This study introduces a new crime hotspot mapping tool called “Hotspot Optimization Tool” (HOT). HOT is an application of spatial data mining to the field of hotspot mapping.
The key component of HOT is the Geospatial Discriminative Patterns (GDPatterns) concept, which can capture the differences between two classes in a spatial dataset. Experiments are done using a real- world dataset from a northeastern city in the United States, and the pros and cons of using related factors in hotspot mapping are discussed. Comparison studies with the Hot Spot Analysis tool implemented by Esri ArcMap 10.1 validate that HOT is capable of accurately mapping crime hotspots. The technique of Hotspot Mapping is widely used in analyzing the spatial characteristics of crimes. The spatial distribution of crime is considered to be related with a variety of socio-economic and crime opportunity factors. But existing methods usually focus on the target crime density as input without using these related factors. (Publisher abstract provided)
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