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Chicago Crime Data Exploration

This repo contains a notebook that explores, visualizes, and analyzes crime data available on the Chicago Data Portal. This repo also shows a cleaner way to organize a small, stand-alone data analysis project, with ETL and plotting functionality is defined in separate .py files and imported and run in a notebook. This both makes the notebook cleaner and enables other notebooks to import the functionality.

Maps, heatmaps (showing frequency by time-of-day and day-of-week, time-of-day and month, and day-of-week and month), and line plots of monthly and annual rates occurance and arrest for different crimes occuring in Chicago can be viewed in the notebook analysis/Chicago_Crime_visualized.ipynb. If GitHub can't load this (admittedly large) notebook, you can view it via this nbViewer link.

Running the code locally

If you want to reproduce this on your own machine, clone this repo and enter the cloned repo via the commands below

(base) user@host: ~/...$ git clone [email protected]:MattTriano/Chicago_Crimes.git
(base) user@host: ~/...$ cd Chicago_Crimes

If you don't already have conda on your machine, I recommend downloading and installing miniconda.

Now that you have conda on your machine, run the commands below 1) to configure conda to strictly pull from the conda-forge channel, 2) recreate the conda env needed to run the notebook code, 3) make that conda env accessible through a jupyter lab/notebook context, and 4) start up a jupyter lab server (which will provide a URL and will probably automatically pass that URL to a browser)

(base) user@host: ~/.../Chicago_Crimes$ conda config --add channels conda-forge
(base) user@host: ~/.../Chicago_Crimes$ conda config --set channel_priority strict
(base) user@host: ~/.../Chicago_Crimes$ conda env create -f environment.yml
(base) user@host: ~/.../Chicago_Crimes$ conda active geo_env
(geo_env) user@host: ~/.../Chicago_Crimes$ python -m ipykernel install --user --name geo_env --display-name "Python (geo_env)"
(geo_env) user@host: ~/.../Chicago_Crimes$ jupyter lab

Open the desired notebook and add parameters force_repull=True and force_remake=True to load_clean_chicago_crimes_data() producing

crimes_gdf = load_clean_chicago_crimes_data(force_repull=True, force_remake=True)

update the start and end date to desired dates

nb_start_date="2015-01-01"
nb_end_date="2022-08-18"

and run the notebook.