An Intelligent Crime Risk Prediction Framework using Behavioral Analysis and Advanced Machine Learning

Criminal acts and the nature of criminality in society is ever increasing, therefore requiring systems that are intelligently developed to analyze, predict and reduce the risk of crime. This paper introduces a new crime risk forecasting framework that combines behavioral analysis, spatiotemporal patterns, and machine learning algorithms to evaluate and visualize prospective hazards in real time. The suggested Dynamic Digital Danger Planning (DDDP) model integrates user-generated information, law enforcement information, and demographic inputs to provide customized safety notices and route suggestions. With visual aids like heatmaps and severity-coded maps, users are able to instantly assess the safety of particular locations. Experimental outcomes based on historical crime datasets prove that the Random Forest classifier had the best accuracy (94%) among tested models, surpassing Decision Trees and SVMs in precision and recall. The system prioritizes ethical data use, transparency, and real-time responsiveness, providing an effective tool for proactive personal safety planning and predictive policing. Unlike current crime forecasting systems, our system combines real-time behavioral data, user-generated information, and adaptive alert systems to provide highly localized, personalized, and timely risk estimation. In addition to this system uses a new geofencing-based alerting feature and route optimization using forecasted hotspot patterns.