Co-offending Networks Research Articles (Page 1)

ABSTRACT The aim of this paper is to examine the relationship between co-offending and the geospatial dispersal of crime events, crime types and crime seriousness. To accomplish this, we use arrest data from a large Australian metropolitan city across a five-year period (2011–2015) comprising 332,860 crime events involving 115,885 unique actors. We employ an innovative combination of Relational Hyperevent Models in concert with geospatial analyses. Our analysis finds that while offenders are more likely to commit new crimes in familiar locations, co-offending for property crime was associated with less geospatial dispersion compared with solo property crime. We also found that medium and high harm crimes are associated with low geospatial dispersion compared with low harm crimes. We offer some initial interpretations of these results based on existing geospatial concepts and research. The paper concludes by considering implications for policy and practice based on the results of the study.

Crimversations: "Homophily promotes stable connections in co-offending networks but limits information diffusion: insights from a simulation study"

Homophily promotes stable connections in co-offending networks but limits information diffusion: insights from a simulation study

PurposeOffenders often select partners based on shared characteristics such as age, sex, or ethnicity, a phenomenon known as homophily. At the same time, co-offenders also face a challenge of choosing between trustworthy partners to maintain stable collaborations and useful partners who provide access to new skills and information. This study investigates how homophily shapes the structure of criminal networks and, consequently, the diffusion of information within these networks.MethodsUsing an Agent-Based Model, we simulate a population of offenders that select partners either randomly or based on high similarity preference. When two agents mutually select each other, they commit a co-offense, forming a social network and exchanging skills.ResultsCompared to the case of the random partner selection, the homophily-driven environment results in sparse networks with a higher number of repeated interactions between agents, but with a slower rate of skill exchange. Moreover, on the individual level, having many partners is more beneficial for diverse skill acquisition, but those partners should belong to different subgroups.ConclusionThe results provide insights into how offender preferences shape the structure and dynamics of criminal networks, particularly in relation to opportunities for collaboration and skill acquisition. The findings highlight a key trade-off introduced by homophily. Although it promotes stable partnerships, it restricts the exchange of information across the broader network.

ABSTRACT The current study examines registration data from the Dutch police pertaining to 2,757 individuals and 10 different crime categories, at the offense, individual, and network levels of analysis. Moreover, a novel estimate is introduced which identifies individuals’ tendency to operate as mentors or mentees in criminal networks. We find co-offending to be conditional on the type of crime and to typically involve groups of two to three offenders. Moreover, we find that men and those aged 18–25 are most likely to co-offend and that co-offenders typically do not show a preference for particular co-offenders. Individuals with central positions in the co-offending network were found to be older and to offend more frequently with a higher average of co-offenders per offense, than those less central. Lastly, we find the novel estimate to be a promising tool for identifying mentor and mentee roles in large quantitative network data. The results of the current study emphasize the importance of studying co-offending on different analytical levels, and to differentiate co-offending by different crime types, network positions, and individual roles, to better inform theory and policy.

The impact of violent behavior on co-offender selection: Evidence of behavioral homophily

Abstract The salience of neighborhoods in shaping crime patterns is one of sociology’s most robust areas of research. One way through which neighborhoods shape outcomes is through the creation and maintenance of social networks, patterns of interactions and relationships among neighborhood residents, organizations, groups, and institutions. This paper explores the relationship between network structures generated through acts of co-offending—when two or more individuals engage in an alleged crime together—and patterns of neighborhood gun violence and gun availability. Using arrest data from New York City, we create co-arrest networks between individuals arrested in the city between 2010 and 2015. We analyze these network patterns to, first, understand the overall structure of co-offending networks and, then, assess how they impact neighborhood levels of gun violence and gun availability. Results show that local and extra-local networks play a central role in predicting neighborhood levels of shootings: neighborhoods with a greater density of local ties have higher shootings rates, and neighborhoods that share social ties have similar rates of violence. In contrast, the network dynamics involved in gun recoveries are almost entirely local: co-offending patterns within neighborhoods are strongly associated with the level of gun recoveries, especially the clustering of co-offending networks indicative of groups. Contrary to previous research, spatial autocorrelation failed to predict either shootings or gun recoveries when demographic features were considered. Social-demographic characteristics seem to explain much of the observed spatial autocorrelation and the precise measurement of network properties might provide better measurements of the neighborhood dynamics involved in urban gun violence.

