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Linking property crime using offender crime scene behaviour: A comparison of methods
Author(s) -
Tonkin Matthew,
Lemeire Jan,
Santtila Pekka,
Winter Jan M.
Publication year - 2019
Publication title -
journal of investigative psychology and offender profiling
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.479
H-Index - 22
eISSN - 1544-4767
pISSN - 1544-4759
DOI - 10.1002/jip.1525
Subject(s) - property crime , logistic regression , statistics , psychology , property (philosophy) , computer science , offender profiling , econometrics , probabilistic logic , criminology , artificial intelligence , machine learning , mathematics , violent crime , bayesian network , philosophy , epistemology
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.