Studies have shown that it is possible to link serial crimes in an accurate fashion based on the statistical analysis of crime scene information. Logistic regression (LR) is one of the most common statistical methods in use and yields relatively accurate linking decisions. However, some research suggests there may be added value in using classification tree (CT) analysis to discriminate between offences committed by the same vs. different offenders. This study explored how three variations of CT analysis can be applied to the crime linkage task. Drawing on a sample of serial sexual assaults from Quebec, Canada, we examine the predictive accuracy of standard, iterative, and multiple CTs, and we contrast the results with LR analysis. Our results revealed that all statistical approaches achieved relatively high (and similar) levels of predictive accuracy, but CTs produce idiographic linking strategies that may be more appealing to practitioners. Future research will need to examine if and how these CTs can be useful as decision aides in operational settings.

研究表明，根据犯罪现场信息的统计分析，可以准确地将连环犯罪联系起来。逻辑回归 (LR) 是最常用的统计方法之一，可以产生相对准确的链接决策。然而，一些研究表明，使用分类树 (CT) 分析来区分相同和不同罪犯所犯的罪行可能会有附加值。本研究探讨了 CT 分析的三种变体如何应用于犯罪关联任务。利用来自加拿大魁北克省的一系列性侵犯样本，我们检查了标准、迭代和多次 CT 的预测准确性，并将结果与​​ LR 分析进行了对比。我们的结果表明，所有统计方法都实现了相对较高（和相似）水平的预测准确性，但 CT 产生了可能对从业者更具吸引力的具体链接策略。未来的研究将需要检查这些 CT 是否以及如何作为操作环境中的决策助手有用。