Using offender crime scene behavior to link stranger sexual assaults: A comparison of three statistical approaches

Matthew Tonkin, Tom Pakkanen, Jukka Siren, Craig Bennell, Jessica Woodhams, Amy Burrell, Hanne Imre, Jan M. Winter, Eva Lam, Gert Jan ten Brinke, Mark Webb, Gerard Labuschagne, Leah Ashmore-Hills, Jasper van der Kemp, Sami Lipponen, Lee Rainbow, Gabrielle Salfati, Pekka Santtila

Research output: Contribution to journalArticle

9 Citations (Scopus)
38 Downloads (Pure)

Abstract

Purpose: This study compared the utility of different statistical methods in differentiating sexual crimes committed by the same person from sexual crimes committed by different persons.
Methods: Logistic regression, iterative classification tree (ICT), and Bayesian analysis were applied to a dataset of 3,364 solved, unsolved, serial, and apparent one-off sexual assaults committed in five countries. Receiver
Operating Characteristic analysis was used to compare the statistical approaches.
Results: All approaches achieved statistically significant levels of discrimination accuracy. Two out of three Bayesian methods achieved a statistically higher level of accuracy (Areas Under the Curve [AUC] =0.89 [Bayesian coding method 1]; AUC =0.91 [Bayesian coding method 3]) than ICT analysis (AUC =0.88), logistic
regression (AUC= 0.87), and Bayesian coding method 2 (AUC =0.86).
Conclusions: The ability to capture/utilize between-offender differences in behavioral consistency appear to be of benefit when linking sexual offenses. Statistical approaches that utilize individual offender behaviors when
generating crime linkage predictions may be preferable to approaches that rely on a single summary score of behavioral similarity. Crime linkage decision-support tools should incorporate a range of statistical methods and
future research must compare these methods in terms of accuracy, usability, and suitability for practice.

Publisher Statement: NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Criminal Justice. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Criminal Justice, [50, (2017)] DOI: 10.1016/j.jcrimjus.2017.04.002

© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Original languageEnglish
Pages (from-to)19-28
Number of pages10
JournalJournal of Criminal Justice
Volume50
Early online date18 Apr 2017
DOIs
Publication statusPublished - May 2017

Keywords

  • Crime linkage
  • Comparative case analysis
  • Bayesian analysis
  • Logistic regression
  • Classification tree analysis
  • Stranger sexual assault

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