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@article{204782,
        author = {Sharvi  Sachin Kotpalliwar and Manasvi Abhay Ainchwar and Shruti Wasudeo Bucche and Chaitanya Jogekar and Soham Sirpurwar and Prof. Madhavi Sadu},
        title = {UrbanShield: A Crime Risk Mapping and Real-Time Alert System for Safer Smart Cities},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {4553-4559},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204782},
        abstract = {Urban crime management presents a persistent challenge for law enforcement agencies in rapidly growing Indian cities where reactive policing methods often fall short. UrbanShield is a full-stack web application that integrates crime prediction, real-time emergency alerts, live location sharing, and community-based incident reporting into a single deployed platform. The backend is built on Node.js with Express.js and MongoDB Atlas for persistent storage. The frontend delivers an interactive crime heatmap using Leaflet.js over OpenStreetMap tiles. Emergency notifications are dispatched via the Brevo HTTP API. The system includes a one-tap SOS emergency alert with live GPS tracking, a voluntary location-sharing module, a crime rate predictor that classifies neighbourhoods into four risk tiers using a weighted scoring algorithm, and a community incident reporting system with Claude AI pre-screening followed by admin review and approval. The application is deployed on Railway.app and is accessible on any device without installation. Testing confirms that all major features function correctly across registration, OTP verification, SOS activation, location sharing, crime reporting, and admin moderation workflows.},
        keywords = {Crime Prediction, SOS Alert, Live Location Sharing, Crime Heatmap, Urban Safety, Node.js, Express.js, MongoDB Atlas, Leaflet.js, Brevo HTTP API, Claude AI, Railway.app, OpenStreetMap},
        month = {June},
        }