The Berkeley City Council recently voted to audit the "calls for service" (CFS) received by the Berkeley Police Department (BPD) to determine the feasibility of transferring the response to certain types of calls to alternative emergency response agencies. Using historical CFS data provided by BPD, this project seeks to simulate the effects on response time, staffing levels, and racial disparities of alternative emergency response strategies.
The project will advance in complexity throughout the semester and perhaps through multiple semesters. We'll start with a cursory analysis of historical data to determine baseline measures, advance to create a multi-agent simulation, and finally explore the possibility of reinforcement learning to simulate more complex emergency CFS patterns.
This is a project for students who want to explore the intersection of machine learning and public policy analysis, engage directly with policy makers, and stretch their data science skillset.
View project submission here.