Methods used to link crimes using behaviour: A literature review
This paper reviews the crime linkage literature to identify how data were pre-processed for analysis, methods used to predict linkage status/series membership, and methods used to assess the accuracy of linkage predictions. Thirteen databases were searched, with 77 papers meeting the inclusion/exclu...
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| Published in: | Aggression and violent behavior Vol. 79; p. 102014 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.11.2024
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| ISSN: | 1359-1789 |
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| Abstract | This paper reviews the crime linkage literature to identify how data were pre-processed for analysis, methods used to predict linkage status/series membership, and methods used to assess the accuracy of linkage predictions. Thirteen databases were searched, with 77 papers meeting the inclusion/exclusion criteria. Methods used to pre-process data were human judgement, similarity metrics (including machine learning approaches), spatial and temporal measures, and Mokken Scaling. Jaccard's coefficient and other measures of similarity (e.g., temporal proximity, inter-crime distance, similarity vectors) are the most common ways of pre-processing data. Methods for predicting linkage status were varied and included human (expert) judgement, logistic regression, multi-dimensional scaling, discriminant function analysis, principal component analysis and multiple correspondence analysis, Bayesian methods, fuzzy logic, and iterative classification trees. A common method used to assess linkage-prediction accuracy was to calculate the hit rate, although position on a ranked list was also used, and receiver operating characteristic (ROC) analysis has emerged as a popular method of assessing accuracy.
•Behaviour can be used to link crime series•Research has examined the accuracy of crime linkage using different methods•Pre-processing of data is required before making predictions•Methods for predicting linkage status are varied•Receiver Operating Characteristic (ROC) is a popular method of assessing accuracy•Accuracy estimation is vital for assessing the validity of crime linkage advice |
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| AbstractList | This paper reviews the crime linkage literature to identify how data were pre-processed for analysis, methods used to predict linkage status/series membership, and methods used to assess the accuracy of linkage predictions. Thirteen databases were searched, with 77 papers meeting the inclusion/exclusion criteria. Methods used to pre-process data were human judgement, similarity metrics (including machine learning approaches), spatial and temporal measures, and Mokken Scaling. Jaccard's coefficient and other measures of similarity (e.g., temporal proximity, inter-crime distance, similarity vectors) are the most common ways of pre-processing data. Methods for predicting linkage status were varied and included human (expert) judgement, logistic regression, multi-dimensional scaling, discriminant function analysis, principal component analysis and multiple correspondence analysis, Bayesian methods, fuzzy logic, and iterative classification trees. A common method used to assess linkage-prediction accuracy was to calculate the hit rate, although position on a ranked list was also used, and receiver operating characteristic (ROC) analysis has emerged as a popular method of assessing accuracy.
•Behaviour can be used to link crime series•Research has examined the accuracy of crime linkage using different methods•Pre-processing of data is required before making predictions•Methods for predicting linkage status are varied•Receiver Operating Characteristic (ROC) is a popular method of assessing accuracy•Accuracy estimation is vital for assessing the validity of crime linkage advice |
| ArticleNumber | 102014 |
| Author | Costello, Benjamin Burrell, Amy Woodhams, Jessica |
| Author_xml | – sequence: 1 givenname: Amy orcidid: 0000-0002-2350-1110 surname: Burrell fullname: Burrell, Amy email: a.burrell@bham.ac.uk – sequence: 2 givenname: Benjamin orcidid: 0000-0003-4744-5659 surname: Costello fullname: Costello, Benjamin email: b.d.costello@bham.ac.uk – sequence: 3 givenname: Jessica orcidid: 0000-0002-9674-6653 surname: Woodhams fullname: Woodhams, Jessica email: j.woodhams@bham.ac.uk |
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| Keywords | Statistical methods Behavioural analysis Comparative case analysis Crime linkage Machine learning |
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