Digital Twins for Simulation of Law Enforcement Operations in Urban Environments

Urban areas law enforcement agencies whose work presents a very dynamic, complex environment in which making good decisions requires a level of situational awareness, fast reaction time, and the optimum use of resources. This paper proposes a digital twin-based framework for simulation and data-driven and scalable analysis of policing strategies in the urban environment, for the purpose of law enforcement operations. The proposed approach includes the integration of urban geographical information system (GIS) data, sensor data in real time, mobility patterns, and agent-based behavior models in a uniform digital twin architecture. Artificial intelligence backed-analytics and optimization are integrated that facilitates-predictive simulation, what-if scenarios analysis and operations plan under uncertainty. Experimental results show that in terms of response time, hotspot coverage, incident mitigation and resources utilization, the results of the proposed planning method are obviously improved compared with traditional planning method. The findings emphasize the usefulness of the digital twins to support the efforts towards improved operational readiness, strategic planning and for effective training, while not violating any policies or denying ethics. This research includes the addition of rational methodology on the adoption of digital twins as decision support and simulation in the modern urban law enforcing systems.

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