JOURNAL ARTICLE

The importance of neighborhood offending networks for gun violence and firearm availability.

  • Published In: Social Forces, 2024, v. 103, n. 2. P. 780 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Papachristos, Andrew V; Murphy, James P; Braga, Anthony; Turchan, Brandon 3 of 3

Abstract

This article examines how co-offending networks—social ties formed when individuals are arrested together for alleged crimes—shape neighborhood patterns of gun violence and gun availability in New York City between 2010 and 2015. Using arrest, shooting, and gun recovery data linked to 188 Neighborhood Tabulation Areas (NTAs), the study finds that neighborhoods with denser local co-offending ties experience higher shooting rates, while neighborhoods connected through inter-neighborhood co-arrest networks tend to have similar levels of gun violence. In contrast, gun recoveries, used as a proxy for illegal gun availability, are primarily associated with local network structures within neighborhoods rather than inter-neighborhood or spatial connections. The research also reveals that traditional spatial autocorrelation effects on shootings and gun recoveries diminish when controlling for social-demographic factors, suggesting that network dynamics provide a more precise understanding of neighborhood gun violence patterns. These findings highlight the importance of incorporating both neighborhood and network perspectives in violence prevention policies and interventions.

Additional Information

  • Source:Social Forces. 2024/12, Vol. 103, Issue 2, p780
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2024
  • ISSN:0037-7732
  • DOI:10.1093/sf/soae099
  • Accession Number:180255641
  • Copyright Statement:Copyright of Social Forces is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.