712,05/07/2003 10:00:00 AM,039XX N JANSSEN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1923,,47,6,14,1165772,1926644,2003,03/22/2006 09:58:07 PM,41.954301288,-87.665992184,"(41.954301288, -87.665992184)"
-2717026,HJ346962,05/07/2003 03:28:00 AM,077XX N HASKINS AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,CTA GARAGE / OTHER PROPERTY,false,false,2422,,49,1,14,1163046,1951395,2003,03/22/2006 09:58:07 PM,42.022276655,-87.675314225,"(42.022276655, -87.675314225)"
-2730246,HJ344561,05/06/2003 11:45:00 PM,048XX S KILDARE AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0815,,23,57,16,1148543,1872422,2003,03/22/2006 09:58:07 PM,41.805860655,-87.730728289,"(41.805860655, -87.730728289)"
-2716389,HJ346742,05/06/2003 09:00:00 PM,095XX S BALTIMORE AVE,1020,ARSON,BY FIRE,VEHICLE NON-COMMERCIAL,false,false,0431,004,10,51,09,1198604,1841934,2003,03/22/2006 09:58:07 PM,41.721089762,-87.548146527,"(41.721089762, -87.548146527)"
-2716495,HJ344385,05/05/2003 10:15:00 PM,007XX W 92ND ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,true,false,2223,,21,73,14,1173006,1843813,2003,03/22/2006 09:58:07 PM,41.726848424,-87.641850661,"(41.726848424, -87.641850661)"
-2717554,HJ342919,05/05/2003 11:35:00 AM,002XX W 57TH ST,141C,WEAPONS VIOLATION,UNLAWFUL USE OTHER DANG WEAPON,"SCHOOL, PUBLIC, BUILDING",false,false,0711,,20,68,15,1175432,1867127,2003,03/22/2006 09:58:07 PM,41.790770977,-87.632267989,"(41.790770977, -87.632267989)"
-2718002,HJ340534,05/04/2003 12:30:00 AM,039XX W ADAMS ST,0330,ROBBERY,AGGRAVATED,SIDEWALK,false,false,1122,,28,26,03,1150423,1898767,2003,03/22/2006 09:58:07 PM,41.878118338,-87.723146602,"(41.878118338, -87.723146602)"
-2710684,HJ339008,05/02/2003 09:35:00 PM,036XX W 57TH PL,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0822,,14,62,14,1153133,1866279,2003,03/22/2006 09:58:07 PM,41.788913828,-87.714056021,"(41.788913828, -87.714056021)"
-2710340,HJ335726,05/01/2003 02:00:00 PM,066XX S OAKLEY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0832,,15,66,08B,1162253,1860679,2003,03/22/2006 09:58:07 PM,41.773361542,-87.680771744,"(41.773361542, -87.680771744)"
-2737208,HJ334055,04/30/2003 07:30:00 PM,033XX W CHICAGO AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1121,,27,23,18,1153604,1905086,2003,03/22/2006 09:58:07 PM,41.895395657,-87.711298451,"(41.895395657, -87.711298451)"
-2707800,HJ334007,04/30/2003 06:30:00 PM,030XX E CHELTENHAM PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0421,,7,43,08B,1197634,1853415,2003,03/22/2006 09:58:07 PM,41.75261875,-87.551317244,"(41.75261875, -87.551317244)"
-2705645,HJ332245,04/29/2003 10:45:00 PM,087XX S COLFAX AVE,0330,ROBBERY,AGGRAVATED,RESIDENCE,false,false,0423,,7,46,03,1195050,1847403,2003,03/22/2006 09:58:07 PM,41.736185395,-87.560984074,"(41.736185395, -87.560984074)"
-2706676,HJ334303,04/29/2003 07:20:00 PM,020XX W HADDON AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1312,,32,24,26,1162699,1907610,2003,03/22/2006 09:58:07 PM,41.902135907,-87.67782387,"(41.902135907, -87.67782387)"
-2706125,HJ332722,04/29/2003 07:00:00 PM,009XX N PARKSIDE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1511,015,29,25,07,1138546,1905986,2003,03/22/2006 09:58:07 PM,41.898151626,-87.7665817,"(41.898151626, -87.7665817)"
-2704766,HJ331372,04/29/2003 07:00:00 AM,050XX W CRYSTAL ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,2533,,37,25,14,1142412,1907809,2003,03/22/2006 09:58:07 PM,41.903083188,-87.75233667,"(41.903083188, -87.75233667)"
-2704035,HJ328886,04/28/2003 03:00:00 PM,064XX S EVANS AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,STREET,false,true,0312,,20,42,04A,1182279,1862734,2003,03/22/2006 09:58:07 PM,41.778560228,-87.607297932,"(41.778560228, -87.607297932)"
-2703305,HJ327392,04/27/2003 09:30:00 PM,036XX W 59TH PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,0822,,13,65,08B,1153126,1864953,2003,03/22/2006 09:58:07 PM,41.785275224,-87.714116704,"(41.785275224, -87.714116704)"
-2701752,HJ328204,04/27/2003 07:30:00 PM,066XX W 64TH PL,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0812,,23,64,14,1132916,1861051,2003,03/22/2006 09:58:07 PM,41.774942902,-87.788307818,"(41.774942902, -87.788307818)"
-2703206,HJ326429,04/26/2003 07:35:00 PM,024XX N LINCOLN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,SMALL RETAIL STORE,false,false,1933,,43,7,14,1170158,1916472,2003,03/22/2006 09:58:07 PM,41.926294047,-87.650166749,"(41.926294047, -87.650166749)"
-2702975,HJ323783,04/26/2003 12:15:00 AM,044XX S KEATING AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0815,,23,56,14,1145467,1875041,2003,03/22/2006 09:58:07 PM,41.81310622,-87.741943967,"(41.81310622, -87.741943967)"
-2699556,HJ322914,04/25/2003 04:35:36 PM,003XX E 57TH ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0234,,20,40,07,1179721,1867345,2003,03/22/2006 09:58:07 PM,41.791272138,-87.616534714,"(41.791272138, -87.616534714)"
-2707944,HJ321543,04/24/2003 10:40:00 PM,108XX S THROOP ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,2234,,34,75,18,1169640,1832489,2003,03/22/2006 09:58:07 PM,41.695847166,-87.654507737,"(41.695847166, -87.654507737)"
-2697221,HJ319692,04/24/2003 12:00:00 AM,074XX N WINCHESTER AVE,0320,ROBBERY,STRONGARM - NO WEAPON,ALLEY,false,false,2424,,49,1,03,1162083,1949532,2003,03/22/2006 09:58:07 PM,42.017184808,-87.67891053,"(42.017184808, -87.67891053)"
-2699944,HJ317710,04/23/2003 08:33:32 AM,045XX S PRAIRIE AVE,0325,ROBBERY,VEHICULAR HIJACKING,STREET,false,false,0221,002,3,38,03,1178762,1874943,2003,03/22/2006 09:58:07 PM,41.812143623,-87.619819865,"(41.812143623, -87.619819865)"
-2801758,HJ454880,04/23/2003 07:50:00 AM,011XX N MONTICELLO AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1112,011,27,23,26,1151794,1907311,2003,03/22/2006 09:58:07 PM,41.901537093,-87.717887561,"(41.901537093, -87.717887561)"
-2710030,HJ317238,04/22/2003 09:42:17 PM,019XX W BELMONT AVE,2220,LIQUOR LAW VIOLATION,ILLEGAL POSSESSION BY MINOR,STREET,true,false,1924,,32,5,22,1162870,1921300,2003,03/22/2006 09:58:07 PM,41.939698582,-87.67681079,"(41.939698582, -87.67681079)"
-2694984,HJ318760,04/22/2003 07:30:00 PM,037XX W AINSLIE ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1712,017,39,14,07,1150500,1932338,2003,03/22/2006 09:58:07 PM,41.970238627,-87.721985059,"(41.970238627, -87.721985059)"
-2694742,HJ316017,04/22/2003 08:00:00 AM,009XX N AVERS AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,1112,011,27,23,05,1150585,1906125,2003,03/22/2006 09:58:07 PM,41.898306305,-87.722359407,"(41.898306305, -87.722359407)"
-2695340,HJ315800,04/21/2003 05:30:00 PM,016XX E 87TH ST,0560,ASSAULT,SIMPLE,DRUG STORE,false,false,0412,,8,45,08A,1188760,1847661,2003,03/22/2006 09:58:07 PM,41.73704596,-87.584019822,"(41.73704596, -87.584019822)"
-2691549,HJ315396,04/21/2003 01:30:00 PM,0000X E ELM ST,0890,THEFT,FROM BUILDING,APARTMENT,false,false,1824,,42,8,06,1176876,1908081,2003,03/22/2006 09:58:07 PM,41.903119284,-87.625735906,"(41.903119284, -87.625735906)"
-2725442,HJ312473,04/20/2003 03:35:00 PM,006XX N LATROBE AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),RESIDENCE,true,false,1524,,28,25,18,1141292,1903601,2003,03/22/2006 09:58:07 PM,41.89155668,-87.756554621,"(41.89155668, -87.756554621)"
-2703414,HJ310904,04/19/2003 06:40:00 PM,055XX S MAY ST,2017,NARCOTICS,MANU/DELIVER:CRACK,SIDEWALK,true,false,0712,,16,68,18,1169633,1868161,2003,03/22/2006 09:58:07 PM,41.793736188,-87.653501504,"(41.793736188, -87.653501504)"
-2689357,HJ309579,04/19/2003 03:15:00 AM,056XX W WEST END AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,1512,,29,25,03,1139007,1900957,2003,03/22/2006 09:58:07 PM,41.884343051,-87.765010804,"(41.884343051, -87.765010804)"
-2689007,HJ309503,04/19/2003 02:05:45 AM,021XX W 83RD ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0614,,18,71,14,1163799,1849554,2003,03/22/2006 09:58:07 PM,41.742800621,-87.675416333,"(41.742800621, -87.675416333)"
-2688217,HJ308151,04/18/2003 12:11:59 PM,002XX W 95TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,ABANDONED BUILDING,false,false,0511,,21,49,14,1176141,1841898,2003,03/22/2006 09:58:07 PM,41.721523727,-87.630424107,"(41.721523727, -87.630424107)"
-2686320,HJ304874,04/16/2003 05:37:18 PM,087XX S SOUTH CHICAGO AVE,1345,CRIMINAL DAMAGE,TO CITY OF CHICAGO PROPERTY,STREET,false,false,0423,,7,48,14,1193741,1847722,2003,03/22/2006 09:58:07 PM,41.737092895,-87.565769275,"(41.737092895, -87.565769275)"
-2695929,HJ319946,04/16/2003 11:24:00 AM,012XX S MICHIGAN AVE,1170,DECEPTIVE PRACTICE,IMPERSONATION,RESIDENCE,false,false,0132,,2,33,11,1177377,1894963,2003,03/22/2006 09:58:07 PM,41.867111445,-87.624293779,"(41.867111445, -87.624293779)"
-2682474,HJ302793,04/15/2003 06:35:00 PM,015XX W WASHBURNE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1231,,2,28,14,1166073,1894507,2003,03/22/2006 09:58:07 PM,41.866108849,-87.665805095,"(41.866108849, -87.665805095)"
-2683064,HJ303009,04/15/2003 09:00:00 AM,018XX W GARFIELD BLVD,0610,BURGLARY,FORCIBLE ENTRY,"SCHOOL, PRIVATE, BUILDING",false,false,0932,,16,61,05,1165282,1868251,2003,03/22/2006 09:58:07 PM,41.79407647,-87.669453787,"(41.79407647, -87.669453787)"
-2680921,HJ301524,04/14/2003 11:50:00 PM,033XX W POLK ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1134,,24,27,14,1154400,1896216,2003,03/22/2006 09:58:07 PM,41.87103961,-87.708612073,"(41.87103961, -87.708612073)"
-2681070,HJ301460,04/14/2003 06:00:00 PM,003XX E 87TH PL,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0632,006,6,44,07,1180284,1846902,2003,03/22/2006 09:58:07 PM,41.735161487,-87.615096229,"(41.735161487, -87.615096229)"
-2722206,HJ341386,04/14/2003 01:00:00 PM,076XX S CICERO AVE,0860,THEFT,RETAIL THEFT,RESTAURANT,true,false,0833,,13,65,06,1145766,1853744,2003,06/02/2010 10:34:17 AM,41.754658085,-87.741385006,"(41.754658085, -87.741385006)"
-2678916,HJ299127,04/14/2003 02:40:00 AM,058XX S SACRAMENTO AVE,031A,ROBBERY,ARMED: HANDGUN,STREET,false,false,0824,,14,63,03,1157341,1865679,2003,03/22/2006 09:58:07 PM,41.787183133,-87.698642843,"(41.787183133, -87.698642843)"
-2681644,HJ298689,04/13/2003 09:06:30 PM,042XX S RICHMOND ST,0460,BATTERY,SIMPLE,STREET,true,false,0912,,14,58,08B,1157370,1876018,2003,03/22/2006 09:58:07 PM,41.815554154,-87.698256423,"(41.815554154, -87.698256423)"
-2679905,HJ298553,04/13/2003 07:54:25 PM,112XX S EDBROOKE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0531,,9,49,08B,1179251,1830138,2003,03/22/2006 09:58:07 PM,41.689182441,-87.619389929,"(41.689182441, -87.619389929)"
-2680572,HJ301111,04/13/2003 03:00:00 PM,122XX S PRINCETON AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,true,0523,,34,53,06,1176525,1823975,2003,03/22/2006 09:58:07 PM,41.672331785,-87.629554013,"(41.672331785, -87.629554013)"
-2678193,HJ297284,04/13/2003 02:55:45 AM,103XX S EWING AVE,1220,DECEPTIVE PRACTICE,THEFT OF LOST/MISLAID PROP,STREET,true,false,0432,,10,52,11,1202117,1837266,2003,06/11/2007 03:52:33 PM,41.70819176,-87.535437913,"(41.70819176, -87.535437913)"
-2678094,HJ295467,04/11/2003 11:00:00 PM,0000X E 102ND PL,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0511,005,9,49,07,1178734,1837071,2003,03/22/2006 09:58:07 PM,41.708219309,-87.621072742,"(41.708219309, -87.621072742)"
-2687576,HJ292511,04/10/2003 08:15:09 PM,059XX S STATE ST,1330,CRIMINAL TRESPASS,TO LAND,GAS STATION,true,false,0233,,20,40,26,1177324,1865895,2003,03/22/2006 09:58:07 PM,41.7873477,-87.625367723,"(41.7873477, -87.625367723)"
-2675688,HJ290805,04/10/2003 03:40:00 AM,057XX S LAFLIN ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0713,,16,67,14,1167277,1866686,2003,03/22/2006 09:58:07 PM,41.789739413,-87.662183016,"(41.789739413, -87.662183016)"
-2673824,HJ290578,04/09/2003 10:00:00 PM,004XX W 45TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,0935,,3,37,08B,1173979,1875053,2003,03/22/2006 09:58:07 PM,41.812553109,-87.637360446,"(41.812553109, -87.637360446)"
-2672809,HJ290727,04/09/2003 05:51:00 PM,046XX S CICERO AVE,0460,BATTERY,SIMPLE,RESTAURANT,false,false,0814,,23,56,08B,1145094,1873703,2003,03/22/2006 09:58:07 PM,41.809441579,-87.74334586,"(41.809441579, -87.74334586)"
-2675782,HJ289926,04/09/2003 05:22:00 PM,020XX N KIMBALL AVE,1330,CRIMINAL TRESPASS,TO LAND,OTHER,false,false,1413,,35,22,26,1153417,1913438,2003,03/22/2006 09:58:07 PM,41.918318026,-87.711762983,"(41.918318026, -87.711762983)"
-2690953,HJ313839,04/07/2003 12:00:00 PM,054XX S KIMBARK AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2131,,4,41,14,1185534,1869843,2003,03/22/2006 09:58:07 PM,41.797991872,-87.595141278,"(41.797991872, -87.595141278)"
-2667583,HJ283299,04/06/2003 11:05:00 AM,025XX E 79TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0421,,7,43,08B,1194397,1853116,2003,03/22/2006 09:58:07 PM,41.751878396,-87.563189066,"(41.751878396, -87.563189066)"
-2665041,HJ280666,04/04/2003 06:00:00 PM,053XX S BLACKSTONE AVE,1220,DECEPTIVE PRACTICE,THEFT OF LOST/MISLAID PROP,STREET,false,false,2131,,4,41,11,1186771,1870408,2003,03/22/2006 09:58:07 PM,41.799513034,-87.590587149,"(41.799513034, -87.590587149)"
-2663492,HJ278703,04/03/2003 04:00:00 PM,028XX W SHERWIN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2411,,50,2,14,1156278,1948571,2003,03/22/2006 09:58:07 PM,42.014667592,-87.700297714,"(42.014667592, -87.700297714)"
-2665045,HJ280676,04/03/2003 02:25:00 PM,073XX S CARPENTER ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0733,,17,68,08B,1170629,1855953,2003,03/22/2006 09:58:07 PM,41.760214302,-87.650204856,"(41.760214302, -87.650204856)"
-2663850,HJ276078,04/02/2003 04:25:50 PM,049XX W MADISON ST,0560,ASSAULT,SIMPLE,RESTAURANT,false,false,1533,,28,25,08A,1143161,1899522,2003,03/22/2006 09:58:07 PM,41.880328732,-87.749792429,"(41.880328732, -87.749792429)"
-2766902,HJ407382,04/01/2003 06:00:00 AM,015XX N HARDING AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2535,,30,23,07,1149719,1910147,2003,03/22/2006 09:58:07 PM,41.909359954,-87.7254355,"(41.909359954, -87.7254355)"
-2663646,HJ271347,03/31/2003 01:17:50 PM,054XX W DIVISION ST,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,ALLEY,false,false,1524,015,37,25,07,1139875,1907424,2003,03/22/2006 09:58:07 PM,41.902073475,-87.761665129,"(41.902073475, -87.761665129)"
-2668893,HJ271106,03/31/2003 11:45:00 AM,065XX S WOOD ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0725,,15,67,18,1165516,1860921,2003,03/22/2006 09:58:07 PM,41.773957058,-87.668803452,"(41.773957058, -87.668803452)"
-2656378,HJ270450,03/31/2003 02:00:00 AM,015XX N MILWAUKEE AVE,0810,THEFT,OVER $500,BAR OR TAVERN,false,false,1424,,1,24,06,1163167,1910229,2003,12/04/2014 12:43:35 PM,41.909312808,-87.676031127,"(41.909312808, -87.676031127)"
-2656737,HJ269927,03/30/2003 05:00:00 PM,032XX W 38TH PL,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0913,009,12,58,05,1155023,1878842,2003,03/22/2006 09:58:07 PM,41.823350855,-87.70679017,"(41.823350855, -87.70679017)"
-2675147,HJ269204,03/30/2003 11:30:00 AM,095XX S BISHOP ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2213,,21,73,18,1168361,1841632,2003,03/22/2006 09:58:07 PM,41.720964566,-87.658928445,"(41.720964566, -87.658928445)"
-2656632,HJ267146,03/28/2003 07:00:00 PM,030XX N PARKSIDE AVE,0810,THEFT,OVER $500,DRIVEWAY - RESIDENTIAL,false,false,2514,,31,19,06,1138213,1919702,2003,12/04/2014 12:43:35 PM,41.935795925,-87.767472403,"(41.935795925, -87.767472403)"
-2657511,HJ266236,03/28/2003 05:21:00 PM,030XX N KILPATRICK AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE PORCH/HALLWAY,true,false,2521,,31,19,26,1144481,1919605,2003,03/22/2006 09:58:07 PM,41.93541391,-87.744439194,"(41.93541391, -87.744439194)"
-2653961,HJ266576,03/28/2003 04:00:00 PM,023XX S LAKE SHORE DR E,0890,THEFT,FROM BUILDING,OTHER,false,false,0133,,2,33,06,1180538,1889157,2003,03/22/2006 09:58:07 PM,41.851107212,-87.612868333,"(41.851107212, -87.612868333)"
-2731317,HJ365741,03/28/2003 12:00:00 PM,011XX E 100TH PL,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,GAS STATION,false,false,0511,,8,50,11,1185335,1838328,2003,03/22/2006 09:58:07 PM,41.711516297,-87.596860359,"(41.711516297, -87.596860359)"
-2653783,HJ265222,03/28/2003 07:50:00 AM,061XX S PEORIA ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,RESIDENCE,true,true,0712,,16,68,04A,1171397,1864203,2003,03/22/2006 09:58:07 PM,41.782836528,-87.647148887,"(41.782836528, -87.647148887)"
-2672532,HJ264027,03/27/2003 04:21:10 PM,076XX S GREENWOOD AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0624,,8,69,18,1185024,1854708,2003,03/22/2006 09:58:07 PM,41.7564721,-87.597486436,"(41.7564721, -87.597486436)"
-2656840,HJ263692,03/27/2003 12:57:06 PM,015XX S WABASH AVE,1570,SEX OFFENSE,PUBLIC INDECENCY,VEHICLE NON-COMMERCIAL,true,false,0132,,2,33,17,1177052,1892908,2003,03/22/2006 09:58:07 PM,41.861479754,-87.625549082,"(41.861479754, -87.625549082)"
-2962974,HJ654724,03/27/2003 12:00:00 AM,001XX W DIVERSEY PKWY,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,OTHER,true,false,2333,019,44,6,11,1174773,1919031,2003,03/22/2006 09:58:07 PM,41.933213888,-87.633132166,"(41.933213888, -87.633132166)"
-2650916,HJ262745,03/26/2003 11:15:00 PM,028XX N DRAKE AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,RESIDENCE PORCH/HALLWAY,false,false,1412,,35,21,04A,1152181,1919065,2003,03/22/2006 09:58:07 PM,41.93378348,-87.716155357,"(41.93378348, -87.716155357)"
-2653043,HJ262169,03/26/2003 05:20:00 PM,019XX W GARFIELD BLVD,1330,CRIMINAL TRESPASS,TO LAND,GAS STATION,true,false,0915,,16,61,26,1164031,1868223,2003,03/22/2006 09:58:07 PM,41.794026053,-87.67404194,"(41.794026053, -87.67404194)"
-2651117,HJ261038,03/26/2003 08:00:00 AM,054XX W AUGUSTA BLVD,0440,BATTERY,AGG: HANDS/FIST/FEET NO/MINOR INJURY,RESIDENCE,false,false,1524,,37,25,08B,1139397,1906164,2003,03/22/2006 09:58:07 PM,41.898624611,-87.76345166,"(41.898624611, -87.76345166)"
-2654534,HJ257295,03/24/2003 11:25:00 AM,063XX S MARYLAND AVE,0330,ROBBERY,AGGRAVATED,SIDEWALK,false,false,0312,,20,42,03,1182961,1863189,2003,03/22/2006 09:58:07 PM,41.779792962,-87.604783572,"(41.779792962, -87.604783572)"
-2645118,HJ256258,03/23/2003 04:00:00 PM,041XX W NEWPORT AVE,0560,ASSAULT,SIMPLE,ALLEY,false,false,1731,,30,16,08A,1148150,1922696,2003,03/22/2006 09:58:07 PM,41.943825928,-87.730875462,"(41.943825928, -87.730875462)"
-2646462,HJ255666,03/23/2003 03:02:12 PM,026XX S TROY ST,0460,BATTERY,SIMPLE,STREET,false,false,1033,,12,30,08B,1155820,1886451,2003,03/22/2006 09:58:07 PM,41.844214893,-87.703661633,"(41.844214893, -87.703661633)"
-2668879,HJ254390,03/22/2003 07:00:00 PM,0000X N KENTON AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1113,,28,25,16,1145748,1900020,2003,03/22/2006 09:58:07 PM,41.881646642,-87.740280495,"(41.881646642, -87.740280495)"
-2649281,HJ260295,03/22/2003 10:00:00 AM,046XX S SPRINGFIELD AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,0821,008,14,57,07,1151110,1873275,2003,03/22/2006 09:58:07 PM,41.808151635,-87.721291107,"(41.808151635, -87.721291107)"
-2642154,HJ252327,03/21/2003 05:45:00 PM,034XX N NAGLE AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1632,,36,17,26,1132797,1922290,2003,03/22/2006 09:58:07 PM,41.942994059,-87.787316383,"(41.942994059, -87.787316383)"
-2645649,HJ257061,03/21/2003 04:00:00 PM,015XX W 99TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,false,false,2213,,19,72,14,1167675,1839122,2003,03/22/2006 09:58:07 PM,41.714091457,-87.661512859,"(41.714091457, -87.661512859)"
-2642766,HJ252752,03/21/2003 04:00:00 PM,078XX S ESSEX AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0421,,7,43,05,1194255,1853642,2003,03/22/2006 09:58:07 PM,41.753325268,-87.563692178,"(41.753325268, -87.563692178)"
-2643265,HJ251084,03/21/2003 04:55:00 AM,076XX S COLFAX AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0421,,7,43,14,1194806,1854932,2003,03/22/2006 09:58:07 PM,41.756851583,-87.561630607,"(41.756851583, -87.561630607)"
-2640432,HJ250177,03/20/2003 03:30:00 PM,001XX N STATE ST,0890,THEFT,FROM BUILDING,DEPARTMENT STORE,false,false,0122,,42,32,06,1176391,1900924,2003,03/22/2006 09:58:07 PM,41.883491075,-87.627733579,"(41.883491075, -87.627733579)"
-2638580,HJ248460,03/19/2003 06:00:00 PM,048XX S PRAIRIE AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,0224,,3,38,08A,1178828,1872640,2003,03/22/2006 09:58:07 PM,41.805822488,-87.619647929,"(41.805822488, -87.619647929)"
-2642493,HJ244030,03/17/2003 04:00:00 PM,091XX S SOUTH CHICAGO AVE,0860,THEFT,RETAIL THEFT,DRUG STORE,true,false,0423,,7,48,06,1196850,1844785,2003,03/22/2006 09:58:07 PM,41.728956871,-87.554476439,"(41.728956871, -87.554476439)"
-2637544,HJ242939,03/17/2003 08:00:00 AM,056XX W FULLERTON AVE,0860,THEFT,RETAIL THEFT,DRUG STORE,false,false,2515,,30,19,06,1138640,1915439,2003,03/22/2006 09:58:07 PM,41.924090052,-87.766006787,"(41.924090052, -87.766006787)"
-2633487,HJ242361,03/16/2003 09:34:28 PM,007XX S HOLDEN CT,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,true,false,0132,,2,32,26,1176651,1897119,2003,03/22/2006 09:58:07 PM,41.873044066,-87.626893856,"(41.873044066, -87.626893856)"
-2632271,HJ240944,03/16/2003 04:02:14 AM,012XX S LAFLIN ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,1231,,2,28,15,1166519,1894782,2003,03/22/2006 09:58:07 PM,41.866853944,-87.66415993,"(41.866853944, -87.66415993)"
-2632002,HJ238968,03/15/2003 07:06:44 AM,064XX N RIDGE BLVD,0560,ASSAULT,SIMPLE,NURSING HOME/RETIREMENT HOME,false,false,2412,,50,2,08A,1162480,1943037,2003,03/22/2006 09:58:07 PM,41.999354032,-87.677632723,"(41.999354032, -87.677632723)"
-2632794,HJ239704,03/14/2003 07:00:00 PM,067XX S COTTAGE GROVE AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0321,,20,42,06,1182672,1860755,2003,12/04/2014 12:43:35 PM,41.773120551,-87.605918552,"(41.773120551, -87.605918552)"
-2651293,HJ235155,03/13/2003 11:35:00 AM,065XX S STEWART AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,"SCHOOL, PUBLIC, BUILDING",true,false,0722,,20,68,18,1174791,1861551,2003,03/22/2006 09:58:07 PM,41.775484151,-87.634784474,"(41.775484151, -87.634784474)"
-2627298,HJ234372,03/12/2003 07:00:00 PM,008XX N MICHIGAN AVE,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,1833,,42,8,06,1177297,1906280,2003,03/22/2006 09:58:07 PM,41.898167718,-87.624244187,"(41.898167718, -87.624244187)"
-2641731,HJ231947,03/11/2003 08:14:00 PM,035XX W 13TH PL,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1021,,24,29,18,1152611,1893627,2003,03/22/2006 09:58:07 PM,41.863970645,-87.715248617,"(41.863970645, -87.715248617)"
-2628382,HJ231891,03/11/2003 05:30:00 PM,076XX S INGLESIDE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0624,,8,69,14,1183846,1854523,2003,03/22/2006 09:58:07 PM,41.755992009,-87.601809291,"(41.755992009, -87.601809291)"
-2646370,HJ230007,03/10/2003 08:30:00 PM,016XX N AUSTIN AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,2531,,29,25,18,1136199,1910235,2003,03/22/2006 09:58:07 PM,41.90985363,-87.775100617,"(41.90985363, -87.775100617)"
-2623302,HJ227459,03/09/2003 02:12:12 PM,002XX W 103RD ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0512,,34,49,08B,1176732,1836606,2003,03/22/2006 09:58:07 PM,41.706988491,-87.628418088,"(41.706988491, -87.628418088)"
-2620618,HJ224040,03/07/2003 04:45:00 PM,063XX S ASHLAND AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,0725,,16,67,08A,1166795,1862762,2003,03/22/2006 09:58:07 PM,41.778981782,-87.664062362,"(41.778981782, -87.664062362)"
-2617018,HJ220750,03/06/2003 12:55:00 AM,031XX W PALMER BLVD,0313,ROBBERY,ARMED: OTHER DANGEROUS WEAPON,SIDEWALK,false,false,1414,,35,22,03,1155104,1914639,2003,03/22/2006 09:58:07 PM,41.921579948,-87.705532493,"(41.921579948, -87.705532493)"
-2616705,HJ218791,03/04/2003 10:00:00 PM,069XX S LAFAYETTE AVE,1330,CRIMINAL TRESPASS,TO LAND,RESTAURANT,true,false,0731,,6,69,26,1176984,1859268,2003,03/22/2006 09:58:07 PM,41.769170188,-87.626813981,"(41.769170188, -87.626813981)"
-2625740,HJ217467,03/04/2003 10:45:00 AM,020XX W CHICAGO AVE,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,1313,,1,24,06,1162716,1905296,2003,03/22/2006 09:58:07 PM,41.895785757,-87.67782637,"(41.895785757, -87.67782637)"
-2619027,HJ216531,03/03/2003 07:00:27 PM,040XX S PRAIRIE AVE,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,0214,,3,38,26,1178663,1878377,2003,03/22/2006 09:58:07 PM,41.82156906,-87.62007844,"(41.82156906, -87.62007844)"
-2640033,HJ247251,03/03/2003 09:00:00 AM,038XX S ASHLAND AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,0922,,11,59,26,1166214,1879402,2003,03/22/2006 09:58:07 PM,41.824656274,-87.665718539,"(41.824656274, -87.665718539)"
-2612763,HJ215058,03/03/2003 12:08:00 AM,010XX W BALMORAL AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2023,,48,77,26,1168456,1936084,2003,03/22/2006 09:58:07 PM,41.980147173,-87.655851076,"(41.980147173, -87.655851076)"
-2616094,HJ213353,03/02/2003 01:53:00 AM,057XX S THROOP ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0713,,16,67,18,1168593,1866881,2003,03/22/2006 09:58:07 PM,41.790246223,-87.657352015,"(41.790246223, -87.657352015)"
-2615265,HJ213132,03/02/2003 12:15:00 AM,022XX W MADISON ST,0460,BATTERY,SIMPLE,STREET,true,false,1211,,2,28,08B,1161094,1899928,2003,03/22/2006 09:58:07 PM,41.881089359,-87.683932823,"(41.881089359, -87.683932823)"
-2612167,HJ213932,03/01/2003 10:00:00 PM,012XX S KEELER AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1011,,24,29,14,1148538,1894071,2003,03/22/2006 09:58:07 PM,41.865268529,-87.730189101,"(41.865268529, -87.730189101)"
-2611611,HJ211723,03/01/2003 07:20:00 AM,005XX E 92ND ST,0320,ROBBERY,STRONGARM - NO WEAPON,RESIDENCE,true,true,0633,,6,44,03,1181572,1844056,2003,03/22/2006 09:58:07 PM,41.727322134,-87.61046513,"(41.727322134, -87.61046513)"
-2613254,HJ211363,03/01/2003 12:36:40 AM,048XX W HADDON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,1531,,37,25,08B,1143977,1907176,2003,03/22/2006 09:58:07 PM,41.901316937,-87.746603948,"(41.901316937, -87.746603948)"
-6487344,HP563459,03/01/2003 12:01:00 AM,042XX N KILDARE AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,1722,,38,16,06,,,2003,09/18/2008 01:03:58 AM,,,
-2633041,HJ211415,02/28/2003 11:55:00 PM,028XX W 23RD PL,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,RESIDENCE,true,false,1033,,12,30,15,1157832,1888309,2003,03/22/2006 09:58:07 PM,41.849272736,-87.696227332,"(41.849272736, -87.696227332)"
-2615943,HJ211206,02/28/2003 10:20:00 PM,079XX S EAST END AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0414,,8,46,18,1188982,1852928,2003,03/22/2006 09:58:07 PM,41.751493803,-87.583038241,"(41.751493803, -87.583038241)"
-2612514,HJ210022,02/28/2003 11:51:01 AM,065XX W DIVERSEY AVE,0820,THEFT,$500 AND UNDER,OTHER,true,false,2512,,36,19,06,1132544,1917860,2003,12/04/2014 12:43:35 PM,41.930842052,-87.788349809,"(41.930842052, -87.788349809)"
-2610440,HJ209311,02/28/2003 12:15:00 AM,003XX N CENTRAL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,1523,,28,25,08B,1139018,1901829,2003,03/22/2006 09:58:07 PM,41.886735731,-87.764949201,"(41.886735731, -87.764949201)"
-2631190,HJ239109,02/27/2003 07:00:00 PM,020XX W 62ND ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0714,,15,67,26,1163846,1863489,2003,03/22/2006 09:58:07 PM,41.78103924,-87.674853278,"(41.78103924, -87.674853278)"
-2613592,HJ216746,02/27/2003 01:45:00 PM,030XX W ADDISON ST,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE PORCH/HALLWAY,false,false,1733,,33,21,26,1155568,1923739,2003,03/22/2006 09:58:07 PM,41.946541687,-87.703582028,"(41.946541687, -87.703582028)"
-2606792,HJ206875,02/25/2003 12:00:00 PM,055XX W JACKSON BLVD,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1522,,29,25,26,1139174,1898073,2003,03/22/2006 09:58:07 PM,41.876425944,-87.764467747,"(41.876425944, -87.764467747)"
-2602745,HJ201395,02/24/2003 02:20:03 AM,108XX S HALSTED ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,true,false,2233,,34,49,14,1172939,1833227,2003,03/22/2006 09:58:07 PM,41.697800369,-87.642407253,"(41.697800369, -87.642407253)"
-2602312,HJ201560,02/24/2003 12:00:00 AM,060XX N CLAREMONT AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2413,,40,2,14,1159508,1940398,2003,03/22/2006 09:58:07 PM,41.992174452,-87.688639001,"(41.992174452, -87.688639001)"
-2602012,HJ201247,02/23/2003 10:30:00 PM,076XX S EBERHART AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0624,,6,69,26,1180930,1854727,2003,03/22/2006 09:58:07 PM,41.756619351,-87.612489414,"(41.756619351, -87.612489414)"
-2601585,HJ200124,02/22/2003 05:00:00 PM,054XX S UNIVERSITY AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2131,,4,41,14,1184756,1869741,2003,03/22/2006 09:58:07 PM,41.797730274,-87.5979975,"(41.797730274, -87.5979975)"
-2600574,HJ198379,02/22/2003 12:45:00 AM,016XX W FULLERTON AVE,0870,THEFT,POCKET-PICKING,TAVERN/LIQUOR STORE,false,false,1811,,32,7,06,1164575,1915959,2003,03/22/2006 09:58:07 PM,41.925006571,-87.670696134,"(41.925006571, -87.670696134)"
-2605695,HJ193227,02/19/2003 06:50:00 PM,001XX N PINE AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,1523,,29,25,18,1139449,1900204,2003,03/22/2006 09:58:07 PM,41.882268679,-87.763406064,"(41.882268679, -87.763406064)"
-2598959,HJ192819,02/19/2003 02:30:00 PM,025XX N RIDGEWAY AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2524,,35,22,06,1150920,1916428,2003,12/04/2014 12:43:35 PM,41.926572145,-87.720858739,"(41.926572145, -87.720858739)"
-2592685,HJ187855,02/17/2003 02:38:42 AM,005XX E 51ST ST,0560,ASSAULT,SIMPLE,HOSPITAL BUILDING/GROUNDS,true,false,0223,,3,38,08A,1180379,1871346,2003,03/22/2006 09:58:07 PM,41.802236159,-87.613999224,"(41.802236159, -87.613999224)"
-2604624,HJ187157,02/16/2003 04:10:39 PM,061XX S EVANS AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0313,,20,42,18,1182326,1864087,2003,03/22/2006 09:58:07 PM,41.782271896,-87.607083736,"(41.782271896, -87.607083736)"
-2591672,HJ186851,02/16/2003 11:00:00 AM,091XX S BURNSIDE AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,0633,,6,49,06,1179896,1844301,2003,12/04/2014 12:43:35 PM,41.728032903,-87.616597041,"(41.728032903, -87.616597041)"
-2594743,HJ190667,02/16/2003 12:00:00 AM,017XX W NEWPORT AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1924,,32,6,06,1164088,1922910,2003,12/04/2014 12:43:35 PM,41.944090823,-87.672288653,"(41.944090823, -87.672288653)"
-2591201,HJ186052,02/15/2003 09:57:00 PM,056XX S MARSHFIELD AVE,2860,PUBLIC PEACE VIOLATION,FALSE POLICE REPORT,RESIDENCE,true,false,0715,,15,67,24,1166268,1867147,2003,03/22/2006 09:58:07 PM,41.791026008,-87.6658696,"(41.791026008, -87.6658696)"
-2604309,HJ183992,02/14/2003 06:55:00 PM,0000X N LATROBE AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1522,,28,25,18,1141346,1899733,2003,03/22/2006 09:58:07 PM,41.880941414,-87.756451798,"(41.880941414, -87.756451798)"
-2590465,HJ184879,02/14/2003 06:30:00 PM,032XX N KIMBALL AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,1732,,35,21,06,1153112,1921344,2003,03/22/2006 09:58:07 PM,41.940018791,-87.712673315,"(41.940018791, -87.712673315)"
-2597316,HJ183689,02/14/2003 04:25:00 PM,0000X E JACKSON BLVD,0820,THEFT,$500 AND UNDER,OTHER,false,false,0123,,42,32,06,1176557,1899045,2003,12/04/2014 12:43:35 PM,41.878331246,-87.627180794,"(41.878331246, -87.627180794)"
-2593378,HJ189239,02/14/2003 03:00:00 PM,040XX W SCHOOL ST,0810,THEFT,OVER $500,STREET,false,false,1731,,31,16,06,1148689,1921578,2003,12/04/2014 12:43:35 PM,41.940747631,-87.728923305,"(41.940747631, -87.728923305)"
-2593088,HJ183259,02/14/2003 12:20:00 PM,023XX W MADISON ST,1330,CRIMINAL TRESPASS,TO LAND,DRUG STORE,true,false,1332,,2,28,26,1160633,1899995,2003,03/22/2006 09:58:07 PM,41.881282773,-87.68562373,"(41.881282773, -87.68562373)"
-2589667,HJ183549,02/14/2003 09:50:00 AM,023XX W JACKSON BLVD,0560,ASSAULT,SIMPLE,CHA HALLWAY/STAIRWELL/ELEVATOR,false,false,1211,,2,28,08A,1160775,1898600,2003,03/22/2006 09:58:07 PM,41.877451825,-87.685141,"(41.877451825, -87.685141)"
-2601053,HJ183083,02/14/2003 09:15:00 AM,056XX S ROCKWELL ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0824,,16,63,08B,1159963,1867082,2003,03/22/2006 09:58:07 PM,41.790979639,-87.688990515,"(41.790979639, -87.688990515)"
-2589773,HJ183889,02/14/2003 07:00:00 AM,041XX W IRVING PARK RD,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,1722,,38,16,06,1147749,1926295,2003,12/04/2014 12:43:35 PM,41.953709602,-87.732256595,"(41.953709602, -87.732256595)"
-2589250,HJ181411,02/13/2003 01:50:00 PM,035XX S WALLACE ST,2840,PUBLIC PEACE VIOLATION,FALSE FIRE ALARM,"SCHOOL, PUBLIC, BUILDING",false,false,0925,,11,60,24,1172909,1881666,2003,03/22/2006 09:58:07 PM,41.830723509,-87.641089764,"(41.830723509, -87.641089764)"
-2588116,HJ181489,02/13/2003 11:00:00 AM,070XX S LOWE AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0732,,6,68,26,1173144,1858192,2003,03/22/2006 09:58:07 PM,41.766303214,-87.640921304,"(41.766303214, -87.640921304)"
-2606162,HJ202732,02/12/2003 12:00:00 PM,001XX E PEARSON ST,0890,THEFT,FROM BUILDING,RESIDENCE,true,false,1833,,42,8,06,1177734,1906117,2003,03/22/2006 09:58:07 PM,41.897710516,-87.622644102,"(41.897710516, -87.622644102)"
-2590248,HJ177528,02/11/2003 01:20:00 PM,035XX E 114TH ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",true,false,0433,,10,52,08B,1201607,1829942,2003,03/22/2006 09:58:07 PM,41.688107014,-87.537553324,"(41.688107014, -87.537553324)"
-2587570,HJ176689,02/11/2003 12:20:00 AM,026XX N PULASKI RD,1563,SEX OFFENSE,CRIMINAL SEXUAL ABUSE,COMMERCIAL / BUSINESS OFFICE,true,false,2524,,30,22,17,1149317,1917019,2003,03/22/2006 09:58:07 PM,41.928225171,-87.726733718,"(41.928225171, -87.726733718)"
-2584502,HJ176455,02/10/2003 09:20:00 PM,007XX N WELLS ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,GAS STATION,true,false,1831,,42,8,04A,1174553,1905589,2003,03/22/2006 09:58:07 PM,41.89633337,-87.634343239,"(41.89633337, -87.634343239)"
-2582674,HJ174847,02/10/2003 07:08:00 AM,047XX W 47TH ST,033A,ROBBERY,ATTEMPT: ARMED-HANDGUN,SIDEWALK,false,false,0815,,23,56,03,1145751,1873014,2003,03/22/2006 09:58:07 PM,41.807538456,-87.740953479,"(41.807538456, -87.740953479)"
-2581790,HJ173340,02/09/2003 03:30:00 AM,010XX N DEARBORN ST,0460,BATTERY,SIMPLE,APARTMENT,true,false,1824,,42,8,08B,1175702,1907500,2003,03/22/2006 09:58:07 PM,41.901551484,-87.630065689,"(41.901551484, -87.630065689)"
-2613053,HJ215215,02/08/2003 12:00:00 PM,001XX E CHICAGO AVE,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,SMALL RETAIL STORE,false,false,1833,,42,8,11,1177130,1905711,2003,03/22/2006 09:58:07 PM,41.896610141,-87.624874814,"(41.896610141, -87.624874814)"
-2584848,HJ176985,02/07/2003 05:00:00 PM,029XX W GRAND AVE,0810,THEFT,OVER $500,DRIVEWAY - RESIDENTIAL,false,false,1311,,26,24,06,1156658,1905262,2003,12/04/2014 12:43:35 PM,41.895817311,-87.700077036,"(41.895817311, -87.700077036)"
-2628499,HJ170607,02/07/2003 03:23:13 PM,005XX N LARAMIE AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,ALLEY,true,false,1532,,28,25,26,1141649,1902910,2003,03/22/2006 09:58:07 PM,41.8896539,-87.755260604,"(41.8896539, -87.755260604)"
-2579928,HJ169412,02/06/2003 08:00:00 PM,031XX W HARRISON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,VEHICLE NON-COMMERCIAL,true,false,1134,,28,27,08B,1155221,1897228,2003,03/22/2006 09:58:07 PM,41.873800207,-87.705570698,"(41.873800207, -87.705570698)"
-2577532,HJ165466,02/04/2003 05:40:00 PM,105XX S COTTAGE GROVE AVE,0880,THEFT,PURSE-SNATCHING,STREET,false,false,0512,,9,50,06,1182451,1835465,2003,03/22/2006 09:58:07 PM,41.703727064,-87.607510551,"(41.703727064, -87.607510551)"
-2574997,HJ164218,02/04/2003 02:00:00 AM,071XX S RHODES AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0323,,6,69,08B,1181106,1857617,2003,03/22/2006 09:58:07 PM,41.764545766,-87.611755571,"(41.764545766, -87.611755571)"
-2573719,HJ162443,02/03/2003 08:30:00 AM,093XX S MERRILL AVE,0281,CRIM SEXUAL ASSAULT,NON-AGGRAVATED,OTHER,true,false,0413,,7,48,02,1192110,1843094,2003,03/22/2006 09:58:07 PM,41.724432987,-87.571894658,"(41.724432987, -87.571894658)"
-2574299,HJ164547,01/31/2003 07:50:00 AM,077XX S COLFAX AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0421,,7,43,14,1194910,1853905,2003,03/22/2006 09:58:07 PM,41.754030856,-87.561283249,"(41.754030856, -87.561283249)"
-2576546,HJ155022,01/30/2003 09:31:12 AM,052XX W QUINCY ST,1822,NARCOTICS,MANU/DEL:CANNABIS OVER 10 GMS,SIDEWALK,true,false,1522,,29,25,18,1141287,1898535,2003,03/22/2006 09:58:07 PM,41.877655038,-87.756698001,"(41.877655038, -87.756698001)"
-2574150,HJ154439,01/29/2003 09:16:00 PM,023XX N NEWLAND AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,true,2512,,36,18,08A,1129729,1915121,2003,03/22/2006 09:58:07 PM,41.923374532,-87.798757305,"(41.923374532, -87.798757305)"
-2577230,HJ154409,01/29/2003 08:45:00 PM,056XX S SACRAMENTO AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0824,,14,63,18,1157293,1867321,2003,03/22/2006 09:58:07 PM,41.791689984,-87.698774403,"(41.791689984, -87.698774403)"
-2566433,HJ151111,01/28/2003 09:20:40 AM,006XX W WASHINGTON BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,COMMERCIAL / BUSINESS OFFICE,false,false,0111,,27,28,14,1171775,1900726,2003,03/22/2006 09:58:07 PM,41.883050646,-87.644689562,"(41.883050646, -87.644689562)"
-2567646,HJ154510,01/28/2003 01:30:00 AM,032XX S EMERALD AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0924,,11,60,26,1171788,1883417,2003,03/22/2006 09:58:07 PM,41.835553126,-87.645151277,"(41.835553126, -87.645151277)"
-2561777,HJ148794,01/27/2003 02:38:00 AM,018XX S THROOP ST,1570,SEX OFFENSE,PUBLIC INDECENCY,RESIDENCE PORCH/HALLWAY,true,false,1222,,25,31,17,1168024,1891511,2003,03/22/2006 09:58:07 PM,41.857845721,-87.658729272,"(41.857845721, -87.658729272)"
-2562567,HJ148802,01/27/2003 01:47:00 AM,0000X W DIVISION ST,0460,BATTERY,SIMPLE,BAR OR TAVERN,true,false,1824,,42,8,08B,1175999,1908331,2003,03/22/2006 09:58:07 PM,41.903825101,-87.628949725,"(41.903825101, -87.628949725)"
-2562672,HJ149434,01/25/2003 10:00:00 PM,006XX S ASHLAND AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1213,,25,28,26,1165885,1896981,2003,03/22/2006 09:58:07 PM,41.872901724,-87.666424695,"(41.872901724, -87.666424695)"
-2560395,HJ146078,01/25/2003 11:00:00 AM,062XX S UNIVERSITY AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0314,,20,42,26,1184884,1863921,2003,06/11/2007 03:52:33 PM,41.781756709,-87.597710726,"(41.781756709, -87.597710726)"
-2569549,HJ144969,01/24/2003 07:55:55 PM,015XX S SAWYER AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1022,,24,29,18,,,2003,03/22/2006 09:58:07 PM,,,
-2564670,HJ144000,01/24/2003 11:01:02 AM,022XX S BLUE ISLAND AVE,0460,BATTERY,SIMPLE,SIDEWALK,true,true,1034,,25,31,08B,1165336,1889137,2003,03/22/2006 09:58:07 PM,41.851388775,-87.668663285,"(41.851388775, -87.668663285)"
-2560282,HJ143622,01/24/2003 01:30:00 AM,073XX S GREEN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,0733,,17,68,08B,1171860,1856442,2003,03/22/2006 09:58:07 PM,41.761529265,-87.6456789,"(41.761529265, -87.6456789)"
-3762471,HL122910,01/24/2003 12:01:00 AM,072XX S HALSTED ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,0732,007,17,68,06,1172264,1856669,2003,03/22/2006 09:58:07 PM,41.76214331,-87.644191541,"(41.76214331, -87.644191541)"
-2567809,HJ142661,01/23/2003 03:00:00 PM,005XX W 104TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,2232,,34,49,18,1174673,1835971,2003,03/22/2006 09:58:07 PM,41.705291963,-87.63597693,"(41.705291963, -87.63597693)"
-2554173,HJ138832,01/20/2003 06:00:00 PM,036XX N LAMON AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1634,,38,15,07,1142984,1923868,2003,03/22/2006 09:58:07 PM,41.947140068,-87.749834172,"(41.947140068, -87.749834172)"
-2553663,HJ136924,01/20/2003 01:30:00 PM,115XX S PEORIA ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,0524,,34,53,08B,1172114,1828300,2003,03/22/2006 09:58:07 PM,41.684298022,-87.645571997,"(41.684298022, -87.645571997)"
-2558398,HJ136522,01/20/2003 09:02:56 AM,098XX S ABERDEEN ST,4650,OTHER OFFENSE,SEX OFFENDER: FAIL TO REGISTER,RESIDENCE,false,false,2223,,21,73,26,1170756,1839674,2003,03/22/2006 09:58:07 PM,41.715539704,-87.650212979,"(41.715539704, -87.650212979)"
-2548751,HJ130419,01/16/2003 10:40:00 PM,034XX W FLOURNOY ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1133,,24,27,08B,1153173,1896770,2003,03/22/2006 09:58:07 PM,41.872584262,-87.713102163,"(41.872584262, -87.713102163)"
-2549237,HJ131334,01/14/2003 09:28:00 AM,028XX W DEVON AVE,0860,THEFT,RETAIL THEFT,DRUG STORE,false,false,2413,,50,2,06,1156200,1942288,2003,03/22/2006 09:58:07 PM,41.997428385,-87.700755546,"(41.997428385, -87.700755546)"
-2558340,HJ122611,01/12/2003 10:30:00 PM,070XX N CLARK ST,2170,NARCOTICS,POSSESSION OF DRUG EQUIPMENT,SIDEWALK,true,false,2424,,49,1,18,1163345,1946915,2003,03/22/2006 09:58:07 PM,42.009977138,-87.67434082,"(42.009977138, -87.67434082)"
-2540841,HJ122404,01/12/2003 07:40:00 PM,020XX W CHICAGO AVE,0460,BATTERY,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),true,false,1313,,1,24,08B,1162818,1905299,2003,03/22/2006 09:58:07 PM,41.89579185,-87.677451663,"(41.89579185, -87.677451663)"
-2544325,HJ125744,01/11/2003 08:00:00 AM,037XX N WILTON AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,STREET,false,false,2324,,44,6,11,,,2003,03/22/2006 09:58:07 PM,,,
-2541751,HJ118014,01/10/2003 10:54:00 AM,029XX W NELSON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,1411,,33,21,08B,1156245,1920288,2003,03/22/2006 09:58:07 PM,41.937058229,-87.701187121,"(41.937058229, -87.701187121)"
-2538749,HJ117606,01/10/2003 05:18:11 AM,059XX N BROADWAY,1310,CRIMINAL DAMAGE,TO PROPERTY,BAR OR TAVERN,false,false,2433,,48,77,14,1167262,1939807,2003,03/22/2006 09:58:07 PM,41.990389011,-87.660134519,"(41.990389011, -87.660134519)"
-2537091,HJ117671,01/09/2003 05:30:00 PM,021XX S MICHIGAN AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,0134,,2,33,06,1177576,1890278,2003,12/04/2014 12:43:35 PM,41.854250992,-87.62370539,"(41.854250992, -87.62370539)"
-2537440,HJ115617,01/09/2003 03:40:00 AM,029XX W PALMER ST,0820,THEFT,$500 AND UNDER,STREET,true,false,1414,,35,22,06,1156384,1914636,2003,12/04/2014 12:43:35 PM,41.921545906,-87.700829506,"(41.921545906, -87.700829506)"
-2546024,HJ115438,01/08/2003 11:30:00 PM,023XX N ST LOUIS AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1413,,26,22,18,1152923,1915077,2003,03/22/2006 09:58:07 PM,41.922825392,-87.713534462,"(41.922825392, -87.713534462)"
-2536552,HJ116438,01/08/2003 06:00:00 PM,025XX W LUNT AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,PARK PROPERTY,false,false,2411,,50,2,14,1158392,1946418,2003,03/22/2006 09:58:07 PM,42.00871653,-87.692578297,"(42.00871653, -87.692578297)"
-2533629,HJ113743,01/08/2003 05:30:00 AM,024XX N LOTUS AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2515,,30,19,26,1139678,1915748,2003,03/22/2006 09:58:07 PM,41.924919073,-87.762185138,"(41.924919073, -87.762185138)"
-2535553,HJ113016,01/07/2003 06:45:00 PM,067XX S WOOD ST,1340,CRIMINAL DAMAGE,TO STATE SUP PROP,OTHER,true,true,0725,,15,67,14,1165546,1860071,2003,03/22/2006 09:58:07 PM,41.771623909,-87.668717558,"(41.771623909, -87.668717558)"
-2547609,HJ112783,01/07/2003 05:06:00 PM,025XX W MOFFAT ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1434,,1,22,18,1158771,1912240,2003,03/22/2006 09:58:07 PM,41.914922454,-87.692124859,"(41.914922454, -87.692124859)"
-2531987,HJ109966,01/06/2003 10:50:00 AM,010XX N ORLEANS ST,0453,BATTERY,AGGRAVATED PO: OTHER DANG WEAP,STREET,true,false,1823,,27,8,04B,1173678,1907393,2003,03/22/2006 09:58:07 PM,41.90130316,-87.637503178,"(41.90130316, -87.637503178)"
-2565168,HJ152702,01/05/2003 12:01:00 AM,056XX W BELMONT AVE,0810,THEFT,OVER $500,COMMERCIAL / BUSINESS OFFICE,false,false,2514,,30,19,06,1137859,1920662,2003,12/04/2014 12:43:35 PM,41.938436671,-87.768750165,"(41.938436671, -87.768750165)"
-2538968,HJ105381,01/03/2003 07:30:00 PM,036XX W FRANKLIN BLVD,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,RESIDENCE,false,false,1122,,27,23,04B,1152213,1903133,2003,03/22/2006 09:58:07 PM,41.890063989,-87.716458857,"(41.890063989, -87.716458857)"
-2532846,HJ112424,01/03/2003 11:00:00 AM,100XX W OHARE ST,0890,THEFT,FROM BUILDING,AIRPORT/AIRCRAFT,false,false,1651,,41,76,06,1100629,1934213,2003,03/22/2006 09:58:07 PM,41.976213976,-87.905334384,"(41.976213976, -87.905334384)"
-2530489,HJ104429,01/03/2003 10:55:00 AM,021XX N CENTRAL PARK AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2525,,26,22,08B,1151993,1913819,2003,03/22/2006 09:58:07 PM,41.919391725,-87.716984833,"(41.919391725, -87.716984833)"
-2526271,HJ104402,01/03/2003 08:00:00 AM,110XX S GREEN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2233,,34,75,08B,1172579,1831572,2003,03/22/2006 09:58:07 PM,41.693266709,-87.643773893,"(41.693266709, -87.643773893)"
-2524228,HJ102134,01/02/2003 05:35:00 AM,031XX E 80TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0422,,7,46,14,1198843,1852575,2003,03/22/2006 09:58:07 PM,41.750283491,-87.546915002,"(41.750283491, -87.546915002)"
-2605167,HJ202312,01/01/2003 09:50:00 AM,023XX N AUSTIN AVE,0810,THEFT,OVER $500,CHURCH/SYNAGOGUE/PLACE OF WORSHIP,false,false,2512,,37,19,06,1135979,1914664,2003,12/04/2014 12:43:35 PM,41.922011257,-87.775803049,"(41.922011257, -87.775803049)"
-6455763,HP535578,01/01/2003 12:01:00 AM,015XX N LINDER AVE,1751,OFFENSE INVOLVING CHILDREN,CRIM SEX ABUSE BY FAM MEMBER,RESIDENCE,false,false,2532,,37,25,20,,,2003,08/29/2008 01:05:02 AM,,,
-5929786,HN730079,01/01/2003 12:00:00 AM,009XX N CENTRAL PARK AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,1112,,27,23,06,,,2003,03/11/2010 03:22:37 PM,,,
-2526052,HJ102292,12/31/2002 10:44:00 PM,0000X W 103RD PL,1720,OFFENSE INVOLVING CHILDREN,CONTRIBUTE DELINQUENCY OF A CHILD,RESIDENCE,false,false,0512,,34,49,20,1177934,1836304,2002,03/30/2006 09:10:16 PM,41.706132676,-87.624025509,"(41.706132676, -87.624025509)"
-2531817,HH870090,12/31/2002 04:00:00 PM,0000X E ELM ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1824,,42,8,14,1176247,1908142,2002,03/30/2006 09:10:16 PM,41.903300884,-87.628044479,"(41.903300884, -87.628044479)"
-2521368,HH867092,12/30/2002 02:15:00 AM,105XX S WESTERN AVE,0610,BURGLARY,FORCIBLE ENTRY,RESTAURANT,false,false,2212,,19,72,05,1162291,1834849,2002,03/30/2006 09:10:16 PM,41.702479207,-87.681349774,"(41.702479207, -87.681349774)"
-2519782,HH866489,12/29/2002 08:30:00 PM,078XX S PEORIA ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0621,,17,71,08B,1171626,1852829,2002,03/30/2006 09:10:16 PM,41.751619863,-87.646642228,"(41.751619863, -87.646642228)"
-2518745,HH863929,12/28/2002 10:50:00 AM,077XX S EXCHANGE AVE,0454,BATTERY,AGG PO HANDS NO/MIN INJURY,RESIDENCE PORCH/HALLWAY,true,false,0421,,7,43,08B,1196358,1854454,2002,03/30/2006 09:10:16 PM,41.755501573,-87.555958748,"(41.755501573, -87.555958748)"
-2533660,HH862933,12/27/2002 08:15:00 PM,009XX N KEYSTONE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1111,,37,23,18,1149244,1906364,2002,03/30/2006 09:10:16 PM,41.898988246,-87.727278637,"(41.898988246, -87.727278637)"
-2517225,HH862777,12/27/2002 05:00:00 PM,012XX E 47TH ST,031A,ROBBERY,ARMED: HANDGUN,VACANT LOT/LAND,false,false,2123,,4,39,03,1185306,1874130,2002,03/30/2006 09:10:16 PM,41.80976109,-87.595842488,"(41.80976109, -87.595842488)"
-2518186,HH861140,12/26/2002 05:03:00 PM,039XX N KILBOURN AVE,4310,OTHER OFFENSE,POSSESSION OF BURGLARY TOOLS,STREET,true,false,1731,,38,16,26,1145342,1926111,2002,03/30/2006 09:10:16 PM,41.953250675,-87.741109742,"(41.953250675, -87.741109742)"
-2514477,HH858337,12/24/2002 09:30:00 PM,045XX S DREXEL BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,2123,,4,39,14,1182918,1874946,2002,03/30/2006 09:10:16 PM,41.812056155,-87.604575812,"(41.812056155, -87.604575812)"
-2514268,HH859026,12/24/2002 09:00:00 PM,066XX S FRANCISCO AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0831,,15,66,05,1158233,1860348,2002,03/30/2006 09:10:16 PM,41.772535988,-87.695517231,"(41.772535988, -87.695517231)"
-2513791,HH857919,12/24/2002 04:39:44 PM,003XX W 106TH ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0512,,34,49,26,1175632,1834587,2002,03/30/2006 09:10:16 PM,41.701472702,-87.63250643,"(41.701472702, -87.63250643)"
-2513870,HH857371,12/24/2002 10:40:00 AM,006XX N LA SALLE DR,0810,THEFT,OVER $500,SIDEWALK,false,false,1832,,42,8,06,1174974,1904917,2002,12/04/2014 12:43:35 PM,41.894479944,-87.632817153,"(41.894479944, -87.632817153)"
-2513876,HH857200,12/24/2002 07:00:00 AM,013XX S WABASH AVE,0810,THEFT,OVER $500,RESIDENCE,false,false,0132,,2,33,06,1176943,1894052,2002,12/04/2014 12:43:35 PM,41.864621429,-87.625914593,"(41.864621429, -87.625914593)"
-2512061,HH856745,12/23/2002 08:00:00 PM,037XX W 86TH PL,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0834,,18,70,07,1153102,1846916,2002,03/30/2006 09:10:16 PM,41.735779235,-87.714680301,"(41.735779235, -87.714680301)"
-2511781,HH856104,12/23/2002 03:30:00 AM,034XX N HALSTED ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESTAURANT,false,false,2331,,44,6,14,1170348,1923243,2002,03/30/2006 09:10:16 PM,41.944869808,-87.649270069,"(41.944869808, -87.649270069)"
-2510185,HH855090,12/23/2002 03:00:00 AM,015XX N KINGSBURY ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1822,,32,8,07,1169404,1910487,2002,03/30/2006 09:10:16 PM,41.909887336,-87.65311176,"(41.909887336, -87.65311176)"
-2512729,HH854997,12/23/2002 01:01:00 AM,072XX S SOUTH SHORE DR,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0334,,7,43,05,1194960,1857761,2002,03/30/2006 09:10:16 PM,41.764610774,-87.560973148,"(41.764610774, -87.560973148)"
-1937,HH854889,12/22/2002 11:00:00 PM,008XX N CHRISTIANA AVE,0110,HOMICIDE,FIRST DEGREE MURDER,YARD,false,false,1121,011,27,23,01A,1153931,1905480,2002,10/31/2014 03:20:56 PM,41.896470319,-87.710086941,"(41.896470319, -87.710086941)"
-2516690,HH858068,12/22/2002 07:15:00 PM,014XX W 73RD ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0734,,17,67,26,1167593,1856367,2002,03/30/2006 09:10:16 PM,41.761415994,-87.661320005,"(41.761415994, -87.661320005)"
-2510755,HH854393,12/22/2002 03:15:00 PM,086XX S MORGAN ST,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,false,false,0613,,21,71,04B,1171110,1847793,2002,03/30/2006 09:10:16 PM,41.737811688,-87.648679956,"(41.737811688, -87.648679956)"
-2512193,HH854445,12/22/2002 01:45:00 PM,083XX S CICERO AVE,0870,THEFT,POCKET-PICKING,GROCERY FOOD STORE,false,false,0834,,18,70,06,1145914,1849091,2002,03/30/2006 09:10:16 PM,41.741886611,-87.740960017,"(41.741886611, -87.740960017)"
-2507754,HH850581,12/19/2002 10:00:00 PM,086XX S PRAIRIE AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0632,,6,44,05,1179584,1847669,2002,03/30/2006 09:10:16 PM,41.737282229,-87.617637356,"(41.737282229, -87.617637356)"
-2509619,HH847938,12/19/2002 12:00:00 PM,003XX W HILL ST,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,1823,,27,8,08A,1173393,1907630,2002,03/30/2006 09:10:16 PM,41.901959839,-87.63854295,"(41.901959839, -87.63854295)"
-2510391,HH847443,12/19/2002 04:25:00 AM,015XX W ESTES AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,2423,,49,1,14,1164623,1947439,2002,03/30/2006 09:10:16 PM,42.011387912,-87.669623686,"(42.011387912, -87.669623686)"
-2503498,HH844490,12/17/2002 01:30:00 PM,058XX N SHERIDAN RD,0460,BATTERY,SIMPLE,NURSING HOME/RETIREMENT HOME,false,false,2022,,48,77,08B,1168531,1939122,2002,03/30/2006 09:10:16 PM,41.988481889,-87.655486834,"(41.988481889, -87.655486834)"
-2504902,HH845634,12/15/2002 02:00:00 PM,108XX S AVENUE H,1340,CRIMINAL DAMAGE,TO STATE SUP PROP,"SCHOOL, PUBLIC, BUILDING",false,false,0432,,10,52,14,1202811,1833840,2002,03/30/2006 09:10:16 PM,41.69877286,-87.533013104,"(41.69877286, -87.533013104)"
-2499115,HH839607,12/15/2002 12:00:00 AM,092XX S ADA ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,2222,,21,73,05,1168908,1843616,2002,03/30/2006 09:10:16 PM,41.726397182,-87.656867795,"(41.726397182, -87.656867795)"
-2513969,HH858958,12/14/2002 03:00:00 PM,050XX S RACINE AVE,0810,THEFT,OVER $500,OTHER,false,false,0921,,16,61,06,1169208,1871630,2002,12/04/2014 12:43:35 PM,41.803264725,-87.654959559,"(41.803264725, -87.654959559)"
-2498424,HH837501,12/14/2002 04:55:00 AM,023XX S WENTWORTH AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,SMALL RETAIL STORE,false,false,2111,,25,34,14,1175298,1888863,2002,03/30/2006 09:10:16 PM,41.850419484,-87.632108886,"(41.850419484, -87.632108886)"
-2575739,HH837142,12/13/2002 11:42:12 PM,009XX S CICERO AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,SIDEWALK,true,false,1533,,24,25,16,1144491,1895516,2002,03/30/2006 09:10:16 PM,41.869310855,-87.745009577,"(41.869310855, -87.745009577)"
-2510956,HH835744,12/13/2002 12:15:00 PM,065XX S ARTESIAN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0832,,15,66,18,1161118,1861461,2002,03/30/2006 09:10:16 PM,41.775531017,-87.684910799,"(41.775531017, -87.684910799)"
-2496434,HH834968,12/13/2002 01:24:59 AM,031XX W 51ST ST,0460,BATTERY,SIMPLE,RESTAURANT,true,false,0911,,14,63,08B,1156100,1870603,2002,03/30/2006 09:10:16 PM,41.800720331,-87.703060713,"(41.800720331, -87.703060713)"
-2496446,HH834343,12/12/2002 06:20:00 PM,014XX S WABASH AVE,0890,THEFT,FROM BUILDING,OTHER,false,false,0132,,2,33,06,1176952,1893703,2002,03/30/2006 09:10:16 PM,41.863663547,-87.625892112,"(41.863663547, -87.625892112)"
-2493841,HH832655,12/11/2002 08:00:00 PM,009XX W VAN BUREN ST,0810,THEFT,OVER $500,STREET,false,false,1213,,2,28,06,1170162,1898329,2002,12/04/2014 12:43:35 PM,41.876508473,-87.650682549,"(41.876508473, -87.650682549)"
-2497567,HH832823,12/11/2002 01:00:00 PM,075XX S VERNON AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0624,,6,69,05,1180506,1855314,2002,03/30/2006 09:10:16 PM,41.75823988,-87.614025294,"(41.75823988, -87.614025294)"
-2498009,HH831280,12/11/2002 11:30:00 AM,062XX S HAMLIN AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",true,false,0823,,13,65,08B,1152065,1863220,2002,03/30/2006 09:10:16 PM,41.780540507,-87.718052317,"(41.780540507, -87.718052317)"
-2505199,HH830183,12/10/2002 06:39:51 PM,067XX N CALIFORNIA AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2412,,50,2,18,1156483,1944536,2002,03/30/2006 09:10:16 PM,42.003591245,-87.699653292,"(42.003591245, -87.699653292)"
-2490693,HH828798,12/09/2002 03:00:00 PM,017XX W 80TH ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,0611,,21,71,07,1165990,1851682,2002,03/30/2006 09:10:16 PM,41.748593912,-87.667328077,"(41.748593912, -87.667328077)"
-2496776,HH824751,12/08/2002 12:17:34 AM,056XX N ORIOLE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1613,,41,10,18,1124716,1936542,2002,03/30/2006 09:10:16 PM,41.98224059,-87.816702825,"(41.98224059, -87.816702825)"
-2558304,HH820450,12/05/2002 08:11:53 PM,052XX N KENMORE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,false,false,2023,,48,77,14,1168345,1934982,2002,03/30/2006 09:10:16 PM,41.977125665,-87.656291323,"(41.977125665, -87.656291323)"
-2486284,HH821532,12/05/2002 07:00:00 PM,036XX W 63RD PL,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0823,,13,65,05,1153171,1862215,2002,06/11/2007 03:52:33 PM,41.777760845,-87.714024014,"(41.777760845, -87.714024014)"
-2484265,HH818086,12/04/2002 03:45:00 PM,008XX N MICHIGAN AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,1833,,42,8,06,1177374,1906115,2002,03/30/2006 09:10:16 PM,41.897713204,-87.623966387,"(41.897713204, -87.623966387)"
-2482767,HH817411,12/04/2002 09:48:37 AM,012XX N MONTICELLO AVE,1025,ARSON,AGGRAVATED,"SCHOOL, PUBLIC, BUILDING",false,false,2535,,26,23,09,1151766,1908151,2002,03/30/2006 09:10:16 PM,41.903842685,-87.717968276,"(41.903842685, -87.717968276)"
-2479058,HH812198,12/01/2002 05:19:24 PM,081XX S ASHLAND AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0614,,21,71,14,1167127,1850821,2002,03/30/2006 09:10:16 PM,41.746206981,-87.663186267,"(41.746206981, -87.663186267)"
-2479384,HH811456,12/01/2002 07:44:17 AM,035XX S INDIANA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,0211,,3,35,14,1178140,1881613,2002,03/30/2006 09:10:16 PM,41.8304608,-87.621898755,"(41.8304608, -87.621898755)"
-2487106,HH820777,12/01/2002 01:00:00 AM,028XX E 77TH ST,0560,ASSAULT,SIMPLE,APARTMENT,false,false,0421,,7,43,08A,1196312,1854409,2002,03/30/2006 09:10:16 PM,41.75537923,-87.556128813,"(41.75537923, -87.556128813)"
-2482915,HH810587,11/30/2002 04:34:00 PM,064XX S EVANS AVE,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,0312,,20,42,18,1182282,1862603,2002,03/30/2006 09:10:16 PM,41.778200682,-87.607290989,"(41.778200682, -87.607290989)"
-2476656,HH810042,11/29/2002 06:30:00 PM,016XX N CLAREMONT AVE,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,1434,,1,24,06,1160395,1911107,2002,12/04/2014 12:43:35 PM,41.911779945,-87.686189886,"(41.911779945, -87.686189886)"
-2476626,HH808899,11/29/2002 05:00:00 PM,066XX S SANGAMON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,false,0723,,17,68,08B,1171162,1860686,2002,03/30/2006 09:10:16 PM,41.773190608,-87.648113214,"(41.773190608, -87.648113214)"
-2479558,HH807358,11/28/2002 04:30:00 PM,047XX W CHICAGO AVE,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,true,false,1111,,37,25,26,1144253,1904918,2002,06/11/2007 03:52:33 PM,41.895115546,-87.745646975,"(41.895115546, -87.745646975)"
-2477477,HH806733,11/28/2002 05:30:00 AM,066XX S JUSTINE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,false,0725,,17,67,08B,1167184,1860884,2002,03/30/2006 09:10:16 PM,41.773819994,-87.662689944,"(41.773819994, -87.662689944)"
-2480845,HH806364,11/27/2002 10:05:00 PM,005XX E BROWNING AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,0212,,4,35,18,1180603,1881458,2002,03/30/2006 09:10:16 PM,41.829979141,-87.6128668,"(41.829979141, -87.6128668)"
-2476765,HH808557,11/27/2002 05:30:00 PM,064XX S DREXEL AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0314,,20,42,05,1183544,1862305,2002,03/30/2006 09:10:16 PM,41.777353611,-87.602673777,"(41.777353611, -87.602673777)"
-2474450,HH805951,11/27/2002 02:00:00 PM,098XX S CALHOUN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0431,,7,51,14,1194806,1839891,2002,03/30/2006 09:10:16 PM,41.715577792,-87.56212455,"(41.715577792, -87.56212455)"
-2560772,HJ144657,11/26/2002 09:00:00 AM,057XX N LINCOLN AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,COMMERCIAL / BUSINESS OFFICE,false,false,2011,,40,2,11,1156408,1938196,2002,03/30/2006 09:10:16 PM,41.986195537,-87.700101698,"(41.986195537, -87.700101698)"
-2480623,HH799963,11/24/2002 09:15:00 PM,064XX W BELMONT AVE,0560,ASSAULT,SIMPLE,CHURCH/SYNAGOGUE/PLACE OF WORSHIP,false,false,2511,,36,19,08A,1132817,1920538,2002,03/30/2006 09:10:16 PM,41.938186026,-87.787283892,"(41.938186026, -87.787283892)"
-2468676,HH799680,11/24/2002 06:15:00 PM,0000X N HAMLIN BLVD,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1122,,28,26,26,1151025,1899809,2002,03/30/2006 09:10:16 PM,41.880965934,-87.72090888,"(41.880965934, -87.72090888)"
-2471864,HH798984,11/24/2002 09:45:00 AM,051XX S DR MARTIN LUTHER KING JR DR,0560,ASSAULT,SIMPLE,OTHER,false,false,0234,,3,40,08A,1179755,1871159,2002,03/30/2006 09:10:16 PM,41.801737322,-87.616293391,"(41.801737322, -87.616293391)"
-2468695,HH798404,11/24/2002 12:45:00 AM,0000X E 68TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,true,0322,,20,69,14,1178246,1859995,2002,03/30/2006 09:10:16 PM,41.771136613,-87.622166081,"(41.771136613, -87.622166081)"
-2468020,HH797920,11/23/2002 06:50:00 PM,043XX N KEELER AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,true,1722,,45,16,14,1147654,1928757,2002,03/30/2006 09:10:16 PM,41.96046735,-87.732542376,"(41.96046735, -87.732542376)"
-2466751,HH796854,11/23/2002 12:00:00 AM,022XX W 72ND ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,0832,,18,66,26,1162425,1856913,2002,03/30/2006 09:10:16 PM,41.763023525,-87.680246054,"(41.763023525, -87.680246054)"
-2467140,HH795446,11/22/2002 02:46:49 PM,012XX W 87TH ST,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,OTHER,true,true,0613,,21,71,26,1169825,1847138,2002,03/30/2006 09:10:16 PM,41.736042231,-87.653406822,"(41.736042231, -87.653406822)"
-2462986,HH791932,11/20/2002 03:00:00 PM,021XX S TAN CT,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2111,,25,34,14,1174990,1890844,2002,03/30/2006 09:10:16 PM,41.85586239,-87.633180007,"(41.85586239, -87.633180007)"
-2474591,HH789087,11/19/2002 01:22:13 PM,046XX S UNION AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,STREET,false,false,0935,,11,61,14,1172379,1873749,2002,03/30/2006 09:10:16 PM,41.809010211,-87.643267636,"(41.809010211, -87.643267636)"
-2463018,HH791499,11/19/2002 09:00:00 AM,044XX N WOLCOTT AVE,0890,THEFT,FROM BUILDING,APARTMENT,false,false,1922,,47,4,06,1163061,1929525,2002,03/30/2006 09:10:16 PM,41.962264384,-87.675876915,"(41.962264384, -87.675876915)"
-2460373,HH788713,11/19/2002 12:00:00 AM,055XX N CHRISTIANA AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1712,,40,13,07,,,2002,03/30/2006 09:10:16 PM,,,
-2459369,HH787904,11/18/2002 08:30:00 PM,007XX S CLAREMONT AVE,031A,ROBBERY,ARMED: HANDGUN,ALLEY,false,false,1224,,2,28,03,1160800,1896544,2002,03/30/2006 09:10:16 PM,41.871809453,-87.685106219,"(41.871809453, -87.685106219)"
-2475048,HH787819,11/18/2002 08:00:00 PM,085XX S COTTAGE GROVE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0632,,6,44,18,1183005,1848752,2002,03/30/2006 09:10:16 PM,41.74017535,-87.605070339,"(41.74017535, -87.605070339)"
-2463058,HH792390,11/18/2002 07:30:00 AM,001XX W 115TH ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0522,,34,53,26,1177108,1828653,2002,03/30/2006 09:10:16 PM,41.68515585,-87.627279925,"(41.68515585, -87.627279925)"
-2456654,HH784108,11/16/2002 09:30:00 PM,062XX N LINCOLN AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,1711,,50,13,06,1152513,1941309,2002,03/30/2006 09:10:16 PM,41.994815901,-87.714344716,"(41.994815901, -87.714344716)"
-2460428,HH783928,11/16/2002 07:10:00 PM,013XX N RIDGEWAY AVE,0460,BATTERY,SIMPLE,RESIDENCE,true,false,2535,,26,23,08B,1151091,1908550,2002,03/30/2006 09:10:16 PM,41.90495084,-87.72043726,"(41.90495084, -87.72043726)"
-2458133,HH783700,11/16/2002 05:24:01 PM,028XX S KEDVALE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,DRIVEWAY - RESIDENTIAL,false,false,1031,,22,30,14,1149214,1884920,2002,03/30/2006 09:10:16 PM,41.840143978,-87.727944302,"(41.840143978, -87.727944302)"
-2457521,HH785219,11/16/2002 05:00:00 PM,072XX W RASCHER AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1613,,41,10,08B,1127114,1935210,2002,03/30/2006 09:10:16 PM,41.978545417,-87.807913445,"(41.978545417, -87.807913445)"
-2455395,HH782404,11/16/2002 12:14:00 AM,023XX E 79TH ST,1330,CRIMINAL TRESPASS,TO LAND,GAS STATION,true,false,0414,,7,46,26,1193505,1853012,2002,03/30/2006 09:10:16 PM,41.751614875,-87.566461172,"(41.751614875, -87.566461172)"
-2456040,HH782959,11/16/2002 12:00:00 AM,076XX S CORNELL AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0414,,8,43,05,1188605,1854514,2002,03/30/2006 09:10:16 PM,41.755854945,-87.58436915,"(41.755854945, -87.58436915)"
-2461128,HH790067,11/15/2002 08:00:00 PM,065XX S CALIFORNIA AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0831,,15,66,26,1158878,1861115,2002,03/30/2006 09:10:16 PM,41.774627597,-87.693131894,"(41.774627597, -87.693131894)"
-2462682,HH780195,11/14/2002 11:27:02 PM,042XX W 77TH PL,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0834,,13,70,18,1149298,1852972,2002,03/30/2006 09:10:16 PM,41.752472167,-87.728460976,"(41.752472167, -87.728460976)"
-2452421,HH777229,11/13/2002 04:00:00 PM,027XX W 38TH PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,ALLEY,false,false,0913,,12,58,08B,1158906,1879052,2002,03/30/2006 09:10:16 PM,41.823848534,-87.69253907,"(41.823848534, -87.69253907)"
-2456673,HH776707,11/13/2002 12:00:00 PM,020XX W 63RD ST,1330,CRIMINAL TRESPASS,TO LAND,OTHER,true,false,0714,,15,67,26,1164017,1862900,2002,03/30/2006 09:10:16 PM,41.779419351,-87.674242906,"(41.779419351, -87.674242906)"
-2453981,HH777373,11/13/2002 02:00:00 AM,037XX S KEDZIE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,SIDEWALK,false,false,0913,,12,58,07,,,2002,03/30/2006 09:10:16 PM,,,
-2452535,HH777358,11/12/2002 05:00:00 PM,023XX S MARSHALL BLVD,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,1033,,12,30,08A,1157036,1888145,2002,03/30/2006 09:10:16 PM,41.84883887,-87.699153199,"(41.84883887, -87.699153199)"
-2450273,HH771405,11/11/2002 11:00:00 AM,027XX W LAWRENCE AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,1911,,47,4,08A,1157317,1931730,2002,03/30/2006 09:10:16 PM,41.968434022,-87.696935093,"(41.968434022, -87.696935093)"
-2449088,HH772157,11/11/2002 09:17:05 AM,045XX S CALUMET AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,false,0222,,3,38,08B,1179151,1875076,2002,03/30/2006 09:10:16 PM,41.812499715,-87.618388975,"(41.812499715, -87.618388975)"
-2453508,HH780506,11/11/2002 09:00:00 AM,020XX N KOSTNER AVE,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,2522,,31,20,06,1146694,1912942,2002,03/30/2006 09:10:16 PM,41.917088007,-87.736476666,"(41.917088007, -87.736476666)"
-2452212,HH777624,11/10/2002 08:00:00 PM,012XX E 55TH ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,CHA APARTMENT,false,false,2133,,5,41,26,1185294,1868736,2002,03/30/2006 09:10:16 PM,41.79495983,-87.596056205,"(41.79495983, -87.596056205)"
-2445568,HH770191,11/10/2002 04:45:00 AM,008XX W GRAND AVE,0460,BATTERY,SIMPLE,STREET,false,false,1323,,27,24,08B,1170617,1903715,2002,03/30/2006 09:10:16 PM,41.89127808,-87.648854217,"(41.89127808, -87.648854217)"
-2448722,HH769952,11/10/2002 01:40:00 AM,013XX N WELLS ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1821,,43,8,08B,1174507,1909115,2002,03/30/2006 09:10:16 PM,41.906009922,-87.634406678,"(41.906009922, -87.634406678)"
-2445171,HH770026,11/09/2002 11:00:00 PM,037XX N HALSTED ST,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,2324,,46,6,06,1170291,1925092,2002,03/30/2006 09:10:16 PM,41.949944781,-87.649425348,"(41.949944781, -87.649425348)"
-2446543,HH769469,11/09/2002 08:20:00 PM,132XX S CORLISS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA PARKING LOT/GROUNDS,false,true,0533,,9,54,08B,1183974,1817992,2002,03/30/2006 09:10:16 PM,41.655743381,-87.602476417,"(41.655743381, -87.602476417)"
-2449085,HH768625,11/09/2002 12:22:00 PM,038XX W DIVERSEY AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,true,false,2524,,35,22,24,1150305,1918304,2002,06/11/2007 03:52:33 PM,41.931732084,-87.723069536,"(41.931732084, -87.723069536)"
-2444171,HH768078,11/09/2002 02:00:00 AM,032XX N HARLEM AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,BAR OR TAVERN,false,false,1632,,36,17,14,1127556,1920613,2002,03/30/2006 09:10:16 PM,41.938482163,-87.806617897,"(41.938482163, -87.806617897)"
-2446305,HH771318,11/07/2002 04:00:00 PM,0000X N HAMLIN BLVD,0820,THEFT,$500 AND UNDER,STREET,false,false,1122,,28,26,06,1151025,1899809,2002,12/04/2014 12:43:35 PM,41.880965934,-87.72090888,"(41.880965934, -87.72090888)"
-2441854,HH764490,11/07/2002 12:59:02 PM,066XX S RHODES AVE,1754,OFFENSE INVOLVING CHILDREN,AGG SEX ASSLT OF CHILD FAM MBR,RESIDENCE,true,false,0321,,20,42,02,1181084,1861031,2002,03/30/2006 09:10:16 PM,41.773914633,-87.611731222,"(41.773914633, -87.611731222)"
-2452975,HH763777,11/07/2002 01:25:00 AM,042XX W JACKSON BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1115,,28,26,08B,1148340,1898392,2002,03/30/2006 09:10:16 PM,41.877129678,-87.730804627,"(41.877129678, -87.730804627)"
-2442351,HH762811,11/06/2002 03:10:00 PM,040XX N CLARK ST,1310,CRIMINAL DAMAGE,TO PROPERTY,GAS STATION,false,false,1923,,47,6,14,1166424,1927359,2002,03/30/2006 09:10:16 PM,41.956249321,-87.66357481,"(41.956249321, -87.66357481)"
-2441475,HH761970,11/06/2002 12:30:00 AM,051XX S ADA ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0933,,16,61,14,1168237,1870745,2002,03/30/2006 09:10:16 PM,41.80085716,-87.658546155,"(41.80085716, -87.658546155)"
-2438791,HH761033,11/05/2002 05:03:16 PM,082XX S EXCHANGE AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,0423,,7,46,08A,1197221,1850710,2002,03/30/2006 09:10:16 PM,41.745206322,-87.552920592,"(41.745206322, -87.552920592)"
-2436937,HH757775,11/04/2002 09:11:17 AM,091XX S GREENWOOD AVE,0460,BATTERY,SIMPLE,STREET,false,false,0413,,8,47,08B,1185097,1844831,2002,03/30/2006 09:10:16 PM,41.729366864,-87.597528416,"(41.729366864, -87.597528416)"
-2437198,HH757722,11/04/2002 08:15:00 AM,002XX E 121ST PL,0560,ASSAULT,SIMPLE,STREET,false,false,0532,,9,53,08A,1179798,1824481,2002,03/30/2006 09:10:16 PM,41.673646347,-87.617559443,"(41.673646347, -87.617559443)"
-2435695,HH756001,11/03/2002 08:52:30 AM,025XX W FLOURNOY ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1135,,2,28,07,1159458,1896912,2002,03/30/2006 09:10:16 PM,41.872846985,-87.690023126,"(41.872846985, -87.690023126)"
-2437667,HH754725,11/02/2002 03:00:00 PM,042XX W POTOMAC AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,2534,,37,23,08B,1147775,1908358,2002,03/30/2006 09:10:16 PM,41.90448834,-87.732622985,"(41.90448834, -87.732622985)"
-2439271,HH762210,11/02/2002 12:00:00 PM,085XX S ADA ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,0613,,21,71,14,1168863,1847933,2002,03/30/2006 09:10:16 PM,41.738244626,-87.656908356,"(41.738244626, -87.656908356)"
-2434414,HH753383,11/01/2002 07:11:00 PM,087XX S ESCANABA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,CTA BUS,false,false,0423,,10,46,14,1196858,1847808,2002,03/30/2006 09:10:16 PM,41.737252033,-87.554346908,"(41.737252033, -87.554346908)"
-2433483,HH753076,11/01/2002 06:00:00 PM,052XX W CHICAGO AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1524,,37,25,14,1141525,1904873,2002,03/30/2006 09:10:16 PM,41.895042899,-87.755667457,"(41.895042899, -87.755667457)"
-2432947,HH751922,11/01/2002 09:10:00 AM,014XX E 53RD ST,0560,ASSAULT,SIMPLE,STREET,true,false,2131,,4,41,08A,1186700,1870370,2002,03/30/2006 09:10:16 PM,41.799410442,-87.590848724,"(41.799410442, -87.590848724)"
-2430480,HH750912,10/31/2002 05:30:00 PM,002XX N CICERO AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1532,,28,25,14,1144300,1901040,2002,03/30/2006 09:10:16 PM,41.884472979,-87.745571916,"(41.884472979, -87.745571916)"
-2431783,HH748830,10/30/2002 07:10:00 PM,059XX N BROADWAY,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,2013,,48,77,03,,,2002,03/30/2006 09:10:16 PM,,,
-2428664,HH749465,10/30/2002 03:00:00 PM,052XX S STATE ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0232,,3,40,08B,1177203,1870284,2002,03/30/2006 09:10:16 PM,41.799394278,-87.625678907,"(41.799394278, -87.625678907)"
-2432151,HH742607,10/27/2002 10:06:30 PM,004XX N DRAKE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,true,false,1123,,27,23,14,1152676,1902816,2002,03/30/2006 09:10:16 PM,41.889184963,-87.714766894,"(41.889184963, -87.714766894)"
-2423257,HH741291,10/27/2002 08:30:00 AM,034XX N KENTON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1731,,30,16,08B,1145154,1922534,2002,03/30/2006 09:10:16 PM,41.94343864,-87.74189159,"(41.94343864, -87.74189159)"
-2438040,HH740633,10/26/2002 09:55:00 PM,016XX N HERMITAGE AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,1433,,32,24,16,1164475,1910734,2002,03/30/2006 09:10:16 PM,41.910670957,-87.671211807,"(41.910670957, -87.671211807)"
-2422556,HH741899,10/26/2002 08:00:00 PM,035XX N KARLOV AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1731,,30,16,06,1148559,1923354,2002,12/04/2014 12:43:35 PM,41.945623636,-87.729355113,"(41.945623636, -87.729355113)"
-2420885,HH738970,10/26/2002 12:01:00 AM,028XX S HARDING AVE,0810,THEFT,OVER $500,STREET,false,false,1031,,22,30,06,1150538,1885057,2002,12/04/2014 12:43:35 PM,41.840494212,-87.723082146,"(41.840494212, -87.723082146)"
-2425905,HH738621,10/25/2002 10:00:00 PM,026XX W EVERGREEN AVE,0560,ASSAULT,SIMPLE,STREET,false,true,1423,,26,24,08A,1158354,1908833,2002,03/30/2006 09:10:16 PM,41.905581918,-87.693750204,"(41.905581918, -87.693750204)"
-2420553,HH738208,10/25/2002 07:05:00 PM,034XX W DOUGLAS BLVD,0460,BATTERY,SIMPLE,APARTMENT,false,false,1021,,24,29,08B,1153785,1893285,2002,03/30/2006 09:10:16 PM,41.863008884,-87.710947989,"(41.863008884, -87.710947989)"
-2419154,HH736960,10/25/2002 09:00:00 AM,075XX S COLFAX AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0421,,7,43,06,1194799,1855207,2002,12/04/2014 12:43:35 PM,41.757606376,-87.56164722,"(41.757606376, -87.56164722)"
-2418777,HH736198,10/24/2002 08:00:00 PM,057XX S SHIELDS AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,0711,,20,68,07,1174904,1867090,2002,03/30/2006 09:10:16 PM,41.790681244,-87.634205132,"(41.790681244, -87.634205132)"
-2416416,HH732272,10/23/2002 02:00:00 AM,103XX S WESTERN AVE,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,2211,,19,72,06,1162167,1836233,2002,03/30/2006 09:10:16 PM,41.706279717,-87.681765488,"(41.706279717, -87.681765488)"
-2427625,HH731783,10/22/2002 08:30:00 PM,005XX W WINNECONNA PKWY,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0621,,17,69,18,1174065,1852887,2002,03/30/2006 09:10:16 PM,41.751725256,-87.637702751,"(41.751725256, -87.637702751)"
-2412712,HH728431,10/21/2002 11:10:00 AM,043XX N CICERO AVE,0460,BATTERY,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1624,,45,15,08B,1143548,1928403,2002,03/30/2006 09:10:16 PM,41.959573948,-87.747647117,"(41.959573948, -87.747647117)"
-2423914,HH743424,10/21/2002 08:20:00 AM,040XX N PONTIAC AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1614,,36,17,26,1119712,1925819,2002,03/30/2006 09:10:16 PM,41.952896739,-87.835335956,"(41.952896739, -87.835335956)"
-2424816,HH741846,10/21/2002 12:01:00 AM,009XX W 19TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,1233,,25,31,14,1170289,1891064,2002,03/30/2006 09:10:16 PM,41.856569981,-87.650428447,"(41.856569981, -87.650428447)"
-2413672,HH727382,10/20/2002 08:04:00 PM,035XX W PALMER ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1413,,26,22,08B,1152375,1914353,2002,03/30/2006 09:10:16 PM,41.920849525,-87.715567171,"(41.920849525, -87.715567171)"
-2413592,HH728030,10/20/2002 06:00:00 PM,079XX S KEDVALE AVE,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,0834,,13,70,07,1150176,1851286,2002,03/30/2006 09:10:16 PM,41.747828507,-87.725287095,"(41.747828507, -87.725287095)"
-2408446,HH723618,10/18/2002 11:30:12 PM,088XX S HERMITAGE AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,SIDEWALK,false,false,2221,,21,71,26,1166269,1845844,2002,03/30/2006 09:10:16 PM,41.732567655,-87.666471491,"(41.732567655, -87.666471491)"
-2417549,HH722334,10/18/2002 01:30:00 PM,013XX S FAIRFIELD AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,1023,,28,29,18,1158204,1893671,2002,03/30/2006 09:10:16 PM,41.863979053,-87.694715677,"(41.863979053, -87.694715677)"
-2412730,HH721588,10/18/2002 05:12:00 AM,014XX W 13TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA APARTMENT,true,true,1231,,2,28,08B,1166838,1894194,2002,03/30/2006 09:10:16 PM,41.865233593,-87.663005702,"(41.865233593, -87.663005702)"
-2421048,HH729070,10/17/2002 06:00:00 PM,063XX S CAMPBELL AVE,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,RESIDENCE,false,true,0825,,15,66,20,1160746,1862770,2002,10/24/2010 12:46:39 AM,41.779130785,-87.68623841,"(41.779130785, -87.68623841)"
-2403879,HH718814,10/16/2002 06:45:00 PM,0000X W SUPERIOR ST,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,1832,,42,8,26,1176213,1905323,2002,03/30/2006 09:10:16 PM,41.895566179,-87.628254468,"(41.895566179, -87.628254468)"
-2406000,HH720548,10/16/2002 06:30:00 PM,061XX S MELVINA AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,"SCHOOL, PUBLIC, BUILDING",false,false,0812,,23,64,14,1136092,1862988,2002,03/30/2006 09:10:16 PM,41.780202564,-87.776618816,"(41.780202564, -87.776618816)"
-2406376,HH718843,10/15/2002 07:30:00 PM,041XX N KENMORE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2322,,46,3,14,1168389,1927546,2002,03/30/2006 09:10:16 PM,41.956720087,-87.656345586,"(41.956720087, -87.656345586)"
-2413705,HH730637,10/14/2002 12:00:00 PM,067XX W TALCOTT AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1613,,41,10,07,1130292,1935999,2002,03/30/2006 09:10:16 PM,41.980656424,-87.796207811,"(41.980656424, -87.796207811)"
-2398367,HH711369,10/13/2002 01:00:00 AM,042XX S CAMPBELL AVE,0920,MOTOR VEHICLE THEFT,ATT: AUTOMOBILE,STREET,false,false,0914,,12,58,07,1160434,1876563,2002,03/30/2006 09:10:16 PM,41.816987011,-87.687002031,"(41.816987011, -87.687002031)"
-2398100,HH710565,10/12/2002 10:00:00 PM,005XX N LA SALLE DR,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,1831,,42,8,06,1175015,1903591,2002,03/30/2006 09:10:16 PM,41.890840409,-87.63270634,"(41.890840409, -87.63270634)"
-2396808,HH707799,10/11/2002 06:25:00 PM,069XX S PEORIA ST,041A,BATTERY,AGGRAVATED: HANDGUN,OTHER,false,false,0733,,17,68,04B,1171549,1858584,2002,03/30/2006 09:10:16 PM,41.767413995,-87.646756079,"(41.767413995, -87.646756079)"
-2400707,HH713055,10/11/2002 06:00:00 PM,085XX S SEELEY AVE,0560,ASSAULT,SIMPLE,STREET,false,false,0614,,18,71,08A,1164143,1847756,2002,03/30/2006 09:10:16 PM,41.737859414,-87.674206361,"(41.737859414, -87.674206361)"
-2405009,HH709843,10/11/2002 01:30:00 PM,033XX W MADISON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1124,,28,27,08B,1153915,1899773,2002,03/30/2006 09:10:16 PM,41.880810064,-87.710297898,"(41.880810064, -87.710297898)"
-2413504,HH709661,10/11/2002 09:00:00 AM,052XX W PALMER ST,0560,ASSAULT,SIMPLE,STREET,true,true,2515,,37,19,08A,1140773,1914158,2002,03/30/2006 09:10:16 PM,41.920535859,-87.758200743,"(41.920535859, -87.758200743)"
-2392705,HH703542,10/09/2002 06:40:00 PM,051XX W DAKIN ST,033A,ROBBERY,ATTEMPT: ARMED-HANDGUN,STREET,false,false,1634,,45,15,03,1141622,1925808,2002,03/30/2006 09:10:16 PM,41.952488941,-87.75479244,"(41.952488941, -87.75479244)"
-2396986,HH703409,10/09/2002 06:20:00 PM,008XX N STATE ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,true,false,1832,,42,8,11,1176184,1905742,2002,03/30/2006 09:10:16 PM,41.89671659,-87.628348332,"(41.89671659, -87.628348332)"
-2404002,HH701905,10/09/2002 01:15:00 AM,062XX S ROCKWELL ST,0460,BATTERY,SIMPLE,STREET,false,false,0825,,15,66,08B,1160065,1863415,2002,03/30/2006 09:10:16 PM,41.78091479,-87.688717311,"(41.78091479, -87.688717311)"
-2390963,HH701243,10/08/2002 06:10:00 PM,128XX S PEORIA ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,0523,,34,53,14,1172635,1819663,2002,03/30/2006 09:10:16 PM,41.660585229,-87.643917728,"(41.660585229, -87.643917728)"
-2387947,HH699710,10/08/2002 02:40:00 AM,037XX N OSCEOLA AVE,0810,THEFT,OVER $500,RESIDENCE PORCH/HALLWAY,false,false,1631,,36,17,06,1125695,1924139,2002,12/04/2014 12:43:35 PM,41.948189112,-87.813379046,"(41.948189112, -87.813379046)"
-2481388,HH815941,10/07/2002 02:00:00 PM,066XX S LANGLEY AVE,0842,THEFT,AGG: FINANCIAL ID THEFT,RESIDENCE,false,false,0321,,20,42,06,1181998,1860922,2002,03/30/2006 09:10:16 PM,41.773594432,-87.608384084,"(41.773594432, -87.608384084)"
-2389913,HH698119,10/07/2002 12:00:00 PM,063XX S HALSTED ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,0723,,20,68,06,1172093,1862948,2002,03/30/2006 09:10:16 PM,41.779377394,-87.644633996,"(41.779377394, -87.644633996)"
-2394215,HH696093,10/06/2002 11:51:30 AM,049XX W NORTH AVE,0560,ASSAULT,SIMPLE,DEPARTMENT STORE,false,true,2533,,37,25,08A,1142980,1910137,2002,03/30/2006 09:10:16 PM,41.909460903,-87.750192135,"(41.909460903, -87.750192135)"
-2397463,HH710241,10/06/2002 10:00:00 AM,045XX N CLARENDON AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,true,2313,,46,3,26,,,2002,03/30/2006 09:10:16 PM,,,
-2395310,HH695128,10/05/2002 10:46:00 PM,032XX W 21ST ST,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1022,,24,30,18,1154974,1889817,2002,03/30/2006 09:10:16 PM,41.853468568,-87.706676181,"(41.853468568, -87.706676181)"
-2386969,HH697263,10/05/2002 10:00:00 PM,026XX N EMMETT ST,0810,THEFT,OVER $500,STREET,false,false,1412,,35,22,06,1154238,1918113,2002,12/04/2014 12:43:35 PM,41.931130236,-87.708621419,"(41.931130236, -87.708621419)"
