Municipal crime and accident management system using geospatial mapping and aibased predictive analytics

Abstract

The present utility model discloses a Municipal Crime and Accident Management System that integrates incident reporting, response monitoring, data analytics, geospatial mapping, and AI-based predictive analytics into a unified platform designed for local government units (LGUs). The system enables citizens to submit crime and accident reports through mobile or web-based devices, which are then visualized on an interactive map and processed through a centralized server architecture. Responders and chiefs of responders utilize role-specific dashboards to monitor incidents in real time, assign field personnel, and track response progress. An AI-based module performs spatio-temporal analysis to forecast hotspots and trends, enabling proactive planning. The system also features facial recognition-based user verification and an audit trail to ensure accountability and transparency. A modular, role-based interface supports distinct functionalities for report senders, responders, chiefs, and administrators, enabling coordinated and data-driven municipal public safety management