The Effect of Police Deployment Strategy on Emergency Response Times: An Agent-based Modelling Investigation

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Abstract

Abstract Objectives: This study investigates the impact of three police deployment strategies on emergency response times using agent-based modelling (ABM). Specifically, it evaluates the effectiveness of random patrol, stationary deployment (optimal spreading), and static deployment (idling at last incident location). It further examines how key variables—urbanisation, call volume, and police capacity—moderate these effects. Methods: A detailed ABM was developed using NetLogo, integrating real-world data: historical calls for service (CFS), jurisdiction shapefiles, and street network data from the Netherlands. The model simulated police travel and response dynamics across 300 runs, varying deployment strategies, urbanisation levels, call volumes, and police capacities. Outputs were analysed to assess response times, fast response rates ( 13 minutes). Methods were preregistered at https://osf.io/yrwdp/ Results: On average, stationary deployment reduced response times by 35% (SD ±14%), increased fast responses by 74% (SD ±40%), and decreased late responses by 66% (SD ±33%) compared to random patrol. Static deployment also outperformed random patrol, reducing response times by 13% (SD ±9%), increasing fast responses by 22% (SD ±14%), and reducing late responses by 42% (SD ±36%). The advantages of stationary and static deployment were most pronounced in rural areas and at lower police capacities. Urbanisation reduced the performance gap between strategies, while higher call volumes modestly diminished the relative benefits of stationary deployment. Conclusions: This study highlights the significant impact of police deployment strategies on response times and rapid interventions. These findings underscore the need for further research on rapid response. The modular ABM framework offers a valuable tool for adapting investigations to different policing contexts, enhancing external validity.