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|Title:||Linking Property Crime Using Offender Crime Scene Behaviour: A Comparison of Methods|
Winter, Jan M.
|Citation:||Journal of Investigative Psychology and Offender Profiling, 2019|
|Abstract:||This study compared the ability of seven statistical models to distinguish between linked and unlinked crimes. The seven models utilized 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 (ROC) analysis and by examining the success with which the seven models could successfully prioritize 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 achievied 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 maximized (in some situations) if behavioural domains are utilized 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.|
|Embargo on file until:||25-Mar-2020|
|Rights:||Copyright © 2019, Wiley. Deposited with reference to the publisher’s open access archiving policy. (http://www.rioxx.net/licenses/all-rights-reserved)|
|Description:||The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.|
|Appears in Collections:||Published Articles, Dept. of Criminology|
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|Tonkin et al. (2019).pdf||Post-review (final submitted author manuscript)||503.33 kB||Adobe PDF||View/Open|
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