  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\%.

Optimization and Control;Machine Learning