Shots fired: Unraveling the 2015 Surrey gang conflict using social network analysis

The ever-changing gang landscape in British Columbia (BC) has seen periods of escalated retaliatory gang violence, most recently in 2015, in Surrey, BC, Canada. The ‘face’ of the gang problem in Surrey is that of South Asian males in their early twenties. Homicide among this population is an unrecognized public health crisis, as over the last decade, there have been over 150 deaths and counting of South Asian males related to gang violence in the Lower Mainland. A cross-disciplinary tool that police can use to advance their understanding of gangs, conflicts and violent victimization is social network analysis (SNA). The ego-networks of the 23 confirmed gang-related gun homicide or attempted homicide victims in Surrey, in 2015, are constructed using police data from 2011 to 2015. The present study a) assesses the overall structure to understand the Surrey gang conflict, b) conducts centrality analyses to identify those individuals (victims and non-victims) at the highest risk of gunshot victimization and c) explores the potential consequences of being central in the victim network. Results indicate that 299 of the 355 individuals in the overall network are connected to each other, including 18 of the 23 victims, who are more likely to be brokers. A high-risk group is identified, with two or more direct connections to victims that are at the highest risk of victimization. Finally, results show that 2016 and 2017 victims are among the most central in the network. Policy and practical implications are discussed with reference to these findings

A Survey of Social Network Forensics

Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models / techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks

The Standard Deviation: Attitude Transference and Perceptions of Deviant Behavior

This dissertation uses a three-article dissertation model to 1) compare how deviance is defined and what is considered deviant comparing the United States to South Korea using content analysis, 2) test socio-demographic and social network variables in the development of one’s approval of deviance using eleven ordinary least squared regression models, and 3) examine the association between social networks and approval of deviant behaviors using social network analysis. All three articles use data from a survey on perceptions of deviant behavior. The survey was conducted in English and Korean. The first article provides comparisons on how deviance is defined and what is defined as deviant. Although the research did not find a consensus, nor did it expect to find a consensus, on how deviance is defined, a strong majority of survey respondents define deviance as behaviors that go against social norms and are negative. This research also reveals that there is a greater consensus as to what behaviors are considered deviant in South Korea than in the United States. The second article tests the hypothesis that perceived approval of one’s social network is a greater predictor (i.e., statistically significant across more models) than traditional socio-demographic variables (i.e., gender, age, and income will not be as strong an indicator as social network) in an individual’s approval of deviance. The results of regression analysis indicate that 1) one’s social network is the greatest predictor of his/her tolerance of deviance behaviors and 2) there is more consensus among South Koreans regarding what is considered deviant than among Americans. The third article finds a statistically significant correlation between an ego’s approval of seven deviant behaviors and that of the perceived approval of his/her network. Respondents reporting that they approve of a behavior have at least one alter that also approves of the behavior but an average of two or three alters approving of the behavior. The research concludes that relational data is more robust than attribute data in the study of perceptions of deviance but emphasizes that attribute data must be understand as a factor in relational data

