Abstract:

Community policing in Sharjah encounters difficulties in resource distribution, crime deterrence, and response enhancement.  This research examines the utilisation of data mining and predictive analytics to improve operational efficiency in community policing.  Utilising historical crime data, real-time event reports, and socio-economic factors, sophisticated analytical models may discern crime patterns, forecast high-risk regions, and enhance patrol allocation.  Machine learning methodologies facilitate anticipatory decision-making, improving reaction times and public safety.  This research underscores the potential of data-driven policing techniques in Sharjah, providing insights into their deployment and influence on law enforcement efficacy.