Predicting Use of Force with the Seattle Police Department

Mission

In order to establish trust between the public and the Seattle Police Department (SPD), it is imperative that there is accountability and transparency with SPD’s processes and procedures. The topic of Use of Force (UOF) is of critical concern to the general public. SPD has clear guidelines and definitions for the UOF, however, a gap exists between actual police activity and public perception. In this project, we have the following objectives:

Build a machine learning model that can be used to predict and audit adequate UOF by officers

Create a dashboard to display visualizations supporting our final model and highlighting the relationship between UOF and significant factors

Overall, this project will serve to provide greater transparency for the SPD and its practices. Transparency between law enforcement and the public can help facilitate trust, accountability, and cooperation. The model developed through this project can serve as a tool for the SPD to identify and understand deviance from regular trends and guide police training. It will also help to identify whether UOF is being underreported or overreported.

Publicly Available Data

The SPD provides data for the public through the City of Seattle Open Data portal. for confidentiality purposes, we are unable to share the data used for this project, but the slider at right contains public variations of the same data used for analysis.

The project has been generously sponsored by Loren Atherly, Director of Performance Analytics & Research at SPD, who has assisted us every step of the way.

Seattle Open Data Portal

Call Data

Crisis Data

Force Data

The Team

Jennifer Ko

Matthew Orcilla

Lauren Santos

Jesse Velasquez

BI Engineer

Data Scientist/Project Manager

Data Scientist