Building the statistical evidence base for crime linkage decision-support tools with sexual offences
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Published online version
Author(s)
Type
Journal Article
Abstract
Objectives
Develop machine learning algorithms to support behavioural crime linkage of serial sexual offences and to test these algorithms in an ecologically valid way.
Methods
Geographical, temporal, and Modus Operandi (MO) information relating to 10,918 solved stranger sexual offences committed in the United Kingdom (UK) were used to compare 35 algorithmic approaches in terms of their ability to successfully distinguish between linked crimes (committed by the same offender) and unlinked crimes (committed by different offenders). The 35 approaches included different types of algorithm (Bayesian, regression and classification tree) and different methods of utilising MO data. The discrimination accuracy of these 35 approaches was compared using six performance metrics.
Results
The algorithm that utilised the new measure of behavioural similarity developed in this study and the Four Quartiles approach clearly outperformed the remaining 34 approaches across all six performance metrics. (% linked pairs in top 100 ranks = 95.00%; % linked pairs in top 500 ranks = 68.20%; AUPRC Mean [SD] = 0.26 [0.10]; AUC Mean [SD] = 0.95 [0.02]; Median First Rank = 2; Median Rank All Series = 5). Collapsing MO variables did not enhance discrimination accuracy. The new similarity metric developed in this study for quantifying behavioural similarity enhanced discrimination accuracy compared to the metric most commonly used by previous crime linkage research, Jaccard’s coefficient.
Conclusions
Machine learning algorithms demonstrate significant potential for supporting the early identification of linked series of sexual offences in the UK. These findings provide a robust evidence base with which to begin building and implementing computer software to support human decision-making in this domain.
Develop machine learning algorithms to support behavioural crime linkage of serial sexual offences and to test these algorithms in an ecologically valid way.
Methods
Geographical, temporal, and Modus Operandi (MO) information relating to 10,918 solved stranger sexual offences committed in the United Kingdom (UK) were used to compare 35 algorithmic approaches in terms of their ability to successfully distinguish between linked crimes (committed by the same offender) and unlinked crimes (committed by different offenders). The 35 approaches included different types of algorithm (Bayesian, regression and classification tree) and different methods of utilising MO data. The discrimination accuracy of these 35 approaches was compared using six performance metrics.
Results
The algorithm that utilised the new measure of behavioural similarity developed in this study and the Four Quartiles approach clearly outperformed the remaining 34 approaches across all six performance metrics. (% linked pairs in top 100 ranks = 95.00%; % linked pairs in top 500 ranks = 68.20%; AUPRC Mean [SD] = 0.26 [0.10]; AUC Mean [SD] = 0.95 [0.02]; Median First Rank = 2; Median Rank All Series = 5). Collapsing MO variables did not enhance discrimination accuracy. The new similarity metric developed in this study for quantifying behavioural similarity enhanced discrimination accuracy compared to the metric most commonly used by previous crime linkage research, Jaccard’s coefficient.
Conclusions
Machine learning algorithms demonstrate significant potential for supporting the early identification of linked series of sexual offences in the UK. These findings provide a robust evidence base with which to begin building and implementing computer software to support human decision-making in this domain.
Date Issued
2026-03-01
Date Acceptance
2025-06-25
Citation
Journal of Quantitative Criminology, 2026, 42 (1), pp.171-201
ISSN
0748-4518
Publisher
Springer Science and Business Media LLC
Start Page
171
End Page
201
Journal / Book Title
Journal of Quantitative Criminology
Volume
42
Issue
1
Copyright Statement
© The Author(s) 2025 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/.
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Publication Status
Published
Date Publish Online
2025-07-03