Proactive Policing: Resource Allocation for Crime Prevention with Deterrence Effect

Optimizing Police Presence to Prevent Crime Before It Happens In their paper “Proactive Policing: Resource Allocation for Crime Prevention with Deterrence Effect,” He, Li, and Zhao introduce a novel optimization framework that reimagines how police resources should be allocated to prevent crimes rather than react to them. Departing from traditional models that respond after incidents occur, the authors propose a proactive approach grounded in the economics of deterrence effect and rational choice theory. By modeling offenders’ location choices using a multinomial logit framework, the study captures how visible police presence not only suppresses crime locally but also redistributes potential criminal activity across space, a phenomenon known as crime displacement and diffusion of crime control. The authors establish the computational complexity of the problem, develop tractable mixed-integer conic reformulations, and extend the framework to dynamic settings. A data-driven case study in New York City demonstrates the model’s potential to guide smarter, prevention-focused deployment of urban policing resources.