Analysis of Local Police Data — The Trace and BuzzFed News
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Analysis of Local Police Data

This repository includes methodologies, data, and code supporting the following articles, published by The Trace and BuzzFeed News:

  • "Shoot Someone In A Major US City, And Odds Are You’ll Get Away With It" (January 24, 2019) — The Trace / BuzzFeed News
  • "5 Things To Know About Cities’ Failure To Arrest Shooters" (January 24, 2019) — The Trace / BuzzFeed News

Click here for additional data and code from The Trace and BuzzFeed News's collaboration.


The Trace and BuzzFeed News analyzed internal data on homicides, aggravated assaults, and non-fatal shootings from 22 municipal police departments in the United States. You can find a description of the standardization process and the data fields in this methodology, and you can download a copy of the standardized offense dataset.

Data Analysis

The notebooks/analyze-local-police-data.ipynb notebook, written in Python, takes the standardized data, and does the following:

  • Identifies the main outcome metric (arrest vs. closure) for each agency/offense combination
  • Calculates arrest/closure rates, using the main outcomes identified above
  • Analyzes Houston's closure rate trends
  • Calculates median arrest disparities by offense and victim race
  • Calculates agggregate arrest disparities by offense and victim race

Note: To reproduce the findings, you'll need to unzip the inputs/ file before running the notebook.


All code in this repository is available under the MIT License. All data files are available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Questions / Comments?

Please contact Jeremy Singer-Vine at