Exploring the Impact of Socioeconomic and Built Environment Factors on Theft Crime Trends in Chicago During the COVID-19 Pandemic

The COVID-19 pandemic has disrupted urban social and economic conditions worldwide, influencing crime trends and patterns. This study examines how socio-economic and built environment factors affect theft crime density in Chicago across different pandemic phases. Using both linear regression and machine learning models, we analyze crime data to identify key factors driving changes in theft crime trends, focusing on the role of built environment elements such as public facilities, commercial venues, and transportation hubs. Results indicate that built environment features have a significant impact on theft density, while socio-economic factors like unemployment and income inequality show variable effects across pandemic periods. These findings suggest that urban and social factors interact to influence crime patterns, with non-linear models capturing these complex relationships more effectively than traditional methods. For policymakers and urban planners, the results underscore the importance of incorporating built environment considerations into crime prevention strategies, particularly in adapting urban spaces to foster safer, more resilient communities in the post-pandemic era.

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Publication Details

Journal
Journal of Applied Economics and Policy Studies
Published
2024-11-29
DOI
https://doi.org/10.54254/2977-5701/2024.17977
Citations
1
Primary Topic
Crime Patterns and Interventions
Type
article
Field-Weighted Citation Impact
1.13
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article

Exploring the Impact of Socioeconomic and Built Environment Factors on Theft Crime Trends in Chicago During the COVID-19 Pandemic

Xuebin Wang
1 citations
Journal of Applied Economics and Policy Studies
Crime Patterns and Interventions
1.13
article

Exploring the Impact of Socioeconomic and Built Environment Factors on Theft Crime Trends in Chicago During the COVID-19 Pandemic

Xuebin Wang
article en
1 citations

Abstract

The COVID-19 pandemic has disrupted urban social and economic conditions worldwide, influencing crime trends and patterns. This study examines how socio-economic and built environment factors affect theft crime density in Chicago across different pandemic phases. Using both linear regression and machine learning models, we analyze crime data to identify key factors driving changes in theft crime trends, focusing on the role of built environment elements such as public facilities, commercial venues, and transportation hubs. Results indicate that built environment features have a significant impact on theft density, while socio-economic factors like unemployment and income inequality show variable effects across pandemic periods. These findings suggest that urban and social factors interact to influence crime patterns, with non-linear models capturing these complex relationships more effectively than traditional methods. For policymakers and urban planners, the results underscore the importance of incorporating built environment considerations into crime prevention strategies, particularly in adapting urban spaces to foster safer, more resilient communities in the post-pandemic era.

Journal of Applied Economics and Policy StudiesVol. 14(1)
University of Hong Kong (HK)
No poverty
Openalex Percentile: Top 15%
Crime Patterns and Interventions
1.13
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