Prior research suggests that members of terrorist groups prioritize forming network ties based on trust to improve their organizational and operational security. The homophily principle, which postulates that individuals tend to form relationships based on shared characteristics, can be a key mechanism through which people identify trustworthy associates. Next to homophily, the mechanism of establishing interconnected relationships through transitivity is also well-known to serve this purpose and shape community structures in social networks. We analyze the community structures of the Islamist co-offending network in the United States, which is highly violent, to assess whether homophily and transitivity determine which extremists form co-offending ties. We rely on a new database on the individual attributes and the co-offending relationships of 494 Islamist offenders radicalized in the United States between 1993 and 2020. Using community detection algorithms, we show that the US Islamist co-offending network is highly clustered, modular, and includes many small but only a few large communities. Furthermore, results from exponential random graph modeling show that transitive relationships as well as spatial proximity, ideological affiliation, and shared socio-cultural characteristics drive co-offending among US Islamist extremists. Overall, these findings demonstrate that the processes of homophily and transitivity shape violent social networks.

Prior research suggests that members of terrorist groups prioritize forming network ties based on trust to improve their organizational and operational security. The homophily principle, which postulates that individuals tend to form relationships based on shared characteristics, can be a key mechanism through which people identify trustworthy associates. Next to homophily, the mechanism of establishing interconnected relationships through transitivity is also well-known to serve this purpose and shape community structures in social networks. We analyze the community structures of the Islamist co-offending network in the United States, which is highly violent, to assess whether homophily and transitivity determine which extremists form co-offending ties. We rely on a new database on the individual attributes and the co-offending relationships of 494 Islamist offenders radicalized in the United States between 1993 and 2020. Using community detection algorithms, we show that the US Islamist co-offending network is highly clustered, modular, and includes many small but only a few large communities. Furthermore, results from exponential random graph modeling show that transitive relationships as well as spatial proximity, ideological affiliation, and shared socio-cultural characteristics drive co-offending among US Islamist extremists. Overall, these findings demonstrate that the processes of homophily and transitivity shape violent social networks.

Is corrections officers' use of illegal force networked? Network structure, brokerage, and key players in the New York City Department of Correction

ObjectivesApproaches to the study of Outlaw Motorcycle Gangs OMCGs tend to focus on offending at the individual level, with limited focus on the nature and extent of co-offending among these affiliates. We aim to examine co-offending by using relational hyper event models (RHEM) to determine what additional insights can be discerned on co-offending above and beyond more traditional network approaches.MethodsUsing de-identified police recorded incident data for affiliates of OMCGs in New South Wales, Australia, including their rank and club affiliation, we examined the positioning of OMCG affiliates in co-offending network structures. The data comprised 2,364 nodes and 12,564 arrest events. We argue that Relational Hyperevent Models (RHEM) are the optimal analytical strategy for co-offending data as it overcomes some of the limitations of traditional co-offending analyses.ResultsWe conducted RHEM modelling and found that co-offending networks were stable over time, whereby actors tended to repeatedly co-offend with the same partners. Lower ranked members were more likely to engage in co-offending compared with office bearers.ConclusionsResults provide some support for the scenario in which OMCGs operate as criminal organisations, but also the protection and distance from offending that is afforded to office bearers. We review implications of the results for law enforcement policy and practice and for the scholarship of OMCGs.

