Optimal Resource Allocation for Coverage Control of City Crimes

Rui Zhu, Faisal Aqlan, Hui Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Protecting citizens from crimes is one of the core responsibilities of governments. However, as the complexity of crimes grows, resources of law enforcement become insufficient. Therefore, current practice of crime analytics calls for the optimal allocation of limited resources to achieve faster responses, reduced costs, and highly efficient operations. A variety of analytical methods have been used to investigate crime data. However, very little has been done to develop data-driven methods to optimally allocate law enforcement resources for coverage control of city crimes. In this paper, we develop a new optimal learning algorithm to characterize multi-scale distributions of crimes and then determine an optimal policy for coverage control of city crimes. First, we categorize crimes into low, medium, and high severity levels. Then, we model crime distributions for various severity levels. Second, we develop an optimal policy for coverage control to allocate limited resources of the law enforcement in areas of interest. Third, the model performance is measured based on the response time of an agent to reach crime scenes. Experimental results demonstrate that the proposed algorithm can effectively and efficiently optimize law enforcement allocation and show a better performance in terms of average response time to crime scenes.

Original languageEnglish (US)
Title of host publicationAI and Analytics for Public Health - Proceedings of the 2020 INFORMS International Conference on Service Science
EditorsHui Yang, Robin Qiu, Weiwei Chen
PublisherSpringer Science and Business Media B.V.
Number of pages13
ISBN (Print)9783030751654
StatePublished - 2022
EventINFORMS International Conference on Service Science, ICSS 2020 - Virtual, Online
Duration: Dec 19 2020Dec 21 2020

Publication series

NameSpringer Proceedings in Business and Economics
ISSN (Print)2198-7246
ISSN (Electronic)2198-7254


ConferenceINFORMS International Conference on Service Science, ICSS 2020
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • General Economics, Econometrics and Finance
  • General Business, Management and Accounting


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