Using offender crime scene behavior to link stranger sexual assaults
| Autor: | J.J. van der Kemp, Jessica Woodhams, Matthew Tonkin, Leah Ashmore-Hills, Amy Burrell, Gerard N. Labuschagne, Tom Pakkanen, Jan Winter, Pekka Santtila, Jukka Sirén, C.G. Salfati, Mark Webb, S. Lipponen, Lee Rainbow, E. Lam, Craig Bennell, Hanne Imre, G. ten Brinke |
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| Přispěvatelé: | Personality and Psychopathology, Faculty of Psychology and Educational Sciences, Clinical and Lifespan Psychology, Transnational Legal Studies, Criminology, A-LAB, Empirical and Normative Studies |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
SDG 16 - Peace
Sociology and Political Science Social Psychology Bayesian probability Bayesian analysis Classification tree analysis Poison control Logistic regression 02 engineering and technology Statistics 0202 electrical engineering electronic engineering information engineering Crime scene Applied Psychology 0505 law Linkage (software) business.industry Crime linkage Decision tree learning 05 social sciences SDG 16 - Peace Justice and Strong Institutions Usability 16. Peace & justice Comparative case analysis Justice and Strong Institutions 050501 criminology 020201 artificial intelligence & image processing Stranger sexual assault business Psychology Law Social psychology Coding (social sciences) |
| Zdroj: | Tonkin, M, Pakkanen, T, Sirén, J, Bennell, C, Woodhams, J, Burrell, A, Imre, H, Winter, J M, Lam, E, ten Brinke, G, Webb, M, Labuschagne, G N, Ashmore-Hills, L, van der Kemp, J J, Lipponen, S, Rainbow, L, Salfati, C G & Santtila, P 2017, ' Using offender crime scene behavior to link stranger sexual assaults : A comparison of three statistical approaches ', Journal of Criminal Justice, vol. 50, pp. 19-28 . https://doi.org/10.1016/j.jcrimjus.2017.04.002 Journal of Criminal Justice, 50, 19-28. Elsevier BV |
| ISSN: | 0047-2352 |
| DOI: | 10.1016/j.jcrimjus.2017.04.002 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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