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The safety of citizens is an integral part of any smart city project. Police patrol provides an effective way to detect suspects and possible crimes. However, policing is a limited resource just like any other service that a Smart City provides. In order to efficiently consume this resource, the city has several aspects that can be controlled to make efficient use of Police patrolling: where (area), what (number), when (hour). In this paper, we utilize the LA County Sheriff's open crime dataset to study the police patrol planning problem. We propose a novel approach to build a network of clusters to efficiently assign patrols based on informational entropy. This minimizes Police time-to-arrival and lowers the overall numbers of police on patrol. Our algorithm relies upon the categories of crimes, and the locations of crimes. Since we use real-time traffic analysis to join crime clusters, our solution is extensible enough to be applied to any metropolitan area.


Peter Faber Business School

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Conference Paper

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