-2385339,HH694823,10/05/2002 07:00:00 PM,112XX S PARNELL AVE,0460,BATTERY,SIMPLE,OTHER,false,false,2233,,34,49,08B,1174594,1830586,2002,03/30/2006 09:10:16 PM,41.690516472,-87.636425777,"(41.690516472, -87.636425777)"
-2395835,HH703430,10/05/2002 02:00:00 AM,018XX N HUMBOLDT BLVD,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1421,,35,22,26,1156043,1912098,2002,03/30/2006 09:10:16 PM,41.914588323,-87.702151065,"(41.914588323, -87.702151065)"
-2385235,HH693705,10/05/2002 12:01:00 AM,0000X E DIVISION ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,1824,,43,8,06,1176393,1908423,2002,03/30/2006 09:10:16 PM,41.904068666,-87.627499704,"(41.904068666, -87.627499704)"
-2380679,HH689065,10/03/2002 02:00:00 AM,026XX W 64TH ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,RESIDENCE,false,false,0825,,15,66,07,1159545,1862042,2002,03/30/2006 09:10:16 PM,41.777157766,-87.690661366,"(41.777157766, -87.690661366)"
-2382040,HH688138,10/02/2002 07:45:00 PM,012XX W PRATT BLVD,1330,CRIMINAL TRESPASS,TO LAND,APARTMENT,true,false,2431,,49,1,26,1166529,1945352,2002,03/30/2006 09:10:16 PM,42.005620386,-87.66267089,"(42.005620386, -87.66267089)"
-2395757,HH687531,10/02/2002 04:00:00 PM,035XX W 74TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0835,,18,66,08B,1154085,1855268,2002,03/30/2006 09:10:16 PM,41.758679074,-87.710857554,"(41.758679074, -87.710857554)"
-2376690,HH683480,09/30/2002 10:00:00 PM,066XX S STEWART AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,false,0722,,6,68,08B,1174739,1860550,2002,03/30/2006 09:10:16 PM,41.772738453,-87.635004896,"(41.772738453, -87.635004896)"
-2374112,HH681471,09/30/2002 01:43:58 AM,018XX S KARLOV AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,CHA HALLWAY/STAIRWELL/ELEVATOR,false,false,1012,,24,29,04B,1149322,1890428,2002,03/30/2006 09:10:16 PM,41.855256543,-87.72740537,"(41.855256543, -87.72740537)"
-2391706,HH680476,09/29/2002 02:55:00 PM,063XX S FAIRFIELD AVE,0880,THEFT,PURSE-SNATCHING,SIDEWALK,false,false,0825,,15,66,06,1159095,1862424,2002,03/30/2006 09:10:16 PM,41.778215245,-87.692300631,"(41.778215245, -87.692300631)"
-2372741,HH679354,09/29/2002 12:31:05 AM,122XX S EGGLESTON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0523,,34,53,14,1175462,1823870,2002,03/30/2006 09:10:16 PM,41.672067407,-87.633447726,"(41.672067407, -87.633447726)"
-2381584,HH679431,09/29/2002 12:15:00 AM,062XX S COTTAGE GROVE AVE,0460,BATTERY,SIMPLE,STREET,false,false,0313,,20,42,08B,1182676,1863795,2002,03/30/2006 09:10:16 PM,41.781462503,-87.605809612,"(41.781462503, -87.605809612)"
-2389072,HH679057,09/28/2002 09:15:00 PM,020XX E 71ST ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0333,,5,43,18,1190791,1858215,2002,03/30/2006 09:10:16 PM,41.765958295,-87.576238726,"(41.765958295, -87.576238726)"
-2375140,HH678777,09/28/2002 02:00:00 AM,076XX S DR MARTIN LUTHER KING JR DR,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0623,,6,69,03,1180183,1854673,2002,03/30/2006 09:10:16 PM,41.756488312,-87.615228658,"(41.756488312, -87.615228658)"
-2372772,HH676860,09/27/2002 08:40:00 PM,032XX N CENTRAL AVE,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,true,false,1633,,38,15,26,1138419,1921064,2002,03/30/2006 09:10:16 PM,41.939529661,-87.76668224,"(41.939529661, -87.76668224)"
-2371120,HH676777,09/27/2002 06:50:00 PM,098XX S PARNELL AVE,0460,BATTERY,SIMPLE,PARK PROPERTY,false,false,2223,,21,73,08B,1174321,1839885,2002,03/30/2006 09:10:16 PM,41.716040358,-87.637150032,"(41.716040358, -87.637150032)"
-2395263,HH706733,09/27/2002 12:00:00 PM,003XX W OAK ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,false,false,1823,,27,8,26,1173432,1907132,2002,03/30/2006 09:10:16 PM,41.900592434,-87.638414517,"(41.900592434, -87.638414517)"
-2372352,HH672652,09/25/2002 10:18:30 PM,116XX S HALSTED ST,031A,ROBBERY,ARMED: HANDGUN,RESTAURANT,false,false,0524,,34,53,03,1173110,1827835,2002,03/30/2006 09:10:16 PM,41.683000111,-87.641939603,"(41.683000111, -87.641939603)"
-2378164,HH673878,09/25/2002 02:00:00 PM,065XX S WOOD ST,0810,THEFT,OVER $500,"SCHOOL, PUBLIC, BUILDING",false,false,0726,,15,67,06,1165424,1861341,2002,12/04/2014 12:43:35 PM,41.775111543,-87.669128816,"(41.775111543, -87.669128816)"
-2367898,HH671129,09/25/2002 09:30:00 AM,111XX S HALE AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),true,false,2212,,19,75,06,1165164,1830576,2002,12/04/2014 12:43:35 PM,41.690693201,-87.670949891,"(41.690693201, -87.670949891)"
-2370144,HH672021,09/25/2002 07:30:00 AM,008XX N CAMBRIDGE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1823,,27,8,14,1172483,1905989,2002,03/30/2006 09:10:16 PM,41.897477018,-87.64193405,"(41.897477018, -87.64193405)"
-2368779,HH670346,09/24/2002 08:53:33 PM,019XX N LA CROSSE AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,2533,,31,19,08A,1143778,1912780,2002,03/30/2006 09:10:16 PM,41.916698651,-87.747194243,"(41.916698651, -87.747194243)"
-2368154,HH669941,09/24/2002 04:50:00 PM,008XX N SEDGWICK ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA APARTMENT,true,false,1823,,27,8,08B,1173307,1906333,2002,03/30/2006 09:10:16 PM,41.898402712,-87.6388974,"(41.898402712, -87.6388974)"
-2380269,HH668185,09/23/2002 08:15:00 PM,051XX S NARRAGANSETT AVE,1330,CRIMINAL TRESPASS,TO LAND,OTHER,true,false,0811,,23,56,26,1134532,1869996,2002,03/30/2006 09:10:16 PM,41.799461363,-87.782173444,"(41.799461363, -87.782173444)"
-2370483,HH668102,09/23/2002 07:45:00 PM,055XX N WINTHROP AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,true,false,2023,,48,77,24,1167820,1937324,2002,03/30/2006 09:10:16 PM,41.983563545,-87.658154086,"(41.983563545, -87.658154086)"
-2364004,HH666245,09/22/2002 10:45:00 PM,0000X W 87TH ST,1330,CRIMINAL TRESPASS,TO LAND,GROCERY FOOD STORE,true,false,0634,,21,44,26,1177043,1847234,2002,03/30/2006 09:10:16 PM,41.736146144,-87.626959873,"(41.736146144, -87.626959873)"
-2365392,HH665580,09/22/2002 04:50:00 PM,060XX S WESTERN AVE,0320,ROBBERY,STRONGARM - NO WEAPON,CAR WASH,false,false,0825,,16,66,03,1161438,1864785,2002,03/30/2006 09:10:16 PM,41.784645902,-87.683645642,"(41.784645902, -87.683645642)"
-2360911,HH665403,09/22/2002 06:30:00 AM,005XX W WEBSTER AVE,0820,THEFT,$500 AND UNDER,HOSPITAL BUILDING/GROUNDS,false,false,1812,,43,7,06,1172020,1914923,2002,12/04/2014 12:43:35 PM,41.922002608,-87.643370627,"(41.922002608, -87.643370627)"
-2361957,HH663073,09/21/2002 12:52:54 PM,045XX S DAMEN AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,0914,,12,61,06,1163704,1874123,2002,03/30/2006 09:10:16 PM,41.810223247,-87.675075348,"(41.810223247, -87.675075348)"
-2362440,HH663557,09/21/2002 11:45:00 AM,025XX S MICHIGAN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,OTHER,false,false,2112,,2,33,08B,1177690,1887533,2002,03/30/2006 09:10:16 PM,41.846715936,-87.623370267,"(41.846715936, -87.623370267)"
-2360023,HH662073,09/21/2002 12:10:00 AM,025XX N CLYBOURN AVE,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,1931,,32,7,06,1163869,1916997,2002,03/30/2006 09:10:16 PM,41.927869849,-87.673260933,"(41.927869849, -87.673260933)"
-2358540,HH661790,09/20/2002 08:30:00 PM,059XX S MOZART ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0824,,16,66,26,1158353,1865176,2002,03/30/2006 09:10:16 PM,41.78578227,-87.694945953,"(41.78578227, -87.694945953)"
-2377163,HH661546,09/20/2002 06:05:00 PM,032XX W HARRISON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1134,,24,27,18,1155010,1897223,2002,03/30/2006 09:10:16 PM,41.873790719,-87.706345526,"(41.873790719, -87.706345526)"
-2380592,HH678050,09/20/2002 12:15:00 PM,044XX S WENTWORTH AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,0935,,3,37,18,1175628,1875758,2002,03/30/2006 09:10:16 PM,41.814450887,-87.631290816,"(41.814450887, -87.631290816)"
-2359024,HH661964,09/20/2002 12:00:00 PM,027XX S CALIFORNIA AVE,0820,THEFT,$500 AND UNDER,GOVERNMENT BUILDING/PROPERTY,false,false,1033,,12,30,06,1158103,1885845,2002,12/04/2014 12:43:35 PM,41.842505723,-87.695299912,"(41.842505723, -87.695299912)"
-2359970,HH664247,09/19/2002 10:20:00 PM,077XX S ESSEX AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0421,,7,43,26,1194250,1853870,2002,03/30/2006 09:10:16 PM,41.753951041,-87.563703027,"(41.753951041, -87.563703027)"
-2357745,HH659307,09/19/2002 08:10:00 PM,005XX W GARFIELD BLVD,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,VACANT LOT/LAND,true,false,0934,,3,61,15,1173668,1868481,2002,03/30/2006 09:10:16 PM,41.794525787,-87.63869601,"(41.794525787, -87.63869601)"
-2355459,HH657970,09/19/2002 08:30:00 AM,003XX W 100TH ST,0560,ASSAULT,SIMPLE,STREET,false,false,0511,,9,49,08A,1175536,1838650,2002,03/30/2006 09:10:16 PM,41.712624299,-87.632736911,"(41.712624299, -87.632736911)"
-2355968,HH658230,09/18/2002 05:45:00 PM,002XX S STATE ST,0610,BURGLARY,FORCIBLE ENTRY,COMMERCIAL / BUSINESS OFFICE,false,false,0123,,42,32,05,1176370,1899426,2002,03/30/2006 09:10:16 PM,41.879380952,-87.62785591,"(41.879380952, -87.62785591)"
-2351893,HH652606,09/16/2002 09:04:00 PM,020XX W 70TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0735,,17,67,08B,1164036,1858194,2002,03/30/2006 09:10:16 PM,41.766505051,-87.674305456,"(41.766505051, -87.674305456)"
-2362738,HH651452,09/16/2002 12:40:26 PM,053XX W VAN BUREN ST,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1522,,29,25,26,1140541,1897526,2002,03/30/2006 09:10:16 PM,41.874899932,-87.759461929,"(41.874899932, -87.759461929)"
-2353113,HH648159,09/14/2002 07:10:00 PM,053XX N BROADWAY,0486,BATTERY,DOMESTIC BATTERY SIMPLE,GROCERY FOOD STORE,false,false,2023,,48,77,08B,1167382,1935813,2002,03/30/2006 09:10:16 PM,41.979426783,-87.659808661,"(41.979426783, -87.659808661)"
-2346533,HH646881,09/14/2002 05:45:00 AM,094XX S EWING AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,false,true,0432,,10,52,26,1201329,1843258,2002,03/30/2006 09:10:16 PM,41.724654285,-87.538120872,"(41.724654285, -87.538120872)"
-2353600,HH645547,09/13/2002 07:00:00 AM,044XX S WALLACE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,false,0935,,11,61,08B,1173074,1875591,2002,03/30/2006 09:10:16 PM,41.814049497,-87.640664069,"(41.814049497, -87.640664069)"
-2357322,HH644137,09/12/2002 09:29:00 PM,075XX N CLAREMONT AVE,2027,NARCOTICS,POSS: CRACK,RESIDENCE,true,false,2411,,49,2,18,1159307,1949719,2002,03/30/2006 09:10:16 PM,42.017755726,-87.689120361,"(42.017755726, -87.689120361)"
-2350169,HH643076,09/12/2002 01:51:00 PM,020XX W PETERSON AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,2413,,40,2,06,1161364,1939893,2002,03/30/2006 09:10:16 PM,41.99075017,-87.681826178,"(41.99075017, -87.681826178)"
-2353181,HH643047,09/12/2002 12:50:40 PM,033XX N CLIFTON AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,true,false,1924,,44,6,14,1167988,1922303,2002,03/30/2006 09:10:16 PM,41.942341761,-87.657971647,"(41.942341761, -87.657971647)"
-2344555,HH642695,09/12/2002 09:30:00 AM,012XX W LUNT AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,2431,,49,1,08B,1166229,1946598,2002,03/30/2006 09:10:16 PM,42.009045875,-87.663738759,"(42.009045875, -87.663738759)"
-2357321,HH641781,09/11/2002 09:00:00 PM,022XX W 50TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0915,,16,63,18,1162172,1871516,2002,03/30/2006 09:10:16 PM,41.803101382,-87.680767151,"(41.803101382, -87.680767151)"
-2349976,HH641412,09/11/2002 06:15:00 PM,015XX N PULASKI RD,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,SIDEWALK,true,false,2534,,30,23,16,1149401,1909710,2002,03/30/2006 09:10:16 PM,41.908166962,-87.726615056,"(41.908166962, -87.726615056)"
-2340441,HH639459,09/10/2002 02:45:00 PM,038XX N PIONEER AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,1631,,36,17,08A,1120678,1924264,2002,03/30/2006 09:10:16 PM,41.948614164,-87.831818242,"(41.948614164, -87.831818242)"
-2352088,HH636171,09/09/2002 11:38:38 AM,044XX S FEDERAL ST,2027,NARCOTICS,POSS: CRACK,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,0221,,3,38,18,1176474,1875517,2002,03/30/2006 09:10:16 PM,41.813770553,-87.628194868,"(41.813770553, -87.628194868)"
-2340953,HH637011,09/09/2002 05:30:00 AM,013XX W 81ST ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0612,,21,71,05,1169050,1851099,2002,03/30/2006 09:10:16 PM,41.746928537,-87.656131948,"(41.746928537, -87.656131948)"
-2362789,HH635409,09/09/2002 12:26:53 AM,012XX S KEELER AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1011,,24,29,08B,1148533,1894257,2002,03/30/2006 09:10:16 PM,41.865779032,-87.730202659,"(41.865779032, -87.730202659)"
-2337085,HH635005,09/08/2002 08:15:00 PM,015XX W 81ST ST,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,0614,,21,71,06,1167237,1850969,2002,12/04/2014 12:43:35 PM,41.746610762,-87.662778976,"(41.746610762, -87.662778976)"
-2345843,HH633010,09/07/2002 08:08:27 PM,038XX W LAKE ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1122,,27,26,18,1150652,1901418,2002,03/30/2006 09:10:16 PM,41.885388499,-87.722236449,"(41.885388499, -87.722236449)"
-2334905,HH632071,09/07/2002 08:30:00 AM,056XX S WOLCOTT AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0715,,15,67,26,1164610,1867273,2002,03/30/2006 09:10:16 PM,41.79140693,-87.671945573,"(41.79140693, -87.671945573)"
-2332387,HH628167,09/05/2002 05:20:00 PM,070XX S PERRY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0731,,6,69,08B,1176581,1858108,2002,03/30/2006 09:10:16 PM,41.765996093,-87.628326032,"(41.765996093, -87.628326032)"
-2336756,HH625413,09/04/2002 01:18:11 PM,048XX S LAMON AVE,0810,THEFT,OVER $500,RESIDENCE,false,false,0814,,23,56,06,1144558,1871771,2002,12/04/2014 12:43:35 PM,41.80414995,-87.745360326,"(41.80414995, -87.745360326)"
-2330627,HH625149,09/04/2002 11:30:51 AM,035XX S PARNELL AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,true,false,0925,,11,60,26,1173175,1881199,2002,03/30/2006 09:10:16 PM,41.829436134,-87.64012763,"(41.829436134, -87.64012763)"
-2325805,HH620806,09/01/2002 11:30:00 PM,020XX W 67TH PL,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,0726,,15,67,07,1163889,1859847,2002,03/30/2006 09:10:16 PM,41.7710442,-87.674797878,"(41.7710442, -87.674797878)"
-2323780,HH616662,08/30/2002 10:00:00 PM,011XX W POLK ST,0917,MOTOR VEHICLE THEFT,"CYCLE, SCOOTER, BIKE W-VIN",STREET,false,false,1213,,25,28,07,1168968,1896646,2002,03/30/2006 09:10:16 PM,41.871916169,-87.655115331,"(41.871916169, -87.655115331)"
-2322635,HH614300,08/30/2002 08:20:00 AM,019XX N KARLOV AVE,2851,PUBLIC PEACE VIOLATION,ARSON THREAT,RESIDENCE,false,true,2534,,30,20,26,1148776,1912714,2002,03/30/2006 09:10:16 PM,41.916422338,-87.728833231,"(41.916422338, -87.728833231)"
-2321716,HH612838,08/29/2002 02:28:03 PM,059XX W ADDISON ST,0860,THEFT,RETAIL THEFT,DRUG STORE,false,false,1633,,38,15,06,1136013,1923287,2002,03/30/2006 09:10:16 PM,41.945673109,-87.775471959,"(41.945673109, -87.775471959)"
-2320805,HH609528,08/28/2002 11:45:00 PM,035XX W CORTLAND ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1422,,26,22,08B,1152475,1912358,2002,03/30/2006 09:10:16 PM,41.915373093,-87.715252576,"(41.915373093, -87.715252576)"
-2320088,HH612613,08/28/2002 07:00:00 PM,025XX N KEDZIE BLVD,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1413,,35,22,14,1154571,1916647,2002,03/30/2006 09:10:16 PM,41.927100753,-87.707437029,"(41.927100753, -87.707437029)"
-2320647,HH613451,08/28/2002 06:00:00 PM,054XX N KENMORE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,false,false,2023,,48,77,26,1168224,1936443,2002,03/30/2006 09:10:16 PM,41.981137311,-87.656693851,"(41.981137311, -87.656693851)"
-2318149,HH609451,08/27/2002 10:50:00 PM,105XX S YATES AVE,1790,OFFENSE INVOLVING CHILDREN,CHILD ABDUCTION,CHA APARTMENT,true,true,0434,,10,51,20,1194165,1835740,2002,03/30/2006 09:10:16 PM,41.704202765,-87.564607875,"(41.704202765, -87.564607875)"
-2317586,HH608906,08/27/2002 06:45:00 PM,106XX S TORRENCE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0434,,10,51,14,1195503,1834893,2002,03/30/2006 09:10:16 PM,41.701845634,-87.559736302,"(41.701845634, -87.559736302)"
-2320621,HH613303,08/27/2002 06:00:00 PM,027XX N MOZART ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,1411,,35,22,05,1156870,1917941,2002,03/30/2006 09:10:16 PM,41.930605224,-87.698953971,"(41.930605224, -87.698953971)"
-2322167,HH606435,08/26/2002 03:45:00 PM,064XX W DIVERSEY AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,PARKING LOT/GARAGE(NON.RESID.),false,true,2512,,36,19,26,,,2002,03/30/2006 09:10:16 PM,,,
-2313708,HH603693,08/25/2002 11:30:02 AM,0000X E 112TH PL,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,false,false,0531,,9,49,14,1178550,1830425,2002,03/30/2006 09:10:16 PM,41.689985925,-87.621947584,"(41.689985925, -87.621947584)"
-2316190,HH603573,08/25/2002 05:00:00 AM,018XX W 37TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0922,,11,59,14,1164381,1880118,2002,03/30/2006 09:10:16 PM,41.826659945,-87.672423063,"(41.826659945, -87.672423063)"
-2312075,HH602985,08/23/2002 10:00:00 PM,028XX S KILDARE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1031,,22,30,14,1148138,1884998,2002,03/30/2006 09:10:16 PM,41.840378764,-87.731890819,"(41.840378764, -87.731890819)"
-2319934,HH600685,08/23/2002 09:53:15 PM,036XX W GRENSHAW ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE PORCH/HALLWAY,false,true,1133,,24,29,08B,1152295,1894757,2002,03/30/2006 09:10:16 PM,41.86707773,-87.716378835,"(41.86707773, -87.716378835)"
-2311047,HH599963,08/23/2002 04:00:00 PM,036XX S FEDERAL ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,0211,,3,35,08B,1176249,1880526,2002,03/30/2006 09:10:16 PM,41.827520746,-87.628869539,"(41.827520746, -87.628869539)"
-2311615,HH602592,08/23/2002 02:00:00 PM,012XX N CAMPBELL AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1423,,26,24,08B,1159556,1908077,2002,03/30/2006 09:10:16 PM,41.903482716,-87.689355689,"(41.903482716, -87.689355689)"
-2309806,HH598648,08/22/2002 11:45:00 PM,024XX S BLUE ISLAND AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1034,,25,31,06,1163400,1888120,2002,12/04/2014 12:43:35 PM,41.848638927,-87.675797435,"(41.848638927, -87.675797435)"
-2318268,HH598259,08/22/2002 08:23:39 PM,047XX N CENTRAL PARK AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1723,,33,14,18,1151489,1931334,2002,03/30/2006 09:10:16 PM,41.967464162,-87.718374927,"(41.967464162, -87.718374927)"
-2307573,HH596570,08/22/2002 01:35:00 AM,003XX W 105TH PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0512,,34,49,08B,1175584,1834918,2002,03/30/2006 09:10:16 PM,41.702382086,-87.632672329,"(41.702382086, -87.632672329)"
-2315267,HH595760,08/21/2002 05:35:52 PM,006XX N LOCKWOOD AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1524,,28,25,26,1140941,1904086,2002,03/30/2006 09:10:16 PM,41.892894046,-87.757831752,"(41.892894046, -87.757831752)"
-2304720,HH593283,08/20/2002 12:00:00 PM,016XX E 73RD ST,0890,THEFT,FROM BUILDING,OTHER,false,false,0324,,8,43,06,1188396,1856865,2002,03/30/2006 09:10:16 PM,41.76231129,-87.585060133,"(41.76231129, -87.585060133)"
-2306805,HH592605,08/20/2002 09:35:00 AM,052XX W MONROE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,1522,,29,25,08B,1141368,1899197,2002,03/30/2006 09:10:16 PM,41.879470157,-87.756384245,"(41.879470157, -87.756384245)"
-2303462,HH592054,08/20/2002 12:31:46 AM,003XX W 110TH PL,0312,ROBBERY,ARMED:KNIFE/CUTTING INSTRUMENT,RESIDENCE,false,true,0513,,34,49,03,1176092,1831609,2002,03/30/2006 09:10:16 PM,41.693290349,-87.630910995,"(41.693290349, -87.630910995)"
-2304704,HH591092,08/19/2002 04:10:00 PM,019XX E 71ST ST,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,true,0332,,5,43,06,1190109,1858344,2002,12/04/2014 12:43:35 PM,41.766328727,-87.578734301,"(41.766328727, -87.578734301)"
-2316159,HH592269,08/18/2002 03:00:00 AM,052XX W DRUMMOND PL,0820,THEFT,$500 AND UNDER,STREET,false,false,2514,,31,19,06,1140732,1917157,2002,12/04/2014 12:43:35 PM,41.928766187,-87.758277491,"(41.928766187, -87.758277491)"
-2302371,HH587825,08/18/2002 01:03:42 AM,057XX S STATE ST,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,0233,,20,40,06,1177288,1867209,2002,12/04/2014 12:43:35 PM,41.79095426,-87.625460051,"(41.79095426, -87.625460051)"
-2300002,HH587482,08/17/2002 09:25:00 PM,100XX S LUELLA AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,PARK PROPERTY,false,false,0431,,7,51,04B,1193195,1838865,2002,03/30/2006 09:10:16 PM,41.712801792,-87.56805809,"(41.712801792, -87.56805809)"
-2298772,HH584997,08/16/2002 06:45:00 PM,082XX S COTTAGE GROVE AVE,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0631,,6,44,03,1182944,1850766,2002,03/30/2006 09:10:16 PM,41.745703403,-87.605231399,"(41.745703403, -87.605231399)"
-2297629,HH581376,08/15/2002 02:29:00 AM,048XX S DR MARTIN LUTHER KING JR DR,1330,CRIMINAL TRESPASS,TO LAND,CHURCH/SYNAGOGUE/PLACE OF WORSHIP,true,false,0223,,3,38,26,1179723,1872754,2002,03/30/2006 09:10:16 PM,41.806114873,-87.616361952,"(41.806114873, -87.616361952)"
-2295544,HH581123,08/14/2002 10:10:00 PM,103XX S AVENUE M,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0432,,10,52,14,1201541,1836947,2002,03/30/2006 09:10:16 PM,41.707331024,-87.537558,"(41.707331024, -87.537558)"
-2294590,HH580832,08/14/2002 01:45:00 PM,061XX N MOZART ST,0890,THEFT,FROM BUILDING,OTHER,false,true,2413,,50,2,06,1156190,1940567,2002,03/30/2006 09:10:16 PM,41.992706089,-87.700839094,"(41.992706089, -87.700839094)"
-2316041,HH579645,08/14/2002 10:05:00 AM,044XX W CONGRESS PKWY,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1131,,24,26,18,1147085,1897286,2002,03/30/2006 09:10:16 PM,41.874118767,-87.735440957,"(41.874118767, -87.735440957)"
-2292912,HH578603,08/13/2002 07:20:00 PM,032XX E 91ST ST,0850,THEFT,ATTEMPT THEFT,ALLEY,false,false,0424,,10,46,06,1199340,1845280,2002,03/30/2006 09:10:16 PM,41.730253001,-87.545338588,"(41.730253001, -87.545338588)"
-2297001,HH575783,08/12/2002 05:20:00 PM,062XX S HALSTED ST,0860,THEFT,RETAIL THEFT,DRUG STORE,true,false,0712,,16,68,06,1172008,1863169,2002,03/30/2006 09:10:16 PM,41.779985712,-87.644939128,"(41.779985712, -87.644939128)"
-2286961,HH571121,08/10/2002 03:18:00 PM,015XX W MORSE AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,OTHER,true,false,2431,,49,1,26,1164762,1946192,2002,03/30/2006 09:10:16 PM,42.007963162,-87.669147845,"(42.007963162, -87.669147845)"
-2297927,HH568736,08/09/2002 02:15:00 PM,048XX W ERIE ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,1532,,28,25,18,1144187,1903918,2002,03/30/2006 09:10:16 PM,41.892372672,-87.745914524,"(41.892372672, -87.745914524)"
-2291551,HH567395,08/08/2002 08:55:00 PM,012XX N STATE PKWY,0820,THEFT,$500 AND UNDER,SIDEWALK,true,false,1824,,43,8,06,1176098,1908774,2002,12/04/2014 12:43:35 PM,41.905038482,-87.628572709,"(41.905038482, -87.628572709)"
-2284139,HH568053,08/08/2002 03:30:00 PM,023XX S LAKE SHORE DR E,0820,THEFT,$500 AND UNDER,OTHER,false,false,0133,,2,33,06,1180538,1889157,2002,12/04/2014 12:43:35 PM,41.851107212,-87.612868333,"(41.851107212, -87.612868333)"
-2296085,HH566113,08/08/2002 11:29:42 AM,008XX N SPRINGFIELD AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1112,,27,23,18,1150201,1905174,2002,03/30/2006 09:10:16 PM,41.89570416,-87.723794637,"(41.89570416, -87.723794637)"
-2287709,HH566183,08/08/2002 11:20:00 AM,074XX S ASHLAND AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,0735,,17,67,08A,1166926,1855615,2002,03/30/2006 09:10:16 PM,41.759366677,-87.663786073,"(41.759366677, -87.663786073)"
-2286871,HH565125,08/07/2002 08:30:00 PM,0000X E 13TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,true,false,0132,,2,33,14,1176993,1894508,2002,06/11/2007 03:52:33 PM,41.865871591,-87.625717248,"(41.865871591, -87.625717248)"
-2282644,HH564556,08/07/2002 04:13:24 PM,111XX S MICHIGAN AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,0531,,9,49,06,1178838,1830760,2002,03/30/2006 09:10:16 PM,41.690898682,-87.620883071,"(41.690898682, -87.620883071)"
-2284042,HH563403,08/07/2002 03:27:58 AM,104XX S MICHIGAN AVE,0460,BATTERY,SIMPLE,STREET,false,false,0512,,9,49,08B,1178933,1835951,2002,03/30/2006 09:10:16 PM,41.705141357,-87.620377954,"(41.705141357, -87.620377954)"
-2284899,HH561920,08/06/2002 01:45:00 PM,012XX W CARMEN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,false,false,2033,,46,3,14,1167236,1934097,2002,03/30/2006 09:10:16 PM,41.974721177,-87.660395161,"(41.974721177, -87.660395161)"
-2284208,HH561844,08/06/2002 12:30:00 PM,009XX N LATROBE AVE,143C,WEAPONS VIOLATION,UNLAWFUL POSS AMMUNITION,STREET,true,false,1524,,37,25,15,1141152,1905524,2002,03/30/2006 09:10:16 PM,41.896836204,-87.757021346,"(41.896836204, -87.757021346)"
-2278102,HH561103,08/06/2002 05:05:00 AM,064XX S KENWOOD AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,RESIDENCE,false,true,0314,,20,42,04A,1186221,1862360,2002,03/30/2006 09:10:16 PM,41.777441701,-87.592858302,"(41.777441701, -87.592858302)"
-2283979,HH560412,08/05/2002 07:42:46 PM,066XX S MORGAN ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0724,,17,68,08B,1170753,1860572,2002,03/30/2006 09:10:16 PM,41.77288671,-87.649615829,"(41.77288671, -87.649615829)"
-2284108,HH566337,08/05/2002 01:00:00 PM,013XX W LUNT AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2431,,49,1,07,1165741,1946585,2002,03/30/2006 09:10:16 PM,42.009020662,-87.665534617,"(42.009020662, -87.665534617)"
-2278100,HH559368,08/05/2002 03:30:00 AM,080XX S MICHIGAN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0623,,6,44,14,1178509,1851672,2002,03/30/2006 09:10:16 PM,41.748291404,-87.621454496,"(41.748291404, -87.621454496)"
-2277898,HH558122,08/04/2002 07:30:00 PM,076XX S LOWE AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,RESIDENCE,false,true,0621,,17,71,04A,1173339,1853901,2002,03/30/2006 09:10:16 PM,41.754523876,-87.640333265,"(41.754523876, -87.640333265)"
-2272249,HH554110,08/02/2002 10:30:00 PM,062XX S NARRAGANSETT AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0812,,23,64,05,1134776,1862484,2002,03/30/2006 09:10:16 PM,41.778842773,-87.781455449,"(41.778842773, -87.781455449)"
-2282997,HH552923,08/02/2002 01:52:12 PM,049XX S FEDERAL ST,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,CHA APARTMENT,true,false,0231,,3,38,18,1176576,1872029,2002,03/30/2006 09:10:16 PM,41.804196865,-87.627925741,"(41.804196865, -87.627925741)"
-2330843,HH552016,08/02/2002 12:47:55 AM,036XX W DOUGLAS BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1011,,24,29,18,1152552,1893259,2002,03/30/2006 09:10:16 PM,41.862961976,-87.715474927,"(41.862961976, -87.715474927)"
-2276916,HH551838,08/01/2002 11:25:00 PM,015XX N WELLS ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,RESTAURANT,true,false,1821,,27,8,11,1174375,1910774,2002,03/30/2006 09:10:16 PM,41.910565248,-87.634841932,"(41.910565248, -87.634841932)"
-2276152,HH551849,08/01/2002 10:00:00 PM,003XX W NORTH AVE,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,true,false,1821,,27,8,07,1173724,1910936,2002,03/30/2006 09:10:16 PM,41.911024307,-87.63722861,"(41.911024307, -87.63722861)"
-2289911,HH551104,08/01/2002 05:20:00 PM,048XX S MARSHFIELD AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,0931,,20,61,08A,1166110,1872849,2002,03/30/2006 09:10:16 PM,41.806676336,-87.666286704,"(41.806676336, -87.666286704)"
-2274378,HH548605,07/31/2002 10:45:00 AM,101XX S WOODLAWN AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0511,,8,50,06,1186320,1838097,2002,12/04/2014 12:43:35 PM,41.71085923,-87.593260361,"(41.71085923, -87.593260361)"
-2269536,HH550466,07/31/2002 01:00:00 AM,060XX W 64TH PL,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0812,,13,64,14,1136961,1861092,2002,03/30/2006 09:10:16 PM,41.774984109,-87.773478108,"(41.774984109, -87.773478108)"
-2271008,HH547490,07/31/2002 12:50:00 AM,014XX W OHIO ST,0460,BATTERY,SIMPLE,STREET,false,false,1324,,27,24,08B,1166485,1904157,2002,03/30/2006 09:10:16 PM,41.892580407,-87.66401634,"(41.892580407, -87.66401634)"
-2271767,HH001238,07/30/2002 03:50:00 AM,001XX N LOCKWOOD AVE,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,ALLEY,true,false,1523,,28,25,18,1141062,1900591,2002,03/30/2006 09:10:16 PM,41.883301108,-87.7574735,"(41.883301108, -87.7574735)"
-2380894,HH542060,07/28/2002 07:18:18 PM,030XX W TAYLOR ST,0460,BATTERY,SIMPLE,STREET,false,false,1134,,28,27,08B,1156479,1895596,2002,06/11/2007 03:52:33 PM,41.869296492,-87.700996048,"(41.869296492, -87.700996048)"
-2261977,HH540869,07/28/2002 02:30:00 AM,025XX S WASHTENAW AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,1034,,12,30,04B,1158722,1887282,2002,03/30/2006 09:10:16 PM,41.84643637,-87.692989025,"(41.84643637, -87.692989025)"
-2260713,HH540232,07/27/2002 04:00:00 PM,045XX W LAWRENCE AVE,0810,THEFT,OVER $500,STREET,false,false,1712,,39,14,06,1145020,1931556,2002,12/04/2014 12:43:35 PM,41.968198295,-87.742155357,"(41.968198295, -87.742155357)"
-2257593,HH534933,07/25/2002 02:25:00 PM,036XX N ELSTON AVE,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,PARKING LOT/GARAGE(NON.RESID.),false,false,1733,,35,16,11,1154035,1923795,2002,03/30/2006 09:10:16 PM,41.946726124,-87.709215391,"(41.946726124, -87.709215391)"
-2259968,HH534723,07/25/2002 10:00:00 AM,0000X N LECLAIRE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1533,,28,25,14,1142344,1899734,2002,03/30/2006 09:10:16 PM,41.880925691,-87.752787139,"(41.880925691, -87.752787139)"
-2255440,HH534039,07/24/2002 04:30:00 AM,023XX W JACKSON BLVD,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,1211,,2,28,06,1160898,1898682,2002,03/30/2006 09:10:16 PM,41.877674291,-87.684687102,"(41.877674291, -87.684687102)"
-2261192,HH533322,07/24/2002 12:01:00 AM,026XX W 55TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0824,,16,63,14,1159441,1868045,2002,03/30/2006 09:10:16 PM,41.793632961,-87.690878195,"(41.793632961, -87.690878195)"
-2254932,HH529069,07/23/2002 12:19:29 AM,060XX S CHAMPLAIN AVE,0460,BATTERY,SIMPLE,APARTMENT,false,false,0313,,20,42,08B,1181649,1864844,2002,03/30/2006 09:10:16 PM,41.784364839,-87.609542417,"(41.784364839, -87.609542417)"
-2255774,HH527396,07/22/2002 11:59:00 AM,132XX S GREEN BAY AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0433,,10,55,18,1200791,1817417,2002,03/30/2006 09:10:16 PM,41.653757771,-87.540962174,"(41.653757771, -87.540962174)"
-2258360,HH524772,07/21/2002 03:40:25 AM,005XX N SAWYER AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1121,,27,23,14,1154570,1903522,2002,03/30/2006 09:10:16 PM,41.891084619,-87.707792429,"(41.891084619, -87.707792429)"
-2248969,HH526148,07/20/2002 07:00:00 PM,089XX S CORNELL AVE,0820,THEFT,$500 AND UNDER,DRIVEWAY - RESIDENTIAL,false,false,0413,,8,48,06,1188846,1845685,2002,12/04/2014 12:43:35 PM,41.731621563,-87.583767797,"(41.731621563, -87.583767797)"
-2250099,HH523691,07/20/2002 04:45:00 PM,008XX N MICHIGAN AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,1833,,42,8,06,1177375,1906125,2002,03/30/2006 09:10:16 PM,41.897740622,-87.62396241,"(41.897740622, -87.62396241)"
-2245210,HH516148,07/16/2002 11:00:00 PM,032XX W 47TH PL,0810,THEFT,OVER $500,RESTAURANT,false,false,0821,,14,58,06,1155582,1872777,2002,12/04/2014 12:43:35 PM,41.806696492,-87.704902113,"(41.806696492, -87.704902113)"
-2239457,HH513767,07/16/2002 11:00:00 AM,022XX N KEELER AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2522,,31,20,26,1147972,1914654,2002,03/30/2006 09:10:16 PM,41.921761396,-87.731737124,"(41.921761396, -87.731737124)"
-2238949,HH512131,07/15/2002 04:51:17 PM,114XX S HOMEWOOD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,2212,,19,75,14,1165262,1828712,2002,03/30/2006 09:10:16 PM,41.685575993,-87.670643601,"(41.685575993, -87.670643601)"
-2238574,HH511931,07/15/2002 03:15:00 PM,040XX N KEDZIE AVE,0820,THEFT,$500 AND UNDER,SMALL RETAIL STORE,false,false,1724,,33,16,06,1154373,1926720,2002,12/04/2014 12:43:35 PM,41.954745763,-87.707894538,"(41.954745763, -87.707894538)"
-2238460,HH510330,07/14/2002 08:30:00 PM,046XX W PATTERSON AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,1731,,38,15,08A,1144682,1923895,2002,03/30/2006 09:10:16 PM,41.947182269,-87.743592034,"(41.947182269, -87.743592034)"
-2243502,HH511330,07/14/2002 01:10:00 AM,009XX N MICHIGAN AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,1833,,42,8,04B,1177362,1906780,2002,03/30/2006 09:10:16 PM,41.899538267,-87.623990266,"(41.899538267, -87.623990266)"
-2244307,HH508486,07/13/2002 11:55:00 PM,068XX S EAST END AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,APARTMENT,false,true,0332,,5,43,26,1188681,1859682,2002,03/30/2006 09:10:16 PM,41.77003457,-87.58392563,"(41.77003457, -87.58392563)"
-2232540,HH503797,07/11/2002 10:45:00 PM,060XX N NEVA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,SIDEWALK,false,false,1612,,41,10,14,1127743,1939575,2002,03/30/2006 09:10:16 PM,41.990512799,-87.805501299,"(41.990512799, -87.805501299)"
-2237727,HH503686,07/11/2002 09:05:00 PM,058XX N SHERIDAN RD,0560,ASSAULT,SIMPLE,PARK PROPERTY,true,false,2022,,48,77,08A,1168623,1938747,2002,03/30/2006 09:10:16 PM,41.987450882,-87.655159373,"(41.987450882, -87.655159373)"
-2265116,HH503481,07/11/2002 08:00:00 PM,005XX W 60TH PL,0820,THEFT,$500 AND UNDER,RESIDENCE PORCH/HALLWAY,false,false,0711,,20,68,06,1173913,1864749,2002,12/04/2014 12:43:35 PM,41.784279345,-87.6379083,"(41.784279345, -87.6379083)"
-2233848,HH502744,07/11/2002 02:50:00 PM,001XX W LAKE ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,true,false,0113,,42,32,11,1175361,1901695,2002,03/30/2006 09:10:16 PM,41.88562992,-87.631492621,"(41.88562992, -87.631492621)"
-2237887,HH511710,07/11/2002 02:30:00 PM,024XX W 79TH ST,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,0835,,18,70,06,1161758,1852242,2002,03/30/2006 09:10:16 PM,41.750219481,-87.682820195,"(41.750219481, -87.682820195)"
-2234082,HH501753,07/11/2002 02:55:00 AM,011XX E 44TH ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,true,false,2123,,4,39,26,1184462,1875856,2002,03/30/2006 09:10:16 PM,41.814517192,-87.598884003,"(41.814517192, -87.598884003)"
-2229684,HH501136,07/10/2002 07:58:45 PM,062XX N OAKLEY AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,false,2413,,50,2,08B,1159895,1941334,2002,03/30/2006 09:10:16 PM,41.994734867,-87.687189513,"(41.994734867, -87.687189513)"
-2230644,HH500966,07/10/2002 05:30:00 PM,109XX S HALSTED ST,0460,BATTERY,SIMPLE,NURSING HOME/RETIREMENT HOME,true,false,2233,,34,49,08B,1172972,1832188,2002,03/30/2006 09:10:16 PM,41.694948466,-87.642316946,"(41.694948466, -87.642316946)"
-2243595,HH518219,07/10/2002 12:15:00 PM,002XX S CANAL ST,0870,THEFT,POCKET-PICKING,OTHER,false,false,0111,,2,28,06,1173137,1899241,2002,03/30/2006 09:10:16 PM,41.878945611,-87.639732344,"(41.878945611, -87.639732344)"
-2229282,HH501509,07/10/2002 04:45:00 AM,0000X E 102ND PL,0820,THEFT,$500 AND UNDER,STREET,true,false,0511,,9,49,06,1178744,1836991,2002,12/04/2014 12:43:35 PM,41.707999551,-87.621038545,"(41.707999551, -87.621038545)"
-2824774,HJ482658,07/09/2002 05:15:00 AM,022XX S BLUE ISLAND AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,false,1034,010,25,31,08B,1164862,1888888,2002,03/30/2006 09:10:16 PM,41.850715549,-87.670410029,"(41.850715549, -87.670410029)"
-2229494,HH501391,07/08/2002 10:00:00 PM,047XX N RAVENSWOOD AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1922,,47,3,06,1163563,1931307,2002,12/04/2014 12:43:35 PM,41.967143685,-87.67398085,"(41.967143685, -87.67398085)"
-2224740,HH495677,07/08/2002 02:45:00 PM,019XX W ELLEN ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1424,,1,24,14,1163486,1908877,2002,03/30/2006 09:10:16 PM,41.90559611,-87.67489739,"(41.90559611, -87.67489739)"
-2237683,HH493966,07/07/2002 08:15:00 PM,065XX S DR MARTIN LUTHER KING JR DR,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0312,,20,69,16,1179999,1861967,2002,03/30/2006 09:10:16 PM,41.776508024,-87.615679945,"(41.776508024, -87.615679945)"
-2223070,HH493350,07/07/2002 09:00:00 AM,018XX S MICHIGAN AVE,0810,THEFT,OVER $500,STREET,false,false,0133,,2,33,06,1177549,1891340,2002,12/04/2014 12:43:35 PM,41.857165803,-87.623772278,"(41.857165803, -87.623772278)"
-2222960,HH492675,07/07/2002 05:00:00 AM,003XX W CHICAGO AVE,0460,BATTERY,SIMPLE,STREET,false,false,1831,,42,8,08B,1174052,1905629,2002,03/30/2006 09:10:16 PM,41.896454318,-87.636182105,"(41.896454318, -87.636182105)"
-2224175,HH492127,07/06/2002 10:20:00 PM,017XX N WINCHESTER AVE,0560,ASSAULT,SIMPLE,ALLEY,false,false,1434,,32,24,08A,1163045,1911454,2002,03/30/2006 09:10:16 PM,41.912676858,-87.676444836,"(41.912676858, -87.676444836)"
-2222735,HH491405,07/06/2002 03:35:02 PM,078XX S HALSTED ST,0860,THEFT,RETAIL THEFT,DRUG STORE,true,false,0621,,17,71,06,1172297,1852510,2002,03/30/2006 09:10:16 PM,41.750729763,-87.644192698,"(41.750729763, -87.644192698)"
-2222388,HH491392,07/06/2002 02:55:00 PM,086XX S COLFAX AVE,041A,BATTERY,AGGRAVATED: HANDGUN,SIDEWALK,false,false,0423,,7,46,04B,1195035,1848073,2002,03/30/2006 09:10:16 PM,41.738024301,-87.561017,"(41.738024301, -87.561017)"
-2236678,HH489713,07/05/2002 07:50:00 PM,028XX W MONROE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1124,,2,27,18,1157311,1899516,2002,03/30/2006 09:10:16 PM,41.880036502,-87.697834995,"(41.880036502, -87.697834995)"
-2250434,HH489408,07/05/2002 05:39:09 PM,011XX N CENTRAL PARK AVE,0334,ROBBERY,ATTEMPT: ARMED-KNIFE/CUT INSTR,STREET,false,false,1112,,26,23,03,1152115,1907619,2002,03/30/2006 09:10:16 PM,41.902375949,-87.716700354,"(41.902375949, -87.716700354)"
-2224488,HH489373,07/05/2002 05:10:00 PM,122XX S THROOP ST,0560,ASSAULT,SIMPLE,STREET,false,false,0524,,34,53,08A,1169844,1823522,2002,03/30/2006 09:10:16 PM,41.671235809,-87.654019694,"(41.671235809, -87.654019694)"
-2221303,HH488465,07/04/2002 11:00:00 PM,057XX N ARTESIAN AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2011,,40,2,07,1159000,1937869,2002,03/30/2006 09:10:16 PM,41.985245252,-87.690577404,"(41.985245252, -87.690577404)"
-2220024,HH487218,07/04/2002 06:52:55 PM,050XX W ADAMS ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1533,,28,25,08B,1143003,1898825,2002,03/30/2006 09:10:16 PM,41.878419025,-87.750389979,"(41.878419025, -87.750389979)"
-2232890,HH500757,07/03/2002 04:39:00 PM,001XX N LAMON AVE,0265,CRIM SEXUAL ASSAULT,AGGRAVATED: OTHER,RESIDENCE,true,false,1532,,28,25,02,1143725,1900705,2002,03/30/2006 09:10:16 PM,41.883564486,-87.747691812,"(41.883564486, -87.747691812)"
-2218119,HH483661,07/02/2002 10:03:48 PM,087XX S STATE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,VEHICLE NON-COMMERCIAL,false,true,0634,,21,44,08B,1177767,1847263,2002,03/30/2006 09:10:16 PM,41.736209387,-87.624306533,"(41.736209387, -87.624306533)"
-2218047,HH482194,07/02/2002 10:45:00 AM,024XX W MADISON ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1332,,2,28,14,1160384,1899988,2002,03/30/2006 09:10:16 PM,41.881268718,-87.68653824,"(41.881268718, -87.68653824)"
-2214973,HH481524,07/01/2002 11:48:12 PM,049XX S FEDERAL ST,0485,BATTERY,AGGRAVATED OF A CHILD,STREET,false,false,0231,,3,38,04B,1176484,1872478,2002,03/30/2006 09:10:16 PM,41.805431035,-87.628249642,"(41.805431035, -87.628249642)"
-2249507,HH481402,07/01/2002 11:00:00 PM,065XX S GREEN ST,0460,BATTERY,SIMPLE,STREET,false,true,0723,,16,68,08B,1171724,1861384,2002,03/30/2006 09:10:16 PM,41.775093697,-87.646032613,"(41.775093697, -87.646032613)"
-2214324,HH481111,07/01/2002 07:45:00 PM,076XX S CICERO AVE,0460,BATTERY,SIMPLE,DEPARTMENT STORE,false,true,0833,,13,65,08B,1145766,1853744,2002,03/30/2006 09:10:16 PM,41.754658085,-87.741385006,"(41.754658085, -87.741385006)"
-2224498,HH480230,07/01/2002 02:02:24 PM,020XX W 67TH PL,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,0726,,15,67,26,1163559,1859839,2002,03/30/2006 09:10:16 PM,41.771029174,-87.676007766,"(41.771029174, -87.676007766)"
-2393114,HH704495,07/01/2002 12:00:00 PM,058XX S NAGLE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0811,,23,56,26,1134438,1864943,2002,03/30/2006 09:10:16 PM,41.785596671,-87.782636877,"(41.785596671, -87.782636877)"
-2214947,HH480971,07/01/2002 11:00:00 AM,037XX N WAYNE AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1923,,44,6,05,1166663,1925307,2002,03/30/2006 09:10:16 PM,41.950613417,-87.662755216,"(41.950613417, -87.662755216)"
-2216915,HH478938,06/30/2002 10:51:34 PM,015XX S ALBANY AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1022,,24,29,14,1155926,1892156,2002,03/30/2006 09:10:16 PM,41.859867924,-87.703118974,"(41.859867924, -87.703118974)"
-2213321,HH478606,06/30/2002 07:50:00 PM,022XX W PERSHING RD,0460,BATTERY,SIMPLE,PARK PROPERTY,false,false,0913,,11,59,08B,1162172,1878822,2002,03/30/2006 09:10:16 PM,41.82314992,-87.680563617,"(41.82314992, -87.680563617)"
-2210968,HH476531,06/29/2002 07:35:00 PM,016XX S HOMAN AVE,041A,BATTERY,AGGRAVATED: HANDGUN,SIDEWALK,false,false,1021,,24,29,04B,1154012,1891492,2002,03/30/2006 09:10:16 PM,41.858084171,-87.710162464,"(41.858084171, -87.710162464)"
-2211564,HH477639,06/29/2002 07:00:00 PM,004XX E 95TH ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0633,,9,49,05,1180748,1842114,2002,03/30/2006 09:10:16 PM,41.722011996,-87.613543019,"(41.722011996, -87.613543019)"
-2245660,HH475483,06/29/2002 10:45:00 AM,028XX W FLOURNOY ST,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1135,,2,27,08B,1157238,1896859,2002,03/30/2006 09:10:16 PM,41.872746915,-87.698175241,"(41.872746915, -87.698175241)"
-2209368,HH474258,06/28/2002 08:00:00 PM,072XX N BELL AVE,1570,SEX OFFENSE,PUBLIC INDECENCY,STREET,false,false,2411,,49,2,17,1160152,1947953,2002,03/30/2006 09:10:16 PM,42.012892283,-87.686060077,"(42.012892283, -87.686060077)"
-2210991,HH477428,06/28/2002 07:00:00 PM,017XX W GRANVILLE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2433,,40,77,14,1163450,1941267,2002,03/30/2006 09:10:16 PM,41.994476665,-87.67411449,"(41.994476665, -87.67411449)"
-2207778,HH472710,06/28/2002 12:00:00 AM,048XX W BERTEAU AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1624,,45,15,14,1142997,1927516,2002,03/30/2006 09:10:16 PM,41.957150261,-87.749695071,"(41.957150261, -87.749695071)"
-2216841,HH471219,06/27/2002 02:15:00 PM,020XX S ALLPORT ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1222,,25,31,18,1168317,1890236,2002,03/30/2006 09:10:16 PM,41.854340689,-87.657690616,"(41.854340689, -87.657690616)"
-2244396,HH518899,06/26/2002 09:00:00 AM,006XX E MARQUETTE RD,1110,DECEPTIVE PRACTICE,BOGUS CHECK,COMMERCIAL / BUSINESS OFFICE,false,false,0321,,20,42,11,1181384,1861423,2002,03/30/2006 09:10:16 PM,41.774983406,-87.610619417,"(41.774983406, -87.610619417)"
-2218872,HH468095,06/26/2002 08:46:00 AM,068XX S SANGAMON ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,0723,,17,68,26,1171203,1859278,2002,03/30/2006 09:10:16 PM,41.769325988,-87.648004046,"(41.769325988, -87.648004046)"
-2211289,HH469556,06/26/2002 07:00:00 AM,048XX N CENTRAL AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1622,,45,11,05,1138079,1931871,2002,03/30/2006 09:10:16 PM,41.96919127,-87.76766969,"(41.96919127, -87.76766969)"
-2204149,HH467537,06/25/2002 07:30:00 PM,043XX N GREENVIEW AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1922,,47,6,14,1165245,1928987,2002,03/30/2006 09:10:16 PM,41.960741826,-87.667862615,"(41.960741826, -87.667862615)"
-2205652,HH466696,06/25/2002 04:35:00 PM,049XX W POLK ST,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1533,,24,25,08B,1143453,1895878,2002,03/30/2006 09:10:16 PM,41.870323688,-87.748811345,"(41.870323688, -87.748811345)"
-2210402,HH469243,06/25/2002 04:00:00 PM,072XX S LOWE AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0732,,17,68,05,1173363,1856709,2002,03/30/2006 09:10:16 PM,41.762228843,-87.640162396,"(41.762228843, -87.640162396)"
-2211530,HH464777,06/24/2002 08:21:00 PM,071XX S KEDZIE AVE,0460,BATTERY,SIMPLE,STREET,true,false,0831,,18,66,08B,1156225,1857330,2002,03/30/2006 09:10:16 PM,41.764294764,-87.702959145,"(41.764294764, -87.702959145)"
-2203330,HH464602,06/24/2002 07:23:36 PM,041XX W WABANSIA AVE,0330,ROBBERY,AGGRAVATED,STREET,false,false,2534,,30,23,03,1148419,1910935,2002,06/11/2007 03:52:33 PM,41.911547487,-87.730190811,"(41.911547487, -87.730190811)"
-2211103,HH461949,06/23/2002 04:30:00 PM,062XX S FAIRFIELD AVE,0560,ASSAULT,SIMPLE,RESIDENCE,true,true,0825,,15,66,08A,1159070,1863327,2002,03/30/2006 09:10:16 PM,41.780693717,-87.692367607,"(41.780693717, -87.692367607)"
-2208492,HH472955,06/22/2002 11:00:00 PM,018XX S KARLOV AVE,0810,THEFT,OVER $500,RESIDENCE,false,false,1012,,24,29,06,1149388,1890891,2002,12/04/2014 12:43:35 PM,41.856525794,-87.72715112,"(41.856525794, -87.72715112)"
-2201380,HH460481,06/22/2002 10:35:00 PM,053XX N BROADWAY,0820,THEFT,$500 AND UNDER,OTHER,false,false,2013,,48,77,06,1167315,1935377,2002,12/04/2014 12:43:35 PM,41.978231832,-87.66006766,"(41.978231832, -87.66006766)"
-2213741,HH457785,06/21/2002 06:35:01 PM,034XX W ROOSEVELT RD,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1133,,24,29,08B,1153749,1894539,2002,03/30/2006 09:10:16 PM,41.866450715,-87.711046775,"(41.866450715, -87.711046775)"
-2208837,HH456945,06/21/2002 12:22:15 PM,057XX S CAMPBELL AVE,1661,GAMBLING,GAME/DICE,STREET,true,false,0824,,16,63,19,1160734,1866164,2002,03/30/2006 09:10:16 PM,41.788444632,-87.686188755,"(41.788444632, -87.686188755)"
-2205394,HH455977,06/20/2002 10:10:00 PM,037XX W DIVISION ST,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,2535,,27,23,18,1151118,1907777,2002,03/30/2006 09:10:16 PM,41.902829124,-87.72035837,"(41.902829124, -87.72035837)"
-2198761,HH460317,06/19/2002 02:30:00 PM,090XX S CONSTANCE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0413,,8,48,14,1190131,1845418,2002,03/30/2006 09:10:16 PM,41.730858084,-87.579068986,"(41.730858084, -87.579068986)"
-2193120,HH070191,06/19/2002 09:00:00 AM,082XX S KINGSTON AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0423,,7,46,07,1194651,1850610,2002,03/30/2006 09:10:16 PM,41.744995488,-87.562340578,"(41.744995488, -87.562340578)"
-2189847,HH449850,06/17/2002 05:30:00 PM,004XX E 111TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,CHURCH/SYNAGOGUE/PLACE OF WORSHIP,false,false,0513,,9,49,14,1180898,1831487,2002,03/30/2006 09:10:16 PM,41.692846669,-87.613319061,"(41.692846669, -87.613319061)"
-2189055,HH448580,06/17/2002 09:00:00 AM,022XX N SHEFFIELD AVE,0810,THEFT,OVER $500,OTHER,false,false,1811,,32,7,06,1169209,1915350,2002,12/04/2014 12:43:35 PM,41.923235916,-87.653686532,"(41.923235916, -87.653686532)"
-2192372,HH447369,06/17/2002 07:39:23 AM,044XX S LA CROSSE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,CHA APARTMENT,false,false,0814,,23,56,14,1144658,1874846,2002,03/30/2006 09:10:16 PM,41.81258635,-87.744916353,"(41.81258635, -87.744916353)"
-2194067,HH447036,06/16/2002 11:45:00 PM,014XX W 114TH PL,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,true,2234,,34,75,14,1168429,1828845,2002,03/30/2006 09:10:16 PM,41.68587353,-87.659046086,"(41.68587353, -87.659046086)"
-2196509,HH446979,06/16/2002 11:15:00 PM,067XX S PARNELL AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,APARTMENT,false,true,0722,,6,68,04A,1173781,1859851,2002,03/30/2006 09:10:16 PM,41.770841609,-87.638537353,"(41.770841609, -87.638537353)"
-2185890,HH444807,06/15/2002 09:30:00 PM,018XX W 22ND PL,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,1034,,25,31,08A,1164601,1889199,2002,03/30/2006 09:10:16 PM,41.851574488,-87.671359149,"(41.851574488, -87.671359149)"
-2185300,HH441992,06/14/2002 04:00:00 PM,032XX E 87TH ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0424,,7,46,07,1199002,1847937,2002,03/30/2006 09:10:16 PM,41.737552497,-87.546487774,"(41.737552497, -87.546487774)"
-2185254,HH440156,06/13/2002 07:58:48 PM,116XX S LAFAYETTE AVE,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,true,false,0522,,34,53,06,1177961,1827683,2002,12/04/2014 12:43:35 PM,41.682474798,-87.624186549,"(41.682474798, -87.624186549)"
-2183582,HH439676,06/13/2002 05:47:45 PM,003XX W 108TH ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0513,,34,49,05,1175872,1833265,2002,03/30/2006 09:10:16 PM,41.697839578,-87.631667068,"(41.697839578, -87.631667068)"
-2181269,HH438401,06/12/2002 10:30:00 PM,035XX S ARCHER AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0913,,11,59,14,1162611,1881239,2002,03/30/2006 09:10:16 PM,41.829773275,-87.678885546,"(41.829773275, -87.678885546)"
-2192577,HH437298,06/12/2002 05:55:58 PM,033XX W MAYPOLE AVE,0460,BATTERY,SIMPLE,STREET,false,false,1123,,28,27,08B,1154211,1900746,2002,03/30/2006 09:10:16 PM,41.883474173,-87.709185019,"(41.883474173, -87.709185019)"
-2204414,HH436248,06/12/2002 10:46:18 AM,041XX S CALIFORNIA AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0912,,14,58,18,1158324,1877638,2002,03/30/2006 09:10:16 PM,41.819980236,-87.694712795,"(41.819980236, -87.694712795)"
-2179285,HH433127,06/10/2002 10:50:00 PM,039XX N SHERIDAN RD,1330,CRIMINAL TRESPASS,TO LAND,CTA PLATFORM,true,false,2324,,44,6,26,1168850,1926540,2002,03/30/2006 09:10:16 PM,41.95394958,-87.65468012,"(41.95394958, -87.65468012)"
-2176876,HH433480,06/10/2002 05:00:00 PM,020XX W CORTEZ ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,1312,,32,24,05,1162513,1907030,2002,03/30/2006 09:10:16 PM,41.900548241,-87.678523335,"(41.900548241, -87.678523335)"
-2177388,HH431428,06/10/2002 01:35:00 AM,045XX N ST LOUIS AVE,041B,BATTERY,AGGRAVATED: OTHER FIREARM,STREET,false,false,1723,,33,14,04B,,,2002,03/30/2006 09:10:16 PM,,,
-2187700,HH429184,06/09/2002 08:30:00 AM,007XX N HAMLIN AVE,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),STREET,true,false,1112,,27,23,18,1150889,1904437,2002,03/30/2006 09:10:16 PM,41.893668316,-87.721287058,"(41.893668316, -87.721287058)"
-2184068,HH428940,06/09/2002 03:10:00 AM,008XX N KEDVALE AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,true,1111,,37,23,04B,1148601,1905416,2002,03/30/2006 09:10:16 PM,41.896399275,-87.729664873,"(41.896399275, -87.729664873)"
-2173466,HH428916,06/09/2002 02:00:00 AM,065XX S CAMPBELL AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,RESIDENCE,false,true,0832,,15,66,04B,1160882,1860910,2002,03/30/2006 09:10:16 PM,41.774023875,-87.685791164,"(41.774023875, -87.685791164)"
-2189824,HH425897,06/08/2002 11:25:00 AM,027XX W LEXINGTON ST,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,STREET,true,false,1135,,2,27,18,1158142,1896547,2002,03/30/2006 09:10:16 PM,41.871872352,-87.694864753,"(41.871872352, -87.694864753)"
-2174107,HH424548,06/06/2002 11:45:00 PM,011XX N LAKE SHORE DR,0820,THEFT,$500 AND UNDER,STREET,false,false,1824,,42,8,06,1177170,1907843,2002,12/04/2014 12:43:35 PM,41.902459541,-87.62466322,"(41.902459541, -87.62466322)"
-2170641,HH423037,06/06/2002 01:47:00 PM,076XX N PAULINA ST,1330,CRIMINAL TRESPASS,TO LAND,STREET,true,false,2422,,49,1,26,1163615,1950440,2002,03/30/2006 09:10:16 PM,42.019644096,-87.673247386,"(42.019644096, -87.673247386)"
-2182694,HH429109,06/05/2002 10:00:00 PM,056XX W NORTH AVE,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,STREET,false,false,2531,,29,25,11,1138737,1910118,2002,03/30/2006 09:10:16 PM,41.909486864,-87.765779753,"(41.909486864, -87.765779753)"
-2173922,HH421407,06/05/2002 06:03:00 PM,022XX N MILWAUKEE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,true,false,1431,,1,22,03,1158233,1914353,2002,03/30/2006 09:10:16 PM,41.920731708,-87.694043541,"(41.920731708, -87.694043541)"
-2168147,HH421043,06/05/2002 03:00:00 PM,035XX W BERTEAU AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1723,,33,16,14,1151762,1927641,2002,03/30/2006 09:10:16 PM,41.95732494,-87.717468725,"(41.95732494, -87.717468725)"
-2171617,HH420308,06/05/2002 10:15:00 AM,007XX W DIVISION ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,true,false,1822,,27,8,26,1171199,1908272,2002,03/30/2006 09:10:16 PM,41.903770001,-87.646582863,"(41.903770001, -87.646582863)"
-2171381,HH416996,06/03/2002 08:05:00 PM,033XX S PRAIRIE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,2112,,3,35,18,1178439,1883013,2002,03/30/2006 09:10:16 PM,41.834295706,-87.620759116,"(41.834295706, -87.620759116)"
-2166981,HH416427,06/03/2002 04:25:00 PM,053XX W WAVELAND AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,true,false,1634,,38,15,26,1140107,1924125,2002,03/30/2006 09:10:16 PM,41.947898559,-87.760403066,"(41.947898559, -87.760403066)"
-2167082,HH416287,06/03/2002 03:30:00 PM,083XX S BURNHAM AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,true,false,0423,,10,46,15,1196325,1849848,2002,03/30/2006 09:10:16 PM,41.742863179,-87.556232134,"(41.742863179, -87.556232134)"
-2162224,HH413664,06/02/2002 02:00:00 AM,009XX W 86TH ST,0460,BATTERY,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),false,false,0613,,21,71,08B,1171257,1847840,2002,03/30/2006 09:10:16 PM,41.737937452,-87.648140015,"(41.737937452, -87.648140015)"
-2161013,HH411612,06/01/2002 02:00:00 PM,107XX S HALSTED ST,0560,ASSAULT,SIMPLE,GROCERY FOOD STORE,false,true,2233,,34,75,08A,1172839,1833862,2002,03/30/2006 09:10:16 PM,41.699545107,-87.642754757,"(41.699545107, -87.642754757)"
-2162672,HH414634,06/01/2002 10:30:00 AM,056XX S TROY ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0824,,14,63,05,1156311,1866797,2002,03/30/2006 09:10:16 PM,41.790271887,-87.702389345,"(41.790271887, -87.702389345)"
-2160854,HH411290,06/01/2002 10:00:00 AM,007XX E 103RD PL,0460,BATTERY,SIMPLE,RESIDENCE,false,true,0512,,9,50,08B,1183040,1836531,2002,03/30/2006 09:10:16 PM,41.706638661,-87.605320769,"(41.706638661, -87.605320769)"
-2268073,HH548739,06/01/2002 09:00:00 AM,114XX S DR MARTIN LUTHER KING JR DR,0840,THEFT,FINANCIAL ID THEFT: OVER $300,APARTMENT,false,false,0531,,9,49,06,1180993,1829149,2002,03/30/2006 09:10:16 PM,41.68642869,-87.613042825,"(41.68642869, -87.613042825)"
-2157849,HH407494,05/30/2002 12:10:00 PM,051XX S TRIPP AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0815,,23,57,14,1148936,1870365,2002,03/30/2006 09:10:16 PM,41.800208368,-87.729339909,"(41.800208368, -87.729339909)"
-2159387,HH405454,05/29/2002 08:26:24 PM,001XX N STATE ST,0460,BATTERY,SIMPLE,DRUG STORE,true,false,0122,,42,32,08B,1176379,1901300,2002,03/30/2006 09:10:16 PM,41.884523111,-87.627766291,"(41.884523111, -87.627766291)"
-2156479,HH403679,05/29/2002 08:37:05 AM,016XX N WELLS ST,0610,BURGLARY,FORCIBLE ENTRY,SMALL RETAIL STORE,false,false,1814,,43,7,05,1174434,1911468,2002,03/30/2006 09:10:16 PM,41.912468298,-87.634604421,"(41.912468298, -87.634604421)"
-2164513,HH400911,05/28/2002 12:30:00 AM,069XX S LAFAYETTE AVE,0460,BATTERY,SIMPLE,STREET,false,false,0731,,6,69,08B,1176984,1859262,2002,03/30/2006 09:10:16 PM,41.769153723,-87.626814161,"(41.769153723, -87.626814161)"
-2152206,HH401320,05/27/2002 05:00:00 PM,053XX N MILWAUKEE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,OTHER,false,false,1622,,45,11,07,1137334,1935330,2002,03/30/2006 09:10:16 PM,41.978696536,-87.770325505,"(41.978696536, -87.770325505)"
-2157187,HH400508,05/27/2002 05:00:00 PM,003XX N AUSTIN BLVD,0560,ASSAULT,SIMPLE,RESIDENCE,true,true,1512,,29,25,08A,1136365,1901525,2002,03/30/2006 09:10:16 PM,41.885949322,-87.774699137,"(41.885949322, -87.774699137)"
-2179629,HH435257,05/25/2002 07:00:00 PM,018XX N FREMONT ST,0810,THEFT,OVER $500,RESIDENCE,false,false,1813,,43,7,06,1170064,1911974,2002,12/04/2014 12:43:35 PM,41.913953358,-87.650643728,"(41.913953358, -87.650643728)"
-2154445,HH394379,05/24/2002 08:30:00 PM,052XX N MILWAUKEE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1623,,45,11,18,1138249,1933856,2002,03/30/2006 09:10:16 PM,41.974635202,-87.766996341,"(41.974635202, -87.766996341)"
-2154131,HH392503,05/24/2002 02:20:00 AM,064XX S LAWNDALE AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0823,,13,65,18,1152772,1861772,2002,03/30/2006 09:10:16 PM,41.77655306,-87.71549844,"(41.77655306, -87.71549844)"
-2157609,HH405508,05/23/2002 11:59:00 PM,049XX S MICHIGAN AVE,0820,THEFT,$500 AND UNDER,RESIDENCE,false,false,0224,,3,38,06,1178042,1871958,2002,12/04/2014 12:43:35 PM,41.803968892,-87.622551341,"(41.803968892, -87.622551341)"
-2154407,HH389896,05/22/2002 09:00:00 PM,042XX W ARMITAGE AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,2522,,30,20,16,1148045,1912996,2002,03/30/2006 09:10:16 PM,41.917210281,-87.731511651,"(41.917210281, -87.731511651)"
-2145631,HH385319,05/20/2002 09:05:00 PM,008XX N MONTICELLO AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1112,,27,23,18,1151940,1905292,2002,03/30/2006 09:10:16 PM,41.895993884,-87.717404526,"(41.895993884, -87.717404526)"
-2157500,HH385432,05/20/2002 08:11:10 PM,0000X E 102ND ST,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,0511,,9,49,18,1178178,1837387,2002,03/30/2006 09:10:16 PM,41.709099058,-87.623099291,"(41.709099058, -87.623099291)"
-2138137,HH383115,05/19/2002 09:31:56 PM,025XX W THOMAS ST,0560,ASSAULT,SIMPLE,RESIDENCE,true,true,1312,,1,24,08A,1158901,1907182,2002,03/30/2006 09:10:16 PM,41.901040229,-87.691786238,"(41.901040229, -87.691786238)"
-2139175,HH381665,05/19/2002 03:30:00 AM,091XX S LAFLIN ST,0460,BATTERY,SIMPLE,STREET,false,false,2222,,21,73,08B,1167987,1843842,2002,03/30/2006 09:10:16 PM,41.727037171,-87.660235035,"(41.727037171, -87.660235035)"
-2137102,HH380029,05/17/2002 11:00:00 PM,015XX S KEDZIE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1022,,24,29,07,,,2002,03/30/2006 09:10:16 PM,,,
-2137374,HH380687,05/17/2002 09:00:00 PM,041XX N LONG AVE,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,RESIDENCE,false,false,1624,,38,15,05,1139650,1926980,2002,03/30/2006 09:10:16 PM,41.95574132,-87.762012959,"(41.95574132, -87.762012959)"
-2140524,HH376970,05/16/2002 09:51:32 PM,020XX W FOSTER AVE,2230,LIQUOR LAW VIOLATION,ILLEGAL CONSUMPTION BY MINOR,STREET,true,false,2032,,47,4,22,1161619,1934481,2002,03/30/2006 09:10:16 PM,41.975894109,-87.681039832,"(41.975894109, -87.681039832)"
-2135156,HH376158,05/16/2002 03:00:00 PM,029XX N NEWCASTLE AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,2511,,36,18,05,1130201,1918900,2002,03/30/2006 09:10:16 PM,41.933736482,-87.796936104,"(41.933736482, -87.796936104)"
-2133842,HH376706,05/16/2002 02:30:00 PM,001XX N STATE ST,0820,THEFT,$500 AND UNDER,DEPARTMENT STORE,false,false,0122,,42,32,06,1176391,1900935,2002,12/04/2014 12:43:35 PM,41.88352126,-87.627733247,"(41.88352126, -87.627733247)"
-2142726,HH375576,05/16/2002 11:22:33 AM,004XX N LAVERGNE AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1532,,28,25,26,1142916,1902513,2002,03/30/2006 09:10:16 PM,41.888540968,-87.750617478,"(41.888540968, -87.750617478)"
-2134664,HH374691,05/15/2002 11:15:00 PM,050XX S DR MARTIN LUTHER KING JR DR,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0224,,3,38,07,1179756,1871324,2002,03/30/2006 09:10:16 PM,41.802190073,-87.616284675,"(41.802190073, -87.616284675)"
-2133736,HH376277,05/15/2002 12:00:00 AM,050XX W GLADYS AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,false,false,1533,,29,25,11,1142809,1897830,2002,03/30/2006 09:10:16 PM,41.875692234,-87.751127097,"(41.875692234, -87.751127097)"
-2139466,HH371955,05/14/2002 07:15:00 PM,030XX W MONTROSE AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1724,,33,16,18,1155573,1929047,2002,03/30/2006 09:10:16 PM,41.96110708,-87.703420243,"(41.96110708, -87.703420243)"
-2133570,HH375920,05/14/2002 12:00:00 AM,004XX S HALSTED ST,1310,CRIMINAL DAMAGE,TO PROPERTY,CTA PLATFORM,false,false,1213,,2,28,14,1171079,1898067,2002,03/30/2006 09:10:16 PM,41.875769464,-87.647323335,"(41.875769464, -87.647323335)"
-2131364,HH369690,05/13/2002 08:00:00 PM,059XX S GREEN ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0712,,16,68,08B,1171625,1865113,2002,03/30/2006 09:10:16 PM,41.785328676,-87.646286312,"(41.785328676, -87.646286312)"
-2130896,HH369119,05/13/2002 04:32:10 PM,024XX S TRUMBULL AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1024,,22,30,26,1153714,1887578,2002,03/30/2006 09:10:16 PM,41.847349627,-87.711360378,"(41.847349627, -87.711360378)"
-2128396,HH369205,05/13/2002 03:30:00 PM,077XX S BURNHAM AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, GROUNDS",false,false,0421,,7,43,08B,1196107,1854103,2002,03/30/2006 09:10:16 PM,41.754544623,-87.55689019,"(41.754544623, -87.55689019)"
-2133080,HH368841,05/13/2002 02:32:36 PM,049XX S STATE ST,0820,THEFT,$500 AND UNDER,CHA HALLWAY/STAIRWELL/ELEVATOR,false,false,0231,,3,38,06,1177075,1872016,2002,12/04/2014 12:43:35 PM,41.804149939,-87.62609605,"(41.804149939, -87.62609605)"
-2127943,HH369035,05/12/2002 11:59:00 PM,008XX N FAIRFIELD AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1311,,26,24,06,1157861,1905648,2002,12/04/2014 12:43:35 PM,41.896852079,-87.695648131,"(41.896852079, -87.695648131)"
-2130991,HH366970,05/12/2002 04:27:02 PM,037XX W HURON ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1122,,27,23,14,1151221,1904368,2002,03/30/2006 09:10:16 PM,41.893472468,-87.720069538,"(41.893472468, -87.720069538)"
-2126644,HH365727,05/11/2002 10:30:00 PM,051XX W MONROE ST,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,1533,,28,25,08A,1141941,1899210,2002,03/30/2006 09:10:16 PM,41.879495241,-87.754279928,"(41.879495241, -87.754279928)"
-2125457,HH363146,05/10/2002 06:30:00 PM,003XX N WELLS ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,true,false,0113,,42,32,11,1174643,1902173,2002,03/30/2006 09:10:16 PM,41.886957663,-87.634114929,"(41.886957663, -87.634114929)"
-2127942,HH360775,05/09/2002 06:43:33 PM,009XX S RACINE AVE,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,1213,,2,28,26,1168481,1895966,2002,03/30/2006 09:10:16 PM,41.870060741,-87.656922972,"(41.870060741, -87.656922972)"
-2124785,HH361648,05/09/2002 02:00:00 PM,021XX N MENARD AVE,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,true,false,2515,,29,19,07,1137349,1913339,2002,03/30/2006 09:10:16 PM,41.918350754,-87.770801134,"(41.918350754, -87.770801134)"
-2128932,HH359276,05/09/2002 07:20:00 AM,040XX S FEDERAL ST,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,CHA PARKING LOT/GROUNDS,true,false,0214,,3,38,18,1176399,1878070,2002,03/30/2006 09:10:16 PM,41.820777898,-87.628393146,"(41.820777898, -87.628393146)"
-2133183,HH359085,05/09/2002 01:00:00 AM,003XX S WESTERN AVE,2027,NARCOTICS,POSS: CRACK,STREET,true,false,1125,,2,28,18,1160428,1898167,2002,03/30/2006 09:10:16 PM,41.876270818,-87.686427074,"(41.876270818, -87.686427074)"
-2123340,HH362302,05/08/2002 05:00:00 PM,035XX W EVERGREEN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1422,,26,23,14,1152255,1908788,2002,03/30/2006 09:10:16 PM,41.905581034,-87.716155215,"(41.905581034, -87.716155215)"
-2126874,HH357399,05/08/2002 10:32:00 AM,043XX W MAYPOLE AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,STREET,true,false,1114,,28,26,26,1147429,1901141,2002,03/30/2006 09:10:16 PM,41.884690751,-87.734079108,"(41.884690751, -87.734079108)"
-2120924,HH356710,05/07/2002 10:14:00 PM,019XX N MAUD AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,1811,,32,7,05,1168608,1912887,2002,03/30/2006 09:10:16 PM,41.91649036,-87.655966289,"(41.91649036, -87.655966289)"
-2119156,HH356627,05/07/2002 10:00:00 PM,045XX S HERMITAGE AVE,0460,BATTERY,SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,0914,,20,61,08B,1165401,1874537,2002,03/30/2006 09:10:16 PM,41.811323473,-87.668839217,"(41.811323473, -87.668839217)"
-2123585,HH355553,05/07/2002 01:25:00 PM,015XX N LONG AVE,0460,BATTERY,SIMPLE,APARTMENT,false,true,2532,,37,25,08B,1140106,1909809,2002,03/30/2006 09:10:16 PM,41.908613965,-87.760758146,"(41.908613965, -87.760758146)"
-2118296,HH352565,05/06/2002 06:45:00 AM,086XX S SACRAMENTO AVE,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,FEDERAL BUILDING,false,false,0835,,18,70,26,1157859,1846784,2002,03/30/2006 09:10:16 PM,41.735321849,-87.697255831,"(41.735321849, -87.697255831)"
-2164655,HH415632,05/05/2002 11:30:00 PM,023XX W HARRISON ST,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1211,,2,28,14,1160548,1897341,2002,03/30/2006 09:10:16 PM,41.874001714,-87.686009347,"(41.874001714, -87.686009347)"
-2255029,HH532649,05/05/2002 10:00:00 AM,002XX E 50TH ST,0890,THEFT,FROM BUILDING,CHURCH/SYNAGOGUE/PLACE OF WORSHIP,false,false,0224,,3,38,06,1178640,1871963,2002,03/30/2006 09:10:16 PM,41.803969022,-87.620358033,"(41.803969022, -87.620358033)"
-2114846,HH349313,05/04/2002 11:00:00 AM,029XX N ALLEN AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,1412,,35,21,03,1152619,1919435,2002,03/30/2006 09:10:16 PM,41.934790126,-87.714535901,"(41.934790126, -87.714535901)"
-2240460,HH515630,05/03/2002 12:00:00 AM,076XX S PHILLIPS AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0421,,7,43,26,1193823,1854520,2002,03/30/2006 09:10:16 PM,41.755745163,-87.56524654,"(41.755745163, -87.56524654)"
-2110413,HH345430,05/02/2002 03:25:00 PM,065XX N SHERIDAN RD,0820,THEFT,$500 AND UNDER,COLLEGE/UNIVERSITY GROUNDS,false,false,2432,,49,1,06,1167110,1943640,2002,12/04/2014 12:43:35 PM,42.00091012,-87.66058284,"(42.00091012, -87.66058284)"
-2113113,HH344684,05/02/2002 11:00:00 AM,049XX W IOWA ST,0560,ASSAULT,SIMPLE,VEHICLE NON-COMMERCIAL,true,false,1531,,37,25,08A,1143493,1905571,2002,03/30/2006 09:10:16 PM,41.896921704,-87.748421942,"(41.896921704, -87.748421942)"
-2110553,HH345891,05/02/2002 08:00:00 AM,031XX S KEELER AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1031,,22,30,26,1148923,1883471,2002,03/30/2006 09:10:16 PM,41.836173352,-87.729049572,"(41.836173352, -87.729049572)"
-2109748,HH343307,05/01/2002 05:05:00 PM,033XX S MORGAN ST,0560,ASSAULT,SIMPLE,STREET,false,false,0924,,11,60,08A,1170220,1883085,2002,03/30/2006 09:10:16 PM,41.834676415,-87.650914431,"(41.834676415, -87.650914431)"
-2112046,HH342261,05/01/2002 01:00:00 AM,051XX S INDIANA AVE,0820,THEFT,$500 AND UNDER,RESIDENCE,false,false,0232,,3,40,06,1178442,1870523,2002,12/04/2014 12:43:35 PM,41.800022036,-87.621127955,"(41.800022036, -87.621127955)"
-2106615,HH339948,04/30/2002 01:46:43 AM,015XX S KILBOURN AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,true,1012,,24,29,08B,1146690,1891695,2002,03/30/2006 09:10:16 PM,41.858783914,-87.737033818,"(41.858783914, -87.737033818)"
-2109585,HH339647,04/29/2002 09:40:00 PM,063XX S ASHLAND AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,SIDEWALK,false,false,0725,,16,67,04A,1166717,1862698,2002,03/30/2006 09:10:16 PM,41.778807823,-87.664350142,"(41.778807823, -87.664350142)"
-2104110,HH337639,04/29/2002 03:45:00 AM,045XX N HARDING AVE,0440,BATTERY,AGG: HANDS/FIST/FEET NO/MINOR INJURY,RESIDENCE,true,true,1723,,39,14,08B,1149284,1929759,2002,03/30/2006 09:10:16 PM,41.963185394,-87.726523555,"(41.963185394, -87.726523555)"
-2104380,HH338244,04/27/2002 12:00:00 PM,036XX S EMERALD AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0925,,11,60,06,1171938,1880686,2002,12/04/2014 12:43:35 PM,41.828055716,-87.644681214,"(41.828055716, -87.644681214)"
-2104578,HH334633,04/27/2002 04:35:00 AM,014XX N MILWAUKEE AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1424,,1,24,08B,1163790,1909809,2002,03/30/2006 09:10:16 PM,41.908147175,-87.673754371,"(41.908147175, -87.673754371)"
-2105710,HH337757,04/26/2002 10:00:00 PM,058XX N PULASKI RD,0820,THEFT,$500 AND UNDER,GOVERNMENT BUILDING/PROPERTY,false,false,1711,,39,13,06,1148723,1938044,2002,12/04/2014 12:43:35 PM,41.985930875,-87.728371059,"(41.985930875, -87.728371059)"
-2104413,HH331961,04/26/2002 10:25:00 AM,015XX N NORTH PARK AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1821,,27,8,14,1173839,1910906,2002,03/30/2006 09:10:16 PM,41.910939423,-87.636807039,"(41.910939423, -87.636807039)"
-2104450,HH338304,04/25/2002 05:00:00 PM,027XX S CALIFORNIA BLVD,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,OTHER,false,true,1034,,12,30,26,1158250,1885974,2002,03/30/2006 09:10:16 PM,41.842856716,-87.694756941,"(41.842856716, -87.694756941)"
diff --git a/work-with-data/dataprep/data/crime_partfiles/part-00007-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv b/work-with-data/dataprep/data/crime_partfiles/part-00007-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv
deleted file mode 100644
index ea61376c..00000000
--- a/work-with-data/dataprep/data/crime_partfiles/part-00007-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv
+++ /dev/null
@@ -1,759 +0,0 @@
-2101077,HH331686,04/25/2002 02:30:00 PM,0000X N MORGAN ST,0560,ASSAULT,SIMPLE,COMMERCIAL / BUSINESS OFFICE,false,false,1212,,27,28,08A,1169732,1900465,2002,03/30/2006 09:10:16 PM,41.882379185,-87.652199117,"(41.882379185, -87.652199117)"
-2103474,HH334487,04/24/2002 11:00:00 PM,018XX E 95TH ST,051A,ASSAULT,AGGRAVATED: HANDGUN,RESIDENCE,false,false,0431,,7,51,04A,1189901,1842296,2002,03/30/2006 09:10:16 PM,41.722296526,-87.580011632,"(41.722296526, -87.580011632)"
-2099681,HH327994,04/24/2002 01:15:00 PM,063XX N RICHMOND ST,1320,CRIMINAL DAMAGE,TO VEHICLE,DRIVEWAY - RESIDENTIAL,false,false,2413,,50,2,14,1155569,1941854,2002,03/30/2006 09:10:16 PM,41.996250238,-87.703088514,"(41.996250238, -87.703088514)"
-2096225,HH327432,04/24/2002 06:30:00 AM,093XX S YATES BL,0810,THEFT,OVER $500,STREET,false,false,0413,,,,06,1193761,1843528,2002,12/04/2014 12:43:35 PM,41.72558369,-87.565833015,"(41.72558369, -87.565833015)"
-2119786,HH325182,04/23/2002 08:15:00 AM,048XX S HALSTED ST,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,0935,,11,61,16,1171820,1873010,2002,03/30/2006 09:10:16 PM,41.806994618,-87.645339629,"(41.806994618, -87.645339629)"
-2096064,HH326438,04/23/2002 07:00:00 AM,039XX W 46TH ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0821,,14,57,07,1151062,1873890,2002,03/30/2006 09:10:16 PM,41.809840221,-87.721451101,"(41.809840221, -87.721451101)"
-2094153,HH325192,04/22/2002 09:30:00 PM,044XX S RICHMOND ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0912,,,,07,1157479,1875053,2002,03/30/2006 09:10:16 PM,41.812903859,-87.697882765,"(41.812903859, -87.697882765)"
-2100649,HH322893,04/22/2002 03:50:00 AM,070XX S HALSTED ST,0460,BATTERY,SIMPLE,RESIDENCE,true,true,0733,,6,68,08B,1172146,1858061,2002,03/30/2006 09:10:16 PM,41.765965722,-87.644583174,"(41.765965722, -87.644583174)"
-2101177,HH322537,04/21/2002 08:30:00 PM,043XX S CICERO AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,CHA PARKING LOT/GROUNDS,false,false,0814,,23,56,04A,1145050,1875212,2002,03/30/2006 09:10:16 PM,41.813583335,-87.743469257,"(41.813583335, -87.743469257)"
-2097671,HH322268,04/21/2002 06:00:00 PM,025XX W CERMAK RD,0820,THEFT,$500 AND UNDER,DEPARTMENT STORE,false,false,1034,,25,31,06,1159501,1889267,2002,12/04/2014 12:43:35 PM,41.851867449,-87.690075557,"(41.851867449, -87.690075557)"
-2090692,HH320103,04/20/2002 03:00:00 PM,018XX N MAYFIELD AV,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2531,,,,26,1136844,1911449,2002,03/30/2006 09:10:16 PM,41.913173447,-87.772701964,"(41.913173447, -87.772701964)"
-2098585,HH329772,04/20/2002 11:00:00 AM,043XX W MAYPOLE AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,true,1114,,28,26,06,1147223,1901137,2002,03/30/2006 09:10:16 PM,41.88468372,-87.73483568,"(41.88468372, -87.73483568)"
-2091887,HH322801,04/19/2002 07:00:00 PM,025XX N CLARK ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1933,,,,06,1172258,1917323,2002,12/04/2014 12:43:35 PM,41.928583056,-87.642425103,"(41.928583056, -87.642425103)"
-2094819,HH316759,04/18/2002 12:12:00 AM,080XX S COTTAGE GROVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0631,,,,16,1182988,1851977,2002,03/30/2006 09:10:16 PM,41.749025496,-87.605032618,"(41.749025496, -87.605032618)"
-2094611,HH314262,04/17/2002 09:00:00 PM,021XX E 71 ST,0820,THEFT,$500 AND UNDER,GROCERY FOOD STORE,true,false,0333,,,,06,1191459,1858232,2002,12/04/2014 12:43:35 PM,41.765988783,-87.573789768,"(41.765988783, -87.573789768)"
-2086302,HH313833,04/17/2002 06:00:00 PM,094XX S GREENWOOD AV,0560,ASSAULT,SIMPLE,STREET,false,false,0413,,,,08A,,,2002,03/30/2006 09:10:16 PM,,,
-2086922,HH313011,04/17/2002 11:51:23 AM,007XX N WABASH AV,0560,ASSAULT,SIMPLE,OTHER,false,false,1833,,,,08A,1176636,1905569,2002,03/30/2006 09:10:16 PM,41.896231666,-87.626693465,"(41.896231666, -87.626693465)"
-2084322,HH310764,04/16/2002 11:20:00 AM,076XX S ESSEX AV,0460,BATTERY,SIMPLE,RESIDENCE,false,true,0421,,,,08B,1194144,1854980,2002,03/30/2006 09:10:16 PM,41.756999568,-87.5640551,"(41.756999568, -87.5640551)"
-2086594,HH310095,04/16/2002 01:18:11 AM,086XX S COTTAGE GROVE,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),true,false,0632,,,,07,1183026,1848012,2002,03/30/2006 09:10:16 PM,41.73814422,-87.605016344,"(41.73814422, -87.605016344)"
-2082250,HH310256,04/15/2002 03:30:00 AM,049XX S KEDZIE AV,1310,CRIMINAL DAMAGE,TO PROPERTY,CTA PLATFORM,false,false,0821,,,,14,1155830,1871914,2002,03/30/2006 09:10:16 PM,41.804323323,-87.704015698,"(41.804323323, -87.704015698)"
-2086258,HH307761,04/14/2002 11:55:00 PM,068XX S STONY ISLAND AV,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,GROCERY FOOD STORE,false,false,0332,,,,05,1187999,1859976,2002,03/30/2006 09:10:16 PM,41.770857611,-87.586416156,"(41.770857611, -87.586416156)"
-2086007,HH307462,04/14/2002 09:50:00 PM,120XX S NORMAL AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,RESIDENCE PORCH/HALLWAY,true,false,0523,,,,18,1175106,1824720,2002,03/30/2006 09:10:16 PM,41.67440787,-87.634725464,"(41.67440787, -87.634725464)"
-2085913,HH309852,04/14/2002 09:00:00 PM,086XX S STONY ISLAND AV,1310,CRIMINAL DAMAGE,TO PROPERTY,GAS STATION,false,false,0412,,,,14,1188259,1848253,2002,03/30/2006 09:10:16 PM,41.738682423,-87.58583647,"(41.738682423, -87.58583647)"
-2081280,HH307525,04/14/2002 07:00:00 PM,042XX W VAN BUREN ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, GROUNDS",false,false,1132,,,,08B,1148424,1897730,2002,03/30/2006 09:10:16 PM,41.875311455,-87.730513272,"(41.875311455, -87.730513272)"
-2084890,HH306585,04/14/2002 01:25:00 PM,063XX S ST LAWRENCE AV,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,true,false,0312,,,,15,1181281,1863211,2002,03/30/2006 09:10:16 PM,41.779892225,-87.610941936,"(41.779892225, -87.610941936)"
-2121803,HH353305,04/13/2002 03:00:00 PM,049XX N WESTERN AVE,1110,DECEPTIVE PRACTICE,BOGUS CHECK,SMALL RETAIL STORE,false,false,2031,,47,4,11,1159499,1933026,2002,03/30/2006 09:10:16 PM,41.971945553,-87.688876131,"(41.971945553, -87.688876131)"
-2079102,HH304357,04/13/2002 11:15:00 AM,068XX S BISHOP ST,0460,BATTERY,SIMPLE,STREET,true,true,0725,,,,08B,1167799,1859617,2002,03/30/2006 09:10:16 PM,41.770330001,-87.660471816,"(41.770330001, -87.660471816)"
-2079024,HH303835,04/13/2002 01:54:44 AM,015XX N LUNA AV,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,2532,,,,04B,1139020,1909457,2002,03/30/2006 09:10:16 PM,41.907667859,-87.764756214,"(41.907667859, -87.764756214)"
-2078076,HH303846,04/12/2002 11:15:00 PM,009XX W ADAMS ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1213,,2,28,07,1170308,1899340,2002,03/30/2006 09:10:16 PM,41.879279542,-87.650116938,"(41.879279542, -87.650116938)"
-2078048,HH303631,04/12/2002 10:45:20 PM,022XX W LELAND AV,0560,ASSAULT,SIMPLE,STREET,true,false,1911,,,,08A,1160522,1931130,2002,03/30/2006 09:10:16 PM,41.966721661,-87.685167148,"(41.966721661, -87.685167148)"
-2078393,HH303643,04/12/2002 10:30:00 PM,087XX W BRYN MAWR AV,0560,ASSAULT,SIMPLE,OTHER,true,false,1614,,,,08A,,,2002,03/30/2006 09:10:16 PM,,,
-2078799,HH302210,04/12/2002 08:15:00 AM,015XX W 51 ST,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,true,false,0932,,,,26,1167183,1870876,2002,03/30/2006 09:10:16 PM,41.80123928,-87.662407779,"(41.80123928, -87.662407779)"
-2077684,HH302498,04/12/2002 08:00:00 AM,076XX S EXCHANGE AV,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,false,false,0421,,,,14,1195684,1855050,2002,03/30/2006 09:10:16 PM,41.757153726,-87.558409062,"(41.757153726, -87.558409062)"
-2075971,HH130121,04/11/2002 08:00:00 PM,044XX N KIMBALL AV,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1723,,,,14,1152886,1929170,2002,03/30/2006 09:10:16 PM,41.961498377,-87.713295854,"(41.961498377, -87.713295854)"
-2092542,HH299641,04/11/2002 08:30:00 AM,049XX S ARCHER AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,"SCHOOL, PUBLIC, BUILDING",true,false,0821,,,,18,1150928,1871413,2002,03/30/2006 09:10:16 PM,41.803045592,-87.722007222,"(41.803045592, -87.722007222)"
-2075522,HH299181,04/11/2002 12:05:32 AM,019XX S TRUMBULL AV,0460,BATTERY,SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,1024,,,,08B,1153770,1890208,2002,03/30/2006 09:10:16 PM,41.854565544,-87.711084915,"(41.854565544, -87.711084915)"
-2080768,HH298735,04/10/2002 08:00:00 PM,028XX N MILWAUKEE AV,0560,ASSAULT,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1412,,,,08A,1152677,1918942,2002,03/30/2006 09:10:16 PM,41.933436146,-87.71433583,"(41.933436146, -87.71433583)"
-2088828,HH298458,04/10/2002 06:00:23 PM,027XX W JACKSON BV,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,1125,,,,18,1157860,1898533,2002,03/30/2006 09:10:16 PM,41.877327883,-87.695845939,"(41.877327883, -87.695845939)"
-2073834,HH298506,04/10/2002 08:15:00 AM,089XX S LOOMIS ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2222,,,,07,1168518,1845657,2002,03/30/2006 09:10:16 PM,41.732006388,-87.658237765,"(41.732006388, -87.658237765)"
-2081516,HH308460,04/08/2002 11:00:00 AM,091XX S SOUTH CHICAGO,0842,THEFT,AGG: FINANCIAL ID THEFT,APARTMENT,false,false,0423,,,,06,1197089,1844669,2002,03/30/2006 09:10:16 PM,41.72863262,-87.553604794,"(41.72863262, -87.553604794)"
-2068545,HH291088,04/07/2002 10:43:43 AM,022XX W MONROE ST,0820,THEFT,$500 AND UNDER,RESIDENCE,false,false,1211,,,,06,1161159,1899452,2002,12/04/2014 12:43:35 PM,41.879781823,-87.683707377,"(41.879781823, -87.683707377)"
-2075557,HH290654,04/07/2002 01:09:30 AM,017XX W 83 ST,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,0614,,,,18,1166186,1849614,2002,03/30/2006 09:10:16 PM,41.742914857,-87.666668571,"(41.742914857, -87.666668571)"
-2075424,HH290461,04/06/2002 10:30:00 PM,067XX S KILDARE AV,2022,NARCOTICS,POSS: COCAINE,ALLEY,true,false,0833,,,,18,1148829,1859481,2002,03/30/2006 09:10:16 PM,41.770343007,-87.730012393,"(41.770343007, -87.730012393)"
-2067379,HH290405,04/06/2002 08:00:00 PM,073XX S MICHIGAN AV,0810,THEFT,OVER $500,STREET,false,false,0323,,,,06,1178467,1856529,2002,12/04/2014 12:43:35 PM,41.761620517,-87.621461131,"(41.761620517, -87.621461131)"
-2067133,HH288820,04/06/2002 05:00:00 AM,118XX S WESTERN AV,0610,BURGLARY,FORCIBLE ENTRY,RESTAURANT,false,false,2212,,,,05,1162486,1826010,2002,03/30/2006 09:10:16 PM,41.678219361,-87.680880879,"(41.678219361, -87.680880879)"
-2066953,HH287533,04/05/2002 03:50:00 PM,023XX S STATE ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,true,false,2113,,,,26,1176649,1888559,2002,03/30/2006 09:10:16 PM,41.849554907,-87.627159699,"(41.849554907, -87.627159699)"
-2071856,HH286557,04/05/2002 08:30:00 AM,043XX W WRIGHTWOOD AV,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,2524,,,,08A,1146795,1916879,2002,03/30/2006 09:10:16 PM,41.927889584,-87.736004831,"(41.927889584, -87.736004831)"
-8212264,HT446293,04/04/2002 12:00:00 PM,044XX S STATE ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,0221,,3,38,06,,,2002,08/24/2011 12:40:17 AM,,,
-2064869,HH284305,04/04/2002 05:35:00 AM,027XX N WESTERN AV,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,1411,,,,04B,1159853,1917902,2002,03/30/2006 09:10:16 PM,41.930437117,-87.687993176,"(41.930437117, -87.687993176)"
-2063931,HH284929,04/03/2002 09:15:00 PM,095XX S EWING AV,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0432,,,,05,1201944,1842278,2002,03/30/2006 09:10:16 PM,41.72194948,-87.535901442,"(41.72194948, -87.535901442)"
-2075450,HH281831,04/02/2002 09:25:00 PM,063XX S HOYNE AV,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0726,,,,16,1163406,1862812,2002,03/30/2006 09:10:16 PM,41.779190696,-87.676485374,"(41.779190696, -87.676485374)"
-2058666,HH280103,04/02/2002 01:45:00 AM,079XX S LA SALLE ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0623,,,,14,1176621,1852135,2002,03/30/2006 09:10:16 PM,41.749604587,-87.628358827,"(41.749604587, -87.628358827)"
