Data-Driven Optimization for Police Beat Design in South Fulton, Georgia

Abstract

We redesign the police patrol beat in South Fulton, Georgia, in collaboration with the South Fulton Police Department (SFPD), using a predictive data-driven optimization approach. Due to rapid urban development and population growth, the existing police beat design done in the 1970s was far from efficient, which leads to low policing efficiency and long 911 call response time. We balance the police workload among different city regions, improve operational efficiency, and reduce 911 call response time by redesigning beat boundaries for the SFPD. We discretize the city into small geographical atoms, which correspond to our decision variables; the decision is to map the atoms into "beats", the basic unit of the police operation. We first analyze workload and trend in each atom using the rich dataset, including police incidents reports and U.S. census data; We then predict future police workload for each atom using spatial statistical regression models; Lastly, we formulate the optimal beat design as a mixed-integer programming (MIP) program with continuity and compactness constraints on the beats' shape. The optimization problem is solved using simulated annealing due to its large-scale and non-convex nature. The simulation results suggest that our proposed beat design can reduce workload variance among beats significantly by over 90\%.