Anna Haensch, Ariana Mendible - Small Town Police Accountability A Data Science Toolkit | SciPy 2023

Small town police accountability requires powerful tools. Learn how a data science team used Altair and machine learning to analyze imperfect data, make it usable, and identify patterns in police behavior.

Key takeaways
  • The talk highlights the importance of small town police accountability and the challenges in accessing and analyzing small town police data.
  • The team used Altair to create a map showing filtering for each different officer, providing spatial patterns on where someone might be policing.
  • The data available is often imperfect and missing, requiring reorganization and cleaning to make it usable.
  • The Freedom of Information Act (FOIA) is a federal law that guarantees access to data, but submitting a FOIA request can be a lengthy and challenging process.
  • The team used supervised machine learning algorithms to analyze the data and identify patterns in police behavior.
  • The project aims to create a reusable code repository so that other data scientists or researchers can replicate the work on their own towns.
  • The talk emphasizes the importance of connecting with communities and involving them in the work to make it impactful.
  • The team wants to create a toolkit that allows anyone, including non-experts, to access and analyze policing data.
  • Currently, the toolkit is available on a GitHub repository.
  • The team encourages listeners to get involved and help promote change in their own towns.