City Crime Hotspot Identification and Visualization Development of a Web Application for Improving Urban Safety and Decision Making

Abstract: Urban crime hotspots present significant challenges to public safety and effective city planning. This study proposes a web-based crime hotspot identification and visualization system that utilizes geospatial mapping, machine learning, and data analytics. The application empowers law enforcement agencies and policymakers by classifying high-risk areas, tracking evolving crime trends, and predicting future crime occurrences. The system’s interactive dashboard delivers real-time, data-driven insights to support strategic decision-making and improve urban safety outcomes. Keywords: Crime Mapping, Hotspot Identification, Geospatial Analysis, Data Visualization, Machine Learning, Predictive Analytics, Data Analysis, Crime Trend Analysis, Risk Classification.