LOCAL GOVERNMENT UNIT 2: PUBLIC SAFETY INCIDENT REPORTING SYSTEM: SYSTEMATIC REPORTING, REAL-TIME NOTIFICATION, ANALYTICS, AND AL-DRIVEN FIRST AID ASSISTANCE

Abstract

Public safety is a fundamental component of societal stability, yet many local government units continue to rely on outdated and fragmented incident reporting methods. These inefficiencies often result in delayed emergency response and diminished public trust. In response to the need for a more efficient and transparent approach, this study introduces the Public Safety Incident Reporting System, a digital platform designed to enhance communication between citizens and authorities, automate incident reporting workflows, and integrate artificial intelligence (AI) for first-aid guidance and data-driven decision-making. The system was developed using a modular microservices architecture and built with HTML, CSS, JavaScript, and PHP, employing asynchronous request handling via the Fetch API. Incident reporting includes X and Y coordinate tagging, with future integration of GPS for real-time location mapping. Development followed the Agile Scrum methodology, executed through iterative sprints to incorporate core features such as systematic reporting, real-time notifications, incident validation, analytics dashboards, and AI-powered first-aid assistance. Development tools included Visual Studio Code for coding, GitHub for version control, Postman for API testing, and UptimeRobot for system monitoring. A centralized database was implemented to ensure secure, efficient storage and retrieval of incident data. Initial deployment of the system led to substantial improvements in public safety operations. Incident reporting time was reduced by 60%, and dispatch coordination improved by 40%. The AI-based first-aid module provided real-time guidance to citizens, helping to mitigate injury severity prior to responder arrival. Real-time notifications ensured that reports were routed immediately to the relevant authorities. The inclusion of visual dashboards allowed local government units to monitor trends, prioritize emergencies, and allocate resources effectively. Increased community satisfaction was also observed due to the enhanced speed and accuracy of response. The findings demonstrate the effectiveness of the Public Safety Incident Reporting System in modernizing local emergency response. Key innovations include multimedia reporting for richer incident context and AI-supported first-aid response, which collectively contribute to more responsive and data-informed governance. Planned future enhancements include the integration of predictive analytics using machine learning, expanded GPS functionality, and real-time interoperability with emergency service providers. These improvements aim to further strengthen coordination, safety, and community trust.