LA County Arrest Mapping Tool for Progressive Organization (using Python/GIS)

Project Overview:

The goal of this Python/Geopandas project was to create a mapping tool to inform a progressive organization's targeted ad campaign. This mapping tool is designed to assist the organization in understanding where to concentrate its decriminalization initiatives. The org asked for a heat map in order to understand this.

I created a heat map using Geopandas, Plotly, Dash and other libraries in Python. I identified the potential to gain deeper insights, and then structured aggregated and specific crime data into two additional tabs. Both tabs accept user input, which allows an interactive visual exploration of crime trends.

The resulting tool includes a heat map, but also maps with the ability to explore patterns at a greater depth.

Limitations:

This mapping tool is dedicated solely to crime data. There is potential to enhance it by incorporating additional layers, such as racial demographics and socio-economic status, to examine patterns and connections related to bias within the criminal justice system.

The heat map may present a skewed representation by emphasizing areas with higher populations.

Next Steps:

• Transform data to normalize based on population density, providing this data as primary or additional heat map layers for greater context.

• Include data from the past five years, spanning 2018-2023, with the ability for the user to select specific years (Updated successfully in Version 2, up on GitHub)

• Provide options to overlay demographic information onto the map in addition to the arrest data points.