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Crime hotspot mapping using the crime related factors—a spatial data mining approach

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Abstract

The technique of Hotspot Mapping is widely used in analysing 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 utilizing these related factors. In this study, we introduce a new crime hotspot mapping tool—Hotspot Optimization Tool (HOT). HOT is an application of spatial data miming 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 utilizing 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.

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Acknowledgements

The work was partially funded by the National Institute of Justice (No. 2009-DE-BX-K219).

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Authors and Affiliations

  1. Department of Computer Science, University of Massachusetts Boston, 100 Morrissey Blvd., Boston, MA, 02125-3393, USA

    Dawei Wang, Wei Ding & Henry Lo

  2. Department of Geography, University of Cincinnati, 2600 Clifton Ave., Cincinnati, OH, 45221, USA

    Tomasz Stepinski

  3. Department of Computer Science, Rice University, 6100 Main, Houston, TX, 77005-1827, USA

    Josue Salazar

  4. Department of Criminology and Criminal Justice, University of Massachusetts Lowell, 1 University Avenue, Lowell, MA, 01854, USA

    Melissa Morabito

Authors
  1. Dawei Wang
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  2. Wei Ding
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  3. Henry Lo
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  4. Tomasz Stepinski
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  5. Josue Salazar
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  6. Melissa Morabito
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Correspondence to Wei Ding.

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Wang, D., Ding, W., Lo, H. et al. Crime hotspot mapping using the crime related factors—a spatial data mining approach. Appl Intell 39, 772–781 (2013). https://doi.org/10.1007/s10489-012-0400-x

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