Linking property crime using offender crime scene behaviour: A comparison of methods. Issue 2 (25th March 2019)

• Record Type: Journal Article
• Authors: Tonkin, Matthew; Lemeire, Jan; Santtila, Pekka; Winter, Jan M.
• Is Part Of: Journal of investigative psychology and offender profiling. Volume 16:Issue 2(2019)
• Page Start: 75
• Page End: 90
• Publication Date: 2019-03-25
• Subjects: classification tree analysis -- crime linkage -- decision‐support tools -- logistic regression -- offender crime scene behaviour -- probabilistic models; Criminal psychology -- Periodicals; Criminal behavior, Prediction of -- Periodicals; 365.3
• DOI: 10.1002/jip.1525
• Languages: English
• ISSNs: 1544-4759

Abstract

Abstract: This study compared the ability of seven statistical models to distinguish between linked and unlinked crimes. The seven models utilised geographical, temporal, and modus operandi information relating to residential burglaries ( n = 180), commercial robberies, ( n = 118), and car thefts ( n = 376). Model performance was assessed using receiver operating characteristic analysis and by examining the success with which the seven models could successfully prioritise linked over unlinked crimes. The regression‐based and probabilistic models achieved comparable accuracy and were generally more accurate than the tree‐based models tested in this study. The Logistic algorithm achieved the highest area under the curve (AUC) for residential burglary (AUC = 0.903) and commercial robbery (AUC = 0.830) and the SimpleLogistic algorithm achieving the highest for car theft (AUC = 0.820). The findings also indicated that discrimination accuracy is maximised (in some situations) if behavioural domains are utilised rather than individual crime scene behaviours and that the AUC should not be used as the sole measure of accuracy in behavioural crime linkage research.