By Daniel Gunnell, Joseph Hillier and Laura Blakeborough

As part of the Home Office’s Ending Gang and Youth Violence programme a commitment was made to help police forces better understand their local gang issues (HM Government, 20131 ). This research aims to meet this commitment by testing the use of social network analysis using police intelligence data, as a tool to more systematically understand gangs and to help direct law enforcement activities. As such, the report serves as one example of how social network analysis can be used, but the approach could also be applied to other types of crime and disorder to explore the networks of people involved (such as those connected to acquisitive crime or sexual abuse). The research was undertaken in partnership with Great Manchester Police and addresses two research questions: 1. What can social network analysis tell us about gangs? 2. How useful are the social network analysis outputs for the police? For this, five individuals living in Manchester and identified as having gang links were chosen as the starting point for the network analysis. Further details about how to conduct social network analysis can be found in the ‘How to guide’ 2 published as an annex to this report.

London: Home Office, 2016. 34p.

By Ryan J. Roberts

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.

Norfolk, VA: Old Dominion University, 2021. 344p.