Investigating Domestic Burglary: Offences, offenders and co-offending

A new Model of Burglary Differentiation is proposed based on the central question: how do the psychological interpretations of the domestic burglary offending styles, patterns and offender characteristics relate to a social hypothesis of this crime? Reiss and Farrington (1991) suggest burglary is most commonly committed in groups. As such, the behavioural variations are investigated in relation to an individual’s position within their social network structure. A unique police database, collected from 2011 to 2015, is examined. The data was obtained from a population of offences within a major metropolitan city in the United Kingdom. It consists of 8,491 domestic burglaries (686 solved and 7,805 unsolved). A further 1,017 convicted burglaries from the Police National Computer database are also included. Initial investigation of the differences between solved and unsolved domestic burglaries provided crucial insight to the validity of modelling crime and the utility of the data. Behavioural analysis identified a good relationship between solved and unsolved domestic burglaries, validating the use of this data in modelling burglary and highlighting the evidence required in burglary detection. To provide further clarification of the sample, the behavioural co-occurrences were examined with the aim of identify distinct variations in domestic burglary. Co-offending burglary was apparent in 60% of cases, thus supporting the social hypothesis of burglary. Smallest-Space Analysis (SSA) systematically revealed thematic behavioural differences between offenders in solved and unsolved offences. It was hypothesised that through examination of the offence characteristics, offender traits, and criminal history, behavioural differentiation of burglary could be determined. Four behavioural patterns are identified: Skilled Domestic, Interpersonal, Forceful, and Non-Domestic. The succeeding study predicted offender characteristics from the previously identified behavioural styles, hypothesising differing criminal experience across offending actions. A new Model of Burglary Differentiation was found, across distinct stages of development based on the offender’s age and experience, labelled: Skilled Domestic, Versatile, Opportunistic and Non-Domestic. The prominence of co-offending within the sample allowed for a social-psychological framework of domestic burglary to be investigated. The analysis identified three distinct types of domestic burglary networks: Starter, Core, and Structured. The criminal histories of the co-offending networks were then examined, finding a robust framework of identifying criminal differentiation, with evidence of specialisation to Material, Power, and Vehicle related crime. The final study demonstrates a social-psychological framework of domestic burglary by drawing on the findings of the previous studies. The findings identify small-scale domestic burglary organisations formed through role differentiation. This has significant implications in the use of quantitative information in drawing psychological interpretations of co-offending information. The research demonstrates the utility of a social network framework for understanding the behavioural, social and psychological characteristics of burglary offenders. This suggests further exploration of the social interdependence between offenders and how individuals provide support in offending behaviours. The implications of uncovering a social-psychological framework of domestic burglary and how it contributes to theoretical, methodological and practical settings are discussed

Re-Spatializing Gangs in the United States: An Analysis of Macro- and Micro-Level Network Structures

Despite the significant contributions from location-based gang studies, the network structure of gangs beyond localized settings remains a neglected but important area of research to better understand the national security implications of gang interconnectivity. The purpose of this dissertation is to examine the network structure of gangs at the macro- and micro-level using social network analysis. At the macro-level, some gangs have formed national alliances in perpetuity with their goals and objectives. In order to study gangs at the macro-level, this research uses open-source data to construct an adjacency matrix of gang alliances and rivalries to map the relationships between gangs and analyze their network centrality across multiple metrics. The results suggest that native gangs are highly influential when compared to immigrant gangs. Some immigrant gangs, however, derive influence by “bridging” the gap between rival gangs. Mexican Drug Trafficking Organizations (MDTOs) play a similar role and feature prominently in the gang network. Moreover, removing MDTOs changes the network structure in favor of ideologically-motivated gangs over profit-oriented gangs. Critics deride macro-level approaches to studying gangs for their lack of national cohesion. In response, this research includes a micro-level analysis of gang member connections by mining Twitter data to analyze the geospatial distribution of gang members and, by proxy, gangs, using an exponential random graph model (ERGM) to test location homophily and better understand the extent to which gang members are localized. The findings show a positive correlation between location and shared gang member connections which is conceptually consistent with the proximity principle. According to the proximity principle, interpersonal relationships are more likely to occur in localized geographic spaces. However, gang member connections appear to be more diffuse than is captured in current location-based gang studies. This dissertation demonstrates that macro- and micro-level gang networks exist in unbounded geographic spaces where the interconnectivity of gangs transpose local issues onto the national security consciousness which challenges law and order, weakens institutions, and negatively impacts the structural integrity of the state

Research on Gang-Related Violence in the 21st Century

Conflict, including the threat or fear of potential violence, or being witness to or a victim of physical violence, constantly surrounds gangs and their communities and is the principal driver sustaining gang life. This Special Issue examines the diverse nature of gang-related violence with the goal of better understanding the growing complexities of gang violence over the last two decades to better inform public policy solutions. The contributions included in this Special Issue highlight the complex nature of gang-related violence in the 21st Century. As much as policy makers, the media, and even scholars like to simplify gang-related violence, all of the studies included in this Special Issue highlight the nuance and variation that exists