TY - GEN
T1 - Optimal Resource Allocation for Coverage Control of City Crimes
AU - Zhu, Rui
AU - Aqlan, Faisal
AU - Yang, Hui
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85126237440&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85126237440&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-75166-1_9
DO - 10.1007/978-3-030-75166-1_9
M3 - Conference contribution
AN - SCOPUS:85126237440
SN - 9783030751654
T3 - Springer Proceedings in Business and Economics
SP - 149
EP - 161
BT - AI and Analytics for Public Health - Proceedings of the 2020 INFORMS International Conference on Service Science
A2 - Yang, Hui
A2 - Qiu, Robin
A2 - Chen, Weiwei
PB - Springer Science and Business Media B.V.
T2 - INFORMS International Conference on Service Science, ICSS 2020
Y2 - 19 December 2020 through 21 December 2020
ER -