  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\%.
