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 also investigate the structural properties of our problem and its relation to mixture-of-logits assortment optimization. Additionally, we provide reformulations for mixed-integer linear/conic programs that can be solved directly using conventional optimization software. Finally, we showcase the efficacy of our model through a data-driven case study on the allocation of surveillance cameras in New York City.
Bio: Long He is an Associate Professor of Decision Sciences at the School of Business, The George Washington University. Prior to joining GWU, he was an Associate Professor in the Department of Analytics & Operations at NUS Business School, National University of Singapore. He received his Ph.D. in Operations Research from University of California, Berkeley and B.Eng. in Logistics Management and Engineering from The Hong Kong University of Science and Technology. His current research involves using optimization and machine learning tools to address problems in Smart City Operations (e.g., shared mobility, electric vehicles, and last-mile delivery), Sustainable Energy Ecosystem, and Supply Chain Management. This line of research has been recognized with the M&SOM Journal Best Paper Award, Transportation Science & Logistics (TSL) Best Paper Award, and ENRE Best Publication Award in Energy from INFORMS.