Real-Time Alert System Based on Crime Area Mapping

In Crime prevention and public safety remain major challenges in rapidly urbanizing cities due to increasing population density, complex socio-economic conditions, and evolvingcriminal patterns. Traditional crime monitoring systems are primarily reactive and rely heavily on static historical data sets, limiting their ability to provide real-time insights and predictiveintelligence. This paper proposes a Real-Time Crime Hotspot Detection and Predictive Alert System that integrates machine learning algorithms, clustering techniques, and Geographic Information System (GIS)-based visualization to enhance proactive crime prevention. The system utilizes K-Means and DBSCAN clustering algorithms to identify spatial crime hotspots, whileRandom Forest and Long Short-Term Memory (LSTM) models are employed to forecast future crime trends based on temporal patterns. In addition, a GPS-enabled alert mechanism isincorporated to notify users when they enter high-risk areas, thereby improving situational awareness and personal safety. The proposed framework also supports user-generated crime reporting to ensure dynamic and continuously updated datasets. Experimental evaluation demonstrates that the hybrid predictive approach improves hotspot detection accuracy and forecasting performancecompared to standalone models. The integration of real-time alerts,predictive analytics, and interactive mapping provides a scalableand intelligent solution for assisting law enforcement agencies and enhancing community safety. The proposed system contributes toward the development of data- driven smart city crime prevention strategies.Keywords— Crime Prediction; Crime Hotspot Detection;Machine Learning; DBSCAN; K-Means; Random Forest; LSTM;Geographic Information System (GIS); Real-Time Alert System;Smart City Safety; Spatio-Temporal Analysis.