Abstract Recent decades have witnessed growing use of social network analysis (SNA) to study criminal activities, including that of co-offending. However, few studies have examined co-offending networks within a historical context. This paper focuses on group-based crime in a large English town during the Victorian period, employing SNA methods to examine the prevalence, structure and composition of co-offending relationships. Networks for property, violent and victimless crimes were partitioned to compare co-offending across crime categories. Results indicate that co-offending groups were typically segregated, although there was a loosely-organised community of property crime offenders connected by ‘brokers’ who collaborated with multiple groups. Evidence also suggests that co-offending was largely characterised by assortative mixing in regard to sex, age and marital status.

This study aims to improve our understanding of criminal accomplice selection by studying the evolution of co-offending networks—i.e., networks that connect those who commit crimes together. To this end, we tested four growth mechanisms (popularity, reinforcement, reciprocity, and triadic closure) on three components observed in a network connecting criminal investigations (M = 286 K) with adult offenders (N = 274 K) in Bogotá (Colombia) between 2005 and 2018. The first component had 4286 offenders (component ‘A’), the second 227 (‘B’), and the third component 211 (‘C’). The evolution of these components was examined using temporal information in tandem with discrete choice models and simulations to understand the mechanisms that could explain how these components grew. The results show that they evolved differently during the period of interest. Popularity yielded negative statistically significant coefficients for ‘A’, suggesting that having more connections reduced the odds of connecting with incoming offenders in this network. Reciprocity and reinforcement yielded mixed results as we observed negative statistically significant coefficients in ‘C’ and positive statistically significant coefficients in ‘A’. Moreover, triadic closure produced positive, statistically significant coefficients in all the networks. The results suggest that a combination of growth mechanisms might explain how co-offending networks grow, highlighting the importance of considering offenders’ network-related characteristics when studying accomplice selection. Besides adding evidence about triadic closure as a universal property of social networks, this result indicates that further analyses are needed to understand better how accomplices shape criminal careers.

Abstract Outlaw motorcycle gangs (OMCGs) have become synonymous with organised crime through engagement in criminal activities including illicit drug production and distribution, firearms trafficking, and serious violent crime. These crimes contribute significant social and economic harms in countries that feature a presence from these groups. The current paper uses network analytics to analyse the extent of co-offending within and across established clubs in Australia, including the relative involvement of senior, or office bearing, members. The majority of affiliates in this sample co-offended with another OMCG affiliate within the sample period, with office bearers, members, nominees and associates represented proportionally among co-offending networks to in the sample at large. However, within these clubs, criminal activities were conducted in small cliques or components of affiliates. This research supports the role of OMCGs as important facilitators of crime, and the role of co-offending in the criminal offending of affiliates. The findings hold important implications for understanding how offending is organised among OMCGs, differences between groups, differing levels of engagement from the club hierarchy.

Little is known about homicide co-offending networks at the individual gang member level. Of particular interest is whether and to what degree gang members who are selected to participate in murders are different from those who are not. The current study constructed the co-offense network of eighteen subjects from the Incarcerated Serious and Violent Young Offender Study who were identified as members of a prominent gang within British Columbia, Canada, referred to as the BC Gang. This gang started to form not long before seven offenders together committed a homicide that was orchestrated by the founder and leader of the BC Gang. After this offense, these seven offenders became some of the most central actors within a large network of co-offenders (n = 137) that was measured at four time periods over a 14 year period. Over this period, a second murder, like the first, was orchestrated by the leader of the BC Gang, offering a rare glimpse into the co-offending recruitment decisions made by a high ranking gang member for two separate homicides. Although only 25% of the 137 co-offenders are BC gang members (n = 35), 100% of the offenders selected for a homicide were members of this gang (n = 13). The network contained 8 separate components at the final measurement period, but all 13 homicide offenders were connected to the same network component of 48 individuals.

