Since existing mapping methods usually identify hotspots without considering the underlying correlates of crime, this study introduces a spatial data mining framework to study crime hotspots through their related variables.
The authors used Geospatial Discriminative Patterns (GDPatterns) to capture the significant difference between two classes (hotspots and normal areas) in a geo-spatial dataset. Utilizing GDPatterns, the authors developed a novel model—Hotspot Optimization Tool (HOT)—to improve the identification of crime hotspots. Finally, based on a similarity measure, we group GDPattern clusters and visualize the distribution and characteristics of crime related variables. The approach was evaluated using a real-world dataset collected from a northeast city in the United States. (Published abstract provided)
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