-2053083,HH270840,03/28/2002 03:10:00 PM,002XX S LA SALLE ST,0810,THEFT,OVER $500,SMALL RETAIL STORE,false,false,0112,,,,06,1175122,1899410,2002,12/04/2014 12:43:35 PM,41.879365106,-87.632438796,"(41.879365106, -87.632438796)"
-2052397,HH269609,03/27/2002 10:15:00 PM,074XX S PARNELL AV,1320,CRIMINAL DAMAGE,TO VEHICLE,VACANT LOT/LAND,false,false,0732,,,,14,1173877,1855458,2002,03/30/2006 09:10:16 PM,41.758784567,-87.638315569,"(41.758784567, -87.638315569)"
-2054666,HH271549,03/27/2002 10:00:00 AM,033XX N HOYNE AV,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1913,,,,26,1161725,1922157,2002,03/30/2006 09:10:16 PM,41.942074225,-87.680995034,"(41.942074225, -87.680995034)"
-2057807,HH266438,03/26/2002 01:33:13 PM,012XX W 71 PL,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,0734,,,,18,1168899,1857319,2002,03/30/2006 09:10:16 PM,41.764000309,-87.656505945,"(41.764000309, -87.656505945)"
-2049210,HH264789,03/25/2002 05:35:00 PM,002XX E 42 ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0214,,,,14,1178418,1877170,2002,03/30/2006 09:10:16 PM,41.81826253,-87.621013927,"(41.81826253, -87.621013927)"
-2041888,HH256723,03/21/2002 12:00:00 AM,028XX W ARMITAGE AV,0810,THEFT,OVER $500,STREET,false,false,1414,,,,06,1156817,1913193,2002,12/04/2014 12:43:35 PM,41.917577425,-87.699277749,"(41.917577425, -87.699277749)"
-2042526,HH255338,03/20/2002 09:30:00 PM,016XX S HOMAN AV,5000,OTHER OFFENSE,OTHER CRIME AGAINST PERSON,RESIDENCE,false,false,1021,,,,26,1154021,1891165,2002,03/30/2006 09:10:16 PM,41.857186667,-87.71013814,"(41.857186667, -87.71013814)"
-2039670,HH251707,03/19/2002 10:10:00 PM,002XX S STATE ST,1330,CRIMINAL TRESPASS,TO LAND,CTA PLATFORM,true,false,0123,,,,26,1176376,1899166,2002,03/30/2006 09:10:16 PM,41.878667362,-87.627841726,"(41.878667362, -87.627841726)"
-2039152,HH253854,03/19/2002 05:00:00 PM,022XX N LISTER AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1432,,32,22,07,1163258,1915073,2002,03/30/2006 09:10:16 PM,41.922603156,-87.675560356,"(41.922603156, -87.675560356)"
-2038345,HH251822,03/19/2002 11:15:00 AM,031XX N BROADWAY,1110,DECEPTIVE PRACTICE,BOGUS CHECK,CURRENCY EXCHANGE,false,false,2332,,,,11,1171653,1921296,2002,03/30/2006 09:10:16 PM,41.939498488,-87.644530959,"(41.939498488, -87.644530959)"
-2038721,HH251420,03/19/2002 05:55:00 AM,025XX N HAMLIN AV,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,true,false,2524,,,,07,1150585,1916446,2002,03/30/2006 09:10:16 PM,41.926628099,-87.722089249,"(41.926628099, -87.722089249)"
-2036453,HH251387,03/18/2002 05:00:00 PM,086XX W BERWYN AV,1310,CRIMINAL DAMAGE,TO PROPERTY,STREET,false,false,1614,,,,14,1117488,1933944,2002,03/30/2006 09:10:16 PM,41.975227926,-87.843340924,"(41.975227926, -87.843340924)"
-2038845,HH253668,03/18/2002 08:00:00 AM,047XX S VINCENNES AV,0820,THEFT,$500 AND UNDER,APARTMENT,false,false,0223,,,,06,1180440,1873459,2002,12/04/2014 12:43:35 PM,41.808033009,-87.613710632,"(41.808033009, -87.613710632)"
-2049919,HH249244,03/18/2002 06:07:44 AM,027XX W HARRISON ST,0560,ASSAULT,SIMPLE,OTHER,false,false,1135,,,,08A,1158284,1897213,2002,03/30/2006 09:10:16 PM,41.873697022,-87.694325209,"(41.873697022, -87.694325209)"
-2091310,HH246237,03/16/2002 01:15:00 PM,067XX S NORMAL AV,0460,BATTERY,SIMPLE,APARTMENT,false,true,0722,,,,08B,1174078,1859944,2002,03/30/2006 09:10:16 PM,41.771090222,-87.637445909,"(41.771090222, -87.637445909)"
-2035354,HH243297,03/15/2002 04:00:00 AM,065XX S COTTAGE GROVE,0460,BATTERY,SIMPLE,APARTMENT,true,true,0321,,,,08B,1182641,1862032,2002,03/30/2006 09:10:16 PM,41.77662548,-87.605992598,"(41.77662548, -87.605992598)"
-2033239,HH241029,03/14/2002 12:30:00 AM,057XX S SANGAMON ST,0460,BATTERY,SIMPLE,STREET,false,false,0712,,,,08B,1171006,1866404,2002,03/30/2006 09:10:16 PM,41.788884878,-87.648518139,"(41.788884878, -87.648518139)"
-2029571,HH239668,03/13/2002 11:55:00 AM,017XX N KOSTNER AV,0820,THEFT,$500 AND UNDER,GROCERY FOOD STORE,false,false,2533,,,,06,1146749,1911068,2002,12/04/2014 12:43:35 PM,41.911944513,-87.73632252,"(41.911944513, -87.73632252)"
-2033751,HH246762,03/13/2002 01:00:00 AM,027XX W ARTHUR AV,0820,THEFT,$500 AND UNDER,STREET,false,false,2412,,,,06,1156705,1943050,2002,12/04/2014 12:43:35 PM,41.999509091,-87.698877068,"(41.999509091, -87.698877068)"
-2031409,HH238423,03/12/2002 07:54:00 PM,059XX W DIVISION ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,VEHICLE NON-COMMERCIAL,true,false,2531,,,,15,1136162,1907412,2002,03/30/2006 09:10:16 PM,41.902107632,-87.775304006,"(41.902107632, -87.775304006)"
-2027921,HH238092,03/12/2002 03:15:00 PM,019XX W POTOMAC AV,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,true,false,1424,,,,05,1163102,1908623,2002,03/30/2006 09:10:16 PM,41.904907195,-87.676315097,"(41.904907195, -87.676315097)"
-2025650,HH236950,03/12/2002 07:00:00 AM,058XX S MARYLAND AV,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,2133,,,,06,1182938,1866293,2002,12/04/2014 12:43:35 PM,41.78831115,-87.604771469,"(41.78831115, -87.604771469)"
-2025417,HH232433,03/09/2002 05:25:00 PM,027XX N WESTERN AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1432,,,,07,1159917,1918503,2002,03/30/2006 09:10:16 PM,41.932084979,-87.687741361,"(41.932084979, -87.687741361)"
-2031235,HH229299,03/09/2002 10:40:00 AM,008XX N ST LOUIS AV,2095,NARCOTICS,ATTEMPT POSSESSION NARCOTICS,STREET,true,false,1121,,,,18,1152924,1905662,2002,03/30/2006 09:10:16 PM,41.896989759,-87.713780664,"(41.896989759, -87.713780664)"
-2021847,HH229844,03/08/2002 02:31:01 PM,002XX S LA SALLE ST,0810,THEFT,OVER $500,SMALL RETAIL STORE,false,false,0112,,,,06,1175122,1899410,2002,12/04/2014 12:43:35 PM,41.879365106,-87.632438796,"(41.879365106, -87.632438796)"
-2023129,HH229817,03/08/2002 12:00:00 PM,0000X N STATE ST,0820,THEFT,$500 AND UNDER,DEPARTMENT STORE,true,false,0122,,,,06,1176405,1900498,2002,12/04/2014 12:43:35 PM,41.882321791,-87.627695033,"(41.882321791, -87.627695033)"
-2021923,HH229392,03/08/2002 10:15:00 AM,018XX S HARDING AV,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,1014,,,,14,1150385,1890831,2002,03/30/2006 09:10:16 PM,41.856341773,-87.723493135,"(41.856341773, -87.723493135)"
-2019963,HH227662,03/07/2002 01:45:00 PM,045XX N BROADWAY,0460,BATTERY,SIMPLE,SIDEWALK,true,false,2311,,,,08B,1168117,1930491,2002,03/30/2006 09:10:16 PM,41.964807164,-87.657260107,"(41.964807164, -87.657260107)"
-2019045,HH226337,03/06/2002 07:30:00 PM,035XX S LAKE PARK AV,0460,BATTERY,SIMPLE,APARTMENT,true,false,2122,,,,08B,1181825,1881875,2002,03/30/2006 09:10:16 PM,41.831095207,-87.60837042,"(41.831095207, -87.60837042)"
-2028291,HH226661,03/06/2002 06:00:00 PM,056XX W DIVERSEY AV,2091,NARCOTICS,FORFEIT PROPERTY,STREET,true,false,2514,,,,26,1138539,1918097,2002,03/30/2006 09:10:16 PM,41.931385725,-87.766313321,"(41.931385725, -87.766313321)"
-2014745,HH221602,03/05/2002 01:46:21 AM,091XX S COMMERCIAL AV,0460,BATTERY,SIMPLE,SMALL RETAIL STORE,false,false,0423,,,,08B,1197686,1845166,2002,03/30/2006 09:10:16 PM,41.729981568,-87.551401361,"(41.729981568, -87.551401361)"
-2066479,HH285639,03/03/2002 01:00:00 PM,049XX N MILWAUKEE AV,1110,DECEPTIVE PRACTICE,BOGUS CHECK,BANK,false,false,1623,,,,11,1139315,1932295,2002,03/30/2006 09:10:16 PM,41.970332279,-87.763114481,"(41.970332279, -87.763114481)"
-2012950,HH219156,03/03/2002 03:15:00 AM,042XX N BROADWAY,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,true,false,2322,,,,14,1169089,1928494,2002,03/30/2006 09:10:16 PM,41.959306229,-87.653744569,"(41.959306229, -87.653744569)"
-2017916,HH217878,03/02/2002 11:54:17 AM,047XX S KING DR,0313,ROBBERY,ARMED: OTHER DANGEROUS WEAPON,SIDEWALK,false,false,0224,,,,03,1179612,1873910,2002,03/30/2006 09:10:16 PM,41.809289576,-87.616733704,"(41.809289576, -87.616733704)"
-2012456,HH217744,03/01/2002 08:00:00 PM,012XX S SPAULDING AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1021,,,,07,1154512,1894462,2002,03/30/2006 09:10:16 PM,41.866224207,-87.70824777,"(41.866224207, -87.70824777)"
-2011616,HH216989,03/01/2002 06:40:00 PM,020XX E 79 ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0414,,,,06,1191393,1853038,2002,12/04/2014 12:43:35 PM,41.751737618,-87.574199674,"(41.751737618, -87.574199674)"
-2018897,HH216389,03/01/2002 03:40:00 PM,064XX S BISHOP ST,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,0725,,,,18,1167734,1861940,2002,03/30/2006 09:10:16 PM,41.776706004,-87.660643475,"(41.776706004, -87.660643475)"
-2011798,HH217528,03/01/2002 02:30:00 PM,0000X E ILLINOIS ST,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,1834,,,,06,1176465,1903575,2002,12/04/2014 12:43:35 PM,41.890763892,-87.627381764,"(41.890763892, -87.627381764)"
-2010536,HH215478,02/28/2002 10:00:00 PM,058XX S MELVINA AV,0820,THEFT,$500 AND UNDER,STREET,false,false,0811,,,,06,1136095,1865162,2002,12/04/2014 12:43:35 PM,41.786168363,-87.776556207,"(41.786168363, -87.776556207)"
-2017294,HH212133,02/27/2002 11:59:41 AM,077XX S THROOP ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0612,,,,18,1169266,1853418,2002,03/30/2006 09:10:16 PM,41.75328752,-87.655273502,"(41.75328752, -87.655273502)"
-2015868,HH212014,02/27/2002 10:35:00 AM,028XX W ROOSEVELT RD,0330,ROBBERY,AGGRAVATED,SIDEWALK,false,false,1135,,,,03,1157790,1894629,2002,03/30/2006 09:10:16 PM,41.866616344,-87.696209358,"(41.866616344, -87.696209358)"
-2005751,HH209285,02/25/2002 10:23:21 PM,013XX S SPRINGFIELD AV,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1011,,,,26,1150557,1893353,2002,03/30/2006 09:10:16 PM,41.863259089,-87.722795972,"(41.863259089, -87.722795972)"
-2008091,HH205283,02/23/2002 10:37:23 PM,011XX S INDEPENDENCE BL,0460,BATTERY,SIMPLE,SIDEWALK,false,true,1133,,,,08B,1151476,1894546,2002,03/30/2006 09:10:16 PM,41.866514834,-87.719391056,"(41.866514834, -87.719391056)"
-2007837,HH205090,02/23/2002 08:05:00 PM,012XX N KEELER AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2534,,,,18,1148119,1907685,2002,03/30/2006 09:10:16 PM,41.902634942,-87.731376712,"(41.902634942, -87.731376712)"
-2003174,HH204778,02/23/2002 02:00:00 PM,050XX S KEDZIE AV,0820,THEFT,$500 AND UNDER,SMALL RETAIL STORE,false,false,0821,,,,06,1155853,1871106,2002,12/04/2014 12:43:35 PM,41.802105599,-87.703953044,"(41.802105599, -87.703953044)"
-1997595,HH198951,02/20/2002 07:44:35 PM,016XX W 47 ST,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,false,false,0914,,,,14,1165818,1873581,2002,03/30/2006 09:10:16 PM,41.808691242,-87.66733686,"(41.808691242, -87.66733686)"
-1996755,HH198612,02/20/2002 05:17:43 PM,002XX S HAMILTON AV,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1211,,,,08B,1162148,1898808,2002,03/30/2006 09:10:16 PM,41.877994031,-87.680093904,"(41.877994031, -87.680093904)"
-1997097,HH197830,02/20/2002 10:30:00 AM,034XX N TRIPP AV,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,1731,,,,03,1147401,1922708,2002,03/30/2006 09:10:16 PM,41.94387327,-87.733628155,"(41.94387327, -87.733628155)"
-1997512,HH196730,02/19/2002 06:45:00 PM,027XX W 51 ST,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,VACANT LOT/LAND,false,false,0911,,,,07,1158887,1870688,2002,03/30/2006 09:10:16 PM,41.800897049,-87.692837472,"(41.800897049, -87.692837472)"
-2005188,HH195761,02/19/2002 11:25:00 AM,025XX W JACKSON BV,0460,BATTERY,SIMPLE,SIDEWALK,true,false,1125,,,,08B,1159583,1898573,2002,03/30/2006 09:10:16 PM,41.877402355,-87.68951845,"(41.877402355, -87.68951845)"
-1994447,HH195569,02/19/2002 09:10:18 AM,007XX S WABASH AV,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,true,false,0132,,,,26,1176864,1897126,2002,03/30/2006 09:10:16 PM,41.873058461,-87.626111629,"(41.873058461, -87.626111629)"
-1994272,HH196095,02/18/2002 11:00:00 PM,020XX W JACKSON BV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1211,,,,07,1162601,1898579,2002,03/30/2006 09:10:16 PM,41.877356162,-87.678437014,"(41.877356162, -87.678437014)"
-1992252,HH193092,02/17/2002 10:15:00 PM,094XX S ASHLAND AV,0820,THEFT,$500 AND UNDER,GROCERY FOOD STORE,true,false,2221,,,,06,1167281,1842343,2002,12/04/2014 12:43:35 PM,41.722938808,-87.662863977,"(41.722938808, -87.662863977)"
-1992406,HH192385,02/17/2002 02:30:00 PM,050XX S PAULINA ST,0560,ASSAULT,SIMPLE,STREET,false,false,0931,,,,08A,1165816,1871515,2002,03/30/2006 09:10:16 PM,41.803021942,-87.667402908,"(41.803021942, -87.667402908)"
-1990849,HH191819,02/17/2002 01:00:00 AM,037XX N OLEANDER AV,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,true,false,1631,,,,07,1125119,1923927,2002,03/30/2006 09:10:16 PM,41.947616927,-87.815501056,"(41.947616927, -87.815501056)"
-2005161,HH187378,02/14/2002 09:40:00 PM,016XX N HERMITAGE AV,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,1433,,,,16,1164475,1910734,2002,03/30/2006 09:10:16 PM,41.910670957,-87.671211807,"(41.910670957, -87.671211807)"
-1987875,HH184572,02/13/2002 02:20:42 PM,0000X E 115 ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0531,,,,08B,1178641,1828771,2002,03/30/2006 09:10:16 PM,41.685445045,-87.621664457,"(41.685445045, -87.621664457)"
-1984204,HH182254,02/12/2002 09:30:00 AM,086XX S MARQUETTE AV,0560,ASSAULT,SIMPLE,STREET,true,false,0423,,,,08A,1195620,1847966,2002,03/30/2006 09:10:16 PM,41.737716258,-87.558877276,"(41.737716258, -87.558877276)"
-1982487,HH180841,02/11/2002 10:00:00 AM,072XX S EBERHART AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0323,,,,07,1180868,1857150,2002,03/30/2006 09:10:16 PM,41.763269745,-87.612642237,"(41.763269745, -87.612642237)"
-1981711,HH178043,02/10/2002 01:55:00 AM,015XX E 67 ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,true,false,0321,,,,14,1187418,1861577,2002,03/30/2006 09:10:16 PM,41.775264717,-87.588495026,"(41.775264717, -87.588495026)"
-1983814,HH182779,02/09/2002 09:00:00 PM,026XX S KARLOV AV,0820,THEFT,$500 AND UNDER,STREET,false,false,1031,,,,06,1149427,1886372,2002,12/04/2014 12:43:35 PM,41.844124332,-87.727125063,"(41.844124332, -87.727125063)"
-1982694,HH176043,02/09/2002 01:52:33 AM,045XX S FEDERAL ST,041A,BATTERY,AGGRAVATED: HANDGUN,CHA PARKING LOT/GROUNDS,false,false,0221,,,,04B,1176490,1874939,2002,03/30/2006 09:10:16 PM,41.812184109,-87.628153577,"(41.812184109, -87.628153577)"
-1989092,HH175719,02/08/2002 09:45:00 PM,062XX S VERNON AV,2110,NARCOTICS,POS: HYPODERMIC NEEDLE,ALLEY,true,false,0313,,,,18,1180292,1863369,2002,03/30/2006 09:10:16 PM,41.780348535,-87.614562873,"(41.780348535, -87.614562873)"
-1979302,HH175103,02/08/2002 04:23:24 PM,094XX S ASHLAND AV,0460,BATTERY,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),false,false,2221,,,,08B,1167281,1842343,2002,12/04/2014 12:43:35 PM,41.722938808,-87.662863977,"(41.722938808, -87.662863977)"
-1998820,HH184165,02/08/2002 10:57:00 AM,075XX S CONSTANCE AV,0545,ASSAULT,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,0414,,,,08A,1189886,1855311,2002,03/30/2006 09:10:16 PM,41.758011284,-87.579649067,"(41.758011284, -87.579649067)"
-1977661,HH172076,02/07/2002 08:10:01 AM,055XX S KEELER AV,0560,ASSAULT,SIMPLE,ALLEY,true,false,0813,,,,08A,1149348,1867582,2002,03/30/2006 09:10:16 PM,41.792563438,-87.727900843,"(41.792563438, -87.727900843)"
-1987268,HH168727,02/05/2002 02:22:29 PM,062XX S KING DR,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0311,,,,18,1180042,1863361,2002,03/30/2006 09:10:16 PM,41.780332313,-87.615479651,"(41.780332313, -87.615479651)"
-1981632,HH168459,02/05/2002 10:45:00 AM,003XX N STATE ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,OTHER,true,false,1831,,,,15,1176264,1902405,2002,03/30/2006 09:10:16 PM,41.887557885,-87.628155238,"(41.887557885, -87.628155238)"
-1970625,HH164176,02/02/2002 11:15:00 PM,047XX N KILDARE AV,1570,SEX OFFENSE,PUBLIC INDECENCY,SIDEWALK,true,false,1722,,,,17,1146889,1931513,2002,03/30/2006 09:10:16 PM,41.968044706,-87.735284197,"(41.968044706, -87.735284197)"
-1970540,HH162217,02/01/2002 10:10:00 PM,074XX S INGLESIDE AV,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,0323,,,,08A,1183815,1855678,2002,03/30/2006 09:10:16 PM,41.759162169,-87.601886908,"(41.759162169, -87.601886908)"
-2099186,HH330889,02/01/2002 12:00:00 PM,005XX W ARLINGTON PL,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,true,false,1933,,43,7,06,1172244,1916695,2002,03/30/2006 09:10:16 PM,41.926860107,-87.64249514,"(41.926860107, -87.64249514)"
-2033850,HH247607,02/01/2002 10:38:00 AM,040XX N MOZART ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1724,,,,14,1156620,1926710,2002,03/30/2006 09:10:16 PM,41.954673026,-87.699634443,"(41.954673026, -87.699634443)"
-1969303,HH160888,02/01/2002 01:00:00 AM,015XX N HALSTED ST,1310,CRIMINAL DAMAGE,TO PROPERTY,MEDICAL/DENTAL OFFICE,false,false,1822,,,,14,1170701,1910042,2002,03/30/2006 09:10:16 PM,41.908637908,-87.648360204,"(41.908637908, -87.648360204)"
-1964963,HH156280,01/29/2002 07:15:00 PM,016XX N DAMEN AV,0460,BATTERY,SIMPLE,APARTMENT,false,true,1434,,,,08B,1162804,1911007,2002,03/30/2006 09:10:16 PM,41.911455321,-87.677342768,"(41.911455321, -87.677342768)"
-1964519,HH154737,01/29/2002 12:24:00 AM,012XX N WESTERN AV,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,OTHER,true,false,1423,,,,04A,1160135,1908480,2002,03/30/2006 09:10:16 PM,41.904576633,-87.687217743,"(41.904576633, -87.687217743)"
-1960959,HH153131,01/28/2002 09:00:00 AM,047XX W PETERSON AV,0820,THEFT,$500 AND UNDER,COMMERCIAL / BUSINESS OFFICE,false,false,1711,,,,06,1143419,1939414,2002,12/04/2014 12:43:35 PM,41.989791398,-87.747844709,"(41.989791398, -87.747844709)"
-1960262,HH152581,01/27/2002 10:40:00 PM,063XX S CLAREMONT AV,0460,BATTERY,SIMPLE,STREET,false,false,0825,,,,08B,1161781,1862763,2002,03/30/2006 09:10:16 PM,41.779090141,-87.682444174,"(41.779090141, -87.682444174)"
-1960302,HH152073,01/27/2002 05:40:00 PM,026XX N CLARK ST,0810,THEFT,OVER $500,SMALL RETAIL STORE,false,false,1933,,,,06,1172044,1917730,2002,12/04/2014 12:43:35 PM,41.929704611,-87.643199435,"(41.929704611, -87.643199435)"
-1962704,HH151616,01/27/2002 01:14:36 PM,078XX S HALSTED ST,0560,ASSAULT,SIMPLE,DRUG STORE,false,false,0621,,,,08A,1172297,1852510,2002,03/30/2006 09:10:16 PM,41.750729763,-87.644192698,"(41.750729763, -87.644192698)"
-1966317,HH151434,01/27/2002 11:05:00 AM,001XX N LOTUS AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1523,,,,18,1139960,1900761,2002,03/30/2006 09:10:16 PM,41.88378783,-87.761516014,"(41.88378783, -87.761516014)"
-1960113,HH152292,01/27/2002 06:00:00 AM,109XX S GREEN ST,0820,THEFT,$500 AND UNDER,RESIDENCE,false,true,2233,,,,06,1172635,1832324,2002,12/04/2014 12:43:35 PM,41.695329084,-87.643546817,"(41.695329084, -87.643546817)"
-1960793,HH149638,01/26/2002 12:30:00 PM,041XX W CULLERTON ST,0460,BATTERY,SIMPLE,STREET,false,false,1012,,,,08B,1148728,1890130,2002,03/30/2006 09:10:16 PM,41.854450276,-87.729593333,"(41.854450276, -87.729593333)"
-1957481,HH147338,01/25/2002 11:30:00 AM,015XX S ASHLAND AV,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,COMMERCIAL / BUSINESS OFFICE,false,false,1211,,,,26,1165936,1892439,2002,03/30/2006 09:10:16 PM,41.860436998,-87.666367011,"(41.860436998, -87.666367011)"
-1956361,HH145290,01/23/2002 09:30:00 PM,065XX N CALIFORNIA AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2412,,,,14,1156518,1943181,2002,03/30/2006 09:10:16 PM,41.999872361,-87.699561427,"(41.999872361, -87.699561427)"
-1955659,HH143659,01/23/2002 03:15:00 PM,032XX W ROOSEVELT RD,0560,ASSAULT,SIMPLE,RESTAURANT,false,false,1134,,,,08A,1155196,1894572,2002,03/30/2006 09:10:16 PM,41.866512365,-87.705733783,"(41.866512365, -87.705733783)"
-1953305,HH142490,01/22/2002 11:00:00 PM,054XX S FAIRFIELD AV,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,0911,,,,08A,1158922,1868577,2002,03/30/2006 09:10:16 PM,41.795103468,-87.692766805,"(41.795103468, -87.692766805)"
-1949701,HH138933,01/20/2002 08:00:00 PM,069XX N SHERIDAN RD,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2431,,,,26,1166804,1946310,2002,03/30/2006 09:10:16 PM,42.008243238,-87.661631492,"(42.008243238, -87.661631492)"
-1950702,HH137915,01/20/2002 03:24:39 PM,029XX W CHICAGO AV,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,OTHER,false,false,1311,,,,07,1156677,1905233,2002,03/30/2006 09:10:16 PM,41.895737348,-87.700008039,"(41.895737348, -87.700008039)"
-1951714,HH137856,01/20/2002 02:45:00 PM,025XX N BERNARD ST,0560,ASSAULT,SIMPLE,STREET,false,false,1413,,,,08A,1152902,1916954,2002,03/30/2006 09:10:16 PM,41.927976453,-87.713561775,"(41.927976453, -87.713561775)"
-1954378,HH139971,01/20/2002 02:30:00 AM,080XX S MERRILL AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,true,0414,,,,14,1191972,1851741,2002,03/30/2006 09:10:16 PM,41.7481645,-87.572119996,"(41.7481645, -87.572119996)"
-1948932,HH137607,01/19/2002 04:00:00 PM,021XX N NEWLAND AV,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,2512,,,,05,1129701,1913375,2002,03/30/2006 09:10:16 PM,41.918583766,-87.798900172,"(41.918583766, -87.798900172)"
-1950737,HH136118,01/19/2002 02:30:00 PM,062XX S BISHOP ST,1245,DECEPTIVE PRACTICE,PAY TV SERVICE OFFENSES,ALLEY,true,false,0713,,,,11,1167687,1863596,2002,03/30/2006 09:10:16 PM,41.781251284,-87.660768294,"(41.781251284, -87.660768294)"
-1947157,HH135200,01/19/2002 01:00:00 AM,071XX S DR MARTN LUTHR KING JR DR,041A,BATTERY,AGGRAVATED: HANDGUN,GAS STATION,false,false,0323,,,,04B,1180106,1857999,2002,03/30/2006 09:10:16 PM,41.765616975,-87.615409118,"(41.765616975, -87.615409118)"
-1945573,HH133129,01/17/2002 09:30:00 PM,046XX S ST LOUIS AV,031A,ROBBERY,ARMED: HANDGUN,VEHICLE NON-COMMERCIAL,false,false,0821,,,,03,1153953,1873408,2002,03/30/2006 09:10:16 PM,41.808460574,-87.710860043,"(41.808460574, -87.710860043)"
-1943297,HH128877,01/15/2002 09:35:00 PM,055XX S ADA ST,0560,ASSAULT,SIMPLE,RESIDENCE,true,false,0713,,,,08A,1168317,1867803,2002,03/30/2006 09:10:16 PM,41.79278225,-87.658337489,"(41.79278225, -87.658337489)"
-1942367,HH129404,01/15/2002 06:30:00 PM,023XX S LAKE SHORE DR,0820,THEFT,$500 AND UNDER,OTHER,false,false,0133,,,,06,1180538,1889157,2002,12/04/2014 12:43:35 PM,41.851107212,-87.612868333,"(41.851107212, -87.612868333)"
-1941348,HH126635,01/14/2002 07:20:00 PM,003XX E 133 ST,1310,CRIMINAL DAMAGE,TO PROPERTY,"SCHOOL, PUBLIC, BUILDING",false,false,0533,,,,14,1180737,1817242,2002,03/30/2006 09:10:16 PM,41.653759968,-87.614343667,"(41.653759968, -87.614343667)"
-1962613,HH155081,01/14/2002 12:00:00 PM,107XX S RHODES AV,1120,DECEPTIVE PRACTICE,FORGERY,STREET,false,false,0513,,,,10,1181778,1833780,2002,03/30/2006 09:10:16 PM,41.699118739,-87.610026744,"(41.699118739, -87.610026744)"
-1943988,HH125536,01/14/2002 11:45:00 AM,062XX S HAMLIN AV,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0823,,,,08B,1152065,1863208,2002,03/30/2006 09:10:16 PM,41.780507577,-87.718052631,"(41.780507577, -87.718052631)"
-1955738,HH145908,01/14/2002 01:30:00 AM,103XX S AVENUE L,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0432,,,,14,1201794,1836682,2002,03/30/2006 09:10:16 PM,41.706597423,-87.536640513,"(41.706597423, -87.536640513)"
-1938105,HH123579,01/13/2002 10:05:00 AM,128XX S MUSKEGON AV,051B,ASSAULT,AGGRAVATED: OTHER FIREARM,RESIDENCE,false,false,0433,,,,04A,1197045,1820617,2002,03/30/2006 09:10:16 PM,41.662632621,-87.554562596,"(41.662632621, -87.554562596)"
-1938634,HH123277,01/13/2002 02:50:00 AM,034XX W EVERGREEN AV,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1422,,,,08B,1153106,1908807,2002,03/30/2006 09:10:16 PM,41.905616327,-87.713028679,"(41.905616327, -87.713028679)"
-1959364,HH150034,01/13/2002 02:00:00 AM,017XX W NORTH SHORE AV,0460,BATTERY,SIMPLE,ALLEY,false,true,2432,,,,08B,1163350,1944507,2002,03/30/2006 09:10:16 PM,42.003369424,-87.674390616,"(42.003369424, -87.674390616)"
-1935642,HH121228,01/12/2002 01:00:00 AM,005XX W NORTH AV,0820,THEFT,$500 AND UNDER,GAS STATION,false,false,1821,,,,06,1172056,1910881,2002,12/04/2014 12:43:35 PM,41.910910367,-87.643357836,"(41.910910367, -87.643357836)"
-1936139,HH121540,01/12/2002 12:01:00 AM,030XX N TROY ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,1411,,,,05,1154884,1920268,2002,03/30/2006 09:10:16 PM,41.937030778,-87.706189554,"(41.937030778, -87.706189554)"
-1934216,HH118625,01/10/2002 07:00:00 PM,050XX S KEDZIE AV,0820,THEFT,$500 AND UNDER,SMALL RETAIL STORE,true,false,0821,,,,06,1155861,1870847,2002,12/04/2014 12:43:35 PM,41.801394706,-87.70393066,"(41.801394706, -87.70393066)"
-1933864,HH118241,01/10/2002 03:45:00 PM,061XX S WESTERN AV,0326,ROBBERY,AGGRAVATED VEHICULAR HIJACKING,PARKING LOT/GARAGE(NON.RESID.),false,false,0825,,,,03,1161472,1863574,2002,03/30/2006 09:10:16 PM,41.781322048,-87.683554536,"(41.781322048, -87.683554536)"
-1932637,HH116221,01/09/2002 04:30:00 PM,063XX N MOBILE AV,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1611,,,,14,1133223,1941403,2002,03/30/2006 09:10:16 PM,41.995434536,-87.78530123,"(41.995434536, -87.78530123)"
-1933898,HH115427,01/09/2002 11:05:56 AM,017XX W 18 ST,0460,BATTERY,SIMPLE,STREET,true,false,1223,,,,08B,1164679,1891481,2002,03/30/2006 09:10:16 PM,41.857834861,-87.671008266,"(41.857834861, -87.671008266)"
-1938304,HH114685,01/08/2002 09:52:09 PM,050XX N SHERIDAN RD,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,2024,,,,18,1168671,1934152,2002,03/30/2006 09:10:16 PM,41.974841045,-87.655116647,"(41.974841045, -87.655116647)"
-1930267,HH114597,01/08/2002 08:10:00 PM,010XX W 98 ST,2850,PUBLIC PEACE VIOLATION,BOMB THREAT,APARTMENT,false,false,2223,,,,26,1170991,1839824,2002,03/30/2006 09:10:16 PM,41.715946207,-87.649347933,"(41.715946207, -87.649347933)"
-1927698,HH112342,01/07/2002 02:00:00 PM,068XX S DORCHESTER AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0321,,5,43,07,,,2002,03/30/2006 09:10:16 PM,,,
-1931080,HH109654,01/06/2002 07:30:00 AM,053XX W WASHINGTON BL,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,RESIDENCE,false,false,1523,,,,04B,1140792,1900275,2002,03/30/2006 09:10:16 PM,41.882438932,-87.758472744,"(41.882438932, -87.758472744)"
-1937338,HH108289,01/05/2002 01:39:13 PM,020XX W 79 ST,0460,BATTERY,SIMPLE,SIDEWALK,true,false,0611,,,,08B,1163945,1852215,2002,03/30/2006 09:10:16 PM,41.750099735,-87.674806765,"(41.750099735, -87.674806765)"
-1924296,HH107650,01/05/2002 02:02:00 AM,107XX S PRAIRIE AV,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,0513,,,,14,1179786,1833541,2002,03/30/2006 09:10:16 PM,41.698508566,-87.617327759,"(41.698508566, -87.617327759)"
-1927211,HH107521,01/05/2002 12:45:23 AM,010XX N STATE ST,0820,THEFT,$500 AND UNDER,RESTAURANT,false,false,1824,,,,06,1176133,1907471,2002,12/04/2014 12:43:35 PM,41.9014622,-87.628483472,"(41.9014622, -87.628483472)"
-1923960,HH107516,01/05/2002 12:33:19 AM,027XX E 89 ST,2850,PUBLIC PEACE VIOLATION,BOMB THREAT,"SCHOOL, PUBLIC, BUILDING",false,false,0423,,,,26,1195939,1846527,2002,03/30/2006 09:10:16 PM,41.733759639,-87.557756078,"(41.733759639, -87.557756078)"
-1934822,HH106270,01/04/2002 11:30:00 AM,119XX S PERRY AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0522,,,,18,1177698,1825418,2002,03/30/2006 09:10:16 PM,41.676265231,-87.625217424,"(41.676265231, -87.625217424)"
-1923000,HH105192,01/03/2002 06:08:12 PM,075XX S RACINE AV,0320,ROBBERY,STRONGARM - NO WEAPON,LIBRARY,false,false,0612,,,,03,1169584,1854898,2002,03/30/2006 09:10:16 PM,41.757341953,-87.654065329,"(41.757341953, -87.654065329)"
-1927747,HH104305,01/03/2002 10:53:07 AM,055XX S STATE ST,0820,THEFT,$500 AND UNDER,ALLEY,true,false,0233,,,,06,1177175,1868400,2002,12/04/2014 12:43:35 PM,41.794225033,-87.625838453,"(41.794225033, -87.625838453)"
-1926462,HH104294,01/03/2002 02:30:00 AM,133XX S GREENWOOD AV,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0533,,,,14,1186094,1817585,2002,03/30/2006 09:10:16 PM,41.654576927,-87.594732006,"(41.654576927, -87.594732006)"
-1929327,HH101791,01/01/2002 10:37:18 PM,008XX N TRUMBULL AV,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1121,,,,18,1153265,1905401,2002,03/30/2006 09:10:16 PM,41.896266785,-87.712535152,"(41.896266785, -87.712535152)"
-1918164,HH102049,01/01/2002 08:00:00 PM,033XX N TROY ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1733,,,,07,1154824,1922256,2002,03/30/2006 09:10:16 PM,41.942487198,-87.706356627,"(41.942487198, -87.706356627)"
-1919094,HH100191,01/01/2002 01:45:00 AM,011XX W ADDISON ST,0460,BATTERY,SIMPLE,STREET,false,false,1923,,,,08B,1167782,1924015,2002,03/30/2006 09:10:16 PM,41.947044016,-87.658679259,"(41.947044016, -87.658679259)"
-1922722,HH100993,12/31/2001 07:00:00 PM,069XX S EBERHART AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0322,,,,14,1180743,1858862,2001,03/31/2006 10:03:38 PM,41.767970523,-87.613047837,"(41.767970523, -87.613047837)"
-1926731,G779506,12/31/2001 05:00:00 PM,022XX S MILLARD AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1013,,,,18,1152341,1889066,2001,03/31/2006 10:03:38 PM,41.851460057,-87.716360095,"(41.851460057, -87.716360095)"
-1916665,G777150,12/30/2001 06:50:00 AM,059XX W FULTON ST,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1512,,,,08B,1136443,1901352,2001,03/31/2006 10:03:38 PM,41.885473193,-87.774416837,"(41.885473193, -87.774416837)"
-1916558,G775231,12/29/2001 01:56:45 AM,025XX N HALSTED ST,0460,BATTERY,SIMPLE,STREET,true,false,1933,,,,08B,1170459,1917427,2001,03/31/2006 10:03:38 PM,41.928908025,-87.649032717,"(41.928908025, -87.649032717)"
-1929325,G774728,12/28/2001 07:40:00 PM,016XX S DRAKE AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1021,,,,18,1152928,1891779,2001,03/31/2006 10:03:38 PM,41.858893253,-87.714133843,"(41.858893253, -87.714133843)"
-1913832,G774490,12/28/2001 07:00:00 AM,042XX W MELROSE ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),true,false,1731,,,,07,1147566,1921310,2001,03/31/2006 10:03:38 PM,41.940033869,-87.733057669,"(41.940033869, -87.733057669)"
-1915357,G777228,12/28/2001 03:30:00 AM,021XX E 67 ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0331,,,,26,1191438,1861588,2001,03/31/2006 10:03:38 PM,41.775198414,-87.573758103,"(41.775198414, -87.573758103)"
-1921225,G768819,12/25/2001 08:35:00 AM,038XX W GLADYS AV,2027,NARCOTICS,POSS: CRACK,STREET,true,false,1133,,,,18,1150701,1898117,2001,03/31/2006 10:03:38 PM,41.876329238,-87.722142838,"(41.876329238, -87.722142838)"
-1910451,G768746,12/25/2001 04:45:00 AM,025XX W MONTROSE AV,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,true,false,1912,,,,14,1158644,1929096,2001,03/31/2006 10:03:38 PM,41.961179034,-87.69212825,"(41.961179034, -87.69212825)"
-1907035,G764683,12/22/2001 03:30:00 PM,028XX W 22 PL,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1033,,,,05,1157268,1889288,2001,03/31/2006 10:03:38 PM,41.851970684,-87.698270712,"(41.851970684, -87.698270712)"
-1907811,G764871,12/22/2001 02:30:00 PM,007XX S LOOMIS ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,1213,,,,05,1167203,1896820,2001,03/31/2006 10:03:38 PM,41.872431725,-87.661590344,"(41.872431725, -87.661590344)"
-1906057,G759775,12/20/2001 07:00:00 AM,052XX W AINSLIE ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1623,,,,05,1140549,1932097,2001,03/31/2006 10:03:38 PM,41.969766324,-87.75858181,"(41.969766324, -87.75858181)"
-1906495,G763715,12/20/2001 03:00:00 AM,021XX N HALSTED ST,0820,THEFT,$500 AND UNDER,ALLEY,false,false,1812,,,,06,1170561,1914417,2001,12/04/2014 12:43:35 PM,41.920646206,-87.648746208,"(41.920646206, -87.648746208)"
-1903745,G757332,12/19/2001 07:05:00 AM,042XX S WENTWORTH AV,1310,CRIMINAL DAMAGE,TO PROPERTY,GAS STATION,false,false,0935,,,,14,1175608,1876478,2001,03/31/2006 10:03:38 PM,41.81642708,-87.631342603,"(41.81642708, -87.631342603)"
-1900910,G755086,12/18/2001 12:01:00 AM,068XX S THROOP ST,0560,ASSAULT,SIMPLE,APARTMENT,false,true,0724,,,,08A,1168802,1859253,2001,03/31/2006 10:03:38 PM,41.769309548,-87.656805719,"(41.769309548, -87.656805719)"
-1912792,G753924,12/17/2001 02:00:00 PM,005XX E BROWNING AV,2027,NARCOTICS,POSS: CRACK,CHA PARKING LOT/GROUNDS,true,false,0212,,,,18,1180944,1881467,2001,03/31/2006 10:03:38 PM,41.829995983,-87.611615407,"(41.829995983, -87.611615407)"
-1899677,G752437,12/16/2001 06:15:00 PM,054XX S HERMITAGE AV,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,SIDEWALK,false,false,0932,,,,04B,1165655,1868253,2001,03/31/2006 10:03:38 PM,41.794074045,-87.668085955,"(41.794074045, -87.668085955)"
-1898847,G751152,12/16/2001 04:00:00 AM,007XX N CLARK ST,0460,BATTERY,SIMPLE,STREET,false,true,1832,,,,08B,1175440,1905491,2001,03/31/2006 10:03:38 PM,41.896044576,-87.631088436,"(41.896044576, -87.631088436)"
-1897021,G750667,12/15/2001 09:10:00 PM,037XX W EDDY ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1732,,,,07,1150874,1923383,2001,03/31/2006 10:03:38 PM,41.945658137,-87.720845183,"(41.945658137, -87.720845183)"
-1895093,G744631,12/12/2001 12:00:00 AM,009XX N AVERS AV,0265,CRIM SEXUAL ASSAULT,AGGRAVATED: OTHER,RESIDENCE,true,false,1112,,,,02,1150512,1905866,2001,03/31/2006 10:03:38 PM,41.89759701,-87.722634304,"(41.89759701, -87.722634304)"
-1888162,G740077,12/10/2001 08:00:00 PM,019XX W BELLE PLAINE AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1923,,,,07,1162662,1927199,2001,03/31/2006 10:03:38 PM,41.95589012,-87.677409334,"(41.95589012, -87.677409334)"
-1889010,G739738,12/10/2001 04:40:00 PM,095XX S HALSTED ST,0820,THEFT,$500 AND UNDER,LIBRARY,false,false,2223,,,,06,1172690,1841813,2001,12/04/2014 12:43:35 PM,41.721367102,-87.64306695,"(41.721367102, -87.64306695)"
-1889742,G737830,12/09/2001 07:45:00 PM,002XX W 57 ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0711,,,,14,1175471,1867207,2001,03/31/2006 10:03:38 PM,41.790989632,-87.632122594,"(41.790989632, -87.632122594)"
-1887494,G737083,12/09/2001 11:49:00 AM,041XX W 26 ST,0460,BATTERY,SIMPLE,OTHER,false,false,1031,,,,08B,1149240,1886361,2001,03/31/2006 10:03:38 PM,41.844097767,-87.727811611,"(41.844097767, -87.727811611)"
-1892822,G736979,12/09/2001 10:30:00 AM,003XX N PINE AV,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,1523,,,,18,1139397,1901818,2001,03/31/2006 10:03:38 PM,41.886698649,-87.763557661,"(41.886698649, -87.763557661)"
-1885865,G736565,12/09/2001 02:00:00 AM,015XX S KILBOURN AV,0312,ROBBERY,ARMED:KNIFE/CUTTING INSTRUMENT,ALLEY,false,false,1012,,,,03,1146592,1892210,2001,03/31/2006 10:03:38 PM,41.860199007,-87.737380425,"(41.860199007, -87.737380425)"
-1889404,G735745,12/08/2001 04:00:00 PM,062XX S ASHLAND AV,0460,BATTERY,SIMPLE,APARTMENT,true,true,0713,,,,08B,1166785,1863137,2001,03/31/2006 10:03:38 PM,41.780011043,-87.664088323,"(41.780011043, -87.664088323)"
-1888256,G737232,12/08/2001 09:00:00 AM,031XX N SHERIDAN RD,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,2332,,,,05,1173067,1920908,2001,03/31/2006 10:03:38 PM,41.938402508,-87.639345693,"(41.938402508, -87.639345693)"
-1890458,G734532,12/07/2001 11:35:00 PM,005XX W SURF ST,0460,BATTERY,SIMPLE,RESIDENCE,true,true,2333,,,,08B,1172519,1919370,2001,03/31/2006 10:03:38 PM,41.934194332,-87.64140532,"(41.934194332, -87.64140532)"
-1884844,G733678,12/07/2001 09:30:00 AM,050XX S LAFLIN ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,0931,,,,05,1167153,1871113,2001,03/31/2006 10:03:38 PM,41.801890278,-87.662511017,"(41.801890278, -87.662511017)"
-2341919,HH640717,12/07/2001 12:00:00 AM,069XX S JUSTINE ST,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,RESIDENCE,false,false,0734,,17,67,06,1167241,1858752,2001,03/31/2006 10:03:38 PM,41.767968289,-87.662541949,"(41.767968289, -87.662541949)"
-1944521,HH131281,12/06/2001 12:00:00 AM,007XX E 111 ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0531,,,,06,1183305,1831462,2001,12/04/2014 12:43:35 PM,41.692722515,-87.604507439,"(41.692722515, -87.604507439)"
-1879787,G727025,12/04/2001 03:28:44 PM,0000X W JACKSON BL,1330,CRIMINAL TRESPASS,TO LAND,HOTEL/MOTEL,true,false,0112,,,,26,1175752,1898934,2001,03/31/2006 10:03:38 PM,41.878044792,-87.630139883,"(41.878044792, -87.630139883)"
-1883373,G727660,12/04/2001 02:00:00 AM,001XX W ERIE ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1832,,,,16,1174780,1904779,2001,03/31/2006 10:03:38 PM,41.894105608,-87.633533784,"(41.894105608, -87.633533784)"
-1881798,G726069,12/04/2001 01:00:00 AM,088XX S ESCANABA AV,0261,CRIM SEXUAL ASSAULT,AGGRAVATED: HANDGUN,APARTMENT,false,false,0423,,,,02,1196960,1847001,2001,03/31/2006 10:03:38 PM,41.735035026,-87.55399999,"(41.735035026, -87.55399999)"
-1873327,G719784,11/30/2001 09:50:00 PM,015XX W WALTON ST,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,1323,,,,08A,1166168,1906394,2001,03/31/2006 10:03:38 PM,41.898725675,-87.665116563,"(41.898725675, -87.665116563)"
-1877938,G719402,11/30/2001 06:30:00 PM,050XX N WINTHROP AV,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,2033,,,,08A,1167910,1934032,2001,03/31/2006 10:03:38 PM,41.97452826,-87.65791855,"(41.97452826, -87.65791855)"
-1874925,G722478,11/30/2001 05:00:00 PM,033XX S CARPENTER ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,0924,,,,14,1169827,1882367,2001,03/31/2006 10:03:38 PM,41.832714714,-87.652377335,"(41.832714714, -87.652377335)"
-1872243,G717136,11/29/2001 05:55:00 PM,049XX W HUBBARD ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,RESIDENCE PORCH/HALLWAY,false,true,1532,,,,04A,1143091,1902513,2001,03/31/2006 10:03:38 PM,41.888537705,-87.749974806,"(41.888537705, -87.749974806)"
-1876916,G714200,11/28/2001 01:10:00 PM,004XX S CLARK ST,1200,DECEPTIVE PRACTICE,STOLEN PROP: BUY/RECEIVE/POS.,RESTAURANT,true,false,0131,,,,13,1175552,1898418,2001,06/02/2010 10:34:17 AM,41.876633351,-87.630889734,"(41.876633351, -87.630889734)"
-1868417,G713829,11/28/2001 06:00:00 AM,024XX W LITHUANIAN PLAZA CT,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0832,,,,08B,1161170,1858790,2001,03/31/2006 10:03:38 PM,41.768200339,-87.684794008,"(41.768200339, -87.684794008)"
-1872895,G712308,11/27/2001 02:45:04 PM,049XX W MADISON ST,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,1533,,,,18,1143660,1899613,2001,03/31/2006 10:03:38 PM,41.880569123,-87.747957852,"(41.880569123, -87.747957852)"
-1869720,G711666,11/26/2001 10:00:00 PM,012XX E 95 ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0511,,,,14,1186097,1842188,2001,03/31/2006 10:03:38 PM,41.722090662,-87.593948361,"(41.722090662, -87.593948361)"
-1869879,G709083,11/26/2001 01:53:18 AM,019XX N CLIFTON AV,2027,NARCOTICS,POSS: CRACK,STREET,true,false,1811,,,,18,1167904,1912466,2001,03/31/2006 10:03:38 PM,41.91535034,-87.658564917,"(41.91535034, -87.658564917)"
-1869754,G706831,11/24/2001 09:30:00 PM,050XX S HALSTED ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0935,,,,16,1171856,1871686,2001,03/31/2006 10:03:38 PM,41.803360634,-87.645246469,"(41.803360634, -87.645246469)"
-1861393,G704680,11/23/2001 09:30:00 PM,012XX S KEELER AV,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1011,,,,26,1148541,1893968,2001,03/31/2006 10:03:38 PM,41.864985827,-87.730180744,"(41.864985827, -87.730180744)"
-1861602,G704783,11/23/2001 06:30:00 PM,009XX W 18 PL,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,false,false,1233,,,,26,1170093,1891306,2001,03/31/2006 10:03:38 PM,41.857238325,-87.651140809,"(41.857238325, -87.651140809)"
-1862004,G703409,11/23/2001 10:30:00 AM,031XX W 26 ST,0560,ASSAULT,SIMPLE,STREET,true,false,1033,,,,08A,1155670,1886523,2001,03/31/2006 10:03:38 PM,41.844415486,-87.704210174,"(41.844415486, -87.704210174)"
-1861718,G702809,11/22/2001 09:19:56 PM,133XX S LANGLEY AV,0460,BATTERY,SIMPLE,CHA APARTMENT,false,false,0533,,,,08B,1183398,1816990,2001,03/31/2006 10:03:38 PM,41.653007124,-87.604615024,"(41.653007124, -87.604615024)"
-1858844,G700979,11/21/2001 06:00:00 PM,044XX N ROCKWELL ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1911,,,,26,1158204,1929316,2001,03/31/2006 10:03:38 PM,41.96179175,-87.69373989,"(41.96179175, -87.69373989)"
-1865034,G700764,11/21/2001 05:01:41 PM,025XX W VAN BUREN ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,true,false,1125,,,,26,1159628,1898123,2001,03/31/2006 10:03:38 PM,41.876166586,-87.689365619,"(41.876166586, -87.689365619)"
-1858779,G700803,11/21/2001 10:00:00 AM,054XX W ADAMS ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,1522,,,,26,1140000,1898833,2001,03/31/2006 10:03:38 PM,41.878496419,-87.761416308,"(41.878496419, -87.761416308)"
-1859292,G698644,11/20/2001 04:20:00 PM,042XX W IRVING PARK RD,1330,CRIMINAL TRESPASS,TO LAND,PARKING LOT/GARAGE(NON.RESID.),true,false,1731,,,,26,1147426,1926207,2001,03/31/2006 10:03:38 PM,41.953474334,-87.733446252,"(41.953474334, -87.733446252)"
-1864187,G696061,11/19/2001 01:19:52 PM,047XX N LAKE SHORE DR,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2312,,,,18,1170262,1931896,2001,03/31/2006 10:03:38 PM,41.96861581,-87.649332309,"(41.96861581, -87.649332309)"
-1864494,G694973,11/18/2001 09:05:00 PM,001XX S CICERO AV,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1533,,,,16,1144371,1898981,2001,03/31/2006 10:03:38 PM,41.878821501,-87.745362993,"(41.878821501, -87.745362993)"
-1867170,G693707,11/18/2001 10:00:00 AM,029XX W HARRISON ST,2027,NARCOTICS,POSS: CRACK,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,1135,,,,18,1156578,1897259,2001,03/31/2006 10:03:38 PM,41.873857932,-87.700587587,"(41.873857932, -87.700587587)"
-1857926,G693291,11/18/2001 02:34:00 AM,010XX N PULASKI RD,2027,NARCOTICS,POSS: CRACK,OTHER,true,false,1112,,,,18,1149573,1906556,2001,03/31/2006 10:03:38 PM,41.89950873,-87.726065231,"(41.89950873, -87.726065231)"
-1858604,G698318,11/17/2001 03:00:00 AM,011XX S MAYFIELD AV,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1513,,,,08B,1137192,1894253,2001,03/31/2006 10:03:38 PM,41.865979153,-87.771836864,"(41.865979153, -87.771836864)"
-1855308,G695633,11/16/2001 02:30:00 PM,099XX S CRANDON AV,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0431,,,,08B,1193433,1839450,2001,03/31/2006 10:03:38 PM,41.714401283,-87.56716741,"(41.714401283, -87.56716741)"
-1934641,HH119494,11/16/2001 09:00:00 AM,0000X S MICHIGAN AV,0840,THEFT,FINANCIAL ID THEFT: OVER $300,COMMERCIAL / BUSINESS OFFICE,false,false,0123,,,,06,1177277,1900263,2001,03/31/2006 10:03:38 PM,41.881657219,-87.624500207,"(41.881657219, -87.624500207)"
-1854763,G684284,11/13/2001 09:24:56 PM,039XX W VAN BUREN ST,2027,NARCOTICS,POSS: CRACK,OTHER,true,false,1132,,,,18,1149825,1897764,2001,03/31/2006 10:03:38 PM,41.875377639,-87.725368423,"(41.875377639, -87.725368423)"
-1842990,G681113,11/12/2001 12:00:00 PM,106XX S WALLACE ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2233,,,,26,1174157,1834104,2001,03/31/2006 10:03:38 PM,41.700180089,-87.637921668,"(41.700180089, -87.637921668)"
-1843867,G680895,11/12/2001 10:49:37 AM,005XX S WELLS ST,051A,ASSAULT,AGGRAVATED: HANDGUN,PARKING LOT/GARAGE(NON.RESID.),false,false,0131,,,,04A,1174845,1897849,2001,03/31/2006 10:03:38 PM,41.875087828,-87.633502621,"(41.875087828, -87.633502621)"
-1842114,G680208,11/11/2001 07:00:00 PM,026XX W DEVON AV,0820,THEFT,$500 AND UNDER,SMALL RETAIL STORE,false,false,2412,,,,06,1157511,1942409,2001,12/04/2014 12:43:35 PM,41.997733736,-87.695929551,"(41.997733736, -87.695929551)"
-1846837,G678863,11/11/2001 06:15:00 AM,036XX W LEXINGTON ST,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1133,,,,08B,1152073,1896409,2001,03/31/2006 10:03:38 PM,41.871615378,-87.717150299,"(41.871615378, -87.717150299)"
-1840732,G677713,11/10/2001 11:00:00 AM,016XX N CLARK ST,0810,THEFT,OVER $500,STREET,false,false,1814,,,,06,1175186,1911032,2001,12/04/2014 12:43:35 PM,41.91125505,-87.631854901,"(41.91125505, -87.631854901)"
-1842600,G679388,11/10/2001 03:00:00 AM,106XX S HOXIE AV,0810,THEFT,OVER $500,RESIDENCE,false,false,0434,,,,06,1195173,1834888,2001,12/04/2014 12:43:35 PM,41.701840041,-87.560944802,"(41.701840041, -87.560944802)"
-1846791,G676190,11/09/2001 08:30:00 PM,029XX S KING DR,2027,NARCOTICS,POSS: CRACK,STREET,true,false,2112,,,,18,1179243,1885818,2001,03/31/2006 10:03:38 PM,41.841974487,-87.617723321,"(41.841974487, -87.617723321)"
-1840283,G676975,11/09/2001 02:00:00 PM,092XX S HARPER AV,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0413,,,,26,1188019,1844085,2001,03/31/2006 10:03:38 PM,41.727250719,-87.58684821,"(41.727250719, -87.58684821)"
-1837881,G673109,11/08/2001 12:15:00 PM,060XX N BROADWAY,0820,THEFT,$500 AND UNDER,GROCERY FOOD STORE,true,false,2433,,,,06,1167254,1940095,2001,12/04/2014 12:43:35 PM,41.991179462,-87.660155617,"(41.991179462, -87.660155617)"
-1854855,G692499,11/08/2001 12:00:00 PM,022XX N JANSSEN AV,1110,DECEPTIVE PRACTICE,BOGUS CHECK,RESIDENCE,false,false,1811,,,,11,1166231,1915143,2001,03/31/2006 10:03:38 PM,41.922732143,-87.66463464,"(41.922732143, -87.66463464)"
-1836859,G671497,11/07/2001 05:38:13 PM,079XX S ASHLAND AV,0460,BATTERY,SIMPLE,STREET,false,true,0611,,,,08B,1167008,1852297,2001,03/31/2006 10:03:38 PM,41.750259875,-87.663580212,"(41.750259875, -87.663580212)"
-1841995,G677730,11/06/2001 10:00:00 AM,065XX S LOWE AV,0820,THEFT,$500 AND UNDER,APARTMENT,false,true,0723,,,,06,1173204,1861445,2001,12/04/2014 12:43:35 PM,41.775228502,-87.640605347,"(41.775228502, -87.640605347)"
-1833299,G667139,11/05/2001 04:50:00 PM,011XX S CENTRAL AV,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,STREET,true,false,1513,,,,04A,1139162,1894773,2001,03/31/2006 10:03:38 PM,41.867370519,-87.764592091,"(41.867370519, -87.764592091)"
-1840124,G676915,11/05/2001 04:00:00 PM,028XX S DR MARTN LUTHR KING JR DR,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2122,,,,26,1179412,1886291,2001,03/31/2006 10:03:38 PM,41.843268566,-87.617088675,"(41.843268566, -87.617088675)"
-1834994,G668209,11/05/2001 02:35:00 PM,014XX E 70 ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0332,,,,08B,1186923,1858906,2001,03/31/2006 10:03:38 PM,41.767947012,-87.590394191,"(41.767947012, -87.590394191)"
-1829714,G665298,11/04/2001 08:25:00 PM,019XX S CANALPORT AV,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,true,true,1233,,,,26,1172533,1891050,2001,03/31/2006 10:03:38 PM,41.856482294,-87.642192245,"(41.856482294, -87.642192245)"
-1830069,G665143,11/04/2001 08:00:00 PM,024XX W CORTLAND ST,051A,ASSAULT,AGGRAVATED: HANDGUN,ALLEY,false,false,1434,,,,04A,1159906,1912592,2001,03/31/2006 10:03:38 PM,41.915865007,-87.687945273,"(41.915865007, -87.687945273)"
-1833686,G665074,11/04/2001 05:30:00 PM,050XX N KILDARE AV,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,1712,,,,08A,1146770,1932914,2001,03/31/2006 10:03:38 PM,41.971891431,-87.73568582,"(41.971891431, -87.73568582)"
-1836633,G668738,11/02/2001 04:30:00 PM,036XX W OAKDALE AV,0820,THEFT,$500 AND UNDER,ALLEY,false,false,2523,,,,06,1151453,1919328,2001,12/04/2014 12:43:35 PM,41.934519524,-87.718823817,"(41.934519524, -87.718823817)"
-1827727,G659818,11/02/2001 10:55:09 AM,112XX S ST LAWRENCE AV,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0531,,,,08B,1182298,1830587,2001,03/31/2006 10:03:38 PM,41.690344719,-87.60822118,"(41.690344719, -87.60822118)"
-1826131,G659649,11/01/2001 05:00:00 PM,043XX N MOZART ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1724,,,,26,1156553,1928923,2001,03/31/2006 10:03:38 PM,41.960746991,-87.699820604,"(41.960746991, -87.699820604)"
-1832679,G658108,11/01/2001 02:20:00 PM,038XX W HURON ST,2012,NARCOTICS,MANU/DELIVER:COCAINE,STREET,true,false,1112,,,,18,1150663,1904440,2001,03/31/2006 10:03:38 PM,41.893680969,-87.722117007,"(41.893680969, -87.722117007)"
-1832591,G657759,11/01/2001 12:45:00 PM,003XX N KILBOURN AV,1821,NARCOTICS,MANU/DEL:CANNABIS 10GM OR LESS,STREET,true,false,1113,,,,18,1146296,1901386,2001,03/31/2006 10:03:38 PM,41.885384695,-87.738233451,"(41.885384695, -87.738233451)"
-1824699,G656960,11/01/2001 02:26:22 AM,020XX W 34 ST,1310,CRIMINAL DAMAGE,TO PROPERTY,CTA PLATFORM,true,false,0913,,,,14,1163327,1882168,2001,03/31/2006 10:03:38 PM,41.832307554,-87.676232471,"(41.832307554, -87.676232471)"
-1821134,G653500,10/30/2001 07:20:00 AM,037XX S ROCKWELL ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0913,,,,06,1159607,1879721,2001,12/04/2014 12:43:35 PM,41.825669974,-87.689948952,"(41.825669974, -87.689948952)"
-1825604,G651966,10/30/2001 12:45:00 AM,106XX S AVENUE C,2022,NARCOTICS,POSS: COCAINE,STREET,true,false,0432,,,,18,1204492,1834881,2001,03/31/2006 10:03:38 PM,41.701586418,-87.52682256,"(41.701586418, -87.52682256)"
-1816732,G648230,10/28/2001 09:45:00 AM,104XX S SPRINGFIELD AV,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,2211,,,,26,1152147,1834995,2001,03/31/2006 10:03:38 PM,41.703084597,-87.718491016,"(41.703084597, -87.718491016)"
-1825338,G659084,10/28/2001 02:45:00 AM,019XX N LACROSSE AV,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,2533,,,,26,1143784,1912630,2001,03/31/2006 10:03:38 PM,41.916286923,-87.747175965,"(41.916286923, -87.747175965)"
-1821177,G647726,10/28/2001 01:26:57 AM,044XX S PRAIRIE AV,0460,BATTERY,SIMPLE,STREET,false,false,0222,,,,08B,1178823,1875624,2001,03/31/2006 10:03:38 PM,41.814010955,-87.619575373,"(41.814010955, -87.619575373)"
-1816403,G647115,10/27/2001 04:55:00 PM,073XX N WESTERN AV,0810,THEFT,OVER $500,SMALL RETAIL STORE,false,false,2411,,,,06,1158975,1948604,2001,12/04/2014 12:43:35 PM,42.014702985,-87.690372872,"(42.014702985, -87.690372872)"
-1822741,G645454,10/26/2001 11:00:00 PM,019XX N AUSTIN AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2515,,,,14,1136062,1912044,2001,03/31/2006 10:03:38 PM,41.914820189,-87.775560689,"(41.914820189, -87.775560689)"
-1817872,G645088,10/26/2001 05:50:00 PM,001XX W CERMAK RD,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,true,false,2111,,,,11,1175600,1889797,2001,03/31/2006 10:03:38 PM,41.852975676,-87.630972464,"(41.852975676, -87.630972464)"
-1818588,G644563,10/26/2001 03:51:03 PM,003XX N ORLEANS ST,0810,THEFT,OVER $500,HOTEL/MOTEL,false,false,1831,,,,06,1173830,1902816,2001,12/04/2014 12:43:35 PM,41.888740233,-87.6370813,"(41.888740233, -87.6370813)"
-1815222,G643706,10/26/2001 08:39:19 AM,038XX N WESTERN AV,0460,BATTERY,SIMPLE,RESIDENCE,true,false,1912,,,,08B,1159722,1925707,2001,03/31/2006 10:03:38 PM,41.951857229,-87.688258735,"(41.951857229, -87.688258735)"
-1812389,G640651,10/24/2001 05:20:23 PM,026XX S HILLOCK AV,0460,BATTERY,SIMPLE,STREET,true,false,0923,,,,08B,1168591,1886921,2001,03/31/2006 10:03:38 PM,41.845238114,-87.656780789,"(41.845238114, -87.656780789)"
-1811303,G639682,10/24/2001 10:15:00 AM,018XX S KEDZIE AV,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,GROCERY FOOD STORE,false,false,1022,,,,04B,1155286,1890569,2001,03/31/2006 10:03:38 PM,41.855525887,-87.705510843,"(41.855525887, -87.705510843)"
-1809893,G639059,10/23/2001 11:15:00 PM,063XX S ASHLAND AV,0460,BATTERY,SIMPLE,STREET,true,false,0725,,,,08B,1166791,1862905,2001,03/31/2006 10:03:38 PM,41.779374277,-87.664072946,"(41.779374277, -87.664072946)"
-1810520,G638530,10/23/2001 06:30:00 PM,087XX S SAGINAW AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0423,,,,14,1195429,1847730,2001,03/31/2006 10:03:38 PM,41.737073369,-87.559584809,"(41.737073369, -87.559584809)"
-1818904,G637543,10/23/2001 11:45:42 AM,044XX S STATE ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,true,false,0221,,,,26,1176971,1875717,2001,03/31/2006 10:03:38 PM,41.814308166,-87.626365813,"(41.814308166, -87.626365813)"
-1810528,G637825,10/23/2001 11:30:00 AM,082XX S EXCHANGE AV,3960,INTIMIDATION,INTIMIDATION,RESIDENCE,true,false,0423,,,,26,1197221,1850710,2001,03/31/2006 10:03:38 PM,41.745206322,-87.552920592,"(41.745206322, -87.552920592)"
-1809226,G636529,10/22/2001 09:48:39 PM,053XX S PULASKI RD,0460,BATTERY,SIMPLE,HOTEL/MOTEL,false,false,0815,,,,08B,1150562,1869117,2001,03/31/2006 10:03:38 PM,41.796752162,-87.723409279,"(41.796752162, -87.723409279)"
-1809793,G636497,10/22/2001 09:27:06 PM,079XX S HERMITAGE AV,0460,BATTERY,SIMPLE,APARTMENT,false,true,0611,,,,08B,1166021,1851964,2001,03/31/2006 10:03:38 PM,41.749367102,-87.667206481,"(41.749367102, -87.667206481)"
-1807675,G636440,10/22/2001 08:39:51 PM,085XX S OGLESBY AV,0820,THEFT,$500 AND UNDER,OTHER,false,false,0412,,,,06,1193280,1848770,2001,12/04/2014 12:43:35 PM,41.739979969,-87.567424038,"(41.739979969, -87.567424038)"
-1807574,G635824,10/22/2001 03:44:34 PM,060XX S ST LAWRENCE AV,1310,CRIMINAL DAMAGE,TO PROPERTY,CHA HALLWAY/STAIRWELL/ELEVATOR,false,false,0313,,,,14,1181237,1864954,2001,03/31/2006 10:03:38 PM,41.784676196,-87.611049569,"(41.784676196, -87.611049569)"
-1812976,G635693,10/22/2001 03:27:21 PM,0000X E GARFIELD BL,0820,THEFT,$500 AND UNDER,RESTAURANT,false,false,0232,,,,06,1177970,1868613,2001,12/04/2014 12:43:35 PM,41.794791534,-87.622916789,"(41.794791534, -87.622916789)"
-1802983,G628497,10/19/2001 10:20:00 AM,032XX W ARMITAGE AV,0313,ROBBERY,ARMED: OTHER DANGEROUS WEAPON,SIDEWALK,false,false,1413,,,,03,1154480,1913147,2001,03/31/2006 10:03:38 PM,41.917498289,-87.707865224,"(41.917498289, -87.707865224)"
-1834773,G626614,10/18/2001 12:45:00 PM,110XX S VERNON AV,5000,OTHER OFFENSE,OTHER CRIME AGAINST PERSON,RESIDENCE,true,false,0513,,,,26,1181239,1832009,2001,03/31/2006 10:03:38 PM,41.69427128,-87.612054603,"(41.69427128, -87.612054603)"
-1800408,G626648,10/17/2001 07:30:00 PM,037XX W FULLERTON AV,1310,CRIMINAL DAMAGE,TO PROPERTY,SMALL RETAIL STORE,false,false,2524,,,,14,1150876,1915731,2001,03/31/2006 10:03:38 PM,41.924660379,-87.721038708,"(41.924660379, -87.721038708)"
-1800868,G627715,10/17/2001 03:00:00 PM,036XX N SHEFFIELD AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2324,,,,14,1168988,1924619,2001,03/31/2006 10:03:38 PM,41.948675282,-87.654228775,"(41.948675282, -87.654228775)"
-1807216,G624102,10/17/2001 09:30:00 AM,021XX E 87 ST,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0412,,,,08A,1191700,1847743,2001,03/31/2006 10:03:38 PM,41.737200229,-87.573246075,"(41.737200229, -87.573246075)"
-1805544,G623282,10/16/2001 10:30:00 PM,054XX W CHICAGO AV,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1524,,,,18,1139746,1904760,2001,03/31/2006 10:03:38 PM,41.894765496,-87.762204112,"(41.894765496, -87.762204112)"
-1797972,G621716,10/15/2001 09:00:00 PM,012XX S MILLARD AV,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,false,false,1011,,,,07,1152273,1894220,2001,03/31/2006 10:03:38 PM,41.865604575,-87.716473767,"(41.865604575, -87.716473767)"
-1794947,G616846,10/14/2001 02:06:07 AM,105XX S YATES AV,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,true,false,0434,,,,26,1194168,1835577,2001,03/31/2006 10:03:38 PM,41.703755403,-87.564602218,"(41.703755403, -87.564602218)"
-1791255,G614522,10/12/2001 09:00:00 PM,046XX N SHERIDAN RD,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2312,,,,14,1168742,1931173,2001,03/31/2006 10:03:38 PM,41.966665039,-87.654942303,"(41.966665039, -87.654942303)"
-1791191,G612633,10/12/2001 10:08:44 AM,045XX N SHERIDAN RD,0560,ASSAULT,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),true,false,2313,,,,08A,1168837,1930075,2001,03/31/2006 10:03:38 PM,41.963650027,-87.654624984,"(41.963650027, -87.654624984)"
-1794573,G612511,10/12/2001 09:07:01 AM,005XX E OAKWOOD BV,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0213,,,,05,1180579,1878757,2001,03/31/2006 10:03:38 PM,41.822567948,-87.61303794,"(41.822567948, -87.61303794)"
-1789589,G611060,10/11/2001 02:25:00 PM,068XX S HERMITAGE AV,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0725,,,,08B,1165813,1859566,2001,03/31/2006 10:03:38 PM,41.770232456,-87.667753148,"(41.770232456, -87.667753148)"
-1785823,G607662,10/10/2001 01:30:00 AM,015XX W ARMITAGE AV,0460,BATTERY,SIMPLE,BAR OR TAVERN,true,false,1433,,,,08B,1165736,1913418,2001,03/31/2006 10:03:38 PM,41.918009217,-87.666502709,"(41.918009217, -87.666502709)"
-1786434,G607016,10/09/2001 08:30:00 AM,005XX N LECLAIRE AV,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1532,,,,14,1142220,1903361,2001,03/31/2006 10:03:38 PM,41.890880923,-87.753152421,"(41.890880923, -87.753152421)"
-1783531,G604959,10/08/2001 09:00:00 PM,039XX N KIMBALL AV,0820,THEFT,$500 AND UNDER,OTHER,false,false,1733,,,,06,1153057,1926272,2001,12/04/2014 12:43:35 PM,41.953542673,-87.712744321,"(41.953542673, -87.712744321)"
-1785428,G605157,10/08/2001 08:00:00 PM,042XX W 76 ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),false,false,0833,,,,07,1149359,1853861,2001,03/31/2006 10:03:38 PM,41.754910559,-87.728214519,"(41.754910559, -87.728214519)"
-1782124,G602717,10/07/2001 08:40:00 PM,132XX S HOUSTON AV,1020,ARSON,BY FIRE,VEHICLE NON-COMMERCIAL,false,false,0433,,,,09,1198720,1817941,2001,03/31/2006 10:03:38 PM,41.655247677,-87.548522275,"(41.655247677, -87.548522275)"
-1781379,G602228,10/07/2001 03:33:59 PM,076XX S COLES AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,0421,,,,07,1196295,1855120,2001,03/31/2006 10:03:38 PM,41.757330687,-87.556167574,"(41.757330687, -87.556167574)"
-1781796,G601899,10/07/2001 12:32:00 PM,069XX S TALMAN AV,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,false,false,0831,,,,26,1159951,1858349,2001,03/31/2006 10:03:38 PM,41.767015307,-87.689274353,"(41.767015307, -87.689274353)"
-1780392,G599363,10/06/2001 04:25:29 AM,031XX W IRVING PARK RD,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,BAR OR TAVERN,false,false,1724,,,,04B,1154529,1926461,2001,03/31/2006 10:03:38 PM,41.954031924,-87.707328009,"(41.954031924, -87.707328009)"
-1785745,G604511,10/06/2001 03:00:00 AM,0000X N PINE AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1522,,,,07,1139537,1899956,2001,03/31/2006 10:03:38 PM,41.881586531,-87.763088974,"(41.881586531, -87.763088974)"
-1779504,G599183,10/06/2001 01:44:15 AM,009XX W WEED ST,1330,CRIMINAL TRESPASS,TO LAND,BAR OR TAVERN,true,false,1822,,,,26,1169930,1910423,2001,03/31/2006 10:03:38 PM,41.909700253,-87.651181335,"(41.909700253, -87.651181335)"
-1778328,G597612,10/05/2001 12:00:00 PM,022XX N KILDARE AV,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,2522,,,,08A,1147308,1914618,2001,03/31/2006 10:03:38 PM,41.921675365,-87.734177786,"(41.921675365, -87.734177786)"
-1784051,G597461,10/05/2001 11:15:00 AM,014XX N MONITOR AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,true,false,2531,,,,14,1137039,1909199,2001,03/31/2006 10:03:38 PM,41.906995679,-87.772039658,"(41.906995679, -87.772039658)"
-1785071,G596928,10/05/2001 02:10:00 AM,009XX N WOOD ST,2111,NARCOTICS,SALE/DEL HYPODERMIC NEEDLE,POLICE FACILITY/VEH PARKING LOT,true,false,1322,,,,26,1164261,1906447,2001,03/31/2006 10:03:38 PM,41.898911655,-87.672119356,"(41.898911655, -87.672119356)"
-1777482,G595907,10/04/2001 04:10:00 PM,076XX S KINGSTON AV,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0421,,,,04B,1194479,1854783,2001,03/31/2006 10:03:38 PM,41.756450758,-87.562833871,"(41.756450758, -87.562833871)"
-1774685,G591274,10/02/2001 04:15:00 PM,004XX N CENTRAL AV,2900,WEAPONS VIOLATION,UNLAWFUL USE/SALE AIR RIFLE,ALLEY,false,false,1523,,,,15,1138995,1902583,2001,03/31/2006 10:03:38 PM,41.888805221,-87.765015326,"(41.888805221, -87.765015326)"
-1773667,G590814,10/02/2001 01:24:00 PM,050XX S WINCHESTER AV,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0915,,,,04B,1164243,1871300,2001,03/31/2006 10:03:38 PM,41.802465254,-87.673177893,"(41.802465254, -87.673177893)"
-1774098,G589162,10/01/2001 06:25:00 PM,050XX N LEAVITT ST,0460,BATTERY,SIMPLE,PARK PROPERTY,false,false,2032,,,,08B,1160814,1933309,2001,03/31/2006 10:03:38 PM,41.972694872,-87.684032788,"(41.972694872, -87.684032788)"
-1772960,G588906,10/01/2001 04:06:00 PM,065XX S EBERHART AV,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0321,,,,08B,1180743,1861601,2001,03/31/2006 10:03:38 PM,41.775486614,-87.612963745,"(41.775486614, -87.612963745)"
-2016086,HH223636,10/01/2001 03:00:00 PM,002XX N LAPORTE AV,1751,OFFENSE INVOLVING CHILDREN,CRIM SEX ABUSE BY FAM MEMBER,RESIDENCE,false,false,1532,,,,20,1143294,1901194,2001,03/31/2006 10:03:38 PM,41.884914421,-87.749262277,"(41.884914421, -87.749262277)"
-1772323,G590679,10/01/2001 12:00:00 PM,053XX N LIEB AV,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,RESIDENCE,false,false,1623,,,,11,1139671,1935194,2001,03/31/2006 10:03:38 PM,41.978280869,-87.761734302,"(41.978280869, -87.761734302)"
-1776683,G586666,09/30/2001 05:11:01 PM,025XX N HARLEM AV,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,DEPARTMENT STORE,false,false,2512,,,,11,1127702,1916091,2001,03/31/2006 10:03:38 PM,41.92607077,-87.806183487,"(41.92607077, -87.806183487)"
-1796585,G586187,09/30/2001 11:44:27 AM,003XX S CICERO AV,0265,CRIM SEXUAL ASSAULT,AGGRAVATED: OTHER,ALLEY,false,false,1131,,,,02,1144475,1898232,2001,03/31/2006 10:03:38 PM,41.876764197,-87.744999973,"(41.876764197, -87.744999973)"
-1766614,G583010,09/28/2001 02:00:00 PM,010XX N LOCKWOOD AV,5001,OTHER OFFENSE,OTHER CRIME INVOLVING PROPERTY,STREET,false,false,1524,,,,26,1140786,1906680,2001,03/31/2006 10:03:38 PM,41.900015144,-87.758337168,"(41.900015144, -87.758337168)"
-1766037,G580540,09/27/2001 07:41:35 PM,005XX N ARMOUR ST,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,true,false,1324,,,,04B,1166034,1903844,2001,03/31/2006 10:03:38 PM,41.891731154,-87.66568162,"(41.891731154, -87.66568162)"
-1772031,G579413,09/27/2001 10:30:00 AM,023XX E 103 ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0434,,,,18,1193689,1837107,2001,03/31/2006 10:03:38 PM,41.707965601,-87.566306289,"(41.707965601, -87.566306289)"
-1765041,G579160,09/27/2001 09:05:00 AM,025XX W CERMAK RD,0820,THEFT,$500 AND UNDER,GROCERY FOOD STORE,true,false,1023,,,,06,1159780,1889354,2001,12/04/2014 12:43:35 PM,41.852100446,-87.689049156,"(41.852100446, -87.689049156)"
-1770136,G578908,09/26/2001 05:00:00 AM,060XX S INDIANA AV,0810,THEFT,OVER $500,APARTMENT,false,true,0311,,,,06,1178586,1865240,2001,12/04/2014 12:43:35 PM,41.785521706,-87.620760457,"(41.785521706, -87.620760457)"
-1763412,G579113,09/25/2001 06:00:00 PM,022XX N CLYBOURN AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1811,,,,14,1165733,1915325,2001,03/31/2006 10:03:38 PM,41.923242204,-87.666459246,"(41.923242204, -87.666459246)"
-1764020,G575580,09/25/2001 10:30:00 AM,039XX S ARCHER AV,5001,OTHER OFFENSE,OTHER CRIME INVOLVING PROPERTY,"SCHOOL, PUBLIC, GROUNDS",false,false,0912,,,,26,1159574,1878714,2001,03/31/2006 10:03:38 PM,41.822907324,-87.690097681,"(41.822907324, -87.690097681)"
-1762315,G574322,09/24/2001 11:30:00 PM,030XX W TAYLOR ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1134,,,,14,1156417,1895594,2001,03/31/2006 10:03:38 PM,41.869292257,-87.701223721,"(41.869292257, -87.701223721)"
-1759557,G574620,09/24/2001 10:00:00 PM,020XX N KEYSTONE AV,0820,THEFT,$500 AND UNDER,STREET,false,false,2525,,,,06,1149098,1913016,2001,12/04/2014 12:43:35 PM,41.91724482,-87.727642369,"(41.91724482, -87.727642369)"
-1762546,G570313,09/23/2001 03:10:00 AM,055XX N WESTERN AV,2027,NARCOTICS,POSS: CRACK,STREET,true,false,2011,,,,18,1159298,1936513,2001,03/31/2006 10:03:38 PM,41.981518188,-87.689518871,"(41.981518188, -87.689518871)"
-1756082,G569705,09/21/2001 10:00:00 PM,016XX N MILWAUKEE AV,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,1434,,,,06,1162692,1910634,2001,12/04/2014 12:43:35 PM,41.910434132,-87.677764693,"(41.910434132, -87.677764693)"
-1753669,G565764,09/21/2001 01:50:00 AM,052XX S PAULINA ST,0330,ROBBERY,AGGRAVATED,TAXICAB,false,false,0932,,,,03,1165932,1870190,2001,03/31/2006 10:03:38 PM,41.799383519,-87.667015152,"(41.799383519, -87.667015152)"
-1752487,G563198,09/19/2001 08:15:00 PM,067XX S HONORE ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0726,,,,26,1165147,1859679,2001,03/31/2006 10:03:38 PM,41.77055666,-87.670191243,"(41.77055666, -87.670191243)"
-1759964,G562782,09/19/2001 05:59:00 PM,002XX S WACKER DR,2850,PUBLIC PEACE VIOLATION,BOMB THREAT,COMMERCIAL / BUSINESS OFFICE,false,false,0112,,,,26,1174011,1899107,2001,03/31/2006 10:03:38 PM,41.87855848,-87.636527202,"(41.87855848, -87.636527202)"
-1764152,G561837,09/19/2001 10:56:29 AM,057XX S HALSTED ST,0820,THEFT,$500 AND UNDER,OTHER,false,false,0712,,,,06,1171915,1866573,2001,12/04/2014 12:43:35 PM,41.789328716,-87.6451802,"(41.789328716, -87.6451802)"
-1750154,G561271,09/19/2001 01:55:55 AM,091XX S PRINCETON AV,031A,ROBBERY,ARMED: HANDGUN,RESIDENCE,false,false,0634,,,,03,1176349,1844573,2001,03/31/2006 10:03:38 PM,41.728859619,-87.629582144,"(41.728859619, -87.629582144)"
-1754041,G562076,09/19/2001 12:00:00 AM,012XX W 83 ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0613,,,,07,1169374,1849699,2001,03/31/2006 10:03:38 PM,41.743079741,-87.654985168,"(41.743079741, -87.654985168)"
-1749975,G559817,09/18/2001 12:25:00 PM,058XX W ADDISON ST,0320,ROBBERY,STRONGARM - NO WEAPON,ALLEY,true,false,1633,,,,03,1136450,1923295,2001,03/31/2006 10:03:38 PM,41.945687246,-87.773865485,"(41.945687246, -87.773865485)"
-1749773,G559559,09/18/2001 10:32:00 AM,078XX S SOUTH SHORE DR,0460,BATTERY,SIMPLE,STREET,false,false,0421,,,,08B,1197550,1854323,2001,03/31/2006 10:03:38 PM,41.75511246,-87.551594828,"(41.75511246, -87.551594828)"
-1747940,G559425,09/18/2001 12:00:00 AM,067XX S CONSTANCE AV,0810,THEFT,OVER $500,STREET,false,false,0332,,,,06,1189735,1860805,2001,12/04/2014 12:43:35 PM,41.773090906,-87.580026119,"(41.773090906, -87.580026119)"
-1746279,G557066,09/17/2001 07:48:10 AM,023XX W HURON ST,1563,SEX OFFENSE,CRIMINAL SEXUAL ABUSE,STREET,false,false,1313,,,,17,1160615,1904570,2001,03/31/2006 10:03:38 PM,41.893837342,-87.685563011,"(41.893837342, -87.685563011)"
-1748381,G558137,09/17/2001 07:10:00 AM,064XX S KING DR,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0312,,,,14,1179981,1862609,2001,03/31/2006 10:03:38 PM,41.778270148,-87.615726292,"(41.778270148, -87.615726292)"
-1748329,G557405,09/16/2001 05:00:00 PM,020XX E 71 ST,0610,BURGLARY,FORCIBLE ENTRY,SMALL RETAIL STORE,false,false,0331,,,,05,1191064,1858364,2001,03/31/2006 10:03:38 PM,41.766360564,-87.575233291,"(41.766360564, -87.575233291)"
-1745014,G555710,09/16/2001 01:30:00 PM,112XX S LANGLEY AV,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0531,,,,26,1182953,1830800,2001,03/31/2006 10:03:38 PM,41.690914064,-87.605816632,"(41.690914064, -87.605816632)"
-1750385,G554160,09/15/2001 04:31:19 PM,006XX N DEARBORN ST,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,BAR OR TAVERN,true,false,1832,,,,05,1175786,1904515,2001,03/31/2006 10:03:38 PM,41.893358604,-87.629847054,"(41.893358604, -87.629847054)"
-1763121,G574187,09/15/2001 12:00:00 PM,100XX W OHARE ST,1330,CRIMINAL TRESPASS,TO LAND,AIRPORT/AIRCRAFT,false,false,1651,,,,26,1100635,1934208,2001,03/31/2006 10:03:38 PM,41.976200173,-87.905312411,"(41.976200173, -87.905312411)"
-1743763,G552816,09/15/2001 01:15:00 AM,004XX W OAK ST,0460,BATTERY,SIMPLE,STREET,false,false,1823,,,,08B,1173075,1907110,2001,03/31/2006 10:03:38 PM,41.900539992,-87.639726439,"(41.900539992, -87.639726439)"
-1801088,G551619,09/14/2001 02:06:00 PM,015XX W OHIO ST,2012,NARCOTICS,MANU/DELIVER:COCAINE,RESIDENCE,true,false,1324,,,,18,1165932,1904143,2001,03/31/2006 10:03:38 PM,41.89255381,-87.666047679,"(41.89255381, -87.666047679)"
-1747316,G551261,09/14/2001 11:00:50 AM,012XX W ROOSEVELT RD,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA APARTMENT,true,false,1231,,,,26,1168492,1894888,2001,03/31/2006 10:03:38 PM,41.867102385,-87.656913773,"(41.867102385, -87.656913773)"
-1741470,G548171,09/12/2001 08:03:48 PM,058XX S RICHMOND ST,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,0824,,,,08A,1157744,1866008,2001,03/31/2006 10:03:38 PM,41.788077782,-87.697156283,"(41.788077782, -87.697156283)"
-1759544,G547549,09/12/2001 02:50:00 PM,050XX W BELMONT AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1634,,,,18,1141920,1920839,2001,03/31/2006 10:03:38 PM,41.938848014,-87.753820467,"(41.938848014, -87.753820467)"
-1736991,G545383,09/10/2001 06:00:00 PM,018XX N LUNA AV,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,RESIDENCE,true,false,2532,,,,11,1139064,1911766,2001,03/31/2006 10:03:38 PM,41.914003222,-87.764538331,"(41.914003222, -87.764538331)"
-1777743,G543076,09/10/2001 01:15:00 PM,016XX W 57 ST,1822,NARCOTICS,MANU/DEL:CANNABIS OVER 10 GMS,VEHICLE NON-COMMERCIAL,true,false,0715,,,,18,1166159,1866945,2001,03/31/2006 10:03:38 PM,41.790474017,-87.666275026,"(41.790474017, -87.666275026)"
-1732875,G541737,09/09/2001 02:00:00 AM,039XX N LINCOLN AV,0460,BATTERY,SIMPLE,RESTAURANT,false,false,1923,,,,08B,1162332,1926419,2001,03/31/2006 10:03:38 PM,41.953756685,-87.678644393,"(41.953756685, -87.678644393)"
-1810666,G639830,09/08/2001 04:00:00 PM,045XX N MENARD AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1622,,,,07,1136823,1929423,2001,03/31/2006 10:03:38 PM,41.962496393,-87.772347004,"(41.962496393, -87.772347004)"
-1752841,G564478,09/07/2001 10:30:00 AM,061XX N LINCOLN AV,1110,DECEPTIVE PRACTICE,BOGUS CHECK,BANK,false,false,1711,,,,11,1152928,1940868,2001,03/31/2006 10:03:38 PM,41.993597533,-87.712829897,"(41.993597533, -87.712829897)"
-1732377,G540582,09/07/2001 03:00:00 AM,016XX N LA SALLE DR,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,1814,,,,14,1174789,1911276,2001,03/31/2006 10:03:38 PM,41.911933498,-87.633306015,"(41.911933498, -87.633306015)"
-1731293,G533426,09/05/2001 10:00:00 PM,091XX S ASHLAND AV,0820,THEFT,$500 AND UNDER,TAVERN/LIQUOR STORE,false,false,2221,,,,06,1167229,1844328,2001,12/04/2014 12:43:35 PM,41.72838706,-87.662997829,"(41.72838706, -87.662997829)"
-1727328,G532592,09/05/2001 08:13:13 PM,006XX N LA SALLE ST,0810,THEFT,OVER $500,SMALL RETAIL STORE,false,false,1832,,,,06,1174987,1904437,2001,12/04/2014 12:43:35 PM,41.893162507,-87.632783802,"(41.893162507, -87.632783802)"
-1725332,G530452,09/04/2001 09:00:00 PM,074XX S STONY ISLAND AV,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,GAS STATION,false,false,0324,,,,04B,1188069,1855700,2001,03/31/2006 10:03:38 PM,41.759122225,-87.586295695,"(41.759122225, -87.586295695)"
-1769971,G586879,09/04/2001 06:00:00 PM,008XX N MENARD AV,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,false,false,1511,,,,26,1137579,1905164,2001,03/31/2006 10:03:38 PM,41.89591343,-87.770153284,"(41.89591343, -87.770153284)"
-1725614,G531455,09/01/2001 10:30:00 PM,064XX S HERMITAGE AV,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0725,,,,26,1165736,1861971,2001,03/31/2006 10:03:38 PM,41.776833727,-87.667967194,"(41.776833727, -87.667967194)"
-1721331,G524586,09/01/2001 08:30:00 PM,012XX W DIVERSEY PW,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,DRIVEWAY - RESIDENTIAL,false,false,1931,,,,07,1167565,1918767,2001,03/31/2006 10:03:38 PM,41.932647941,-87.659628489,"(41.932647941, -87.659628489)"
-1727232,G522352,08/31/2001 10:45:00 PM,006XX N LARAMIE AV,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1524,,,,18,1141539,1903780,2001,03/31/2006 10:03:38 PM,41.892043318,-87.755643066,"(41.892043318, -87.755643066)"
-1720900,G522202,08/31/2001 08:45:00 PM,008XX W GARFIELD BL,0820,THEFT,$500 AND UNDER,GAS STATION,false,false,0712,,,,06,1171894,1868214,2001,12/04/2014 12:43:35 PM,41.793832261,-87.645209039,"(41.793832261, -87.645209039)"
-2166278,HH418605,08/31/2001 12:00:00 PM,106XX S COTTAGE GROVE AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,0512,,9,50,06,1182360,1834392,2001,03/31/2006 10:03:38 PM,41.700784716,-87.607876869,"(41.700784716, -87.607876869)"
-1739989,G520846,08/31/2001 10:10:00 AM,022XX S STATE ST,2093,NARCOTICS,FOUND SUSPECT NARCOTICS,CHA PARKING LOT/GROUNDS,true,false,2111,,,,26,1176630,1889325,2001,03/31/2006 10:03:38 PM,41.851657294,-87.627206314,"(41.851657294, -87.627206314)"
-1771905,G587690,08/31/2001 08:00:00 AM,014XX N OGDEN AV,0810,THEFT,OVER $500,"SCHOOL, PUBLIC, GROUNDS",false,false,1822,,,,06,1171431,1909712,2001,12/04/2014 12:43:35 PM,41.907716342,-87.645688276,"(41.907716342, -87.645688276)"
-1718365,G521303,08/30/2001 04:00:00 PM,054XX S LAKE PARK AV,0820,THEFT,$500 AND UNDER,SMALL RETAIL STORE,false,false,2132,,,,06,1187645,1869651,2001,12/04/2014 12:43:35 PM,41.797415003,-87.587406103,"(41.797415003, -87.587406103)"
-1725728,G518601,08/30/2001 10:30:00 AM,0000X W MAPLE ST,2024,NARCOTICS,POSS: HEROIN(WHITE),OTHER,true,false,1824,,,,18,1175987,1907660,2001,03/31/2006 10:03:38 PM,41.901984115,-87.629014039,"(41.901984115, -87.629014039)"
-1725451,G518194,08/30/2001 05:50:00 AM,001XX N WALLER AV,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),STREET,true,false,1512,,,,18,1138089,1900692,2001,03/31/2006 10:03:38 PM,41.88363249,-87.768388272,"(41.88363249, -87.768388272)"
-1724895,G519665,08/29/2001 06:00:00 PM,004XX W OAK ST,1310,CRIMINAL DAMAGE,TO PROPERTY,CHA PARKING LOT/GROUNDS,false,false,1823,,,,14,1173075,1907110,2001,03/31/2006 10:03:38 PM,41.900539992,-87.639726439,"(41.900539992, -87.639726439)"
-1715061,G515169,08/28/2001 08:02:09 PM,042XX N CICERO AV,1570,SEX OFFENSE,PUBLIC INDECENCY,STREET,true,false,1624,,,,17,1143576,1927506,2001,03/31/2006 10:03:38 PM,41.957111979,-87.747566718,"(41.957111979, -87.747566718)"
-1713791,G514739,08/28/2001 04:36:37 PM,007XX W 51 ST,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,APARTMENT,true,false,0935,,,,26,1172259,1871092,2001,03/31/2006 10:03:38 PM,41.801721774,-87.643785956,"(41.801721774, -87.643785956)"
-1710904,G513686,08/27/2001 08:45:00 PM,054XX W HIRSCH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2532,,,,14,1139401,1908813,2001,03/31/2006 10:03:38 PM,41.905893709,-87.763372324,"(41.905893709, -87.763372324)"
-1725771,G531629,08/27/2001 01:00:00 PM,031XX N FRANCISCO AV,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1411,,,,14,1156463,1920533,2001,03/31/2006 10:03:38 PM,41.937726113,-87.700379288,"(41.937726113, -87.700379288)"
-1711278,G510146,08/26/2001 03:05:00 PM,065XX N BOSWORTH AV,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,OTHER,false,true,2432,,,,26,1164797,1943502,2001,03/31/2006 10:03:38 PM,42.000580999,-87.669095858,"(42.000580999, -87.669095858)"
-1717296,G519524,08/26/2001 12:00:00 PM,051XX N NEWLAND AV,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,RESIDENCE,false,false,1613,,,,06,1129233,1933833,2001,03/31/2006 10:03:38 PM,41.97473086,-87.800152109,"(41.97473086, -87.800152109)"
-1708487,G509782,08/26/2001 11:00:00 AM,002XX S MICHIGAN AV,0810,THEFT,OVER $500,COMMERCIAL / BUSINESS OFFICE,false,false,0123,,,,06,1177289,1899271,2001,12/04/2014 12:43:35 PM,41.878934844,-87.62448623,"(41.878934844, -87.62448623)"
-1708663,G508702,08/25/2001 11:10:00 PM,024XX W NORTH AV,0810,THEFT,OVER $500,STREET,false,false,1423,,,,06,1159770,1910524,2001,12/04/2014 12:43:35 PM,41.910193066,-87.68850205,"(41.910193066, -87.68850205)"
-1707062,G507781,08/25/2001 02:00:00 AM,063XX N CLAREMONT AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2413,,,,07,1159532,1942233,2001,03/31/2006 10:03:38 PM,41.997209263,-87.688499894,"(41.997209263, -87.688499894)"
-1706732,G505653,08/24/2001 04:04:07 PM,050XX S KEDZIE AV,0820,THEFT,$500 AND UNDER,DEPARTMENT STORE,true,false,0821,,,,06,1155864,1870748,2001,12/04/2014 12:43:35 PM,41.801122976,-87.703922316,"(41.801122976, -87.703922316)"
-1706394,G505365,08/24/2001 01:53:40 PM,004XX N LECLAIRE AV,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1532,,,,08B,1142243,1902686,2001,03/31/2006 10:03:38 PM,41.889028215,-87.753084716,"(41.889028215, -87.753084716)"
-1717866,G504544,08/23/2001 11:58:44 PM,025XX W DIVISION ST,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,1312,,,,18,1159224,1907852,2001,03/31/2006 10:03:38 PM,41.90287213,-87.690581393,"(41.90287213, -87.690581393)"
-1704898,G502607,08/23/2001 09:00:00 AM,014XX N ARTESIAN AV,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,OTHER,false,false,1423,,,,04A,1159855,1909393,2001,03/31/2006 10:03:38 PM,41.90708776,-87.688221042,"(41.90708776, -87.688221042)"
-1703644,G501712,08/21/2001 08:30:00 PM,010XX N LATROBE AV,0460,BATTERY,SIMPLE,SIDEWALK,false,true,1524,,,,08B,1141122,1906686,2001,03/31/2006 10:03:38 PM,41.900025422,-87.757102867,"(41.900025422, -87.757102867)"
-1703241,G498266,08/21/2001 10:41:59 AM,077XX S LOOMIS ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0612,,,,14,1168379,1853456,2001,03/31/2006 10:03:38 PM,41.753410934,-87.658522933,"(41.753410934, -87.658522933)"
-1700541,G497807,08/21/2001 01:30:00 AM,071XX S CONSTANCE AV,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0324,,,,03,1189567,1858027,2001,03/31/2006 10:03:38 PM,41.765471883,-87.580731063,"(41.765471883, -87.580731063)"
-1698535,G497907,08/21/2001 12:48:00 AM,032XX W 55 ST,0610,BURGLARY,FORCIBLE ENTRY,CLEANING STORE,false,false,0822,,,,05,1155891,1868013,2001,03/31/2006 10:03:38 PM,41.793617212,-87.703896741,"(41.793617212, -87.703896741)"
-1696760,G493432,08/18/2001 06:00:00 PM,024XX W MARQUETTE RD,0810,THEFT,OVER $500,OTHER,false,false,0832,,,,06,1161028,1860194,2001,12/04/2014 12:43:35 PM,41.772056052,-87.685275731,"(41.772056052, -87.685275731)"
-1695751,G492739,08/18/2001 05:00:00 PM,062XX S GREEN ST,0460,BATTERY,SIMPLE,STREET,false,true,0712,,,,08B,1171618,1863656,2001,03/31/2006 10:03:38 PM,41.781330655,-87.646354658,"(41.781330655, -87.646354658)"
-1694805,G492627,08/18/2001 11:00:00 AM,060XX N OLYMPIA AV,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1612,,,,26,1124365,1938937,2001,03/31/2006 10:03:38 PM,41.988818521,-87.817940739,"(41.988818521, -87.817940739)"
-1693967,G491586,08/18/2001 04:35:00 AM,008XX W SUNNYSIDE AV,0820,THEFT,$500 AND UNDER,ALLEY,false,false,2313,,,,06,1169910,1930047,2001,12/04/2014 12:43:35 PM,41.963549804,-87.650680758,"(41.963549804, -87.650680758)"
-1696550,G491400,08/18/2001 01:00:00 AM,024XX W CORTLAND ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,true,false,1434,,,,14,1159867,1912511,2001,03/31/2006 10:03:38 PM,41.915643542,-87.688090796,"(41.915643542, -87.688090796)"
-1694038,G490772,08/17/2001 07:40:37 PM,014XX W JONQUIL TR,031A,ROBBERY,ARMED: HANDGUN,RESIDENCE PORCH/HALLWAY,false,false,2422,,,,03,1165174,1950953,2001,03/31/2006 10:03:38 PM,42.02101864,-87.667495755,"(42.02101864, -87.667495755)"
-1703713,G489856,08/17/2001 12:55:00 PM,062XX S LOOMIS ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0713,,,,18,1168020,1863576,2001,03/31/2006 10:03:38 PM,41.781189248,-87.659548018,"(41.781189248, -87.659548018)"
-1694309,G489534,08/16/2001 08:30:00 PM,052XX S WOOD ST,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,RESIDENCE,true,false,0932,,,,20,1165205,1869599,2001,03/31/2006 10:03:38 PM,41.797777177,-87.669697982,"(41.797777177, -87.669697982)"
-1704926,G486392,08/15/2001 09:25:00 PM,030XX W MADISON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1331,,,,18,1156128,1899904,2001,03/31/2006 10:03:38 PM,41.881125168,-87.702168364,"(41.881125168, -87.702168364)"
-1690991,G484712,08/15/2001 07:30:00 AM,034XX W BERTEAU AV,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1723,,,,08B,1152576,1927741,2001,03/31/2006 10:03:38 PM,41.957583255,-87.714473535,"(41.957583255, -87.714473535)"
-1688074,G483618,08/14/2001 04:45:00 PM,028XX S KOLIN AV,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1031,,,,08B,1147806,1884990,2001,03/31/2006 10:03:38 PM,41.840363184,-87.733109346,"(41.840363184, -87.733109346)"
-1699402,G482238,08/14/2001 02:54:52 AM,016XX N SHEFFIELD AV,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,PARKING LOT/GARAGE(NON.RESID.),true,false,1811,,,,16,1169349,1911194,2001,03/31/2006 10:03:38 PM,41.911828581,-87.653293214,"(41.911828581, -87.653293214)"
-1697441,G481984,08/13/2001 10:01:10 PM,063XX S MORGAN ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0723,,,,18,1170773,1862767,2001,03/31/2006 10:03:38 PM,41.778909617,-87.649478529,"(41.778909617, -87.649478529)"
-1686253,G481109,08/13/2001 04:30:00 PM,066XX S MOZART ST,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,RESIDENCE,true,true,0831,,,,04B,1158561,1860489,2001,03/31/2006 10:03:38 PM,41.772916231,-87.694311025,"(41.772916231, -87.694311025)"
-1692096,G480476,08/13/2001 11:25:00 AM,068XX S CORNELL AV,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,0332,,,,08A,1188336,1860164,2001,03/31/2006 10:03:38 PM,41.771365461,-87.585174865,"(41.771365461, -87.585174865)"
-1684831,G478928,08/12/2001 03:51:23 PM,082XX S COTTAGE GROVE,1330,CRIMINAL TRESPASS,TO LAND,RESTAURANT,true,false,0631,,,,26,1183039,1850251,2001,03/31/2006 10:03:38 PM,41.744287981,-87.604899279,"(41.744287981, -87.604899279)"
