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

Chen, Tongxin; Bowers, Kate; Cheng, Tao; Zhang, Yang; Chen, Peng

Authors

Kate Bowers

Tao Cheng

Yang Zhang

Peng Chen



Abstract

Offender homogeneity occurs when the same criminal group is composed of offenders with similar attributes (e.g., socio-economic-demographics). Exploring the homogeneity of offenders 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 offenders 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 offences/offender 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 offenders (and offences) 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 offenders with similar social demographics, referred to as homogeneous offender groups are detectable.

Citation

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(1), Article 9. https://doi.org/10.1186/s40163-020-00115-8

Journal Article Type Article
Acceptance Date May 7, 2020
Online Publication Date Jun 10, 2020
Publication Date Jun 10, 2020
Deposit Date Nov 6, 2024
Publicly Available Date Nov 7, 2024
Journal Crime Science
Print ISSN 2193-7680
Electronic ISSN 2193-7680
Publisher SpringerOpen
Peer Reviewed Peer Reviewed
Volume 9
Issue 1
Article Number 9
DOI https://doi.org/10.1186/s40163-020-00115-8
Keywords Spatio-temporal clustering; Homogenous offenders; ST-DBSCAN; Entropy; Near-repeat victimization; Unsupervised learning
Public URL https://hull-repository.worktribe.com/output/4909983

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0

Copyright Statement
© The Author(s) 2020.
Open Access 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.




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