Data-Driven Optimization for Atlanta Police-Zone Design

Shixiang Zhu (), He Wang () and Yao Xie ()
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Shixiang Zhu: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
He Wang: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Yao Xie: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332

Interfaces, 2022, vol. 52, issue 5, 412-432

Abstract: We present a data-driven optimization framework for redesigning police patrol zones in an urban environment. The objectives are to rebalance police workload along 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 incident 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 Atlanta’s zones by 43%.

Keywords: police operations; data analytics; queueing model; optimization; mixed-integer programming (search for similar items in EconPapers)
Date: 2022
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