STRATEGIC PATROL OPTIMIZATION: A PROFICIENCY-AWARE MODEL FOR CRIME PREVENTION IN URBAN HOTSPOTS

STRATEGIC PATROL OPTIMIZATION

Abstract

 This study presents the Capacitated Police Patrol Area Covering Model (CPPACM), a ground-breaking optimization framework that introduces three transformative dimensions to police resource allocation: (1) crime-severity-weighted hotspot prioritization \( L_{i}^{c,s} \), (2) proficiency-aware team deployment \( P_{t}^{c,s} \) and (3) scenario-driven stochastic coverage \( Q_{i}^{c,s} \). Through comprehensive sensitivity analysis, we demonstrated how coverage radius and team availability interacts to affect system performance. Formulated as a binary integer program, CPPACM achieved 96\% coverage of 25 hotspots using 1\,km radius of coverage and 10 available patrol teams with computational efficiency ($<$4 seconds). The model's unique parameters will enable low enforcement agencies to quantify trade-offs between resource investment and public safety outcomes.