ABSTRACT Outlaw motorcycle gangs (OMGs) have received increased attention from both law enforcement agencies and the research community. This study investigates the criminal collaboration patterns of two OMGs with a long history of hostilities. We use government data on individuals registered as belonging to Hells Angels MC, Bandidos MC and individuals with multiple OMG memberships, and suspicion data from 2011 to 2016 to build co-offending networks. Our results show that members of multiple OMGs tend to have higher centrality and clustering. These members also have the highest levels of suspicions per capita, and most of the co-offending is related to nexus links involving multiple membership individuals. They can be described as ‘criminal nomads’, collaborating with individuals from different organisations. Our results suggest that core members tend to engage in white-collar crime to a greater extent than those on the periphery, which tend to engage more in violence and drug crime.

Reducing explanations of victimization to a person’s risky lifestyle has stalled growth in theories of victimization. Drawing from Carlo Morselli’s contributions to social network analysis, the current study extended past research on community-based co-offending networks and victimization in two ways. First, the current study more comprehensively measured a person’s criminogenic network by also examining the contribution of conflict ties and social ties to victimization. Second, we investigated whether serious victimization was prospectively associated with social network characteristics. Data were used on 99 participants from the Incarcerated Serious Violent Young Offender Study who had criminogenic connections within the city of Surrey, BC. Time-dependent covariate survival analysis was used to model the relationship between network characteristics and time to victimization. Time-series ordinary least squares regression was used to examine whether serious victimization predicted network characteristics. Participants with a greater number of co-offending ties experienced serious victimization significantly later. As evidence of the reciprocal nature of the victimization–network relationship, victimization predicted a greater number of future criminogenic connections in the co-offending tie, social tie, and prison tie networks. Findings have implications for network-based intervention models.

The interurban network of criminal collaboration in Canada is described, and possible explanations for its structure are explored. The data include all police-reported co-offences in the 32 major cities of Canada during 2006–09. Component analysis and graph drawings in network space and in geospace elucidate the structure of the network. Quadratic assignment procedure multiple regressions, repeated separately on the networks of instrumental and noninstrumental co-offences, test hypotheses about possible determinants of the network structure. The cities form one connected component, containing two clusters connected by a link between Toronto and Vancouver. One cluster, centred on the triad of Toronto, Montreal, and Ottawa, comprises the cities in Ontario and Quebec, with weak links to cities in the Atlantic provinces. The other cluster, centred on Vancouver, comprises the cities in the four western provinces. The structure is strongly correlated with the residential mobility of the general population, which in turn is strongly correlated with intercity distances. The correlation with mobility is less strong for instrumental than for noninstrumental crimes. The structure of this co-offending network can be explained by criminals’ routine activities, namely ordinary residential mobility, but the alternative explanation of purposive interurban criminal collaboration is more plausible for instrumental crime.

We measured triadic closure in co-offending networks – i.e., the tendency of two individuals to co-offend if they share an accomplice – using a method that addresses the risk of overestimating clustering coefficients when using one-mode projections. We also assess the statistical significance of clustering coefficients using null models. The data relates to adult offenders (N = 274,689) connected to criminal investigations (N = 286,591) in Colombia. The observed coefficients range between 0.05 and 0.53 and are statistically significant, indicating that accomplices become sources of information about potential associates. They support the idea of preventing crime by targeting offenders’ trustworthiness and disrupting information flows.

ABSTRACT The current study aims to expand the geographic breadth of co-offending research by providing one of the first examinations of co-offending within Australia. We find co-offending was more common for some crimes than others. Individuals arrested for homicide had some of the highest co-offending rates and were more frequently observed in the core of the co-offending network. Females had higher rates of co-offending than males, and differences between sexes were most pronounced for sexual assault. However, females were underrepresented in the core of the network as compared to males. Lastly, co-offending declined with age, with the exception of drug offences for which co-offending was slightly more common among older age groups. Despite declines in co-offending overall, all age groups were equally represented in the network’s core. Results emphasise the importance of disaggregating co-offending by crime type and examining co-offending across international contexts to better inform theory and policy.