Ride Shield: Bike Theft Analysis

Abstract

Bike theft remains a recurring urban problem that causes significant financial loss and social inconvenience. Conventional crime monitoring approaches are largely reactive, relying on manual reporting and post-incident investigation, which limits timely preventive action. This paper presents Ride Shield, a spatial–temporal bike theft analysis and mapping system designed to transform historical crime data into actionable visual intelligence for smart city decision support. The proposed framework integrates a MongoDB database, Fast API backend, and a React–Tailwind interactive dashboard to process multi-year theft records. Robust data preprocessing techniques, including field normalization, geolocation validation, and temporal parsing, ensure reliable analytical inputs. Spatial aggregation and time-based profiling are performed to identify high-risk zones, peak theft hours, and frequently targeted vehicle categories. The system generates interactive geospatial heatmaps and trend visualizations that highlight theft density and recurring patterns across police jurisdictions. Experimental findings reveal consistent hotspot concentrations in high-traffic urban regions and peak activity during evening hours (18:00–22:00). By converting raw crime records into intuitive visual analytics, Ride Shield enhances situational awareness and supports data-driven patrol allocation and preventive planning. The framework establishes a scalable foundation for future integration of predictive modeling and real-time crime data streams within smart city infrastructures.