The Corner and the Crew: The Influence of Geography and Social Networks on Gang Violence

Abstract

More than a century of empirical research examines how neighborhoods' emergent properties influence a host of phenomena such as crime, poverty, health, civic engagement, immigration, and economic inequality (for a recent review, see Sampson 2012). These studies typically conceive of such neighborhood effects as influencing behavior through neighborhood characteristics (e.g., population heterogeneity, level of segregation, or economic disadvantage) or social processes (e.g., collective efficacy), or as spatial processes between neighborhoods (e.g., diffusion). Theoretically bundled within these neighborhood effects are the social networks of actors and institutions. In fact, the presence, vitality, and stability of neighborhood social networks are often at the Abstract Nearly a century of empirical research examines how neighborhood properties influence a host of phenomena such as crime, poverty, health, civic engagement, immigration, and economic inequality. Theoretically bundled within these neighborhood effects are institutions' and actors' social networks that are the foundation of other neighborhood-level processes such as social control, mobilization, and cultural assimilation. Yet, despite such long-standing theoretical links between neighborhoods and social networks, empirical research rarely considers or measures dimensions of geography and social network mechanisms simultaneously. The present study seeks to fill this gap by analyzing how both geography and social networks influence an important social problem in urban America: gang violence. Using detailed data on fatal and non-fatal shootings, we examine effects of geographic proximity, organizational memory, and additional group processes (e.g., reciprocity, transitivity, and status seeking) on gang violence in Chicago and Boston. Results show adjacency of gang turf and prior conflict between gangs are strong predictors of subsequent gang violence. Furthermore, important network processes, including reciprocity and status seeking, also contribute to observed patterns of gang violence. In fact, we find that these spatial and network processes mediate racial effects, suggesting the primacy of place and the group in generating gang violence.

Determinants of knowledge-sharing networks in primary care

“…If the presence of ties between i and j and between j and z lead to the formation of a tie from z to i, then we observe cyclic closure. Transitive closure manifests as a general force toward local hierarchy and, in professional contexts, implies deference among social actors (Papachristos et al, 2013). Recent empirical studies have shown that physicians tend to form transitive triads in multiple health organizations and systems (Paul et al, 2004;Zappa, 2011).…”

Forecasting Organized Crime Homicides: Risk Terrain Modeling of Camorra Violence in Naples, Italy

“…Group mechanisms that facilitate violence include intergroup violence, group status seeking and management, and reciprocity (Hopkins et al, 2013;Miethe & Drass, 1999;Papachristos et al, 2013). Gangs usually identify themselves with a place; the protection of the gang's turf or "set space" is one of the factors that may lead to inter-gang violence (Klein, 1995;Tita, Cohen, & Engberg, 2005).…”

“…M. Kennedy et al, 1997;Papachristos, 2009;Papachristos et al, 2013;Tita & Greenbaum, 2009;Tita & Radil, 2011). Indeed, conflicts are often retaliatory in nature (Decker, 1996), and groups use violence as a form of justice to reciprocate a transgression or to respond to a threat by another group (Chinnici & Santino, 1989;Hopkins et al, 2013;Papachristos, 2009;Papachristos et al, 2013).…”

Diagnosing Multicollinearity in Exponential Random Graph Models

“…Exponential random graph models (ERGM) have been widely applied to test theories of racial boundaries (Wimmer and Lewis 2010), group processes (Goodreau, Kitts, and Morris 2009), gang violence (Papachristos 2009;Papachristos, Hureau, and Braga 2013), and social structure in total institutions (Kreager et al, forthcoming), among other topics. The main benefit of ERGM is the ability to simultaneously model and compare (exogenous) actor level attributes against contextual (endogenous) network effects (Robins et al 2007; Lusher, Robins and Koskinen 2013).…”