Space-Time Urban Explorer: A Visual Tool for Exploring Spatiotemporal Crime and Patrolling Data

Spatiotemporal urban data gathered by public security departments holds immense potential for in-depth analysis, enhancing decision-making in areas such as crime prevention and patrolling strategies. However, extracting meaningful patterns from vast and complex spatiotemporal datasets presents a considerable challenge in the field of big data analytics. We introduce a novel visualization-assisted tool tailored for handling massive spatiotemporal urban datasets, with a specific focus on public security data. At the core of this tool are spatial graph vertex ordering algorithms that perform dimensionality reduction on the vertices' locations. To make this tool practical for handling massive spatiotemporal datasets, we present efficient preprocessing techniques. These techniques are carefully crafted to distill and represent urban spatiotemporal datasets, ensuring efficient data exploration. The effectiveness of the proposed solution is validated through case studies using real datasets from the Military Police of the State of Alagoas - Brazil. We demonstrate the effectiveness of the proposed solutions, showcasing the versatility of the visual tool in accomplishing various relevant analytical tasks.