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An Exploration of Crime Prediction Using Data Mining on Open Data

Author

Listed:
  • Ginger Saltos

    (Facultad de Ingeniería en Electricidad y Computación, Escuela Superior Politécnica del Litoral, ESPOL, Ecuador)

  • Mihaela Cocea

    (#x2020;School of Computing, University of Portsmouth, UK)

Abstract

The increase in crime data recording coupled with data analytics resulted in the growth of research approaches aimed at extracting knowledge from crime records to better understand criminal behavior and ultimately prevent future crimes. While many of these approaches make use of clustering and association rule mining techniques, there are fewer approaches focusing on predictive models of crime. In this paper, we explore models for predicting the frequency of several types of crimes by LSOA code (Lower Layer Super Output Areas — an administrative system of areas used by the UK police) and the frequency of anti-social behavior crimes. Three algorithms are used from different categories of approaches: instance-based learning, regression and decision trees. The data are from the UK police and contain over 600,000 records before preprocessing. The results, looking at predictive performance as well as processing time, indicate that decision trees (M5P algorithm) can be used to reliably predict crime frequency in general as well as anti-social behavior frequency.

Suggested Citation

  • Ginger Saltos & Mihaela Cocea, 2017. "An Exploration of Crime Prediction Using Data Mining on Open Data," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(05), pages 1155-1181, September.
    • Handle: RePEc:wsi:ijitdm:v:16:y:2017:i:05:n:s0219622017500250
    DOI: 10.1142/S0219622017500250
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    References listed on IDEAS

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    Cited by:

    1. Azam Amir & Michael Henry, 2023. "Reverse Engineering of Maintenance Budget Allocation Using Decision Tree Analysis for Data-Driven Highway Network Management," Sustainability, MDPI, vol. 15(13), pages 1-18, July.

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