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Exploring the homogeneity of theft offenders in spatio-temporal crime hotspots

Chen, T; Bowers, K; Cheng, T; Zhang, Y; Chen, P; (2020) Exploring the homogeneity of theft offenders in spatio-temporal crime hotspots. Crime Science , 9 , Article 9. 10.1186/s40163-020-00115-8. Green open access

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Abstract

Ofender homogeneity occurs when the same criminal group is composed of ofenders with similar attributes (e.g., socio-economic-demographics). Exploring the homogeneity of ofenders within spatio-temporal crime hotspots (STCHs) is useful for understanding not only the generational mechanisms of crime hotspots, but also has crime prevention implications. However, the homogeneity of ofenders within STCHs has not been explored in criminological studies hitherto. Indeed, current techniques of STCH detection are limited to using statistical clustering methods in existing studies that lack the ability to identify the shape of STCHs or the distribution and variety of ofences/ofender activity with them. In this study, we utilise a spatio-temporal clustering algorithm called ST-DBSCAN to determine STCHs. We then propose novel entropy-based indices that measure the similarity of ofenders (and ofences) within STCHs. The method is demonstrated using theft crime records in the central area of Beijing, China. The results show that theft in the city is concentrated in a narrow space and time span (STCHs) and that within these associated ofenders with similar social demographics, referred to as homogeneous ofender groups are detectable

Type: Article
Title: Exploring the homogeneity of theft offenders in spatio-temporal crime hotspots
Open access status: An open access version is available from UCL Discovery
DOI: 10.1186/s40163-020-00115-8
Publisher version: https://doi.org/10.1186/s40163-020-00115-8
Language: English
Additional information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: Spatio-temporal clustering, Homogenous ofenders, ST-DBSCAN, Entropy, Near-repeat victimization, Unsupervised learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science
URI: https://discovery.ucl.ac.uk/id/eprint/10100541
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