Linking commercial burglaries by modus operandi: tests using regression and ROC analysis

One and a half centuries of serial homicide in Italy: offender, victim and offence characteristics

The present study reports offender, victim and offence characteristics of the entire population of known serial homicide offenders identified in Italy between 1848 and 2019 (59 offenders, 244 victims, of whom 50.4% were men, and 21.7% were sex workers). We found that most of the offenders (72.4%) had a personality disorder. The offenders' age during their series was 35.1 (SD = 11.3) years, on average. Sexual elements were found in 28.4% of the crime scenes. The median time interval between homicides was 2.8 months. Also, we investigated the consistency of the offence characteristics over the series and found significant correlations between the offence characteristics between a previous and the subsequent homicide.

Characteristics and Behaviors of Anonymous Users of Dark Web Platforms Suspected of Child Sexual Offenses

International law enforcement have noted a rise in the use of the Dark Web to facilitate and commit sexual offenses against children, both prior to and since the start of the COVID-19 pandemic. The study presented here therefore aimed to investigate the characteristics and behaviors of anonymous users of Dark Web platforms who were suspected of engaging in the sexual abuse of children. Naturally-occurring data on 53 anonymous suspects, who were active on the Dark Web and had come to police attention in the United Kingdom (UK), were sampled. Analysis of the data yielded 462 features that could be coded reliably. Analysis of these features provided novel insights into suspects’ characteristics, their motivations for using the Dark Web, the nature of the offending behavior they reported engaging in, their technical and security precautions, sexual interests, and the content of their interactions with one another. Findings are discussed in relation to theoretical and practical implications, as well as directions for future research.

MATCH: A New Approach for Differentiating & Linking Series of Sex Worker Homicides and Sexual Assaults

Sex workers as a group are one of the more common targets in serial homicide, yet the most likely to go unsolved. Part of the reason for this is the difficulty in linking individual crime scenes to a series, especially in those series where offenders not only target sex worker victims but also target non-sex worker victims. Inconsistencies in both victim targeting and behaviors engaged in across series add to the difficulties of linking and solvability in these types of crimes. The current study aimed to add to the current body of literature on serial crime linkage by examining not only the most salient behavioral indicators useful for crime scene classification of serial homicides that involve sex worker victims but also examine the trajectories of behavioral change that can help link apparently inconsistent crime scenes and proposes the new Model for the Analysis of Trajectories and Consistency in Homicide (MATCH). The study examines 83 homicide series, including 44 (53%) series where all victims were sex workers and 39 (47%) series that included a mix of sex workers and non-sex worker victims. Using the MATCH system allowed for the majority of series to be classified to a dominant trajectory pattern, over half as many as a traditional consistency analysis that focusses on behavioral similarity matching. Results further showed that Sex Worker Victim series were almost three times more consistent across their series than Mixed-Victim series, not only in victim selection but also in the overall behavioral patterns. Findings are discussed in line with theoretical and psychological issues relating to understanding the nature of behavioral consistency and the importance of going beyond simple matching toward a model that allows for the identification of consistency in seemingly inconsistent series, as well as investigative implications relating to linking serial crimes.

Evaluating evidence in linked crimes with multiple offenders

In de Zoete et al. (2015) a framework for the evaluation of evidence when an individual is a suspect of two separate offenses (based on Evett et al., 2006) is implemented using a Bayesian network. Here, we extend this to situations with multiple offenders. When we have multiple offenders, new questions arise: (1) Can we distinguish between the offenders, even if we do not know their identity? (2) Do we know that certain pieces of evidence originate from the same person? (3) Do we know the number of offenders? With the aid of a mock case example, we show that such subtle differences between situations can lead to substantially different conclusions in terms of posterior probabilities of a certain suspect being one of the offenders in a particular crime. We reach our conclusions by constructing appropriate Bayesian networks for each situation. Although we find it undesirable that Bayesian networks are demonstrated in court, they can be very helpful in guiding expert and legal reasoning, identifying pitfalls and assist in preventing them. Bayesian networks can be used as a tool to understand how the different pieces of evidence influence each others evidential value, and the probabilities of the hypotheses of interest.

