Incident Reporting and Hotspot Detection

Abstract

The rapid rise in urban crime and public safety concerns has driven the need for inno vative technological solutions. Traditional incident reporting methods lack real-time data processing and predictive capabilities, leading to delayed responses and inefficient crime management. This project aims to develop an Safety-driven incident reporting and hotspot detection application that integrates machine learning, large language models (LLMs), and data visualization. The application will enable users to report incidents in real-time, predict high-risk zones, and provide community verification fea tures to filter out false reports. By leveraging advanced data-driven approaches, the system seeks to enhance public safety, optimize resource allocation, and assist law enforcement agencies in proactive crime prevention