Proactive Policing: Resource Allocation for Crime Prevention with Deterrence Effect

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.

Funding: X. Li was supported in part by the National Natural Science Foundation of China [Grants 72331004 and 72171156] and by the Singapore Ministry of Education Academic Research Fund [Tier 1 Grants 23-0619-P0001 and 24-0500-A0001; Tier 3 Grant MOE-2019-T3-1-010]. Y. Zhao was partially supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 [Grant MOE-2019-T3-1-010].

Supplemental Material: All supplemental materials, including the code, data, and files required to reproduce the results, are available at https://doi.org/10.1287/opre.2023.0415.