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There is an abundance of literature on serial homicide from a Western perspective that outlines operational definitions, types of offenders and how they prey upon their victims. However, currently, there is a lack of studies that compare serial homicide in different countries. The current study aims to give an overview of the demographics of serial homicide offenders and victims in South Africa and compare these to the demographics of offenders and victims from other currently available empirical studies of other countries. The sample consisted of 33 out of the total 54 solved series in South Africa between 1936-2007, which includes a total of 33 offenders, 302 victims, and 254 crime scenes. Results of the sample as a whole showed that South African serial homicide offenders are similar to offenders in other countries in terms of their actions at the crime scene and victim choice, with some notable exceptions. Additional analysis looked at the offender's consistency of targeting certain types of victims across their homicide series in comparison with the patterns of serial homicide offenders in other countries.

Purpose To conduct a test of the principles underpinning crime linkage (behavioural consistency and distinctiveness) with a sample more closely reflecting the volume and nature of sexual crimes with which practitioners work, and to assess whether solved series are characterized by greater behavioural similarity than unsolved series. Method A sample of 3,364 sexual crimes (including 668 series) was collated from five countries. For the first time, the sample included solved and unsolved but linked‐by‐DNA sexual offence series, as well as solved one‐off offences. All possible crime pairings in the data set were created, and the degree of similarity in crime scene behaviour shared by the crimes in each pair was quantified using Jaccard's coefficient. The ability to distinguish same‐offender and different‐offender pairs using similarity in crime scene behaviour was assessed using Receiver Operating Characteristic analysis. The relative amount of behavioural similarity and distinctiveness seen in solved and unsolved crime pairs was assessed. Results An Area Under the Curve of .86 was found, which represents an excellent level of discrimination accuracy. This decreased to .85 when using a data set that contained one‐off offences, and both one‐off offences and unsolved crime series. Discrimination accuracy also decreased when using a sample composed solely of unsolved but linked‐by‐DNA series (AUC = .79). Conclusions Crime linkage is practised by police forces globally, and its use in legal proceedings requires demonstration that its underlying principles are reliable. Support was found for its two underpinning principles with a more ecologically valid sample.

Purpose: This study compared the utility of different statistical methods in differentiating sexual crimes committed by the same person from sexual crimes committed by different persons.Methods: Logistic regression, iterative classification tree (ICT), and Bayesian analysis were applied to a dataset of 3,364 solved, unsolved, serial, and apparent one-off sexual assaults committed in five countries. Receiver Operating Characteristic analysis was used to compare the statistical approaches.Results: All approaches achieved statistically significant levels of discrimination accuracy.Two out of three Bayesian methods achieved a statistically higher level of accuracy (Areas Under the Curve [AUC] = 0.89 [Bayesian coding method 1]; AUC = 0.91 [Bayesian coding method 3]) than ICT analysis (AUC = 0.88), logistic regression (AUC = 0.87), and Bayesian coding method 2 (AUC = 0.86). Conclusions:The ability to capture/utilize between-offender differences in behavioral consistency appear to be of benefit when linking sexual offenses. Statistical approaches that utilize individual offender behaviors when generating crime linkage predictions may be preferable to approaches that rely on a single summary score of behavioral similarity. Crime linkage decision-support tools should incorporate a range of statistical methods and future 2 research must compare these methods in terms of accuracy, usability, and suitability for practice.

Abstract