Network Spillovers and Neighborhood Crime: A Computational Statistics Analysis of Employment-Based Networks of Neighborhoods

Corina Graif, Brittany N. Freelin, Yu Hsuan Kuo, Hongjian Wang, Zhenhui Li, Daniel Kifer

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Research on communities and crime has predominantly focused on social conditions within an area or in its immediate proximity. However, a growing body of research shows that people often travel to areas away from home, contributing to connections between places. A few studies highlight the criminological implications of such connections, focusing on important but rare ties like co-offending or gang conflicts. The current study extends this idea by analyzing more common ties based on commuting across Chicago communities. It integrates standard criminological methods with machine learning and computational statistics approaches to investigate the extent to which neighborhood crime depends on the disadvantage of areas connected to it through commuting. The findings suggest that connected communities can influence each other from a distance and that connectivity to less disadvantaged work hubs may decrease local crime–with implications for advancing knowledge on the relational ecology of crime, social isolation, and ecological networks.

Original languageEnglish (US)
JournalJustice Quarterly
DOIs
StatePublished - Jan 1 2019

All Science Journal Classification (ASJC) codes

  • Pathology and Forensic Medicine
  • Law

Fingerprint Dive into the research topics of 'Network Spillovers and Neighborhood Crime: A Computational Statistics Analysis of Employment-Based Networks of Neighborhoods'. Together they form a unique fingerprint.

  • Cite this