-1682879,G477592,08/12/2001 12:23:40 AM,124XX S WABASH AV,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,RESIDENCE,false,false,0532,,,,05,1178798,1821992,2001,03/31/2006 10:03:38 PM,41.666838883,-87.621294792,"(41.666838883, -87.621294792)"
-1721115,G514100,08/11/2001 11:00:00 PM,034XX W 79 ST,1120,DECEPTIVE PRACTICE,FORGERY,STREET,false,false,0835,,,,10,1155065,1852077,2001,03/31/2006 10:03:38 PM,41.749902937,-87.707350976,"(41.749902937, -87.707350976)"
-1682770,G476880,08/11/2001 05:00:00 PM,028XX N LEAVITT ST,0460,BATTERY,SIMPLE,CHA PARKING LOT/GROUNDS,false,false,1913,,,,08B,1161662,1918838,2001,03/31/2006 10:03:38 PM,41.932968005,-87.681319366,"(41.932968005, -87.681319366)"
-1682478,G476648,08/11/2001 03:00:00 PM,040XX N MONITOR AV,0820,THEFT,$500 AND UNDER,RESIDENCE,false,false,1624,,,,06,1136588,1926278,2001,12/04/2014 12:43:35 PM,41.953870427,-87.773286591,"(41.953870427, -87.773286591)"
-1681166,G474385,08/10/2001 11:30:00 AM,005XX W DIVERSEY PW,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,2333,,,,14,1171993,1918829,2001,03/31/2006 10:03:38 PM,41.932721439,-87.643354345,"(41.932721439, -87.643354345)"
-1680276,G471205,08/09/2001 07:45:00 AM,133XX S VERNON AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0533,,,,14,1181517,1816776,2001,03/31/2006 10:03:38 PM,41.652463303,-87.611503969,"(41.652463303, -87.611503969)"
-1678332,G470429,08/08/2001 09:09:56 PM,0000X E GARFIELD BL,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0233,,,,08B,1177540,1868400,2001,03/31/2006 10:03:38 PM,41.794216782,-87.624500023,"(41.794216782, -87.624500023)"
-1677494,G470185,08/08/2001 07:00:00 AM,083XX S HOUSTON AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0424,,,,14,1197892,1850496,2001,03/31/2006 10:03:38 PM,41.744602364,-87.550469128,"(41.744602364, -87.550469128)"
-1704477,G468859,08/07/2001 07:45:00 PM,073XX S WOODLAWN AV,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0324,,,,05,1185444,1856628,2001,03/31/2006 10:03:38 PM,41.761730896,-87.595886919,"(41.761730896, -87.595886919)"
-1675273,G467587,08/07/2001 05:30:00 PM,025XX W LYNDALE ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1431,,,,06,1158746,1914939,2001,12/04/2014 12:43:35 PM,41.922329226,-87.692142569,"(41.922329226, -87.692142569)"
-1679889,G467967,08/07/2001 11:20:00 AM,001XX E RANDOLPH ST,0820,THEFT,$500 AND UNDER,COMMERCIAL / BUSINESS OFFICE,false,false,0124,,,,06,1177602,1901333,2001,12/04/2014 12:43:35 PM,41.884585984,-87.623274323,"(41.884585984, -87.623274323)"
-1671630,G463592,08/06/2001 01:18:00 AM,032XX W DIVISION ST,051A,ASSAULT,AGGRAVATED: HANDGUN,SIDEWALK,true,false,1121,,,,04A,1154531,1907758,2001,03/31/2006 10:03:38 PM,41.902709381,-87.707822237,"(41.902709381, -87.707822237)"
-1670089,G459723,08/04/2001 09:54:56 AM,085XX S MARQUETTE AV,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0423,,,,26,1195679,1849020,2001,03/31/2006 10:03:38 PM,41.740607064,-87.558626358,"(41.740607064, -87.558626358)"
-1670278,G459505,08/04/2001 12:30:00 AM,077XX W GREGORY ST,0460,BATTERY,SIMPLE,RESIDENCE,false,false,1613,,,,08B,1123697,1935958,2001,03/31/2006 10:03:38 PM,41.980654836,-87.820463397,"(41.980654836, -87.820463397)"
-1674912,G458943,08/03/2001 10:27:13 PM,074XX S COTTAGE GROVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,0323,,,,18,1182884,1855987,2001,03/31/2006 10:03:38 PM,41.760031768,-87.605289363,"(41.760031768, -87.605289363)"
-1676307,G458982,08/03/2001 10:00:00 PM,004XX W GARFIELD BL,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,STREET,true,false,0934,,,,18,1174192,1868498,2001,03/31/2006 10:03:38 PM,41.794560802,-87.636774019,"(41.794560802, -87.636774019)"
-1671558,G456801,08/03/2001 12:50:00 AM,069XX S HARVARD AV,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,false,false,0731,,,,04B,1175108,1859162,2001,03/31/2006 10:03:38 PM,41.768921394,-87.633693637,"(41.768921394, -87.633693637)"
-1667496,G456779,08/03/2001 12:13:00 AM,038XX W 13 ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,1011,,,,15,1151205,1893814,2001,03/31/2006 10:03:38 PM,41.864511456,-87.720405118,"(41.864511456, -87.720405118)"
-1672876,G465098,08/02/2001 12:00:00 PM,004XX W FULLERTON AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1814,,,,14,1173031,1916201,2001,03/31/2006 10:03:38 PM,41.925487114,-87.639617972,"(41.925487114, -87.639617972)"
-1664838,G454499,08/01/2001 10:50:00 PM,006XX W HARRISON ST,0820,THEFT,$500 AND UNDER,CTA BUS,false,false,0131,,,,06,1172038,1897598,2001,12/04/2014 12:43:35 PM,41.874461408,-87.6438161,"(41.874461408, -87.6438161)"
-1666944,G456034,08/01/2001 10:00:00 PM,055XX S ROCKWELL ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0824,,,,14,1159951,1867469,2001,03/31/2006 10:03:38 PM,41.792041866,-87.689023882,"(41.792041866, -87.689023882)"
-1666365,G455099,08/01/2001 07:00:00 PM,056XX N ST LOUIS AV,0820,THEFT,$500 AND UNDER,STREET,false,false,1711,,,,06,1152068,1937352,2001,12/04/2014 12:43:35 PM,41.983966495,-87.716086552,"(41.983966495, -87.716086552)"
-1669442,G457704,08/01/2001 10:00:00 AM,007XX N DEARBORN ST,0560,ASSAULT,SIMPLE,RESIDENCE,true,false,1832,,,,08A,1175757,1905627,2001,03/31/2006 10:03:38 PM,41.896410641,-87.629920078,"(41.896410641, -87.629920078)"
-1754010,G562646,07/30/2001 09:00:00 AM,039XX W IRVING PARK RD,1120,DECEPTIVE PRACTICE,FORGERY,OTHER,true,false,1723,,,,10,1149383,1926331,2001,03/31/2006 10:03:38 PM,41.953776784,-87.726248853,"(41.953776784, -87.726248853)"
-1673662,G455896,07/30/2001 08:15:00 AM,075XX S CHAMPLAIN AV,5001,OTHER OFFENSE,OTHER CRIME INVOLVING PROPERTY,STREET,true,false,0624,,,,26,1181836,1854917,2001,03/31/2006 10:03:38 PM,41.757119851,-87.609163264,"(41.757119851, -87.609163264)"
-1661908,G447638,07/30/2001 12:26:16 AM,007XX S KENNETH AV,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,1131,,,,14,1146846,1896250,2001,03/31/2006 10:03:38 PM,41.871280424,-87.736344919,"(41.871280424, -87.736344919)"
-1663406,G449043,07/29/2001 11:50:00 PM,034XX N LARAMIE AV,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1634,,,,08B,1141056,1922194,2001,03/31/2006 10:03:38 PM,41.94258225,-87.756962442,"(41.94258225, -87.756962442)"
-1657484,G444845,07/28/2001 05:00:00 PM,079XX S ESCANABA AV,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0422,,,,08B,1196841,1852823,2001,03/31/2006 10:03:38 PM,41.751014001,-87.55424284,"(41.751014001, -87.55424284)"
-1658188,G443387,07/28/2001 12:20:31 AM,038XX W ARMITAGE AV,0340,ROBBERY,ATTEMPT: STRONGARM-NO WEAPON,SIDEWALK,true,false,2525,,,,03,1150642,1913057,2001,03/31/2006 10:03:38 PM,41.917327265,-87.72196858,"(41.917327265, -87.72196858)"
-1661272,G441719,07/27/2001 10:37:00 AM,026XX E 75 ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0334,,,,06,1195324,1855881,2001,12/04/2014 12:43:35 PM,41.759442942,-87.55970099,"(41.759442942, -87.55970099)"
-1657517,G440948,07/26/2001 10:30:00 PM,047XX S ELLIS AV,0460,BATTERY,SIMPLE,RESIDENCE,true,true,2124,,,,08B,1183756,1873774,2001,03/31/2006 10:03:38 PM,41.808820558,-87.601538692,"(41.808820558, -87.601538692)"
-1663027,G439105,07/26/2001 08:07:37 AM,001XX W 87 ST,0810,THEFT,OVER $500,GROCERY FOOD STORE,true,false,0622,,,,06,1176895,1847317,2001,12/04/2014 12:43:35 PM,41.736377238,-87.627499596,"(41.736377238, -87.627499596)"
-1676405,G439282,07/26/2001 06:30:00 AM,004XX W 38 ST,1710,OFFENSE INVOLVING CHILDREN,ENDANGERING LIFE/HEALTH CHILD,RESIDENCE,false,false,0925,,,,26,1173661,1879684,2001,03/31/2006 10:03:38 PM,41.825268064,-87.638389483,"(41.825268064, -87.638389483)"
-1656700,G444229,07/26/2001 12:00:00 AM,053XX N SHERIDAN RD,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,RESIDENCE,true,false,2023,,,,07,1168637,1935631,2001,03/31/2006 10:03:38 PM,41.9789002,-87.65519862,"(41.9789002, -87.65519862)"
-1850014,G438298,07/25/2001 07:31:00 PM,036XX S KING DR,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0211,,,,14,1179417,1880820,2001,03/31/2006 10:03:38 PM,41.828255632,-87.617237703,"(41.828255632, -87.617237703)"
-1652070,G439109,07/25/2001 07:30:00 PM,064XX S KENWOOD AV,0820,THEFT,$500 AND UNDER,STREET,false,false,0314,,,,06,1186142,1862339,2001,12/04/2014 12:43:35 PM,41.777385942,-87.593148575,"(41.777385942, -87.593148575)"
-1651588,G438313,07/25/2001 07:00:00 PM,003XX S KILBOURN AV,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1131,,,,26,1146412,1897797,2001,03/31/2006 10:03:38 PM,41.87553385,-87.737898913,"(41.87553385, -87.737898913)"
-1763290,G438197,07/25/2001 06:30:00 PM,024XX S STATE ST,2093,NARCOTICS,FOUND SUSPECT NARCOTICS,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,2113,,,,26,1176662,1888120,2001,03/31/2006 10:03:38 PM,41.848349966,-87.627125238,"(41.848349966, -87.627125238)"
-1651044,G432781,07/23/2001 03:45:00 PM,001XX E ERIE ST,0850,THEFT,ATTEMPT THEFT,RESTAURANT,false,false,1834,,,,06,1177543,1904778,2001,03/31/2006 10:03:38 PM,41.894040577,-87.62338631,"(41.894040577, -87.62338631)"
-1645916,G432008,07/22/2001 03:00:00 AM,001XX E WALTON ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,1833,,,,07,1177362,1906991,2001,03/31/2006 10:03:38 PM,41.900117261,-87.623983858,"(41.900117261, -87.623983858)"
-1650357,G428281,07/21/2001 01:30:00 PM,013XX N LARRABEE ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA APARTMENT,true,false,1822,,,,26,1172048,1909151,2001,03/31/2006 10:03:38 PM,41.906163332,-87.643438342,"(41.906163332, -87.643438342)"
-1643618,G427541,07/21/2001 05:30:00 AM,064XX N RIDGE BL,031A,ROBBERY,ARMED: HANDGUN,OTHER,false,false,2412,,,,03,1162572,1942776,2001,03/31/2006 10:03:38 PM,41.998635907,-87.677301636,"(41.998635907, -87.677301636)"
-1652451,G427020,07/20/2001 11:03:45 PM,002XX E 48 ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0224,,,,16,1178629,1873291,2001,03/31/2006 10:03:38 PM,41.807613422,-87.620357973,"(41.807613422, -87.620357973)"
-1646995,G425712,07/20/2001 12:15:00 PM,057XX W WASHINGTON BL,0460,BATTERY,SIMPLE,STREET,false,true,1512,,,,08B,1138123,1900207,2001,03/31/2006 10:03:38 PM,41.882300973,-87.768275146,"(41.882300973, -87.768275146)"
-1665491,G425391,07/20/2001 10:15:00 AM,008XX W WINDSOR AV,1310,CRIMINAL DAMAGE,TO PROPERTY,SIDEWALK,true,false,2313,,,,14,1169982,1930437,2001,03/31/2006 10:03:38 PM,41.964618401,-87.650404611,"(41.964618401, -87.650404611)"
-1641775,G424262,07/19/2001 06:45:00 PM,114XX S MICHIGAN AV,0820,THEFT,$500 AND UNDER,STREET,false,false,0531,,,,06,1178800,1829346,2001,12/04/2014 12:43:35 PM,41.687019323,-87.621064995,"(41.687019323, -87.621064995)"
-1760534,G420249,07/18/2001 12:37:00 AM,012XX W GREENLEAF AV,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,RESIDENCE,true,false,2423,,,,18,1166995,1947064,2001,03/31/2006 10:03:38 PM,42.010308111,-87.66090697,"(42.010308111, -87.66090697)"
-1636958,G419676,07/17/2001 07:30:00 PM,085XX S MARSHFIELD AV,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0614,,,,26,1166875,1847839,2001,03/31/2006 10:03:38 PM,41.738029324,-87.664194616,"(41.738029324, -87.664194616)"
-1635351,G415302,07/15/2001 11:30:00 PM,103XX S EWING AV,0460,BATTERY,SIMPLE,STREET,false,false,0432,,,,08B,1202121,1837014,2001,03/31/2006 10:03:38 PM,41.70750015,-87.535431816,"(41.70750015, -87.535431816)"
-1647014,G414016,07/15/2001 01:00:00 AM,080XX S CHAMPLAIN AV,1320,CRIMINAL DAMAGE,TO VEHICLE,RESIDENCE,false,true,0631,,,,14,1181996,1851967,2001,03/31/2006 10:03:38 PM,41.749021039,-87.608667956,"(41.749021039, -87.608667956)"
-1634183,G414773,07/14/2001 11:10:00 AM,075XX S STONY ISLAND AV,0460,BATTERY,SIMPLE,HOSPITAL BUILDING/GROUNDS,false,false,0411,,,,08B,1188271,1855225,2001,03/31/2006 10:03:38 PM,41.757813967,-87.585570517,"(41.757813967, -87.585570517)"
-1630386,G409321,07/13/2001 10:30:00 AM,025XX N NEWCASTLE AV,0320,ROBBERY,STRONGARM - NO WEAPON,RESIDENCE,false,false,2512,,,,03,1130370,1915940,2001,03/31/2006 10:03:38 PM,41.92561098,-87.796383147,"(41.92561098, -87.796383147)"
-1641432,G407657,07/12/2001 02:57:36 PM,056XX S LAFAYETTE AV,1661,GAMBLING,GAME/DICE,SIDEWALK,true,false,0233,,,,19,1176983,1867483,2001,03/31/2006 10:03:38 PM,41.791713029,-87.626570149,"(41.791713029, -87.626570149)"
-1631341,G407139,07/12/2001 10:55:00 AM,060XX N WESTERN AV,0460,BATTERY,SIMPLE,STREET,false,false,2413,,,,08B,1159190,1939816,2001,03/31/2006 10:03:38 PM,41.990583984,-87.689824793,"(41.990583984, -87.689824793)"
-1626035,G403952,07/10/2001 09:45:00 PM,086XX S DANTE AV,0460,BATTERY,SIMPLE,RESIDENCE,false,true,0412,,,,08B,1187339,1847801,2001,03/31/2006 10:03:38 PM,41.737463967,-87.589221429,"(41.737463967, -87.589221429)"
-1625182,G403642,07/10/2001 07:29:16 PM,061XX S WASHTENAW AV,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0825,,,,08B,1159385,1863959,2001,03/31/2006 10:03:38 PM,41.782421564,-87.691195448,"(41.782421564, -87.691195448)"
-1628322,G405000,07/10/2001 06:00:00 PM,056XX S WABASH AV,0820,THEFT,$500 AND UNDER,ALLEY,false,false,0233,,,,06,1177630,1867877,2001,12/04/2014 12:43:35 PM,41.792779583,-87.62418582,"(41.792779583, -87.62418582)"
-1631228,G411713,07/10/2001 10:00:00 AM,024XX S SAWYER AV,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,false,false,1024,,,,07,1155034,1887835,2001,03/31/2006 10:03:38 PM,41.848028529,-87.70650906,"(41.848028529, -87.70650906)"
-1624676,G402746,07/10/2001 07:15:00 AM,039XX N FREMONT ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2324,,,,07,1169593,1926442,2001,03/31/2006 10:03:38 PM,41.953664486,-87.651951647,"(41.953664486, -87.651951647)"
-1622730,G399609,07/09/2001 03:50:00 AM,014XX S HARDING AV,031A,ROBBERY,ARMED: HANDGUN,PARK PROPERTY,false,false,1011,,,,03,1150330,1892475,2001,03/31/2006 10:03:38 PM,41.860854179,-87.723652165,"(41.860854179, -87.723652165)"
-1644903,G398029,07/08/2001 09:46:20 AM,036XX W MC LEAN AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2525,,,,14,1151536,1913412,2001,03/31/2006 10:03:38 PM,41.918283882,-87.71867464,"(41.918283882, -87.71867464)"
-1620784,G395905,07/07/2001 07:30:00 PM,064XX N CLAREMONT AV,0610,BURGLARY,FORCIBLE ENTRY,SMALL RETAIL STORE,false,false,2412,,,,05,1159442,1942523,2001,03/31/2006 10:03:38 PM,41.998006893,-87.688822939,"(41.998006893, -87.688822939)"
-1642000,G396819,07/07/2001 06:00:00 PM,026XX N PARKSIDE AV,0460,BATTERY,SIMPLE,RESIDENCE,true,true,2514,,,,08B,1138221,1916978,2001,03/31/2006 10:03:38 PM,41.928320834,-87.76750907,"(41.928320834, -87.76750907)"
-1620797,G396403,07/07/2001 02:15:00 PM,068XX N CLARK ST,0820,THEFT,$500 AND UNDER,OTHER,true,false,2424,,,,06,1163594,1945536,2001,12/04/2014 12:43:35 PM,42.006187868,-87.673463774,"(42.006187868, -87.673463774)"
-1626231,G395390,07/07/2001 12:35:38 AM,037XX N AVONDALE AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1732,,,,18,1150364,1924234,2001,03/31/2006 10:03:38 PM,41.948003332,-87.722697489,"(41.948003332, -87.722697489)"
-1619569,G393968,07/06/2001 12:15:00 PM,039XX W IRVING PARK RD,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,1723,,,,04B,1149665,1926338,2001,03/31/2006 10:03:38 PM,41.953790506,-87.725212,"(41.953790506, -87.725212)"
-1617346,G393186,07/05/2001 11:36:55 PM,014XX W HOWARD ST,0440,BATTERY,AGG: HANDS/FIST/FEET NO/MINOR INJURY,RESIDENCE PORCH/HALLWAY,false,false,2422,,,,08B,1165112,1950337,2001,03/31/2006 10:03:38 PM,42.019329651,-87.667741541,"(42.019329651, -87.667741541)"
-1616091,G393167,07/05/2001 10:50:00 PM,081XX S EVANS AV,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",STREET,false,false,0631,,,,07,1182606,1851029,2001,03/31/2006 10:03:38 PM,41.746432946,-87.606461743,"(41.746432946, -87.606461743)"
-1618090,G390784,07/04/2001 11:40:00 PM,102XX S EWING AV,0460,BATTERY,SIMPLE,RESIDENCE,true,true,0432,,,,08B,1202113,1837804,2001,03/31/2006 10:03:38 PM,41.709668178,-87.535434305,"(41.709668178, -87.535434305)"
-1617518,G390325,07/04/2001 05:30:00 PM,051XX S FRANCISCO AV,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0911,,,,08B,1157880,1870106,2001,03/31/2006 10:03:38 PM,41.799320495,-87.69654632,"(41.799320495, -87.69654632)"
-1614111,G389831,07/04/2001 12:45:00 AM,029XX N SACRAMENTO AV,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1411,,,,14,1155828,1919409,2001,03/31/2006 10:03:38 PM,41.934654617,-87.702743424,"(41.934654617, -87.702743424)"
-1613744,G387222,07/02/2001 10:00:00 PM,056XX S ARCHER AV,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0814,,,,26,1142848,1869326,2001,03/31/2006 10:03:38 PM,41.797472403,-87.751692579,"(41.797472403, -87.751692579)"
-1613918,G387303,07/02/2001 08:00:00 PM,051XX S HARPER AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2132,,,,14,1187196,1871300,2001,03/31/2006 10:03:38 PM,41.801950658,-87.58900026,"(41.801950658, -87.58900026)"
-1616792,G386211,07/02/2001 07:00:00 PM,061XX S ABERDEEN ST,0560,ASSAULT,SIMPLE,APARTMENT,false,false,0712,,,,08A,1170077,1863970,2001,03/31/2006 10:03:38 PM,41.782225953,-87.651995176,"(41.782225953, -87.651995176)"
-1612200,G387334,07/01/2001 11:30:00 AM,024XX W OHIO ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,1313,,,,05,1159700,1903885,2001,03/31/2006 10:03:38 PM,41.89197655,-87.688942399,"(41.89197655, -87.688942399)"
-1608747,G382352,06/30/2001 11:00:00 PM,014XX E 71 PL,0460,BATTERY,SIMPLE,STREET,false,false,0324,,,,08B,1187219,1857918,2001,03/31/2006 10:03:38 PM,41.765228831,-87.589340556,"(41.765228831, -87.589340556)"
-1607145,G379491,06/29/2001 05:25:00 PM,0000X E JACKSON BV,0460,BATTERY,SIMPLE,SMALL RETAIL STORE,false,false,0123,,,,08B,1176537,1899045,2001,03/31/2006 10:03:38 PM,41.878331697,-87.627254229,"(41.878331697, -87.627254229)"
-1606518,G375995,06/28/2001 04:15:00 AM,070XX S NORMAL BL,0460,BATTERY,SIMPLE,RESIDENCE,true,true,0732,,,,08B,1174122,1858364,2001,03/31/2006 10:03:38 PM,41.766753542,-87.637331489,"(41.766753542, -87.637331489)"
-1610698,G375693,06/27/2001 11:00:00 PM,012XX S KEELER AV,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,1011,,,,18,1148615,1894198,2001,03/31/2006 10:03:38 PM,41.865615547,-87.729903152,"(41.865615547, -87.729903152)"
-1604334,G374180,06/27/2001 10:45:00 AM,007XX W 59 ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,RESIDENCE,false,true,0711,,,,04A,1172351,1865705,2001,03/31/2006 10:03:38 PM,41.78693724,-87.643607065,"(41.78693724, -87.643607065)"
-1601084,G373092,06/26/2001 09:30:00 AM,077XX S EXCHANGE AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0421,,,,14,1196216,1854407,2001,03/31/2006 10:03:38 PM,41.755376121,-87.556480687,"(41.755376121, -87.556480687)"
-1602767,G371349,06/26/2001 02:05:25 AM,031XX N AUSTIN AV,0810,THEFT,OVER $500,STREET,false,false,2511,,,,06,1135789,1920613,2001,12/04/2014 12:43:35 PM,41.938339367,-87.77635918,"(41.938339367, -87.77635918)"
-1599121,G367221,06/24/2001 06:26:00 AM,059XX S SACRAMENTO AV,1220,DECEPTIVE PRACTICE,THEFT OF LOST/MISLAID PROP,ALLEY,true,false,0824,,,,11,1157447,1864778,2001,03/31/2006 10:03:38 PM,41.784708513,-87.698278585,"(41.784708513, -87.698278585)"
-1596439,G367092,06/24/2001 01:30:00 AM,057XX S MARSHFIELD AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,0715,,,,07,1166282,1866628,2001,03/31/2006 10:03:38 PM,41.78960151,-87.66583304,"(41.78960151, -87.66583304)"
-1596880,G367741,06/23/2001 10:00:00 PM,040XX N PAULINA ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1923,,,,14,1164466,1926842,2001,03/31/2006 10:03:38 PM,41.954872415,-87.670787578,"(41.954872415, -87.670787578)"
-1599830,G366563,06/23/2001 08:10:00 PM,002XX N STATE ST,0820,THEFT,$500 AND UNDER,CTA TRAIN,false,false,0122,,,,06,1176287,1901755,2001,12/04/2014 12:43:35 PM,41.885773731,-87.628090393,"(41.885773731, -87.628090393)"
-1615328,G388091,06/23/2001 03:00:00 PM,0000X S WHIPPLE ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,1124,,,,06,1156204,1899724,2001,03/31/2006 10:03:38 PM,41.880629697,-87.701894158,"(41.880629697, -87.701894158)"
-1595728,G363963,06/22/2001 03:30:00 PM,008XX W 89 ST,0820,THEFT,$500 AND UNDER,OTHER,false,false,2223,,,,06,1172190,1845924,2001,12/04/2014 12:43:35 PM,41.732659251,-87.644777902,"(41.732659251, -87.644777902)"
-1594327,G362799,06/22/2001 12:55:00 AM,102XX S AVENUE N,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0432,,,,14,1201206,1837690,2001,03/31/2006 10:03:38 PM,41.709378367,-87.538759636,"(41.709378367, -87.538759636)"
-1593056,G362338,06/21/2001 07:50:00 PM,056XX S LAFAYETTE AV,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0233,,,,26,1176896,1867756,2001,03/31/2006 10:03:38 PM,41.79246413,-87.626880934,"(41.79246413, -87.626880934)"
-1592976,G360714,06/21/2001 04:20:00 AM,062XX S MARSHFIELD AV,0460,BATTERY,SIMPLE,RESIDENCE,false,true,0714,,,,08B,1166373,1863227,2001,03/31/2006 10:03:38 PM,41.780266803,-87.665596216,"(41.780266803, -87.665596216)"
-1593190,G360563,06/21/2001 12:51:57 AM,075XX S PEORIA ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,STREET,false,false,0621,,,,11,1171574,1854735,2001,03/31/2006 10:03:38 PM,41.756851311,-87.646777046,"(41.756851311, -87.646777046)"
-1590425,G357085,06/19/2001 02:30:00 PM,035XX W MELROSE ST,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,1732,,,,08A,1152388,1921349,2001,03/31/2006 10:03:38 PM,41.940046864,-87.715334128,"(41.940046864, -87.715334128)"
-1599255,G356962,06/19/2001 02:25:45 PM,002XX N MICHIGAN AV,0850,THEFT,ATTEMPT THEFT,RESTAURANT,false,false,0124,,,,06,1177220,1901790,2001,03/31/2006 10:03:38 PM,41.885848681,-87.624663204,"(41.885848681, -87.624663204)"
-1589929,G355964,06/19/2001 02:25:07 AM,070XX S BELL AV,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,true,false,0832,,,,14,1162580,1857738,2001,03/31/2006 10:03:38 PM,41.765284211,-87.67965497,"(41.765284211, -87.67965497)"
-1599273,G355257,06/18/2001 07:23:04 PM,017XX W HURON ST,0460,BATTERY,SIMPLE,SIDEWALK,true,true,1324,,,,08B,1164700,1904773,2001,03/31/2006 10:03:38 PM,41.89430878,-87.670554429,"(41.89430878, -87.670554429)"
-1594744,G363908,06/18/2001 06:00:00 PM,001XX W SCHILLER ST,0810,THEFT,OVER $500,STREET,false,false,1821,,,,06,1174613,1909683,2001,12/04/2014 12:43:35 PM,41.907566171,-87.634000295,"(41.907566171, -87.634000295)"
-1591662,G353202,06/18/2001 12:30:00 AM,008XX N PULASKI RD,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1111,,,,14,1149537,1905099,2001,03/31/2006 10:03:38 PM,41.895511271,-87.726235328,"(41.895511271, -87.726235328)"
-1584588,G351805,06/17/2001 05:30:00 AM,038XX N KOSTNER AV,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,1731,,,,05,1146134,1925194,2001,03/31/2006 10:03:38 PM,41.950719296,-87.738221666,"(41.950719296, -87.738221666)"
-1586911,G354730,06/16/2001 12:00:00 AM,001XX E WACKER DR,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,HOTEL/MOTEL,false,false,0124,,,,11,1177784,1902578,2001,03/31/2006 10:03:38 PM,41.887998194,-87.622568135,"(41.887998194, -87.622568135)"
-1585597,G348884,06/15/2001 10:50:00 PM,061XX S MARSHFIELD AV,041A,BATTERY,AGGRAVATED: HANDGUN,APARTMENT,false,true,0714,,,,04B,1166442,1863650,2001,03/31/2006 10:03:38 PM,41.781426098,-87.665331203,"(41.781426098, -87.665331203)"
-1583502,G348125,06/14/2001 06:00:00 PM,020XX N MILWAUKEE AV,0560,ASSAULT,SIMPLE,STREET,true,false,1431,,,,08A,1159246,1913540,2001,03/31/2006 10:03:38 PM,41.918479993,-87.690343958,"(41.918479993, -87.690343958)"
-1581993,G345118,06/14/2001 11:38:06 AM,011XX S FRANCISCO AV,0460,BATTERY,SIMPLE,RESIDENCE,true,false,1135,,,,08B,1157152,1895163,2001,03/31/2006 10:03:38 PM,41.868094665,-87.698537036,"(41.868094665, -87.698537036)"
-1581364,G345963,06/14/2001 08:00:00 AM,112XX S CHAMPLAIN AV,0560,ASSAULT,SIMPLE,STREET,false,true,0531,,,,08A,1182551,1830501,2001,03/31/2006 10:03:38 PM,41.690102875,-87.607297598,"(41.690102875, -87.607297598)"
-1580112,G342961,06/13/2001 01:11:50 PM,013XX S KILDARE AV,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,1011,,,,08A,1147900,1893121,2001,03/31/2006 10:03:38 PM,41.862673889,-87.732555647,"(41.862673889, -87.732555647)"
-1581989,G347476,06/13/2001 04:30:00 AM,038XX W IRVING PARK RD,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,1723,,,,26,1150241,1926356,2001,03/31/2006 10:03:38 PM,41.953828664,-87.723094075,"(41.953828664, -87.723094075)"
-1578733,G341076,06/12/2001 03:22:00 PM,073XX S INDIANA AV,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,true,0323,,,,04B,1178824,1856624,2001,03/31/2006 10:03:38 PM,41.761873093,-87.620149814,"(41.761873093, -87.620149814)"
-1577879,G339935,06/12/2001 04:12:45 AM,039XX W 56 ST,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0822,,,,08B,1150816,1867130,2001,03/31/2006 10:03:38 PM,41.791294585,-87.722529595,"(41.791294585, -87.722529595)"
-1576153,G338467,06/11/2001 12:49:52 PM,025XX W CERMAK RD,1110,DECEPTIVE PRACTICE,BOGUS CHECK,GROCERY FOOD STORE,true,false,1023,,,,11,1159780,1889354,2001,03/31/2006 10:03:38 PM,41.852100446,-87.689049156,"(41.852100446, -87.689049156)"
-1572848,G336365,06/10/2001 04:17:17 PM,021XX W SUPERIOR ST,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1313,,,,08B,1162051,1904942,2001,03/31/2006 10:03:38 PM,41.894828269,-87.680278663,"(41.894828269, -87.680278663)"
-1575874,G340339,06/10/2001 09:00:00 AM,114XX S THROOP ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2234,,,,06,1169751,1828920,2001,12/04/2014 12:43:35 PM,41.686050846,-87.654204349,"(41.686050846, -87.654204349)"
-1571730,G334787,06/09/2001 08:30:00 PM,015XX W 18 PL,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1222,,,,08B,1166306,1891196,2001,03/31/2006 10:03:38 PM,41.8570182,-87.66504435,"(41.8570182, -87.66504435)"
-1570422,G332659,06/08/2001 08:41:18 PM,025XX W HADDON AV,0460,BATTERY,SIMPLE,STREET,false,true,1312,,,,08B,1159308,1907595,2001,03/31/2006 10:03:38 PM,41.902165173,-87.690279921,"(41.902165173, -87.690279921)"
-1568641,G330510,06/07/2001 07:30:00 PM,081XX S JUSTINE ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0614,,,,07,1167389,1850425,2001,03/31/2006 10:03:38 PM,41.745114697,-87.662237556,"(41.745114697, -87.662237556)"
-1578082,G329156,06/07/2001 10:12:00 AM,044XX S PRAIRIE AV,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,0221,,,,16,1178743,1875622,2001,03/31/2006 10:03:38 PM,41.81400729,-87.619868879,"(41.81400729, -87.619868879)"
-1571732,G328612,06/06/2001 11:44:24 PM,008XX N KEDZIE AV,1310,CRIMINAL DAMAGE,TO PROPERTY,GOVERNMENT BUILDING/PROPERTY,false,false,1311,,,,14,1154933,1905177,2001,03/31/2006 10:03:38 PM,41.895618826,-87.706414892,"(41.895618826, -87.706414892)"
-1567115,G328913,06/06/2001 08:00:00 PM,012XX N HOYNE AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1424,,,,07,1162128,1908545,2001,03/31/2006 10:03:38 PM,41.904713572,-87.679895073,"(41.904713572, -87.679895073)"
-1566847,G328419,06/06/2001 06:51:00 PM,033XX W JACKSON BL,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1134,,,,26,1154395,1898458,2001,03/31/2006 10:03:38 PM,41.877191993,-87.708570516,"(41.877191993, -87.708570516)"
-1601738,G370801,06/04/2001 06:00:00 PM,022XX W BERTEAU AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1912,,,,07,1160922,1927898,2001,03/31/2006 10:03:38 PM,41.95784458,-87.683786492,"(41.95784458, -87.683786492)"
-1569479,G321218,06/03/2001 04:42:22 PM,023XX W SHAKESPEARE AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1432,,,,18,1160671,1914382,2001,03/31/2006 10:03:38 PM,41.920761064,-87.685085005,"(41.920761064, -87.685085005)"
-1580475,G345405,06/03/2001 04:00:00 PM,078XX S ELLIS AV,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,0624,,,,14,1184291,1853236,2001,03/31/2006 10:03:38 PM,41.752449956,-87.600218675,"(41.752449956, -87.600218675)"
-1562630,G320144,06/03/2001 12:45:00 AM,069XX S WINCHESTER AV,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0735,,,,08B,1164597,1858527,2001,03/31/2006 10:03:38 PM,41.767407035,-87.672239791,"(41.767407035, -87.672239791)"
-1567823,G320098,06/03/2001 12:06:11 AM,076XX S KING DR,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0623,,,,14,1180277,1854121,2001,03/31/2006 10:03:38 PM,41.75497141,-87.614901062,"(41.75497141, -87.614901062)"
-847,G317944,06/02/2001 08:45:00 PM,015XX W CHESTNUT ST,0110,HOMICIDE,FIRST DEGREE MURDER,STREET,true,false,1323,012,1,24,01A,1165823,1906051,2001,08/25/2009 02:50:36 PM,41.897791825,-87.66639352,"(41.897791825, -87.66639352)"
-1563782,G319804,06/02/2001 08:20:00 PM,068XX S ELIZABETH ST,0460,BATTERY,SIMPLE,RESIDENCE,false,true,0724,,,,08B,1169211,1859326,2001,03/31/2006 10:03:38 PM,41.769501031,-87.655304405,"(41.769501031, -87.655304405)"
-1566445,G319643,06/02/2001 07:11:52 PM,019XX S KEELER AV,2024,NARCOTICS,POSS: HEROIN(WHITE),ALLEY,true,false,1012,,,,18,1148732,1890218,2001,03/31/2006 10:03:38 PM,41.854691681,-87.72957638,"(41.854691681, -87.72957638)"
-1562284,G319827,06/02/2001 06:00:00 PM,057XX S CICERO AV,0820,THEFT,$500 AND UNDER,AIRPORT/AIRCRAFT,false,false,0813,,,,06,1145592,1866396,2001,12/04/2014 12:43:35 PM,41.789380622,-87.741703718,"(41.789380622, -87.741703718)"
-1566063,G318175,06/02/2001 03:00:00 AM,049XX W CHICAGO AV,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1531,,,,08B,1143323,1904821,2001,03/31/2006 10:03:38 PM,41.894866796,-87.749065092,"(41.894866796, -87.749065092)"
-1558993,G315951,06/01/2001 05:30:00 AM,040XX N KENMORE AV,0820,THEFT,$500 AND UNDER,ALLEY,false,false,2322,,,,06,1168392,1927454,2001,12/04/2014 12:43:35 PM,41.95646757,-87.656337229,"(41.95646757, -87.656337229)"
-1556205,G314109,05/31/2001 09:40:00 AM,063XX S MAPLEWOOD AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0825,,,,07,1160418,1862675,2001,03/31/2006 10:03:38 PM,41.778876858,-87.687443512,"(41.778876858, -87.687443512)"
-1554116,G311772,05/29/2001 11:30:00 PM,064XX N ROCKWELL ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2412,,,,06,1157774,1942863,2001,12/04/2014 12:43:35 PM,41.998974153,-87.694949617,"(41.998974153, -87.694949617)"
-1554079,G310825,05/29/2001 07:30:00 PM,013XX W 47 ST,0850,THEFT,ATTEMPT THEFT,OTHER,true,false,0921,,,,06,1168264,1873560,2001,03/31/2006 10:03:38 PM,41.808581251,-87.658366065,"(41.808581251, -87.658366065)"
-1556090,G310106,05/29/2001 02:45:00 PM,103XX S MICHIGAN AV,0820,THEFT,$500 AND UNDER,DRUG STORE,true,false,0512,,,,06,1178931,1836694,2001,12/04/2014 12:43:35 PM,41.707180296,-87.620362752,"(41.707180296, -87.620362752)"
-1557804,G309696,05/29/2001 11:30:00 AM,049XX S STATE ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,CHA PARKING LOT/GROUNDS,true,false,0231,,,,04B,1177075,1872016,2001,03/31/2006 10:03:38 PM,41.804149939,-87.62609605,"(41.804149939, -87.62609605)"
-1554907,G312488,05/29/2001 10:30:00 AM,042XX N MILWAUKEE AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1624,,,,14,1142373,1927657,2001,03/31/2006 10:03:38 PM,41.957548816,-87.751985595,"(41.957548816, -87.751985595)"
-1552437,G308260,05/28/2001 05:40:00 PM,042XX S ELLIS AV,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,2123,,,,08A,1183680,1876903,2001,03/31/2006 10:03:38 PM,41.81740854,-87.601719735,"(41.81740854, -87.601719735)"
-1555690,G308070,05/28/2001 04:00:00 PM,038XX W HURON ST,0460,BATTERY,SIMPLE,STREET,false,true,1112,,,,08B,1150279,1904431,2001,03/31/2006 10:03:38 PM,41.89366377,-87.723527553,"(41.89366377, -87.723527553)"
-1553324,G306598,05/27/2001 07:45:00 PM,014XX W 13 ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA PARKING LOT/GROUNDS,true,false,1231,,,,26,1166838,1894194,2001,03/31/2006 10:03:38 PM,41.865233593,-87.663005702,"(41.865233593, -87.663005702)"
-1617711,G394178,05/27/2001 05:12:00 PM,002XX N LA SALLE ST,1206,DECEPTIVE PRACTICE,"THEFT BY LESSEE,MOTOR VEH",PARKING LOT/GARAGE(NON.RESID.),false,false,0113,,,,11,1175139,1901763,2001,03/31/2006 10:03:38 PM,41.885821495,-87.6323058,"(41.885821495, -87.6323058)"
-1549252,G304155,05/26/2001 02:00:00 PM,027XX E 76 ST,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0421,,,,08B,1195835,1855198,2001,03/31/2006 10:03:38 PM,41.757556115,-87.557850794,"(41.757556115, -87.557850794)"
-1553536,G302193,05/25/2001 03:47:25 PM,072XX S WENTWORTH AV,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),SIDEWALK,true,false,0731,,,,18,1176181,1856604,2001,03/31/2006 10:03:38 PM,41.761877933,-87.629837248,"(41.761877933, -87.629837248)"
-1548695,G301874,05/25/2001 12:50:00 PM,066XX S DAMEN AV,0560,ASSAULT,SIMPLE,STREET,false,true,0726,,,,08A,1164206,1860834,2001,03/31/2006 10:03:38 PM,41.773745995,-87.673608114,"(41.773745995, -87.673608114)"
-1554715,G300698,05/24/2001 07:55:00 PM,034XX S LA SALLE ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0924,,,,18,1175920,1882479,2001,03/31/2006 10:03:38 PM,41.832887336,-87.630017923,"(41.832887336, -87.630017923)"
-1545873,G300656,05/24/2001 05:00:00 PM,021XX E 69 ST,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0331,,,,26,1191694,1859677,2001,03/31/2006 10:03:38 PM,41.769948277,-87.5728816,"(41.769948277, -87.5728816)"
-1553802,G299298,05/24/2001 10:42:52 AM,048XX W SUPERIOR ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1531,,,,18,1144105,1904488,2001,03/31/2006 10:03:38 PM,41.893938357,-87.746201355,"(41.893938357, -87.746201355)"
-1546814,G297588,05/23/2001 02:20:00 PM,112XX S WALLACE ST,0545,ASSAULT,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",true,false,2233,,,,08A,1174272,1830372,2001,03/31/2006 10:03:38 PM,41.689936366,-87.637610961,"(41.689936366, -87.637610961)"
-1542479,G296090,05/22/2001 07:30:00 PM,016XX S KEDVALE AV,1330,CRIMINAL TRESPASS,TO LAND,ABANDONED BUILDING,true,false,1012,,,,26,1149026,1891547,2001,03/31/2006 10:03:38 PM,41.858332944,-87.728462902,"(41.858332944, -87.728462902)"
-1545877,G297668,05/22/2001 03:15:00 PM,066XX S ELLIS AV,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0321,,,,08B,1183993,1861055,2001,03/31/2006 10:03:38 PM,41.773913017,-87.601066784,"(41.773913017, -87.601066784)"
-1538441,G290719,05/20/2001 12:33:50 PM,101XX S LUELLA AV,0460,BATTERY,SIMPLE,STREET,true,true,0431,,,,08B,1193126,1838291,2001,03/31/2006 10:03:38 PM,41.711228361,-87.568329462,"(41.711228361, -87.568329462)"
-1542813,G290927,05/19/2001 01:00:00 AM,056XX W IRVING PARK RD,0460,BATTERY,SIMPLE,STREET,false,true,1624,,,,08B,1138281,1926078,2001,03/31/2006 10:03:38 PM,41.953291079,-87.767067692,"(41.953291079, -87.767067692)"
-1538615,G292271,05/18/2001 11:00:00 PM,020XX N WINCHESTER AV,0810,THEFT,OVER $500,STREET,false,false,1432,,,,06,1163001,1913346,2001,12/04/2014 12:43:35 PM,41.91786956,-87.676553252,"(41.91786956, -87.676553252)"
-1536378,G285623,05/17/2001 10:10:00 PM,006XX W 61 ST,0460,BATTERY,SIMPLE,APARTMENT,false,false,0711,,,,08B,1172824,1864472,2001,03/31/2006 10:03:38 PM,41.783543326,-87.641909151,"(41.783543326, -87.641909151)"
-1534150,G283560,05/17/2001 03:00:00 AM,065XX S TALMAN AV,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0831,,,,08B,1159874,1861156,2001,03/31/2006 10:03:38 PM,41.774719702,-87.689479567,"(41.774719702, -87.689479567)"
-1532666,G283476,05/16/2001 11:15:00 PM,037XX S FEDERAL ST,1340,CRIMINAL DAMAGE,TO STATE SUP PROP,CHA APARTMENT,false,false,0211,,,,14,1176242,1880053,2001,03/31/2006 10:03:38 PM,41.826222952,-87.628909449,"(41.826222952, -87.628909449)"
-1533771,G282643,05/16/2001 05:06:31 PM,002XX S RIVERSIDE PZ,0560,ASSAULT,SIMPLE,COMMERCIAL / BUSINESS OFFICE,false,false,0111,,,,08A,1173452,1899110,2001,03/31/2006 10:03:38 PM,41.878579147,-87.638579626,"(41.878579147, -87.638579626)"
-1532188,G282715,05/15/2001 07:30:00 PM,033XX N OAKLEY AV,0810,THEFT,OVER $500,OTHER,false,false,1913,,,,06,1160479,1922374,2001,12/04/2014 12:43:35 PM,41.942695615,-87.685568599,"(41.942695615, -87.685568599)"
-1532903,G279107,05/15/2001 07:30:00 AM,066XX S YALE AV,0820,THEFT,$500 AND UNDER,STREET,false,false,0722,,,,06,1175611,1860858,2001,12/04/2014 12:43:35 PM,41.773564165,-87.631799184,"(41.773564165, -87.631799184)"
-1540796,G278759,05/14/2001 09:03:15 PM,031XX W WARREN BV,2027,NARCOTICS,POSS: CRACK,STREET,true,false,1331,,,,18,1155263,1900140,2001,03/31/2006 10:03:38 PM,41.881790189,-87.705338264,"(41.881790189, -87.705338264)"
-1525878,G274578,05/12/2001 09:03:42 PM,008XX E 133 ST,1200,DECEPTIVE PRACTICE,STOLEN PROP: BUY/RECEIVE/POS.,CHA PARKING LOT/GROUNDS,false,false,0533,,,,13,1184100,1817474,2001,03/31/2006 10:03:38 PM,41.654318979,-87.602031457,"(41.654318979, -87.602031457)"
-1527494,G273660,05/12/2001 12:38:21 PM,027XX W JACKSON BV,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,1125,,,,03,1158288,1898543,2001,03/31/2006 10:03:38 PM,41.87734659,-87.694274165,"(41.87734659, -87.694274165)"
-1533728,G272729,05/11/2001 09:50:31 PM,046XX S CALUMET AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0222,,,,18,1179176,1874210,2001,03/31/2006 10:03:38 PM,41.810122767,-87.618323704,"(41.810122767, -87.618323704)"
-1523675,G261748,05/11/2001 02:40:00 PM,002XX N PINE AV,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,1523,,,,08B,1139494,1901274,2001,03/31/2006 10:03:38 PM,41.885204076,-87.763214719,"(41.885204076, -87.763214719)"
-1522419,G269985,05/10/2001 06:15:00 PM,083XX S BOND AV,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,0424,,,,14,1198652,1849641,2001,03/31/2006 10:03:38 PM,41.742237174,-87.547713057,"(41.742237174, -87.547713057)"
-1522006,G269771,05/10/2001 02:30:00 PM,025XX W 43 ST,0460,BATTERY,SIMPLE,ALLEY,false,false,0914,,,,08B,1160323,1876035,2001,03/31/2006 10:03:38 PM,41.815540405,-87.687423768,"(41.815540405, -87.687423768)"
-1523969,G269178,05/10/2001 12:26:34 PM,064XX N RIDGE BL,0460,BATTERY,SIMPLE,NURSING HOME/RETIREMENT HOME,false,false,2412,,,,08B,1162486,1943020,2001,03/31/2006 10:03:38 PM,41.999307258,-87.677611129,"(41.999307258, -87.677611129)"
-1520765,G267349,05/09/2001 04:03:07 PM,052XX S PULASKI RD,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,RESTAURANT,true,false,0822,,,,11,1150623,1869757,2001,03/31/2006 10:03:38 PM,41.79850723,-87.723168923,"(41.79850723, -87.723168923)"
-1520634,G265738,05/08/2001 08:00:00 AM,035XX N DAMEN AV,0820,THEFT,$500 AND UNDER,STREET,false,false,1913,,,,06,1162339,1923788,2001,12/04/2014 12:43:35 PM,41.946536936,-87.678692536,"(41.946536936, -87.678692536)"
-1519581,G263961,05/08/2001 05:00:00 AM,053XX W CHICAGO AV,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,false,false,1524,,,,07,1140544,1904774,2001,03/31/2006 10:03:38 PM,41.894789299,-87.759272895,"(41.894789299, -87.759272895)"
-1518503,G262804,05/07/2001 04:00:00 PM,006XX E 75 ST,0820,THEFT,$500 AND UNDER,CLEANING STORE,false,false,0323,,,,06,1182083,1855473,2001,12/04/2014 12:43:35 PM,41.758639864,-87.608240882,"(41.758639864, -87.608240882)"
-1516934,G260452,05/06/2001 10:30:00 AM,024XX W LAWRENCE AV,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,2031,,,,04A,1159482,1931851,2001,03/31/2006 10:03:38 PM,41.968721647,-87.688971139,"(41.968721647, -87.688971139)"
-1515823,G261259,05/06/2001 12:01:00 AM,081XX S MARYLAND AV,0810,THEFT,OVER $500,STREET,false,false,0631,,,,06,1183366,1850946,2001,12/04/2014 12:43:35 PM,41.746187534,-87.603679534,"(41.746187534, -87.603679534)"
-1523014,G256103,05/04/2001 03:14:57 PM,0000X S STATE ST,0820,THEFT,$500 AND UNDER,DEPARTMENT STORE,true,false,0123,,,,06,1176418,1900379,2001,12/04/2014 12:43:35 PM,41.881994955,-87.62765089,"(41.881994955, -87.62765089)"
-1514082,G256452,05/04/2001 11:00:00 AM,046XX N DOVER ST,0810,THEFT,OVER $500,RESIDENCE,false,false,2311,,,,06,1165908,1930811,2001,12/04/2014 12:43:35 PM,41.965732798,-87.665372822,"(41.965732798, -87.665372822)"
-1515500,G254742,05/03/2001 10:35:00 PM,054XX N LINCOLN AV,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,2011,,,,18,1158363,1936334,2001,03/31/2006 10:03:38 PM,41.981046237,-87.69296245,"(41.981046237, -87.69296245)"
-1510705,G254667,05/03/2001 10:00:00 PM,062XX N LINCOLN AV,0820,THEFT,$500 AND UNDER,DEPARTMENT STORE,true,false,1711,,,,06,1152513,1941309,2001,12/04/2014 12:43:35 PM,41.994815901,-87.714344716,"(41.994815901, -87.714344716)"
-1508976,G249256,05/01/2001 03:40:00 PM,038XX W THOMAS ST,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1112,,,,08B,1150781,1907026,2001,03/31/2006 10:03:38 PM,41.900774907,-87.721615912,"(41.900774907, -87.721615912)"
-1508188,G248972,05/01/2001 12:50:00 PM,064XX S CICERO AV,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,0813,,,,14,1145456,1861294,2001,03/31/2006 10:03:38 PM,41.775382452,-87.742330975,"(41.775382452, -87.742330975)"
-1508244,G248546,05/01/2001 10:00:00 AM,014XX N PARKSIDE AV,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,2531,,,,08B,1138363,1909089,2001,03/31/2006 10:03:38 PM,41.906669949,-87.767178638,"(41.906669949, -87.767178638)"
-1503704,G246662,04/30/2001 01:30:00 PM,071XX S RIDGELAND AV,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,false,false,0324,,,,04A,1189147,1857902,2001,03/31/2006 10:03:38 PM,41.765138947,-87.582274472,"(41.765138947, -87.582274472)"
-1501778,G244751,04/29/2001 04:24:40 PM,043XX N HERMITAGE AV,0820,THEFT,$500 AND UNDER,OTHER,false,false,1922,,,,06,1164052,1928992,2001,12/04/2014 12:43:35 PM,41.960780886,-87.672248535,"(41.960780886, -87.672248535)"
-1504349,G244488,04/29/2001 01:30:00 PM,071XX S UNION AV,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0732,,,,08B,1173032,1857218,2001,03/31/2006 10:03:38 PM,41.763632914,-87.641360544,"(41.763632914, -87.641360544)"
-1504161,G244305,04/29/2001 12:09:20 PM,018XX W 62 ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0714,,,,06,1164859,1863595,2001,12/04/2014 12:43:35 PM,41.78130877,-87.671136408,"(41.78130877, -87.671136408)"
-1505523,G250011,04/28/2001 12:00:00 PM,012XX W WEBSTER AV,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,1811,,,,26,1167530,1914798,2001,03/31/2006 10:03:38 PM,41.921757548,-87.659871669,"(41.921757548, -87.659871669)"
-1499308,G241122,04/27/2001 08:42:23 PM,011XX W ERIE ST,2850,PUBLIC PEACE VIOLATION,BOMB THREAT,CHA PARKING LOT/GROUNDS,false,false,1323,,,,26,1168508,1904469,2001,03/31/2006 10:03:38 PM,41.893393012,-87.656577678,"(41.893393012, -87.656577678)"
-1501402,G240286,04/27/2001 02:20:00 PM,061XX S VERNON AV,0560,ASSAULT,SIMPLE,RESIDENCE PORCH/HALLWAY,false,true,0313,,,,08A,1180274,1864034,2001,03/31/2006 10:03:38 PM,41.782173772,-87.614608486,"(41.782173772, -87.614608486)"
-1497563,G239281,04/27/2001 03:20:00 AM,084XX S STONY ISLAND AV,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,false,false,0412,,,,04B,1188236,1849158,2001,03/31/2006 10:03:38 PM,41.741166381,-87.585891938,"(41.741166381, -87.585891938)"
-1510116,G237044,04/26/2001 03:16:11 AM,003XX S WESTERN AV,2027,NARCOTICS,POSS: CRACK,STREET,true,false,1125,,,,18,1160407,1898606,2001,03/31/2006 10:03:38 PM,41.877475908,-87.686492031,"(41.877475908, -87.686492031)"
-1499710,G238275,04/25/2001 12:00:00 PM,077XX S OGLESBY AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,OTHER,false,false,0414,,,,07,1193245,1854337,2001,03/31/2006 10:03:38 PM,41.755257138,-87.567370703,"(41.755257138, -87.567370703)"
-1496439,G235179,04/25/2001 09:00:00 AM,102XX S WESTERN AV,0820,THEFT,$500 AND UNDER,GAS STATION,false,false,2211,,,,06,1162165,1836300,2001,12/04/2014 12:43:35 PM,41.706463618,-87.681770956,"(41.706463618, -87.681770956)"
-1493884,G234567,04/24/2001 09:18:16 PM,079XX S KING DR,0326,ROBBERY,AGGRAVATED VEHICULAR HIJACKING,STREET,false,false,0623,,,,03,1180234,1852680,2001,03/31/2006 10:03:38 PM,41.751018132,-87.615102729,"(41.751018132, -87.615102729)"
-1494279,G230706,04/22/2001 11:00:00 PM,026XX S WESTERN AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1034,,,,14,1160741,1886708,2001,03/31/2006 10:03:38 PM,41.844819703,-87.685595267,"(41.844819703, -87.685595267)"
-1488198,G225703,04/20/2001 08:00:00 PM,047XX S HONORE ST,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0931,,,,08B,1164864,1872911,2001,03/31/2006 10:03:38 PM,41.806872918,-87.670854875,"(41.806872918, -87.670854875)"
-1492311,G224688,04/20/2001 12:29:00 PM,066XX S COTTAGE GROVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0321,,,,18,1182666,1860980,2001,03/31/2006 10:03:38 PM,41.773738112,-87.605933571,"(41.773738112, -87.605933571)"
-1490136,G227526,04/20/2001 01:30:00 AM,024XX N MILWAUKEE AV,1563,SEX OFFENSE,CRIMINAL SEXUAL ABUSE,OTHER,false,false,1414,,,,17,1156366,1915864,2001,03/31/2006 10:03:38 PM,41.924915997,-87.700862364,"(41.924915997, -87.700862364)"
-1504475,G246658,04/19/2001 02:00:00 AM,014XX N KINGSBURY ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,1822,,,,04B,1170196,1909087,2001,03/31/2006 10:03:38 PM,41.906028384,-87.650243255,"(41.906028384, -87.650243255)"
-1489053,G221304,04/18/2001 07:55:00 PM,069XX S PAXTON AV,0460,BATTERY,SIMPLE,RESIDENCE,true,true,0331,,,,08B,1192128,1859015,2001,03/31/2006 10:03:38 PM,41.768121158,-87.57131227,"(41.768121158, -87.57131227)"
-1482959,G219165,04/17/2001 07:30:00 PM,072XX S CARPENTER ST,0460,BATTERY,SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,0733,,,,08B,1170521,1856957,2001,03/31/2006 10:03:38 PM,41.762971759,-87.650571472,"(41.762971759, -87.650571472)"
-1481652,G218119,04/17/2001 10:45:51 AM,012XX W VAN BUREN ST,0460,BATTERY,SIMPLE,STREET,false,false,1213,,,,08B,1167895,1898342,2001,03/31/2006 10:03:38 PM,41.876593323,-87.659005818,"(41.876593323, -87.659005818)"
-1483182,G218129,04/17/2001 10:30:00 AM,002XX S CICERO AV,4651,OTHER OFFENSE,SEX OFFENDER: FAIL REG NEW ADD,SIDEWALK,true,false,1533,,,,26,1144376,1898796,2001,03/31/2006 10:03:38 PM,41.878313744,-87.745349287,"(41.878313744, -87.745349287)"
-1492033,G217357,04/16/2001 09:19:49 PM,012XX N WASHTENAW AV,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1423,,,,18,1158219,1907890,2001,03/31/2006 10:03:38 PM,41.902997009,-87.694271912,"(41.902997009, -87.694271912)"
-1490646,G216951,04/16/2001 05:45:00 PM,025XX N LOCKWOOD AV,0460,BATTERY,SIMPLE,APARTMENT,false,true,2515,,,,08B,1140662,1916374,2001,03/31/2006 10:03:38 PM,41.926618841,-87.758554009,"(41.926618841, -87.758554009)"
-1480247,G216912,04/16/2001 05:28:04 PM,022XX S WESTERN AV,0560,ASSAULT,SIMPLE,CLEANING STORE,false,true,1034,,,,08A,1160684,1888780,2001,03/31/2006 10:03:38 PM,41.85050667,-87.685747109,"(41.85050667, -87.685747109)"
-1480269,G216932,04/16/2001 05:05:00 PM,062XX S KING DR,1563,SEX OFFENSE,CRIMINAL SEXUAL ABUSE,APARTMENT,false,false,0312,,,,17,1179958,1863504,2001,03/31/2006 10:03:38 PM,41.780726643,-87.615783232,"(41.780726643, -87.615783232)"
-1479997,G216385,04/16/2001 01:30:00 PM,078XX S CORNELL AV,0460,BATTERY,SIMPLE,RESIDENCE,false,true,0411,,,,08B,1188558,1853329,2001,03/31/2006 10:03:38 PM,41.75260432,-87.584579186,"(41.75260432, -87.584579186)"
-1477215,G213510,04/14/2001 09:38:25 PM,048XX W NORTH AV,0820,THEFT,$500 AND UNDER,GROCERY FOOD STORE,true,false,2533,,,,06,1143859,1910159,2001,12/04/2014 12:43:35 PM,41.90950483,-87.746962481,"(41.90950483, -87.746962481)"
-1490079,G213205,04/14/2001 06:33:48 PM,074XX S BLACKSTONE AV,0460,BATTERY,SIMPLE,STREET,false,false,0324,,,,08B,1187452,1856012,2001,03/31/2006 10:03:38 PM,41.759993062,-87.588547039,"(41.759993062, -87.588547039)"
-1477478,G212878,04/14/2001 03:00:00 PM,013XX E 47 ST,0820,THEFT,$500 AND UNDER,GROCERY FOOD STORE,true,false,2123,,,,06,1185546,1874136,2001,12/04/2014 12:43:35 PM,41.809771899,-87.594962029,"(41.809771899, -87.594962029)"
-1477168,G212868,04/14/2001 03:00:00 PM,054XX S RICHMOND ST,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0911,,,,08B,1157674,1868434,2001,03/31/2006 10:03:38 PM,41.794736484,-87.697347144,"(41.794736484, -87.697347144)"
-1484754,G222265,04/14/2001 02:00:00 PM,009XX N WALLER AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1511,,,,07,1138136,1906000,2001,03/31/2006 10:03:38 PM,41.898197466,-87.76808728,"(41.898197466, -87.76808728)"
-1481174,G211864,04/14/2001 02:00:00 AM,037XX W 63 ST,2022,NARCOTICS,POSS: COCAINE,STREET,true,false,0823,,,,18,1152342,1862608,2001,03/31/2006 10:03:38 PM,41.778855641,-87.717052854,"(41.778855641, -87.717052854)"
-1474618,G208828,04/12/2001 05:04:00 PM,011XX W 111 ST,0460,BATTERY,SIMPLE,STREET,false,false,2234,,,,08B,1170937,1831154,2001,03/31/2006 10:03:38 PM,41.692155565,-87.649797744,"(41.692155565, -87.649797744)"
-1486248,G208387,04/12/2001 01:50:00 PM,074XX N GREENVIEW AV,2027,NARCOTICS,POSS: CRACK,STREET,true,false,2422,,,,18,1164961,1949509,2001,03/31/2006 10:03:38 PM,42.017060828,-87.668320874,"(42.017060828, -87.668320874)"
-1474976,G209851,04/12/2001 12:14:00 PM,044XX S TALMAN AV,0560,ASSAULT,SIMPLE,VEHICLE NON-COMMERCIAL,false,false,0912,,,,08A,1159399,1875233,2001,03/31/2006 10:03:38 PM,41.813358626,-87.690835177,"(41.813358626, -87.690835177)"
-1476349,G209093,04/12/2001 05:00:00 AM,020XX S STATE ST,0460,BATTERY,SIMPLE,CHA APARTMENT,false,true,2111,,,,08B,1176604,1890373,2001,03/31/2006 10:03:38 PM,41.854533666,-87.627270111,"(41.854533666, -87.627270111)"
-1474373,G207441,04/12/2001 01:24:26 AM,038XX N LAKEWOOD AV,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1923,,,,08B,1166901,1925694,2001,03/31/2006 10:03:38 PM,41.951670248,-87.661869204,"(41.951670248, -87.661869204)"
-1490370,G207350,04/11/2001 11:50:00 PM,059XX W PATTERSON AV,2230,LIQUOR LAW VIOLATION,ILLEGAL CONSUMPTION BY MINOR,ALLEY,true,false,1633,,,,22,1136042,1923619,2001,03/31/2006 10:03:38 PM,41.946583634,-87.775357419,"(41.946583634, -87.775357419)"
-1480206,G202948,04/09/2001 11:44:08 PM,039XX W ARTHINGTON ST,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1132,,,,18,1150137,1895781,2001,03/31/2006 10:03:38 PM,41.86992999,-87.724274534,"(41.86992999, -87.724274534)"
-1471135,G202838,04/09/2001 09:54:42 PM,088XX S INDIANA AV,1310,CRIMINAL DAMAGE,TO PROPERTY,"SCHOOL, PUBLIC, GROUNDS",false,false,0632,,,,14,1179172,1846656,2001,03/31/2006 10:03:38 PM,41.734511826,-87.619177591,"(41.734511826, -87.619177591)"
-1467116,G200181,04/08/2001 07:15:00 PM,054XX S ABERDEEN ST,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,false,false,0934,,,,04B,1169949,1868657,2001,03/31/2006 10:03:38 PM,41.795090404,-87.652328347,"(41.795090404, -87.652328347)"
-1465602,G196648,04/07/2001 02:32:21 AM,079XX S LAFAYETTE AV,0560,ASSAULT,SIMPLE,STREET,false,false,0623,,,,08A,1177269,1852609,2001,03/31/2006 10:03:38 PM,41.750890705,-87.625970021,"(41.750890705, -87.625970021)"
-1466691,G194842,04/06/2001 02:40:00 PM,034XX N MAJOR AV,041B,BATTERY,AGGRAVATED: OTHER FIREARM,STREET,false,false,1633,,,,04B,1137794,1922326,2001,03/31/2006 10:03:38 PM,41.943004033,-87.768948794,"(41.943004033, -87.768948794)"
-1463114,G194870,04/06/2001 08:00:00 AM,014XX S KOSTNER AV,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,1011,,,,26,1147327,1892627,2001,03/31/2006 10:03:38 PM,41.861329275,-87.734671729,"(41.861329275, -87.734671729)"
-1459497,G189157,04/03/2001 04:50:00 PM,119XX S PRINCETON AV,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0522,,,,14,1176400,1825387,2001,03/31/2006 10:03:38 PM,41.676209336,-87.62996933,"(41.676209336, -87.62996933)"
-1460552,G190565,04/03/2001 09:30:00 AM,012XX W BARRY AV,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1932,,,,05,1167491,1920757,2001,03/31/2006 10:03:38 PM,41.938110195,-87.65984298,"(41.938110195, -87.65984298)"
-1459087,G189335,04/03/2001 08:30:00 AM,047XX S WESTERN AV,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,true,false,0915,,,,05,1161367,1872983,2001,03/31/2006 10:03:38 PM,41.807143747,-87.683678807,"(41.807143747, -87.683678807)"
-1456493,G187505,04/02/2001 09:45:00 PM,031XX W NORTH AV,0820,THEFT,$500 AND UNDER,STREET,false,false,1421,,,,06,1154763,1910498,2001,12/04/2014 12:43:35 PM,41.910223539,-87.706896556,"(41.910223539, -87.706896556)"
-1462476,G190453,04/02/2001 08:00:00 PM,037XX W DICKENS AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2525,,,,07,1151336,1913740,2001,03/31/2006 10:03:38 PM,41.919187874,-87.719400832,"(41.919187874, -87.719400832)"
-1467694,G186913,04/02/2001 05:05:00 PM,048XX W CONCORD PL,0460,BATTERY,SIMPLE,RESIDENCE,false,true,2533,,,,08B,1143457,1910563,2001,03/31/2006 10:03:38 PM,41.910620981,-87.748429152,"(41.910620981, -87.748429152)"
-1457480,G185112,04/01/2001 07:30:00 PM,053XX W MONROE ST,0460,BATTERY,SIMPLE,OTHER,false,true,1522,,,,08B,1140736,1899182,2001,03/31/2006 10:03:38 PM,41.87944063,-87.758705253,"(41.87944063, -87.758705253)"
-1547715,G302632,04/01/2001 12:00:00 AM,018XX S SPRINGFIELD AV,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1014,,,,26,1150721,1890982,2001,03/31/2006 10:03:38 PM,41.85674958,-87.722255886,"(41.85674958, -87.722255886)"
-1454451,G184503,03/31/2001 12:30:00 AM,041XX S ROCKWELL ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0912,,,,06,1159671,1877343,2001,12/04/2014 12:43:35 PM,41.819143141,-87.689779497,"(41.819143141, -87.689779497)"
-1459326,G181415,03/30/2001 09:42:51 PM,026XX W MADISON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1331,,,,18,1158676,1899953,2001,03/31/2006 10:03:38 PM,41.881207826,-87.692810896,"(41.881207826, -87.692810896)"
-1468186,G179699,03/30/2001 04:37:33 AM,060XX W WELLINGTON AV,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,2511,,,,14,1135522,1919353,2001,03/31/2006 10:03:38 PM,41.934886545,-87.777370512,"(41.934886545, -87.777370512)"
-1448638,G174386,03/27/2001 04:22:03 PM,042XX W 18 ST,142B,WEAPONS VIOLATION,UNLAWFUL SALE OTHER FIREARM,SIDEWALK,true,false,1012,,,,15,1148477,1891003,2001,03/31/2006 10:03:38 PM,41.856850736,-87.730492119,"(41.856850736, -87.730492119)"
-1452130,G174427,03/27/2001 12:30:00 PM,002XX N CENTRAL AV,0820,THEFT,$500 AND UNDER,"SCHOOL, PUBLIC, BUILDING",false,false,1512,,,,06,1138959,1901197,2001,12/04/2014 12:43:35 PM,41.885002514,-87.765181234,"(41.885002514, -87.765181234)"
-1447725,G172546,03/26/2001 07:00:00 PM,006XX W DIVISION ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,CHA APARTMENT,true,false,1822,,,,26,1171830,1908290,2001,03/31/2006 10:03:38 PM,41.903805509,-87.644264537,"(41.903805509, -87.644264537)"
-1467354,G168915,03/24/2001 10:30:00 PM,035XX S FEDERAL ST,2024,NARCOTICS,POSS: HEROIN(WHITE),CHA PARKING LOT/GROUNDS,true,false,0211,,,,18,1176254,1881288,2001,03/31/2006 10:03:38 PM,41.829611625,-87.628828268,"(41.829611625, -87.628828268)"
-1439726,G166215,03/23/2001 04:00:00 PM,113XX S EDBROOKE AV,0460,BATTERY,SIMPLE,RESIDENCE,true,true,0531,,,,08B,1179192,1829519,2001,03/31/2006 10:03:38 PM,41.687485157,-87.619624701,"(41.687485157, -87.619624701)"
-1439766,G166216,03/23/2001 03:38:00 PM,014XX N CICERO AV,0320,ROBBERY,STRONGARM - NO WEAPON,CHA PARKING LOT/GROUNDS,false,false,2533,,,,03,1144070,1909425,2001,03/31/2006 10:03:38 PM,41.907486694,-87.746205806,"(41.907486694, -87.746205806)"
-1437557,G161730,03/21/2001 03:10:00 PM,062XX N HAMLIN AV,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,1711,,,,08B,1149868,1940981,2001,03/31/2006 10:03:38 PM,41.993967902,-87.724082918,"(41.993967902, -87.724082918)"
-1447645,G161307,03/21/2001 01:30:00 PM,119XX S LOWE AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0524,,,,18,1174097,1825418,2001,03/31/2006 10:03:38 PM,41.676345684,-87.638397933,"(41.676345684, -87.638397933)"
-1449408,G161265,03/21/2001 10:30:00 AM,049XX S WABASH AV,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0231,,,,08A,1177513,1872170,2001,03/31/2006 10:03:38 PM,41.804562628,-87.624485028,"(41.804562628, -87.624485028)"
-1436732,G160516,03/21/2001 06:05:00 AM,012XX W COLUMBIA AV,0820,THEFT,$500 AND UNDER,STREET,true,false,2432,,,,06,1166991,1944918,2001,12/04/2014 12:43:35 PM,42.00441954,-87.660983698,"(42.00441954, -87.660983698)"
-1436905,G160469,03/21/2001 03:51:12 AM,035XX W 63 ST,0460,BATTERY,SIMPLE,STREET,false,false,0823,,,,08B,1153981,1862575,2001,03/31/2006 10:03:38 PM,41.778732694,-87.711044957,"(41.778732694, -87.711044957)"
-1443933,G163293,03/20/2001 09:20:00 PM,074XX S MERRILL AV,0820,THEFT,$500 AND UNDER,STREET,false,false,0333,,,,06,1191888,1855734,2001,12/04/2014 12:43:35 PM,41.759123666,-87.572298368,"(41.759123666, -87.572298368)"
-1434829,G159774,03/20/2001 07:10:00 PM,045XX N HAZEL ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,2313,,,,14,1169483,1930443,2001,03/31/2006 10:03:38 PM,41.964645764,-87.652239112,"(41.964645764, -87.652239112)"
-1439370,G158868,03/20/2001 12:14:16 PM,052XX W NORTH AV,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,2532,,,,16,1141077,1910172,2001,03/31/2006 10:03:38 PM,41.909592236,-87.757182157,"(41.909592236, -87.757182157)"
-1434229,G156720,03/18/2001 11:30:00 PM,0000X E 75 ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0323,,,,14,1178204,1855347,2001,03/31/2006 10:03:38 PM,41.758382945,-87.622460845,"(41.758382945, -87.622460845)"
-1431983,G153956,03/18/2001 01:50:00 AM,058XX N CAMPBELL AV,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,2011,,,,05,1158556,1938936,2001,03/31/2006 10:03:38 PM,41.988182277,-87.692181023,"(41.988182277, -87.692181023)"
-1429858,G153702,03/17/2001 08:30:00 PM,069XX N GLENWOOD AV,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,2431,,,,03,1165528,1946220,2001,03/31/2006 10:03:38 PM,42.008023652,-87.666328766,"(42.008023652, -87.666328766)"
-1429045,G152067,03/17/2001 02:00:00 AM,009XX W NEWPORT AV,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,2331,,,,14,1169486,1923054,2001,03/31/2006 10:03:38 PM,41.944370017,-87.65244393,"(41.944370017, -87.65244393)"
-1429449,G152865,03/16/2001 10:00:00 PM,047XX N KILPATRICK AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1722,,,,14,1144209,1931300,2001,03/31/2006 10:03:38 PM,41.967511128,-87.745143848,"(41.967511128, -87.745143848)"
-1429965,G153084,03/16/2001 09:00:00 PM,065XX N WASHTENAW AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2412,,,,14,1157099,1943280,2001,03/31/2006 10:03:38 PM,42.0001322,-87.697421357,"(42.0001322, -87.697421357)"
-1429036,G150163,03/16/2001 09:16:44 AM,014XX W GARFIELD BL,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,true,false,0933,,,,14,1167476,1868309,2001,03/31/2006 10:03:38 PM,41.794188853,-87.66140683,"(41.794188853, -87.66140683)"
-1431846,G150260,03/15/2001 02:25:00 PM,024XX N KOSTNER AV,0460,BATTERY,SIMPLE,SIDEWALK,false,false,2524,,,,08B,1146663,1916245,2001,03/31/2006 10:03:38 PM,41.926152351,-87.736506102,"(41.926152351, -87.736506102)"
-1429275,G148362,03/15/2001 11:59:25 AM,030XX S DR MARTN LUTHR KING JR DR,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,2112,,,,08B,1179262,1885167,2001,03/31/2006 10:03:38 PM,41.840187663,-87.617673503,"(41.840187663, -87.617673503)"
-1425357,G146987,03/14/2001 05:55:00 PM,016XX W MARQUETTE RD,0560,ASSAULT,SIMPLE,ALLEY,false,false,0725,,,,08A,1166598,1860321,2001,03/31/2006 10:03:38 PM,41.772287568,-87.664854144,"(41.772287568, -87.664854144)"
-1480348,G143189,03/12/2001 08:01:00 PM,109XX S RACINE AV,2230,LIQUOR LAW VIOLATION,ILLEGAL CONSUMPTION BY MINOR,STREET,true,false,2234,,,,22,1170324,1831941,2001,03/31/2006 10:03:38 PM,41.694328546,-87.652019238,"(41.694328546, -87.652019238)"
-1431896,G142993,03/12/2001 07:51:00 PM,107XX S EDBROOKE AV,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0513,,,,18,1179099,1834018,2001,03/31/2006 10:03:38 PM,41.699833166,-87.619828729,"(41.699833166, -87.619828729)"
-1421338,G142557,03/12/2001 04:24:06 PM,028XX N LEAVITT ST,0820,THEFT,$500 AND UNDER,RESIDENCE,false,false,1913,,,,06,1161731,1918772,2001,12/04/2014 12:43:35 PM,41.932785457,-87.681067642,"(41.932785457, -87.681067642)"
-1421137,G142769,03/12/2001 08:00:00 AM,130XX S GREENWOOD AV,0620,BURGLARY,UNLAWFUL ENTRY,CHA APARTMENT,false,false,0533,,,,05,1185831,1818718,2001,03/31/2006 10:03:38 PM,41.657692223,-87.595658839,"(41.657692223, -87.595658839)"
-1419480,G139643,03/11/2001 05:31:47 AM,057XX N LEONARD AV,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,true,false,1622,,,,05,1137747,1938468,2001,03/31/2006 10:03:38 PM,41.987300009,-87.768730585,"(41.987300009, -87.768730585)"
-1420162,G141253,03/11/2001 01:30:00 AM,029XX W BELDEN AV,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,1414,,,,14,1156501,1915225,2001,03/31/2006 10:03:38 PM,41.923159798,-87.700383643,"(41.923159798, -87.700383643)"
-1419186,G141247,03/10/2001 09:00:00 PM,039XX N ALBANY AV,0920,MOTOR VEHICLE THEFT,ATT: AUTOMOBILE,STREET,false,false,1733,,,,07,1154980,1925945,2001,03/31/2006 10:03:38 PM,41.952606934,-87.705683963,"(41.952606934, -87.705683963)"
-1417373,G137018,03/09/2001 08:44:39 PM,038XX W ROOSEVELT RD,0460,BATTERY,SIMPLE,GAS STATION,false,false,1011,,,,08B,1151234,1894403,2001,03/31/2006 10:03:38 PM,41.866127172,-87.720283218,"(41.866127172, -87.720283218)"
-1421316,G135487,03/09/2001 07:15:00 AM,035XX N MILWAUKEE AV,051B,ASSAULT,AGGRAVATED: OTHER FIREARM,PARKING LOT/GARAGE(NON.RESID.),false,false,1731,,,,04A,1147010,1923398,2001,03/31/2006 10:03:38 PM,41.945774186,-87.735047602,"(41.945774186, -87.735047602)"
-1425247,G134359,03/08/2001 04:56:15 PM,133XX S LANGLEY AV,2027,NARCOTICS,POSS: CRACK,CHA PARKING LOT/GROUNDS,true,false,0533,,,,18,1183373,1817105,2001,03/31/2006 10:03:38 PM,41.653323281,-87.604702939,"(41.653323281, -87.604702939)"
-1426082,G133111,03/08/2001 01:15:58 AM,007XX N PINE AV,2027,NARCOTICS,POSS: CRACK,OTHER,true,false,1524,,,,18,1139342,1904589,2001,03/31/2006 10:03:38 PM,41.894303621,-87.763692084,"(41.894303621, -87.763692084)"
-1421756,G137414,03/05/2001 11:00:00 AM,077XX S EUCLID AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0414,,,,14,1190506,1854021,2001,03/31/2006 10:03:38 PM,41.754456486,-87.577418412,"(41.754456486, -87.577418412)"
-1431498,G117564,03/05/2001 09:50:00 AM,008XX N TRUMBULL AV,2090,NARCOTICS,ALTER/FORGE PRESCRIPTION,STREET,true,false,1121,,,,18,1153177,1905645,2001,03/31/2006 10:03:38 PM,41.896938092,-87.712851879,"(41.896938092, -87.712851879)"
-1422194,G125064,03/04/2001 02:15:00 AM,076XX N ROGERS AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2422,,,,18,1165187,1950533,2001,03/31/2006 10:03:38 PM,42.019865877,-87.66745994,"(42.019865877, -87.66745994)"
-1408197,G125548,03/03/2001 09:00:00 PM,035XX N CLARK ST,0820,THEFT,$500 AND UNDER,BAR OR TAVERN,false,false,2331,,,,06,1168411,1924085,2001,12/04/2014 12:43:35 PM,41.94722249,-87.656365212,"(41.94722249, -87.656365212)"
-1410982,G124346,03/03/2001 06:43:00 PM,036XX S MICHIGAN AV,0560,ASSAULT,SIMPLE,APARTMENT,false,true,0211,,,,08A,1177828,1881070,2001,03/31/2006 10:03:38 PM,41.82897785,-87.623059954,"(41.82897785, -87.623059954)"
-1406864,G122316,03/02/2001 06:15:00 PM,048XX S ASHLAND AV,0460,BATTERY,SIMPLE,SMALL RETAIL STORE,true,false,0931,,,,08B,1166444,1872794,2001,03/31/2006 10:03:38 PM,41.80651829,-87.66506327,"(41.80651829, -87.66506327)"
-1405220,G120634,03/01/2001 09:30:00 PM,065XX S UNION AV,0820,THEFT,$500 AND UNDER,STREET,false,false,0723,,,,06,1172600,1861265,2001,12/04/2014 12:43:35 PM,41.77474789,-87.642824826,"(41.77474789, -87.642824826)"
-1404966,G119509,03/01/2001 10:16:00 AM,001XX N WABASH AV,5001,OTHER OFFENSE,OTHER CRIME INVOLVING PROPERTY,CURRENCY EXCHANGE,false,false,0122,,,,26,1176749,1901418,2001,03/31/2006 10:03:38 PM,41.884838553,-87.626404048,"(41.884838553, -87.626404048)"
-1404550,G119259,03/01/2001 12:01:00 AM,026XX W WASHINGTON BL,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1331,,,,05,1158473,1900617,2001,03/31/2006 10:03:38 PM,41.883034059,-87.693538125,"(41.883034059, -87.693538125)"
-1403867,G119213,03/01/2001 12:00:00 AM,076XX S LAFLIN ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0612,,,,07,1167404,1853969,2001,03/31/2006 10:03:38 PM,41.754839607,-87.662081285,"(41.754839607, -87.662081285)"
-1404200,G119542,02/28/2001 12:30:00 PM,031XX S DR MARTN LUTHR KING JR DR,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,2112,,,,08B,1179284,1884489,2001,03/31/2006 10:03:38 PM,41.83832668,-87.617613505,"(41.83832668, -87.617613505)"
-1704552,G504298,02/28/2001 12:10:00 AM,113XX S EDBROOKE AV,1751,OFFENSE INVOLVING CHILDREN,CRIM SEX ABUSE BY FAM MEMBER,RESIDENCE,false,false,0531,,,,20,1179258,1829938,2001,01/14/2007 05:31:37 AM,41.688633453,-87.619370371,"(41.688633453, -87.619370371)"
-1412121,G112510,02/25/2001 08:50:00 PM,045XX S FEDERAL ST,2090,NARCOTICS,ALTER/FORGE PRESCRIPTION,CHA HALLWAY/STAIRWELL/ELEVATOR,true,false,0221,,,,18,1176499,1874647,2001,03/31/2006 10:03:38 PM,41.811382632,-87.628129354,"(41.811382632, -87.628129354)"
-1399247,G112334,02/25/2001 09:00:00 AM,027XX W LELAND AV,0820,THEFT,$500 AND UNDER,STREET,false,false,1911,,,,06,1157385,1931066,2001,12/04/2014 12:43:35 PM,41.966610583,-87.696703202,"(41.966610583, -87.696703202)"
-1395413,G110579,02/24/2001 05:00:00 PM,031XX N CLARK ST,0820,THEFT,$500 AND UNDER,SMALL RETAIL STORE,true,false,2332,,,,06,1170332,1920953,2001,12/04/2014 12:43:35 PM,41.938586306,-87.649396035,"(41.938586306, -87.649396035)"
-1392294,G105659,02/21/2001 05:00:00 PM,031XX W ARMITAGE AV,0460,BATTERY,SIMPLE,RESTAURANT,true,false,1414,,,,08B,1154877,1913155,2001,03/31/2006 10:03:38 PM,41.917512287,-87.706406414,"(41.917512287, -87.706406414)"
-1393424,G105093,02/21/2001 10:15:00 AM,099XX S CRANDON AV,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0431,,,,08B,1193433,1839450,2001,03/31/2006 10:03:38 PM,41.714401283,-87.56716741,"(41.714401283, -87.56716741)"
-1429081,G144381,02/20/2001 02:00:00 PM,093XX S COLFAX AV,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0423,,,,14,1195098,1843440,2001,03/31/2006 10:03:38 PM,41.725309394,-87.560938511,"(41.725309394, -87.560938511)"
-1404175,G101155,02/19/2001 10:45:00 AM,035XX S FEDERAL ST,2024,NARCOTICS,POSS: HEROIN(WHITE),CHA PARKING LOT/GROUNDS,true,false,0211,,,,18,1176246,1881571,2001,03/31/2006 10:03:38 PM,41.83038838,-87.628849105,"(41.83038838, -87.628849105)"
-1387919,G099631,02/18/2001 12:06:33 PM,081XX S KING DR,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0631,,,,14,1180366,1850754,2001,03/31/2006 10:03:38 PM,41.745729946,-87.614677968,"(41.745729946, -87.614677968)"
-1392624,G097558,02/17/2001 05:15:00 AM,035XX W WALNUT ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,RESIDENCE,false,true,1123,,,,04B,1152934,1901505,2001,03/31/2006 10:03:38 PM,41.885582338,-87.713854165,"(41.885582338, -87.713854165)"
-1385129,G096620,02/16/2001 03:25:00 PM,010XX N LOCKWOOD AV,0560,ASSAULT,SIMPLE,STREET,false,false,1524,,,,08A,1140785,1906742,2001,03/31/2006 10:03:38 PM,41.900185298,-87.758339315,"(41.900185298, -87.758339315)"
-1384311,G094637,02/15/2001 05:00:00 PM,011XX S MOZART ST,0460,BATTERY,SIMPLE,APARTMENT,false,true,1135,,,,08B,1157493,1894893,2001,03/31/2006 10:03:38 PM,41.867346831,-87.697292499,"(41.867346831, -87.697292499)"
-1397656,G094603,02/15/2001 04:34:03 PM,002XX S LOTUS AV,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1522,,,,18,1139951,1898677,2001,03/31/2006 10:03:38 PM,41.878069231,-87.761600044,"(41.878069231, -87.761600044)"
-1382397,G093344,02/15/2001 12:45:00 AM,027XX S DEARBORN ST,0460,BATTERY,SIMPLE,CHA APARTMENT,false,false,2113,,,,08B,1176354,1886628,2001,03/31/2006 10:03:38 PM,41.844262752,-87.628300558,"(41.844262752, -87.628300558)"
-1381966,G093195,02/14/2001 07:30:00 PM,004XX W 58 ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,true,false,0711,,,,05,1174518,1866535,2001,03/31/2006 10:03:38 PM,41.78916687,-87.635637008,"(41.78916687, -87.635637008)"
-1388773,G098031,02/14/2001 04:00:00 PM,023XX S STATE ST,0810,THEFT,OVER $500,CHA APARTMENT,false,false,2113,,,,06,1176649,1888559,2001,12/04/2014 12:43:35 PM,41.849554907,-87.627159699,"(41.849554907, -87.627159699)"
-1383868,G091625,02/14/2001 12:01:00 AM,042XX S WASHTENAW AV,0337,ROBBERY,ATTEMPT: ARMED-OTHER DANG WEAP,STREET,false,false,0912,,,,03,1159107,1876529,2001,03/31/2006 10:03:38 PM,41.816920998,-87.691870768,"(41.816920998, -87.691870768)"
-1379556,G091127,02/13/2001 10:00:00 PM,005XX W CHESTNUT ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1823,,,,26,1172447,1906084,2001,03/31/2006 10:03:38 PM,41.8977385,-87.642063461,"(41.8977385, -87.642063461)"
-1379405,G089888,02/13/2001 10:30:00 AM,071XX S EMERALD AV,0460,BATTERY,SIMPLE,STREET,false,true,0732,,,,08B,1172535,1857678,2001,03/31/2006 10:03:38 PM,41.764906167,-87.643168622,"(41.764906167, -87.643168622)"
-1377526,G086961,02/12/2001 12:06:21 AM,049XX W ERIE ST,0460,BATTERY,SIMPLE,SIDEWALK,false,true,1532,,,,08B,1143459,1903825,2001,03/31/2006 10:03:38 PM,41.892131114,-87.748590525,"(41.892131114, -87.748590525)"
-1384550,G086743,02/11/2001 08:50:00 PM,087XX S PEORIA ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2222,,,,18,1171787,1846992,2001,03/31/2006 10:03:38 PM,41.735598828,-87.646223035,"(41.735598828, -87.646223035)"
-1380272,G086084,02/10/2001 11:00:00 PM,022XX W DIVERSEY AV,0460,BATTERY,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),false,true,1432,,,,08B,1161039,1918520,2001,03/31/2006 10:03:38 PM,41.932108371,-87.683617692,"(41.932108371, -87.683617692)"
-1377525,G084005,02/10/2001 10:40:00 AM,006XX W 63 ST,0560,ASSAULT,SIMPLE,GROCERY FOOD STORE,true,false,0711,,,,08A,1173102,1863153,2001,03/31/2006 10:03:38 PM,41.779917703,-87.640928851,"(41.779917703, -87.640928851)"
-1374827,G083327,02/09/2001 10:30:00 PM,076XX S PHILLIPS AV,0460,BATTERY,SIMPLE,RESIDENCE,false,true,0421,,,,08B,1193905,1854460,2001,03/31/2006 10:03:38 PM,41.755578509,-87.564947999,"(41.755578509, -87.564947999)"
-1377070,G078117,02/07/2001 12:40:00 PM,063XX S CALUMET AV,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0312,,,,08A,1179581,1863162,2001,03/31/2006 10:03:38 PM,41.779796785,-87.617175815,"(41.779796785, -87.617175815)"
-1369655,G075911,02/06/2001 08:20:00 AM,0000X E GRAND AV,0820,THEFT,$500 AND UNDER,DEPARTMENT STORE,true,false,1834,,,,06,1176874,1903879,2001,12/04/2014 12:43:35 PM,41.89158884,-87.625870527,"(41.89158884, -87.625870527)"
-1372960,G074566,02/05/2001 06:30:00 PM,046XX S VINCENNES AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0222,,,,18,1180357,1873982,2001,03/31/2006 10:03:38 PM,41.809470071,-87.613998996,"(41.809470071, -87.613998996)"
-1367090,G074170,02/05/2001 02:50:00 PM,011XX W 111 ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,true,2234,,,,08B,1170523,1831224,2001,03/31/2006 10:03:38 PM,41.692356661,-87.65131143,"(41.692356661, -87.65131143)"
-1372792,G073042,02/05/2001 02:00:00 AM,088XX S CALUMET AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0632,,,,18,1179983,1846364,2001,03/31/2006 10:03:38 PM,41.733692038,-87.616215384,"(41.733692038, -87.616215384)"
-1391605,G070986,02/03/2001 09:30:00 PM,042XX W MONROE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1115,,,,18,1147894,1899358,2001,03/31/2006 10:03:38 PM,41.879789074,-87.732417391,"(41.879789074, -87.732417391)"
-1363716,G070658,02/03/2001 06:20:00 PM,066XX N GLENWOOD AV,1570,SEX OFFENSE,PUBLIC INDECENCY,STREET,false,false,2432,,,,17,1165686,1944293,2001,03/31/2006 10:03:38 PM,42.002732548,-87.665802729,"(42.002732548, -87.665802729)"
-1364400,G069245,02/03/2001 12:33:57 AM,009XX E 84 ST,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,0632,,,,08A,1183817,1849444,2001,03/31/2006 10:03:38 PM,41.742055378,-87.602073759,"(41.742055378, -87.602073759)"
-1362946,G069230,02/03/2001 12:25:31 AM,119XX S UNION AV,0460,BATTERY,SIMPLE,RESIDENCE,true,true,0524,,,,08B,1173765,1825514,2001,03/31/2006 10:03:38 PM,41.676616465,-87.639610305,"(41.676616465, -87.639610305)"
-1368099,G075862,02/02/2001 08:30:00 AM,027XX S QUINN ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0923,,,,05,1169647,1886282,2001,03/31/2006 10:03:38 PM,41.843461748,-87.65292399,"(41.843461748, -87.65292399)"
-1361758,G066417,02/01/2001 03:40:00 PM,020XX S MICHIGAN AV,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE PORCH/HALLWAY,true,false,2111,,,,26,1177566,1890708,2001,03/31/2006 10:03:38 PM,41.855431167,-87.62372905,"(41.855431167, -87.62372905)"
-1360910,G066353,02/01/2001 03:20:00 PM,055XX S HOMAN AV,0560,ASSAULT,SIMPLE,STREET,false,true,0822,,,,08A,1154608,1867905,2001,03/31/2006 10:03:38 PM,41.793346526,-87.708604327,"(41.793346526, -87.708604327)"
-1361049,G067151,02/01/2001 08:50:00 AM,028XX S CALUMET AV,0820,THEFT,$500 AND UNDER,RESIDENCE,false,false,2112,,,,06,1178911,1886079,2001,12/04/2014 12:43:35 PM,41.842698274,-87.618933678,"(41.842698274, -87.618933678)"
-676,G065240,02/01/2001 12:10:00 AM,016XX W HOWARD ST,0110,HOMICIDE,FIRST DEGREE MURDER,STREET,false,false,2422,024,49,1,01A,1163938,1950386,2001,08/25/2009 02:50:36 PM,42.019489078,-87.672060316,"(42.019489078, -87.672060316)"
-1357834,G062955,01/30/2001 08:26:00 PM,063XX S KEATING AV,0453,BATTERY,AGGRAVATED PO: OTHER DANG WEAP,ALLEY,true,false,0813,,,,04B,1145841,1862091,2001,03/31/2006 10:03:38 PM,41.777562285,-87.74089946,"(41.777562285, -87.74089946)"
-1356489,G059773,01/29/2001 01:15:00 PM,073XX S YALE AV,0460,BATTERY,SIMPLE,RESIDENCE,true,true,0731,,,,08B,1175937,1856141,2001,03/31/2006 10:03:38 PM,41.76061288,-87.630745391,"(41.76061288, -87.630745391)"
-1368379,G059569,01/29/2001 11:58:00 AM,009XX E 79 ST,2017,NARCOTICS,MANU/DELIVER:CRACK,STREET,true,false,0624,,,,18,1183847,1852781,2001,03/31/2006 10:03:38 PM,41.75121176,-87.601859903,"(41.75121176, -87.601859903)"
-1359540,G059519,01/29/2001 11:35:00 AM,049XX S FEDERAL ST,0460,BATTERY,SIMPLE,CHA APARTMENT,false,true,0231,,,,08B,1176576,1872029,2001,03/31/2006 10:03:38 PM,41.804196865,-87.627925741,"(41.804196865, -87.627925741)"
-1352664,G055242,01/27/2001 05:00:00 AM,015XX N MAPLEWOOD AV,0460,BATTERY,SIMPLE,RESIDENCE,false,true,1423,,,,08B,1159165,1910146,2001,03/31/2006 10:03:38 PM,41.909168263,-87.690734988,"(41.909168263, -87.690734988)"
-1361279,G053951,01/26/2001 04:15:00 PM,010XX N LECLAIRE AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1531,,,,18,1142211,1906418,2001,03/31/2006 10:03:38 PM,41.899269858,-87.75310955,"(41.899269858, -87.75310955)"
-1360351,G051290,01/25/2001 11:55:00 AM,028XX W ROOSEVELT RD,2027,NARCOTICS,POSS: CRACK,STREET,true,false,1022,,,,18,1157524,1894542,2001,03/31/2006 10:03:38 PM,41.86638302,-87.697188242,"(41.86638302, -87.697188242)"
-1365947,G051186,01/25/2001 09:27:00 AM,031XX W ARMITAGE AV,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1414,,,,16,1155158,1913160,2001,03/31/2006 10:03:38 PM,41.917520366,-87.705373874,"(41.917520366, -87.705373874)"
-1348969,G049821,01/24/2001 03:00:00 PM,023XX N CENTRAL PARK AV,0460,BATTERY,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1413,,,,08B,1152018,1915659,2001,03/31/2006 10:03:38 PM,41.924440352,-87.716844377,"(41.924440352, -87.716844377)"
-1349497,G047657,01/22/2001 02:00:00 PM,037XX N PANAMA AV,5000,OTHER OFFENSE,OTHER CRIME AGAINST PERSON,"SCHOOL, PUBLIC, BUILDING",false,false,1631,,,,26,1121355,1924041,2001,03/31/2006 10:03:38 PM,41.947991326,-87.829334474,"(41.947991326, -87.829334474)"
-1343027,G043676,01/21/2001 01:00:00 PM,040XX W POTOMAC AV,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,2534,,,,26,1149302,1908399,2001,03/31/2006 10:03:38 PM,41.904571368,-87.727012776,"(41.904571368, -87.727012776)"
-1341944,G042370,01/20/2001 05:00:00 PM,043XX W NORTH AV,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),false,false,2534,,,,07,1147071,1910318,2001,03/31/2006 10:03:38 PM,41.909880281,-87.735158791,"(41.909880281, -87.735158791)"
-1358327,G040771,01/20/2001 12:36:54 AM,035XX N CICERO AV,2022,NARCOTICS,POSS: COCAINE,TAVERN/LIQUOR STORE,true,false,1634,,,,18,1143717,1922874,2001,03/31/2006 10:03:38 PM,41.944398718,-87.747164818,"(41.944398718, -87.747164818)"
-1341148,G040527,01/19/2001 08:35:52 PM,078XX S EXCHANGE AV,0820,THEFT,$500 AND UNDER,APARTMENT,false,false,0421,,,,06,1197037,1853402,2001,12/04/2014 12:43:35 PM,41.752597946,-87.553505384,"(41.752597946, -87.553505384)"
-1344184,G044973,01/19/2001 06:00:00 PM,061XX S PULASKI RD,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,false,false,0813,,,,14,1150718,1863499,2001,03/31/2006 10:03:38 PM,41.78133247,-87.722983459,"(41.78133247, -87.722983459)"
-1338946,G038108,01/18/2001 06:00:00 PM,053XX N MILWAUKEE AV,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,1622,,,,06,1137492,1935100,2001,12/04/2014 12:43:35 PM,41.978062545,-87.769750009,"(41.978062545, -87.769750009)"
-1350766,G037035,01/18/2001 10:32:09 AM,015XX N KINGSBURY ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1822,,,,16,1169639,1910112,2001,03/31/2006 10:03:38 PM,41.908853197,-87.652259411,"(41.908853197, -87.652259411)"
-1342961,G036444,01/17/2001 11:10:00 PM,070XX S CARPENTER ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,true,false,0733,,,,15,1170490,1858118,2001,03/31/2006 10:03:38 PM,41.766158364,-87.65065132,"(41.766158364, -87.65065132)"
-1336773,G035260,01/17/2001 01:30:00 AM,043XX S WABASH AV,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0221,,,,14,1177392,1876448,2001,03/31/2006 10:03:38 PM,41.816304578,-87.624799443,"(41.816304578, -87.624799443)"
-1335810,G033134,01/16/2001 11:15:00 AM,046XX W DIVERSEY AV,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),false,false,2521,,,,07,1144877,1918250,2001,03/31/2006 10:03:38 PM,41.931688189,-87.743018145,"(41.931688189, -87.743018145)"
-1334386,G032106,01/15/2001 09:00:00 PM,071XX S SEELEY AV,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0735,,,,08B,1163975,1857003,2001,03/31/2006 10:03:38 PM,41.763238063,-87.674562478,"(41.763238063, -87.674562478)"
-1332275,G030498,01/14/2001 08:00:00 PM,005XX W WRIGHTWOOD AV,0820,THEFT,$500 AND UNDER,STREET,false,false,2333,,,,06,1172382,1917980,2001,12/04/2014 12:43:35 PM,41.930383149,-87.641949981,"(41.930383149, -87.641949981)"
-1332853,G028467,01/14/2001 01:00:07 AM,020XX W 79 ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0611,,,,04B,1163945,1852215,2001,03/31/2006 10:03:38 PM,41.750099735,-87.674806765,"(41.750099735, -87.674806765)"
-1350635,G028262,01/13/2001 10:45:00 PM,052XX W VAN BUREN ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,1522,,,,18,1141115,1897460,2001,03/31/2006 10:03:38 PM,41.874708269,-87.757356037,"(41.874708269, -87.757356037)"
-1331306,G027632,01/13/2001 05:00:00 PM,011XX W BRYN MAWR AV,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,true,false,2023,,,,11,1167635,1937312,2001,03/31/2006 10:03:38 PM,41.983534617,-87.658834827,"(41.983534617, -87.658834827)"
-1328417,G023112,01/11/2001 05:00:00 PM,044XX N HAMILTON AV,1330,CRIMINAL TRESPASS,TO LAND,APARTMENT,true,false,1911,,,,26,1161297,1929315,2001,03/31/2006 10:03:38 PM,41.961725091,-87.68236829,"(41.961725091, -87.68236829)"
-1331296,G028311,01/10/2001 10:00:00 AM,075XX N RIDGE BL,0820,THEFT,$500 AND UNDER,OTHER,false,false,2411,,,,06,1160521,1949918,2001,12/04/2014 12:43:35 PM,42.018276627,-87.684647544,"(42.018276627, -87.684647544)"
-1323005,G017080,01/08/2001 11:30:00 PM,110XX S ESMOND ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,RESIDENCE,true,false,2212,,,,04B,1166186,1831485,2001,03/31/2006 10:03:38 PM,41.693166026,-87.667182531,"(41.693166026, -87.667182531)"
-1322900,G015838,01/08/2001 11:05:00 AM,045XX N WESTERN AV,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,RESIDENCE,false,false,1911,,,,11,1159593,1930080,2001,03/31/2006 10:03:38 PM,41.963859642,-87.688611996,"(41.963859642, -87.688611996)"
-1325993,G015366,01/08/2001 08:00:00 AM,062XX S STATE ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0311,,,,06,1177384,1863310,2001,12/04/2014 12:43:35 PM,41.78025284,-87.625225785,"(41.78025284, -87.625225785)"
-1326211,G014640,01/07/2001 08:29:31 PM,026XX W HIRSCH ST,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,ALLEY,true,false,1423,,,,07,1158370,1909246,2001,03/31/2006 10:03:38 PM,41.906714897,-87.693680117,"(41.906714897, -87.693680117)"
-1320069,G012340,01/06/2001 04:52:52 PM,0000X E 102 PL,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,false,false,0511,,,,26,1178308,1836978,2001,03/31/2006 10:03:38 PM,41.707973763,-87.622635581,"(41.707973763, -87.622635581)"
-1318856,G011167,01/06/2001 02:00:00 AM,005XX S CLINTON ST,0610,BURGLARY,FORCIBLE ENTRY,OTHER,false,false,0131,,,,05,1172768,1897890,2001,03/31/2006 10:03:38 PM,41.875246552,-87.641127239,"(41.875246552, -87.641127239)"
-1323242,G011954,01/05/2001 10:00:00 PM,076XX S COTTAGE GROVE AV,0820,THEFT,$500 AND UNDER,TAVERN/LIQUOR STORE,false,false,0624,,,,06,1182848,1854470,2001,12/04/2014 12:43:35 PM,41.7558698,-87.605468342,"(41.7558698, -87.605468342)"
-1347392,G009796,01/05/2001 03:00:00 PM,035XX S FEDERAL ST,2024,NARCOTICS,POSS: HEROIN(WHITE),CHA PARKING LOT/GROUNDS,true,false,0211,,,,18,1176254,1881288,2001,03/31/2006 10:03:38 PM,41.829611625,-87.628828268,"(41.829611625, -87.628828268)"
