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

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

Fingerprint

Crime
statistics
offense
community
Social Isolation
social factors
ecology
social isolation
Social Conditions
Vulnerable Populations
Ecology
travel
Research
learning

All Science Journal Classification (ASJC) codes

  • Pathology and Forensic Medicine
  • Law

Cite this

@article{f471e3d0f9cd4624a395b63b4e921aeb,
title = "Network Spillovers and Neighborhood Crime: A Computational Statistics Analysis of Employment-Based Networks of Neighborhoods",
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.",
author = "Corina Graif and Freelin, {Brittany N.} and Kuo, {Yu Hsuan} and Hongjian Wang and Zhenhui Li and Daniel Kifer",
year = "2019",
month = "1",
day = "1",
doi = "10.1080/07418825.2019.1602160",
language = "English (US)",
journal = "Justice Quarterly",
issn = "0741-8825",
publisher = "Taylor and Francis Ltd.",

}

Network Spillovers and Neighborhood Crime : A Computational Statistics Analysis of Employment-Based Networks of Neighborhoods. / Graif, Corina; Freelin, Brittany N.; Kuo, Yu Hsuan; Wang, Hongjian; Li, Zhenhui; Kifer, Daniel.

In: Justice Quarterly, 01.01.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Network Spillovers and Neighborhood Crime

T2 - A Computational Statistics Analysis of Employment-Based Networks of Neighborhoods

AU - Graif, Corina

AU - Freelin, Brittany N.

AU - Kuo, Yu Hsuan

AU - Wang, Hongjian

AU - Li, Zhenhui

AU - Kifer, Daniel

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85065057516&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85065057516&partnerID=8YFLogxK

U2 - 10.1080/07418825.2019.1602160

DO - 10.1080/07418825.2019.1602160

M3 - Article

AN - SCOPUS:85065057516

JO - Justice Quarterly

JF - Justice Quarterly

SN - 0741-8825

ER -