Multi-officer Routing for Patrolling High Risk Areas Jointly Learned from Check-ins, Crime and Incident Response Data

TL;DR

This paper introduces a joint learning and optimization approach for planning police patrol routes for multiple officers in high-risk areas, utilizing diverse mobility and crime data, and demonstrates its effectiveness with real-world datasets.

Contribution

It presents a novel multi-officer patrol planning framework that integrates check-in, crime, incident, and POI data with meta-heuristic algorithms for optimal routing.

Findings

Outperforms existing methods in real-world tests

Effectively identifies high-risk areas for patrol

Enhances community safety through optimized patrol routes

Abstract

A well-crafted police patrol route design is vital in providing community safety and security in the society. Previous works have largely focused on predicting crime events with historical crime data. The usage of large-scale mobility data collected from Location-Based Social Network, or check-ins, and Point of Interests (POI) data for designing an effective police patrol is largely understudied. Given that there are multiple police officers being on duty in a real-life situation, this makes the problem more complex to solve. In this paper, we formulate the dynamic crime patrol planning problem for multiple police officers using check-ins, crime, incident response data, and POI information. We propose a joint learning and non-random optimisation method for the representation of possible solutions where multiple police officers patrol the high crime risk areas simultaneously first rather than the low crime risk areas. Later, meta-heuristic Genetic Algorithm (GA) and Cuckoo Search (CS) are implemented to find the optimal routes. The performance of the proposed solution is verified and compared with several state-of-art methods using real-world datasets.