-1323811,G009485,01/05/2001 10:55:00 AM,062XX S NAGLE AV,5001,OTHER OFFENSE,OTHER CRIME INVOLVING PROPERTY,STREET,false,false,0812,,,,26,1134448,1862309,2001,03/31/2006 10:03:38 PM,41.77836831,-87.782662059,"(41.77836831, -87.782662059)"
-1314868,G005912,01/03/2001 06:07:46 PM,078XX S ESSEX AV,0560,ASSAULT,SIMPLE,APARTMENT,true,false,0421,,,,08A,1194175,1853638,2001,03/31/2006 10:03:38 PM,41.753316255,-87.563985474,"(41.753316255, -87.563985474)"
-1350449,G004267,01/02/2001 09:05:50 PM,034XX W CARROLL AV,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1123,,,,18,1153228,1902143,2001,03/31/2006 10:03:38 PM,41.887327246,-87.712757591,"(41.887327246, -87.712757591)"
-1310863,G001452,01/01/2001 04:00:00 PM,033XX N MILWAUKEE AV,1310,CRIMINAL DAMAGE,TO PROPERTY,SMALL RETAIL STORE,false,false,1731,,,,14,1149156,1921775,2001,03/31/2006 10:03:38 PM,41.941279167,-87.727201778,"(41.941279167, -87.727201778)"
-1311253,G000194,01/01/2001 01:39:18 AM,015XX W JARVIS AV,1310,CRIMINAL DAMAGE,TO PROPERTY,CTA PLATFORM,true,false,2423,,,,14,1164761,1949091,2001,03/31/2006 10:03:38 PM,42.015918091,-87.669068759,"(42.015918091, -87.669068759)"
-3182762,HK189377,01/01/2001 12:00:00 AM,086XX S DAMEN AVE,1720,OFFENSE INVOLVING CHILDREN,CONTRIBUTE DELINQUENCY OF A CHILD,RESIDENCE,false,false,0614,006,18,71,20,1164559,1847620,2001,03/31/2006 10:03:38 PM,41.737477456,-87.672686062,"(41.737477456, -87.672686062)"
diff --git a/work-with-data/dataprep/data/json.json b/work-with-data/dataprep/data/json.json
deleted file mode 100644
index dfce4329..00000000
--- a/work-with-data/dataprep/data/json.json
+++ /dev/null
@@ -1,1306 +0,0 @@
-{
-  "inspections": [
-    {
-      "business": {
-        "business_id": "16162",
-        "name": "Quick-N-Ezee Indian Foods",
-        "address": "3861 24th St ",
-        "city": "SF",
-        "postal_code": "94114",
-        "latitude": "",
-        "longitude": "",
-        "phone_number": "",
-        "TaxCode": "H34",
-        "business_certificate": "467114",
-        "application_date": "May  9 2005 12:00AM",
-        "owner_name": "Jagpreet Enterprises",
-        "owner_address": "23682 Clawiter Road\n Hayward\n CA\n 94545"
-      },
-      "Score": "100",
-      "date": "20130223",
-      "type": "Routine - Unscheduled",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "69707",
-        "name": "Little Green Cyclo 2",
-        "address": " Off The Grid  ",
-        "city": "",
-        "postal_code": "",
-        "latitude": "",
-        "longitude": "",
-        "phone_number": "",
-        "TaxCode": "H79",
-        "business_certificate": "453248",
-        "application_date": "Jul 12 2012 12:00AM",
-        "owner_name": "LITTLEGREENCYCLO LLC",
-        "owner_address": "100 Esplanade Ave., Apt. 99\n Pacifica\n CA\n 94044"
-      },
-      "Score": "93",
-      "date": "20130224",
-      "type": "Routine - Unscheduled",
-      "violations": [ { "description": "103112: No hot water or running water (High Risk)" } ]
-    },
-    {
-      "business": {
-        "business_id": "67565",
-        "name": "King of Thai Noodles Cafe",
-        "address": "1541 TARAVAL St ",
-        "city": "SAN FRANCISCO",
-        "postal_code": "94116",
-        "latitude": "37.7427",
-        "longitude": "-122.483",
-        "phone_number": "",
-        "TaxCode": "H25",
-        "business_certificate": "",
-        "application_date": "Oct 12 2011 12:00AM",
-        "owner_name": "Royal Thai Noodles, Inc",
-        "owner_address": "2410 19th Ave\n SF\n CA\n 94116"
-      },
-      "Score": "79",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [
-        { "description": "103139: Improper food storage (Low Risk)" },
-        { "description": "103119: Inadequate and inaccessible handwashing facilities (Moderate Risk)" },
-        { "description": "103120: Moderate risk food holding temperature  (Moderate Risk)" },
-        { "description": "103144: Unapproved or unmaintained equipment or utensils (Low Risk)" },
-        { "description": "103161: Low risk vermin infestation (Low Risk)" },
-        { "description": "103105: Improper cooling methods (High Risk)" }
-      ]
-    },
-    {
-      "business": {
-        "business_id": "67565",
-        "name": "King of Thai Noodles Cafe",
-        "address": "1541 TARAVAL St ",
-        "city": "SAN FRANCISCO",
-        "postal_code": "94116",
-        "latitude": "37.7427",
-        "longitude": "-122.483",
-        "phone_number": "",
-        "TaxCode": "H25",
-        "business_certificate": "",
-        "application_date": "Oct 12 2011 12:00AM",
-        "owner_name": "Royal Thai Noodles, Inc",
-        "owner_address": "2410 19th Ave\n SF\n CA\n 94116"
-      },
-      "Score": "",
-      "date": "20130225",
-      "type": "Complaint",
-      "violations": [
-        { "description": "103139: Improper food storage (Low Risk)" },
-        { "description": "103119: Inadequate and inaccessible handwashing facilities (Moderate Risk)" },
-        { "description": "103120: Moderate risk food holding temperature  (Moderate Risk)" },
-        { "description": "103144: Unapproved or unmaintained equipment or utensils (Low Risk)" },
-        { "description": "103161: Low risk vermin infestation (Low Risk)" },
-        { "description": "103105: Improper cooling methods (High Risk)" }
-      ]
-    },
-    {
-      "business": {
-        "business_id": "68701",
-        "name": "Grindz",
-        "address": "832 Clement St ",
-        "city": "SF",
-        "postal_code": "94118",
-        "latitude": "37.7828",
-        "longitude": "-122.468",
-        "phone_number": "",
-        "TaxCode": "H25",
-        "business_certificate": "467498",
-        "application_date": "Mar 16 2012 12:00AM",
-        "owner_name": "Ono Grindz, LLC",
-        "owner_address": "1055 Granada St.\n Vallejo\n CA\n 94591"
-      },
-      "Score": "100",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "69186",
-        "name": "Premier Catering & Events, Inc.",
-        "address": "1255 22nd St ",
-        "city": "S.F.",
-        "postal_code": "94107",
-        "latitude": "",
-        "longitude": "",
-        "phone_number": "14155530288",
-        "TaxCode": "H30",
-        "business_certificate": "362812",
-        "application_date": "Apr 30 2012 12:00AM",
-        "owner_name": "Premier Catering & Events, Inc.",
-        "owner_address": "298 Magellan Ave.\n SF\n CA\n 94116"
-      },
-      "Score": "",
-      "date": "20130225",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "2689",
-        "name": "THE BLUE PLATE",
-        "address": "3218 MISSION St ",
-        "city": "SF",
-        "postal_code": "94110",
-        "latitude": "37.7452",
-        "longitude": "-122.42",
-        "phone_number": "14155286777",
-        "TaxCode": "H25",
-        "business_certificate": "325714",
-        "application_date": "",
-        "owner_name": "BLUE ENCLAVE LLC",
-        "owner_address": "3218 MISSION ST.\n SAN FRANCISCO\n CA\n 94110"
-      },
-      "Score": "98",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [ { "description": "103143: Inadequate warewashing facilities or equipment (Low Risk)" } ]
-    },
-    {
-      "business": {
-        "business_id": "15806",
-        "name": "Vital Tea Leaf",
-        "address": "1044 Grant Ave",
-        "city": "San Francisco",
-        "postal_code": "94133",
-        "latitude": "37.7966",
-        "longitude": "-122.407",
-        "phone_number": "",
-        "TaxCode": "H24",
-        "business_certificate": "388301",
-        "application_date": "May 23 2005 12:00AM",
-        "owner_name": "Minh H. Duong",
-        "owner_address": "1044 Grant Ave\n San Francisco\n CA\n 94133"
-      },
-      "Score": "98",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [ { "description": "103157: Food safety certificate or food handler card not available (Low Risk)" } ]
-    },
-    {
-      "business": {
-        "business_id": "21807",
-        "name": "The Front Porch",
-        "address": "65 29th St A",
-        "city": "SF",
-        "postal_code": "94110",
-        "latitude": "37.7439",
-        "longitude": "-122.422",
-        "phone_number": "",
-        "TaxCode": "H25",
-        "business_certificate": "398500",
-        "application_date": "Jun  7 2006 12:00AM",
-        "owner_name": "Front Porch Restaurant LLC",
-        "owner_address": "65A 29th Street\n SF\n CA\n 94110"
-      },
-      "Score": "",
-      "date": "20130225",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "69041",
-        "name": "Washington Cafe",
-        "address": "826 Washington St ",
-        "city": "San Francisco",
-        "postal_code": "94108",
-        "latitude": "37.7951",
-        "longitude": "-122.407",
-        "phone_number": "",
-        "TaxCode": "H26",
-        "business_certificate": "468548",
-        "application_date": "Apr 18 2012 12:00AM",
-        "owner_name": "Washington Caf�, Inc. / Louis Kuang",
-        "owner_address": "333 Third Avenue\n Daly City\n CA\n 94014"
-      },
-      "Score": "65",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [
-        { "description": "103120: Moderate risk food holding temperature  (Moderate Risk)" },
-        { "description": "103156: Permit license or inspection report not posted (Low Risk)" },
-        { "description": "103154: Unclean or degraded floors walls or ceilings (Low Risk)" },
-        { "description": "103133: Foods not protected from contamination (Moderate Risk)" },
-        { "description": "103114: High risk vermin infestation (High Risk)" },
-        { "description": "103109: Unclean or unsanitary food contact surfaces (High Risk)" },
-        { "description": "103108: Contaminated or adulterated food (High Risk)" },
-        { "description": "103144: Unapproved or unmaintained equipment or utensils (Low Risk)" }
-      ]
-    },
-    {
-      "business": {
-        "business_id": "6018",
-        "name": "3RD BAPTIST FELLOWSHIP HALL",
-        "address": "1399 MCALLISTER St ",
-        "city": "SAN FRANCISCO",
-        "postal_code": "94115",
-        "latitude": "37.7782",
-        "longitude": "-122.435",
-        "phone_number": "",
-        "TaxCode": "AA",
-        "business_certificate": "",
-        "application_date": "",
-        "owner_name": "3RD BAPTIST FELLOWSHIP HALL",
-        "owner_address": "1399 MC ALLISTER STREET\n SAN FRANCISCO\n CA\n 94115"
-      },
-      "Score": "98",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [ { "description": "103143: Inadequate warewashing facilities or equipment (Low Risk)" } ]
-    },
-    {
-      "business": {
-        "business_id": "23702",
-        "name": "Doo Bu Restaurant",
-        "address": "1723 Buchanan St",
-        "city": "San Francisco",
-        "postal_code": "94115",
-        "latitude": "37.786",
-        "longitude": "-122.43",
-        "phone_number": "",
-        "TaxCode": "H25",
-        "business_certificate": "205289",
-        "application_date": "Jun 30 2006 12:00AM",
-        "owner_name": "Jung Un Hong",
-        "owner_address": "1723 Buchanan St\n San Francisco\n CA\n 94115"
-      },
-      "Score": "90",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [
-        { "description": "103154: Unclean or degraded floors walls or ceilings (Low Risk)" },
-        { "description": "103120: Moderate risk food holding temperature  (Moderate Risk)" },
-        { "description": "103131: Moderate risk vermin infestation (Moderate Risk)" }
-      ]
-    },
-    {
-      "business": {
-        "business_id": "23702",
-        "name": "Doo Bu Restaurant",
-        "address": "1723 Buchanan St",
-        "city": "San Francisco",
-        "postal_code": "94115",
-        "latitude": "37.786",
-        "longitude": "-122.43",
-        "phone_number": "",
-        "TaxCode": "H25",
-        "business_certificate": "205289",
-        "application_date": "Jun 30 2006 12:00AM",
-        "owner_name": "Jung Un Hong",
-        "owner_address": "1723 Buchanan St\n San Francisco\n CA\n 94115"
-      },
-      "Score": "",
-      "date": "20130225",
-      "type": "Complaint",
-      "violations": [
-        { "description": "103154: Unclean or degraded floors walls or ceilings (Low Risk)" },
-        { "description": "103120: Moderate risk food holding temperature  (Moderate Risk)" },
-        { "description": "103131: Moderate risk vermin infestation (Moderate Risk)" }
-      ]
-    },
-    {
-      "business": {
-        "business_id": "34244",
-        "name": "Vital Tea Leaf Inc",
-        "address": "905 Grant Ave ",
-        "city": "San Francisco",
-        "postal_code": "94108",
-        "latitude": "37.7953",
-        "longitude": "-122.407",
-        "phone_number": "",
-        "TaxCode": "H25",
-        "business_certificate": "417300",
-        "application_date": "May 17 2007 12:00AM",
-        "owner_name": "Vital Tea Leaf Inc   Minh Hong Duong",
-        "owner_address": "905 Grant Ave\n San Francisco\n CA\n 94108"
-      },
-      "Score": "100",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "63503",
-        "name": "Mason Street Deli and Market",
-        "address": "39 MASON",
-        "city": "SF",
-        "postal_code": "94102",
-        "latitude": "37.7841",
-        "longitude": "-122.409",
-        "phone_number": "14155624312",
-        "TaxCode": "H07",
-        "business_certificate": "450988",
-        "application_date": "Jun 28 2010 12:00AM",
-        "owner_name": "Hifdallah Ahmed Homran and Arkan Ali Hassan Alwasyi",
-        "owner_address": "39 Mason Street\n San Francisco\n CA\n 94102"
-      },
-      "Score": "100",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "1427",
-        "name": "MIFUNE DON",
-        "address": "22 PEACE PLAZA  275",
-        "city": "SF",
-        "postal_code": "94115",
-        "latitude": "37.785",
-        "longitude": "-122.43",
-        "phone_number": "14155341993",
-        "TaxCode": "H24",
-        "business_certificate": "314685",
-        "application_date": "",
-        "owner_name": "Osaka Eiko, Inc",
-        "owner_address": "22 PEACE PLAZA, #275\n SF\n CA\n 94115"
-      },
-      "Score": "86",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [
-        { "description": "103102: Unclean hands or improper use of gloves (High Risk)" },
-        { "description": "103114: High risk vermin infestation (High Risk)" }
-      ]
-    },
-    {
-      "business": {
-        "business_id": "3331",
-        "name": "HARVEY'S",
-        "address": "500 CASTRO St ",
-        "city": "SAN FRANCISCO",
-        "postal_code": "94114",
-        "latitude": "37.7607",
-        "longitude": "-122.435",
-        "phone_number": "14155434278",
-        "TaxCode": "H26",
-        "business_certificate": "945743",
-        "application_date": "",
-        "owner_name": "LANGLEY ENTERPRISES",
-        "owner_address": "500 CASTRO ST\n SF\n CA\n 94114"
-      },
-      "Score": "83",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [
-        { "description": "103149: Wiping cloths not clean or properly stored or inadequate sanitizer (Low Risk)" },
-        { "description": "103147: Inadequate ventilation or lighting (Low Risk)" },
-        { "description": "103131: Moderate risk vermin infestation (Moderate Risk)" },
-        { "description": "103154: Unclean or degraded floors walls or ceilings (Low Risk)" },
-        { "description": "103103: High risk food holding temperature  (High Risk)" }
-      ]
-    },
-    {
-      "business": {
-        "business_id": "5907",
-        "name": "LILIENTHAL ELEMENTARY SCHOOL",
-        "address": "3630 DIVISADERO St ",
-        "city": "S.F.",
-        "postal_code": "94118",
-        "latitude": "37.8035",
-        "longitude": "-122.444",
-        "phone_number": "14155743516",
-        "TaxCode": "H91",
-        "business_certificate": "",
-        "application_date": "",
-        "owner_name": "San Francisco Unified School District",
-        "owner_address": "3630 Divisadero Street\n San Francisco\n CA\n 94123"
-      },
-      "Score": "",
-      "date": "20130225",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "65050",
-        "name": "House of Banquet",
-        "address": "939 Clement St ",
-        "city": "San Francisco",
-        "postal_code": "94118",
-        "latitude": "37.7827",
-        "longitude": "-122.469",
-        "phone_number": "14155780203",
-        "TaxCode": "H26",
-        "business_certificate": "455173",
-        "application_date": "Jan 11 2011 12:00AM",
-        "owner_name": "LYS Trading, Inc. / Yong Shong",
-        "owner_address": "939 Clement Street\n San Francisco\n CA\n 94118"
-      },
-      "Score": "87",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [
-        { "description": "103149: Wiping cloths not clean or properly stored or inadequate sanitizer (Low Risk)" },
-        { "description": "103103: High risk food holding temperature  (High Risk)" },
-        { "description": "103120: Moderate risk food holding temperature  (Moderate Risk)" }
-      ]
-    },
-    {
-      "business": {
-        "business_id": "71534",
-        "name": "7 Eleven #2366-36039A",
-        "address": "644 Mission St ",
-        "city": "San Francisco",
-        "postal_code": "94105",
-        "latitude": "",
-        "longitude": "",
-        "phone_number": "14155610309",
-        "TaxCode": "H07",
-        "business_certificate": "475970",
-        "application_date": "Jan 24 2013 12:00AM",
-        "owner_name": "Bhrighu Muniwar, Inc.",
-        "owner_address": "538 Pyramid Ct.\n Fairfield\n CA\n 94534"
-      },
-      "Score": "100",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "71769",
-        "name": "Lucky One Mini Mart",
-        "address": "1010 Market St ",
-        "city": "San Francisco",
-        "postal_code": "94102",
-        "latitude": "37.7819",
-        "longitude": "-122.411",
-        "phone_number": "",
-        "TaxCode": "H24",
-        "business_certificate": "473612",
-        "application_date": "Feb 15 2013 12:00AM",
-        "owner_name": "Charles Ahn",
-        "owner_address": "1010 Market Street\n San Francisco\n CA\n 94102"
-      },
-      "Score": "100",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "5884",
-        "name": "Gordon J Lau Elementary School",
-        "address": "950 Clay St",
-        "city": "San Francisco",
-        "postal_code": "94108",
-        "latitude": "37.794",
-        "longitude": "-122.409",
-        "phone_number": "",
-        "TaxCode": "H91",
-        "business_certificate": "",
-        "application_date": "",
-        "owner_name": "S.F. Unified School District",
-        "owner_address": "950  Clay St\n San Francisco\n CA\n 94108"
-      },
-      "Score": "96",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [ { "description": "103120: Moderate risk food holding temperature  (Moderate Risk)" } ]
-    },
-    {
-      "business": {
-        "business_id": "69635",
-        "name": "Sophie's Crepes",
-        "address": "1581 Webster St 275",
-        "city": "S.F.",
-        "postal_code": "94115",
-        "latitude": "",
-        "longitude": "",
-        "phone_number": "",
-        "TaxCode": "H28",
-        "business_certificate": "470456",
-        "application_date": "Jul  5 2012 12:00AM",
-        "owner_name": "Sophie's Crepes, LLC",
-        "owner_address": "1581 Webster St., #275\n SF\n CA\n 94115"
-      },
-      "Score": "",
-      "date": "20130225",
-      "type": "Non-inspection site visit",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "3633",
-        "name": "Magnolia Pub & Brewery",
-        "address": "1398 Haight St",
-        "city": "San Francisco",
-        "postal_code": "94117",
-        "latitude": "37.7703",
-        "longitude": "-122.445",
-        "phone_number": "",
-        "TaxCode": "H26",
-        "business_certificate": "306120",
-        "application_date": "",
-        "owner_name": "McLean Breweries, Inc",
-        "owner_address": "1398 Haight St\n San Francisco\n CA\n 94117"
-      },
-      "Score": "",
-      "date": "20130225",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "5905",
-        "name": "MARINA MIDDLE SCHOOL",
-        "address": "3500 FILLMORE St ",
-        "city": "S.F.",
-        "postal_code": "94123",
-        "latitude": "37.8018",
-        "longitude": "-122.436",
-        "phone_number": "14155743495",
-        "TaxCode": "H91",
-        "business_certificate": "",
-        "application_date": "",
-        "owner_name": "San Francisco Unified School district",
-        "owner_address": "3500 Fillmore Street\n San Francisco\n CA\n 94123"
-      },
-      "Score": "",
-      "date": "20130225",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "19277",
-        "name": "Farmer Brown",
-        "address": "25 Mason St ",
-        "city": "San Francisco",
-        "postal_code": "94102",
-        "latitude": "37.7837",
-        "longitude": "-122.409",
-        "phone_number": "",
-        "TaxCode": "H26",
-        "business_certificate": "397481",
-        "application_date": "Apr 20 2006 12:00AM",
-        "owner_name": "Black Rabbit Hospitality",
-        "owner_address": "25 Mason St\n San Francisco\n CA\n 94102"
-      },
-      "Score": "100",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "1775",
-        "name": "Sapporo-Ya",
-        "address": "1581 WEBSTER St ",
-        "city": "SF",
-        "postal_code": "94115",
-        "latitude": "37.7852",
-        "longitude": "-122.431",
-        "phone_number": "14155567400",
-        "TaxCode": "H25",
-        "business_certificate": "98407",
-        "application_date": "",
-        "owner_name": "TURNER SHIBATA INC.",
-        "owner_address": "1581 WEBSTER ST\n SF\n CA\n 94115"
-      },
-      "Score": "",
-      "date": "20130225",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "61135",
-        "name": "Old Mandarin Islamic Restaurant",
-        "address": "3132 VICENTE St ",
-        "city": "SF",
-        "postal_code": "94116",
-        "latitude": "37.7382",
-        "longitude": "-122.501",
-        "phone_number": "",
-        "TaxCode": "H24",
-        "business_certificate": "442429",
-        "application_date": "Sep 14 2009 12:00AM",
-        "owner_name": "Shuai Yang",
-        "owner_address": "3132 Vicente St.\n SF\n CA\n 94116"
-      },
-      "Score": "84",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [
-        { "description": "103144: Unapproved or unmaintained equipment or utensils (Low Risk)" },
-        { "description": "103132: Improper thawing methods  (Moderate Risk)" },
-        { "description": "103154: Unclean or degraded floors walls or ceilings (Low Risk)" },
-        { "description": "103142: Unclean nonfood contact surfaces (Low Risk)" },
-        { "description": "103139: Improper food storage (Low Risk)" },
-        { "description": "103124: Inadequately cleaned or sanitized food contact surfaces (Moderate Risk)" }
-      ]
-    },
-    {
-      "business": {
-        "business_id": "765",
-        "name": "APERTO RESTAURANT",
-        "address": "1434 18th St ",
-        "city": "S.F.",
-        "postal_code": "94107",
-        "latitude": "37.7626",
-        "longitude": "-122.397",
-        "phone_number": "",
-        "TaxCode": "H24",
-        "business_certificate": "921479",
-        "application_date": "Apr 16 1992 12:00AM",
-        "owner_name": "APERTO INC.",
-        "owner_address": "245 LAWTON ST.\n S.F.\n CA\n 94122"
-      },
-      "Score": "",
-      "date": "20130225",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "3373",
-        "name": "New Delhi Restaurant",
-        "address": "160 Ellis St ",
-        "city": "San Francisco",
-        "postal_code": "94102",
-        "latitude": "37.7855",
-        "longitude": "-122.409",
-        "phone_number": "",
-        "TaxCode": "H26",
-        "business_certificate": "173398",
-        "application_date": "",
-        "owner_name": "New Delhi Restaurant",
-        "owner_address": "160 Ellis St\n San Francisco\n CA\n 94102"
-      },
-      "Score": "",
-      "date": "20130225",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "3373",
-        "name": "New Delhi Restaurant",
-        "address": "160 Ellis St ",
-        "city": "San Francisco",
-        "postal_code": "94102",
-        "latitude": "37.7855",
-        "longitude": "-122.409",
-        "phone_number": "",
-        "TaxCode": "H26",
-        "business_certificate": "173398",
-        "application_date": "",
-        "owner_name": "New Delhi Restaurant",
-        "owner_address": "160 Ellis St\n San Francisco\n CA\n 94102"
-      },
-      "Score": "",
-      "date": "20130225",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "33891",
-        "name": "Harlot",
-        "address": "46 Minna St ",
-        "city": "San Francisco",
-        "postal_code": "94105",
-        "latitude": "37.7887",
-        "longitude": "-122.398",
-        "phone_number": "",
-        "TaxCode": "H87",
-        "business_certificate": "404920",
-        "application_date": "Apr 26 2007 12:00AM",
-        "owner_name": "Vespertine, Inc   Robert Nunez",
-        "owner_address": "46 Minna Street\n San Francisco\n CA\n 94105"
-      },
-      "Score": "87",
-      "date": "20130225",
-      "type": "Routine - Unscheduled",
-      "violations": [
-        { "description": "103119: Inadequate and inaccessible handwashing facilities (Moderate Risk)" },
-        { "description": "103162: Other low risk violation (Low Risk)" },
-        { "description": "103109: Unclean or unsanitary food contact surfaces (High Risk)" }
-      ]
-    },
-    {
-      "business": {
-        "business_id": "28828",
-        "name": "The Sandwich Shop",
-        "address": "635 19th St ",
-        "city": "S.F.",
-        "postal_code": "94107",
-        "latitude": "37.7617",
-        "longitude": "-122.389",
-        "phone_number": "",
-        "TaxCode": "H28",
-        "business_certificate": "402498",
-        "application_date": "Oct 11 2006 12:00AM",
-        "owner_name": "Hye Hwa Cho",
-        "owner_address": "635 19th Street\n SF\n CA\n 94107"
-      },
-      "Score": "88",
-      "date": "20130226",
-      "type": "Routine - Unscheduled",
-      "violations": [
-        { "description": "103124: Inadequately cleaned or sanitized food contact surfaces (Moderate Risk)" },
-        { "description": "103120: Moderate risk food holding temperature  (Moderate Risk)" },
-        { "description": "103119: Inadequate and inaccessible handwashing facilities (Moderate Risk)" }
-      ]
-    },
-    {
-      "business": {
-        "business_id": "60933",
-        "name": "Mayes",
-        "address": "1233 Polk St ",
-        "city": "San Francisco",
-        "postal_code": "94109",
-        "latitude": "37.7883",
-        "longitude": "-122.42",
-        "phone_number": "",
-        "TaxCode": "H26",
-        "business_certificate": "441175",
-        "application_date": "Aug 27 2009 12:00AM",
-        "owner_name": "1233 Polk Street LLC   Fred Duncan",
-        "owner_address": "1233 Polk St\n San Francisco\n CA\n 94109"
-      },
-      "Score": "85",
-      "date": "20130226",
-      "type": "Routine - Unscheduled",
-      "violations": [
-        { "description": "103114: High risk vermin infestation (High Risk)" },
-        { "description": "103119: Inadequate and inaccessible handwashing facilities (Moderate Risk)" },
-        { "description": "103150: Improper or defective plumbing (Low Risk)" },
-        { "description": "103154: Unclean or degraded floors walls or ceilings (Low Risk)" }
-      ]
-    },
-    {
-      "business": {
-        "business_id": "62688",
-        "name": "Auntie April's",
-        "address": "4618 03rd St ",
-        "city": "SF",
-        "postal_code": "94124",
-        "latitude": "37.7363",
-        "longitude": "-122.39",
-        "phone_number": "",
-        "TaxCode": "H24",
-        "business_certificate": "428950",
-        "application_date": "Apr  2 2010 12:00AM",
-        "owner_name": "April Spears",
-        "owner_address": "4618 3rd Street\n SF\n CA\n 94124"
-      },
-      "Score": "",
-      "date": "20130226",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "3373",
-        "name": "New Delhi Restaurant",
-        "address": "160 Ellis St ",
-        "city": "San Francisco",
-        "postal_code": "94102",
-        "latitude": "37.7855",
-        "longitude": "-122.409",
-        "phone_number": "",
-        "TaxCode": "H26",
-        "business_certificate": "173398",
-        "application_date": "",
-        "owner_name": "New Delhi Restaurant",
-        "owner_address": "160 Ellis St\n San Francisco\n CA\n 94102"
-      },
-      "Score": "",
-      "date": "20130226",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "2756",
-        "name": "STARBUCKS",
-        "address": "3595 CALIFORNIA St ",
-        "city": "SF",
-        "postal_code": "94118",
-        "latitude": "37.7864",
-        "longitude": "-122.453",
-        "phone_number": "",
-        "TaxCode": "H25",
-        "business_certificate": "911744",
-        "application_date": "Oct 30 1995 12:00AM",
-        "owner_name": "STARBUCKS COFFEE CO, INC.",
-        "owner_address": "3595 CALIFORNIA ST\n SF\n CA\n 94118"
-      },
-      "Score": "",
-      "date": "20130226",
-      "type": "Reinspection/Followup",
-      "violations": [ { "description": "103157: Food safety certificate or food handler card not available (Low Risk)" } ]
-    },
-    {
-      "business": {
-        "business_id": "7489",
-        "name": "GAS & SHOP",
-        "address": "599 South Van Ness Ave ",
-        "city": "S.F.",
-        "postal_code": "94110",
-        "latitude": "37.7638",
-        "longitude": "-122.417",
-        "phone_number": "14155866835",
-        "TaxCode": "H10",
-        "business_certificate": "922993",
-        "application_date": "Aug 13 1993 12:00AM",
-        "owner_name": "SELF SERVE PETROLEUM, INC.",
-        "owner_address": "2240 SKY FARM\n HILLSBOROUGH\n CA\n 94010"
-      },
-      "Score": "",
-      "date": "20130226",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "19094",
-        "name": "Rigolo",
-        "address": "3465 California St ",
-        "city": "SF",
-        "postal_code": "94118",
-        "latitude": "37.7867",
-        "longitude": "-122.451",
-        "phone_number": "",
-        "TaxCode": "H25",
-        "business_certificate": "373994",
-        "application_date": "Mar  1 2004 12:00AM",
-        "owner_name": "Laurel Village Bakery LLC",
-        "owner_address": "2132 Pine Street\n SF\n CA\n 94118"
-      },
-      "Score": "",
-      "date": "20130226",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "65051",
-        "name": "Mission Cheese",
-        "address": "736 Valencia St ",
-        "city": "SF",
-        "postal_code": "94110",
-        "latitude": "37.7611",
-        "longitude": "-122.422",
-        "phone_number": "14155558667",
-        "TaxCode": "H24",
-        "business_certificate": "455293",
-        "application_date": "Jan 11 2011 12:00AM",
-        "owner_name": "Mission Cheese LLC",
-        "owner_address": "363 Hermann St.\n SF\n CA\n 94117"
-      },
-      "Score": "",
-      "date": "20130226",
-      "type": "Non-inspection site visit",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "3802",
-        "name": "THE GREAT AMERICAN MUSIC HALL",
-        "address": "859 O'FARRELL St ",
-        "city": "SAN FRANCISCO",
-        "postal_code": "94109",
-        "latitude": "37.785",
-        "longitude": "-122.419",
-        "phone_number": "",
-        "TaxCode": "H26",
-        "business_certificate": "358240",
-        "application_date": "",
-        "owner_name": "MUSIC HALL, LLC",
-        "owner_address": "859 O'FARRELL St\n SAN FRANCISCO\n CA\n 94109"
-      },
-      "Score": "94",
-      "date": "20130226",
-      "type": "Routine - Unscheduled",
-      "violations": [
-        { "description": "103161: Low risk vermin infestation (Low Risk)" },
-        { "description": "103142: Unclean nonfood contact surfaces (Low Risk)" },
-        { "description": "103154: Unclean or degraded floors walls or ceilings (Low Risk)" }
-      ]
-    },
-    {
-      "business": {
-        "business_id": "475",
-        "name": "Mara's",
-        "address": "503 Columbus Ave ",
-        "city": "San Francisco",
-        "postal_code": "94133",
-        "latitude": "37.7995",
-        "longitude": "-122.409",
-        "phone_number": "",
-        "TaxCode": "H24",
-        "business_certificate": "147903",
-        "application_date": "",
-        "owner_name": "Filomena & Gennaro Torrano",
-        "owner_address": "503 Columbus Ave\n San Francisco\n CA\n 94133"
-      },
-      "Score": "",
-      "date": "20130226",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "1193",
-        "name": "PEET'S COFFEES & TEAS",
-        "address": "3419 CALIFORNIA St ",
-        "city": "SAN FRANCISCO",
-        "postal_code": "94118",
-        "latitude": "37.7868",
-        "longitude": "-122.45",
-        "phone_number": "",
-        "TaxCode": "H24",
-        "business_certificate": "196325",
-        "application_date": "",
-        "owner_name": "Peet's Coffee",
-        "owner_address": "3419 California\n SF\n CA\n 94118"
-      },
-      "Score": "",
-      "date": "20130226",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "2548",
-        "name": "NOAH'S NEW YORK BAGELS #2120",
-        "address": "3519 CALIFORNIA St ",
-        "city": "SF",
-        "postal_code": "94118",
-        "latitude": "37.7866",
-        "longitude": "-122.452",
-        "phone_number": "",
-        "TaxCode": "H25",
-        "business_certificate": "353177",
-        "application_date": "Mar 15 2002 12:00AM",
-        "owner_name": "EINSTEIN AND NOAH CORP.",
-        "owner_address": "555 Zang St., Suite 300\n Lakewood\n CO\n 80228"
-      },
-      "Score": "",
-      "date": "20130226",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "6913",
-        "name": "XTRA OIL CORP",
-        "address": "3750 03rd St ",
-        "city": "S.F.",
-        "postal_code": "94124",
-        "latitude": "37.7432",
-        "longitude": "-122.388",
-        "phone_number": "",
-        "TaxCode": "H07",
-        "business_certificate": "144903",
-        "application_date": "May 17 1993 12:00AM",
-        "owner_name": "EDWARD T SIMAS",
-        "owner_address": "2307 PACIFIC AVE.\n ALAMEDA\n CA\n 94501"
-      },
-      "Score": "",
-      "date": "20130226",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "4923",
-        "name": "TED'S MARKET",
-        "address": "1530 HOWARD St ",
-        "city": "S.F.",
-        "postal_code": "94103",
-        "latitude": "37.7727",
-        "longitude": "-122.416",
-        "phone_number": "",
-        "TaxCode": "H28",
-        "business_certificate": "321004",
-        "application_date": "",
-        "owner_name": "ZOUZOUNIS DAVID P",
-        "owner_address": "1530 HOWARD ST\n SAN FRANCISCO\n CA\n 94103"
-      },
-      "Score": "100",
-      "date": "20130226",
-      "type": "Routine - Unscheduled",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "36020",
-        "name": "Cadillac Market",
-        "address": "499 Eddy St",
-        "city": "San Francisco",
-        "postal_code": "94109",
-        "latitude": "37.7835",
-        "longitude": "-122.416",
-        "phone_number": "",
-        "TaxCode": "H07",
-        "business_certificate": "379395",
-        "application_date": "Oct 29 2007 12:00AM",
-        "owner_name": "Irfan Barkat Ali",
-        "owner_address": "499 Eddy St\n San Francisco\n CA\n 94109"
-      },
-      "Score": "98",
-      "date": "20130226",
-      "type": "Routine - Unscheduled",
-      "violations": [ { "description": "103143: Inadequate warewashing facilities or equipment (Low Risk)" } ]
-    },
-    {
-      "business": {
-        "business_id": "3935",
-        "name": "Le Colonial, SF, LLC",
-        "address": "20 Cosmo Pl ",
-        "city": "San Francisco",
-        "postal_code": "94109",
-        "latitude": "37.7881",
-        "longitude": "-122.412",
-        "phone_number": "",
-        "TaxCode": "H26",
-        "business_certificate": "317110",
-        "application_date": "",
-        "owner_name": "Le Colonial, SF, LLC",
-        "owner_address": "20  Cosmo PL\n San Francisco\n CA\n 94109"
-      },
-      "Score": "",
-      "date": "20130226",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "1259",
-        "name": "KD'S GROG & GROCERY",
-        "address": "2416 MARKET St ",
-        "city": "SF",
-        "postal_code": "94114",
-        "latitude": "37.7626",
-        "longitude": "-122.435",
-        "phone_number": "14155863736",
-        "TaxCode": "H24",
-        "business_certificate": "198913",
-        "application_date": "",
-        "owner_name": "KIM, SUNG U.",
-        "owner_address": "2416 MARKET ST.\n SAN FRANCISCO\n CA\n 94114"
-      },
-      "Score": "",
-      "date": "20130226",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "65396",
-        "name": "Carmelina's Cafe",
-        "address": "1855 Folsom St ",
-        "city": "SF",
-        "postal_code": "94103",
-        "latitude": "37.7677",
-        "longitude": "-122.415",
-        "phone_number": "",
-        "TaxCode": "H24",
-        "business_certificate": "181141",
-        "application_date": "Mar  2 2011 12:00AM",
-        "owner_name": "Carmelina Narciso",
-        "owner_address": "1855 Folsom Street\n SF\n CA\n 94103"
-      },
-      "Score": "",
-      "date": "20130226",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "477",
-        "name": "Il Pollaio",
-        "address": "555 Columbus Ave ",
-        "city": "San Francisco",
-        "postal_code": "94133",
-        "latitude": "37.8",
-        "longitude": "-122.41",
-        "phone_number": "",
-        "TaxCode": "H24",
-        "business_certificate": "147775",
-        "application_date": "",
-        "owner_name": "Antonio & Jose Castellucci",
-        "owner_address": "555 Columbus Ave\n San Francisco\n CA\n 94133"
-      },
-      "Score": "90",
-      "date": "20130226",
-      "type": "Routine - Unscheduled",
-      "violations": [
-        { "description": "103154: Unclean or degraded floors walls or ceilings (Low Risk)" },
-        { "description": "103124: Inadequately cleaned or sanitized food contact surfaces (Moderate Risk)" },
-        { "description": "103120: Moderate risk food holding temperature  (Moderate Risk)" }
-      ]
-    },
-    {
-      "business": {
-        "business_id": "1199",
-        "name": "PANCHO'S",
-        "address": "3440 GEARY Blvd ",
-        "city": "SAN FRANCISCO",
-        "postal_code": "94118",
-        "latitude": "37.7815",
-        "longitude": "-122.456",
-        "phone_number": "",
-        "TaxCode": "H24",
-        "business_certificate": "318180",
-        "application_date": "Jun 17 1998 12:00AM",
-        "owner_name": "CAL. SOL. CONCEPTS LLC",
-        "owner_address": "3440  GEARY Blvd\n S.F.\n CA\n 94118"
-      },
-      "Score": "",
-      "date": "20130226",
-      "type": "Reinspection/Followup",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "1549",
-        "name": "Cafe Bellini",
-        "address": "235 Powell St ",
-        "city": "San Francisco",
-        "postal_code": "94102",
-        "latitude": "37.787",
-        "longitude": "-122.408",
-        "phone_number": "",
-        "TaxCode": "H25",
-        "business_certificate": "384285",
-        "application_date": "",
-        "owner_name": "SEGA FOOD GROUP INC",
-        "owner_address": "\t235 POWELL ST\n SAN FRANCISCO\n CA\n 94102"
-      },
-      "Score": "96",
-      "date": "20130226",
-      "type": "Routine - Unscheduled",
-      "violations": [ { "description": "103124: Inadequately cleaned or sanitized food contact surfaces (Moderate Risk)" } ]
-    },
-    {
-      "business": {
-        "business_id": "4257",
-        "name": "DELUXE",
-        "address": "1509 HAIGHT St ",
-        "city": "SF",
-        "postal_code": "94117",
-        "latitude": "37.7699",
-        "longitude": "-122.447",
-        "phone_number": "14155556949",
-        "TaxCode": "H25",
-        "business_certificate": "",
-        "application_date": "",
-        "owner_name": "JAY JOHNSON",
-        "owner_address": "1509 HAIGHT STREET\n SAN FRANCISCO\n CA\n 94117"
-      },
-      "Score": "",
-      "date": "20130226",
-      "type": "Administrative or Document Review",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "68775",
-        "name": "Dandelion Chocolate",
-        "address": "740 Valencia St ",
-        "city": "San Francisco",
-        "postal_code": "94110",
-        "latitude": "37.761",
-        "longitude": "-122.422",
-        "phone_number": "",
-        "TaxCode": "H26",
-        "business_certificate": "466375",
-        "application_date": "Mar 21 2012 12:00AM",
-        "owner_name": "Cocoa Co., Inc.",
-        "owner_address": "334 Santana Row #335\n San Jose\n Ca\n 95128"
-      },
-      "Score": "",
-      "date": "20130226",
-      "type": "Complaint",
-      "violations": [ ]
-    },
-    {
-      "business": {
-        "business_id": "68037",
-        "name": "Focaccia Market Bakery",
-        "address": "185 BERRY St #1430",
-        "city": "S.F.",
-        "postal_code": "94107",
-        "latitude": "37.776",
-        "longitude": "-122.393",
-        "phone_number": "",
-        "TaxCode": "H26",
-        "business_certificate": "455335",
-        "application_date": "Dec  1 2011 12:00AM",
-        "owner_name": "Focaccia Market Bakery, Inc.",
-        "owner_address": "119 Sacramento St.\n SF\n CA\n 94111"
-      },
-      "Score": "91",
-      "date": "20130226",
-      "type": "Routine - Unscheduled",
-      "violations": [
-        { "description": "103109: Unclean or unsanitary food contact surfaces (High Risk)" },
-        { "description": "103150: Improper or defective plumbing (Low Risk)" }
-      ]
-    },
-    {
-      "business": {
-        "business_id": "69945",
-        "name": "Linda's Catering",
-        "address": "290 Townsend St ",
-        "city": "SF",
-        "postal_code": "94107",
-        "latitude": "",
-        "longitude": "",
-        "phone_number": "",
-        "TaxCode": "H79",
-        "business_certificate": "470527",
-        "application_date": "Aug 15 2012 12:00AM",
-        "owner_name": "Hong Yan Fang-Li",
-        "owner_address": "3623 Kirkham St.\n SF\n CA\n 94122"
-      },
-      "Score": "96",
-      "date": "20130226",
-      "type": "Routine - Unscheduled",
-      "violations": [
-        { "description": "103142: Unclean nonfood contact surfaces (Low Risk)" },
-        { "description": "103147: Inadequate ventilation or lighting (Low Risk)" }
-      ]
-    },
-    {
-      "business": {
-        "business_id": "70920",
-        "name": "Stock in Trade",
-        "address": "2036 LOMBARD St ",
-        "city": "SF",
-        "postal_code": "94123",
-        "latitude": "",
-        "longitude": "",
-        "phone_number": "",
-        "TaxCode": "H26",
-        "business_certificate": "470757",
-        "application_date": "Nov  9 2012 12:00AM",
-        "owner_name": "4 Item Combo Plate / Tod Alsman, Chris Fogarty",
-        "owner_address": "2036 Lombard Street\n San Francisco\n CA\n 94123"
-      },
-      "Score": "90",
-      "date": "20130226",
-      "type": "Routine - Unscheduled",
-      "violations": [
-        { "description": "103149: Wiping cloths not clean or properly stored or inadequate sanitizer (Low Risk)" },
-        { "description": "103139: Improper food storage (Low Risk)" },
-        { "description": "103145: Improper storage of equipment utensils or linens (Low Risk)" },
-        { "description": "103150: Improper or defective plumbing (Low Risk)" },
-        { "description": "103142: Unclean nonfood contact surfaces (Low Risk)" }
-      ]
-    }
-  ]
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/data/large_dflow.json b/work-with-data/dataprep/data/large_dflow.json
deleted file mode 100644
index 8cff8b07..00000000
--- a/work-with-data/dataprep/data/large_dflow.json
+++ /dev/null
@@ -1,4415 +0,0 @@
-{
-  "blocks": [
-    {
-      "id": "36442a99-73bd-4307-985e-11e3a496abdd",
-      "type": "Microsoft.DPrep.ReferenceAndInverseSplitBlock",
-      "arguments": {
-        "sourceFilter": {
-          "referenceType": 2,
-          "referenceContainerPath": null,
-          "referencedStep": {
-            "referencedStepId": "00000000-0000-0000-0000-000000000000",
-            "fallback": 1
-          },
-          "anonymousSteps": [
-            {
-              "id": "509f625c-951b-410c-b2a5-ba7d77ac5805",
-              "description": {
-                "localDataProperties": [],
-                "blockGroup": 0,
-                "supportedTargets": [
-                  4
-                ],
-                "supportedFieldTypes": null,
-                "supportedMessages": [],
-                "requiresInitialization": false,
-                "allowDefaultAdd": true,
-                "documentation": "Creates a reference to an existing activity object.",
-                "type": "Microsoft.DPrep.ReferenceBlock",
-                "propertyDescriptions": [
-                  {
-                    "name": "reference",
-                    "type": 21,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The reference activity."
-                  }
-                ],
-                "dataEffectDetails": {
-                  "dataEffect": 1
-                }
-              },
-              "type": "Microsoft.DPrep.ReferenceBlock",
-              "dataEffectDetails": {
-                "dataEffect": 1
-              },
-              "propertyDescriptions": [
-                {
-                  "name": "reference",
-                  "type": 21,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The reference activity."
-                }
-              ],
-              "arguments": {
-                "reference": {
-                  "referenceType": 1,
-                  "referenceContainerPath": "dataset://35f16a99-532a-4a47-9e93-00305f6c40f2/maytest/mayworkspace/train_raw",
-                  "referencedStep": null,
-                  "anonymousSteps": null
-                }
-              }
-            },
-            {
-              "id": "49f2a633-169b-412c-8565-4cfe9a3e10bd",
-              "description": {
-                "localDataProperties": [],
-                "blockGroup": 5,
-                "supportedTargets": [
-                  1
-                ],
-                "supportedFieldTypes": null,
-                "supportedMessages": [],
-                "requiresInitialization": false,
-                "allowDefaultAdd": true,
-                "documentation": "Takes a random sample of the available records.",
-                "type": "Microsoft.DPrep.TakeSampleBlock",
-                "propertyDescriptions": [
-                  {
-                    "name": "probability",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 4,
-                      "details": {
-                        "min": 0.0,
-                        "max": 1.0
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 0.1,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "The probability of a record being included in the sample."
-                  },
-                  {
-                    "name": "seed",
-                    "type": 2,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 4,
-                      "details": {
-                        "min": 0.0,
-                        "max": 4294967295.0
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "The seed to use for the random generator."
-                  }
-                ],
-                "dataEffectDetails": {
-                  "dataEffect": 6
-                }
-              },
-              "type": "Microsoft.DPrep.TakeSampleBlock",
-              "dataEffectDetails": {
-                "dataEffect": 6
-              },
-              "propertyDescriptions": [
-                {
-                  "name": "probability",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 4,
-                    "details": {
-                      "min": 0.0,
-                      "max": 1.0
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": 0.1,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "The probability of a record being included in the sample."
-                },
-                {
-                  "name": "seed",
-                  "type": 2,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 4,
-                    "details": {
-                      "min": 0.0,
-                      "max": 4294967295.0
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "The seed to use for the random generator."
-                }
-              ],
-              "arguments": {
-                "probability": 0.2,
-                "seed": 3923004803
-              }
-            },
-            {
-              "id": "8eb37cb0-0e45-4230-a69e-70a9b481d9a3",
-              "description": {
-                "localDataProperties": [],
-                "blockGroup": 6,
-                "supportedTargets": [
-                  2,
-                  3
-                ],
-                "supportedFieldTypes": null,
-                "supportedMessages": [],
-                "requiresInitialization": false,
-                "allowDefaultAdd": true,
-                "documentation": "Drops the specified columns.",
-                "type": "Microsoft.DPrep.DropColumnsBlock",
-                "propertyDescriptions": [
-                  {
-                    "name": "columns",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 1,
-                        "fieldTypes": []
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source columns."
-                  }
-                ],
-                "dataEffectDetails": {
-                  "columnsRemovedProperties": [
-                    {
-                      "propertyPath": [
-                        "columns"
-                      ]
-                    }
-                  ],
-                  "dataEffect": 4
-                }
-              },
-              "type": "Microsoft.DPrep.DropColumnsBlock",
-              "dataEffectDetails": {
-                "columnsRemovedProperties": [
-                  {
-                    "propertyPath": [
-                      "columns"
-                    ]
-                  }
-                ],
-                "dataEffect": 4
-              },
-              "propertyDescriptions": [
-                {
-                  "name": "columns",
-                  "type": 5,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 12,
-                    "details": {
-                      "selectorDomainType": 1,
-                      "fieldTypes": []
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 0,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The source columns."
-                }
-              ],
-              "arguments": {
-                "columns": {
-                  "type": 0,
-                  "details": {
-                    "selectedColumns": [
-                      "AVProductStatesIdentifier",
-                      "AVProductsEnabled",
-                      "AutoSampleOptIn",
-                      "AvSigVersion",
-                      "AvSigVersionEncoded",
-                      "Census_ActivationChannel",
-                      "Census_ChassisTypeName",
-                      "Census_DeviceFamily",
-                      "Census_GenuineStateName",
-                      "Census_InternalBatteryNumberOfCharges",
-                      "Census_InternalBatteryType",
-                      "Census_InternalPrimaryDiagonalDisplaySizeInInches",
-                      "Census_InternalPrimaryDisplayResolutionHorizontal",
-                      "Census_InternalPrimaryDisplayResolutionVertical",
-                      "Census_IsFlightingInternal",
-                      "Census_IsFlightsDisabled",
-                      "Census_IsPortableOperatingSystem",
-                      "Census_IsSecureBootEnabled",
-                      "Census_IsWIMBootEnabled",
-                      "Census_MDC2FormFactor",
-                      "Census_OSArchitecture",
-                      "Census_OSBranch",
-                      "Census_OSBuildNumber",
-                      "Census_OSBuildRevision",
-                      "Census_OSEdition",
-                      "Census_OSInstallTypeName",
-                      "Census_OSSkuName",
-                      "Census_OSVersion",
-                      "Census_OSWUAutoUpdateOptionsName",
-                      "Census_PowerPlatformRoleName",
-                      "Census_PrimaryDiskTypeName",
-                      "Census_ProcessorClass",
-                      "Census_SystemVolumeTotalCapacity",
-                      "Census_ThresholdOptIn",
-                      "CountryIdentifier",
-                      "DefaultBrowsersIdentifier",
-                      "Firewall",
-                      "HasTpm",
-                      "IsBeta",
-                      "MachineIdentifier",
-                      "OrganizationIdentifier",
-                      "OsBuild",
-                      "OsBuildLab",
-                      "OsPlatformSubRelease",
-                      "OsSuite",
-                      "OsVer",
-                      "Platform",
-                      "Processor",
-                      "ProductName",
-                      "PuaMode",
-                      "SMode",
-                      "SkuEdition",
-                      "SmartScreen",
-                      "UacLuaenable"
-                    ]
-                  }
-                }
-              }
-            },
-            {
-              "id": "164eb9f4-bc3b-485e-8d76-55a5fcd1b2a8",
-              "description": {
-                "localDataProperties": [],
-                "blockGroup": 7,
-                "supportedTargets": [
-                  2,
-                  3
-                ],
-                "supportedFieldTypes": [
-                  0,
-                  2
-                ],
-                "supportedMessages": [],
-                "requiresInitialization": false,
-                "allowDefaultAdd": true,
-                "documentation": "Converts the values in the specified columns to floating point numbers.",
-                "type": "Microsoft.DPrep.ToNumberBlock",
-                "propertyDescriptions": [
-                  {
-                    "name": "columns",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 1,
-                        "fieldTypes": []
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source columns."
-                  },
-                  {
-                    "name": "decimalPoint",
-                    "type": 6,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 1,
-                      "details": {
-                        "enumName": "DecimalMark",
-                        "enumType": "Microsoft.DPrep.Engine.Blocks.ToNumberBlock+DecimalMark, Microsoft.DPrep.Engine, Version=1.0.0.0, Culture=neutral, PublicKeyToken=null",
-                        "enumValues": [
-                          0,
-                          1
-                        ]
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": 0,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "The symbol to use as the decimal mark."
-                  }
-                ],
-                "dataEffectDetails": {
-                  "columnsTransformedProperties": [
-                    {
-                      "propertyPath": [
-                        "columns"
-                      ]
-                    }
-                  ],
-                  "dataEffect": 5
-                }
-              },
-              "type": "Microsoft.DPrep.ToNumberBlock",
-              "dataEffectDetails": {
-                "columnsTransformedProperties": [
-                  {
-                    "propertyPath": [
-                      "columns"
-                    ]
-                  }
-                ],
-                "dataEffect": 5
-              },
-              "propertyDescriptions": [
-                {
-                  "name": "columns",
-                  "type": 5,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 12,
-                    "details": {
-                      "selectorDomainType": 1,
-                      "fieldTypes": []
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 0,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The source columns."
-                },
-                {
-                  "name": "decimalPoint",
-                  "type": 6,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 1,
-                    "details": {
-                      "enumName": "DecimalMark",
-                      "enumType": "Microsoft.DPrep.Engine.Blocks.ToNumberBlock+DecimalMark, Microsoft.DPrep.Engine, Version=1.0.0.0, Culture=neutral, PublicKeyToken=null",
-                      "enumValues": [
-                        0,
-                        1
-                      ]
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": 0,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "The symbol to use as the decimal mark."
-                }
-              ],
-              "arguments": {
-                "columns": {
-                  "type": 0,
-                  "details": {
-                    "selectedColumns": [
-                      "AVProductsInstalled",
-                      "Census_ProcessorCoreCount",
-                      "Census_PrimaryDiskTotalCapacity",
-                      "Census_TotalPhysicalRAM"
-                    ]
-                  }
-                },
-                "decimalPoint": 0
-              }
-            },
-            {
-              "id": "7e2a6355-de6b-4d03-b869-9d6097eb2012",
-              "description": {
-                "localDataProperties": [],
-                "blockGroup": 3,
-                "supportedTargets": [
-                  2,
-                  3
-                ],
-                "supportedFieldTypes": [
-                  1,
-                  4,
-                  3,
-                  2,
-                  0,
-                  5
-                ],
-                "supportedMessages": [],
-                "requiresInitialization": false,
-                "allowDefaultAdd": true,
-                "documentation": "Replaces values in the specified columns with nulls. You can choose to use the default list, supply your own, or both.",
-                "type": "Microsoft.DPrep.ReplaceNaBlock",
-                "propertyDescriptions": [
-                  {
-                    "name": "useDefaultNaList",
-                    "type": 4,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": true,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Use the default list and replace 'null', 'NaN', 'NA', and 'N/A' with null."
-                  },
-                  {
-                    "name": "useEmptyStringAsNa",
-                    "type": 4,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": true,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Replace empty strings with null."
-                  },
-                  {
-                    "name": "useNanAsNa",
-                    "type": 4,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": true,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Replace NaNs with Null."
-                  },
-                  {
-                    "name": "customNaList",
-                    "type": 1,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Provide a comma separated list of values to replace with null."
-                  },
-                  {
-                    "name": "columns",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 1,
-                        "fieldTypes": []
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source columns."
-                  }
-                ],
-                "dataEffectDetails": {
-                  "columnsTransformedProperties": [
-                    {
-                      "propertyPath": [
-                        "columns"
-                      ]
-                    }
-                  ],
-                  "dataEffect": 5
-                }
-              },
-              "type": "Microsoft.DPrep.ReplaceNaBlock",
-              "dataEffectDetails": {
-                "columnsTransformedProperties": [
-                  {
-                    "propertyPath": [
-                      "columns"
-                    ]
-                  }
-                ],
-                "dataEffect": 5
-              },
-              "propertyDescriptions": [
-                {
-                  "name": "useDefaultNaList",
-                  "type": 4,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": true,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "Use the default list and replace 'null', 'NaN', 'NA', and 'N/A' with null."
-                },
-                {
-                  "name": "useEmptyStringAsNa",
-                  "type": 4,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": true,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "Replace empty strings with null."
-                },
-                {
-                  "name": "useNanAsNa",
-                  "type": 4,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": true,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "Replace NaNs with Null."
-                },
-                {
-                  "name": "customNaList",
-                  "type": 1,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "Provide a comma separated list of values to replace with null."
-                },
-                {
-                  "name": "columns",
-                  "type": 5,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 12,
-                    "details": {
-                      "selectorDomainType": 1,
-                      "fieldTypes": []
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 0,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The source columns."
-                }
-              ],
-              "arguments": {
-                "columns": {
-                  "type": 1,
-                  "details": {
-                    "ignoreCase": false,
-                    "term": ".*",
-                    "useRegex": true,
-                    "matchWholeWord": false,
-                    "invert": false
-                  }
-                },
-                "useDefaultNaList": true,
-                "useEmptyStringAsNa": true,
-                "useNanAsNa": true
-              }
-            },
-            {
-              "id": "9d738031-d05e-41ab-a45c-3a6859155198",
-              "description": {
-                "localDataProperties": [],
-                "blockGroup": 4,
-                "supportedTargets": [
-                  2,
-                  3
-                ],
-                "supportedFieldTypes": null,
-                "supportedMessages": [],
-                "requiresInitialization": false,
-                "allowDefaultAdd": false,
-                "documentation": "Replaces null values in a column with the specified replacement value.",
-                "type": "Microsoft.DPrep.FillNullsBlock",
-                "propertyDescriptions": [
-                  {
-                    "name": "columns",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 1,
-                        "fieldTypes": []
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source columns."
-                  },
-                  {
-                    "name": "replaceWithType",
-                    "type": 6,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 1,
-                      "details": {
-                        "enumName": "FieldType",
-                        "enumType": "Microsoft.DPrep.Engine.FieldType, Microsoft.DPrep.EngineAPI, Version=1.0.0.0, Culture=neutral, PublicKeyToken=null",
-                        "enumValues": [
-                          0,
-                          1,
-                          3,
-                          4,
-                          6,
-                          10
-                        ]
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 0,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": null,
-                    "documentation": "The replacement value type."
-                  },
-                  {
-                    "name": "stringReplaceWith",
-                    "type": 1,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": {
-                      "condition": {
-                        "property": {
-                          "propertyPath": [
-                            "replaceWithType"
-                          ]
-                        },
-                        "comparison": 0,
-                        "value": 0
-                      },
-                      "aggregate": null
-                    },
-                    "documentation": "The replacement value."
-                  },
-                  {
-                    "name": "doubleReplaceWith",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": {
-                      "condition": {
-                        "property": {
-                          "propertyPath": [
-                            "replaceWithType"
-                          ]
-                        },
-                        "comparison": 0,
-                        "value": 3
-                      },
-                      "aggregate": null
-                    },
-                    "documentation": "The replacement value."
-                  },
-                  {
-                    "name": "datetimeReplaceWith",
-                    "type": 20,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": {
-                      "condition": {
-                        "property": {
-                          "propertyPath": [
-                            "replaceWithType"
-                          ]
-                        },
-                        "comparison": 0,
-                        "value": 4
-                      },
-                      "aggregate": null
-                    },
-                    "documentation": "The replacement value."
-                  },
-                  {
-                    "name": "booleanReplaceWith",
-                    "type": 6,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 1,
-                      "details": {
-                        "enumName": "ReplaceBoolValues",
-                        "enumType": "Microsoft.DPrep.Engine.ReplaceBoolValues, Microsoft.DPrep.EngineAPI, Version=1.0.0.0, Culture=neutral, PublicKeyToken=null",
-                        "enumValues": [
-                          0,
-                          1
-                        ]
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 1,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": {
-                      "condition": {
-                        "property": {
-                          "propertyPath": [
-                            "replaceWithType"
-                          ]
-                        },
-                        "comparison": 0,
-                        "value": 1
-                      },
-                      "aggregate": null
-                    },
-                    "documentation": "The replacement value."
-                  }
-                ],
-                "dataEffectDetails": {
-                  "columnsTransformedProperties": [
-                    {
-                      "propertyPath": [
-                        "columns"
-                      ]
-                    }
-                  ],
-                  "dataEffect": 5
-                }
-              },
-              "type": "Microsoft.DPrep.FillNullsBlock",
-              "dataEffectDetails": {
-                "columnsTransformedProperties": [
-                  {
-                    "propertyPath": [
-                      "columns"
-                    ]
-                  }
-                ],
-                "dataEffect": 5
-              },
-              "propertyDescriptions": [
-                {
-                  "name": "columns",
-                  "type": 5,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 12,
-                    "details": {
-                      "selectorDomainType": 1,
-                      "fieldTypes": []
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 0,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The source columns."
-                },
-                {
-                  "name": "replaceWithType",
-                  "type": 6,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 1,
-                    "details": {
-                      "enumName": "FieldType",
-                      "enumType": "Microsoft.DPrep.Engine.FieldType, Microsoft.DPrep.EngineAPI, Version=1.0.0.0, Culture=neutral, PublicKeyToken=null",
-                      "enumValues": [
-                        0,
-                        1,
-                        3,
-                        4,
-                        6,
-                        10
-                      ]
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": 0,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": null,
-                  "documentation": "The replacement value type."
-                },
-                {
-                  "name": "stringReplaceWith",
-                  "type": 1,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": {
-                    "condition": {
-                      "property": {
-                        "propertyPath": [
-                          "replaceWithType"
-                        ]
-                      },
-                      "comparison": 0,
-                      "value": 0
-                    },
-                    "aggregate": null
-                  },
-                  "documentation": "The replacement value."
-                },
-                {
-                  "name": "doubleReplaceWith",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": {
-                    "condition": {
-                      "property": {
-                        "propertyPath": [
-                          "replaceWithType"
-                        ]
-                      },
-                      "comparison": 0,
-                      "value": 3
-                    },
-                    "aggregate": null
-                  },
-                  "documentation": "The replacement value."
-                },
-                {
-                  "name": "datetimeReplaceWith",
-                  "type": 20,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": {
-                    "condition": {
-                      "property": {
-                        "propertyPath": [
-                          "replaceWithType"
-                        ]
-                      },
-                      "comparison": 0,
-                      "value": 4
-                    },
-                    "aggregate": null
-                  },
-                  "documentation": "The replacement value."
-                },
-                {
-                  "name": "booleanReplaceWith",
-                  "type": 6,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 1,
-                    "details": {
-                      "enumName": "ReplaceBoolValues",
-                      "enumType": "Microsoft.DPrep.Engine.ReplaceBoolValues, Microsoft.DPrep.EngineAPI, Version=1.0.0.0, Culture=neutral, PublicKeyToken=null",
-                      "enumValues": [
-                        0,
-                        1
-                      ]
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": 1,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": {
-                    "condition": {
-                      "property": {
-                        "propertyPath": [
-                          "replaceWithType"
-                        ]
-                      },
-                      "comparison": 0,
-                      "value": 1
-                    },
-                    "aggregate": null
-                  },
-                  "documentation": "The replacement value."
-                }
-              ],
-              "arguments": {
-                "booleanReplaceWith": 1,
-                "columns": {
-                  "type": 1,
-                  "details": {
-                    "ignoreCase": false,
-                    "term": ".*",
-                    "useRegex": true,
-                    "matchWholeWord": false,
-                    "invert": false
-                  }
-                },
-                "doubleReplaceWith": 0.0,
-                "replaceWithType": 3
-              }
-            },
-            {
-              "id": "bd960a6b-0959-4062-91aa-ba072f0e8058",
-              "description": {
-                "localDataProperties": [],
-                "blockGroup": 4,
-                "supportedTargets": [
-                  2,
-                  3
-                ],
-                "supportedFieldTypes": null,
-                "supportedMessages": [],
-                "requiresInitialization": false,
-                "allowDefaultAdd": false,
-                "documentation": "Replaces values in a column that match the specified search value.",
-                "type": "Microsoft.DPrep.ReplaceBlock",
-                "propertyDescriptions": [
-                  {
-                    "name": "columns",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 1,
-                        "fieldTypes": []
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source columns."
-                  },
-                  {
-                    "name": "valueToFindType",
-                    "type": 6,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 1,
-                      "details": {
-                        "enumName": "FieldType",
-                        "enumType": "Microsoft.DPrep.Engine.FieldType, Microsoft.DPrep.EngineAPI, Version=1.0.0.0, Culture=neutral, PublicKeyToken=null",
-                        "enumValues": [
-                          0,
-                          1,
-                          3,
-                          4,
-                          6,
-                          7,
-                          10
-                        ]
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 0,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": null,
-                    "documentation": "The value to find type."
-                  },
-                  {
-                    "name": "stringValueToFind",
-                    "type": 1,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": {
-                      "condition": {
-                        "property": {
-                          "propertyPath": [
-                            "valueToFindType"
-                          ]
-                        },
-                        "comparison": 0,
-                        "value": 0
-                      },
-                      "aggregate": null
-                    },
-                    "documentation": "The value to find."
-                  },
-                  {
-                    "name": "doubleValueToFind",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": {
-                      "condition": {
-                        "property": {
-                          "propertyPath": [
-                            "valueToFindType"
-                          ]
-                        },
-                        "comparison": 0,
-                        "value": 3
-                      },
-                      "aggregate": null
-                    },
-                    "documentation": "The value to find."
-                  },
-                  {
-                    "name": "datetimeValueToFind",
-                    "type": 20,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": {
-                      "condition": {
-                        "property": {
-                          "propertyPath": [
-                            "valueToFindType"
-                          ]
-                        },
-                        "comparison": 0,
-                        "value": 4
-                      },
-                      "aggregate": null
-                    },
-                    "documentation": "The value to find."
-                  },
-                  {
-                    "name": "booleanValueToFind",
-                    "type": 6,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 1,
-                      "details": {
-                        "enumName": "ReplaceBoolValues",
-                        "enumType": "Microsoft.DPrep.Engine.ReplaceBoolValues, Microsoft.DPrep.EngineAPI, Version=1.0.0.0, Culture=neutral, PublicKeyToken=null",
-                        "enumValues": [
-                          0,
-                          1
-                        ]
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 1,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": {
-                      "condition": {
-                        "property": {
-                          "propertyPath": [
-                            "valueToFindType"
-                          ]
-                        },
-                        "comparison": 0,
-                        "value": 1
-                      },
-                      "aggregate": null
-                    },
-                    "documentation": "The value to find."
-                  },
-                  {
-                    "name": "replaceWithType",
-                    "type": 6,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 1,
-                      "details": {
-                        "enumName": "FieldType",
-                        "enumType": "Microsoft.DPrep.Engine.FieldType, Microsoft.DPrep.EngineAPI, Version=1.0.0.0, Culture=neutral, PublicKeyToken=null",
-                        "enumValues": [
-                          0,
-                          1,
-                          3,
-                          4,
-                          6,
-                          7,
-                          10
-                        ]
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 0,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": null,
-                    "documentation": "The replacement value type."
-                  },
-                  {
-                    "name": "stringReplaceWith",
-                    "type": 1,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": {
-                      "condition": {
-                        "property": {
-                          "propertyPath": [
-                            "replaceWithType"
-                          ]
-                        },
-                        "comparison": 0,
-                        "value": 0
-                      },
-                      "aggregate": null
-                    },
-                    "documentation": "The replacement value."
