Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2017

Abstract

In a security-conscious world, and with the rapid increase in the global urbanized population, there is a growing challenge for law enforcement agencies to efficiently respond to emergency calls. We consider the problem of spatially and temporally optimizing the allocation of law enforcement resources such that the quality of service (QoS) in terms of emergency response time can be guaranteed. To solve this problem, we provide a spatio-temporal MILP optimization model, which we learn from a real-world dataset of incidents and dispatching records, and solve by existing solvers. One key feature of our proposed model is the introduction of risk values that allow a planner to flexibly make a tradeoff between their resource budget and the targeted service quality. Experimental results on real-world incident data, and simulations run on learned synthetic data, show a significant reduction in resource requirements over current practice, with violating QoS or abusing resource utilization.

Keywords

Resource Allocation, Law Enforcement Staffing, Data-Driven

Discipline

Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

2017 IEEE Symposium Series on Computational Intelligence SSCI: Honolulu, November 27 - December 1: Proceedings

First Page

1074

Last Page

1080

ISBN

9781538627266

Identifier

10.1109/SSCI.2017.8285326

Publisher

IEEE

City or Country

Pistacaway, NJ

Embargo Period

12-19-2019

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

Additional URL

https://doi.org/10.1109/SSCI.2017.8285326

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