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.

中文翻译：

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使用罪犯犯罪现场行为将财产犯罪联系起来：方法的比较

这项研究比较了七个统计模型区分关联犯罪和非关联犯罪的能力。这七个模型利用了与住宅盗窃（n  = 180），商业抢劫（n  = 118）和汽车盗窃（n = 376）。使用接收者的操作特征分析并通过检查成功使用这七个模型来成功确定关联犯罪优先于未关联犯罪的能力来评估模型性能。基于回归的模型和概率模型的准确性可比，并且通常比本研究中测试的基于树的模型更准确。Logistic算法实现了住宅盗窃（AUC = 0.903）和商业抢劫（AUC = 0.830）的曲线下面积（AUC）最高，而SimpleLogistic算法实现了汽车盗窃的最大值（AUC = 0.820）。