Utilising Data Mining and Predictive Analytics to Enhance Operational Efficiency in Community Policing in Sharjah

This study is motivated by the increasing need for proactive policing strategies to respond to contemporary public safety challenges effectively. A critical void in the existing literature relates to the limited integration of advanced analytics and real-time visualizations within community policing frameworks, especially in the context of the Community Police Department in Sharjah, UAE. This research employs sophisticated methodologies including predictive analytics, geospatial analysis, and Natural Language Processing (NLP) to address this gap. Specifically, the study utilizes the Autoregressive Integrated Moving Average (ARIMA) model for forecasting trends, Kernel Density Estimation (KDE) for geospatial hotspot analysis, and sentiment analysis techniques to examine textual data from police reports. The findings underscore notable enhancements in predictive accuracy, precise resource allocation, and improved community engagement strategies. The implementation of an interactive dashboard for real-time insights significantly bolstered operational responsiveness. This research presents a scalable, practical model bridging theoretical insights with policing operations, providing actionable outcomes and substantial improvements in public safety management.