-                  },
-                  {
-                    "name": "doubleReplaceWith",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": {
-                      "condition": {
-                        "property": {
-                          "propertyPath": [
-                            "replaceWithType"
-                          ]
-                        },
-                        "comparison": 0,
-                        "value": 3
-                      },
-                      "aggregate": null
-                    },
-                    "documentation": "The replacement value."
-                  },
-                  {
-                    "name": "datetimeReplaceWith",
-                    "type": 20,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": {
-                      "condition": {
-                        "property": {
-                          "propertyPath": [
-                            "replaceWithType"
-                          ]
-                        },
-                        "comparison": 0,
-                        "value": 4
-                      },
-                      "aggregate": null
-                    },
-                    "documentation": "The replacement value."
-                  },
-                  {
-                    "name": "booleanReplaceWith",
-                    "type": 6,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 1,
-                      "details": {
-                        "enumName": "ReplaceBoolValues",
-                        "enumType": "Microsoft.DPrep.Engine.ReplaceBoolValues, Microsoft.DPrep.EngineAPI, Version=1.0.0.0, Culture=neutral, PublicKeyToken=null",
-                        "enumValues": [
-                          0,
-                          1
-                        ]
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 1,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": {
-                      "condition": {
-                        "property": {
-                          "propertyPath": [
-                            "replaceWithType"
-                          ]
-                        },
-                        "comparison": 0,
-                        "value": 1
-                      },
-                      "aggregate": null
-                    },
-                    "documentation": "The replacement value."
-                  },
-                  {
-                    "name": "errorReplaceWith",
-                    "type": 1,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": {
-                      "condition": {
-                        "property": {
-                          "propertyPath": [
-                            "replaceWithType"
-                          ]
-                        },
-                        "comparison": 0,
-                        "value": 6
-                      },
-                      "aggregate": null
-                    },
-                    "documentation": "The replacement error code."
-                  }
-                ],
-                "dataEffectDetails": {
-                  "columnsTransformedProperties": [
-                    {
-                      "propertyPath": [
-                        "columns"
-                      ]
-                    }
-                  ],
-                  "dataEffect": 5
-                }
-              },
-              "type": "Microsoft.DPrep.ReplaceBlock",
-              "dataEffectDetails": {
-                "columnsTransformedProperties": [
-                  {
-                    "propertyPath": [
-                      "columns"
-                    ]
-                  }
-                ],
-                "dataEffect": 5
-              },
-              "propertyDescriptions": [
-                {
-                  "name": "columns",
-                  "type": 5,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 12,
-                    "details": {
-                      "selectorDomainType": 1,
-                      "fieldTypes": []
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 0,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The source columns."
-                },
-                {
-                  "name": "valueToFindType",
-                  "type": 6,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 1,
-                    "details": {
-                      "enumName": "FieldType",
-                      "enumType": "Microsoft.DPrep.Engine.FieldType, Microsoft.DPrep.EngineAPI, Version=1.0.0.0, Culture=neutral, PublicKeyToken=null",
-                      "enumValues": [
-                        0,
-                        1,
-                        3,
-                        4,
-                        6,
-                        7,
-                        10
-                      ]
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": 0,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": null,
-                  "documentation": "The value to find type."
-                },
-                {
-                  "name": "stringValueToFind",
-                  "type": 1,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": {
-                    "condition": {
-                      "property": {
-                        "propertyPath": [
-                          "valueToFindType"
-                        ]
-                      },
-                      "comparison": 0,
-                      "value": 0
-                    },
-                    "aggregate": null
-                  },
-                  "documentation": "The value to find."
-                },
-                {
-                  "name": "doubleValueToFind",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": {
-                    "condition": {
-                      "property": {
-                        "propertyPath": [
-                          "valueToFindType"
-                        ]
-                      },
-                      "comparison": 0,
-                      "value": 3
-                    },
-                    "aggregate": null
-                  },
-                  "documentation": "The value to find."
-                },
-                {
-                  "name": "datetimeValueToFind",
-                  "type": 20,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": {
-                    "condition": {
-                      "property": {
-                        "propertyPath": [
-                          "valueToFindType"
-                        ]
-                      },
-                      "comparison": 0,
-                      "value": 4
-                    },
-                    "aggregate": null
-                  },
-                  "documentation": "The value to find."
-                },
-                {
-                  "name": "booleanValueToFind",
-                  "type": 6,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 1,
-                    "details": {
-                      "enumName": "ReplaceBoolValues",
-                      "enumType": "Microsoft.DPrep.Engine.ReplaceBoolValues, Microsoft.DPrep.EngineAPI, Version=1.0.0.0, Culture=neutral, PublicKeyToken=null",
-                      "enumValues": [
-                        0,
-                        1
-                      ]
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": 1,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": {
-                    "condition": {
-                      "property": {
-                        "propertyPath": [
-                          "valueToFindType"
-                        ]
-                      },
-                      "comparison": 0,
-                      "value": 1
-                    },
-                    "aggregate": null
-                  },
-                  "documentation": "The value to find."
-                },
-                {
-                  "name": "replaceWithType",
-                  "type": 6,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 1,
-                    "details": {
-                      "enumName": "FieldType",
-                      "enumType": "Microsoft.DPrep.Engine.FieldType, Microsoft.DPrep.EngineAPI, Version=1.0.0.0, Culture=neutral, PublicKeyToken=null",
-                      "enumValues": [
-                        0,
-                        1,
-                        3,
-                        4,
-                        6,
-                        7,
-                        10
-                      ]
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": 0,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": null,
-                  "documentation": "The replacement value type."
-                },
-                {
-                  "name": "stringReplaceWith",
-                  "type": 1,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": {
-                    "condition": {
-                      "property": {
-                        "propertyPath": [
-                          "replaceWithType"
-                        ]
-                      },
-                      "comparison": 0,
-                      "value": 0
-                    },
-                    "aggregate": null
-                  },
-                  "documentation": "The replacement value."
-                },
-                {
-                  "name": "doubleReplaceWith",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": {
-                    "condition": {
-                      "property": {
-                        "propertyPath": [
-                          "replaceWithType"
-                        ]
-                      },
-                      "comparison": 0,
-                      "value": 3
-                    },
-                    "aggregate": null
-                  },
-                  "documentation": "The replacement value."
-                },
-                {
-                  "name": "datetimeReplaceWith",
-                  "type": 20,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": {
-                    "condition": {
-                      "property": {
-                        "propertyPath": [
-                          "replaceWithType"
-                        ]
-                      },
-                      "comparison": 0,
-                      "value": 4
-                    },
-                    "aggregate": null
-                  },
-                  "documentation": "The replacement value."
-                },
-                {
-                  "name": "booleanReplaceWith",
-                  "type": 6,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 1,
-                    "details": {
-                      "enumName": "ReplaceBoolValues",
-                      "enumType": "Microsoft.DPrep.Engine.ReplaceBoolValues, Microsoft.DPrep.EngineAPI, Version=1.0.0.0, Culture=neutral, PublicKeyToken=null",
-                      "enumValues": [
-                        0,
-                        1
-                      ]
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": 1,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": {
-                    "condition": {
-                      "property": {
-                        "propertyPath": [
-                          "replaceWithType"
-                        ]
-                      },
-                      "comparison": 0,
-                      "value": 1
-                    },
-                    "aggregate": null
-                  },
-                  "documentation": "The replacement value."
-                },
-                {
-                  "name": "errorReplaceWith",
-                  "type": 1,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": {
-                    "condition": {
-                      "property": {
-                        "propertyPath": [
-                          "replaceWithType"
-                        ]
-                      },
-                      "comparison": 0,
-                      "value": 6
-                    },
-                    "aggregate": null
-                  },
-                  "documentation": "The replacement error code."
-                }
-              ],
-              "arguments": {
-                "booleanReplaceWith": 1,
-                "booleanValueToFind": 1,
-                "columns": {
-                  "type": 0,
-                  "details": {
-                    "selectedColumns": [
-                      "Census_PrimaryDiskTotalCapacity"
-                    ]
-                  }
-                },
-                "doubleReplaceWith": 0.0,
-                "replaceWithType": 3,
-                "valueToFindType": 6
-              }
-            },
-            {
-              "id": "6397d50d-e60f-4121-9970-9596cfa945b1",
-              "description": {
-                "localDataProperties": [],
-                "blockGroup": 3,
-                "supportedTargets": [
-                  2,
-                  3
-                ],
-                "supportedFieldTypes": [
-                  3,
-                  2
-                ],
-                "supportedMessages": [],
-                "requiresInitialization": false,
-                "allowDefaultAdd": false,
-                "documentation": "Clips values so that all values are between the lower and upper boundaries.",
-                "type": "Microsoft.DPrep.ClipBlock",
-                "propertyDescriptions": [
-                  {
-                    "name": "columns",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 1,
-                        "fieldTypes": []
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source columns."
-                  },
-                  {
-                    "name": "lower",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "All values lower than this value will be set to this value."
-                  },
-                  {
-                    "name": "upper",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "All values higher than this value will be set to this value."
-                  },
-                  {
-                    "name": "useValues",
-                    "type": 4,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": true,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "If true, values outside boundaries will be set to the boundary values. If false, those values will be set to null."
-                  }
-                ],
-                "dataEffectDetails": {
-                  "columnsTransformedProperties": [
-                    {
-                      "propertyPath": [
-                        "columns"
-                      ]
-                    }
-                  ],
-                  "dataEffect": 5
-                }
-              },
-              "type": "Microsoft.DPrep.ClipBlock",
-              "dataEffectDetails": {
-                "columnsTransformedProperties": [
-                  {
-                    "propertyPath": [
-                      "columns"
-                    ]
-                  }
-                ],
-                "dataEffect": 5
-              },
-              "propertyDescriptions": [
-                {
-                  "name": "columns",
-                  "type": 5,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 12,
-                    "details": {
-                      "selectorDomainType": 1,
-                      "fieldTypes": []
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 0,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The source columns."
-                },
-                {
-                  "name": "lower",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "All values lower than this value will be set to this value."
-                },
-                {
-                  "name": "upper",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "All values higher than this value will be set to this value."
-                },
-                {
-                  "name": "useValues",
-                  "type": 4,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": true,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "If true, values outside boundaries will be set to the boundary values. If false, those values will be set to null."
-                }
-              ],
-              "arguments": {
-                "columns": {
-                  "type": 0,
-                  "details": {
-                    "selectedColumns": [
-                      "Census_TotalPhysicalRAM"
-                    ]
-                  }
-                },
-                "lower": 0.0,
-                "upper": 16384.0,
-                "useValues": true
-              }
-            },
-            {
-              "id": "2b897794-0e8c-4c05-9147-730ec9e10770",
-              "description": {
-                "localDataProperties": [],
-                "blockGroup": 13,
-                "supportedTargets": [
-                  2
-                ],
-                "supportedFieldTypes": [
-                  0
-                ],
-                "supportedMessages": [],
-                "requiresInitialization": false,
-                "allowDefaultAdd": true,
-                "documentation": "Creates a new column where values in the source column have been replaced with encoded labels.",
-                "type": "Microsoft.DPrep.LabelEncoderBlock",
-                "propertyDescriptions": [
-                  {
-                    "name": "column",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 0,
-                        "fieldTypes": []
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source column."
-                  },
-                  {
-                    "name": "newColumnId",
-                    "type": 1,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 3,
-                      "details": null
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The name of the newly added column that contains the encoded labels."
-                  },
-                  {
-                    "name": "encodedLabelsMap",
-                    "type": 7,
-                    "multipleValues": true,
-                    "domain": {
-                      "type": 0,
-                      "details": [
-                        {
-                          "name": "valueToLabel",
-                          "type": 1,
-                          "multipleValues": false,
-                          "domain": null,
-                          "multiValueDetails": null,
-                          "isRequired": false,
-                          "origin": 1,
-                          "defaultValue": null,
-                          "initializeFromProperty": null,
-                          "telemetryStrategy": 2,
-                          "condition": null,
-                          "documentation": "The value to label."
-                        },
-                        {
-                          "name": "encodedLabel",
-                          "type": 2,
-                          "multipleValues": false,
-                          "domain": null,
-                          "multiValueDetails": null,
-                          "isRequired": false,
-                          "origin": 1,
-                          "defaultValue": null,
-                          "initializeFromProperty": null,
-                          "telemetryStrategy": 2,
-                          "condition": null,
-                          "documentation": "The encoded label."
-                        }
-                      ]
-                    },
-                    "multiValueDetails": {
-                      "isOrdered": false
-                    },
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": null,
-                    "documentation": "The values to label and their encoded labels."
-                  }
-                ],
-                "dataEffectDetails": {
-                  "columnsAddedProperties": [
-                    {
-                      "propertyPath": [
-                        "newColumnId"
-                      ]
-                    }
-                  ],
-                  "dataEffect": 3
-                }
-              },
-              "type": "Microsoft.DPrep.LabelEncoderBlock",
-              "dataEffectDetails": {
-                "columnsAddedProperties": [
-                  {
-                    "propertyPath": [
-                      "newColumnId"
-                    ]
-                  }
-                ],
-                "dataEffect": 3
-              },
-              "propertyDescriptions": [
-                {
-                  "name": "column",
-                  "type": 5,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 12,
-                    "details": {
-                      "selectorDomainType": 0,
-                      "fieldTypes": []
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 0,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The source column."
-                },
-                {
-                  "name": "newColumnId",
-                  "type": 1,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 3,
-                    "details": null
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The name of the newly added column that contains the encoded labels."
-                },
-                {
-                  "name": "encodedLabelsMap",
-                  "type": 7,
-                  "multipleValues": true,
-                  "domain": {
-                    "type": 0,
-                    "details": [
-                      {
-                        "name": "valueToLabel",
-                        "type": 1,
-                        "multipleValues": false,
-                        "domain": null,
-                        "multiValueDetails": null,
-                        "isRequired": false,
-                        "origin": 1,
-                        "defaultValue": null,
-                        "initializeFromProperty": null,
-                        "telemetryStrategy": 2,
-                        "condition": null,
-                        "documentation": "The value to label."
-                      },
-                      {
-                        "name": "encodedLabel",
-                        "type": 2,
-                        "multipleValues": false,
-                        "domain": null,
-                        "multiValueDetails": null,
-                        "isRequired": false,
-                        "origin": 1,
-                        "defaultValue": null,
-                        "initializeFromProperty": null,
-                        "telemetryStrategy": 2,
-                        "condition": null,
-                        "documentation": "The encoded label."
-                      }
-                    ]
-                  },
-                  "multiValueDetails": {
-                    "isOrdered": false
-                  },
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": null,
-                  "documentation": "The values to label and their encoded labels."
-                }
-              ],
-              "arguments": {
-                "column": {
-                  "type": 2,
-                  "details": {
-                    "selectedColumn": "EngineVersion"
-                  }
-                },
-                "encodedLabelsMap": [
-                  {
-                    "encodedLabel": 0,
-                    "valueToLabel": "1.1.15200.1"
-                  },
-                  {
-                    "encodedLabel": 1,
-                    "valueToLabel": "1.1.15100.1"
-                  },
-                  {
-                    "encodedLabel": 2,
-                    "valueToLabel": "1.1.15000.2"
-                  },
-                  {
-                    "encodedLabel": 3,
-                    "valueToLabel": "1.1.14901.4"
-                  },
-                  {
-                    "encodedLabel": 4,
-                    "valueToLabel": "1.1.14600.4"
-                  },
-                  {
-                    "encodedLabel": 5,
-                    "valueToLabel": "1.1.14800.3"
-                  },
-                  {
-                    "encodedLabel": 6,
-                    "valueToLabel": "1.1.15300.6"
-                  },
-                  {
-                    "encodedLabel": 7,
-                    "valueToLabel": "1.1.14104.0"
-                  },
-                  {
-                    "encodedLabel": 8,
-                    "valueToLabel": "1.1.13504.0"
-                  },
-                  {
-                    "encodedLabel": 9,
-                    "valueToLabel": "1.1.15300.5"
-                  },
-                  {
-                    "encodedLabel": 10,
-                    "valueToLabel": "1.1.14700.5"
-                  },
-                  {
-                    "encodedLabel": 11,
-                    "valueToLabel": "1.1.14500.5"
-                  },
-                  {
-                    "encodedLabel": 12,
-                    "valueToLabel": "1.1.14405.2"
-                  },
-                  {
-                    "encodedLabel": 13,
-                    "valueToLabel": "1.1.14306.0"
-                  },
-                  {
-                    "encodedLabel": 14,
-                    "valueToLabel": "1.1.14202.0"
-                  },
-                  {
-                    "encodedLabel": 15,
-                    "valueToLabel": "1.1.14003.0"
-                  },
-                  {
-                    "encodedLabel": 16,
-                    "valueToLabel": "1.1.13903.0"
-                  },
-                  {
-                    "encodedLabel": 17,
-                    "valueToLabel": "1.1.13303.0"
-                  },
-                  {
-                    "encodedLabel": 18,
-                    "valueToLabel": "1.1.13804.0"
-                  },
-                  {
-                    "encodedLabel": 19,
-                    "valueToLabel": "1.1.13407.0"
-                  },
-                  {
-                    "encodedLabel": 20,
-                    "valueToLabel": "1.1.13601.0"
-                  },
-                  {
-                    "encodedLabel": 21,
-                    "valueToLabel": "1.1.12902.0"
-                  },
-                  {
-                    "encodedLabel": 22,
-                    "valueToLabel": "1.1.13701.0"
-                  },
-                  {
-                    "encodedLabel": 23,
-                    "valueToLabel": "1.1.14305.0"
-                  },
-                  {
-                    "encodedLabel": 24,
-                    "valueToLabel": "1.1.13704.0"
-                  },
-                  {
-                    "encodedLabel": 25,
-                    "valueToLabel": "1.1.13202.0"
-                  },
-                  {
-                    "encodedLabel": 26,
-                    "valueToLabel": "1.1.13103.0"
-                  },
-                  {
-                    "encodedLabel": 27,
-                    "valueToLabel": "1.1.13000.0"
-                  },
-                  {
-                    "encodedLabel": 28,
-                    "valueToLabel": "1.1.12805.0"
-                  },
-                  {
-                    "encodedLabel": 29,
-                    "valueToLabel": "1.1.15000.1"
-                  },
-                  {
-                    "encodedLabel": 30,
-                    "valueToLabel": "1.1.14901.3"
-                  },
-                  {
-                    "encodedLabel": 31,
-                    "valueToLabel": "1.1.14700.3"
-                  },
-                  {
-                    "encodedLabel": 32,
-                    "valueToLabel": "1.1.14700.4"
-                  },
-                  {
-                    "encodedLabel": 33,
-                    "valueToLabel": "1.1.14800.1"
-                  },
-                  {
-                    "encodedLabel": 34,
-                    "valueToLabel": "1.1.14303.0"
-                  },
-                  {
-                    "encodedLabel": 35,
-                    "valueToLabel": "1.1.14500.2"
-                  },
-                  {
-                    "encodedLabel": 36,
-                    "valueToLabel": "1.1.14201.0"
-                  },
-                  {
-                    "encodedLabel": 37,
-                    "valueToLabel": "1.1.14103.0"
-                  },
-                  {
-                    "encodedLabel": 38,
-                    "valueToLabel": "1.1.12101.0"
-                  },
-                  {
-                    "encodedLabel": 39,
-                    "valueToLabel": "1.1.11701.0"
-                  },
-                  {
-                    "encodedLabel": 40,
-                    "valueToLabel": "1.1.13902.0"
-                  },
-                  {
-                    "encodedLabel": 41,
-                    "valueToLabel": "1.1.14102.0"
-                  },
-                  {
-                    "encodedLabel": 42,
-                    "valueToLabel": "1.1.13803.0"
-                  },
-                  {
-                    "encodedLabel": 43,
-                    "valueToLabel": "1.1.13802.0"
-                  },
-                  {
-                    "encodedLabel": 44,
-                    "valueToLabel": "1.1.14001.0"
-                  },
-                  {
-                    "encodedLabel": 45,
-                    "valueToLabel": "1.1.14002.0"
-                  },
-                  {
-                    "encodedLabel": 46,
-                    "valueToLabel": "1.1.13406.0"
-                  },
-                  {
-                    "encodedLabel": 47,
-                    "valueToLabel": "1.1.12400.0"
-                  },
-                  {
-                    "encodedLabel": 48,
-                    "valueToLabel": "1.1.12603.0"
-                  },
-                  {
-                    "encodedLabel": 49,
-                    "valueToLabel": "1.1.12804.0"
-                  },
-                  {
-                    "encodedLabel": 50,
-                    "valueToLabel": "1.1.9700.0"
-                  },
-                  {
-                    "encodedLabel": 51,
-                    "valueToLabel": "1.1.12505.0"
-                  },
-                  {
-                    "encodedLabel": 52,
-                    "valueToLabel": "1.1.13102.0"
-                  },
-                  {
-                    "encodedLabel": 53,
-                    "valueToLabel": "1.1.10302.0"
-                  },
-                  {
-                    "encodedLabel": 54,
-                    "valueToLabel": "1.1.12706.0"
-                  },
-                  {
-                    "encodedLabel": 55,
-                    "valueToLabel": "1.1.12205.0"
-                  },
-                  {
-                    "encodedLabel": 56,
-                    "valueToLabel": "1.1.12300.0"
-                  },
-                  {
-                    "encodedLabel": 57,
-                    "valueToLabel": "1.1.11602.0"
-                  },
-                  {
-                    "encodedLabel": 58,
-                    "valueToLabel": "1.1.12002.0"
-                  },
-                  {
-                    "encodedLabel": 59,
-                    "valueToLabel": "1.1.11804.0"
-                  },
-                  {
-                    "encodedLabel": 60,
-                    "valueToLabel": "1.1.10401.0"
-                  }
-                ],
-                "newColumnId": "EngineVersionEncoded"
-              }
-            },
-            {
-              "id": "3e931e3f-efbb-4896-9ffe-e69d7c298618",
-              "description": {
-                "localDataProperties": [],
-                "blockGroup": 13,
-                "supportedTargets": [
-                  2
-                ],
-                "supportedFieldTypes": [
-                  0
-                ],
-                "supportedMessages": [],
-                "requiresInitialization": false,
-                "allowDefaultAdd": true,
-                "documentation": "Creates a new column where values in the source column have been replaced with encoded labels.",
-                "type": "Microsoft.DPrep.LabelEncoderBlock",
-                "propertyDescriptions": [
-                  {
-                    "name": "column",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 0,
-                        "fieldTypes": []
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source column."
-                  },
-                  {
-                    "name": "newColumnId",
-                    "type": 1,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 3,
-                      "details": null
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The name of the newly added column that contains the encoded labels."
-                  },
-                  {
-                    "name": "encodedLabelsMap",
-                    "type": 7,
-                    "multipleValues": true,
-                    "domain": {
-                      "type": 0,
-                      "details": [
-                        {
-                          "name": "valueToLabel",
-                          "type": 1,
-                          "multipleValues": false,
-                          "domain": null,
-                          "multiValueDetails": null,
-                          "isRequired": false,
-                          "origin": 1,
-                          "defaultValue": null,
-                          "initializeFromProperty": null,
-                          "telemetryStrategy": 2,
-                          "condition": null,
-                          "documentation": "The value to label."
-                        },
-                        {
-                          "name": "encodedLabel",
-                          "type": 2,
-                          "multipleValues": false,
-                          "domain": null,
-                          "multiValueDetails": null,
-                          "isRequired": false,
-                          "origin": 1,
-                          "defaultValue": null,
-                          "initializeFromProperty": null,
-                          "telemetryStrategy": 2,
-                          "condition": null,
-                          "documentation": "The encoded label."
-                        }
-                      ]
-                    },
-                    "multiValueDetails": {
-                      "isOrdered": false
-                    },
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": null,
-                    "documentation": "The values to label and their encoded labels."
-                  }
-                ],
-                "dataEffectDetails": {
-                  "columnsAddedProperties": [
-                    {
-                      "propertyPath": [
-                        "newColumnId"
-                      ]
-                    }
-                  ],
-                  "dataEffect": 3
-                }
-              },
-              "type": "Microsoft.DPrep.LabelEncoderBlock",
-              "dataEffectDetails": {
-                "columnsAddedProperties": [
-                  {
-                    "propertyPath": [
-                      "newColumnId"
-                    ]
-                  }
-                ],
-                "dataEffect": 3
-              },
-              "propertyDescriptions": [
-                {
-                  "name": "column",
-                  "type": 5,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 12,
-                    "details": {
-                      "selectorDomainType": 0,
-                      "fieldTypes": []
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 0,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The source column."
-                },
-                {
-                  "name": "newColumnId",
-                  "type": 1,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 3,
-                    "details": null
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The name of the newly added column that contains the encoded labels."
-                },
-                {
-                  "name": "encodedLabelsMap",
-                  "type": 7,
-                  "multipleValues": true,
-                  "domain": {
-                    "type": 0,
-                    "details": [
-                      {
-                        "name": "valueToLabel",
-                        "type": 1,
-                        "multipleValues": false,
-                        "domain": null,
-                        "multiValueDetails": null,
-                        "isRequired": false,
-                        "origin": 1,
-                        "defaultValue": null,
-                        "initializeFromProperty": null,
-                        "telemetryStrategy": 2,
-                        "condition": null,
-                        "documentation": "The value to label."
-                      },
-                      {
-                        "name": "encodedLabel",
-                        "type": 2,
-                        "multipleValues": false,
-                        "domain": null,
-                        "multiValueDetails": null,
-                        "isRequired": false,
-                        "origin": 1,
-                        "defaultValue": null,
-                        "initializeFromProperty": null,
-                        "telemetryStrategy": 2,
-                        "condition": null,
-                        "documentation": "The encoded label."
-                      }
-                    ]
-                  },
-                  "multiValueDetails": {
-                    "isOrdered": false
-                  },
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": null,
-                  "documentation": "The values to label and their encoded labels."
-                }
-              ],
-              "arguments": {
-                "column": {
-                  "type": 2,
-                  "details": {
-                    "selectedColumn": "AppVersion"
-                  }
-                },
-                "encodedLabelsMap": [
-                  {
-                    "encodedLabel": 0,
-                    "valueToLabel": "4.18.1807.18075"
-                  },
-                  {
-                    "encodedLabel": 1,
-                    "valueToLabel": "4.18.1806.18062"
-                  },
-                  {
-                    "encodedLabel": 2,
-                    "valueToLabel": "4.12.16299.15"
-                  },
-                  {
-                    "encodedLabel": 3,
-                    "valueToLabel": "4.10.209.0"
-                  },
-                  {
-                    "encodedLabel": 4,
-                    "valueToLabel": "4.13.17134.1"
-                  },
-                  {
-                    "encodedLabel": 5,
-                    "valueToLabel": "4.16.17656.18052"
-                  },
-                  {
-                    "encodedLabel": 6,
-                    "valueToLabel": "4.13.17134.228"
-                  },
-                  {
-                    "encodedLabel": 7,
-                    "valueToLabel": "4.8.10240.17443"
-                  },
-                  {
-                    "encodedLabel": 8,
-                    "valueToLabel": "4.9.10586.1106"
-                  },
-                  {
-                    "encodedLabel": 9,
-                    "valueToLabel": "4.14.17639.18041"
-                  },
-                  {
-                    "encodedLabel": 10,
-                    "valueToLabel": "4.12.17007.18022"
-                  },
-                  {
-                    "encodedLabel": 11,
-                    "valueToLabel": "4.9.10586.0"
-                  },
-                  {
-                    "encodedLabel": 12,
-                    "valueToLabel": "4.11.15063.447"
-                  },
-                  {
-                    "encodedLabel": 13,
-                    "valueToLabel": "4.10.14393.0"
-                  },
-                  {
-                    "encodedLabel": 14,
-                    "valueToLabel": "4.11.15063.0"
-                  },
-                  {
-                    "encodedLabel": 15,
-                    "valueToLabel": "4.12.17007.18011"
-                  },
-                  {
-                    "encodedLabel": 16,
-                    "valueToLabel": "4.14.17613.18039"
-                  },
-                  {
-                    "encodedLabel": 17,
-                    "valueToLabel": "4.8.10240.16384"
-                  },
-                  {
-                    "encodedLabel": 18,
-                    "valueToLabel": "4.10.14393.1794"
-                  },
-                  {
-                    "encodedLabel": 19,
-                    "valueToLabel": "4.11.15063.1155"
-                  },
-                  {
-                    "encodedLabel": 20,
-                    "valueToLabel": "4.9.10586.494"
-                  },
-                  {
-                    "encodedLabel": 21,
-                    "valueToLabel": "4.10.14393.1198"
-                  },
-                  {
-                    "encodedLabel": 22,
-                    "valueToLabel": "4.9.10586.672"
-                  },
-                  {
-                    "encodedLabel": 23,
-                    "valueToLabel": "4.13.17134.191"
-                  },
-                  {
-                    "encodedLabel": 24,
-                    "valueToLabel": "4.10.14393.1613"
-                  },
-                  {
-                    "encodedLabel": 25,
-                    "valueToLabel": "4.18.1809.2"
-                  },
-                  {
-                    "encodedLabel": 26,
-                    "valueToLabel": "4.12.17007.17123"
-                  },
-                  {
-                    "encodedLabel": 27,
-                    "valueToLabel": "4.9.10586.589"
-                  },
-                  {
-                    "encodedLabel": 28,
-                    "valueToLabel": "4.13.17134.112"
-                  },
-                  {
-                    "encodedLabel": 29,
-                    "valueToLabel": "4.9.10586.1045"
-                  },
-                  {
-                    "encodedLabel": 30,
-                    "valueToLabel": "4.10.14393.1593"
-                  },
-                  {
-                    "encodedLabel": 31,
-                    "valueToLabel": "4.10.14393.953"
-                  },
-                  {
-                    "encodedLabel": 32,
-                    "valueToLabel": "4.10.14393.1066"
-                  },
-                  {
-                    "encodedLabel": 33,
-                    "valueToLabel": "4.9.218.0"
-                  },
-                  {
-                    "encodedLabel": 34,
-                    "valueToLabel": "4.9.10586.916"
-                  },
-                  {
-                    "encodedLabel": 35,
-                    "valueToLabel": "4.9.10586.965"
-                  },
-                  {
-                    "encodedLabel": 36,
-                    "valueToLabel": "4.8.10240.17946"
-                  },
-                  {
-                    "encodedLabel": 37,
-                    "valueToLabel": "4.9.10586.962"
-                  },
-                  {
-                    "encodedLabel": 38,
-                    "valueToLabel": "4.9.10586.839"
-                  },
-                  {
-                    "encodedLabel": 39,
-                    "valueToLabel": "4.9.10586.873"
-                  },
-                  {
-                    "encodedLabel": 40,
-                    "valueToLabel": "4.8.207.0"
-                  },
-                  {
-                    "encodedLabel": 41,
-                    "valueToLabel": "4.5.218.0"
-                  },
-                  {
-                    "encodedLabel": 42,
-                    "valueToLabel": "4.8.10240.17202"
-                  },
-                  {
-                    "encodedLabel": 43,
-                    "valueToLabel": "4.8.10240.17914"
-                  },
-                  {
-                    "encodedLabel": 44,
-                    "valueToLabel": "4.8.10240.17889"
-                  },
-                  {
-                    "encodedLabel": 45,
-                    "valueToLabel": "4.13.17134.319"
-                  },
-                  {
-                    "encodedLabel": 46,
-                    "valueToLabel": "4.8.204.0"
-                  },
-                  {
-                    "encodedLabel": 47,
-                    "valueToLabel": "4.8.10240.17394"
-                  },
-                  {
-                    "encodedLabel": 48,
-                    "valueToLabel": "4.8.10240.17071"
-                  },
-                  {
-                    "encodedLabel": 49,
-                    "valueToLabel": "4.10.205.0"
-                  },
-                  {
-                    "encodedLabel": 50,
-                    "valueToLabel": "4.8.10240.17146"
-                  },
-                  {
-                    "encodedLabel": 51,
-                    "valueToLabel": "4.8.10240.17319"
-                  },
-                  {
-                    "encodedLabel": 52,
-                    "valueToLabel": "4.14.17613.18038"
-                  },
-                  {
-                    "encodedLabel": 53,
-                    "valueToLabel": "4.10.14393.2273"
-                  },
-                  {
-                    "encodedLabel": 54,
-                    "valueToLabel": "4.6.305.0"
-                  },
-                  {
-                    "encodedLabel": 55,
-                    "valueToLabel": "4.8.10240.17354"
-                  },
-                  {
-                    "encodedLabel": 56,
-                    "valueToLabel": "4.7.205.0"
-                  },
-                  {
-                    "encodedLabel": 57,
-                    "valueToLabel": "4.8.10240.17861"
-                  },
-                  {
-                    "encodedLabel": 58,
-                    "valueToLabel": "4.8.10240.17797"
-                  },
-                  {
-                    "encodedLabel": 59,
-                    "valueToLabel": "4.8.10240.17184"
-                  },
-                  {
-                    "encodedLabel": 60,
-                    "valueToLabel": "4.18.1807.20063"
-                  },
-                  {
-                    "encodedLabel": 61,
-                    "valueToLabel": "4.4.304.0"
-                  },
-                  {
-                    "encodedLabel": 62,
-                    "valueToLabel": "4.8.10240.17918"
-                  },
-                  {
-                    "encodedLabel": 63,
-                    "valueToLabel": "4.8.10240.17113"
-                  },
-                  {
-                    "encodedLabel": 64,
-                    "valueToLabel": "4.8.10240.17609"
-                  },
-                  {
-                    "encodedLabel": 65,
-                    "valueToLabel": "4.10.14393.2248"
-                  },
-                  {
-                    "encodedLabel": 66,
-                    "valueToLabel": "4.5.216.0"
-                  },
-                  {
-                    "encodedLabel": 67,
-                    "valueToLabel": "4.10.14393.726"
-                  },
-                  {
-                    "encodedLabel": 68,
-                    "valueToLabel": "4.18.1807.18072"
-                  },
-                  {
-                    "encodedLabel": 69,
-                    "valueToLabel": "4.17.17685.20082"
-                  },
-                  {
-                    "encodedLabel": 70,
-                    "valueToLabel": "4.17.17672.1000"
-                  },
-                  {
-                    "encodedLabel": 71,
-                    "valueToLabel": "4.8.10240.17770"
-                  },
-                  {
-                    "encodedLabel": 72,
-                    "valueToLabel": "4.17.17686.1003"
-                  },
-                  {
-                    "encodedLabel": 73,
-                    "valueToLabel": "4.11.15063.994"
-                  },
-                  {
-                    "encodedLabel": 74,
-                    "valueToLabel": "4.10.14393.1532"
-                  },
-                  {
-                    "encodedLabel": 75,
-                    "valueToLabel": "4.18.1806.20021"
-                  },
-                  {
-                    "encodedLabel": 76,
-                    "valueToLabel": "4.10.14393.2457"
-                  },
-                  {
-                    "encodedLabel": 77,
-                    "valueToLabel": "4.17.17682.1000"
-                  },
-                  {
-                    "encodedLabel": 78,
-                    "valueToLabel": "4.17.17677.1000"
-                  },
-                  {
-                    "encodedLabel": 79,
-                    "valueToLabel": "4.15.17666.1000"
-                  },
-                  {
-                    "encodedLabel": 80,
-                    "valueToLabel": "4.9.10586.456"
-                  },
-                  {
-                    "encodedLabel": 81,
-                    "valueToLabel": "4.13.17639.1000"
-                  },
-                  {
-                    "encodedLabel": 82,
-                    "valueToLabel": "4.16.17656.18051"
-                  },
-                  {
-                    "encodedLabel": 83,
-                    "valueToLabel": "4.9.10586.1177"
-                  },
-                  {
-                    "encodedLabel": 84,
-                    "valueToLabel": "4.15.17650.1001"
-                  },
-                  {
-                    "encodedLabel": 85,
-                    "valueToLabel": "4.4.306.0"
-                  },
-                  {
-                    "encodedLabel": 86,
-                    "valueToLabel": "4.18.1806.20033"
-                  },
-                  {
-                    "encodedLabel": 87,
-                    "valueToLabel": "4.12.17007.17121"
-                  },
-                  {
-                    "encodedLabel": 88,
-                    "valueToLabel": "4.15.17661.1001"
-                  },
-                  {
-                    "encodedLabel": 89,
-                    "valueToLabel": "4.8.10240.17533"
-                  },
-                  {
-                    "encodedLabel": 90,
-                    "valueToLabel": "4.11.15063.1154"
-                  },
-                  {
-                    "encodedLabel": 91,
-                    "valueToLabel": "4.8.10240.17446"
-                  },
-                  {
-                    "encodedLabel": 92,
-                    "valueToLabel": "4.18.1806.20015"
-                  },
-                  {
-                    "encodedLabel": 93,
-                    "valueToLabel": "4.18.1807.18070"
-                  },
-                  {
-                    "encodedLabel": 94,
-                    "valueToLabel": "4.13.17634.1000"
-                  },
-                  {
-                    "encodedLabel": 95,
-                    "valueToLabel": "4.13.17627.1000"
-                  },
-                  {
-                    "encodedLabel": 96,
-                    "valueToLabel": "4.9.219.0"
-                  },
-                  {
-                    "encodedLabel": 97,
-                    "valueToLabel": "4.12.17007.18021"
-                  },
-                  {
-                    "encodedLabel": 98,
-                    "valueToLabel": "4.12.16299.0"
-                  },
-                  {
-                    "encodedLabel": 99,
-                    "valueToLabel": "4.7.209.0"
-                  },
-                  {
-                    "encodedLabel": 100,
-                    "valueToLabel": "4.15.17655.1000"
-                  }
-                ],
-                "newColumnId": "AppVersionEncoded"
-              }
-            },
-            {
-              "id": "e64c7560-76e1-4fb1-b558-491bf6166cfc",
-              "description": {
-                "localDataProperties": [],
-                "blockGroup": 13,
-                "supportedTargets": [
-                  2
-                ],
-                "supportedFieldTypes": [
-                  0
-                ],
-                "supportedMessages": [],
-                "requiresInitialization": false,
-                "allowDefaultAdd": true,
-                "documentation": "Creates a new column where values in the source column have been replaced with encoded labels.",
-                "type": "Microsoft.DPrep.LabelEncoderBlock",
-                "propertyDescriptions": [
-                  {
-                    "name": "column",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 0,
-                        "fieldTypes": []
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source column."
-                  },
-                  {
-                    "name": "newColumnId",
-                    "type": 1,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 3,
-                      "details": null
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The name of the newly added column that contains the encoded labels."
-                  },
-                  {
-                    "name": "encodedLabelsMap",
-                    "type": 7,
-                    "multipleValues": true,
-                    "domain": {
-                      "type": 0,
-                      "details": [
-                        {
-                          "name": "valueToLabel",
-                          "type": 1,
-                          "multipleValues": false,
-                          "domain": null,
-                          "multiValueDetails": null,
-                          "isRequired": false,
-                          "origin": 1,
-                          "defaultValue": null,
-                          "initializeFromProperty": null,
-                          "telemetryStrategy": 2,
-                          "condition": null,
-                          "documentation": "The value to label."
-                        },
-                        {
-                          "name": "encodedLabel",
-                          "type": 2,
-                          "multipleValues": false,
-                          "domain": null,
-                          "multiValueDetails": null,
-                          "isRequired": false,
-                          "origin": 1,
-                          "defaultValue": null,
-                          "initializeFromProperty": null,
-                          "telemetryStrategy": 2,
-                          "condition": null,
-                          "documentation": "The encoded label."
-                        }
-                      ]
-                    },
-                    "multiValueDetails": {
-                      "isOrdered": false
-                    },
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 2,
-                    "condition": null,
-                    "documentation": "The values to label and their encoded labels."
-                  }
-                ],
-                "dataEffectDetails": {
-                  "columnsAddedProperties": [
-                    {
-                      "propertyPath": [
-                        "newColumnId"
-                      ]
-                    }
-                  ],
-                  "dataEffect": 3
-                }
-              },
-              "type": "Microsoft.DPrep.LabelEncoderBlock",
-              "dataEffectDetails": {
-                "columnsAddedProperties": [
-                  {
-                    "propertyPath": [
-                      "newColumnId"
-                    ]
-                  }
-                ],
-                "dataEffect": 3
-              },
-              "propertyDescriptions": [
-                {
-                  "name": "column",
-                  "type": 5,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 12,
-                    "details": {
-                      "selectorDomainType": 0,
-                      "fieldTypes": []
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 0,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The source column."
-                },
-                {
-                  "name": "newColumnId",
-                  "type": 1,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 3,
-                    "details": null
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The name of the newly added column that contains the encoded labels."
-                },
-                {
-                  "name": "encodedLabelsMap",
-                  "type": 7,
-                  "multipleValues": true,
-                  "domain": {
-                    "type": 0,
-                    "details": [
-                      {
-                        "name": "valueToLabel",
-                        "type": 1,
-                        "multipleValues": false,
-                        "domain": null,
-                        "multiValueDetails": null,
-                        "isRequired": false,
-                        "origin": 1,
-                        "defaultValue": null,
-                        "initializeFromProperty": null,
-                        "telemetryStrategy": 2,
-                        "condition": null,
-                        "documentation": "The value to label."
-                      },
-                      {
-                        "name": "encodedLabel",
-                        "type": 2,
-                        "multipleValues": false,
-                        "domain": null,
-                        "multiValueDetails": null,
-                        "isRequired": false,
-                        "origin": 1,
-                        "defaultValue": null,
-                        "initializeFromProperty": null,
-                        "telemetryStrategy": 2,
-                        "condition": null,
-                        "documentation": "The encoded label."
-                      }
-                    ]
-                  },
-                  "multiValueDetails": {
-                    "isOrdered": false
-                  },
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 2,
-                  "condition": null,
-                  "documentation": "The values to label and their encoded labels."
-                }
-              ],
-              "arguments": {
-                "column": {
-                  "type": 2,
-                  "details": {
-                    "selectedColumn": "Census_FlightRing"
-                  }
-                },
-                "encodedLabelsMap": [
-                  {
-                    "encodedLabel": 0,
-                    "valueToLabel": "Retail"
-                  },
-                  {
-                    "encodedLabel": 1,
-                    "valueToLabel": "NOT_SET"
-                  },
-                  {
-                    "encodedLabel": 2,
-                    "valueToLabel": "Unknown"
-                  },
-                  {
-                    "encodedLabel": 3,
-                    "valueToLabel": "WIS"
-                  },
-                  {
-                    "encodedLabel": 4,
-                    "valueToLabel": "RP"
-                  },
-                  {
-                    "encodedLabel": 5,
-                    "valueToLabel": "WIF"
-                  },
-                  {
-                    "encodedLabel": 6,
-                    "valueToLabel": "Disabled"
-                  },
-                  {
-                    "encodedLabel": 7,
-                    "valueToLabel": "Canary"
-                  }
-                ],
-                "newColumnId": "Census_FlightRingEncoded"
-              }
-            },
-            {
-              "id": "a76a615b-8b6b-4292-8a57-6f26b96c54c6",
-              "description": {
-                "localDataProperties": [],
-                "blockGroup": 6,
-                "supportedTargets": [
-                  2,
-                  3
-                ],
-                "supportedFieldTypes": null,
-                "supportedMessages": [],
-                "requiresInitialization": false,
-                "allowDefaultAdd": true,
-                "documentation": "Drops the specified columns.",
-                "type": "Microsoft.DPrep.DropColumnsBlock",
-                "propertyDescriptions": [
-                  {
-                    "name": "columns",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 1,
-                        "fieldTypes": []
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source columns."
-                  }
-                ],
-                "dataEffectDetails": {
-                  "columnsRemovedProperties": [
-                    {
-                      "propertyPath": [
-                        "columns"
-                      ]
-                    }
-                  ],
-                  "dataEffect": 4
-                }
-              },
-              "type": "Microsoft.DPrep.DropColumnsBlock",
-              "dataEffectDetails": {
-                "columnsRemovedProperties": [
-                  {
-                    "propertyPath": [
-                      "columns"
-                    ]
-                  }
-                ],
-                "dataEffect": 4
-              },
-              "propertyDescriptions": [
-                {
-                  "name": "columns",
-                  "type": 5,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 12,
-                    "details": {
-                      "selectorDomainType": 1,
-                      "fieldTypes": []
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 0,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The source columns."
-                }
-              ],
-              "arguments": {
-                "columns": {
-                  "type": 0,
-                  "details": {
-                    "selectedColumns": [
-                      "EngineVersion",
-                      "AppVersion",
-                      "Census_FlightRing"
-                    ]
-                  }
-                }
-              }
-            },
-            {
-              "id": "4a4443a7-caa6-445c-b9bc-1932765da295",
-              "description": {
-                "localDataProperties": [],
-                "blockGroup": 13,
-                "supportedTargets": [
-                  2
-                ],
-                "supportedFieldTypes": [
-                  3,
-                  2
-                ],
-                "supportedMessages": [],
-                "requiresInitialization": false,
-                "allowDefaultAdd": true,
-                "documentation": "Scale values in the column to lie within the specified range (default=[0, 1]).",
-                "type": "Microsoft.DPrep.MinMaxScalerBlock",
-                "propertyDescriptions": [
-                  {
-                    "name": "column",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 0,
-                        "fieldTypes": [
-                          3,
-                          2
-                        ]
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source column."
-                  },
-                  {
-                    "name": "rangeMin",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 0,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "This will be the minimum value after scaling. Default is 0."
-                  },
-                  {
-                    "name": "rangeMax",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 1,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "This will be the maximum value after scaling. Default is 1."
-                  },
-                  {
-                    "name": "dataMin",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Minimum value of the data. If none is provided, a scan of the data will be performed to find the minimum."
-                  },
-                  {
-                    "name": "dataMax",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Maximum value of the data. If none is provided, a scan of the data will be performed to find the maximum."
-                  }
-                ],
-                "dataEffectDetails": {
-                  "columnsTransformedProperties": [
-                    {
-                      "propertyPath": [
-                        "column"
-                      ]
-                    }
-                  ],
-                  "dataEffect": 5
-                }
-              },
-              "type": "Microsoft.DPrep.MinMaxScalerBlock",
-              "dataEffectDetails": {
-                "columnsTransformedProperties": [
-                  {
-                    "propertyPath": [
-                      "column"
-                    ]
-                  }
-                ],
-                "dataEffect": 5
-              },
-              "propertyDescriptions": [
-                {
-                  "name": "column",
-                  "type": 5,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 12,
-                    "details": {
-                      "selectorDomainType": 0,
-                      "fieldTypes": [
-                        3,
-                        2
-                      ]
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 0,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The source column."
-                },
-                {
-                  "name": "rangeMin",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": 0,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "This will be the minimum value after scaling. Default is 0."
-                },
-                {
-                  "name": "rangeMax",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": 1,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "This will be the maximum value after scaling. Default is 1."
-                },
-                {
-                  "name": "dataMin",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "Minimum value of the data. If none is provided, a scan of the data will be performed to find the minimum."
-                },
-                {
-                  "name": "dataMax",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "Maximum value of the data. If none is provided, a scan of the data will be performed to find the maximum."
-                }
-              ],
-              "arguments": {
-                "column": {
-                  "type": 2,
-                  "details": {
-                    "selectedColumn": "AVProductsInstalled"
-                  }
-                },
-                "dataMax": 6.0,
-                "dataMin": 0.0,
-                "rangeMax": 1.0,
-                "rangeMin": 0.0
-              }
-            },
-            {
-              "id": "28dc01c4-036c-4562-9d9a-7ebee4c5f396",
-              "description": {
-                "localDataProperties": [],
-                "blockGroup": 13,
-                "supportedTargets": [
-                  2
-                ],
-                "supportedFieldTypes": [
-                  3,
-                  2
-                ],
-                "supportedMessages": [],
-                "requiresInitialization": false,
-                "allowDefaultAdd": true,
-                "documentation": "Scale values in the column to lie within the specified range (default=[0, 1]).",
-                "type": "Microsoft.DPrep.MinMaxScalerBlock",
-                "propertyDescriptions": [
-                  {
-                    "name": "column",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 0,
-                        "fieldTypes": [
-                          3,
-                          2
-                        ]
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source column."
-                  },
-                  {
-                    "name": "rangeMin",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 0,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "This will be the minimum value after scaling. Default is 0."
-                  },
-                  {
-                    "name": "rangeMax",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 1,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "This will be the maximum value after scaling. Default is 1."
-                  },
-                  {
-                    "name": "dataMin",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Minimum value of the data. If none is provided, a scan of the data will be performed to find the minimum."
-                  },
-                  {
-                    "name": "dataMax",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Maximum value of the data. If none is provided, a scan of the data will be performed to find the maximum."
-                  }
-                ],
-                "dataEffectDetails": {
-                  "columnsTransformedProperties": [
-                    {
-                      "propertyPath": [
-                        "column"
-                      ]
-                    }
-                  ],
-                  "dataEffect": 5
-                }
-              },
-              "type": "Microsoft.DPrep.MinMaxScalerBlock",
-              "dataEffectDetails": {
-                "columnsTransformedProperties": [
-                  {
-                    "propertyPath": [
-                      "column"
-                    ]
-                  }
-                ],
-                "dataEffect": 5
-              },
-              "propertyDescriptions": [
-                {
-                  "name": "column",
-                  "type": 5,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 12,
-                    "details": {
-                      "selectorDomainType": 0,
-                      "fieldTypes": [
-                        3,
-                        2
-                      ]
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 0,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The source column."
-                },
-                {
-                  "name": "rangeMin",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": 0,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "This will be the minimum value after scaling. Default is 0."
-                },
-                {
-                  "name": "rangeMax",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": 1,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "This will be the maximum value after scaling. Default is 1."
-                },
-                {
-                  "name": "dataMin",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "Minimum value of the data. If none is provided, a scan of the data will be performed to find the minimum."
-                },
-                {
-                  "name": "dataMax",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "Maximum value of the data. If none is provided, a scan of the data will be performed to find the maximum."
-                }
-              ],
-              "arguments": {
-                "column": {
-                  "type": 2,
-                  "details": {
-                    "selectedColumn": "Census_ProcessorCoreCount"
-                  }
-                },
-                "dataMax": 112.0,
-                "dataMin": 0.0,
-                "rangeMax": 1.0,
-                "rangeMin": 0.0
-              }
-            },
-            {
-              "id": "826a8b62-1f79-4261-bbc6-0608393c65fb",
-              "description": {
-                "localDataProperties": [],
-                "blockGroup": 13,
-                "supportedTargets": [
-                  2
-                ],
-                "supportedFieldTypes": [
-                  3,
-                  2
-                ],
-                "supportedMessages": [],
-                "requiresInitialization": false,
-                "allowDefaultAdd": true,
-                "documentation": "Scale values in the column to lie within the specified range (default=[0, 1]).",
-                "type": "Microsoft.DPrep.MinMaxScalerBlock",
-                "propertyDescriptions": [
-                  {
-                    "name": "column",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 0,
-                        "fieldTypes": [
-                          3,
-                          2
-                        ]
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source column."
-                  },
-                  {
-                    "name": "rangeMin",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 0,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "This will be the minimum value after scaling. Default is 0."
-                  },
-                  {
-                    "name": "rangeMax",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 1,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "This will be the maximum value after scaling. Default is 1."
-                  },
-                  {
-                    "name": "dataMin",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Minimum value of the data. If none is provided, a scan of the data will be performed to find the minimum."
-                  },
-                  {
-                    "name": "dataMax",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Maximum value of the data. If none is provided, a scan of the data will be performed to find the maximum."
-                  }
-                ],
-                "dataEffectDetails": {
-                  "columnsTransformedProperties": [
-                    {
-                      "propertyPath": [
-                        "column"
-                      ]
-                    }
-                  ],
-                  "dataEffect": 5
-                }
-              },
-              "type": "Microsoft.DPrep.MinMaxScalerBlock",
-              "dataEffectDetails": {
-                "columnsTransformedProperties": [
-                  {
-                    "propertyPath": [
-                      "column"
-                    ]
-                  }
-                ],
-                "dataEffect": 5
-              },
-              "propertyDescriptions": [
-                {
-                  "name": "column",
-                  "type": 5,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 12,
-                    "details": {
-                      "selectorDomainType": 0,
-                      "fieldTypes": [
-                        3,
-                        2
-                      ]
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 0,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The source column."
-                },
-                {
-                  "name": "rangeMin",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": 0,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "This will be the minimum value after scaling. Default is 0."
-                },
-                {
-                  "name": "rangeMax",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": 1,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "This will be the maximum value after scaling. Default is 1."
-                },
-                {
-                  "name": "dataMin",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "Minimum value of the data. If none is provided, a scan of the data will be performed to find the minimum."
-                },
-                {
-                  "name": "dataMax",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "Maximum value of the data. If none is provided, a scan of the data will be performed to find the maximum."
-                }
-              ],
-              "arguments": {
-                "column": {
-                  "type": 2,
-                  "details": {
-                    "selectedColumn": "Census_PrimaryDiskTotalCapacity"
-                  }
-                },
-                "dataMax": 47687668.0,
-                "dataMin": 0.0,
-                "rangeMax": 1.0,
-                "rangeMin": 0.0
-              }
-            },
-            {
-              "id": "245c19bb-97be-4879-bfd7-fd6e6e408d4c",
-              "description": {
-                "localDataProperties": [],
-                "blockGroup": 13,
-                "supportedTargets": [
-                  2
-                ],
-                "supportedFieldTypes": [
-                  3,
-                  2
-                ],
-                "supportedMessages": [],
-                "requiresInitialization": false,
-                "allowDefaultAdd": true,
-                "documentation": "Scale values in the column to lie within the specified range (default=[0, 1]).",
-                "type": "Microsoft.DPrep.MinMaxScalerBlock",
-                "propertyDescriptions": [
-                  {
-                    "name": "column",
-                    "type": 5,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 12,
-                      "details": {
-                        "selectorDomainType": 0,
-                        "fieldTypes": [
-                          3,
-                          2
-                        ]
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 0,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 1,
-                    "condition": null,
-                    "documentation": "The source column."
-                  },
-                  {
-                    "name": "rangeMin",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 0,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "This will be the minimum value after scaling. Default is 0."
-                  },
-                  {
-                    "name": "rangeMax",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 1,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "This will be the maximum value after scaling. Default is 1."
-                  },
-                  {
-                    "name": "dataMin",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Minimum value of the data. If none is provided, a scan of the data will be performed to find the minimum."
-                  },
-                  {
-                    "name": "dataMax",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "Maximum value of the data. If none is provided, a scan of the data will be performed to find the maximum."
-                  }
-                ],
-                "dataEffectDetails": {
-                  "columnsTransformedProperties": [
-                    {
-                      "propertyPath": [
-                        "column"
-                      ]
-                    }
-                  ],
-                  "dataEffect": 5
-                }
-              },
-              "type": "Microsoft.DPrep.MinMaxScalerBlock",
-              "dataEffectDetails": {
-                "columnsTransformedProperties": [
-                  {
-                    "propertyPath": [
-                      "column"
-                    ]
-                  }
-                ],
-                "dataEffect": 5
-              },
-              "propertyDescriptions": [
-                {
-                  "name": "column",
-                  "type": 5,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 12,
-                    "details": {
-                      "selectorDomainType": 0,
-                      "fieldTypes": [
-                        3,
-                        2
-                      ]
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 0,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 1,
-                  "condition": null,
-                  "documentation": "The source column."
-                },
-                {
-                  "name": "rangeMin",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": 0,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "This will be the minimum value after scaling. Default is 0."
-                },
-                {
-                  "name": "rangeMax",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": 1,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "This will be the maximum value after scaling. Default is 1."
-                },
-                {
-                  "name": "dataMin",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "Minimum value of the data. If none is provided, a scan of the data will be performed to find the minimum."
-                },
-                {
-                  "name": "dataMax",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "Maximum value of the data. If none is provided, a scan of the data will be performed to find the maximum."
-                }
-              ],
-              "arguments": {
-                "column": {
-                  "type": 2,
-                  "details": {
-                    "selectedColumn": "Census_TotalPhysicalRAM"
-                  }
-                },
-                "dataMax": 16384.0,
-                "dataMin": 0.0,
-                "rangeMax": 1.0,
-                "rangeMin": 0.0
-              }
-            },
-            {
-              "id": "b5bded0f-6aad-41a4-b556-d08b2da1c71b",
-              "description": {
-                "type": "Microsoft.DPrep.RandomSplitStepDescription",
-                "propertyDescriptions": [
-                  {
-                    "name": "probability",
-                    "type": 3,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 4,
-                      "details": {
-                        "min": 0.0,
-                        "max": 1.0
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": 0.1,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "The probability of a record being included in the sample."
-                  },
-                  {
-                    "name": "seed",
-                    "type": 2,
-                    "multipleValues": false,
-                    "domain": {
-                      "type": 4,
-                      "details": {
-                        "min": 0.0,
-                        "max": 4294967295.0
-                      }
-                    },
-                    "multiValueDetails": null,
-                    "isRequired": true,
-                    "origin": 1,
-                    "defaultValue": null,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": "The seed to use for the random generator."
-                  },
-                  {
-                    "name": "invert",
-                    "type": 4,
-                    "multipleValues": false,
-                    "domain": null,
-                    "multiValueDetails": null,
-                    "isRequired": false,
-                    "origin": 1,
-                    "defaultValue": false,
-                    "initializeFromProperty": null,
-                    "telemetryStrategy": 0,
-                    "condition": null,
-                    "documentation": null
-                  }
-                ],
-                "dataEffectDetails": {
-                  "dataEffect": 6
-                }
-              },
-              "type": "Microsoft.DPrep.RandomSplitStepDescription",
-              "dataEffectDetails": {
-                "dataEffect": 6
-              },
-              "propertyDescriptions": [
-                {
-                  "name": "probability",
-                  "type": 3,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 4,
-                    "details": {
-                      "min": 0.0,
-                      "max": 1.0
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": 0.1,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "The probability of a record being included in the sample."
-                },
-                {
-                  "name": "seed",
-                  "type": 2,
-                  "multipleValues": false,
-                  "domain": {
-                    "type": 4,
-                    "details": {
-                      "min": 0.0,
-                      "max": 4294967295.0
-                    }
-                  },
-                  "multiValueDetails": null,
-                  "isRequired": true,
-                  "origin": 1,
-                  "defaultValue": null,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": "The seed to use for the random generator."
-                },
-                {
-                  "name": "invert",
-                  "type": 4,
-                  "multipleValues": false,
-                  "domain": null,
-                  "multiValueDetails": null,
-                  "isRequired": false,
-                  "origin": 1,
-                  "defaultValue": false,
-                  "initializeFromProperty": null,
-                  "telemetryStrategy": 0,
-                  "condition": null,
-                  "documentation": null
-                }
-              ],
-              "arguments": {
-                "invert": false,
-                "probability": 0.3,
-                "seed": 3285947786
-              }
-            }
-          ]
-        }
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "84f3b9c2-96a1-4f9c-b906-558f2842cee8",
-      "type": "Microsoft.DPrep.DropColumnsBlock",
-      "arguments": {
-        "columns": {
-          "type": 0,
-          "details": {
-            "selectedColumns": [
-              "HasDetections"
-            ]
-          }
-        }
-      },
-      "localData": {},
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    }
-  ],
-  "inspectors": []
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/data/map_func.py b/work-with-data/dataprep/data/map_func.py
deleted file mode 100644
index 7b97a8d1..00000000
--- a/work-with-data/dataprep/data/map_func.py
+++ /dev/null
@@ -1,4 +0,0 @@
-def transform(df, index):
-    df['Latitude'].fillna('0', inplace=True)
-    df['Longitude'].fillna('0', inplace=True)
-    return df
diff --git a/work-with-data/dataprep/data/median_income.csv b/work-with-data/dataprep/data/median_income.csv
deleted file mode 100644
index 742c4007..00000000
--- a/work-with-data/dataprep/data/median_income.csv
+++ /dev/null
@@ -1,251 +0,0 @@
-median_income
-4.4896
-2.1029
-2.3889
-3.707
-6.4788
-4.4074
-5.2907
-1.5156
-8.4411
-4.4085
-2.1439
-2.8971
-6.1008
-3.5258
-2.7694
-2.2356
-1.9509
-4.0905
-3.6726
-3.1696
-2.5389
-3.0319
-4.6779
-2.9076
-2.8616
-1.4722
-5.6413
-2.1167
-4.7308
-4.8173
-2.3438
-1.7333
-1.4429
-2.3253
-2.4022
-3.4048
-6.6073
-4.1080000000000005
-4.2829
-1.5727
-2.5211
-4.2679
-4.7328
-4.7069
-2.465
-5.0267
-2.8043
-2.4053
-1.2176
-2.39
-3.6364
-6.0162
-2.8088
-3.3984
-4.5
-3.9079
-4.9618
-2.9344
-2.4283
-3.7388
-1.6021
-2.3352
-4.0982
-1.9531
-3.2386
-5.1169
-4.692
-4.0
-6.4238
-3.7375
-2.8233
-2.8009
-3.767
-3.6761
-5.0282
-3.5296
-5.215
-4.0125
-9.4667
-5.9062
-3.9864
-2.0734
-2.875
-3.3611
-2.8214
-0.9946
-4.5446
-4.6908
-9.3198
-1.2826
-2.4943
-10.1882
-4.6731
-4.375
-2.8173
-2.0903
-2.725
-2.8547
-2.25
-1.9444
-1.7167
-1.9342
-4.9524
-3.65
-3.0856
-3.2396
-2.9324
-3.495
-1.9818
-4.6964
-3.925
-3.625
-2.9688
-4.0417
-9.7956
-3.8732
-2.6998
-2.006
-4.25
-3.1839999999999997
-5.9658
-2.628
-2.5057
-5.155
-4.6
-4.6681
-5.5942
-5.1104
-3.0759
-3.5757
-3.6845
-6.4667
-5.273
-3.0635
-11.2866
-4.0444
-5.2541
-5.5791
-4.5375
-9.8144
-6.7257
-4.1442
-4.0313
-2.2791
-4.1679
-3.2852
-3.2768
-5.021
-4.875
-4.419
-3.3272
-4.2386
-1.245
-5.152
-4.8125
-2.1638
-7.1621
-1.5372
-10.0481
-3.3869
-5.4591
-4.4318
-6.5044
-4.2865
-3.0461
-11.3283
-2.7026
-3.016
-3.0943
-3.225
-6.187
-3.8158
-3.0147
-15.0
-3.1364
-2.9
-5.5941
-3.4028
-6.0062
-8.3792
-3.8036
-2.0926
-6.7703
-4.2569
-4.744
-9.7037
-5.1292
-2.3148
-3.3021
-1.95
-3.025
-2.6523
-1.2188
-5.827999999999999
-3.1587
-2.45
-2.3851
-2.1221
-3.5313
-3.4821
-7.8252
-5.1878
-3.7459
-6.0097
-2.3194
-4.2061
-2.267
-2.2109
-2.7589
-2.6553
-6.3325
-5.7233
-4.337
-3.9667
-5.8623
-1.6806
-3.5851
-2.9716
-3.9
-2.7431
-3.3621
-1.9464
-7.3518
-4.775
-3.5968
-6.221
-10.0968
-1.9483
-2.0469
-3.725
-3.675
-1.8529
-1.7159
-1.7386
-3.6687
-3.4671
-4.8233
-4.3036
-1.6488
-2.9453
-5.0096
-3.175
-4.2031
-3.1667
-5.7204
-3.375
-6.5483
-4.2206
-2.6631
-3.5363
diff --git a/work-with-data/dataprep/data/median_income_transformed.csv b/work-with-data/dataprep/data/median_income_transformed.csv
deleted file mode 100644
index c6ca88e4..00000000
--- a/work-with-data/dataprep/data/median_income_transformed.csv
+++ /dev/null
@@ -1,251 +0,0 @@
-median_income,median_income_uniform,median_income_normal
-4.4896,0.688927015969381,0.4928112398942898
-2.1029,0.16242159563866576,-0.9845540061415601
-2.3889,0.20433495515563627,-0.8262365643809355
-3.707,0.5167832475474021,0.04208177981426486
-6.4788,0.7918154943685715,0.8127367410844715
-4.4074,0.6708459812590735,0.44225037403262457
-5.2907,0.7627885954411082,0.71530142355699
-1.5156,0.07635265842077493,-1.430040774223022
-8.4411,0.8397571522806675,0.9934602851577232
-4.4085,0.6710879415775812,0.44291928632820865
-2.1439,0.16843015417081886,-0.960387338010023
-2.8971,0.3028380993334766,-0.5162551753422808
-6.1008,0.7825804402531089,0.780937730437047
-3.5258,0.47180713824983866,-0.07072794893657437
-2.7694,0.2685175231133089,-0.6173027607303029
-2.2356,0.18186880825370766,-0.9082661605741446
-1.9509,0.14014596400726886,-1.0796637810446446
-4.0905,0.6011394131362455,0.25629744014575906
-3.6726,0.5092164884958867,0.023104366055624576
-3.1696,0.3760750376263169,-0.31580558952782856
-2.5389,0.2263174863708306,-0.7510293827903433
-3.0319,0.3390668673403568,-0.4150111583096138
-4.6779,0.7303462232193919,0.61386044030116
-2.9076,0.3056600731025585,-0.5081899386067169
-2.8616,0.29329714039991395,-0.5437779548183906
-1.4722,0.0699923793891787,-1.475847787309027
-5.6413,0.7713542302900003,0.7433140959507171
-2.1167,0.1644439885104636,-0.9763562034967139
-4.7308,0.7419823149003563,0.6494688564486208
-4.8173,0.7512227895726955,0.6783427156995535
-2.3438,0.19772554077026783,-0.8497733992585795
-1.7333,0.10825663872442697,-1.235852818864947
-1.4429,0.06569845829181076,-1.5086161030849279
-2.3253,0.19501436192039387,-0.8595652745311925
-2.4022,0.20628407292338352,-0.8193826233304142
-3.4048,0.4392872500537518,-0.15277653846062797
-6.6073,0.7949549241406269,0.8237349930522744
-4.1080000000000005,0.6049887818397783,0.2662814785234653
-4.2829,0.6434604724825127,0.3677239840415221
-1.5727,0.0847206753033589,-1.3740010688161062
-2.5211,0.22370889266662755,-0.7597270013437459
-4.2679,0.6401610135937705,0.35888920867930674
-4.7328,0.7424222427521885,0.6508311116191982
-4.7069,0.7367251770709602,0.6332817852325006
-2.465,0.21548742599214485,-0.7875245686101101
-5.0267,0.7563387163763406,0.6945735910123612
-2.8043,0.277897226402924,-0.5890996132954226
-2.4053,0.20673837856849753,-0.8177906162820535
-1.2176,0.03268069640658888,-1.8427782558151542
-2.39,0.2044961603845477,-0.8256682268514074
-3.6364,0.501253794377722,0.0031428016114447123
-6.0162,0.780513547189172,0.7739287859106961
-2.8088,0.2791066437325306,-0.5854974399696314
-3.3984,0.4375671898516448,-0.15714017005326586
-4.5,0.6912146407989088,0.4992962189785684
-3.9079,0.5609740002639567,0.1534391166508579
-4.9618,0.7547531211062519,0.6895237101278
-2.9344,0.3128628251988819,-0.4877518079508566
-2.4283,0.21010903335482733,-0.8060429810797759
-3.7388,0.5237781003915357,0.05963819257139614
-1.6021,0.089029251421537,-1.346757015256811
-2.3352,0.1964652089805967,-0.8543151087700283
-4.0982,0.6028331353658,0.26068721102120757
-1.9531,0.1404683744650917,-1.078217399304828
-3.2386,0.39461943668028376,-0.26729910818310126
-5.1169,0.7585424250568029,0.7016216442421952
-4.692,0.7334477145748096,0.6232738623769376
-4.0,0.5812326778408341,0.20504797284322906
-6.4238,0.7904717695634116,0.8080592738489488
-3.7375,0.5234921472878448,0.05892015392758171
-2.8233,0.28300365512792947,-0.5739416160725967
-2.8009,0.2769834444205546,-0.5918263314054893
-3.767,0.5299810831023711,0.07522231004048434
-3.6761,0.5099863622365932,0.025034712726592745
-5.0282,0.7563753634164814,0.6946905154674345
-3.5296,0.47282842399483976,-0.06816178418735802
-5.215,0.7609391414820063,0.70932677431432
-4.0125,0.583982226914786,0.21209163422132077
-9.4667,0.8648139551928855,1.1022060793492992
-5.9062,0.7778260975788522,0.7648719390554228
-3.9864,0.5782411684483745,0.19739599549652825
-2.0734,0.15809836449967754,-1.0023041289181829
-2.875,0.29689851644807563,-0.5333417486766315
-3.3611,0.4275424639862395,-0.1826343528680402
-2.8214,0.2824930122554289,-0.5754514381784691
-0.9946,9.999999977795539e-08,-5.199337582605575
-4.5446,0.7010250318947692,0.5273508961812362
-4.6908,0.7331837578637103,0.6224705781821348
-9.3198,0.861224988395104,1.085839464021226
-1.2826,0.04220645993317308,-1.725635992615424
-2.4943,0.2197813470895128,-0.7729318836756727
-10.1882,0.8824411815005742,1.1872788772739642
-4.6731,0.7292903963749944,0.6106682834354926
-4.375,0.6637191500593902,0.42263484449515154
-2.8173,0.281391098688454,-0.578713956288219
-2.0903,0.16057506301658947,-0.9920971718209968
-2.725,0.2565846054611911,-0.6539109002088199
-2.8547,0.29144270049451715,-0.5491749392049887
-2.25,0.18397913125036633,-0.9003044332369591
-1.9444,0.13919338765461042,-1.0839504359991745
-1.7167,0.10582390526994544,-1.2490472005138797
-1.9342,0.13769857553197723,-1.0907176288996137
-4.9524,0.7545234663213701,0.6887937530976251
-3.65,0.5042453037701816,0.010641599310222728
-3.0856,0.35349924747366146,-0.37589024233948426
-3.2396,0.394888196086863,-0.26660099147199706
-2.9324,0.3123253063857234,-0.48926992156445664
-3.495,0.46352934852719846,-0.09154607537767347
-1.9818,0.14467436543759887,-1.0595514059995472
-4.6964,0.7344155558488406,0.6262226873574688
-3.925,0.5647353833971228,0.16298628506764115
-3.625,0.4984680713824984,-0.003839985024398658
-2.9688,0.3221081487852074,-0.4618117868349911
-4.0417,0.5904051735515374,0.22858735593517016
-9.7956,0.8728494295277418,1.1399643807904891
-3.8732,0.5533412520346663,0.13410759250347515
-2.6998,0.24989741485432906,-0.6748126069650576
-2.006,0.1482208804736502,-1.0440943039822579
-4.25,0.6362236593198715,0.34838284885436693
-3.1839999999999997,0.3799451730810577,-0.3056247863400478
-5.9658,0.7792822066404437,0.7697712668826051
-2.628,0.23937510991265604,-0.7083141049607123
-2.5057,0.2214520194618676,-0.7672985355850374
-5.155,0.7594732598763774,0.7046091860898294
-4.6,0.7132110333905237,0.5627899373325242
-4.6681,0.7281905767454135,0.6073497232884044
-5.5942,0.7702035132295815,0.7395172425174374
-5.1104,0.7583836212161931,0.7011125840899696
-3.0759,0.35089228122984295,-0.38291260834816176
-3.5757,0.4852182326381423,-0.037060878177008386
-3.6845,0.5118340592142888,0.02966793907608783
-6.4667,0.7915198749114363,0.8117061751243242
-5.273,0.7623561603674476,0.7139021637992619
-3.0635,0.3475596645882605,-0.3919172875113563
-11.2866,0.9092765874276221,1.3363135019854007
-4.0444,0.5909990761515111,0.23011572277987793
-5.2541,0.7618944076616745,0.7124095804237914
-5.5791,0.7698345996921648,0.7383022482134728
-4.5375,0.6994632880207644,0.5228574972480653
-9.8144,0.8733087390975055,1.1421720249180776
-6.7257,0.7978475971757347,0.8339577268169219
-4.1442,0.6129514759579427,0.28701994562086086
-4.0313,0.5881175487220095,0.22270526607462063
-2.2791,0.18824374230611404,-0.88438672867467
-4.1679,0.6181646210021556,0.3006639542199363
-3.2852,0.40714362502687595,-0.23489883984002982
-3.2768,0.4048860460116104,-0.24072005796234255
-5.021,0.7561994576238059,0.6941293646505097
-4.875,0.7526324790501087,0.682797132481279
-4.419,0.6733975627997006,0.44931439580522814
-3.3272,0.41843152010320356,-0.20590766210173994
-4.2386,0.6337160705644274,0.341711709817628
-1.245,0.03669617210856439,-1.7903831065617093
-5.152,0.7593999657960959,0.704373718687922
-4.8125,0.7511055190442452,0.6779727645490474
-2.1638,0.1713465033120347,-0.9488576690059357
-7.1621,0.8085094427206763,0.872416661000832
-1.5372,0.0795181429157629,-1.408320169672634
-10.0481,0.8790183479514304,1.1700935990858978
-3.3869,0.43447645667598356,-0.16498865287495923
-5.4591,0.7669028364809068,0.7286850708477646
-4.4318,0.6762131010514274,0.4571353017049396
-6.5044,0.7924409371869732,0.8149199625504349
-4.2865,0.644252342615811,0.3698485817763834
-3.0461,0.3428832509137819,-0.40460687823111086
-11.3283,0.9102953751435343,1.3425761736583905
-2.7026,0.25056439475381626,-0.6727147380915197
-3.016,0.33479359277574705,-0.4267146406076349
-3.0943,0.35583745431090086,-0.3696075717076287
-3.225,0.39096430875080623,-0.2768065942137824
-6.187,0.7846864234931958,0.7881189136255171
-3.8158,0.5407153226870792,0.10223599868489697
-3.0147,0.33444420554719406,-0.42767409705501475
-15.0,0.9999999000000003,5.19933758270342
-3.1364,0.36715222532788644,-0.33940526790689773
-2.9,0.30361750161255635,-0.5140242974144523
-5.5941,0.7702010700935721,0.739509192607556
-3.4028,0.4387497312405934,-0.15413985695864643
-6.0062,0.7802692335882339,0.7731028191405914
-8.3792,0.8382448510908602,0.9872699911993058
-3.8036,0.5380317627909023,0.09547634997560216
-2.0926,0.1609121284952224,-0.9907160390730209
-6.7703,0.798937235835919,0.837831173103705
-4.2569,0.6377414104086929,0.3524281695696263
-4.744,0.7448858387224493,0.6584822133838069
-9.7037,0.87060418753512,1.1292518063856445
-5.1292,0.7588429307859569,0.7025854406375655
-2.3148,0.19347558473533027,-0.8651596484776254
-3.3021,0.41168565899806486,-0.22321096549396113
-1.95,0.1400140688199777,-1.0802561341135346
-3.025,0.33721242743496016,-0.42008295383958816
-2.6523,0.2429362799695175,-0.6968885244090585
-1.2188,0.032856556656310446,-1.8403755990446928
-5.827999999999999,0.7759155652195158,0.7584713308686281
-3.1587,0.3731455600946033,-0.3235336598161438
-2.45,0.21328917287062546,-0.7950604390156254
-2.3851,0.20377806436485135,-0.8282019693528496
-2.1221,0.16523535963421065,-0.9731661745087966
-3.5313,0.47328531498602444,-0.06701390942662634
-3.4821,0.4600623521823264,-0.10027663775339715
-7.8252,0.8247098775988859,0.9334643965884332
-5.1878,0.7602746084874545,0.7071861842977014
-3.7459,0.5253398442655404,0.06356034060479637
-6.0097,0.7803547433485621,0.773391847490294
-2.3194,0.19414971569259623,-0.862705456837268
-4.2061,0.6265672429721525,0.32277517627250846
-2.267,0.18647048478808834,-0.8909780060851004
-2.2109,0.1782490181136057,-0.9220585498888423
-2.7589,0.265695549344227,-0.6258838950234518
-2.6553,0.2433759305938214,-0.6954842771160817
-6.3325,0.7882411863868461,0.8003334507140982
-5.7233,0.7733576018176932,0.7499500376839813
-4.337,0.6553605208745764,0.3998337668630456
-3.9667,0.5739078791078263,0.18633222024962756
-5.8623,0.7767535608707337,0.7612749184789059
-1.6806,0.1005334427574887,-1.2785178769957448
-3.5851,0.48774457105998714,-0.03072463804138443
-2.9716,0.32286067512362926,-0.45971423541894674
-3.9,0.5592362852492191,0.14903320783722215
-2.7431,0.26144915072027514,-0.6388841474573944
-3.3621,0.4278112233928187,-0.18194938611936773
-1.9464,0.13948648807081301,-1.0826293461973695
-7.3518,0.8131440717304732,0.8895420092252918
-4.775,0.750189343040727,0.6750857069958222
-3.5968,0.4908890561169641,-0.022839735128387998
-6.221,0.7855170897363856,0.7909625929859541
-10.0968,0.8802081551879993,1.1760279942069634
-1.9483,0.1397649334662055,-1.0813760586453744
-2.0469,0.15421478398499322,-1.0185228067876306
-3.725,0.5207425982138929,0.05201743189676979
-3.675,0.5097444019180853,0.02442802263182327
-1.8529,0.1257840436133419,-1.1465489456932518
-1.7159,0.10570666510346441,-1.2496885811666654
-1.7386,0.10903335482736382,-1.2316851754273808
-3.6687,0.5083586291848137,0.020953509422343072
-3.4671,0.45603096108363783,-0.11043812057602183
-4.8233,0.7513693777332584,0.6788052852389578
-4.3036,0.6480137257489771,0.3799634475571365
-1.6488,0.0958731461398675,-1.3054305179573955
-2.9453,0.3157923027305955,-0.47949769802476955
-5.0096,0.7559209401187363,0.6932413225605799
-3.175,0.3775263384218447,-0.31198400815546146
-4.2031,0.625907351194404,0.32103311272888746
-3.1667,0.3752956353472371,-0.3178598218476214
-5.7204,0.7732867508734211,0.7497147894780406
-3.375,0.43127821973769076,-0.17312084617136517
-6.5483,0.7935134738950917,0.818672917584347
-4.2206,0.6297567198979367,0.33120908249828585
-2.6631,0.2445190222170115,-0.6918396326662699
-3.5363,0.4746291120189207,-0.06363831319524592
diff --git a/work-with-data/dataprep/data/parquet.parquet b/work-with-data/dataprep/data/parquet.parquet
deleted file mode 100644
index bc61f97c..00000000
Binary files a/work-with-data/dataprep/data/parquet.parquet and /dev/null differ
diff --git a/work-with-data/dataprep/data/parquet_dataset/Arrest=false/part-00000-34f8a7a7-c3cd-4926-92b2-ba2dcd3f95b7.gz.parquet b/work-with-data/dataprep/data/parquet_dataset/Arrest=false/part-00000-34f8a7a7-c3cd-4926-92b2-ba2dcd3f95b7.gz.parquet
deleted file mode 100644
index 25d1b247..00000000
Binary files a/work-with-data/dataprep/data/parquet_dataset/Arrest=false/part-00000-34f8a7a7-c3cd-4926-92b2-ba2dcd3f95b7.gz.parquet and /dev/null differ
diff --git a/work-with-data/dataprep/data/parquet_dataset/Arrest=true/part-00000-34f8a7a7-c3cd-4926-92b2-ba2dcd3f95b7.gz.parquet b/work-with-data/dataprep/data/parquet_dataset/Arrest=true/part-00000-34f8a7a7-c3cd-4926-92b2-ba2dcd3f95b7.gz.parquet
deleted file mode 100644
index 0d47b550..00000000
Binary files a/work-with-data/dataprep/data/parquet_dataset/Arrest=true/part-00000-34f8a7a7-c3cd-4926-92b2-ba2dcd3f95b7.gz.parquet and /dev/null differ
diff --git a/work-with-data/dataprep/data/secrets.dprep b/work-with-data/dataprep/data/secrets.dprep
deleted file mode 100644
index bf156e3c..00000000
--- a/work-with-data/dataprep/data/secrets.dprep
+++ /dev/null
@@ -1,63 +0,0 @@
-{
-  "id": "b308e5b8-9b2a-47f8-9d32-0f542b4a34a4",
-  "name": "read_csv_duplicate_headers",
-  "blocks": [
-    {
-      "id": "8d9ec228-6a4b-4abf-afb7-65f58dda1581",
-      "type": "Microsoft.DPrep.GetFilesBlock",
-      "arguments": {
-        "path": {
-          "target": 1,
-          "resourceDetails": [
-            {
-              "path": "https://dpreptestfiles.blob.core.windows.net/testfiles/read_csv_duplicate_headers.csv",
-              "sas": {
-                "id": "https://dpreptestfiles.blob.core.windows.net/testfiles/read_csv_duplicate_headers.csv",
-                "secretType": "AzureMLSecret"
-              },
-              "storageAccountName": null,
-              "storageAccountKey": null
-            }
-          ]
-        }
-      },
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "4ad0460f-ec65-47c0-a0a4-44345404a462",
-      "type": "Microsoft.DPrep.ParseDelimitedBlock",
-      "arguments": {
-        "columnHeadersMode": 3,
-        "fileEncoding": 0,
-        "handleQuotedLineBreaks": false,
-        "preview": false,
-        "separator": ",",
-        "skipRows": 0,
-        "skipRowsMode": 0
-      },
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "1a3e11ba-5854-48da-aa47-53af61beb782",
-      "type": "Microsoft.DPrep.DropColumnsBlock",
-      "arguments": {
-        "columns": {
-          "type": 0,
-          "details": {
-            "selectedColumns": [
-              "Path"
-            ]
-          }
-        }
-      },
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    }
-  ],
-  "inspectors": []
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/data/stream-path.csv b/work-with-data/dataprep/data/stream-path.csv
deleted file mode 100644
index 175f3801..00000000
--- a/work-with-data/dataprep/data/stream-path.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-Stream Path
-https://dataset.blob.core.windows.net/blobstore/container/2019/01/01/train.csv
-https://dataset.blob.core.windows.net/blobstore/container/2019/01/02/train.csv
-https://dataset.blob.core.windows.net/blobstore/container/2019/01/03/train.csv
-https://dataset.blob.core.windows.net/blobstore/container/2019/01/04/train.csv
-https://dataset.blob.core.windows.net/blobstore/container/2019/01/05/train.csv
-https://dataset.blob.core.windows.net/blobstore/container/2019/01/06/train.csv
-https://dataset.blob.core.windows.net/blobstore/container/2019/01/07/train.csv
-https://dataset.blob.core.windows.net/blobstore/container/2019/01/08/train.csv
-https://dataset.blob.core.windows.net/blobstore/container/2019/01/09/train.csv
-https://dataset.blob.core.windows.net/blobstore/container/2019/01/10/train.csv
diff --git a/work-with-data/dataprep/how-to-guides/add-column-using-expression.ipynb b/work-with-data/dataprep/how-to-guides/add-column-using-expression.ipynb
deleted file mode 100644
index 3fa0e65e..00000000
--- a/work-with-data/dataprep/how-to-guides/add-column-using-expression.ipynb
+++ /dev/null
@@ -1,360 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/add-column-using-expression.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Add Column using Expression\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "With Azure ML Data Prep you can add a new column to data with `Dataflow.add_column` by using a Data Prep expression to calculate the value from existing columns. This is similar to using Python to create a [new script column](./custom-python-transforms.ipynb#New-Script-Column) except the Data Prep expressions are more limited and will execute faster. The expressions used are the same as for [filtering rows](./filtering.ipynb#Filtering-rows) and hence have the same functions and operators available.\n",
-        "<p>\n",
-        "Here we add additional columns. First we get input data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# loading data\n",
-        "dflow = dprep.auto_read_file('../data/crime-spring.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### `substring(start, length)`\n",
-        "Add a new column \"Case Category\" using the `substring(start, length)` expression to extract the prefix from the \"Case Number\" column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "case_category = dflow.add_column(new_column_name='Case Category',\n",
-        "                                 prior_column='Case Number',\n",
-        "                                 expression=dflow['Case Number'].substring(0, 2))\n",
-        "case_category.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### `substring(start)`\n",
-        "Add a new column \"Case Id\" using the `substring(start)` expression to extract just the number from \"Case Number\" column and then convert it to numeric."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "case_id = dflow.add_column(new_column_name='Case Id',\n",
-        "                           prior_column='Case Number',\n",
-        "                           expression=dflow['Case Number'].substring(2))\n",
-        "case_id = case_id.to_number('Case Id')\n",
-        "case_id.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### `length()`\n",
-        "Using the length() expression, add a new numeric column \"Length\", which contains the length of the string in \"Primary Type\"."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_length = dflow.add_column(new_column_name='Length',\n",
-        "                                prior_column='Primary Type',\n",
-        "                                expression=dflow['Primary Type'].length())\n",
-        "dflow_length.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### `to_upper()`\n",
-        "Using the to_upper() expression, add a new numeric column \"Upper Case\", which contains the length of the string in \"Primary Type\"."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_to_upper = dflow.add_column(new_column_name='Upper Case',\n",
-        "                                prior_column='Primary Type',\n",
-        "                                expression=dflow['Primary Type'].to_upper())\n",
-        "dflow_to_upper.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### `to_lower()`\n",
-        "Using the to_lower() expression, add a new numeric column \"Lower Case\", which contains the length of the string in \"Primary Type\"."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_to_lower = dflow.add_column(new_column_name='Lower Case',\n",
-        "                                prior_column='Primary Type',\n",
-        "                                expression=dflow['Primary Type'].to_lower())\n",
-        "dflow_to_lower.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### `RegEx.extract_record()`\n",
-        "Using the `RegEx.extract_record()` expression, add a new record column \"Stream Date Record\", which contains the name capturing groups in the regex with value."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_regex_extract_record = dprep.auto_read_file('../data/stream-path.csv')\n",
-        "regex = dprep.RegEx('\\/(?<year>\\d{4})\\/(?<month>\\d{2})\\/(?<day>\\d{2})\\/')\n",
-        "dflow_regex_extract_record = dflow_regex_extract_record.add_column(new_column_name='Stream Date Record',\n",
-        "                                                                   prior_column='Stream Path',\n",
-        "                                                                   expression=regex.extract_record(dflow_regex_extract_record['Stream Path']))\n",
-        "dflow_regex_extract_record.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### `create_datetime()`\n",
-        "Using the `create_datetime()` expression, add a new column \"Stream Date\", which contains datetime values constructed from year, month, day values extracted from a record column \"Stream Date Record\"."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "year = dprep.col('year', dflow_regex_extract_record['Stream Date Record'])\n",
-        "month = dprep.col('month', dflow_regex_extract_record['Stream Date Record'])\n",
-        "day = dprep.col('day', dflow_regex_extract_record['Stream Date Record'])\n",
-        "dflow_create_datetime = dflow_regex_extract_record.add_column(new_column_name='Stream Date',\n",
-        "                                                              prior_column='Stream Date Record',\n",
-        "                                                              expression=dprep.create_datetime(year, month, day))\n",
-        "dflow_create_datetime.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### `col(column1) + col(column2)`\n",
-        "Add a new column \"Total\" to show the result of adding the values in the \"FBI Code\" column to the \"Community Area\" column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_total = dflow.add_column(new_column_name='Total',\n",
-        "                               prior_column='FBI Code',\n",
-        "                               expression=dflow['Community Area']+dflow['FBI Code'])\n",
-        "dflow_total.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### `col(column1) - col(column2)`\n",
-        "Add a new column \"Subtract\" to show the result of subtracting the values in the \"FBI Code\" column from the \"Community Area\" column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_diff = dflow.add_column(new_column_name='Difference',\n",
-        "                              prior_column='FBI Code',\n",
-        "                              expression=dflow['Community Area']-dflow['FBI Code'])\n",
-        "dflow_diff.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### `col(column1) * col(column2)`\n",
-        "Add a new column \"Product\" to show the result of multiplying the values in the \"FBI Code\" column to the \"Community Area\" column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_prod = dflow.add_column(new_column_name='Product',\n",
-        "                              prior_column='FBI Code',\n",
-        "                              expression=dflow['Community Area']*dflow['FBI Code'])\n",
-        "dflow_prod.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### `col(column1) / col(column2)`\n",
-        "Add a new column \"True Quotient\" to show the result of true (decimal) division of the values in \"Community Area\" column by the \"FBI Code\" column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_true_div = dflow.add_column(new_column_name='True Quotient',\n",
-        "                             prior_column='FBI Code',\n",
-        "                             expression=dflow['Community Area']/dflow['FBI Code'])\n",
-        "dflow_true_div.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### `col(column1) // col(column2)`\n",
-        "Add a new column \"Floor Quotient\" to show the result of floor (integer) division of the values in \"Community Area\" column by the \"FBI Code\" column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_floor_div = dflow.add_column(new_column_name='Floor Quotient',\n",
-        "                             prior_column='FBI Code',\n",
-        "                             expression=dflow['Community Area']//dflow['FBI Code'])\n",
-        "dflow_floor_div.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### `col(column1) % col(column2)`\n",
-        "Add a new column \"Mod\" to show the result of applying the modulo operation on the \"FBI Code\" column and the \"Community Area\" column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_mod = dflow.add_column(new_column_name='Mod',\n",
-        "                             prior_column='FBI Code',\n",
-        "                             expression=dflow['Community Area']%dflow['FBI Code'])\n",
-        "dflow_mod.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### `col(column1) ** col(column2)`\n",
-        "Add a new column \"Power\" to show the result of applying the exponentiation operation when the base is the \"Community Area\" column and the exponent is \"FBI Code\" column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_pow = dflow.add_column(new_column_name='Power',\n",
-        "                             prior_column='FBI Code',\n",
-        "                             expression=dflow['Community Area']**dflow['FBI Code'])\n",
-        "dflow_pow.head(5)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/append-columns-and-rows.ipynb b/work-with-data/dataprep/how-to-guides/append-columns-and-rows.ipynb
deleted file mode 100644
index 51a55e4a..00000000
--- a/work-with-data/dataprep/how-to-guides/append-columns-and-rows.ipynb
+++ /dev/null
@@ -1,251 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/append-columns-and-rows.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Append Columns and Rows\n",
-        "\n",
-        "Often the data we want does not come in a single dataset: they are coming from different locations, have features that are separated, or are simply not homogeneous. Unsurprisingly, we typically want to work with a single dataset at a time.\n",
-        "\n",
-        "Azure ML Data Prep allows the concatenation of two or more dataflows by means of column and row appends.\n",
-        "\n",
-        "We will demonstrate this by defining a single dataflow that will pull data from multiple datasets.\n",
-        "\n",
-        "## Table of Contents\n",
-        "[append_columns(dataflows)](#append_columns)<br>\n",
-        "[append_rows(dataflows)](#append_rows)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"append_columns\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## `append_columns(dataflows)`\n",
-        "We can append data width-wise, which will change some or all existing rows and potentially adding rows (based on an assumption that data in two datasets are aligned on row number).\n",
-        "\n",
-        "However we cannot do this if the reference dataflows have clashing schema with the target dataflow. Observe:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import auto_read_file"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_chicago = auto_read_file(path='../data/chicago-aldermen-2015.csv')\n",
-        "dflow_chicago.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import ExecutionError\n",
-        "try:\n",
-        "    dflow_combined_by_column = dflow.append_columns([dflow_chicago])\n",
-        "    dflow_combined_by_column.head(5)\n",
-        "except ExecutionError:\n",
-        "    print('Cannot append_columns with schema clash!')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "As expected, we cannot call `append_columns` with target dataflows that have clashing schema.\n",
-        "\n",
-        "We can make the call once we rename or drop the offending columns. In more complex scenarios, we could opt to skip or filter to make rows align before appending columns. Here we will choose to simply drop the clashing column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_combined_by_column = dflow.append_columns([dflow_chicago.drop_columns(['Ward'])])\n",
-        "dflow_combined_by_column.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Notice that the resultant schema has more columns in the first N records (N being the number of records in `dataflow` and the extra columns being the width of the schema of our reference dataflow, chicago, minus the `Ward` column). From the N+1th record onwards, we will only have a schema width matching that of the `Ward`-less chicago set.\n",
-        "\n",
-        "Why is this? As much as possible, the data from the reference dataflow(s) will be attached to existing rows in the target dataflow. If there are not enough rows in the target dataflow to attach to, we simply append them as new rows.\n",
-        "\n",
-        "Note that these are appends, not joins (for joins please reference [Join](join.ipynb)), so the append may not be logically correct, but will take effect as long as there are no schema clashes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Ward-less data after we skip the first N rows\n",
-        "dflow_len = dflow.row_count\n",
-        "dflow_combined_by_column.skip(dflow_len).head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"append_rows\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## `append_rows(dataflows)`\n",
-        "We can append data length-wise, which will only have the effect of adding new rows. No existing data will be changed."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import auto_read_file"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_spring = auto_read_file(path='../data/crime-spring.csv')\n",
-        "dflow_spring.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_chicago = auto_read_file(path='../data/chicago-aldermen-2015.csv')\n",
-        "dflow_chicago.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_combined_by_row = dflow.append_rows([dflow_chicago, dflow_spring])\n",
-        "dflow_combined_by_row.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Notice that neither schema nor data has changed for the target dataflow.\n",
-        "\n",
-        "If we skip ahead, we will see our target dataflows' data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# chicago data\n",
-        "dflow_len = dflow.row_count\n",
-        "dflow_combined_by_row.skip(dflow_len).head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# crimes spring data\n",
-        "dflow_chicago_len = dflow_chicago.row_count\n",
-        "dflow_combined_by_row.skip(dflow_len + dflow_chicago_len).head(5)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/assertions.ipynb b/work-with-data/dataprep/how-to-guides/assertions.ipynb
deleted file mode 100644
index b33a30ed..00000000
--- a/work-with-data/dataprep/how-to-guides/assertions.ipynb
+++ /dev/null
@@ -1,133 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/assertions.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Assertions\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Frequently, the data we work with while cleaning and preparing data is just a subset of the total data we will need to work with in production. It is also common to be working on a snapshot of a live dataset that is continuously updated and augmented.\n",
-        "\n",
-        "In these cases, some of the assumptions we make as part of our cleaning might turn out to be false. Columns that originally only contained numbers within a certain range might actually contain a wider range of values in later executions. These errors often result in either broken pipelines or bad data.\n",
-        "\n",
-        "Azure ML Data Prep supports creating assertions on data, which are evaluated as the pipeline is executed. These assertions enable us to verify that our assumptions on the data continue to be accurate and, when not, to handle failures in a clean way."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To demonstrate, we will load a dataset and then add some assertions based on what we can see in the first few rows."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import auto_read_file\n",
-        "\n",
-        "dflow = auto_read_file('../data/crime-dirty.csv')\n",
-        "dflow.get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can see there are latitude and longitude columns present in this dataset. By definition, these are constrained to specific ranges of values. We can assert that this is indeed the case so that if any records come through with invalid values, we detect them."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import value\n",
-        "\n",
-        "dflow = dflow.assert_value('Latitude', (value <= 90) & (value >= -90), error_code='InvalidLatitude')\n",
-        "dflow = dflow.assert_value('Longitude', (value <= 180) & (value >= -180), error_code='InvalidLongitude')\n",
-        "dflow.keep_columns(['Latitude', 'Longitude']).get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Any assertion failures are represented as Errors in the resulting dataset. From the profile above, you can see that the Error Count for both of these columns is 1. We can use a filter to retrieve the error and see what value caused the assertion to fail."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import col\n",
-        "\n",
-        "dflow_error = dflow.filter(col('Latitude').is_error())\n",
-        "error = dflow_error.head(10)['Latitude'][0]\n",
-        "print(error.originalValue)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Our assertion failed because we were not removing missing values from our data. At this point, we have two options: we can go back and edit our code to avoid this error in the first place or we can resolve it now. In this case, we will just filter these out."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import LocalFileOutput\n",
-        "dflow_clean = dflow.filter(~dflow['Latitude'].is_error())\n",
-        "dflow_clean.get_profile()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/auto-read-file.ipynb b/work-with-data/dataprep/how-to-guides/auto-read-file.ipynb
deleted file mode 100644
index 2a12288c..00000000
--- a/work-with-data/dataprep/how-to-guides/auto-read-file.ipynb
+++ /dev/null
@@ -1,189 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/auto-read-file.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Auto Read File\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep has the ability to load different kinds of text files. The `auto_read_file` entry point can take any text based file (including excel, json and parquet) and auto-detect how to parse the file. It will also attempt to auto-detect the types of each column and apply type transformations to the columns it detects.\n",
-        "\n",
-        "The result will be a Dataflow object that has all the steps added that are required to read the given file(s) and convert their columns to the predicted types. No parameters are required beyond the file path or `FileDataSource` object."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_auto = dprep.auto_read_file('../data/crime_multiple_separators.csv')\n",
-        "dflow_auto.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_auto1 = dprep.auto_read_file('../data/crime.xlsx')\n",
-        "dflow_auto1.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_auto2 = dprep.auto_read_file('../data/crime.parquet')\n",
-        "dflow_auto2.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Looking at the data, we can see that there are two empty columns either side of the 'Completed' column.\n",
-        "If we compare the dataframe to a few rows from the original file:\n",
-        "```\n",
-        "ID |CaseNumber| |Completed|\n",
-        "10140490 |HY329907| |Y|\n",
-        "10139776 |HY329265| |Y|\n",
-        "```\n",
-        "We can see that the `|`'s have disappeared in the dataframe. This is because `|` is a very common separator character in csv files, so `auto_read_file` guessed it was the column separator. For this data we actually want the `|`'s to remain and instead use space as the column separator.\n",
-        "\n",
-        "To achieve this we can use `detect_file_format`. It takes a file path or datasource object and gives back a `FileFormatBuilder` which has learnt some information about the supplied data.\n",
-        "This is what `auto_read_file` is using behind the scenes to 'learn' the contents of the given file and determine how to parse it. With the `FileFormatBuilder` we can take advantage of the intelligent learning aspect of `auto_read_file` but have the chance to modify some of the learnt information."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ffb = dprep.detect_file_format('../data/crime_multiple_separators.csv')\n",
-        "ffb_2 = dprep.detect_file_format('../data/crime.xlsx')\n",
-        "ffb_3 = dprep.detect_file_format('../data/crime_fixed_width_file.txt')\n",
-        "ffb_4 = dprep.detect_file_format('../data/json.json')\n",
-        "\n",
-        "print(ffb.file_format)\n",
-        "print(ffb_2.file_format)\n",
-        "print(ffb_3.file_format)\n",
-        "print(type(ffb_4.file_format))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "After calling `detect_file_format` we get a `FileFormatBuilder` that has had `learn` called on it. This means the `file_format` attribute will be populated with a `<Parse|Read><type>Properties` object, it contains all the information that was learnt about the file. As we can see above different file types have corresponding file_formats detected. \n",
-        "Continuing with our delimited example we can change any of these values and then call `ffb.to_dataflow()` to create a `Dataflow` that has the steps required to parse the datasource."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ffb.file_format.separator = ' '\n",
-        "dflow = ffb.to_dataflow()\n",
-        "df = dflow.to_pandas_dataframe()\n",
-        "df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The result is our desired dataframe with `|`'s included.\n",
-        "\n",
-        "If we refer back to the original data output by `auto_read_file`, the 'ID' column was also detected as numeric and converted to a number data type instead of remaining a string like in the data above.\n",
-        "We can perform type inference on our new dataflow using the `dataflow.builders` property. This property exposes different builders that can `learn` from a dataflow and `apply` the learning to produce a new dataflow, very similar to the pattern we used above for the `FileFormatBuilder`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ctb = dflow.builders.set_column_types()\n",
-        "ctb.learn()\n",
-        "ctb.conversion_candidates"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "After learning `ctb.conversion_candidates` has been populated with information about the inferred types for each column, it is possible for there to be multiple candidate types per column, in this example there is only one type for each column.\n",
-        "\n",
-        "The candidates look correct, we only want to convert `ID` to be an integer column, so applying this `ColumnTypesBuilder` should result in a Dataflow with our columns converted to their respective types."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_converted = ctb.to_dataflow()\n",
-        "\n",
-        "df_converted = dflow_converted.to_pandas_dataframe()\n",
-        "df_converted"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/cache.ipynb b/work-with-data/dataprep/how-to-guides/cache.ipynb
deleted file mode 100644
index fd47cf0f..00000000
--- a/work-with-data/dataprep/how-to-guides/cache.ipynb
+++ /dev/null
@@ -1,194 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/cache.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Cache\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "A Dataflow can be cached as a file on your disk during a local run by calling `dflow_cached = dflow.cache(directory_path)`. Doing this will run all the steps in the Dataflow, `dflow`, and save the cached data to the specified `directory_path`. The returned Dataflow, `dflow_cached`, has a Caching Step added at the end. Any subsequent runs on on the Dataflow `dflow_cached` will reuse the cached data, and the steps before the Caching Step will not be run again.\n",
-        "\n",
-        "Caching avoids running transforms multiple times, which can make local runs more efficient. Here are common places to use Caching:\n",
-        "- after reading data from remote\n",
-        "- after expensive transforms, such as Sort\n",
-        "- after transforms that change the shape of data, such as Sampling, Filter and Summarize\n",
-        "\n",
-        "Caching Step will be ignored during scale-out run invoked by `to_spark_dataframe()`."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We will start by reading in a dataset and applying some transforms to the Dataflow."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "dflow = dprep.read_csv(path='../data/crime-spring.csv')\n",
-        "dflow = dflow.take_sample(probability=0.2, seed=7)\n",
-        "dflow = dflow.sort_asc(columns='Primary Type')\n",
-        "dflow = dflow.keep_columns(['ID', 'Case Number', 'Date', 'Primary Type'])\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Next, we will choose a directory to store the cached data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "from pathlib import Path\n",
-        "cache_dir = str(Path(os.getcwd(), 'dataflow-cache'))\n",
