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.

中文翻译：

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社区犯罪网络对枪支暴力和枪支供应的重要性


社区在塑造犯罪模式方面的显著性是社会学最强大的研究领域之一。社区塑造结果的一种方式是通过创建和维护社交网络、社区居民、组织、团体和机构之间的互动模式和关系。本文探讨了通过共同犯罪行为（当两个或多个人一起参与被指控的犯罪时）产生的网络结构与社区枪支暴力模式和枪支可用性之间的关系。使用纽约市的逮捕数据，我们在 2010 年至 2015 年期间在该市被捕的个人之间建立了共同逮捕网络。我们分析这些网络模式，首先了解共同犯罪网络的整体结构，然后评估它们如何影响社区的枪支暴力水平和枪支供应。结果表明，本地和本地外网络在预测社区枪击事件水平方面起着核心作用：地方关系密度较高的社区枪击率较高，而具有社会关系的社区具有相似的暴力率。相比之下，枪支追回所涉及的网络动态几乎完全是局部的：社区内的共同犯罪模式与枪支追回水平密切相关，尤其是表明群体的共同犯罪网络的聚集。与之前的研究相反，当考虑人口统计特征时，空间自相关无法预测枪击事件或枪支回收率。 社会人口学特征似乎可以解释大部分观察到的空间自相关，而网络属性的精确测量可能会更好地测量城市枪支暴力中涉及的邻里动态。