Network exposure and homicide victimization in an African American community

Objectives: We estimated the association of an individual's exposure to homicide in a social network and the risk of individual homicide victimization across a high-crime African American community. Methods: Combining 5 years of homicide and police records, we analyzed a network of 3718 high-risk individuals that was created by instances of cooffending. We used logistic regression to model the odds of being a gunshot homicide victim by individual characteristics, network position, and indirect exposure to homicide. Results: Forty-one percent of all gun homicides occurred within a network component containing less than 4% of the neighborhood's population. Network-level indicators reduced the association between individual risk factors and homicide victimization and improved the overall prediction of individual victimization. Network exposure to homicide was strongly associated with victimization: the closer one is to a homicide victim, the greater the risk of victimization. Regression models show that exposure diminished with social distance: each social tie removed from a homicide victim decreased one's odds of being a homicide victim by 57%. Conclusions: Risk of homicide in urban areas is even more highly concentrated than previously thought. We found that most of the risk of gun violence was concentrated in networks of identifiable individuals. Understanding these networks may improve prediction of individual homicide victimization within disadvantaged communities.