-        "cache_dir"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We will now call `dflow.cache(directory_path)` to cache the Dataflow to your directory."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_cached = dflow.cache(directory_path=cache_dir)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now we will check steps in the `dflow_cached` to see that all of the previous steps were cached."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "[s.step_type for s in dflow_cached._get_steps()]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We also check the data stored in the cache directory."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "os.listdir(cache_dir)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Running against `dflow_cached` will reuse the cached data and skip running all of the previous steps again."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_cached.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Adding additional steps to `dflow_cached` will also reuse the cache data and skip running the steps prior to the Cache Step."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_cached_take = dflow_cached.take(10)\n",
-        "dflow_cached_skip = dflow_cached.skip(10).take(10)\n",
-        "\n",
-        "df_cached_take = dflow_cached_take.to_pandas_dataframe()\n",
-        "df_cached_skip = dflow_cached_skip.to_pandas_dataframe()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# shutil.rmtree will then clean up the cached data \n",
-        "import shutil\n",
-        "shutil.rmtree(path=cache_dir)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/column-manipulations.ipynb b/work-with-data/dataprep/how-to-guides/column-manipulations.ipynb
deleted file mode 100644
index bf1836f9..00000000
--- a/work-with-data/dataprep/how-to-guides/column-manipulations.ipynb
+++ /dev/null
@@ -1,563 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/column-manipulations.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Column Manipulations\n",
-        "\n",
-        "Azure ML Data Prep has many methods for manipulating columns, including basic CUD operations and several other more complex manipulations.\n",
-        "\n",
-        "This notebook will focus primarily on data-agnostic operations. For all other column manipulation operations, we will link to their specific how-to guide.\n",
-        "\n",
-        "## Table of Contents\n",
-        "[ColumnSelector](#ColumnSelector)<br>\n",
-        "[add_column](#add_column)<br>\n",
-        "[append_columns](#append_columns)<br>\n",
-        "[drop_columns](#drop_columns)<br>\n",
-        "[duplicate_column](#duplicate_column)<br>\n",
-        "[fuzzy_group_column](#fuzzy_group_column)<br>\n",
-        "[keep_columns](#keep_columns)<br>\n",
-        "[map_column](#map_column)<br>\n",
-        "[new_script_column](#new_script_column)<br>\n",
-        "[rename_columns](#rename_columns)<br>\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"ColumnSelector\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## ColumnSelector\n",
-        "`ColumnSelector` is a Data Prep class that allows us to select columns by name. The idea is to be able to describe columns generally instead of explicitly, using a search term or regex expression, with various options.\n",
-        "\n",
-        "Note that a `ColumnSelector` does not represent the columns they match themselves, but the selector of the described columns. Therefore if we use the same `ColumnSelector` on two different dataflows, we may get different results depending on the columns of each dataflow.\n",
-        "\n",
-        "Column manipulations that can utilize `ColumnSelector` will be noted in their respective sections in this book."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import auto_read_file\n",
-        "dflow = auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "All parameters to a `ColumnSelector` are shown here for completeness. We will use `keep_columns` in our example, which will keep only the columns in the dataflow that we tell it to keep.\n",
-        "\n",
-        "In the below example, we match all columns with the letter 'i'. Because we set `ignore_case` to false and `match_whole_word` to false, then any column that contains 'i' or 'I' will be selected."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import ColumnSelector\n",
-        "column_selector = ColumnSelector(term=\"i\",\n",
-        "                                 use_regex=False,\n",
-        "                                 ignore_case=True,\n",
-        "                                 match_whole_word=False,\n",
-        "                                 invert=False)\n",
-        "dflow_selected = dflow.keep_columns(column_selector)\n",
-        "dflow_selected.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If we set `invert` to true, we get the opposite of what we matched earlier."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "column_selector = ColumnSelector(term=\"i\",\n",
-        "                                 use_regex=False,\n",
-        "                                 ignore_case=True,\n",
-        "                                 match_whole_word=False,\n",
-        "                                 invert=True)\n",
-        "dflow_selected = dflow.keep_columns(column_selector)\n",
-        "dflow_selected.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If we change the search term to 'I' and set case sensitivity to true, we get only the handful of columns that contain an upper case 'I'."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "column_selector = ColumnSelector(term=\"I\",\n",
-        "                                 use_regex=False,\n",
-        "                                 ignore_case=False,\n",
-        "                                 match_whole_word=False,\n",
-        "                                 invert=False)\n",
-        "dflow_selected = dflow.keep_columns(column_selector)\n",
-        "dflow_selected.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "And if we set `match_whole_word` to true, we get no results at all as there is no column called 'I'."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "column_selector = ColumnSelector(term=\"I\",\n",
-        "                                 use_regex=False,\n",
-        "                                 ignore_case=False,\n",
-        "                                 match_whole_word=True,\n",
-        "                                 invert=False)\n",
-        "dflow_selected = dflow.keep_columns(column_selector)\n",
-        "dflow_selected.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Finally, the `use_regex` flag dictates whether or not to treat the search term as a regex. It can be combined still with the other options.\n",
-        "\n",
-        "Here we define all columns that begin with the capital letter 'I'."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "column_selector = ColumnSelector(term=\"I.*\",\n",
-        "                                 use_regex=True,\n",
-        "                                 ignore_case=True,\n",
-        "                                 match_whole_word=True,\n",
-        "                                 invert=False)\n",
-        "dflow_selected = dflow.keep_columns(column_selector)\n",
-        "dflow_selected.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"add_column\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## add_column"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Please see [add-column-using-expression](add-column-using-expression.ipynb)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"append_columns\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## append_columns"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Please see [append-columns-and-rows](append-columns-and-rows.ipynb)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"drop_columns\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## drop_columns"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep supports dropping columns one or more columns in a single statement. Supports `ColumnSelector`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import auto_read_file\n",
-        "dflow = auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Note that there are 22 columns to begin with. We will now drop the 'ID' column and observe that the resulting dataflow contains 21 columns."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_dropped = dflow.drop_columns('ID')\n",
-        "dflow_dropped.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can also drop more than one column at once by passing a list of column names."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_dropped = dflow_dropped.drop_columns(['IUCR', 'Description'])\n",
-        "dflow_dropped.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"duplicate_column\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## duplicate_column"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep supports duplicating columns one or more columns in a single statement.\n",
-        "\n",
-        "Duplicated columns are placed to the immediate right of their source column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import auto_read_file\n",
-        "dflow = auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We decide which column(s) to duplicate and what the new column name(s) should be with a key value pairing (dictionary)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_dupe = dflow.duplicate_column({'ID': 'ID2', 'IUCR': 'IUCR_Clone'})\n",
-        "dflow_dupe.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"fuzzy_group_column\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## fuzzy_group_column"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Please see [fuzzy-group](fuzzy-group.ipynb)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"keep_columns\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## keep_columns"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep supports keeping one or more columns in a single statement. The resulting dataflow will contain only the column(s) specified; dropping all the other columns. Supports `ColumnSelector`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import auto_read_file\n",
-        "dflow = auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_keep = dflow.keep_columns(['ID', 'Date', 'Description'])\n",
-        "dflow_keep.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Similar to `drop_columns`, we can pass a single column name or a list of them."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_keep = dflow_keep.keep_columns('ID')\n",
-        "dflow_keep.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"map_column\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## map_column"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep supports string mapping. For a column containing strings, we can provide specific mappings from an original value to a new value, and then produce a new column that contains the mapped values.\n",
-        "\n",
-        "The mapped columns are placed to the immediate right of their source column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import auto_read_file\n",
-        "dflow = auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import ReplacementsValue\n",
-        "replacements = [ReplacementsValue('THEFT', 'THEFT2'), ReplacementsValue('BATTERY', 'BATTERY!!!')]\n",
-        "dflow_mapped = dflow.map_column(column='Primary Type', \n",
-        "                                new_column_id='Primary Type V2',\n",
-        "                                replacements=replacements)\n",
-        "dflow_mapped.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"new_script_column\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## new_script_column"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Please see [custom-python-transforms](custom-python-transforms.ipynb)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"rename_columns\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## rename_columns"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep supports renaming one or more columns in a single statement."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import auto_read_file\n",
-        "dflow = auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We decide which column(s) to rename and what the new column name(s) should be with a key value pairing (dictionary)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_renamed = dflow.rename_columns({'ID': 'ID2', 'IUCR': 'IUCR_Clone'})\n",
-        "dflow_renamed.head(5)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/column-type-transforms.ipynb b/work-with-data/dataprep/how-to-guides/column-type-transforms.ipynb
deleted file mode 100644
index bfc4e73f..00000000
--- a/work-with-data/dataprep/how-to-guides/column-type-transforms.ipynb
+++ /dev/null
@@ -1,473 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/column-type-transforms.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Column Type Transforms\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "When consuming a data set, it is highly useful to know as much as possible about the data. Column types can help you understand more about each column, and enable type-specific transformations later. This provides much more insight than treating all data as strings.\n",
-        "\n",
-        "In this notebook, you will learn about:\n",
-        "- [Built-in column types](#types)\n",
-        "- How to:\n",
-        " - [Convert to long (integer)](#long)\n",
-        " - [Convert to double (floating point or decimal number)](#double)\n",
-        " - [Convert to boolean](#boolean)\n",
-        " - [Convert to datetime](#datetime)\n",
-        "- [How to use `ColumnTypesBuilder` to get suggested column types and convert them](#builder)\n",
-        "- [How to convert column type for multiple columns if types are known](#multiple-columns)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Set up"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dprep.read_csv('../data/crime-winter.csv')\n",
-        "dflow = dflow.keep_columns(['Case Number', 'Date', 'IUCR', 'Arrest', 'Longitude', 'Latitude'])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"types\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Built-in column types"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Currently, Data Prep supports the following column types: string, long (integer), double (floating point or decimal number), boolean, and datetime.\n",
-        "\n",
-        "In the previous step, a data set was read in as a Dataflow, with only a few interesting columns kept. We will use this Dataflow to explore column types throughout the notebook."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "From the first few rows of the Dataflow, you can see that the columns contain different types of data. However, by looking at `dtypes`, you can see that `read_csv()` treats all columns as string columns.\n",
-        "\n",
-        "Note that `auto_read_file()` is a data ingestion function that infers column types. Learn more about it [here](./auto-read-file.ipynb)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow.dtypes"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"long\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Converting to long (integer)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Suppose the \"IUCR\" column should only contain integers. You can call `to_long` to convert the column type of \"IUCR\" to `FieldType.INTEGER`. If you look at the data profile ([learn more about data profiles](./data-profile.ipynb)), you will see numeric metrics populated for that column such as mean, variance, quantiles, etc. This is helpful for understanding the shape and distribution of numeric data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_conversion = dflow.to_long('IUCR')\n",
-        "profile = dflow_conversion.get_profile()\n",
-        "profile"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"double\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Converting to double (floating point or decimal number)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Suppose the \"Latitude\" and \"Longitude\" columns should only contain decimal numbers. You can call `to_double` to convert the column type of \"Latitude\" and \"Longitude\" to `FieldType.DECIMAL`. In the data profile, you will see numeric metrics populated for these columns as well. Note that after converting the column types, you can see that there are missing values in these columns. Metrics like this can be helpful for noticing issues with the data set."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_conversion = dflow_conversion.to_number(['Latitude', 'Longitude'])\n",
-        "profile = dflow_conversion.get_profile()\n",
-        "profile"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"boolean\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Converting to boolean"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Suppose the \"Arrest\" column should only contain boolean values. You can call `to_bool` to convert the column type of \"Arrest\" to `FieldType.BOOLEAN`.\n",
-        "\n",
-        "The `to_bool` function allows you to specify which values should map to `True` and which values should map to `False`. To do so, you can provide those values in an array as parameters `true_values` and `false_values`. Additionally, you can specify whether all other values should become `True`, `False` or Error by using the `mismatch_as` parameter."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_conversion.to_bool('Arrest', \n",
-        "                         true_values=[1],\n",
-        "                         false_values=[0],\n",
-        "                         mismatch_as=dprep.MismatchAsOption.ASERROR).head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In the previous conversion, all the values in the \"Arrest\" column became `DataPrepError`, because 'FALSE' didn't match any of the `false_values` nor any of the `true_values`, and all the unmatched values were set to become errors. Let's try the conversion again with different `false_values`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_conversion = dflow_conversion.to_bool('Arrest',\n",
-        "                                            true_values=['1', 'TRUE'],\n",
-        "                                            false_values=['0', 'FALSE'],\n",
-        "                                            mismatch_as=dprep.MismatchAsOption.ASERROR)\n",
-        "dflow_conversion.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This time, all the string values 'FALSE' have been successfully converted to the boolean value `False`. Take another look at the data profile."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "profile = dflow_conversion.get_profile()\n",
-        "profile"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"datetime\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Suppose the \"Date\" column should only contain datetime values. You can convert its column type to `FieldType.DateTime` using the `to_datetime` function. Typically, datetime formats can be confusing or inconsistent. Next, we will show you all the tools that can help correctly converting the column to `DateTime`."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In the first example, directly call `to_datetime` with only the column name. Data Prep will inspect the data in this column and learn what format should be used for the conversion.\n",
-        "\n",
-        "Note that if there is data in the column that cannot be converted to datetime, an Error value will be created in that cell."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_conversion_date = dflow_conversion.to_datetime('Date')\n",
-        "dflow_conversion_date.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In this case, we can see that '1/10/2016 11:00' was converted using the format `%m/%d/%Y %H:%M`.\n",
-        "\n",
-        "The data in this column is actually somewhat ambiguous. Should the dates be 'October 1' or 'January 10'? The function `to_datetime` determines that both are possible, but defaults to month-first (US format).\n",
-        "\n",
-        "If the data was supposed to be day-first, you can customize the conversion."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_alternate_conversion = dflow_conversion.to_datetime('Date', date_time_formats=['%d/%m/%Y %H:%M'])\n",
-        "dflow_alternate_conversion.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"builder\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Using `ColumnTypesBuilder`"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep can help you automatically detect what are the likely column types.\n",
-        "\n",
-        "You can call `dflow.builders.set_column_types()` to get a `ColumnTypesBuilder`. Then, calling `learn()` on it will trigger Data Prep to inspect the data in each column. As a result, you can see the suggested column types for each column (conversion candidates)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder = dflow.builders.set_column_types()\n",
-        "builder.learn()\n",
-        "builder"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In this case, Data Prep suggested the correct column types for \"Arrest\", \"Case Number\", \"Latitude\", and \"Longitude\".\n",
-        "\n",
-        "However, for \"Date\", it has suggested two possible date formats: month-first, or day-first. The ambiguity must be resolved before you complete the conversion. To use the month-first format, you can call `builder.ambiguous_date_conversions_keep_month_day()`. Otherwise, call `builder.ambiguous_date_conversions_keep_day_month()`. Note that if there were multiple datetime columns with ambiguous date conversions, calling one of these functions will apply the resolution to all of them.\n",
-        "\n",
-        "If you want to skip all the ambiguous date column conversions instead, you can call: `builder.ambiguous_date_conversions_drop()`"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.ambiguous_date_conversions_keep_month_day()\n",
-        "builder.conversion_candidates"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The conversion candidate for \"IUCR\" is currently `FieldType.INTEGER`. If you know that \"IUCR\" should be floating point (called `FieldType.DECIMAL`), you can tweak the builder to change the conversion candidate for that specific column. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.conversion_candidates['IUCR'] = dprep.FieldType.DECIMAL\n",
-        "builder"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In this case we are happy with \"IUCR\" as `FieldType.INTEGER`. So we set it back. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.conversion_candidates['IUCR'] = dprep.FieldType.INTEGER\n",
-        "builder"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once you are happy with the conversion candidates, you can complete the conversion by calling `builder.to_dataflow()`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_converion_using_builder = builder.to_dataflow()\n",
-        "dflow_converion_using_builder.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"multiple-columns\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Convert column types for multiple columns"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If you already know the column types, you can simply call `dflow.set_column_types()`. This function allows you to specify multiple columns, and the desired column type for each one. Here's how you can convert all five columns at once.\n",
-        "\n",
-        "Note that `set_column_types` only supports a subset of column type conversions. For example, we cannot specify the true/false values for a boolean conversion, so the results of this operation is incorrect for the \"Arrest\" column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_conversion_using_set = dflow.set_column_types({\n",
-        "    'IUCR': dprep.FieldType.INTEGER,\n",
-        "    'Latitude': dprep.FieldType.DECIMAL,\n",
-        "    'Longitude': dprep.FieldType.DECIMAL,\n",
-        "    'Arrest': dprep.FieldType.BOOLEAN,\n",
-        "    'Date': (dprep.FieldType.DATE, ['%m/%d/%Y %H:%M']),\n",
-        "})\n",
-        "dflow_conversion_using_set.head(5)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/custom-python-transforms.ipynb b/work-with-data/dataprep/how-to-guides/custom-python-transforms.ipynb
deleted file mode 100644
index 30dcb113..00000000
--- a/work-with-data/dataprep/how-to-guides/custom-python-transforms.ipynb
+++ /dev/null
@@ -1,231 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/custom-python-transforms.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Custom Python Transforms\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "There will be scenarios when the easiest thing for you to do is just to write some Python code. This SDK provides three extension points that you can use.\n",
-        "\n",
-        "1. New Script Column\n",
-        "2. New Script Filter\n",
-        "3. Transform Partition\n",
-        "\n",
-        "Each of these are supported in both the scale-up and the scale-out runtime. A key advantage of using these extension points is that you don't need to pull all of the data in order to create a dataframe. Your custom python code will be run just like other transforms, at scale, by partition, and typically in parallel."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initial data prep"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We start by loading crime data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "col = dprep.col\n",
-        "\n",
-        "dflow = dprep.read_csv(path='../data/crime-spring.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We trim the dataset down and keep only the columns we are interested in. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.keep_columns(['Case Number','Primary Type', 'Description', 'Latitude', 'Longitude'])\n",
-        "dflow = dflow.replace_na(columns=['Latitude', 'Longitude'], custom_na_list='')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We look for null values using a filter. We found some, so now we'll look at a way to fill these missing values."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow.filter(col('Latitude').is_null()).head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Transform Partition"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We want to replace all null values with a 0, so we decide to use a handy pandas function. This code will be run by partition, not on all of the dataset at a time. This means that on a large dataset, this code may run in parallel as the runtime processes the data partition by partition."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "pt_dflow = dflow\n",
-        "dflow = pt_dflow.transform_partition(\"\"\"\n",
-        "def transform(df, index):\n",
-        "    df['Latitude'].fillna('0',inplace=True)\n",
-        "    df['Longitude'].fillna('0',inplace=True)\n",
-        "    return df\n",
-        "\"\"\")\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Transform Partition With File"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Being able to use any python code to manipulate your data as a pandas DataFrame is extremely useful for complex and specific data operations that DataPrep doesn't handle natively. Though the code isn't very testable unfortunately, it's just sitting inside a string.\n",
-        "So to improve code testability and ease of script writing there is another transform_partiton interface that takes the path to a python script which must contain a function matching the 'transform' signature defined above.\n",
-        "\n",
-        "The `script_path` argument should be a relative path to ensure Dataflow portability. Here `map_func.py` contains the same code as in the previous example."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = pt_dflow.transform_partition_with_file('../data/map_func.py')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## New Script Column"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We want to create a new column that has both the latitude and longitude. We can achieve it easily using [Data Prep expression](./add-column-using-expression.ipynb), which is faster in execution. Alternatively, We can do this using Python code by using the `new_script_column()` method on the dataflow. Note that we use custom Python code here for demo purpose only. In practise, you should always use Data Prep native functions as a preferred method, and use custom Python code when the functionality is not available in Data Prep. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.new_script_column(new_column_name='coordinates', insert_after='Longitude', script=\"\"\"\n",
-        "def newvalue(row):\n",
-        "    return '(' + row['Latitude'] + ', ' + row['Longitude'] + ')'\n",
-        "\"\"\")\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## New Script Filter"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now we want to filter the dataset down to only the crimes that incurred over $300 in loss. We can build a Python expression that returns True if we want to keep the row, and False to drop the row."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.new_script_filter(\"\"\"\n",
-        "def includerow(row):\n",
-        "    val = row['Description']\n",
-        "    return 'OVER $ 300' in val\n",
-        "\"\"\")\n",
-        "dflow.head(5)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/data-ingestion.ipynb b/work-with-data/dataprep/how-to-guides/data-ingestion.ipynb
deleted file mode 100644
index fc82dbda..00000000
--- a/work-with-data/dataprep/how-to-guides/data-ingestion.ipynb
+++ /dev/null
@@ -1,978 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/data-ingestion.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Data Ingestion\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep has the ability to load different types of input data. You can use auto-reading functionality to detect the type of a file, or directly specify a file type and its parameters."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Table of Contents\n",
-        "[Read Lines](#lines)<br>\n",
-        "[Read CSV](#csv)<br>\n",
-        "[Read Compressed CSV](#compressed-csv)<br>\n",
-        "[Read Excel](#excel)<br>\n",
-        "[Read Fixed Width Files](#fixed-width)<br>\n",
-        "[Read Parquet](#parquet)<br>\n",
-        "[Read Part Files Using Globbing](#globbing)<br>\n",
-        "[Read JSON](#json)<br>\n",
-        "[Read SQL](#sql)<br>\n",
-        "[Read PostgreSQL](#postgresql)<br>\n",
-        "[Read From Azure Blob](#azure-blob)<br>\n",
-        "[Read From ADLS](#adls)<br>\n",
-        "[Read Pandas DataFrame](#pandas-df)<br>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"lines\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Read Lines"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "One of the simplest ways to read data using Data Prep is to just read it as text lines."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dprep.read_lines(path='../data/crime.txt')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "With ingestion done, you can go ahead and start prepping the dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "df = dflow.to_pandas_dataframe()\n",
-        "df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"csv\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Read CSV"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "When reading delimited files, the only required parameter is `path`. Other parameters (e.g. separator, encoding, whether to use headers, etc.) are available to modify default behavior.\n",
-        "In this case, you can read a file by specifying only its location, then retrieve the first 5 rows to evaluate the result."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_duplicate_headers = dprep.read_csv(path='../data/crime_duplicate_headers.csv')\n",
-        "dflow_duplicate_headers.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "From the result, you can see that the delimiter and encoding were correctly detected. Column headers were also detected. However, the first line seems to be a duplicate of the column headers. One of the parameters is a number of lines to skip from the files being read. You can use this to filter out the duplicate line."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_skip_headers = dprep.read_csv(path='../data/crime_duplicate_headers.csv', skip_rows=1)\n",
-        "dflow_skip_headers.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now the data set contains the correct headers and the extraneous row has been skipped by `read_csv`. Next, look at the data types of the columns."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_skip_headers.dtypes"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Unfortunately, all of the columns came back as strings. This is because, by default, Data Prep will not change the type of the data. Since the data source is a text file, all values are kept as strings. In this case, however, numeric columns should be parsed as numbers. To do this, set the `infer_column_types` parameter to `True`, which will trigger type inference to be performed.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_inferred_types = dprep.read_csv(path='../data/crime_duplicate_headers.csv',\n",
-        "                          skip_rows=1,\n",
-        "                          infer_column_types=True)\n",
-        "dflow_inferred_types.dtypes"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now several of the columns were correctly detected as numbers and their `FieldType` is Decimal.\n",
-        "\n",
-        "With ingestion done, the data set is ready to start preparing."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "df = dflow_inferred_types.to_pandas_dataframe()\n",
-        "df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"compressed-csv\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Read Compressed CSV"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep can also read delimited files compressed in an archive. The `archive_options` parameter specifies the type of archive and glob pattern of entries in the archive.\n",
-        "\n",
-        "At this moment, only reading from ZIP archives is supported."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import ArchiveOptions, ArchiveType\n",
-        "\n",
-        "dflow = dprep.read_csv(path='../data/crime.zip',\n",
-        "                          archive_options=ArchiveOptions(archive_type=ArchiveType.ZIP, entry_glob='*10-20.csv'))\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"excel\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Read Excel"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep can also load Excel files using the `read_excel` method."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_default_sheet = dprep.read_excel(path='../data/crime.xlsx')\n",
-        "dflow_default_sheet.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Here, the first sheet of the Excel document has been loaded. You could achieve the same result by specifying the name of the desired sheet explicitly."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_second_sheet = dprep.read_excel(path='../data/crime.xlsx', sheet_name='Sheet2')\n",
-        "dflow_second_sheet.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "As you can see, the table in the second sheet had headers as well as three empty rows, so you can modify the arguments accordingly."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_skipped_rows = dprep.read_excel(path='../data/crime.xlsx',\n",
-        "                                      sheet_name='Sheet2',\n",
-        "                                      use_column_headers=True,\n",
-        "                                      skip_rows=3)\n",
-        "dflow_skipped_rows.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "df = dflow_skipped_rows.to_pandas_dataframe()\n",
-        "df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"fixed-width\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Read Fixed Width Files"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "For fixed-width files, you can specify a list of offsets. The first column is always assumed to start at offset 0."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_fixed_width = dprep.read_fwf('../data/crime.txt', offsets=[8, 17, 26, 33, 56, 58, 74])\n",
-        "dflow_fixed_width.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Looking at the data, you can see that the first row was used as headers. In this particular case, however, there are no headers in the file, so the first row should be treated as data.\n",
-        "\n",
-        "Passing in `PromoteHeadersMode.NONE` to the `header` keyword argument avoids header detection and gets the correct data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_no_headers = dprep.read_fwf('../data/crime.txt',\n",
-        "                          offsets=[8, 17, 26, 33, 56, 58, 74],\n",
-        "                          header=dprep.PromoteHeadersMode.NONE)\n",
-        "dflow_no_headers.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "df = dflow_no_headers.to_pandas_dataframe()\n",
-        "df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"parquet\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Read Parquet"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep has two different methods for reading data stored as Parquet.\n",
-        "\n",
-        "Currently, both methods require the `pyarrow` package to be installed in your Python environment. This can be done via `pip install azureml-dataprep[parquet]`."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Read Parquet File"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "For reading single `.parquet` files, or a folder full of only Parquet files, use `read_parquet_file`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dprep.read_parquet_file('../data/crime.parquet')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Parquet data is explicitly typed so no type inference is needed."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow.dtypes"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Read Parquet Dataset"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "A Parquet Dataset is different from a Parquet file in that it could be a folder containing a number of Parquet files within a complex directory structure. It may have a hierarchical structure that partitions the data by value of a column. These more complex forms of Parquet data are commonly produced by Spark/HIVE.\n",
-        "\n",
-        "For these more complex data sets, you can use `read_parquet_dataset`, which uses pyarrow to handle complex Parquet layouts. This will also handle single Parquet files, though these are better read using `read_parquet_file`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dprep.read_parquet_dataset('../data/parquet_dataset')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The above data was partitioned by the value of the `Arrest` column. It is a boolean column in the original crime0 data set and hence was partitioned by `Arrest=true` and `Arrest=false`.\n",
-        "\n",
-        "The directory structure is printed below for clarity."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "for path, dirs, files in os.walk('../data/parquet_dataset'):\n",
-        "    level = path.replace('../data/parquet_dataset', '').count(os.sep)\n",
-        "    indent = '   ' * (level)\n",
-        "    print(indent + os.path.basename(path) + '/')\n",
-        "    fileindent = '   ' * (level + 1)\n",
-        "    for f in files:\n",
-        "        print(fileindent + f)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"globbing\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Read Part Files Using Globbing"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep supports globbing, which allows you to read partitioned files (or any other type of files) in a folder. Globbing is supported by all of the read transformations that take in file paths, such as `read_csv`, `read_lines`, etc. By specifying `../data/crime_partfiles/part-*` in the path, we will read all files start with `part-`in `crime_partfiles` folder and return them in one Dataflow. [`auto_read_file`](./auto-read-file.ipynb) will detect column types of your part files and parse them automatically."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_partfiles = dprep.auto_read_file(path='../data/crime_partfiles/part-*')\n",
-        "dflow_partfiles.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"json\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Read JSON"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep can also load JSON files."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_json = dprep.read_json(path='../data/json.json')\n",
-        "dflow_json.head(15)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "When you use `read_json`, Data Prep will attempt to extract data from the file into a table. You can also control the file encoding Data Prep should use as well as whether Data Prep should flatten nested JSON arrays.\n",
-        "\n",
-        "Choosing the option to flatten nested arrays could result in a much larger number of rows."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_flat_arrays = dprep.read_json(path='../data/json.json', flatten_nested_arrays=True)\n",
-        "dflow_flat_arrays.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"sql\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Read SQL"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep can also fetch data from SQL servers. Currently, only Microsoft SQL Server is supported.\n",
-        "\n",
-        "To read data from a SQL server, first create a data source object that contains the connection information."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "secret = dprep.register_secret(value=\"dpr3pTestU$er\", id=\"dprepTestUser\")\n",
-        "ds = dprep.MSSQLDataSource(server_name=\"dprep-sql-test.database.windows.net\",\n",
-        "                           database_name=\"dprep-sql-test\",\n",
-        "                           user_name=\"dprepTestUser\",\n",
-        "                           password=secret)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "As you can see, the password parameter of `MSSQLDataSource` accepts a Secret object. You can get a Secret object in two ways:\n",
-        "1. Register the secret and its value with the execution engine.\n",
-        "2. Create the secret with just an id (useful if the secret value was already registered in the execution environment).\n",
-        "\n",
-        "Now that you have created a data source object, you can proceed to read data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dprep.read_sql(ds, \"SELECT top 100 * FROM [SalesLT].[Product]\")\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "df = dflow.to_pandas_dataframe()\n",
-        "df.dtypes"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"postgresql\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Read PostgreSQL"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep can also fetch data from Azure PostgreSQL servers.\n",
-        "\n",
-        "To read data from a PostgreSQL server, first create a data source object that contains the connection information."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "secret = dprep.register_secret(value=\"dpr3pTestU$er\", id=\"dprepPostgresqlUser\")\n",
-        "ds = dprep.PostgreSQLDataSource(server_name=\"dprep-postgresql-test.postgres.database.azure.com\",\n",
-        "                                database_name=\"dprep-postgresql-testdb\",\n",
-        "                                user_name=\"dprepPostgresqlReadOnlyUser@dprep-postgresql-test\",\n",
-        "                                password=secret)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "As you can see, the password parameter of `PostgreSQLDataSource` accepts a Secret object as well.\n",
-        "Now that you have created a PostgreSQL data source object, you can proceed to read data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dprep.read_postgresql(ds, \"SELECT * FROM public.people\")\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow.dtypes"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"azure-blob\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Read from Azure Blob"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can read files stored in public Azure Blob by directly passing a file url. To read file from a protected Blob, pass SAS (Shared Access Signature) URI with both resource URI and SAS token in the path."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dprep.read_csv(path='https://dpreptestfiles.blob.core.windows.net/testfiles/read_csv_duplicate_headers.csv', skip_rows=1)\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"adls\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Read from ADLS"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "There are two ways the Data Prep API can acquire the necessary OAuth token to access Azure DataLake Storage:\n",
-        "1. Retrieve the access token from a recent login session of the user's [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) login.\n",
-        "2. Use a ServicePrincipal (SP) and a certificate as a secret."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Using Access Token from a recent Azure CLI session"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "On your local machine, run the following command:\n",
-        "```\n",
-        "az login\n",
-        "```\n",
-        "If your user account is a member of more than one Azure tenant, you need to specify the tenant, either in the AAD url hostname form '<your_domain>.onmicrosoft.com' or the tenantId GUID. The latter can be retrieved as follows:\n",
-        "```\n",
-        "az account show --query tenantId\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "```python\n",
-        "dflow = read_csv(path = DataLakeDataSource(path='adl://dpreptestfiles.azuredatalakestore.net/farmers-markets.csv', tenant='microsoft.onmicrosoft.com'))\n",
-        "head = dflow.head(5)\n",
-        "head\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create a ServicePrincipal via Azure CLI"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "A ServicePrincipal and the corresponding certificate can be created via [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest).\n",
-        "This particular SP is configured as Reader, with its scope reduced to just the ADLS account 'dpreptestfiles'.\n",
-        "```\n",
-        "az account set --subscription \"Data Wrangling development\"\n",
-        "az ad sp create-for-rbac -n \"SP-ADLS-dpreptestfiles\" --create-cert --role reader --scopes /subscriptions/35f16a99-532a-4a47-9e93-00305f6c40f2/resourceGroups/dpreptestfiles/providers/Microsoft.DataLakeStore/accounts/dpreptestfiles\n",
-        "```\n",
-        "This command emits the appId and the path to the certificate file (usually in the home folder). The .crt file contains both the public certificate and the private key in PEM format.\n",
-        "\n",
-        "Extract the thumbprint with:\n",
-        "```\n",
-        "openssl x509 -in adls-dpreptestfiles.crt -noout -fingerprint\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Configure ADLS Account for ServicePrincipal"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To configure the ACL for the ADLS filesystem, use the objectId of the user or, here, ServicePrincipal:\n",
-        "```\n",
-        "az ad sp show --id \"8dd38f34-1fcb-4ff9-accd-7cd60b757174\" --query objectId\n",
-        "```\n",
-        "Configure Read and Execute access for the ADLS file system. Since the underlying HDFS ACL model doesn't support inheritance, folders and files need to be ACL-ed individually.\n",
-        "```\n",
-        "az dls fs access set-entry --account dpreptestfiles --acl-spec \"user:e37b9b1f-6a5e-4bee-9def-402b956f4e6f:r-x\" --path /\n",
-        "az dls fs access set-entry --account dpreptestfiles --acl-spec \"user:e37b9b1f-6a5e-4bee-9def-402b956f4e6f:r--\" --path /farmers-markets.csv\n",
-        "```\n",
-        "\n",
-        "References:\n",
-        "- [az ad sp](https://docs.microsoft.com/en-us/cli/azure/ad/sp?view=azure-cli-latest)\n",
-        "- [az dls fs access](https://docs.microsoft.com/en-us/cli/azure/dls/fs/access?view=azure-cli-latest)\n",
-        "- [ACL model for ADLS](https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/data-lake-store/data-lake-store-access-control.md)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "certThumbprint = 'C2:08:9D:9E:D1:74:FC:EB:E9:7E:63:96:37:1C:13:88:5E:B9:2C:84'\n",
-        "certificate = ''\n",
-        "with open('../data/adls-dpreptestfiles.crt', 'rt', encoding='utf-8') as crtFile:\n",
-        "    certificate = crtFile.read()\n",
-        "\n",
-        "servicePrincipalAppId = \"8dd38f34-1fcb-4ff9-accd-7cd60b757174\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Acquire an OAuth Access Token"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Use the adal package (via: `pip install adal`) to create an authentication context on the MSFT tenant and acquire an OAuth access token. Note that for ADLS, the `resource` in the token request must be for 'datalake.azure.net', which is different from most other Azure resources."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import adal\n",
-        "from azureml.dataprep.api.datasources import DataLakeDataSource\n",
-        "\n",
-        "ctx = adal.AuthenticationContext('https://login.microsoftonline.com/microsoft.onmicrosoft.com')\n",
-        "token = ctx.acquire_token_with_client_certificate('https://datalake.azure.net/', servicePrincipalAppId, certificate, certThumbprint)\n",
-        "dflow = dprep.read_csv(path = DataLakeDataSource(path='adl://dpreptestfiles.azuredatalakestore.net/crime-spring.csv', accessToken=token['accessToken']))\n",
-        "dflow.to_pandas_dataframe().head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"pandas-df\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Read Pandas DataFrame"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "There are situations where you may already have some data in the form of a pandas DataFrame.\n",
-        "The steps taken to get to this DataFrame may be non-trivial or not easy to convert to Data Prep Steps. The `read_pandas_dataframe` reader can take a DataFrame and use it as the data source for a Dataflow.\n",
-        "\n",
-        "You can pass in a path to a directory (that doesn't exist yet) for Data Prep to store the contents of the DataFrame; otherwise, a temporary directory will be made in the system's temp folder. The files written to this directory will be named `part-00000` and so on; they are written out in Data Prep's internal row-based file format."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dprep.read_excel(path='../data/crime.xlsx')\n",
-        "dflow = dflow.drop_columns(columns=['Column1'])\n",
-        "df = dflow.to_pandas_dataframe()\n",
-        "df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "After loading in the data you can now do `read_pandas_dataframe`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import shutil\n",
-        "cache_dir = 'dflow_df'\n",
-        "shutil.rmtree(cache_dir, ignore_errors=True)\n",
-        "dflow_df = dprep.read_pandas_dataframe(df, cache_dir)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_df.head(5)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/data-profile.ipynb b/work-with-data/dataprep/how-to-guides/data-profile.ipynb
deleted file mode 100644
index 97b42ee1..00000000
--- a/work-with-data/dataprep/how-to-guides/data-profile.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/data-profile.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Data Profile\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "A DataProfile collects summary statistics on each column of the data produced by a Dataflow. This can be used to:\n",
-        "- Understand the input data.\n",
-        "- Determine which columns might need further preparation.\n",
-        "- Verify that data preparation operations produced the desired result.\n",
-        "\n",
-        "`Dataflow.get_profile()` executes the Dataflow, calculates profile information, and returns a newly constructed DataProfile."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "\n",
-        "dflow = dprep.auto_read_file('../data/crime-spring.csv')\n",
-        "\n",
-        "profile = dflow.get_profile()\n",
-        "profile"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "A DataProfile contains a collection of ColumnProfiles, indexed by column name. Each ColumnProfile has attributes for the calculated column statistics. For non-numeric columns, profiles include only basic statistics like min, max, and error count. For numeric columns, profiles also include statistical moments and estimated quantiles."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "profile.columns['Beat']"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also extract and filter data from profiles by using list and dict comprehensions."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "variances = [c.variance for c in profile.columns.values() if c.variance]\n",
-        "variances"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "column_types = {c.name: c.type for c in profile.columns.values()}\n",
-        "column_types"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If a column has fewer than a thousand unique values, its ColumnProfile contains a summary of values with their respective counts."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "profile.columns['Primary Type'].value_counts"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Numeric ColumnProfiles include an estimated histogram of the data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "profile.columns['District'].histogram"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To configure the number of bins in the histogram, you can pass an integer as the `number_of_histogram_bins` parameter."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "profile_more_bins = dflow.get_profile(number_of_histogram_bins=5)\n",
-        "profile_more_bins.columns['District'].histogram"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "For columns containing data of mixed types, the ColumnProfile also provides counts of each type."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "profile.columns['X Coordinate'].type_counts"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/datastore.ipynb b/work-with-data/dataprep/how-to-guides/datastore.ipynb
deleted file mode 100644
index d6c99a0e..00000000
--- a/work-with-data/dataprep/how-to-guides/datastore.ipynb
+++ /dev/null
@@ -1,215 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/datastore.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Reading from and Writing to Datastores"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "A datastore is a reference that points to an Azure storage service like a blob container for example. It belongs to a workspace and a workspace can have many datastores.\n",
-        "\n",
-        "A data path points to a path on the underlying Azure storage service the datastore references. For example, given a datastore named `blob` that points to an Azure blob container, a data path can point to `/test/data/titanic.csv` in the blob container."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Read data from Datastore"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep supports reading data from a `Datastore` or a `DataPath` or a `DataReference`. \n",
-        "\n",
-        "Passing in a datastore into all the `read_*` methods of Data Prep will result in reading everything in the underlying Azure storage service. To read a specific folder or file in the underlying storage, you have to pass in a data reference."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace, Datastore\n",
-        "from azureml.data.datapath import DataPath\n",
-        "\n",
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "First, get or create a workspace. Feel free to replace `subscription_id`, `resource_group`, and `workspace_name` with other values."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "subscription_id = '35f16a99-532a-4a47-9e93-00305f6c40f2'\n",
-        "resource_group = 'DataStoreTest'\n",
-        "workspace_name = 'dataprep-centraleuap'\n",
-        "\n",
-        "workspace = Workspace(subscription_id=subscription_id, resource_group=resource_group, workspace_name=workspace_name)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "workspace.datastores"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can now read a crime data set from the datastore. If you are using your own workspace, the `crime0-10.csv` will not be there by default. You will have to upload the data to the datastore yourself."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "datastore = Datastore(workspace=workspace, name='dataprep_blob')\n",
-        "dflow = dprep.read_csv(path=datastore.path('crime0-10.csv'))\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also read from an Azure SQL database. To do that, you will first get an Azure SQL database datastore instance and pass it to Data Prep for reading."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "datastore = Datastore(workspace=workspace, name='test_sql')\n",
-        "dflow_sql = dprep.read_sql(data_source=datastore, query='SELECT * FROM team')\n",
-        "dflow_sql.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also read from a PostgreSQL database. To do that, you will first get a PostgreSQL database datastore instance and pass it to Data Prep for reading."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "datastore = Datastore(workspace=workspace, name='postgre_test')\n",
-        "dflow_sql = dprep.read_postgresql(data_source=datastore, query='SELECT * FROM public.people')\n",
-        "dflow_sql.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Write data to Datastore"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also write a dataflow to a datastore. The code below will write the file you read in earlier to the folder in the datastore."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dest_datastore = Datastore(workspace, 'dataprep_blob_key')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow.write_to_csv(directory_path=dest_datastore.path('output/crime0-10')).run_local()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now you can read all the files in the `dataprep_adls` datastore which references an Azure Data Lake store."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "datastore = Datastore(workspace=workspace, name='dataprep_adls')\n",
-        "dflow_adls = dprep.read_csv(path=DataPath(datastore, path_on_datastore='/input/crime0-10.csv'))\n",
-        "dflow_adls.head(5)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/derive-column-by-example.ipynb b/work-with-data/dataprep/how-to-guides/derive-column-by-example.ipynb
deleted file mode 100644
index 5d1db4ee..00000000
--- a/work-with-data/dataprep/how-to-guides/derive-column-by-example.ipynb
+++ /dev/null
@@ -1,187 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/derive-column-by-example.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Derive Column By Example\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "One of the more advanced tools in Data Prep is the ability to derive columns by providing examples of desired results and letting Data Prep generate code to achieve the intended derivation."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dprep.read_csv(path = '../data/crime-spring.csv')\n",
-        "df = dflow.head(5)\n",
-        "df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "As you can see, this is a fairly simple file, but let's assume that we need to be able to join this with a dataset where date and time come in a format 'Apr 4, 2016 | 10PM-12AM'.\n",
-        "\n",
-        "Let's wrangle the data into the shape we need."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder = dflow.builders.derive_column_by_example(source_columns = ['Date'], new_column_name = 'date_timerange')\n",
-        "builder.add_example(source_data = df.iloc[0], example_value = 'Apr 4, 2016 10PM-12AM')\n",
-        "builder.preview() # will preview top 10 rows"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The code above first creates a builder for the derived column by providing an array of source columns to consider ('DATE') and name for the new column to be added.\n",
-        "\n",
-        "Then, we provide the first example by passing in the first row (index 0) of the DataFrame printed above and giving an expected value for the derived column.\n",
-        "\n",
-        "Finally, we call `builder.preview()` and observe the derived column next to the source column."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Everything looks good here. However, we just noticed that it's not quite what we wanted. We forgot to separate date and time range by '|' to generate the format we need.\n",
-        "\n",
-        "To fix that, we will add another example. This time, instead of passing in a row from the preview, we just construct a dictionary of column name to value for the source_data parameter."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.add_example(source_data = {'Date': '4/15/2016 10:00'}, example_value = 'Apr 15, 2016 | 10AM-12PM')\n",
-        "builder.preview()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This clearly had negative effects, as now the only rows that have any values in derived column are the ones that match exactly with the examples we have provided.\n",
-        "\n",
-        "Let's look at the examples:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "examples = builder.list_examples()\n",
-        "examples"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Here we can see that we have provided inconsistent examples. To fix the issue, we need to replace the first example with a correct one (including '|' between date and time).\n",
-        "\n",
-        "We can achieve this by deleting examples that are incorrect (by either passing in example_row from examples DataFrame, or by just passing in example_id value) and then adding new modified examples back."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.delete_example(example_id = -1)\n",
-        "builder.add_example(examples.iloc[0], 'Apr 4, 2016 | 10PM-12AM')\n",
-        "builder.preview()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now this looks correct and we can finally call to_dataflow() on the builder, which would return a dataflow with the desired derived columns added."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = builder.to_dataflow()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "df = dflow.to_pandas_dataframe()\n",
-        "df"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/external-references.ipynb b/work-with-data/dataprep/how-to-guides/external-references.ipynb
deleted file mode 100644
index 579d9087..00000000
--- a/work-with-data/dataprep/how-to-guides/external-references.ipynb
+++ /dev/null
@@ -1,118 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/external-references.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# External References\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In addition to opening existing Dataflows in code and modifying them, it is also possible to create and persist Dataflows that reference another Dataflow that has been persisted to a .dprep file. In this case, executing this Dataflow will load and execute the referenced Dataflow dynamically, and then execute the steps in the referencing Dataflow."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To demonstrate, we will create a Dataflow that loads and transforms some data. After that, we will persist this Dataflow to disk. To learn more about saving and opening .dprep files, see: [Opening and Saving Dataflows](./open-save-dataflows.ipynb)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "import tempfile\n",
-        "import os\n",
-        "\n",
-        "dflow = dprep.auto_read_file('../data/crime.txt')\n",
-        "dflow = dflow.drop_errors(['Column7', 'Column8', 'Column9'], dprep.ColumnRelationship.ANY)\n",
-        "dflow_path = os.path.join(tempfile.gettempdir(), 'package.dprep')\n",
-        "dflow.save(dflow_path)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now that we have a .dprep file, we can create a new Dataflow that references it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_new = dprep.Dataflow.reference(dprep.ExternalReference(dflow_path))\n",
-        "dflow_new.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "When executed, the new Dataflow returns the same results as the one we saved to the .dprep file. Since this reference is resolved on execution, updating the referenced Dataflow results in the changes being visible when re-executing the referencing Dataflow."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.take(5)\n",
-        "dflow.save(dflow_path)\n",
-        "\n",
-        "dflow_new.head(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "As we can see, even though we did not modify `dflow_new`, it now returns only 5 records, as the referenced Dataflow was updated with the result from `dflow.take(5)`."
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/filtering.ipynb b/work-with-data/dataprep/how-to-guides/filtering.ipynb
deleted file mode 100644
index 545fd3ca..00000000
--- a/work-with-data/dataprep/how-to-guides/filtering.ipynb
+++ /dev/null
@@ -1,220 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/filtering.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Filtering\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Azure ML Data Prep has the ability to filter out columns or rows using `Dataflow.drop_columns` or `Dataflow.filter`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# initial set up\n",
-        "import azureml.dataprep as dprep\n",
-        "from datetime import datetime\n",
-        "dflow = dprep.read_csv(path='../data/crime-spring.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Filtering columns"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To filter columns, use `Dataflow.drop_columns`. This method takes a list of columns to drop or a more complex argument called `ColumnSelector`."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Filtering columns with list of strings"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In this example, `drop_columns` takes a list of strings. Each string should exactly match the desired column to drop."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.drop_columns(['ID', 'Location Description', 'Ward', 'Community Area', 'FBI Code'])\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Filtering columns with regex"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Alternatively, a `ColumnSelector` can be used to drop columns that match a regex expression. In this example, we drop all the columns that match the expression `Column*|.*longitud|.*latitude`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.drop_columns(dprep.ColumnSelector('Column*|.*longitud|.*latitude', True, True))\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Filtering rows"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To filter rows, use `DataFlow.filter`. This method takes an `Expression` as an argument, and returns a new dataflow with the rows in which the expression evaluates to `True`. Expressions are built by indexing the `Dataflow` with a column name (`dataflow['myColumn']`) and regular operators (`>`, `<`, `>=`, `<=`, `==`, `!=`)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Filtering rows with simple expressions"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Index into the Dataflow specifying the column name as a string argument `dataflow['column_name']` and in combination with one of the following standard operators `>, <, >=, <=, ==, !=`, build an expression such as `dataflow['District'] > 9`.  Finally, pass the built expression into the `Dataflow.filter` function.\n",
-        "\n",
-        "In this example, `dataflow.filter(dataflow['District'] > 9)` returns a new dataflow with the rows in which the value of \"District\" is greater than '10' \n",
-        "\n",
-        "*Note that \"District\" is first converted to numeric, which allows us to build an expression comparing it against other numeric values.*"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.to_number(['District'])\n",
-        "dflow = dflow.filter(dflow['District'] > 9)\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Filtering rows with complex expressions"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To filter using complex expressions, combine one or more simple expressions with the operators `&`, `|`, and `~`. Please note that the precedence of these operators is lower than that of the comparison operators; therefore, you'll need to use parentheses to group clauses together. \n",
-        "\n",
-        "In this example, `Dataflow.filter` returns a new dataflow with the rows in which \"Primary Type\" equals 'DECEPTIVE PRACTICE' and \"District\" is greater than or equal to '10'."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.to_number(['District'])\n",
-        "dflow = dflow.filter((dflow['Primary Type'] == 'DECEPTIVE PRACTICE') & (dflow['District'] >= 10))\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "It is also possible to filter rows combining more than one expression builder to create a nested expression.\n",
-        "\n",
-        "*Note that `'Date'` and `'Updated On'` are first converted to datetime, which allows us to build an expression comparing it against other datetime values.*"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.to_datetime(['Date', 'Updated On'], ['%Y-%m-%d %H:%M:%S'])\n",
-        "dflow = dflow.to_number(['District', 'Y Coordinate'])\n",
-        "comparison_date = datetime(2016,4,13)\n",
-        "dflow = dflow.filter(\n",
-        "    ((dflow['Date'] > comparison_date) | (dflow['Updated On'] > comparison_date))\n",
-        "    | ((dflow['Y Coordinate'] > 1900000) & (dflow['District'] > 10.0)))\n",
-        "dflow.head(5)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/fuzzy-group.ipynb b/work-with-data/dataprep/how-to-guides/fuzzy-group.ipynb
deleted file mode 100644
index 53f309a7..00000000
--- a/work-with-data/dataprep/how-to-guides/fuzzy-group.ipynb
+++ /dev/null
@@ -1,211 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/fuzzy-group.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Fuzzy Grouping\n",
-        "\n",
-        "Unprepared data often represents the same entity with multiple values; examples include different spellings, varying capitalizations, and abbreviations. This is common when working with data gathered from multiple sources or through human input. One way to canonicalize and reconcile these variants is to use Data Prep's fuzzy_group_column (also known as \"text clustering\") functionality.\n",
-        "\n",
-        "Data Prep inspects a column to determine clusters of similar values. A new column is added in which clustered values are replaced with the canonical value of its cluster, thus significantly reducing the number of distinct values. You can control the degree of similarity required for values to be clustered together, override canonical form, and set clusters if automatic clustering did not provide the desired results.\n",
-        "\n",
-        "Let's explore the capabilities of `fuzzy_group_column` by first reading in a dataset and inspecting it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dprep.read_json(path='../data/json.json')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "As you can see above, the column `inspections.business.city` contains several forms of the city name \"San Francisco\".\n",
-        "Let's add a column with values replaced by the automatically detected canonical form. To do so call fuzzy_group_column() on an existing Dataflow:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_clean = dflow.fuzzy_group_column(source_column='inspections.business.city',\n",
-        "                                       new_column_name='city_grouped',\n",
-        "                                       similarity_threshold=0.8,\n",
-        "                                       similarity_score_column_name='similarity_score')\n",
-        "dflow_clean.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The arguments `source_column` and `new_column_name` are required, whereas the others are optional.\n",
-        "If `similarity_threshold` is provided, it will be used to control the required similarity level for the values to be grouped together.\n",
-        "If `similarity_score_column_name` is provided, a second new column will be added to show similarity score between every pair of original and canonical values.\n",
-        "\n",
-        "In the resulting data set, you can see that all the different variations of representing \"San Francisco\" in the data were normalized to the same string, \"San Francisco\".\n",
-        "\n",
-        "But what if you want more control over what gets grouped, what doesn't, and what the canonical value should be? \n",
-        "\n",
-        "To get more control over grouping, canonical values, and exceptions, you need to use the `FuzzyGroupBuilder` class.\n",
-        "Let's see what it has to offer below:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder = dflow.builders.fuzzy_group_column(source_column='inspections.business.city',\n",
-        "                                            new_column_name='city_grouped',\n",
-        "                                            similarity_threshold=0.8,\n",
-        "                                            similarity_score_column_name='similarity_score')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# calling learn() to get fuzzy groups\n",
-        "builder.learn()\n",
-        "builder.groups"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Here you can see that `fuzzy_group_column` detected one group with four values that all map to \"San Francisco\" as the canonical value.\n",
-        "You can see the effects of changing the similarity threshold next:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.similarity_threshold = 0.9\n",
-        "builder.learn()\n",
-        "builder.groups"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now that you are using a similarity threshold of `0.9`, two distinct groups of values are generated.\n",
-        "\n",
-        "Let's tweak some of the detected groups before completing the builder and getting back the Dataflow with the resulting fuzzy grouped column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.similarity_threshold = 0.8\n",
-        "builder.learn()\n",
-        "groups = builder.groups\n",
-        "groups"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# change the canonical value for the first group\n",
-        "groups[0]['canonicalValue'] = 'SANFRAN'\n",
-        "duplicates = groups[0]['duplicates']\n",
-        "# remove the last duplicate value from the cluster\n",
-        "duplicates = duplicates[:-1]\n",
-        "# assign modified duplicate array back\n",
-        "groups[0]['duplicates'] = duplicates\n",
-        "# assign modified groups back to builder\n",
-        "builder.groups = groups\n",
-        "builder.groups"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Here, the canonical value is modified to be used for the single fuzzy group and removed 'S.F.' from this group's duplicates list.\n",
-        "\n",
-        "You can mutate the copy of the `groups` list from the builder (be careful to keep the structure of objects inside this list). After getting the desired groups in the list, you can update the builder with it.\n",
-        "\n",
-        "Now you can get a dataflow with the FuzzyGroup step in it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_clean = builder.to_dataflow()\n",
-        "\n",
-        "df = dflow_clean.to_pandas_dataframe()\n",
-        "df"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/impute-missing-values.ipynb b/work-with-data/dataprep/how-to-guides/impute-missing-values.ipynb
deleted file mode 100644
index a1b9e46e..00000000
--- a/work-with-data/dataprep/how-to-guides/impute-missing-values.ipynb
+++ /dev/null
@@ -1,147 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/impute-missing-values.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Impute missing values\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Azure ML Data Prep has the ability to impute missing values in specified columns. In this case, we will attempt to impute the missing _Latitude_ and _Longitude_ values in the input data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# loading input data\n",
-        "dflow = dprep.read_csv(path= '../data/crime-spring.csv')\n",
-        "dflow = dflow.keep_columns(['ID', 'Arrest', 'Latitude', 'Longitude'])\n",
-        "dflow = dflow.to_number(['Latitude', 'Longitude'])\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The third record from input data has _Latitude_ and _Longitude_ missing. To impute those missing values, we can use `ImputeMissingValuesBuilder` to learn a fixed program which imputes the columns with either a calculated `MIN`, `MAX` or `MEAN` value or a `CUSTOM` value. When `group_by_columns` is specified, missing values will be imputed by group with `MIN`, `MAX` and `MEAN` calculated per group."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Firstly, let us quickly see check the `MEAN` value of _Latitude_ column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_mean = dflow.summarize(group_by_columns=['Arrest'],\n",
-        "                       summary_columns=[dprep.SummaryColumnsValue(column_id='Latitude',\n",
-        "                                                                 summary_column_name='Latitude_MEAN',\n",
-        "                                                                 summary_function=dprep.SummaryFunction.MEAN)])\n",
-        "dflow_mean = dflow_mean.filter(dprep.col('Arrest') == 'FALSE')\n",
-        "dflow_mean.head(1)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The `MEAN` value of _Latitude_ looks good. So we will impute _Latitude_ with it. As for `Longitude`, we will impute it using `42` based on external knowledge."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# impute with MEAN\n",
-        "impute_mean = dprep.ImputeColumnArguments(column_id='Latitude',\n",
-        "                                          impute_function=dprep.ReplaceValueFunction.MEAN)\n",
-        "# impute with custom value 42\n",
-        "impute_custom = dprep.ImputeColumnArguments(column_id='Longitude',\n",
-        "                                            custom_impute_value=42)\n",
-        "# get instance of ImputeMissingValuesBuilder\n",
-        "impute_builder = dflow.builders.impute_missing_values(impute_columns=[impute_mean, impute_custom],\n",
-        "                                                   group_by_columns=['Arrest'])\n",
-        "# call learn() to learn a fixed program to impute missing values\n",
-        "impute_builder.learn()\n",
-        "# call to_dataflow() to get a dataflow with impute step added\n",
-        "dflow_imputed = impute_builder.to_dataflow()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# check impute result\n",
-        "dflow_imputed.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "As the result above, the missing _Latitude_ has been imputed with the `MEAN` value of `Arrest=='false'` group, and the missing _Longitude_ has been imputed with `42`."
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/join.ipynb b/work-with-data/dataprep/how-to-guides/join.ipynb
deleted file mode 100644
index 2f8c2e47..00000000
--- a/work-with-data/dataprep/how-to-guides/join.ipynb
+++ /dev/null
@@ -1,265 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/join.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Join\n",
-        "\n",
-        "In Data Prep you can easily join two Dataflows."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "First, get the left side of the data into a shape that is ready for the join."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# get the first Dataflow and derive desired key column\n",
-        "dflow_left = dprep.read_csv(path='https://dpreptestfiles.blob.core.windows.net/testfiles/BostonWeather.csv')\n",
-        "dflow_left = dflow_left.derive_column_by_example(source_columns='DATE', new_column_name='date_timerange',\n",
-        "                                                 example_data=[('11/11/2015 0:54', 'Nov 11, 2015 | 12AM-2AM'),\n",
-        "                                                              ('2/1/2015 0:54', 'Feb 1, 2015 | 12AM-2AM'),\n",
-        "                                                              ('1/29/2015 20:54', 'Jan 29, 2015 | 8PM-10PM')])\n",
-        "dflow_left = dflow_left.drop_columns(['DATE'])\n",
-        "\n",
-        "# convert types and summarize data\n",
-        "dflow_left = dflow_left.set_column_types(type_conversions={'HOURLYDRYBULBTEMPF': dprep.TypeConverter(dprep.FieldType.DECIMAL)})\n",
-        "dflow_left = dflow_left.filter(expression=~dflow_left['HOURLYDRYBULBTEMPF'].is_error())\n",
-        "dflow_left = dflow_left.summarize(group_by_columns=['date_timerange'],summary_columns=[dprep.SummaryColumnsValue('HOURLYDRYBULBTEMPF', dprep.api.engineapi.typedefinitions.SummaryFunction.MEAN, 'HOURLYDRYBULBTEMPF_Mean')] )\n",
-        "\n",
-        "# cache the result so the steps above are not executed every time we pull on the data\n",
-        "import os\n",
-        "from pathlib import Path\n",
-        "cache_dir = str(Path(os.getcwd(), 'dataflow-cache'))\n",
-        "dflow_left.cache(directory_path=cache_dir)\n",
-        "dflow_left.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now let's prepare the data for the right side of the join."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# get the second Dataflow and desired key column\n",
-        "dflow_right = dprep.read_csv(path='https://dpreptestfiles.blob.core.windows.net/bike-share/*-hubway-tripdata.csv')\n",
-        "dflow_right = dflow_right.keep_columns(['starttime', 'start station id'])\n",
-        "dflow_right = dflow_right.derive_column_by_example(source_columns='starttime', new_column_name='l_date_timerange',\n",
-        "                                                 example_data=[('2015-01-01 00:21:44', 'Jan 1, 2015 | 12AM-2AM')])\n",
-        "dflow_right = dflow_right.drop_columns('starttime')\n",
-        "\n",
-        "# cache the results\n",
-        "dflow_right.cache(directory_path=cache_dir)\n",
-        "dflow_right.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "There are three ways you can join two Dataflows in Data Prep:\n",
-        "1. Create a `JoinBuilder` object for interactive join configuration.\n",
-        "2. Call ```join()``` on one of the Dataflows and pass in the other along with all other arguments.\n",
-        "3. Call ```Dataflow.join()``` method and pass in two Dataflows along with all other arguments.\n",
-        "\n",
-        "We will explore the builder object as it simplifies the determination of correct arguments. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# construct a builder for joining dataflow_l with dataflow_r\n",
-        "join_builder = dflow_left.builders.join(right_dataflow=dflow_right, left_column_prefix='l', right_column_prefix='r')\n",
-        "\n",
-        "join_builder"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "So far the builder has no properties set except default values.\n",
-        "From here you can set each of the options and preview its effect on the join result or use Data Prep to determine some of them.\n",
-        "\n",
-        "Let's start with determining appropriate column prefixes for left and right side of the join and lists of columns that would not conflict and therefore don't need to be prefixed."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "join_builder.detect_column_info()\n",
-        "join_builder"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can see that Data Prep has performed a pull on both Dataflows to determine the column names in them. Given that `dataflow_r` already had a column starting with `l_` new prefix got generated which would not collide with any column names that are already present.\n",
-        "Additionally columns in each Dataflow that won't conflict during join would remain unprefixed.\n",
-        "This apprach to column naming is crucial for join robustness to schema changes in the data. Let's say that at some time in future the data consumed by left Dataflow will also have `l_date_timerange` column in it.\n",
-        "Configured as above the join will still run as expected and the new column will be prefixed with `l2_` ensuring that ig column `l_date_timerange` was consumed by some other future transformation it remains unaffected.\n",
-        "\n",
-        "Note: `KEY_generated` is appended to both lists and is reserved for Data Prep use in case Autojoin is performed.\n",
-        "\n",
-        "### Autojoin\n",
-        "Autojoin is a Data prep feature that determines suitable join arguments given data on both sides. In some cases Autojoin can even derive a key column from a number of available columns in the data.\n",
-        "Here is how you can use Autojoin:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# generate join suggestions\n",
-        "join_builder.generate_suggested_join()\n",
-        "\n",
-        "# list generated suggestions\n",
-        "join_builder.list_join_suggestions()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now let's select the first suggestion and preview the result of the join."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# apply first suggestion\n",
-        "join_builder.apply_suggestion(0)\n",
-        "\n",
-        "join_builder.preview(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now, get our new joined Dataflow."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_autojoined = join_builder.to_dataflow().drop_columns(['l_date_timerange'])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Joining two Dataflows without pulling the data\n",
-        "\n",
-        "If you don't want to pull on data and know what join should look like, you can always use the join method on the Dataflow."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_joined = dprep.Dataflow.join(left_dataflow=dflow_left,\n",
-        "                                      right_dataflow=dflow_right,\n",
-        "                                      join_key_pairs=[('date_timerange', 'l_date_timerange')],\n",
-        "                                      left_column_prefix='l2_',\n",
-        "                                      right_column_prefix='r_')\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_joined.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_joined = dflow_joined.filter(expression=dflow_joined['r_start station id'] == '67')\n",
-        "df = dflow_joined.to_pandas_dataframe()\n",
-        "df"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.5"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/label-encoder.ipynb b/work-with-data/dataprep/how-to-guides/label-encoder.ipynb
deleted file mode 100644
index bc7b78c1..00000000
--- a/work-with-data/dataprep/how-to-guides/label-encoder.ipynb
+++ /dev/null
@@ -1,168 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/label-encoder.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Label Encoder\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep has the ability to encode labels with values between 0 and (number of classes - 1) using `label_encode`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "from datetime import datetime\n",
-        "dflow = dprep.read_csv(path='../data/crime-spring.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To use `label_encode` from a Dataflow, simply specify the source column and the new column name. `label_encode` will figure out all the distinct values or classes in the source column, and it will return a new Dataflow with a new column containing the labels."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.label_encode(source_column='Primary Type', new_column_name='Primary Type Label')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To have more control over the encoded labels, create a builder with `dataflow.builders.label_encode`.\n",
-        "The builder allows you to preview and modify the encoded labels before generating a new Dataflow with the results. \n",
-        "To get started, create a builder object with `dataflow.builders.label_encode` specifying the source column and the new column name.  "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder = dflow.builders.label_encode(source_column='Location Description', new_column_name='Location Description Label')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To generate the encoded labels, call the `learn` method on the builder object:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.learn()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To check the result, access the generated labels through the property `encoded_labels`:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.encoded_labels"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To modify the generated results, just assign a new value to `encoded_labels`. The following example adds a missing label not found in the sample data. `builder.encoded_labels` is saved into a variable `encoded_labels`, modified, and assigned back to `builder.encoded_labels`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "encoded_labels = builder.encoded_labels\n",
-        "encoded_labels['TOWNHOUSE'] = 6\n",
-        "\n",
-        "builder.encoded_labels = encoded_labels\n",
-        "builder.encoded_labels"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once the desired results are achieved, call `builder.to_dataflow` to get the new Dataflow with the encoded labels."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dataflow = builder.to_dataflow()\n",
-        "dataflow.head(5)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/min-max-scaler.ipynb b/work-with-data/dataprep/how-to-guides/min-max-scaler.ipynb
deleted file mode 100644
index a7e5fd65..00000000
--- a/work-with-data/dataprep/how-to-guides/min-max-scaler.ipynb
+++ /dev/null
@@ -1,239 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/min-max-scaler.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Min-Max Scaler\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The min-max scaler scales all values in a column to a desired range (typically [0, 1]). This is also known as feature scaling or unity-based normalization. Min-max scaling is commonly used to normalize numeric columns in a data set for machine learning algorithms."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "First, load a data set containing information about crime in Chicago. Keep only a few columns."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dprep.read_csv('../data/crime-spring.csv')\n",
-        "dflow = dflow.keep_columns(columns=['ID', 'District', 'FBI Code'])\n",
-        "dflow = dflow.to_number(columns=['District', 'FBI Code'])\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Using `get_profile()`, you can see the shape of the numeric columns such as the minimum, maximum, count, and number of error values."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow.get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To apply min-max scaling, call the function `min_max_scaler` on the Dataflow and specify the column name. This will trigger a full data scan over the column to determine the min and max values and perform the scaling. Note that the min and max values of the column are preserved at this point. If the same dataflow steps are performed over a different dataset, the min-max scaler must be re-executed."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_district = dflow.min_max_scale(column='District')\n",
-        "dflow_district.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Look at the data profile to see that the \"District\" column is now scaled; the min is 0 and the max is 1. Any error values and missing values from the source column are preserved."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_district.get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also specify a custom range for the scaling. Instead of [0, 1], let's choose [-10, 10]."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_district_range = dflow.min_max_scale(column='District', range_min=-10, range_max=10)\n",
-        "dflow_district_range.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In some cases, you may want to manually provide the min and max of the data in the source column. For example, you may want to avoid a full data scan because the dataset is large and we already know the min and max. You can provide the known min and max to the `min_max_scaler` function. The column will be scaled using the provided values. For example, if you want to scale the `FBI Code` column with 6  (`data_min`) becoming 0 (`range_min`), the program will scan the data to get `data_max`, which will become 1 (`range_max`)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_fbi = dflow.min_max_scale(column='FBI Code', data_min=6)\n",
-        "dflow_fbi.get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Using a Min-Max Scaler builder"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "For more flexibility when constructing the arguments for the min-max scaling, you can use a Min-Max Scaler builder."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder = dflow.builders.min_max_scale(column='District')\n",
-        "builder"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Calling `builder.learn()` will trigger a full data scan to see what `data_min` and `data_max` are. You can choose whether to use these values or set custom values."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.learn()\n",
-        "builder"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If you want to provide custom values for any of the arguments, you can update the builder object."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.range_max = 10\n",
-        "builder.data_min = 6\n",
-        "builder"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "When you are satisfied with the arguments, you will call `builder.to_dataflow()` to get the result. Note that the min and max values of the source column is preserved by the builder at this point. If you need to get the true `data_min` and `data_max` values again, you will need to set those arguments on the builder to `None` and then call `builder.learn()` again."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_builder = builder.to_dataflow()\n",
-        "dflow_builder.head(5)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/one-hot-encoder.ipynb b/work-with-data/dataprep/how-to-guides/one-hot-encoder.ipynb
deleted file mode 100644
index 72918540..00000000
--- a/work-with-data/dataprep/how-to-guides/one-hot-encoder.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/one-hot-encoder.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# One Hot Encoder\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Azure ML Data Prep has the ability to perform one hot encoding on a selected column using `one_hot_encode`. The result Dataflow will have a new binary column for each categorical label encountered in the selected column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "dflow = dprep.read_csv(path='../data/crime-spring.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To use `one_hot_encode` from a Dataflow, simply specify the source column. `one_hot_encode` will figure out all the distinct values or categorical labels in the source column using the current data, and it will return a new Dataflow with a new binary column for each categorical label. Note that the categorical labels are remembered in the Dataflow step."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_result = dflow.one_hot_encode(source_column='Location Description')\n",
-        "dflow_result.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "By default, all the new columns will use the `source_column` name as a prefix. However, if you would like to specify your own prefix, simply pass a `prefix` string as a second parameter."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_result = dflow.one_hot_encode(source_column='Location Description', prefix='LOCATION_')\n",
-        "dflow_result.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To have more control over the categorical labels, create a builder using `dataflow.builders.one_hot_encode`. The builder allows to preview and modify the categorical labels before generating a new Dataflow with the results."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder = dflow.builders.one_hot_encode(source_column='Location Description', prefix='LOCATION_')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To generate the categorical labels, call the `learn` method on the builder object:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.learn()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To preview the categorical labels, simply access them through the property `categorical_labels` on the builder object:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.categorical_labels"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To modify the generated `categorical_labels`, assign a new value to `categorical_labels` or modify the existing one. The following example adds a missing label not found on the sample data to `categorical_labels`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.categorical_labels.append('TOWNHOUSE')\n",
-        "builder.categorical_labels"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once the desired results are achieved, call `builder.to_dataflow` to get the new Dataflow with the encoded labels."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_result = builder.to_dataflow()\n",
-        "dflow_result.head(5)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/open-save-dataflows.ipynb b/work-with-data/dataprep/how-to-guides/open-save-dataflows.ipynb
deleted file mode 100644
index 92064377..00000000
--- a/work-with-data/dataprep/how-to-guides/open-save-dataflows.ipynb
+++ /dev/null
@@ -1,171 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/open-save-dataflows.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Opening and Saving Dataflows\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once you have built a Dataflow, you can save it to a `.dprep` file. This persists all of the information in your Dataflow including steps you've added, examples and programs from by-example steps, computed aggregations, etc.\n",
-        "\n",
-        "You can also open `.dprep` files to access any Dataflows you have previously persisted."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Open\n",
-        "\n",
-        "Use the `open()` method of the Dataflow class to load existing `.dprep` files."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "dflow_path = os.path.join(os.getcwd(), '..', 'data', 'crime.dprep')\n",
-        "print(dflow_path)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import Dataflow"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = Dataflow.open(dflow_path)\n",
-        "head = dflow.head(5)\n",
-        "head"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Edit\n",
-        "\n",
-        "After a Dataflow is loaded, it can be further edited as needed. In this example, a filter is added."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.dataprep import col"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.filter(col('Description') != 'SIMPLE')\n",
-        "head = dflow.head(5)\n",
-        "head"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Save\n",
-        "\n",
-        "Use the `save()` method of the Dataflow class to write out the `.dprep` file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import tempfile\n",
-        "temp_dir = tempfile._get_default_tempdir()\n",
-        "temp_file_name = next(tempfile._get_candidate_names())\n",
-        "temp_dflow_path = os.path.join(temp_dir, temp_file_name + '.dprep')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow.save(temp_dflow_path)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Round-trip\n",
-        "\n",
-        "This illustrates the ability to load the edited Dataflow back in and use it, in this case to get a pandas DataFrame."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_to_open = Dataflow.open(temp_dflow_path)\n",
-        "df = dflow_to_open.to_pandas_dataframe()\n",
-        "df"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if os.path.isfile(temp_dflow_path):\n",
-        "    os.remove(temp_dflow_path)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/quantile-transformation.ipynb b/work-with-data/dataprep/how-to-guides/quantile-transformation.ipynb
deleted file mode 100644
index 883bc5c8..00000000
--- a/work-with-data/dataprep/how-to-guides/quantile-transformation.ipynb
+++ /dev/null
@@ -1,91 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/quantile-transformation.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Quantile Transformation\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "DataPrep has the ability to perform quantile transformation to a numeric column. This transformation can transform the data into a normal or uniform distribution. Values bigger than the learnt boundaries will simply be clipped to the learnt boundaries when applying quantile transformation.\n",
-        "\n",
-        "Let's load a sample of the median income of california households in different suburbs from the 1990 census data. From the data profile, we can see that the minimum value and maximum value is 0.9946 and 15 respectively."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "\n",
-        "dflow = dprep.read_csv(path='../data/median_income.csv').set_column_types(type_conversions={\n",
-        "    'median_income': dprep.TypeConverter(dprep.FieldType.DECIMAL)\n",
-        "})\n",
-        "dflow.get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's now apply quantile transformation to `median_income` and see how that affects the data. We will apply quantile transformation twice, one that maps the data to a Uniform(0, 1) distribution, one that maps it to a Normal(0, 1) distribution.\n",
-        "\n",
-        "From the data profile, we can see that the min and max of the uniform median income is strictly between 0 and 1 and the mean and standard deviation of the normal median income is close to 0 and 1 respectively.\n",
-        "\n",
-        "*Note: for normal distribution, we will clip the values at the ends as the 0th percentile and the 100th percentile are -Inf and Inf respectively.*"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.quantile_transform(source_column='median_income', new_column='median_income_uniform', quantiles_count=5)\n",
-        "dflow = dflow.quantile_transform(source_column='median_income', new_column='median_income_normal', \n",
-        "                           quantiles_count=5, output_distribution=\"Normal\")\n",
-        "dflow.get_profile()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/random-split.ipynb b/work-with-data/dataprep/how-to-guides/random-split.ipynb
deleted file mode 100644
index 4f87af22..00000000
--- a/work-with-data/dataprep/how-to-guides/random-split.ipynb
+++ /dev/null
@@ -1,145 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/random-split.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Random Split\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Azure ML Data Prep provides the functionality of splitting a data set into two. When training a machine learning model, it is often desirable to train the model on a subset of data, then validate the model on a different subset."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The `random_split(percentage, seed=None)` function in Data Prep takes in a Dataflow and randomly splitting it into two distinct subsets (approximately by the percentage specified)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The `seed` parameter is optional. If a seed is not provided, a stable one is generated, ensuring that the results for a specific Dataflow remain consistent. Different calls to `random_split` will receive different seeds."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To demonstrate, you can go through the following example. First, you can read the first 10,000 lines from a file. Since the contents of the file don't matter, just the first two columns can be used for a simple example."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dprep.read_csv(path='https://dpreptestfiles.blob.core.windows.net/testfiles/crime0.csv').take(10000)\n",
-        "dflow = dflow.keep_columns(['ID', 'Date'])\n",
-        "profile = dflow.get_profile()\n",
-        "print('Row count: %d' % (profile.columns['ID'].count))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Next, you can call `random_split` with the percentage set to 10% (the actual split ratio will be an approximation of `percentage`). You can take a look at the row count of the first returned Dataflow. You should see that `dflow_test` has approximately 1,000 rows (10% of 10,000)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "(dflow_test, dflow_train) = dflow.random_split(percentage=0.1)\n",
-        "profile_test = dflow_test.get_profile()\n",
-        "print('Row count of \"test\": %d' % (profile_test.columns['ID'].count))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now you can take a look at the row count of the second returned Dataflow. The row count of `dflow_test` and `dflow_train` sums exactly to 10,000, because `random_split` results in two subsets that make up the original Dataflow."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "profile_train = dflow_train.get_profile()\n",
-        "print('Row count of \"train\": %d' % (profile_train.columns['ID'].count))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To specify a fixed seed, simply provide it to the `random_split` function."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "(dflow_test, dflow_train) = dflow.random_split(percentage=0.1, seed=12345)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/replace-datasource-replace-reference.ipynb b/work-with-data/dataprep/how-to-guides/replace-datasource-replace-reference.ipynb
deleted file mode 100644
index e8d62acf..00000000
--- a/work-with-data/dataprep/how-to-guides/replace-datasource-replace-reference.ipynb
+++ /dev/null
@@ -1,130 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/replace-datasource-replace-reference.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Replace DataSource Reference\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "A common practice when performing DataPrep is to build up a script or set of cleaning operations on a smaller example file locally. This is quicker and easier than dealing with large amounts of data initially.\n",
-        "\n",
-        "After building a Dataflow that performs the desired steps, it's time to run it against the larger dataset, which may be stored in the cloud, or even locally just in a different file. This is where we can use `Dataflow.replace_datasource` to get a Dataflow identical to the one built on the small data, but referencing the newly specified DataSource."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "\n",
-        "dflow = dprep.read_csv('../data/crime-spring.csv')\n",
-        "df = dflow.to_pandas_dataframe()\n",
-        "df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Here we have the first 10 rows of a dataset called 'Crime'. The original dataset is over 100MB (admittedly not that large of a dataset but this is just an example).\n",
-        "\n",
-        "We'll perform a few cleaning operations."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_dropped = dflow.drop_columns(['Location', 'Updated On', 'X Coordinate', 'Y Coordinate', 'Description'])\n",
-        "sctb = dflow_dropped.builders.set_column_types()\n",
-        "sctb.learn(inference_arguments=dprep.InferenceArguments(day_first=False))\n",
-        "dflow_typed = sctb.to_dataflow()\n",
-        "dflow_typed.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now that we have a Dataflow with all our desired steps, we're ready to run against the 'full' dataset stored in Azure Blob.\n",
-        "All we need to do is pass the BlobDataSource into `replace_datasource` and we'll get back an identical Dataflow with the new DataSource substituted in."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_replaced = dflow_typed.replace_datasource(dprep.BlobDataSource('https://dpreptestfiles.blob.core.windows.net/testfiles/crime0.csv'))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "'replaced_dflow' will now pull data from the 168MB (729734 rows) version of Crime0.csv stored in Azure Blob!\n",
-        "\n",
-        "NOTE: Dataflows can also be created by referencing a different Dataflow. Instead of using `replace_datasource`, there is a corresponding `replace_reference` method.\n",
-        "\n",
-        "We should be careful now since pulling all that data down and putting it in a pandas dataframe isn't an ideal way to inspect the result of our Dataflow. So instead, to see that our steps are being applied to all the new data, we can add a `take_sample` step, which will select records at random (based on a given probability) to be returned.\n",
-        "\n",
-        "The probability below takes the ~730000 rows down to a more inspectable ~73, though the number will vary each time `to_pandas_dataframe()` is run, since they are being randomly selected based on the probability."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_random_sample= dflow_replaced.take_sample(probability=0.0001)\n",
-        "sample = dflow_random_sample.to_pandas_dataframe()\n",
-        "sample"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/replace-fill-error.ipynb b/work-with-data/dataprep/how-to-guides/replace-fill-error.ipynb
deleted file mode 100644
index 04dad995..00000000
--- a/work-with-data/dataprep/how-to-guides/replace-fill-error.ipynb
+++ /dev/null
@@ -1,239 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/replace-fill-error.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Replace, Fill, Error\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can use the methods in this notebook to change values in your dataset.\n",
-        "\n",
-        "* <a href='#replace'>replace</a> - use this method to replace a value with another value. You can also use this to replace null with a value, or a value with null\n",
-        "* <a href='#error'>error</a> - use this method to replace a value with an error.\n",
-        "* <a href='#fill_nulls'>fill_nulls</a> - this method lets you fill all nulls in a column with a certain value.\n",
-        "* <a href='#fill_errors'>fill_errors</a> - this method lets you fill all errors in a column with a certain value."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Setup"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dprep.read_csv('../data/crime-spring.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.to_datetime('Date', ['%m/%d/%Y %H:%M'])\n",
-        "dflow = dflow.to_number(['IUCR', 'District', 'FBI Code'])\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Replace <a id='replace'></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### String\n",
-        "Use `replace` to swap a string value with another string value."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.replace('Primary Type', 'THEFT', 'STOLEN')\n",
-        "head = dflow.head(5)\n",
-        "head"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Use `replace` to remove a certain string value from the column, replacing it with null. Note that Pandas shows null values as None."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.replace('Primary Type', 'DECEPTIVE PRACTICE', None)\n",
-        "head = dflow.head(5)\n",
-        "head"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Numeric\n",
-        "Use `replace` to swap a numeric value with another numeric value."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.replace('District', 5, 1)\n",
-        "head = dflow.head(5)\n",
-        "head"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Date\n",
-        "Use `replace` to swap in a new Date for an existing Date in the data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from datetime import datetime, timezone\n",
-        "dflow = dflow.replace('Date', \n",
-        "                 datetime(2016, 4, 15, 9, 0, tzinfo=timezone.utc), \n",
-        "                 datetime(2018, 7, 4, 0, 0, tzinfo=timezone.utc))\n",
-        "head = dflow.head(5)\n",
-        "head"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Error <a id='error'></a>\n",
-        "\n",
-        "The `error` method lets you create Error values. You can pass to this function the value that you want to find, along with the Error code to use in any Errors created."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.error('IUCR', 890, 'Invalid value')\n",
-        "head = dflow.head(5)\n",
-        "head"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Fill Nulls <a id='fill_nulls'></a>\n",
-        "\n",
-        "Use the `fill_nulls` method to replace all null values in columns with another value. This is similar to Panda's fillna() method."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.fill_nulls('Primary Type', 'N/A')\n",
-        "head = dflow.head(5)\n",
-        "head"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Fill Errors <a id='fill_errors'></a>\n",
-        "\n",
-        "Use the `fill_errors` method to replace all error values in columns with another value."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.fill_errors('IUCR', -1)\n",
-        "head = dflow.head(5)\n",
-        "head"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/secrets.ipynb b/work-with-data/dataprep/how-to-guides/secrets.ipynb
deleted file mode 100644
index c77169c9..00000000
--- a/work-with-data/dataprep/how-to-guides/secrets.ipynb
+++ /dev/null
@@ -1,140 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/secrets.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Providing Secrets\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Currently, secrets are only persisted for the lifetime of the engine process. Even if the dataflow is saved to a file, the secrets are not persisted in the dprep file. If you started a new session (i.e. start a new engine process), loaded a dataflow and wanted to run it, you will need to call `use_secrets` to register the required secrets to use during execution, otherwise the execution will fail as the required secrets are not available.\n",
-        "\n",
-        "In this notebook, we will:\n",
-        "1. Loading a previously saved dataflow\n",
-        "2. Call `get_missing_secrets` to determine the missing secrets\n",
-        "3. Call `use_secrets` and pass in the missing secrets to register it with the engine for this session\n",
-        "4. Call `head` to see the a preview of the data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "\n",
-        "import os"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's load the previously saved dataflow."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dprep.Dataflow.open(file_path='../data/secrets.dprep')\n",
-        "dflow"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can call `get_missing_secrets` to see which required secrets are missing in the engine."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow.get_missing_secrets()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can now read the secrets from an environment variable, put it in a secret dictionary, and call `use_secrets` with the secrets. This will register the secrets in the engine so you don't need to provide them again in this session.\n",
-        "\n",
-        "_Note: It is a bad practice to have secrets in files that will be checked into source control._"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "sas = os.environ['SCENARIOS_SECRETS']\n",
-        "secrets = {\n",
-        "    'https://dpreptestfiles.blob.core.windows.net/testfiles/read_csv_duplicate_headers.csv': sas\n",
-        "}\n",
-        "dflow.use_secrets(secrets=secrets)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can now call `head` without passing in `secrets` and the engine will successfully execute. Here is a preview of the data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow.head(5)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/semantic-types.ipynb b/work-with-data/dataprep/how-to-guides/semantic-types.ipynb
deleted file mode 100644
index 266353df..00000000
--- a/work-with-data/dataprep/how-to-guides/semantic-types.ipynb
+++ /dev/null
@@ -1,164 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/semantic-types.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Semantic Types\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Some string values can be recognized as semantic types. For example, email addresses, US zip codes or IP addresses have specific formats that can be recognized, and then split in specific ways.\n",
-        "\n",
-        "When getting a DataProfile you can optionally ask to collect counts of values recognized as semantic types. [`Dataflow.get_profile()`](./data-profile.ipynb) executes the Dataflow, calculates profile information, and returns a newly constructed DataProfile. Semantic type counts can be included in the data profile by calling `get_profile` with the `include_stype_counts` argument set to true.\n",
-        "\n",
-        "The `stype_counts` property of the DataProfile will then include entries for columns where some semantic types were recognized for some values."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "dflow = dprep.read_json(path='../data/json.json')\n",
-        "\n",
-        "profile = dflow.get_profile(include_stype_counts=True)\n",
-        "\n",
-        "print(\"row count: \" + str(profile.row_count))\n",
-        "profile.stype_counts"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To see all the supported semantic types, you can examine the `SType` enumeration. More types will be added over time."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "[t.name for t in dprep.SType]\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can filter the found semantic types down to just those where all non-empty values matched. The `DataProfile.stype_counts` gives a list of semantic type counts for each column, where at least some matches were found. Those lists are in desecending order of count, so here we consider only the first in each list, as that will be the one with the highest count of values that match.\n",
-        "\n",
-        "In this example, the column `inspections.business.postal_code` looks to be a US zip code."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "stypes_counts = profile.stype_counts\n",
-        "all_match = [\n",
-        "    (column, stypes_counts[column][0].stype)\n",
-        "    for column in stypes_counts\n",
-        "    if profile.row_count - profile.columns[column].empty_count == stypes_counts[column][0].count\n",
-        "]\n",
-        "all_match"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can use semantic types to compute new columns. The new columns are the values split up into elements, or canonicalized.\n",
-        "\n",
-        "Here we reduce our data down to just the `postal` column so we can better see what a `split_stype` operation can do."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_postal = dflow.keep_columns(['inspections.business.postal_code']).rename_columns({'inspections.business.postal_code': 'postal'})\n",
-        "dflow_postal.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "With `SType.ZipCode`, values are split into their basic five digit zip code and the plus-four add-on of the Zip+4 format."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_split = dflow_postal.split_stype('postal', dprep.SType.ZIPCODE)\n",
-        "dflow_split.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "`split_stype` also allows you to specify the fields of the stype to use and the name of the new columns. For example, if you just needed to strip the plus four from our zip codes, you could use this."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_no_plus4 = dflow_postal.split_stype('postal', dprep.SType.ZIPCODE, ['zip'], ['zipNoPlus4'])\n",
-        "dflow_no_plus4.head(5)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/split-column-by-example.ipynb b/work-with-data/dataprep/how-to-guides/split-column-by-example.ipynb
deleted file mode 100644
index 9b709207..00000000
--- a/work-with-data/dataprep/how-to-guides/split-column-by-example.ipynb
+++ /dev/null
@@ -1,220 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/split-column-by-example.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Split column by example\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "DataPrep also offers you a way to easily split a column into multiple columns.\n",
-        "The SplitColumnByExampleBuilder class lets you generate a proper split program that will work even when the cases are not trivial, like in example below."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dprep.read_lines(path='../data/crime.txt')\n",
-        "df = dflow.head(10)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "df['Line'].iloc[0]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "As you can see above, you can't split this particular file by space character as it will create too many columns.\n",
-        "That's where split_column_by_example could be quite useful."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder = dflow.builders.split_column_by_example('Line', keep_delimiters=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.preview()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Couple things to take note of here. No examples were given, and yet DataPrep was able to generate quite reasonable split program. \n",
-        "We have passed keep_delimiters=True so we can see all the data split into columns. In practice, though, delimiters are rarely useful, so let's exclude them."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.keep_delimiters = False\n",
-        "builder.preview()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This looks pretty good already, except that one case number is split into 2 columns. Taking the first row as an example, we want to keep case number as \"HY329907\" instead of \"HY\" and \"329907\" seperately.  \n",
-        "If we request generation of suggested examples we will get a list of examples that require input."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "suggestions = builder.generate_suggested_examples()\n",
-        "suggestions"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "suggestions.iloc[0]['Line']"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Having retrieved source value we can now provide an example of desired split.\n",
-        "Notice that we chose not to split date and time but rather keep them together in one column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.add_example(example=(suggestions['Line'].iloc[0], ['10140490','HY329907','7/5/2015  23:50','050XX N NEWLAND AVE','820','THEFT']))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.preview()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "As we can see from the preview, some of the crime types (`Line_6`) do not show up as expected. Let's try to add one more example. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.add_example(example=(df['Line'].iloc[1],['10139776','HY329265','7/5/2015  23:30','011XX W MORSE AVE','460','BATTERY']))\n",
-        "builder.preview()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This looks just like what we need. Let's get a dataflow with splited columns and drop original column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = builder.to_dataflow()\n",
-        "dflow = dflow.drop_columns(['Line'])\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now we have successfully split the data into useful columns through examples. "
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/subsetting-sampling.ipynb b/work-with-data/dataprep/how-to-guides/subsetting-sampling.ipynb
deleted file mode 100644
index d1abb62f..00000000
--- a/work-with-data/dataprep/how-to-guides/subsetting-sampling.ipynb
+++ /dev/null
@@ -1,217 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/subsetting-sampling.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Sampling and Subsetting\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once a Dataflow has been created, it is possible to act on only a subset of the records contained in it. This can help when working with very large datasets or when only a portion of the records is truly relevant."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Head\n",
-        "\n",
-        "The `head` method will take the number of records specified, run them through the transformations in the Dataflow, and then return the result as a Pandas dataframe."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "\n",
-        "dflow = dprep.read_csv('../data/crime_duplicate_headers.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Take\n",
-        "\n",
-        "The `take` method adds a step to the Dataflow that will keep the number of records specified (counting from the beginning) and drop the rest. Unlike `head`, which does not modify the Dataflow, all operations applied on a Dataflow on which `take` has been applied will affect only the records kept."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_top_five = dflow.take(5)\n",
-        "dflow_top_five.to_pandas_dataframe()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Skip\n",
-        "\n",
-        "It is also possible to skip a certain number of records in a Dataflow, such that transformations are only applied after a specific point. Depending on the underlying data source, a Dataflow with a `skip` step might still have to scan through the data in order to skip past the records."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_skip_top_one = dflow_top_five.skip(1)\n",
-        "dflow_skip_top_one.to_pandas_dataframe()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Take Sample\n",
-        "\n",
-        "In addition to taking records from the top of the dataset, it's also possible to take a random sample of the dataset. This is done through the `take_sample(probability, seed=None)` method. This method will scan through all of the records available in the Dataflow and include them based on the probability specified. The `seed` parameter is optional. If a seed is not provided, a stable one is generated, ensuring that the results for a specific Dataflow remain consistent. Different calls to `take_sample` will receive different seeds."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_sampled = dflow.take_sample(0.1)\n",
-        "dflow_sampled.to_pandas_dataframe()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "`skip`, `take`, and `take_sample` can all be combined. With this, we can achieve behaviors like getting a random 10% sample fo the middle N records of a dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "seed = 1\n",
-        "dflow_nested_sample = dflow.skip(1).take(5).take_sample(0.5, seed)\n",
-        "dflow_nested_sample.to_pandas_dataframe()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Take Stratified Sample\n",
-        "Besides sampling all by a probability, we also have stratified sampling, provided the strata and strata weights, the probability to sample each stratum with.\n",
-        "This is done through the `take_stratified_sample(columns, fractions, seed=None)` method.\n",
-        "For all records, we will group each record by the columns specified to stratify, and based on the stratum x weight information in `fractions`, include said record.\n",
-        "\n",
-        "Seed behavior is same as in `take_sample`.\n",
-        "\n",
-        "If a stratum is not specified or the record cannot be grouped by said stratum, we default the weight to sample by to 0 (it will not be included).\n",
-        "\n",
-        "The order of `fractions` must match the order of `columns`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "fractions = {}\n",
-        "fractions[('ASSAULT',)] = 0.5\n",
-        "fractions[('BATTERY',)] = 0.2\n",
-        "fractions[('ARSON',)] = 0.5\n",
-        "fractions[('THEFT',)] = 1.0\n",
-        "\n",
-        "columns = ['Primary Type']\n",
-        "\n",
-        "single_strata_sample = dflow.take_stratified_sample(columns=columns, fractions = fractions, seed = 42)\n",
-        "single_strata_sample.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Stratified sampling on multiple columns is also supported."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "fractions = {}\n",
-        "fractions[('ASSAULT', '560')] = 0.5\n",
-        "fractions[('BATTERY', '460')] = 0.2\n",
-        "fractions[('ARSON', '1020')] = 0.5\n",
-        "fractions[('THEFT', '820')] = 1.0\n",
-        "\n",
-        "columns = ['Primary Type', 'IUCR']\n",
-        "\n",
-        "multi_strata_sample = dflow.take_stratified_sample(columns=columns, fractions = fractions, seed = 42)\n",
-        "multi_strata_sample.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Caching\n",
-        "It is usually a good idea to cache the sampled Dataflow for later uses.\n",
-        "\n",
-        "See [here](cache.ipynb) for more details about caching."
