Crime and Settlement
[Case Study] This project offers an interactive map of crime clusters in the City of Chicago with 2010-2020 crime trends.
Chicago Crime Clustering and Trends 
Overview
In this project we’ve developed an interactive presentation of the crime data in the city of Chicago for (2010-2020). This project was designed to showcase two novel techniques:
1. Use of VRAM (GPU memory) and embarrassingly parallel approach for processing big data using RAPIDS.
2. GMM clustering sped up by a K-Means hybrid-step.
Demo
Basic Functionality
Prerequisites
Data
Please make sure the data prerequisites are met by following instructions presented in the README.md file here.
Tokens
Please obtain Google API and Mapbox API keys and place them in their approperiate files *-token.txt within the tokens folder.
Environment
Run the following code in terminal and use the chicago kernel to run the main.ipynb notebook:
conda create -n chicago -c conda-forge python==3.8 -y;
conda activate chicago;
pip install -r requirements.txt
As long as you have the nb_conda_kernels package installed in your Jupyter environment, you should be able to access chicago kernel inside the Jupyter. We recommend using JupyterLab instead of Jupyter Notebook.