Smart Policing Models for Enhancing Efficiency, Accountability, and Public Safety Outcomes

Abstract

Smart policing models integrate data analytics, digital technologies, and evidence-based strategies to improve crime prevention, operational efficiency, and community engagement. This study examines the implementation and effectiveness of smart policing approaches by analyzing data from three metropolitan police departments adopting predictive analytics, geospatial mapping, real-time crime centers, surveillance networks, and integrated communication systems. Using a mixed-methods design combining quantitative crime data analysis (2018–2023), interviews with 28 police personnel, and field observations, the research identifies key factors influencing the performance of smart policing. Results show a 17.8% reduction in property crime, a 12.4% improvement in response times, and a 31% increase in case clearance rates following the deployment of digital tools and data-driven decision models. Interview insights highlight improved situational awareness, streamlined coordination, and enhanced transparency due to technology integration. However, challenges persist, including data governance concerns, unequal technological access across units, and the need for continuous training. This study concludes that smart policing offers a robust framework for strengthening operational capability and institutional accountability. The research contributes to policing science by presenting an evidence-based model for integrating technology, analytics, and community-oriented practices in modern police operations.