[Linkage analysis across different types of crime, by using geographical and temporal proximity]

A number of studies have investigated behavioral case linkage between crimes of a specified type, such as linking one residential burglary with another residential burglary. However, only a few studies have investigated the effectiveness of case linkage across crime types, which have been limited to the UK. This study examined whether linking across crime types using spatio-temporal proximity was possible in samples that were different from the UK in terms of the structure of crime classification. This was accomplished by calculating the discrimination accuracy between linked crime pairs (two offenses committed by the same offender) and unlinked crime pairs (two offenses committed by different offenders) using geographical (inter-crime distance) and temporal proximity (number of days between offenses) across various crimes committed in Japan. Both the geographical proximity and temporal proximity had statistically significant levels of discrimination accuracy across crime types as assessed by Receiver Operating Characteristic (ROC) analysis. This suggests the possibility of identifying a crime series by geographical and temporal proximity across multiple crime types in Japan.

[Linkage analysis of serial sex crimes]

The purpose of this study was twofold: first, to create an index for a behavioral linkage analysis of serial sex crimes, and second, to construct a predictive model for the analysis. Data on 720 sex crimes (rape, indecent assault) committed by 360 offenders arrested between 1993 and 2005 throughout Japan were collected. The following seven behaviors were examined during a series of analyses aimed at illustrating the effectiveness of crime linkage in serial sex crimes: victim age group, area type, publicness of offense site, weapon, time, contact method, and day of the week. The results indicated that six of the seven behaviors (excluding "day of the week") significantly distinguished between linked and unlinked crime pairs. Under a logistic regression of these six variables, which were dichotomously coded in terms of the concordance or discordance between each pair of incidents, the area under the receiver operating characteristic (ROC) curve was 0.85 (95% CI = 0.82-0.87), indicating a high level of discriminative accuracy in identifying disparate sex crimes committed by the same person.

Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores

One of the most challenging problems facing crime analysts is that of identifying crime series, which are sets of crimes committed by the same individual or group. Detecting crime series can be an important step in predictive policing, as knowledge of a pattern can be of paramount importance toward finding the offenders or stopping the pattern. Currently, crime analysts detect crime series manually; our goal is to assist them by providing automated tools for discovering crime series from within a database of crimes. Our approach relies on a key hypothesis that each crime series possesses at least one core of crimes that are very similar to each other, which can be used to characterize the modus operandi (M.O.) of the criminal. Based on this assumption, as long as we find all of the cores in the database, we have found a piece of each crime series. We propose a subspace clustering method, where the subspace is the M.O. of the series. The method has three steps: We first construct a similarity graph to link crimes that are generally similar, second we find cores of crime using an integer linear programming approach, and third we construct the rest of the crime series by merging cores to form the full crime series. To judge whether a set of crimes is indeed a core, we consider both pattern-general similarity, which can be learned from past crime series, and pattern-specific similarity, which is specific to the M.O. of the series and cannot be learned. Our method can be used for general pattern detection beyond crime series detection, as cores exist for patterns in many domains.

Can serial rapists be distinguished from one-off rapists?

There are investigative advantages to being able to determine early in a police investigation whether a rape has been committed by a serial or a one-off rapist. Previous research has found some differences in the crime-scene behaviors of serial and one-off rapists; however, this research suffers from the limitation of utilizing a sample of rapes within which there was a mixture of victim-offender relationships. To address this limitation, this study sampled 38 serial (two or more convictions) and 50 one-off (one conviction) stranger rapists and compared their crime scene behavior across four domains (control, sex, escape and style behaviors). Serial and one-off rapists differed in some control and sexual behaviors, in particular, in the type of victim targeted, the offense locations, methods of control and the sexual acts forced upon the victim. However, the results did not indicate a striking difference in the offending behavior of the two groups. The implications of these findings for criminal investigations are discussed.

