Abstract
In the current economic climate, law enforcement agencies are facing resource shortages. The effective and efficient use of scarce resources is therefore of the utmost importance to provide a high standard public safety service. Optimization models specifically tailored to the necessity of police agencies can help to ameliorate their use. The Multicriteria Police Districting Problem (MC-PDP) on a graph concerns the definition of sound patrolling sectors in a police district. The objective of this problem is to partition a graph into convex and continuous subsets, while ensuring efficiency and workload balance among the subsets. The model was originally formulated in collaboration with the Spanish National Police Corps. We propose for its solution three local search algorithms: a Simple Hill Climbing, a Steepest Descent Hill Climbing, and a Tabu Search. To improve their diversification capabilities, all the algorithms implement a multistart procedure, initialized by randomized greedy solutions. The algorithms are empirically tested on a case study on the Central District of Madrid. Our experiments show that the solutions identified by the novel Tabu Search outperform the other algorithms. Finally, research guidelines for future developments on the MC-PDP are given.
Highlights
The Police Districting Problem concerns the definition of sound patrolling sectors in a police district
We propose for its solution three local search algorithms: a Simple Hill Climbing, a Steepest Descent Hill Climbing, and a Tabu Search
The newest member of this family is the Multicriteria Police Districting Problem (MC-PDP) [1]. The novelty of this model stands in that it evaluates the workload associated with a specific patrol sector according to multiple criteria, such as area, crime risk, diameter, and isolation, and that it finds a balance between global efficiency and workload distribution among the agents, according to the preferences of a decision-maker
Summary
The Police Districting Problem concerns the definition of sound patrolling sectors in a police district. When combined with Predictive Policing methodologies [2], the MC-PDP allows designing patrolling configurations that focus the distribution of resources on the most relevant locations, with a consequential improvement in the effectiveness of patrolling operations. This is the rationale of the paper by Camacho-Collados and Liberatore [3] that presented a Decision Support System (DSS) for the implementation of a paradigm of Predictive Patrolling in the SNPC. Their performances are compared and analyzed statistically. We conclude the paper with some guidelines for future research
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
