Proactive Policing: Resource Allocation for Crime Prevention with Deterrence Effect

• 成果类型: Article; Early Access
• 署名作者: He, Long; Li, Xiaobo; Zhao, Yue
• 署名单位: George Washington University; National University of Singapore; Peking University Shenzhen Graduate School (PKU Shenzhen); Peking University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2023.0415
• 发表日期: 2025-11-27
• 关键词: proactive policing; resource allocation; Crime deterrence; Multinomial logit model; conic programming; criminal deterrence; electoral cycles; queuing model; reduce crime; patrol; optimization; management; deployment

摘要

This paper addresses police resource allocation across multiple locations, aiming to minimize the overall cost of potential crimes. Unlike previous literature focused on reactive police tasks, we propose a proactive approach that emphasizes crime prevention through deterrence. To account for the deterrence effect of police resources on crime, we employ the multinomial logit model to calibrate the distribution of crime locations. Our model sheds light on two facets of the deterrence effect in proactive policing-crime control diffusion and crime displacement-relevant to modern crime patterns from both criminology and economics perspectives. We establish the NP-hardness of our problem and provide mixed-integer linear/conic reformulations solvable with standard optimization software. Additionally, we extend our results to a dynamic model over multiple time periods. Finally, we showcase the efficacy of our model through a data-driven case study on the allocation of surveillance cameras in New York City.