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/summarize.ipynb b/work-with-data/dataprep/how-to-guides/summarize.ipynb
deleted file mode 100644
index 56a37bee..00000000
--- a/work-with-data/dataprep/how-to-guides/summarize.ipynb
+++ /dev/null
@@ -1,590 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/summarize.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Summarize\n",
-        "\n",
-        "Azure ML Data Prep can help summarize your data by providing you a synopsis based on aggregates over specific columns.\n",
-        "\n",
-        "## Table of Contents\n",
-        "[Overview](#overview)<br>\n",
-        "[Summmary Functions](#summary)<br>\n",
-        "* [SummaryFunction.MIN](#min)<br>\n",
-        "* [SummaryFunction.MAX](#max)<br>\n",
-        "* [SummaryFunction.MEAN](#mean)<br>\n",
-        "* [SummaryFunction.MEDIAN](#median)<br>\n",
-        "* [SummaryFunction.VAR](#var)<br>\n",
-        "* [SummaryFunction.SD](#sd)<br>\n",
-        "* [SummaryFunction.COUNT](#count)<br>\n",
-        "* [SummaryFunction.SUM](#sum)<br>\n",
-        "* [SummaryFunction.SKEWNESS](#skewness)<br>\n",
-        "* [SummaryFunction.KURTOSIS](#kurtosis)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"overview\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Overview\n",
-        "Before we drill down into each aggregate function, let us observe `summarize` end to end.\n",
-        "\n",
-        "We will start by reading some data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "dflow = dprep.auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow.head(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Next we count (`SummaryFunction.COUNT`) the number of rows with column ID with non-null values grouped by Primary Type."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_summarize = dflow.summarize(\n",
-        "        summary_columns=[\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='ID',\n",
-        "                summary_column_name='Primary Type ID Counts', \n",
-        "                summary_function=dprep.SummaryFunction.COUNT)],\n",
-        "        group_by_columns=['Primary Type'])\n",
-        "dflow_summarize.head(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If we choose to not group by anything, we will instead get a single record over the entire dataset. Here we will get the number of rows that have the column ID with non-null values."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_summarize_nogroup = dflow.summarize(\n",
-        "        summary_columns=[\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='ID',\n",
-        "                summary_column_name='ID Count', \n",
-        "                summary_function=dprep.SummaryFunction.COUNT)])\n",
-        "dflow_summarize_nogroup.head(1)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Conversely, we can group by multiple columns."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_summarize_2group = dflow.summarize(\n",
-        "        summary_columns=[\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='ID',\n",
-        "                summary_column_name='Primary Type & Location Description ID Counts', \n",
-        "                summary_function=dprep.SummaryFunction.COUNT)],\n",
-        "        group_by_columns=['Primary Type', 'Location Description'])\n",
-        "dflow_summarize_2group.head(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In a similar vein, we can compute multiple aggregates in a single summary. Each aggregate function is independent and it is possible to aggregate the same column multiple times."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_summarize_multi_agg = dflow.summarize(\n",
-        "        summary_columns=[\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='ID',\n",
-        "                summary_column_name='Primary Type ID Counts', \n",
-        "                summary_function=dprep.SummaryFunction.COUNT),\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='ID',\n",
-        "                summary_column_name='Primary Type Min ID', \n",
-        "                summary_function=dprep.SummaryFunction.MIN),\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='Date',\n",
-        "                summary_column_name='Primary Type Max Date', \n",
-        "                summary_function=dprep.SummaryFunction.MAX)],\n",
-        "        group_by_columns=['Primary Type'])\n",
-        "dflow_summarize_multi_agg.head(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If we wanted this summary data back into our original data set, we can make use of `join_back` and optionally `join_back_columns_prefix` for easy naming distinctions. Summary columns will be added to the end. `group_by_columns` is not necessary for using `join_back`, however the behavior will be more like an append instead of a join."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_summarize_join = dflow.summarize(\n",
-        "        summary_columns=[\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='ID',\n",
-        "                summary_column_name='Primary Type ID Counts', \n",
-        "                summary_function=dprep.SummaryFunction.COUNT)],\n",
-        "        group_by_columns=['Primary Type'],\n",
-        "        join_back=True,\n",
-        "        join_back_columns_prefix='New_')\n",
-        "dflow_summarize_join.head(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"summary\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Summary Functions\n",
-        "Here we will go over all the possible aggregates in Data Prep.\n",
-        "The most up to date set of functions can be found by enumerating the `SummaryFunction` enum."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "[x.name for x in dprep.SummaryFunction]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"min\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### SummaryFunction.MIN\n",
-        "Data Prep can aggregate and find the minimum value of a column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "dflow = dprep.auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow_min = dflow.summarize(\n",
-        "        summary_columns=[\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='Date',\n",
-        "                summary_column_name='Primary Type Min Date', \n",
-        "                summary_function=dprep.SummaryFunction.MIN)],\n",
-        "        group_by_columns=['Primary Type'])\n",
-        "dflow_min.head(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"max\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### SummaryFunction.MAX\n",
-        "Data Prep can find the maximum value of a column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "dflow = dprep.auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow_min = dflow.summarize(\n",
-        "        summary_columns=[\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='Date',\n",
-        "                summary_column_name='Primary Type Max Date', \n",
-        "                summary_function=dprep.SummaryFunction.MAX)],\n",
-        "        group_by_columns=['Primary Type'])\n",
-        "dflow_min.head(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"mean\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### SummaryFunction.MEAN\n",
-        "Data Prep can find the statistical mean of a column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "dflow = dprep.auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow_min = dflow.summarize(\n",
-        "        summary_columns=[\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='Latitude',\n",
-        "                summary_column_name='Primary Type Latitude Mean', \n",
-        "                summary_function=dprep.SummaryFunction.MEAN)],\n",
-        "        group_by_columns=['Primary Type'])\n",
-        "dflow_min.head(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"median\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### SummaryFunction.MEDIAN\n",
-        "Data Prep can find the median value of a column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "dflow = dprep.auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow_min = dflow.summarize(\n",
-        "        summary_columns=[\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='Latitude',\n",
-        "                summary_column_name='Primary Type Latitude Median', \n",
-        "                summary_function=dprep.SummaryFunction.MEDIAN)],\n",
-        "        group_by_columns=['Primary Type'])\n",
-        "dflow_min.head(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"var\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### SummaryFunction.VAR\n",
-        "Data Prep can find the statistical variance of a column. We will need more than one data point to calculate this, otherwise we will be unable to give results."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "dflow = dprep.auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow_min = dflow.summarize(\n",
-        "        summary_columns=[\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='Latitude',\n",
-        "                summary_column_name='Primary Type Latitude Variance', \n",
-        "                summary_function=dprep.SummaryFunction.VAR)],\n",
-        "        group_by_columns=['Primary Type'])\n",
-        "dflow_min.head(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Note that despite there being two cases of BATTERY, one of them is missing geographical location, thus only CRIMINAL DAMAGE can yield variance information. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"sd\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### SummaryFunction.SD\n",
-        "Data Prep can find the standard deviation of a column. We will need more than one data point to calculate this, otherwise we will be unable to give results."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "dflow = dprep.auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow_min = dflow.summarize(\n",
-        "        summary_columns=[\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='Latitude',\n",
-        "                summary_column_name='Primary Type Latitude Standard Deviation', \n",
-        "                summary_function=dprep.SummaryFunction.SD)],\n",
-        "        group_by_columns=['Primary Type'])\n",
-        "dflow_min.head(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Similar to when we calculate variance, despite there being two cases of BATTERY, one of them is missing geographical location, thus only CRIMINAL DAMAGE can yield variance information. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"count\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### SummaryFunction.COUNT\n",
-        "Data Prep can count the number of rows that have a column with non-null values."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "dflow = dprep.auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow_min = dflow.summarize(\n",
-        "        summary_columns=[\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='Latitude',\n",
-        "                summary_column_name='Primary Type Latitude Count', \n",
-        "                summary_function=dprep.SummaryFunction.COUNT)],\n",
-        "        group_by_columns=['Primary Type'])\n",
-        "dflow_min.head(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Note that despite there being two cases of BATTERY, one of them is missing geographical location, thus when we group by Primary Type, we only get a count of one for Latitude."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"sum\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### SummaryFunction.SUM\n",
-        "Data Prep can aggregate and sum the values of a column. Our dataset does not have many numerical facts, but here we sum IDs grouped by Primary Type."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "dflow = dprep.auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow_min = dflow.summarize(\n",
-        "        summary_columns=[\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='ID',\n",
-        "                summary_column_name='Primary Type ID Sum', \n",
-        "                summary_function=dprep.SummaryFunction.SUM)],\n",
-        "        group_by_columns=['Primary Type'])\n",
-        "dflow_min.head(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"skewness\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### SummaryFunction.SKEWNESS\n",
-        "Data Prep can calculate the skewness of data in a column. We will need more than one data point to calculate this, otherwise we will be unable to give results."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "dflow = dprep.auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow_min = dflow.summarize(\n",
-        "        summary_columns=[\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='Latitude',\n",
-        "                summary_column_name='Primary Type Latitude Skewness', \n",
-        "                summary_function=dprep.SummaryFunction.SKEWNESS)],\n",
-        "        group_by_columns=['Primary Type'])\n",
-        "dflow_min.head(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"kurtosis\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### SummaryFunction.KURTOSIS\n",
-        "Data Prep can calculate the kurtosis of data in a column. We will need more than one data point to calculate this, otherwise we will be unable to give results."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "dflow = dprep.auto_read_file(path='../data/crime-dirty.csv')\n",
-        "dflow_min = dflow.summarize(\n",
-        "        summary_columns=[\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='Latitude',\n",
-        "                summary_column_name='Primary Type Latitude Kurtosis', \n",
-        "                summary_function=dprep.SummaryFunction.KURTOSIS)],\n",
-        "        group_by_columns=['Primary Type'])\n",
-        "dflow_min.head(10)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/working-with-file-streams.ipynb b/work-with-data/dataprep/how-to-guides/working-with-file-streams.ipynb
deleted file mode 100644
index e92c1e1c..00000000
--- a/work-with-data/dataprep/how-to-guides/working-with-file-streams.ipynb
+++ /dev/null
@@ -1,192 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/working-with-file-streams.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Working With File Streams\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In addition to loading and parsing tabular data (see [here](./data-ingestion.ipynb) for more details), Data Prep also supports a variety of operations on raw file streams. \n",
-        "\n",
-        "File streams are usually created by calling `Dataflow.get_files`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dprep.Dataflow.get_files(path='../data/*.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The result of this operation is a Dataflow with a single column named \"Path\". This column contains values of type `StreamInfo`, each of which represents a different file matched by the search pattern specified when calling `get_files`. The string representation of a `StreamInfo` follows this pattern:\n",
-        "\n",
-        "StreamInfo(_Location_://_ResourceIdentifier_\\[_Arguments_\\])\n",
-        "\n",
-        "Location is the type of storage where the stream is located (e.g. Azure Blob, Local, or ADLS); ResouceIdentifier is the name of the file within that storage, such as a file path; and Arguments is a list of arguments required to load and read the file.\n",
-        "\n",
-        "On their own, `StreamInfo` objects are not particularly useful; however, you can use them as input to other functions."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Retrieving File Names\n",
-        "\n",
-        "In the example above, we matched a set of CSV files by using a search pattern and got back a column with several `StreamInfo` objects, each representing a different file. Now, we will extract the file path and name for each of these values into a new string column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.add_column(expression=dprep.get_stream_name(dflow['Path']),\n",
-        "                         new_column_name='FilePath',\n",
-        "                         prior_column='Path')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The `get_stream_name` function will return the full name of the file referenced by a `StreamInfo`. In the case of a local file, this will be an absolute path. From here, you can use the `derive_column_by_example` method to extract just the file name."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "first_file_path = dflow.head(1)['FilePath'][0]\n",
-        "first_file_name = os.path.basename(first_file_path)\n",
-        "dflow = dflow.derive_column_by_example(new_column_name='FileName',\n",
-        "                                       source_columns=['FilePath'],\n",
-        "                                       example_data=(first_file_path, first_file_name))\n",
-        "dflow = dflow.drop_columns(['FilePath'])\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Writing Streams\n",
-        "\n",
-        "Whenever you have a column containing `StreamInfo` objects, it's possible to write these out to any of the locations Data Prep supports. You can do this by calling `Dataflow.write_streams`:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow.write_streams(streams_column='Path', base_path=dprep.LocalFileOutput('./test_out/')).run_local()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The `base_path` parameter specifies the location the files will be written to. By default, the name of the file will be the resource identifier of the stream with any invalid characters replaced by `_`. In the case of streams referencing local files, this would be the full path of the original file. You can also specify the desired file names by referencing a column containing them:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow.write_streams(streams_column='Path',\n",
-        "                    base_path=dprep.LocalFileOutput('./test_out/'),\n",
-        "                    file_names_column='FileName').run_local()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Using this functionality, you can transfer files from any source to any destination supported by Data Prep. In addition, since the streams are just values in the Dataflow, you can use all of the functionality available.\n",
-        "\n",
-        "Here, for example, we will write out only the files that start with the prefix \"crime-\". The resulting file names will have the prefix stripped and will be written to a folder named \"crime\"."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "prefix = 'crime-'\n",
-        "dflow = dflow.filter(dflow['FileName'].starts_with(prefix))\n",
-        "dflow = dflow.add_column(expression=dflow['FileName'].substring(len(prefix)),\n",
-        "                         new_column_name='CleanName',\n",
-        "                         prior_column='FileName')\n",
-        "dflow.write_streams(streams_column='Path',\n",
-        "                    base_path=dprep.LocalFileOutput('./test_out/crime/'),\n",
-        "                    file_names_column='CleanName').run_local()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/how-to-guides/writing-data.ipynb b/work-with-data/dataprep/how-to-guides/writing-data.ipynb
deleted file mode 100644
index bfbe3865..00000000
--- a/work-with-data/dataprep/how-to-guides/writing-data.ipynb
+++ /dev/null
@@ -1,183 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/writing-data.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Writing Data\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "It is possible to write out the data at any point in a Dataflow. These writes are added as steps to the resulting Dataflow and will be executed every time the Dataflow is executed. Since there are no limitations to how many write steps there are in a pipeline, this makes it easy to write out intermediate results for troubleshooting or to be picked up by other pipelines.\n",
-        "\n",
-        "It is important to note that the execution of each write results in a full pull of the data in the Dataflow. For example, a Dataflow with three write steps will read and process every record in the dataset three times."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Writing to Files"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data can be written to files in any of our supported locations (Local File System, Azure Blob Storage, and Azure Data Lake Storage). In order to parallelize the write, the data is written to multiple partition files. A sentinel file named SUCCESS is also output once the write has completed. This makes it possible to identify when an intermediate write has completed without having to wait for the whole pipeline to complete.\n",
-        "\n",
-        "> When running a Dataflow in Spark, attempting to execute a write to an existing folder will fail. It is important to ensure the folder is empty or use a different target location per execution.\n",
-        "\n",
-        "The following file formats are currently supported:\n",
-        "- Delimited Files (CSV, TSV, etc.)\n",
-        "- Parquet Files"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We'll start by loading data into a Dataflow which will be re-used with different formats."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dprep.auto_read_file('../data/crime.txt')\n",
-        "dflow = dflow.to_number('Column2')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Delimited Files"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Here we create a dataflow with a write step.\n",
-        "\n",
-        "This operation is lazy until we invoke `run_local` (or any operation that forces execution like `to_pandas_dataframe`), only then will we execute the write operation."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_write = dflow.write_to_csv(directory_path=dprep.LocalFileOutput('./test_out/'))\n",
-        "\n",
-        "dflow_write.run_local()\n",
-        "\n",
-        "dflow_written_files = dprep.read_csv('./test_out/part-*')\n",
-        "dflow_written_files.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The data we wrote out contains several errors in the numeric columns due to numbers that we were unable to parse. When written out to CSV, these are replaced with the string \"ERROR\" by default. We can parameterize this as part of our write call. In the same vein, it is also possible to set what string to use to represent null values."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_write_errors = dflow.write_to_csv(directory_path=dprep.LocalFileOutput('./test_out/'), \n",
-        "                                        error='BadData',\n",
-        "                                        na='NA')\n",
-        "dflow_write_errors.run_local()\n",
-        "dflow_written = dprep.read_csv('./test_out/part-*')\n",
-        "dflow_written.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Parquet Files"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Similar to `write_to_csv`, `write_to_parquet` returns a new Dataflow with a Write Parquet Step which hasn't been executed yet.\n",
-        "\n",
-        "Then we run the Dataflow with `run_local`, which executes the write operation."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_write_parquet = dflow.write_to_parquet(directory_path=dprep.LocalFileOutput('./test_parquet_out/'),\n",
-        "                                             error='MiscreantData')\n",
-        "\n",
-        "dflow_write_parquet.run_local()\n",
-        "\n",
-        "dflow_written_parquet = dprep.read_parquet_file('./test_parquet_out/part-*')\n",
-        "dflow_written_parquet.head(5)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/dataprep/tutorials/getting-started/getting-started.ipynb b/work-with-data/dataprep/tutorials/getting-started/getting-started.ipynb
deleted file mode 100644
index 729af0db..00000000
--- a/work-with-data/dataprep/tutorials/getting-started/getting-started.ipynb
+++ /dev/null
@@ -1,441 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Getting started with Azure ML Data Prep SDK\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n",
-        "Wonder how you can make the most of the Azure ML Data Prep SDK? In this \"Getting Started\" guide, we'll demonstrate how to do your normal data wrangling with this SDK and showcase a few highlights that make this SDK shine. Using a sample of this [Kaggle crime dataset](https://www.kaggle.com/currie32/crimes-in-chicago/home) as an example, we'll cover how to:\n",
-        "\n",
-        "* [Read in data](#Read)\n",
-        "* [Profile your data](#Profile)\n",
-        "* [Append rows](#Append)\n",
-        "* [Apply common data science transforms](#Data-science-transforms)\n",
-        "    * [Summarize](#Summarize)\n",
-        "    * [Join](#Join)\n",
-        "    * [Filter](#Filter)\n",
-        "    * [Replace](#Replace)\n",
-        "* [Consume your cleaned dataset](#Consume)\n",
-        "* [Explore advanced features](#Explore)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from IPython.display import display\n",
-        "from os import path\n",
-        "from tempfile import mkdtemp\n",
-        "\n",
-        "import pandas as pd\n",
-        "import azureml.dataprep as dprep\n",
-        "\n",
-        "# Paths for datasets\n",
-        "file_crime_dirty  = '../../data/crime-dirty.csv'\n",
-        "file_crime_spring = '../../data/crime-spring.csv'\n",
-        "file_crime_winter = '../../data/crime-winter.csv'\n",
-        "file_aldermen     = '../../data/chicago-aldermen-2015.csv'\n",
-        "\n",
-        "# Seed\n",
-        "RAND_SEED = 7251"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"Read\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Read in data\n",
-        "\n",
-        "Azure ML Data Prep supports many different file reading formats (i.e. CSV, Excel, Parquet) and the ability to infer column types automatically. To see how powerful the `auto_read_file` capability is, let's take a peek at the `dirty-crime.csv`:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dprep.read_csv(path=file_crime_dirty).head(7)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "A common occurrence in many datasets is to have a column of values with commas; in our case, the last column represents location in the form of longitude-latitude pair. The default CSV reader interprets this comma as a delimiter and thus splits the data into two columns. Furthermore, it incorrectly reads in the header as the column name. Normally, we would need to `skip` the header and specify the delimiter as `|`, but our `auto_read_file` eliminates that work:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "crime_dirty = dprep.auto_read_file(path=file_crime_dirty)\n",
-        "\n",
-        "crime_dirty.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "__Advanced features:__ if you'd like to specify the file type and adjust how you want to read files in, you can see the list of our specialized file readers and how to use them [here](../../how-to-guides/data-ingestion.ipynb)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"Profile\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Profile your data\n",
-        "\n",
-        "Let's understand what our data looks like. Azure ML Data Prep facilitates this process by offering data profiles that help us glimpse into column types and column summary statistics. Notice that our auto file reader automatically guessed the column type:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "crime_dirty.get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"Append\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Append rows\n",
-        "\n",
-        "What if your data is split across multiple files? We support the ability to append multiple datasets column-wise and row-wise. Here, we demonstrate how you can coalesce datasets row-wise:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Datasets with the same schema as crime_dirty\n",
-        "crime_winter = dprep.auto_read_file(path=file_crime_winter)\n",
-        "crime_spring = dprep.auto_read_file(path=file_crime_spring)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "crime = (crime_dirty.append_rows(dataflows=[crime_winter, crime_spring]))\n",
-        "\n",
-        "crime.take_sample(probability=0.25, seed=RAND_SEED).head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "__Advanced features:__ you can learn how to append column-wise and how to deal with appending data with different schemas [here](../../how-to-guides/append-columns-and-rows.ipynb)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"Data-science-transforms\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Apply common data science transforms\n",
-        "\n",
-        "Azure ML Data Prep supports almost all common data science transforms found in other industry-standard data science libraries. Here, we'll explore the ability to `summarize`, `join`, `filter`, and `replace`. \n",
-        "\n",
-        "__Advanced features:__\n",
-        "* We also provide \"smart\" transforms not found in pandas that use machine learning to [derive new columns](../../how-to-guides/derive-column-by-example.ipynb), [split columns](../../how-to-guides/split-column-by-example.ipynb), and [fuzzy grouping](../../how-to-guides/fuzzy-group.ipynb).\n",
-        "* Finally, we also help featurize your dataset to prepare it for machine learning; learn more about our featurizers like [one-hot encoder](../../how-to-guides/one-hot-encoder.ipynb), [label encoder](../../how-to-guides/label-encoder.ipynb), [min-max scaler](../../how-to-guides/min-max-scaler.ipynb), and [random (train-test) split](../../how-to-guides/random-split.ipynb).\n",
-        "* Our complete list of example Notebooks for transforms can be found in our [How-to Guides](../../how-to-guides)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"Summarize\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Summarize\n",
-        "\n",
-        "Let's see which wards had the most crimes in our sample dataset:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "crime_summary = (crime\n",
-        "    .summarize(\n",
-        "        summary_columns=[\n",
-        "            dprep.SummaryColumnsValue(\n",
-        "                column_id='ID', \n",
-        "                summary_column_name='total_ward_crimes', \n",
-        "                summary_function=dprep.SummaryFunction.COUNT\n",
-        "            )\n",
-        "        ],\n",
-        "        group_by_columns=['Ward']\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "(crime_summary\n",
-        "     .sort(sort_order=[('total_ward_crimes', True)])\n",
-        "     .head(5)\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"Join\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Join\n",
-        "\n",
-        "Let's annotate each observation with more information about the ward where the crime occurred. Let's do so by joining `crime` with a dataset which lists the current aldermen for each ward:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "aldermen = dprep.auto_read_file(path=file_aldermen)\n",
-        "\n",
-        "aldermen.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "crime.join(\n",
-        "    left_dataflow=crime,\n",
-        "    right_dataflow=aldermen,\n",
-        "    join_key_pairs=[\n",
-        "        ('Ward', 'Ward')\n",
-        "    ]\n",
-        ").head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "__Advanced features:__ [Learn more](../../how-to-guides/join.ipynb) about how you can do all variants of `join`, like inner-, left-, right-, anti-, and semi-joins."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"Filter\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Filter\n",
-        "\n",
-        "Let's look at theft crimes:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "theft = crime.filter(crime['Primary Type'] == 'THEFT')\n",
-        "\n",
-        "theft.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"Replace\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Replace\n",
-        "\n",
-        "Notice that our `theft` dataset has empty strings in column `Location`. Let's replace those with a missing value:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "theft_replaced = (theft\n",
-        "    .replace_na(\n",
-        "        columns=['Location'], \n",
-        "        use_empty_string_as_na=True\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "theft_replaced.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "__Advanced features:__ [Learn more](../../how-to-guides/replace-fill-error.ipynb) about more advanced `replace` and `fill` capabilities."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"Consume\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Consume your cleaned dataset\n",
-        "\n",
-        "Azure ML Data Prep allows you to \"choose your own adventure\" once you're done wrangling. You can:\n",
-        "\n",
-        "1. Write to a pandas dataframe\n",
-        "2. Execute on Spark\n",
-        "3. Consume directly in Azure Machine Learning models\n",
-        "\n",
-        "In this quickstart guide, we'll show how you can export to a pandas dataframe.\n",
-        "\n",
-        "__Advanced features:__ \n",
-        "* One of the beautiful features of Azure ML Data Prep is that you only need to write your code once and choose whether to scale up or out.\n",
-        "* You can directly consume your new DataFlow in model builders like Azure Machine Learning's [automated machine learning](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.ipynb)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "theft_replaced.to_pandas_dataframe()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"Explore\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explore advanced features\n",
-        "\n",
-        "Congratulations on finishing your introduction to the Azure ML Data Prep SDK! If you'd like more detailed tutorials on how to construct machine learning datasets or dive deeper into all of its functionality, you can find more information in our detailed notebooks [here](https://github.com/Microsoft/PendletonDocs). There, we cover topics including how to:\n",
-        "\n",
-        "* [Cache your Dataflow to speed up your iterations](../../how-to-guides/cache.ipynb)\n",
-        "* [Add your custom Python transforms](../../how-to-guides/custom-python-transforms.ipynb)\n",
-        "* [Impute missing values](../../how-to-guides/impute-missing-values.ipynb)\n",
-        "* [Sample your data](../../how-to-guides/subsetting-sampling.ipynb)\n",
-        "* [Reference and link between Dataflows](../../how-to-guides/join.ipynb)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/tutorials/getting-started/getting-started.png)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.2"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/datasets/README.md b/work-with-data/datasets/README.md
deleted file mode 100644
index 26b22054..00000000
--- a/work-with-data/datasets/README.md
+++ /dev/null
@@ -1,159 +0,0 @@
-# Azure Machine Learning Datasets (preview)
-
-Azure Machine Learning Datasets (preview) make it easier to access and work with your data. Datasets manage data in various scenarios such as model training and pipeline creation. Using the Azure Machine Learning SDK, you can access underlying storage, explore and prepare data, manage the life cycle of different Dataset definitions, and compare between Datasets used in training and in production.
-
-## Create and Register Datasets
-
-It's easy to create Datasets from either local files, or Azure Datastores.
-
-```Python
-from azureml.core.workspace import Workspace
-from azureml.core.datastore import Datastore
-from azureml.core.dataset import Dataset
-
-datastore_name = 'your datastore name'
-
-# get existing workspace
-workspace = Workspace.from_config()
-
-# get Datastore from the workspace
-dstore = Datastore.get(workspace, datastore_name)
-
-# create an in-memory Dataset on your local machine
-dataset = Dataset.from_delimited_files(dstore.path('data/src/crime.csv'))
-```
-
-To consume Datasets across various scenarios in Azure Machine Learning service such as automated machine learning, model training and pipeline creation, you need to register the Datasets with your workspace. By doing so, you can also share and reuse the Datasets within your organization.
-
-```Python
-dataset = dataset.register(workspace = workspace,
-                           name = 'dataset_crime',
-                           description = 'Training data'
-                           )
-```
-
-## Sampling
-
-Sampling can be particular useful with Datasets that are too large to efficiently analyze in full. It enables data scientists to work with a manageable amount of data to build and train machine learning models. At this time, the [`sample()`](https://docs.microsoft.com//python/api/azureml-core/azureml.core.dataset(class)?view=azure-ml-py#sample-sample-strategy--arguments-) method from the Dataset class supports Top N, Simple Random, and Stratified sampling strategies.
-
-After sampling, you can convert your sampled Dataset to pandas DataFrame for training. By using  the native [`sample()`](https://docs.microsoft.com//python/api/azureml-core/azureml.core.dataset(class)?view=azure-ml-py#sample-sample-strategy--arguments-) method from the Dataset class, you will load the sampled data on the fly instead of loading full data into memory.
-
-### Top N sample
-
-For Top N sampling, the first n records of your Dataset are your sample. This is helpful if you are just trying  to get an idea of what your data records look like or to see what fields are in your data.
-
-```Python
-top_n_sample_dataset = dataset.sample('top_n', {'n': 5})
-top_n_sample_dataset.to_pandas_dataframe()
-```
-
-### Simple random sample
-
-In Simple Random sampling, every member of the data population has an equal chance of being selected as a part of the sample. In the `simple_random` sample strategy, the records from your Dataset are selected based on the probability specified and returns a modified Dataset. The seed parameter is optional.
-
-```Python
-simple_random_sample_dataset = dataset.sample('simple_random', {'probability':0.3, 'seed': seed})
-simple_random_sample_dataset.to_pandas_dataframe()
-```
-
-### Stratified sample
-
-Stratified samples ensure that certain groups of a population are represented in the sample. In the `stratified` sample strategy, the population is divided into strata, or subgroups, based on similarities, and records are randomly selected from each strata according to the strata weights indicated by the `fractions` parameter.
-
-In the following example, we group each record by the specified columns, and include said record based on the strata X weight information in `fractions`. If a strata is not specified or the record cannot be grouped, the default weight to sample is 0.
-
-```Python
-# take 50% of records with `Primary Type` as `THEFT` and 20% of records with `Primary Type` as `DECEPTIVE PRACTICE` into sample Dataset
-fractions = {}
-fractions[('THEFT',)] = 0.5
-fractions[('DECEPTIVE PRACTICE',)] = 0.2
-
-sample_dataset = dataset.sample('stratified', {'columns': ['Primary Type'], 'fractions': fractions, 'seed': seed})
-
-sample_dataset.to_pandas_dataframe()
-```
-
-## Explore with summary statistics
-
- Detect anomalies, missing values, or error counts with the [`get_profile()`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#get-profile-arguments-none--generate-if-not-exist-true--workspace-none--compute-target-none-) method. This function gets the profile and summary statistics of your data, which in turn helps determine the necessary data preparation operations to apply.
-
-```Python
-# get pre-calculated profile
-# if there is no precalculated profile available or the precalculated profile is not up-to-date, this method will generate a new profile of the Dataset
-dataset.get_profile()
-```
-
-||Type|Min|Max|Count|Missing Count|Not Missing Count|Percent missing|Error Count|Empty count|0.1% Quantile|1% Quantile|5% Quantile|25% Quantile|50% Quantile|75% Quantile|95% Quantile|99% Quantile|99.9% Quantile|Mean|Standard Deviation|Variance|Skewness|Kurtosis
--|----|---|---|-----|-------------|-----------------|---------------|-----------|-----------|-------------|-----------|-----------|------------|------------|------------|------------|------------|--------------|----|------------------|--------|--------|--------
-ID|FieldType.INTEGER|1.04986e+07|1.05351e+07|10.0|0.0|10.0|0.0|0.0|0.0|1.04986e+07|1.04992e+07|1.04986e+07|1.05166e+07|1.05209e+07|1.05259e+07|1.05351e+07|1.05351e+07|1.05351e+07|1.05195e+07|12302.7|1.51358e+08|-0.495701|-1.02814
-Case Number|FieldType.STRING|HZ239907|HZ278872|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
-Date|FieldType.DATE|2016-04-04 23:56:00+00:00|2016-04-15 17:00:00+00:00|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
-Block|FieldType.STRING|004XX S KILBOURN AVE|113XX S PRAIRIE AVE|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
-IUCR|FieldType.INTEGER|810|1154|10.0|0.0|10.0|0.0|0.0|0.0|810|850|810|890|1136|1153|1154|1154|1154|1058.5|137.285|18847.2|-0.785501|-1.3543
-Primary Type|FieldType.STRING|DECEPTIVE PRACTICE|THEFT|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
-Description|FieldType.STRING|BOGUS CHECK|OVER $500|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
-Location Description|FieldType.STRING||SCHOOL, PUBLIC, BUILDING|10.0|0.0|10.0|0.0|0.0|1.0||||||||||||||
-Arrest|FieldType.BOOLEAN|False|False|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
-Domestic|FieldType.BOOLEAN|False|False|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
-Beat|FieldType.INTEGER|531|2433|10.0|0.0|10.0|0.0|0.0|0.0|531|531|531|614|1318.5|1911|2433|2433|2433|1371.1|692.094|478994|0.105418|-1.60684
-District|FieldType.INTEGER|5|24|10.0|0.0|10.0|0.0|0.0|0.0|5|5|5|6|13|19|24|24|24|13.5|6.94822|48.2778|0.0930109|-1.62325
-Ward|FieldType.INTEGER|1|48|10.0|0.0|10.0|0.0|0.0|0.0|1|5|1|9|22.5|40|48|48|48|24.5|16.2635|264.5|0.173723|-1.51271
-Community Area|FieldType.INTEGER|4|77|10.0|0.0|10.0|0.0|0.0|0.0|4|8.5|4|24|37.5|71|77|77|77|41.2|26.6366|709.511|0.112157|-1.73379
-FBI Code|FieldType.INTEGER|6|11|10.0|0.0|10.0|0.0|0.0|0.0|6|6|6|6|11|11|11|11|11|9.4|2.36643|5.6|-0.702685|-1.59582
-X Coordinate|FieldType.INTEGER|1.16309e+06|1.18336e+06|10.0|7.0|3.0|0.7|0.0|0.0|1.16309e+06|1.16309e+06|1.16309e+06|1.16401e+06|1.16678e+06|1.17921e+06|1.18336e+06|1.18336e+06|1.18336e+06|1.17108e+06|10793.5|1.165e+08|0.335126|-2.33333
-Y Coordinate|FieldType.INTEGER|1.8315e+06|1.908e+06|10.0|7.0|3.0|0.7|0.0|0.0|1.8315e+06|1.8315e+06|1.8315e+06|1.83614e+06|1.85005e+06|1.89352e+06|1.908e+06|1.908e+06|1.908e+06|1.86319e+06|39905.2|1.59243e+09|0.293465|-2.33333
-Year|FieldType.INTEGER|2016|2016|10.0|0.0|10.0|0.0|0.0|0.0|2016|2016|2016|2016|2016|2016|2016|2016|2016|2016|0|0|NaN|NaN
-Updated On|FieldType.DATE|2016-05-11 15:48:00+00:00|2016-05-27 15:45:00+00:00|10.0|0.0|10.0|0.0|0.0|0.0||||||||||||||
-Latitude|FieldType.DECIMAL|41.6928|41.9032|10.0|7.0|3.0|0.7|0.0|0.0|41.6928|41.6928|41.6928|41.7057|41.7441|41.8634|41.9032|41.9032|41.9032|41.78|0.109695|0.012033|0.292478|-2.33333
-Longitude|FieldType.DECIMAL|-87.6764|-87.6043|10.0|7.0|3.0|0.7|0.0|0.0|-87.6764|-87.6764|-87.6764|-87.6734|-87.6645|-87.6194|-87.6043|-87.6043|-87.6043|-87.6484|0.0386264|0.001492|0.344429|-2.33333
-Location|FieldType.STRING||(41.903206037, -87.676361925)|10.0|0.0|10.0|0.0|0.0|7.0||||||||||||||
-
-
-## Training with Dataset
-
-Now that you have registered your Dataset, you can call up the Dataset and convert it to pandas DataFrame or Spark DataFrame easily in your train.py script.
-
-```Python
-# Sample train.py script
-import azureml.core
-import pandas as pd
-import datetime
-import shutil
-from azureml.core import Workspace, Datastore, Dataset, Experiment, Run
-from sklearn.model_selection import train_test_split
-from azureml.core.compute import ComputeTarget, AmlCompute
-from azureml.core.compute_target import ComputeTargetException
-from sklearn.tree import DecisionTreeClassifier
-
-run = Run.get_context()
-workspace = run.experiment.workspace
-
-# Access Dataset registered with the workspace by name
-dataset_name = 'training_data'
-dataset = Dataset.get(workspace=workspace, name=dataset_name)
-
-ds_def = dataset.get_definition()
-dataset_val, dataset_train = ds_def.random_split(percentage=0.3)
-y_df = dataset_train.keep_columns(['HasDetections']).to_pandas_dataframe()
-x_df = dataset_train.drop_columns(['HasDetections']).to_pandas_dataframe()
-y_val = dataset_val.keep_columns(['HasDetections']).to_pandas_dataframe()
-x_val = dataset_val.drop_columns(['HasDetections']).to_pandas_dataframe()
-
-data = {"train": {"X": x_df, "y": y_df},
-        "validation": {"X": x_val, "y": y_val}}
-
-clf = DecisionTreeClassifier().fit(data["train"]["X"], data["train"]["y"])
-print('Accuracy of Decision Tree classifier on training set: {:.2f}'.format(clf.score(x_df, y_df)))
-print('Accuracy of Decision Tree classifier on validation set: {:.2f}'.format(clf.score(x_val, y_val)))
-```
-
-For an end-to-end tutorial, you may refer to [Dataset tutorial](datasets-tutorial.ipynb). You will learn how to:
-- Explore and prepare data for training the model.
-- Register the Dataset in your workspace for easy access in training.
-- Take snapshots of data to ensure models can be trained with the same data every time.
-- Use registered Dataset in your training script.
-- Create and use multiple Dataset definitions to ensure that updates to the definition don't break existing pipelines/scripts.
-
- 
-
-![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/datasets/README.png) 
\ No newline at end of file
diff --git a/work-with-data/datasets/datasets-tutorial/datasets-tutorial.ipynb b/work-with-data/datasets/datasets-tutorial/datasets-tutorial.ipynb
deleted file mode 100644
index b28adf5c..00000000
--- a/work-with-data/datasets/datasets-tutorial/datasets-tutorial.ipynb
+++ /dev/null
@@ -1,376 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Tutorial: Learn how to use Datasets in Azure ML"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In this tutorial, you learn how to use Azure ML Datasets to train a regression model with the Azure Machine Learning SDK for Python. You will\n",
-        "\n",
-        "* Explore and prepare data for training the model\n",
-        "* Register the Dataset in your workspace to share it with others\n",
-        "* Create and use multiple Dataset definitions to ensure that updates to the definition don't break existing pipelines/scripts\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In this tutorial, you:\n",
-        "\n",
-        "&#x2611; Setup a Python environment and import packages\n",
-        "\n",
-        "&#x2611; Load the Titanic data from your Azure Blob Storage. (The [original data](https://www.kaggle.com/c/titanic/data) can be found on Kaggle)\n",
-        "\n",
-        "&#x2611; Explore and cleanse the data to remove anomalies\n",
-        "\n",
-        "&#x2611; Register the Dataset in your workspace, allowing you to use it in model training \n",
-        "\n",
-        "&#x2611; Make changes to the dataset's definition without breaking the production model or the daily data pipeline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Pre-requisites:\n",
-        "Skip to Set up your development environment to read through the notebook steps, or use the instructions below to get the notebook and run it on Azure Notebooks or your own notebook server. To run the notebook you will need:\n",
-        "\n",
-        "A Python 3.6 notebook server with the following installed:\n",
-        "* The Azure Machine Learning SDK for Python\n",
-        "* The Azure Machine Learning Data Prep SDK for Python\n",
-        "* The tutorial notebook\n",
-        "\n",
-        "Data and train.py script to store in your Azure Blob Storage Account.\n",
-        " * [Titanic data](./train-dataset/Titanic.csv)\n",
-        " * [train.py](./train-dataset/train.py)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To create and register Datasets you need:\n",
-        "\n",
-        "   * An Azure subscription. If you don\u00e2\u20ac\u2122t have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning service](https://aka.ms/AMLFree) today.\n",
-        "\n",
-        "   * An Azure Machine Learning service workspace. See the [Create an Azure Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/setup-create-workspace?branch=release-build-amls).\n",
-        "\n",
-        "   * The Azure Machine Learning SDK for Python (version 1.0.21 or later). To install or update to the latest version of the SDK, see [Install or update the SDK](https://docs.microsoft.com/python/api/overview/azure/ml/install?view=azure-ml-py).\n",
-        "\n",
-        "\n",
-        "For more information on how to set up your workspace, see the [Create an Azure Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/setup-create-workspace?branch=release-build-amls).\n",
-        "\n",
-        "The first part that needs to be done is setting up your python environment. You will need to import all of your python packages including `azureml.dataprep` and `azureml.core.dataset`. Then access your workspace through your Azure subscription and set up your compute target. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "import azureml.core\n",
-        "import pandas as pd\n",
-        "import logging\n",
-        "import os\n",
-        "import shutil\n",
-        "from azureml.core import Workspace, Datastore, Dataset\n",
-        "\n",
-        "# Get existing workspace from config.json file in the same folder as the tutorial notebook\n",
-        "# You can download the config file from your workspace\n",
-        "workspace = Workspace.from_config()\n",
-        "print(\"Workspace\")\n",
-        "print(workspace)\n",
-        "print(\"Compute targets\")\n",
-        "print(workspace.compute_targets)\n",
-        "\n",
-        "# Get compute target that has already been attached to the workspace\n",
-        "# Pick the right compute target from the list of computes attached to your workspace\n",
-        "\n",
-        "compute_target_name = 'datasetBugBash'\n",
-        "remote_compute_target = workspace.compute_targets[compute_target_name]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To load data to your dataset, you will access the data through your datastore. After you create your dataset, you can use `get_profile()` to see your data's statistics.\n",
-        "\n",
-        "We will now upload the [original data](https://www.kaggle.com/c/titanic/data) to the default datastore(blob) within your workspace.."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "datastore = workspace.get_default_datastore()\n",
-        "datastore.upload_files(files=['./train-dataset/Titanic.csv'],\n",
-        "                       target_path='train-dataset/',\n",
-        "                       overwrite=True,\n",
-        "                       show_progress=True)\n",
-        "\n",
-        "dataset = Dataset.auto_read_files(path=datastore.path('train-dataset/Titanic.csv'))\n",
-        "\n",
-        "#Display Dataset Profile of the Titanic Dataset\n",
-        "dataset.get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To predict if a person survived the Titanic's sinking or not, the columns that are relevant to train the model are 'Survived','Pclass', 'Sex','SibSp', and 'Parch'. You can update your dataset's deinition and only keep these columns you will need. You will also need to convert values (\"male\",\"female\") in the \"Sex\" column to 0 or 1, because the algorithm in the train.py file will be using numeric values instead of strings.\n",
-        "\n",
-        "For more examples of preparing data with Datasets, see [Explore and prepare data with the Dataset class](aka.ms/azureml/howto/exploreandpreparedata)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ds_def = dataset.get_definition()\n",
-        "ds_def = ds_def.keep_columns(['Survived','Pclass', 'Sex','SibSp', 'Parch', 'Fare'])\n",
-        "ds_def = ds_def.replace('Sex','male', 0)\n",
-        "ds_def = ds_def.replace('Sex','female', 1)\n",
-        "ds_def.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once you have cleaned your data, you can register your dataset in your workspace. \n",
-        "\n",
-        "Registering your dataset allows you to easily have access to your processed data and share it with other people in your organization using the same workspace. It can be accessed in any notebook or script that is connected to your workspace."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dataset = dataset.update_definition(ds_def, 'Cleaned Data')\n",
-        "\n",
-        "dataset.generate_profile(compute_target='local').get_result()\n",
-        "\n",
-        "dataset_name = 'clean_Titanic_tutorial'\n",
-        "dataset = dataset.register(workspace=workspace,\n",
-        "                           name=dataset_name,\n",
-        "                           description='training dataset',\n",
-        "                           tags = {'year':'2019', 'month':'Apr'},\n",
-        "                           exist_ok=True)\n",
-        "workspace.datasets"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n",
-        "The following code snippet will train your model locally using the train.py script."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Experiment, RunConfiguration\n",
-        "\n",
-        "experiment_name = 'training-datasets'\n",
-        "experiment = Experiment(workspace = workspace, name = experiment_name)\n",
-        "project_folder = './train-dataset/'\n",
-        "\n",
-        "# create a new RunConfig object\n",
-        "run_config = RunConfiguration()\n",
-        "\n",
-        "run_config.environment.python.user_managed_dependencies = True\n",
-        "\n",
-        "from azureml.core import Run\n",
-        "from azureml.core import ScriptRunConfig\n",
-        "\n",
-        "src = ScriptRunConfig(source_directory=project_folder, \n",
-        "                      script='train.py', \n",
-        "                      run_config=run_config) \n",
-        "run = experiment.submit(config=src)\n",
-        "run.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also use the same script with your dataset for your Pipeline's Python Script Step.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.pipeline.core import Pipeline, PipelineData\n",
-        "from azureml.pipeline.steps import PythonScriptStep\n",
-        "from azureml.data.data_reference import DataReference\n",
-        "\n",
-        "trainStep = PythonScriptStep(script_name=\"train.py\",\n",
-        "                             compute_target=remote_compute_target,\n",
-        "                             source_directory=project_folder)\n",
-        "\n",
-        "pipeline = Pipeline(workspace=workspace,\n",
-        "                    steps=trainStep)\n",
-        "\n",
-        "pipeline_run = experiment.submit(pipeline)\n",
-        "pipeline_run.wait_for_completion()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can make changes to the dataset's definition without breaking the production model or the daily data pipeline. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can call get_definitions to see that there are several versions. After each change to a dataset's version, another one is added."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dataset.get_definitions()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dataset = Dataset.get(workspace=workspace, name=dataset_name)\n",
-        "ds_def = dataset.get_definition()\n",
-        "ds_def = ds_def.drop_columns(['Fare'])\n",
-        "dataset = dataset.update_definition(ds_def, 'Dropping Fare as PClass and Fare are strongly correlated')\n",
-        "dataset.generate_profile(compute_target='local').get_result()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Dataset definitions can be deprecated when usage is no longer recommended and a replacement is available. When a deprecated dataset definition is used in an AML Experimentation/Pipeline scenario, a warning message gets returned but execution will not be blocked. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Deprecate dataset definition 1 by the 2nd definition\n",
-        "ds_def = dataset.get_definition('1')\n",
-        "ds_def.deprecate(deprecate_by_dataset_id=dataset._id, deprecated_by_definition_version='2')\n",
-        "dataset.get_definitions()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Dataset definitions can be archived when definitions are not supposed to be used for any reasons (such as underlying data no longer available). When an archived dataset definition is used in an AML Experimentation/Pipeline scenario, execution will be blocked with error. No further actions can be performed on archived Dataset definitions, but the references will be kept intact. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Archive the deprecated dataset definition #1\n",
-        "ds_def = dataset.get_definition('1')\n",
-        "ds_def.archive()\n",
-        "dataset.get_definitions()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also reactivate any defition that you archived for later use."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ds_def = dataset.get_definition('1')\n",
-        "ds_def.reactivate()\n",
-        "dataset.get_definitions()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You have now finished using a dataset from start to finish of your experiment!"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/datasets/datasets-tutorial/datasets-tutorial.png)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "cforbe"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/work-with-data/datasets/datasets-tutorial/train-dataset/Titanic.csv b/work-with-data/datasets/datasets-tutorial/train-dataset/Titanic.csv
deleted file mode 100644
index 5cc466e9..00000000
--- a/work-with-data/datasets/datasets-tutorial/train-dataset/Titanic.csv
+++ /dev/null
@@ -1,892 +0,0 @@
-PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
-1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
-2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
-3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
-4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
-5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
-6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
-7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
-8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
-9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
-10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
-11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
-12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
-13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
-14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
-15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
-16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
-17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
-18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
-19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
-20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
-21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
-22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
-23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
-24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
-25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
-26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
-27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
-28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
-29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
-30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
-31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
-32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
-33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
-34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
-35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
-36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
-37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
-38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
-39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
-40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
-41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
-42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
-43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
-44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
-45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
-46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
-47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
-48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
-49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
-50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
-51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
-52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
-53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
-54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
-55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
-56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
-57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
-58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
-59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
-60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
-61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
-62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
-63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
-64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
-65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
-66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
-67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
-68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
-69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
-70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
-71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
-72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
-73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
-74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
-75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
-76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
-77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
-78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
-79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
-80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
-81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
-82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
-83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
-84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
-85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
-86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
-87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
-88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
-89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
-90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
-91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
-92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
-93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
-94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
-95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
-96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
-97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
-98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
-99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
-100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
-101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
-102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
-103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S
-104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S
-105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S
-106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
-107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
-108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
-109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
-110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
-111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
-112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
-113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
-114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
-115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
-116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
-117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
-118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
-119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
-120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
-121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
-122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
-123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
-124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
-125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
-126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
-127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
-128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
-129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
-130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
-131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
-132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
-133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S
-134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S
-135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S
-136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C
-137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S
-138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S
-139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S
-140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C
-141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
-142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
-143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
-144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
-145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
-146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
-147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S
-148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S
-149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S
-150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S
-151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S
-152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S
-153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S
-154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S
-155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S
-156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
-157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q
-158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S
-159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S
-160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S
-161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S
-162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S
-163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S
-164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S
-165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S
-166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S
-167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S
-168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S
-169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S
-170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S
-171,0,1,"Van der hoef, Mr. Wyckoff",male,61,0,0,111240,33.5,B19,S
-172,0,3,"Rice, Master. Arthur",male,4,4,1,382652,29.125,,Q
-173,1,3,"Johnson, Miss. Eleanor Ileen",female,1,1,1,347742,11.1333,,S
-174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S
-175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C
-176,0,3,"Klasen, Mr. Klas Albin",male,18,1,1,350404,7.8542,,S
-177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S
-178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C
-179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S
-180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S
-181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S
-182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C
-183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S
-184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S
-185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4,0,2,315153,22.025,,S
-186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50,A32,S
-187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q
-188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S
-189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q
-190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S
-191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S
-192,0,2,"Carbines, Mr. William",male,19,0,0,28424,13,,S
-193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19,1,0,350046,7.8542,,S
-194,1,2,"Navratil, Master. Michel M",male,3,1,1,230080,26,F2,S
-195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44,0,0,PC 17610,27.7208,B4,C
-196,1,1,"Lurette, Miss. Elise",female,58,0,0,PC 17569,146.5208,B80,C
-197,0,3,"Mernagh, Mr. Robert",male,,0,0,368703,7.75,,Q
-198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42,0,1,4579,8.4042,,S
-199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q
-200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S
-201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28,0,0,345770,9.5,,S
-202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S
-203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S
-204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C
-205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S
-206,0,3,"Strom, Miss. Telma Matilda",female,2,0,1,347054,10.4625,G6,S
-207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S
-208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C
-209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q
-210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C
-211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S
-212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S
-213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S
-214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S
-215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q
-216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C
-217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S
-218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42,1,0,243847,27,,S
-219,1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C
-220,0,2,"Harris, Mr. Walter",male,30,0,0,W/C 14208,10.5,,S
-221,1,3,"Sunderland, Mr. Victor Francis",male,16,0,0,SOTON/OQ 392089,8.05,,S
-222,0,2,"Bracken, Mr. James H",male,27,0,0,220367,13,,S
-223,0,3,"Green, Mr. George Henry",male,51,0,0,21440,8.05,,S
-224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S
-225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38,1,0,19943,90,C93,S
-226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22,0,0,PP 4348,9.35,,S
-227,1,2,"Mellors, Mr. William John",male,19,0,0,SW/PP 751,10.5,,S
-228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S
-229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18,0,0,236171,13,,S
-230,0,3,"Lefebre, Miss. Mathilde",female,,3,1,4133,25.4667,,S
-231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35,1,0,36973,83.475,C83,S
-232,0,3,"Larsson, Mr. Bengt Edvin",male,29,0,0,347067,7.775,,S
-233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59,0,0,237442,13.5,,S
-234,1,3,"Asplund, Miss. Lillian Gertrud",female,5,4,2,347077,31.3875,,S
-235,0,2,"Leyson, Mr. Robert William Norman",male,24,0,0,C.A. 29566,10.5,,S
-236,0,3,"Harknett, Miss. Alice Phoebe",female,,0,0,W./C. 6609,7.55,,S
-237,0,2,"Hold, Mr. Stephen",male,44,1,0,26707,26,,S
-238,1,2,"Collyer, Miss. Marjorie ""Lottie""",female,8,0,2,C.A. 31921,26.25,,S
-239,0,2,"Pengelly, Mr. Frederick William",male,19,0,0,28665,10.5,,S
-240,0,2,"Hunt, Mr. George Henry",male,33,0,0,SCO/W 1585,12.275,,S
-241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C
-242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q
-243,0,2,"Coleridge, Mr. Reginald Charles",male,29,0,0,W./C. 14263,10.5,,S
-244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22,0,0,STON/O 2. 3101275,7.125,,S
-245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C
-246,0,1,"Minahan, Dr. William Edward",male,44,2,0,19928,90,C78,Q
-247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25,0,0,347071,7.775,,S
-248,1,2,"Hamalainen, Mrs. William (Anna)",female,24,0,2,250649,14.5,,S
-249,1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S
-250,0,2,"Carter, Rev. Ernest Courtenay",male,54,1,0,244252,26,,S
-251,0,3,"Reed, Mr. James George",male,,0,0,362316,7.25,,S
-252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S
-253,0,1,"Stead, Mr. William Thomas",male,62,0,0,113514,26.55,C87,S
-254,0,3,"Lobb, Mr. William Arthur",male,30,1,0,A/5. 3336,16.1,,S
-255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S
-256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C
-257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C
-258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S
-259,1,1,"Ward, Miss. Anna",female,35,0,0,PC 17755,512.3292,,C
-260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S
-261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q
-262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S
-263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S
-264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S
-265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q
-266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S
-267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S
-268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S
-269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S
-270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S
-271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S
-272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S
-273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S
-274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C
-275,1,3,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q
-276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S
-277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S
-278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S
-279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q
-280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S
-281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q
-282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S
-283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S
-284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S
-285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S
-286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C
-287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S
-288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S
-289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S
-290,1,3,"Connolly, Miss. Kate",female,22,0,0,370373,7.75,,Q
-291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S
-292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C
-293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C
-294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S
-295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S
-296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C
-297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C
-298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S
-299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S
-300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C
-301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q
-302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q
-303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S
-304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q
-305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S
-306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S
-307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C
-308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17,1,0,PC 17758,108.9,C65,C
-309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C
-310,1,1,"Francatelli, Miss. Laura Mabel",female,30,0,0,PC 17485,56.9292,E36,C
-311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C
-312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C
-313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S
-314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S
-315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S
-316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S
-317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S
-318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S
-319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S
-320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C
-321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S
-322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S
-323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q
-324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S
-325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S
-326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C
-327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S
-328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S
-329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S
-330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C
-331,1,3,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q
-332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S
-333,0,1,"Graham, Mr. George Edward",male,38,0,1,PC 17582,153.4625,C91,S
-334,0,3,"Vander Planke, Mr. Leo Edmondus",male,16,2,0,345764,18,,S
-335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S
-336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S
-337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S
-338,1,1,"Burns, Miss. Elizabeth Margaret",female,41,0,0,16966,134.5,E40,C
-339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S
-340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S
-341,1,2,"Navratil, Master. Edmond Roger",male,2,1,1,230080,26,F2,S
-342,1,1,"Fortune, Miss. Alice Elizabeth",female,24,3,2,19950,263,C23 C25 C27,S
-343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S
-344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S
-345,0,2,"Fox, Mr. Stanley Hubert",male,36,0,0,229236,13,,S
-346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S
-347,1,2,"Smith, Miss. Marion Elsie",female,40,0,0,31418,13,,S
-348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S
-349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S
-350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S
-351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S
-352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S
-353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C
-354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S
-355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C
-356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S
-357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S
-358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S
-359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q
-360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q
-361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S
-362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C
-363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C
-364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S
-365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q
-366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S
-367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60,1,0,110813,75.25,D37,C
-368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C
-369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q
-370,1,1,"Aubart, Mme. Leontine Pauline",female,24,0,0,PC 17477,69.3,B35,C
-371,1,1,"Harder, Mr. George Achilles",male,25,1,0,11765,55.4417,E50,C
-372,0,3,"Wiklund, Mr. Jakob Alfred",male,18,1,0,3101267,6.4958,,S
-373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S
-374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C
-375,0,3,"Palsson, Miss. Stina Viola",female,3,3,1,349909,21.075,,S
-376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C
-377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S
-378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C
-379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C
-380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S
-381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C
-382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C
-383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S
-384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35,1,0,113789,52,,S
-385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S
-386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S
-387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S
-388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S
-389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q
-390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C
-391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S
-392,1,3,"Jansson, Mr. Carl Olof",male,21,0,0,350034,7.7958,,S
-393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S
-394,1,1,"Newell, Miss. Marjorie",female,23,1,0,35273,113.275,D36,C
-395,1,3,"Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)",female,24,0,2,PP 9549,16.7,G6,S
-396,0,3,"Johansson, Mr. Erik",male,22,0,0,350052,7.7958,,S
-397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S
-398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S
-399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S
-400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S
-401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S
-402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S
-403,0,3,"Jussila, Miss. Mari Aina",female,21,1,0,4137,9.825,,S
-404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S
-405,0,3,"Oreskovic, Miss. Marija",female,20,0,0,315096,8.6625,,S
-406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S
-407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.75,,S
-408,1,2,"Richards, Master. William Rowe",male,3,1,1,29106,18.75,,S
-409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S
-410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S
-411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S
-412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q
-413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q
-414,0,2,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0,,S
-415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S
-416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S
-417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S
-418,1,2,"Silven, Miss. Lyyli Karoliina",female,18,0,2,250652,13,,S
-419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S
-420,0,3,"Van Impe, Miss. Catharina",female,10,0,2,345773,24.15,,S
-421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C
-422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q
-423,0,3,"Zimmerman, Mr. Leo",male,29,0,0,315082,7.875,,S
-424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S
-425,0,3,"Rosblom, Mr. Viktor Richard",male,18,1,1,370129,20.2125,,S
-426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S
-427,1,2,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28,1,0,2003,26,,S
-428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S
-429,0,3,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q
-430,1,3,"Pickard, Mr. Berk (Berk Trembisky)",male,32,0,0,SOTON/O.Q. 392078,8.05,E10,S
-431,1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28,0,0,110564,26.55,C52,S
-432,1,3,"Thorneycroft, Mrs. Percival (Florence Kate White)",female,,1,0,376564,16.1,,S
-433,1,2,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",female,42,1,0,SC/AH 3085,26,,S
-434,0,3,"Kallio, Mr. Nikolai Erland",male,17,0,0,STON/O 2. 3101274,7.125,,S
-435,0,1,"Silvey, Mr. William Baird",male,50,1,0,13507,55.9,E44,S
-436,1,1,"Carter, Miss. Lucile Polk",female,14,1,2,113760,120,B96 B98,S
-437,0,3,"Ford, Miss. Doolina Margaret ""Daisy""",female,21,2,2,W./C. 6608,34.375,,S
-438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S
-439,0,1,"Fortune, Mr. Mark",male,64,1,4,19950,263,C23 C25 C27,S
-440,0,2,"Kvillner, Mr. Johan Henrik Johannesson",male,31,0,0,C.A. 18723,10.5,,S
-441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45,1,1,F.C.C. 13529,26.25,,S
-442,0,3,"Hampe, Mr. Leon",male,20,0,0,345769,9.5,,S
-443,0,3,"Petterson, Mr. Johan Emil",male,25,1,0,347076,7.775,,S
-444,1,2,"Reynaldo, Ms. Encarnacion",female,28,0,0,230434,13,,S
-445,1,3,"Johannesen-Bratthammer, Mr. Bernt",male,,0,0,65306,8.1125,,S
-446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S
-447,1,2,"Mellinger, Miss. Madeleine Violet",female,13,0,1,250644,19.5,,S
-448,1,1,"Seward, Mr. Frederic Kimber",male,34,0,0,113794,26.55,,S
-449,1,3,"Baclini, Miss. Marie Catherine",female,5,2,1,2666,19.2583,,C
-450,1,1,"Peuchen, Major. Arthur Godfrey",male,52,0,0,113786,30.5,C104,S
-451,0,2,"West, Mr. Edwy Arthur",male,36,1,2,C.A. 34651,27.75,,S
-452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S
-453,0,1,"Foreman, Mr. Benjamin Laventall",male,30,0,0,113051,27.75,C111,C
-454,1,1,"Goldenberg, Mr. Samuel L",male,49,1,0,17453,89.1042,C92,C
-455,0,3,"Peduzzi, Mr. Joseph",male,,0,0,A/5 2817,8.05,,S
-456,1,3,"Jalsevac, Mr. Ivan",male,29,0,0,349240,7.8958,,C
-457,0,1,"Millet, Mr. Francis Davis",male,65,0,0,13509,26.55,E38,S
-458,1,1,"Kenyon, Mrs. Frederick R (Marion)",female,,1,0,17464,51.8625,D21,S
-459,1,2,"Toomey, Miss. Ellen",female,50,0,0,F.C.C. 13531,10.5,,S
-460,0,3,"O'Connor, Mr. Maurice",male,,0,0,371060,7.75,,Q
-461,1,1,"Anderson, Mr. Harry",male,48,0,0,19952,26.55,E12,S
-462,0,3,"Morley, Mr. William",male,34,0,0,364506,8.05,,S
-463,0,1,"Gee, Mr. Arthur H",male,47,0,0,111320,38.5,E63,S
-464,0,2,"Milling, Mr. Jacob Christian",male,48,0,0,234360,13,,S
-465,0,3,"Maisner, Mr. Simon",male,,0,0,A/S 2816,8.05,,S
-466,0,3,"Goncalves, Mr. Manuel Estanslas",male,38,0,0,SOTON/O.Q. 3101306,7.05,,S
-467,0,2,"Campbell, Mr. William",male,,0,0,239853,0,,S
-468,0,1,"Smart, Mr. John Montgomery",male,56,0,0,113792,26.55,,S
-469,0,3,"Scanlan, Mr. James",male,,0,0,36209,7.725,,Q
-470,1,3,"Baclini, Miss. Helene Barbara",female,0.75,2,1,2666,19.2583,,C
-471,0,3,"Keefe, Mr. Arthur",male,,0,0,323592,7.25,,S
-472,0,3,"Cacic, Mr. Luka",male,38,0,0,315089,8.6625,,S
-473,1,2,"West, Mrs. Edwy Arthur (Ada Mary Worth)",female,33,1,2,C.A. 34651,27.75,,S
-474,1,2,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23,0,0,SC/AH Basle 541,13.7917,D,C
-475,0,3,"Strandberg, Miss. Ida Sofia",female,22,0,0,7553,9.8375,,S
-476,0,1,"Clifford, Mr. George Quincy",male,,0,0,110465,52,A14,S
-477,0,2,"Renouf, Mr. Peter Henry",male,34,1,0,31027,21,,S
-478,0,3,"Braund, Mr. Lewis Richard",male,29,1,0,3460,7.0458,,S
-479,0,3,"Karlsson, Mr. Nils August",male,22,0,0,350060,7.5208,,S
-480,1,3,"Hirvonen, Miss. Hildur E",female,2,0,1,3101298,12.2875,,S
-481,0,3,"Goodwin, Master. Harold Victor",male,9,5,2,CA 2144,46.9,,S
-482,0,2,"Frost, Mr. Anthony Wood ""Archie""",male,,0,0,239854,0,,S
-483,0,3,"Rouse, Mr. Richard Henry",male,50,0,0,A/5 3594,8.05,,S
-484,1,3,"Turkula, Mrs. (Hedwig)",female,63,0,0,4134,9.5875,,S
-485,1,1,"Bishop, Mr. Dickinson H",male,25,1,0,11967,91.0792,B49,C
-486,0,3,"Lefebre, Miss. Jeannie",female,,3,1,4133,25.4667,,S
-487,1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35,1,0,19943,90,C93,S
-488,0,1,"Kent, Mr. Edward Austin",male,58,0,0,11771,29.7,B37,C
-489,0,3,"Somerton, Mr. Francis William",male,30,0,0,A.5. 18509,8.05,,S
-490,1,3,"Coutts, Master. Eden Leslie ""Neville""",male,9,1,1,C.A. 37671,15.9,,S
-491,0,3,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S
-492,0,3,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.25,,S
-493,0,1,"Molson, Mr. Harry Markland",male,55,0,0,113787,30.5,C30,S
-494,0,1,"Artagaveytia, Mr. Ramon",male,71,0,0,PC 17609,49.5042,,C
-495,0,3,"Stanley, Mr. Edward Roland",male,21,0,0,A/4 45380,8.05,,S
-496,0,3,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C
-497,1,1,"Eustis, Miss. Elizabeth Mussey",female,54,1,0,36947,78.2667,D20,C
-498,0,3,"Shellard, Mr. Frederick William",male,,0,0,C.A. 6212,15.1,,S
-499,0,1,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25,1,2,113781,151.55,C22 C26,S
-500,0,3,"Svensson, Mr. Olof",male,24,0,0,350035,7.7958,,S
-501,0,3,"Calic, Mr. Petar",male,17,0,0,315086,8.6625,,S
-502,0,3,"Canavan, Miss. Mary",female,21,0,0,364846,7.75,,Q
-503,0,3,"O'Sullivan, Miss. Bridget Mary",female,,0,0,330909,7.6292,,Q
-504,0,3,"Laitinen, Miss. Kristina Sofia",female,37,0,0,4135,9.5875,,S
-505,1,1,"Maioni, Miss. Roberta",female,16,0,0,110152,86.5,B79,S
-506,0,1,"Penasco y Castellana, Mr. Victor de Satode",male,18,1,0,PC 17758,108.9,C65,C
-507,1,2,"Quick, Mrs. Frederick Charles (Jane Richards)",female,33,0,2,26360,26,,S
-508,1,1,"Bradley, Mr. George (""George Arthur Brayton"")",male,,0,0,111427,26.55,,S
-509,0,3,"Olsen, Mr. Henry Margido",male,28,0,0,C 4001,22.525,,S
-510,1,3,"Lang, Mr. Fang",male,26,0,0,1601,56.4958,,S
-511,1,3,"Daly, Mr. Eugene Patrick",male,29,0,0,382651,7.75,,Q
-512,0,3,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.05,,S
-513,1,1,"McGough, Mr. James Robert",male,36,0,0,PC 17473,26.2875,E25,S
-514,1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54,1,0,PC 17603,59.4,,C
-515,0,3,"Coleff, Mr. Satio",male,24,0,0,349209,7.4958,,S
-516,0,1,"Walker, Mr. William Anderson",male,47,0,0,36967,34.0208,D46,S
-517,1,2,"Lemore, Mrs. (Amelia Milley)",female,34,0,0,C.A. 34260,10.5,F33,S
-518,0,3,"Ryan, Mr. Patrick",male,,0,0,371110,24.15,,Q
-519,1,2,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36,1,0,226875,26,,S
-520,0,3,"Pavlovic, Mr. Stefo",male,32,0,0,349242,7.8958,,S
-521,1,1,"Perreault, Miss. Anne",female,30,0,0,12749,93.5,B73,S
-522,0,3,"Vovk, Mr. Janko",male,22,0,0,349252,7.8958,,S
-523,0,3,"Lahoud, Mr. Sarkis",male,,0,0,2624,7.225,,C
-524,1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44,0,1,111361,57.9792,B18,C
-525,0,3,"Kassem, Mr. Fared",male,,0,0,2700,7.2292,,C
-526,0,3,"Farrell, Mr. James",male,40.5,0,0,367232,7.75,,Q
-527,1,2,"Ridsdale, Miss. Lucy",female,50,0,0,W./C. 14258,10.5,,S
-528,0,1,"Farthing, Mr. John",male,,0,0,PC 17483,221.7792,C95,S
-529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S
-530,0,2,"Hocking, Mr. Richard George",male,23,2,1,29104,11.5,,S
-531,1,2,"Quick, Miss. Phyllis May",female,2,1,1,26360,26,,S
-532,0,3,"Toufik, Mr. Nakli",male,,0,0,2641,7.2292,,C
-533,0,3,"Elias, Mr. Joseph Jr",male,17,1,1,2690,7.2292,,C
-534,1,3,"Peter, Mrs. Catherine (Catherine Rizk)",female,,0,2,2668,22.3583,,C
-535,0,3,"Cacic, Miss. Marija",female,30,0,0,315084,8.6625,,S
-536,1,2,"Hart, Miss. Eva Miriam",female,7,0,2,F.C.C. 13529,26.25,,S
-537,0,1,"Butt, Major. Archibald Willingham",male,45,0,0,113050,26.55,B38,S
-538,1,1,"LeRoy, Miss. Bertha",female,30,0,0,PC 17761,106.425,,C
-539,0,3,"Risien, Mr. Samuel Beard",male,,0,0,364498,14.5,,S
-540,1,1,"Frolicher, Miss. Hedwig Margaritha",female,22,0,2,13568,49.5,B39,C
-541,1,1,"Crosby, Miss. Harriet R",female,36,0,2,WE/P 5735,71,B22,S
-542,0,3,"Andersson, Miss. Ingeborg Constanzia",female,9,4,2,347082,31.275,,S
-543,0,3,"Andersson, Miss. Sigrid Elisabeth",female,11,4,2,347082,31.275,,S
-544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S
-545,0,1,"Douglas, Mr. Walter Donald",male,50,1,0,PC 17761,106.425,C86,C
-546,0,1,"Nicholson, Mr. Arthur Ernest",male,64,0,0,693,26,,S
-547,1,2,"Beane, Mrs. Edward (Ethel Clarke)",female,19,1,0,2908,26,,S
-548,1,2,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C
-549,0,3,"Goldsmith, Mr. Frank John",male,33,1,1,363291,20.525,,S
-550,1,2,"Davies, Master. John Morgan Jr",male,8,1,1,C.A. 33112,36.75,,S
-551,1,1,"Thayer, Mr. John Borland Jr",male,17,0,2,17421,110.8833,C70,C
-552,0,2,"Sharp, Mr. Percival James R",male,27,0,0,244358,26,,S
-553,0,3,"O'Brien, Mr. Timothy",male,,0,0,330979,7.8292,,Q
-554,1,3,"Leeni, Mr. Fahim (""Philip Zenni"")",male,22,0,0,2620,7.225,,C
-555,1,3,"Ohman, Miss. Velin",female,22,0,0,347085,7.775,,S
-556,0,1,"Wright, Mr. George",male,62,0,0,113807,26.55,,S
-557,1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48,1,0,11755,39.6,A16,C
-558,0,1,"Robbins, Mr. Victor",male,,0,0,PC 17757,227.525,,C
-559,1,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",female,39,1,1,110413,79.65,E67,S
-560,1,3,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",female,36,1,0,345572,17.4,,S
-561,0,3,"Morrow, Mr. Thomas Rowan",male,,0,0,372622,7.75,,Q
-562,0,3,"Sivic, Mr. Husein",male,40,0,0,349251,7.8958,,S
-563,0,2,"Norman, Mr. Robert Douglas",male,28,0,0,218629,13.5,,S
-564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S
-565,0,3,"Meanwell, Miss. (Marion Ogden)",female,,0,0,SOTON/O.Q. 392087,8.05,,S
-566,0,3,"Davies, Mr. Alfred J",male,24,2,0,A/4 48871,24.15,,S
-567,0,3,"Stoytcheff, Mr. Ilia",male,19,0,0,349205,7.8958,,S
-568,0,3,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",female,29,0,4,349909,21.075,,S
-569,0,3,"Doharr, Mr. Tannous",male,,0,0,2686,7.2292,,C
-570,1,3,"Jonsson, Mr. Carl",male,32,0,0,350417,7.8542,,S
-571,1,2,"Harris, Mr. George",male,62,0,0,S.W./PP 752,10.5,,S
-572,1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53,2,0,11769,51.4792,C101,S
-573,1,1,"Flynn, Mr. John Irwin (""Irving"")",male,36,0,0,PC 17474,26.3875,E25,S
-574,1,3,"Kelly, Miss. Mary",female,,0,0,14312,7.75,,Q
-575,0,3,"Rush, Mr. Alfred George John",male,16,0,0,A/4. 20589,8.05,,S
-576,0,3,"Patchett, Mr. George",male,19,0,0,358585,14.5,,S
-577,1,2,"Garside, Miss. Ethel",female,34,0,0,243880,13,,S
-578,1,1,"Silvey, Mrs. William Baird (Alice Munger)",female,39,1,0,13507,55.9,E44,S
-579,0,3,"Caram, Mrs. Joseph (Maria Elias)",female,,1,0,2689,14.4583,,C
-580,1,3,"Jussila, Mr. Eiriik",male,32,0,0,STON/O 2. 3101286,7.925,,S
-581,1,2,"Christy, Miss. Julie Rachel",female,25,1,1,237789,30,,S
-582,1,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",female,39,1,1,17421,110.8833,C68,C
-583,0,2,"Downton, Mr. William James",male,54,0,0,28403,26,,S
-584,0,1,"Ross, Mr. John Hugo",male,36,0,0,13049,40.125,A10,C
-585,0,3,"Paulner, Mr. Uscher",male,,0,0,3411,8.7125,,C
-586,1,1,"Taussig, Miss. Ruth",female,18,0,2,110413,79.65,E68,S
-587,0,2,"Jarvis, Mr. John Denzil",male,47,0,0,237565,15,,S
-588,1,1,"Frolicher-Stehli, Mr. Maxmillian",male,60,1,1,13567,79.2,B41,C
-589,0,3,"Gilinski, Mr. Eliezer",male,22,0,0,14973,8.05,,S
-590,0,3,"Murdlin, Mr. Joseph",male,,0,0,A./5. 3235,8.05,,S
-591,0,3,"Rintamaki, Mr. Matti",male,35,0,0,STON/O 2. 3101273,7.125,,S
-592,1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52,1,0,36947,78.2667,D20,C
-593,0,3,"Elsbury, Mr. William James",male,47,0,0,A/5 3902,7.25,,S
-594,0,3,"Bourke, Miss. Mary",female,,0,2,364848,7.75,,Q
-595,0,2,"Chapman, Mr. John Henry",male,37,1,0,SC/AH 29037,26,,S
-596,0,3,"Van Impe, Mr. Jean Baptiste",male,36,1,1,345773,24.15,,S
-597,1,2,"Leitch, Miss. Jessie Wills",female,,0,0,248727,33,,S
-598,0,3,"Johnson, Mr. Alfred",male,49,0,0,LINE,0,,S
-599,0,3,"Boulos, Mr. Hanna",male,,0,0,2664,7.225,,C
-600,1,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",male,49,1,0,PC 17485,56.9292,A20,C
-601,1,2,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",female,24,2,1,243847,27,,S
-602,0,3,"Slabenoff, Mr. Petco",male,,0,0,349214,7.8958,,S
-603,0,1,"Harrington, Mr. Charles H",male,,0,0,113796,42.4,,S
-604,0,3,"Torber, Mr. Ernst William",male,44,0,0,364511,8.05,,S
-605,1,1,"Homer, Mr. Harry (""Mr E Haven"")",male,35,0,0,111426,26.55,,C
-606,0,3,"Lindell, Mr. Edvard Bengtsson",male,36,1,0,349910,15.55,,S
-607,0,3,"Karaic, Mr. Milan",male,30,0,0,349246,7.8958,,S
-608,1,1,"Daniel, Mr. Robert Williams",male,27,0,0,113804,30.5,,S
-609,1,2,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",female,22,1,2,SC/Paris 2123,41.5792,,C
-610,1,1,"Shutes, Miss. Elizabeth W",female,40,0,0,PC 17582,153.4625,C125,S
-611,0,3,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",female,39,1,5,347082,31.275,,S
-612,0,3,"Jardin, Mr. Jose Neto",male,,0,0,SOTON/O.Q. 3101305,7.05,,S
-613,1,3,"Murphy, Miss. Margaret Jane",female,,1,0,367230,15.5,,Q
-614,0,3,"Horgan, Mr. John",male,,0,0,370377,7.75,,Q
-615,0,3,"Brocklebank, Mr. William Alfred",male,35,0,0,364512,8.05,,S
-616,1,2,"Herman, Miss. Alice",female,24,1,2,220845,65,,S
-617,0,3,"Danbom, Mr. Ernst Gilbert",male,34,1,1,347080,14.4,,S
-618,0,3,"Lobb, Mrs. William Arthur (Cordelia K Stanlick)",female,26,1,0,A/5. 3336,16.1,,S
-619,1,2,"Becker, Miss. Marion Louise",female,4,2,1,230136,39,F4,S
-620,0,2,"Gavey, Mr. Lawrence",male,26,0,0,31028,10.5,,S
-621,0,3,"Yasbeck, Mr. Antoni",male,27,1,0,2659,14.4542,,C
-622,1,1,"Kimball, Mr. Edwin Nelson Jr",male,42,1,0,11753,52.5542,D19,S
-623,1,3,"Nakid, Mr. Sahid",male,20,1,1,2653,15.7417,,C
-624,0,3,"Hansen, Mr. Henry Damsgaard",male,21,0,0,350029,7.8542,,S
-625,0,3,"Bowen, Mr. David John ""Dai""",male,21,0,0,54636,16.1,,S
-626,0,1,"Sutton, Mr. Frederick",male,61,0,0,36963,32.3208,D50,S
-627,0,2,"Kirkland, Rev. Charles Leonard",male,57,0,0,219533,12.35,,Q
-628,1,1,"Longley, Miss. Gretchen Fiske",female,21,0,0,13502,77.9583,D9,S
-629,0,3,"Bostandyeff, Mr. Guentcho",male,26,0,0,349224,7.8958,,S
-630,0,3,"O'Connell, Mr. Patrick D",male,,0,0,334912,7.7333,,Q
-631,1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80,0,0,27042,30,A23,S
-632,0,3,"Lundahl, Mr. Johan Svensson",male,51,0,0,347743,7.0542,,S
-633,1,1,"Stahelin-Maeglin, Dr. Max",male,32,0,0,13214,30.5,B50,C
-634,0,1,"Parr, Mr. William Henry Marsh",male,,0,0,112052,0,,S
-635,0,3,"Skoog, Miss. Mabel",female,9,3,2,347088,27.9,,S
-636,1,2,"Davis, Miss. Mary",female,28,0,0,237668,13,,S
-637,0,3,"Leinonen, Mr. Antti Gustaf",male,32,0,0,STON/O 2. 3101292,7.925,,S
-638,0,2,"Collyer, Mr. Harvey",male,31,1,1,C.A. 31921,26.25,,S
-639,0,3,"Panula, Mrs. Juha (Maria Emilia Ojala)",female,41,0,5,3101295,39.6875,,S
-640,0,3,"Thorneycroft, Mr. Percival",male,,1,0,376564,16.1,,S
-641,0,3,"Jensen, Mr. Hans Peder",male,20,0,0,350050,7.8542,,S
-642,1,1,"Sagesser, Mlle. Emma",female,24,0,0,PC 17477,69.3,B35,C
-643,0,3,"Skoog, Miss. Margit Elizabeth",female,2,3,2,347088,27.9,,S
-644,1,3,"Foo, Mr. Choong",male,,0,0,1601,56.4958,,S
-645,1,3,"Baclini, Miss. Eugenie",female,0.75,2,1,2666,19.2583,,C
-646,1,1,"Harper, Mr. Henry Sleeper",male,48,1,0,PC 17572,76.7292,D33,C
-647,0,3,"Cor, Mr. Liudevit",male,19,0,0,349231,7.8958,,S
-648,1,1,"Simonius-Blumer, Col. Oberst Alfons",male,56,0,0,13213,35.5,A26,C
-649,0,3,"Willey, Mr. Edward",male,,0,0,S.O./P.P. 751,7.55,,S
-650,1,3,"Stanley, Miss. Amy Zillah Elsie",female,23,0,0,CA. 2314,7.55,,S
-651,0,3,"Mitkoff, Mr. Mito",male,,0,0,349221,7.8958,,S
-652,1,2,"Doling, Miss. Elsie",female,18,0,1,231919,23,,S
-653,0,3,"Kalvik, Mr. Johannes Halvorsen",male,21,0,0,8475,8.4333,,S
-654,1,3,"O'Leary, Miss. Hanora ""Norah""",female,,0,0,330919,7.8292,,Q
-655,0,3,"Hegarty, Miss. Hanora ""Nora""",female,18,0,0,365226,6.75,,Q
-656,0,2,"Hickman, Mr. Leonard Mark",male,24,2,0,S.O.C. 14879,73.5,,S
-657,0,3,"Radeff, Mr. Alexander",male,,0,0,349223,7.8958,,S
-658,0,3,"Bourke, Mrs. John (Catherine)",female,32,1,1,364849,15.5,,Q
-659,0,2,"Eitemiller, Mr. George Floyd",male,23,0,0,29751,13,,S
-660,0,1,"Newell, Mr. Arthur Webster",male,58,0,2,35273,113.275,D48,C
-661,1,1,"Frauenthal, Dr. Henry William",male,50,2,0,PC 17611,133.65,,S
-662,0,3,"Badt, Mr. Mohamed",male,40,0,0,2623,7.225,,C
-663,0,1,"Colley, Mr. Edward Pomeroy",male,47,0,0,5727,25.5875,E58,S
-664,0,3,"Coleff, Mr. Peju",male,36,0,0,349210,7.4958,,S
-665,1,3,"Lindqvist, Mr. Eino William",male,20,1,0,STON/O 2. 3101285,7.925,,S
-666,0,2,"Hickman, Mr. Lewis",male,32,2,0,S.O.C. 14879,73.5,,S
-667,0,2,"Butler, Mr. Reginald Fenton",male,25,0,0,234686,13,,S
-668,0,3,"Rommetvedt, Mr. Knud Paust",male,,0,0,312993,7.775,,S
-669,0,3,"Cook, Mr. Jacob",male,43,0,0,A/5 3536,8.05,,S
-670,1,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",female,,1,0,19996,52,C126,S
-671,1,2,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",female,40,1,1,29750,39,,S
-672,0,1,"Davidson, Mr. Thornton",male,31,1,0,F.C. 12750,52,B71,S
-673,0,2,"Mitchell, Mr. Henry Michael",male,70,0,0,C.A. 24580,10.5,,S
-674,1,2,"Wilhelms, Mr. Charles",male,31,0,0,244270,13,,S
-675,0,2,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0,,S
-676,0,3,"Edvardsson, Mr. Gustaf Hjalmar",male,18,0,0,349912,7.775,,S
-677,0,3,"Sawyer, Mr. Frederick Charles",male,24.5,0,0,342826,8.05,,S
-678,1,3,"Turja, Miss. Anna Sofia",female,18,0,0,4138,9.8417,,S
-679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43,1,6,CA 2144,46.9,,S
-680,1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36,0,1,PC 17755,512.3292,B51 B53 B55,C
-681,0,3,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q
-682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C
-683,0,3,"Olsvigen, Mr. Thor Anderson",male,20,0,0,6563,9.225,,S
-684,0,3,"Goodwin, Mr. Charles Edward",male,14,5,2,CA 2144,46.9,,S
-685,0,2,"Brown, Mr. Thomas William Solomon",male,60,1,1,29750,39,,S
-686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25,1,2,SC/Paris 2123,41.5792,,C
-687,0,3,"Panula, Mr. Jaako Arnold",male,14,4,1,3101295,39.6875,,S
-688,0,3,"Dakic, Mr. Branko",male,19,0,0,349228,10.1708,,S
-689,0,3,"Fischer, Mr. Eberhard Thelander",male,18,0,0,350036,7.7958,,S
-690,1,1,"Madill, Miss. Georgette Alexandra",female,15,0,1,24160,211.3375,B5,S
-691,1,1,"Dick, Mr. Albert Adrian",male,31,1,0,17474,57,B20,S
-692,1,3,"Karun, Miss. Manca",female,4,0,1,349256,13.4167,,C
-693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S
-694,0,3,"Saad, Mr. Khalil",male,25,0,0,2672,7.225,,C
-695,0,1,"Weir, Col. John",male,60,0,0,113800,26.55,,S
-696,0,2,"Chapman, Mr. Charles Henry",male,52,0,0,248731,13.5,,S
-697,0,3,"Kelly, Mr. James",male,44,0,0,363592,8.05,,S
-698,1,3,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q
-699,0,1,"Thayer, Mr. John Borland",male,49,1,1,17421,110.8833,C68,C
-700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42,0,0,348121,7.65,F G63,S
-701,1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18,1,0,PC 17757,227.525,C62 C64,C
-702,1,1,"Silverthorne, Mr. Spencer Victor",male,35,0,0,PC 17475,26.2875,E24,S
-703,0,3,"Barbara, Miss. Saiide",female,18,0,1,2691,14.4542,,C
-704,0,3,"Gallagher, Mr. Martin",male,25,0,0,36864,7.7417,,Q
-705,0,3,"Hansen, Mr. Henrik Juul",male,26,1,0,350025,7.8542,,S
-706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39,0,0,250655,26,,S
-707,1,2,"Kelly, Mrs. Florence ""Fannie""",female,45,0,0,223596,13.5,,S
-708,1,1,"Calderhead, Mr. Edward Pennington",male,42,0,0,PC 17476,26.2875,E24,S
-709,1,1,"Cleaver, Miss. Alice",female,22,0,0,113781,151.55,,S
-710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C
-711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C
-712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S
-713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S
-714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S
-715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S
-716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S
-717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C
-718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S
-719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q
-720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S
-721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S
-722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S
-723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S
-724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S
-725,1,1,"Chambers, Mr. Norman Campbell",male,27,1,0,113806,53.1,E8,S
-726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S
-727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S
-728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q
-729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S
-730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S
-731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S
-732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C
-733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S
-734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S
-735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S
-736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S
-737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S
-738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C
-739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S
-740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S
-741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S
-742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S
-743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C
-744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S
-745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S
-746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S
-747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S
-748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S
-749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S
-750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q
-751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S
-752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S
-753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S
-754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S
-755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S
-756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S
-757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S
-758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S
-759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S
-760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S
-761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S
-762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S
-763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C
-764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36,1,2,113760,120,B96 B98,S
-765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S
-766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S
-767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C
-768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q
-769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q
-770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S
-771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S
-772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S
-773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S
-774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C
-775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S
-776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S
-777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q
-778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S
-779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q
-780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S
-781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C
-782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S
-783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S
-784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S
-785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S
-786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S
-787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S
-788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q
-789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S
-790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C
-791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q
-792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S
-793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S
-794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C
-795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S
-796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S
-797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S
-798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S
-799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C
-800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S
-801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S
-802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S
-803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S
-804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C
-805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S
-806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S
-807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S
-808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S
-809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S
-810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S
-811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S
-812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S
-813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S
-814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S
-815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S
-816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S
-817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S
-818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C
-819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S
-820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S
-821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
-822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
-823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S
-824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S
-825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
-826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
-827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
-828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C
-829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
-830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
-831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C
-832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
-833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
-834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S
-835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S
-836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
-837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
-838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
-839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
-840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
-841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
-842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
-843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
-844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
-845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
-846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
-847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
-848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
-849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
-850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
-851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
-852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
-853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
-854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
-855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
-856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
-857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
-858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
-859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
-860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
-861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
-862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
-863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
-864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
-865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
-866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
-867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
-868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
-869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
-870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
-871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
-872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
-873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
-874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
-875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
-876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
-877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
-878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
-879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
-880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
-881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
-882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
-883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
-884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
-885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
-886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
-887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
-888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
-889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
-890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
-891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q