Data-Driven Optimization for Atlanta Police Zone Design
This addresses urban policing efficiency for law enforcement agencies, with incremental improvements in optimization methods applied to real-world data.
The paper tackled the problem of redesigning police patrol zones in Atlanta to rebalance workload and reduce emergency response times, resulting in a 5.8% reduction in response time for high-priority calls and a 43% decrease in workload imbalance.
We present a data-driven optimization framework for redesigning police patrol zones in an urban environment. The objectives are to rebalance police workload among geographical areas and to reduce response time to emergency calls. We develop a stochastic model for police emergency response by integrating multiple data sources, including police incidents reports, demographic surveys, and traffic data. Using this stochastic model, we optimize zone redesign plans using mixed-integer linear programming. Our proposed design was implemented by the Atlanta Police Department in March 2019. By analyzing data before and after the zone redesign, we show that the new design has reduced the response time to high priority 911 calls by 5.8\% and the imbalance of police workload among different zones by 43\%.