What works in offender profiling? A comparison of typological, thematic, and multivariate models

Utilizing a sample of 85 stranger rapists, three models (Hazelwood's (1987) Power and Anger FBI model, the Behavioral Thematic evaluation of Canter, Bennell, Alison, and Reddy (2003), and the Massachusetts Treatment Center: Rape classification system revision 3 (MTC:R3, Knight & Prentky, 1990)) were contrasted with a multivariate regression approach to assess their ability to predict an offender's previous convictions from crime scene information. In respect of the three aforementioned models, logistic regression and AUC analysis indicated that the Power and Anger FBI model was the most effective, followed by the MTC:R3, and then the Behavioral Thematic evaluation. However, predictive analyses based on a multivariate approach using a mixture of crime scene behaviors, as opposed to the grouping of behaviors into themes or types as in the three models, far exceeded the predictive ability of the three models under AUC analysis. The results suggest that emphasis should be placed on further exploration of the predictive validity of each of the individual behaviors that comprise existing thematic, typological, and multivariate classification systems, especially those that are subject to inter-situational variation.

Application of the behavioral investigative support system for profiling perpetrators of serial sexual assaults

The authors developed a data-based profiling system in order to support offender profiling. The system stored incident records of prior offenders. Inputting offence details of an unsolved incident, a probability score was assigned to each prior offender in the system; the score represented the behavioral similarity with the unsolved incident. The system then ranked all offenders in the system according to the probability scores, and prioritized the high-ranked offenders as possible suspects. Moreover, the system inferred the characteristics of unknown offenders by accumulating characteristics of the high-ranked offenders. The system achieved promising accuracy, especially for linking crimes to perpetrators. In 45 out of 81 simulation trials, the target offenders were retrieved as a rank score of 1 from among 868 sex offenders.

When is profiling possible? Offense planning and aggression as moderators in predicting offender age from victim age in stranger rape

Debate continues with regard to the possibility of inferring an offender's characteristic features from crime scene details (the process of so-called "offender profiling"). We argue that psychology generally has benefited from appreciating context in predicting behavior. In the same way, profiling would benefit from an appreciation of context in predicting characteristics. This "reverse" process is contingent on various "if...then" relationships. As one example, this paper demonstrates how profiling offender age from victim age is contingent on (i) the level of planning and (ii) the level of aggression displayed during the offense. Eighty-five stranger rape case records formed the data set. Moderated regression analysis revealed that victim age is a significant predictor of offender age only in cases where the offender has (i) shown evidence of planning the attack and/or (ii) acts in a gratuitously aggressive manner. The theoretical bases for these findings may lie in the extent to which offenders disparately plan and target victims and how extreme aggressiveness in stranger rapes may relate to a near-peer proxy for an offender's anger. The implications of the results for the processes and methods involved in offender profiling suggest that certain crime scene factors can have differential moderating effects on predictive outcomes.

An examination of behavioral consistency using individual behaviors or groups of behaviors in serial homicide

Establishing behavioral consistency in serial homicide offending behavior is essential when linking homicides together and to a common offender. An examination of 35 serial homicide behaviors utilized by 90 offenders in 450 serial homicide cases was carried out to identify whether these offenders consistently performed the same behaviors across their series of homicides and whether it is more effective to examine individual behaviors or groupings of behaviors in order to identify behavioral consistency. This study provides the first empirically based evidence testing the use of using single behaviors as linking factors in linking homicide cases in a series, and shows that this method has inherent difficulties in accomplishing this, thus providing the first wave of empirical testing that questions current theories in the literature on serial homicide, which has suggested that signatures or specific key behaviors are important in understanding the consistency in an offender's behavior across the crimes. Recommendations for future studies on linking using signatures are suggested, including a more in-depth qualitative empirical evaluation of individual series.