Linking Property Crime Using Offender Crime Scene Behaviour: A Comparison of Methods

Matthew Tonkin, Jan Lemeire, Jan Winter, Pekka Santtila

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


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 regressionbased and probabilistic models achieved comparable accuracy and were generally more accurate than the treebased 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.
Original languageEnglish
Pages (from-to)75-90
Number of pages16
JournalJournal of Investigative Psychology and Offender Profiling
Issue number2
Publication statusPublished - Jun 2019


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