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Abstract: In contemporary law enforcement, the need for proactive strategies to combat crime and ensure public safety is paramount. This paper presents the development and implementation of a predictive modeling framework aimed at identifying crime hotspots and optimizing resource allocation for law enforcement agencies. The model leverages historical crime data, geographical information, and socio- economic factors to forecast areas at elevated risk of criminal activity. Through a multistage process encompassing data collection, preprocessing, feature engineering, and model training, the predictive model enables law enforcement agencies to anticipate and prioritize areas with the highest likelihood of crime occurrence. By strategically deploying resources to these identified hotspots, law enforcement agencies can intervene early, deter criminal activity, and reduce overall crime rates. This research contributes to the advancement of evidence-based policing practices by offering a scalable framework for crime hotspot mapping that prioritizes efficiency, effectiveness, and community partnership.
Suryawanshi Sejal (Thu,) studied this question.