An Exploration of Crime Prediction Using Data Mining on Open Data

Ginger Saltos () and Mihaela Cocea ()
Additional contact information
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

International Journal of Information Technology & Decision Making (IJITDM), 2017, vol. 16, issue 05, 1155-1181

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.

Keywords: Crime prediction; data mining; open data; regression; decision trees; instance-based learning (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622017500250
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:16:y:2017:i:05:n:s0219622017500250

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219622017500250

Access Statistics for this article

International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi

More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().