Investigating the differences in influencing factors on burglaries in migrant communities, local communities, and mixed communities: a case study of ZG City

Burglaries have detrimental effects on urban residents’ life quality and property safety. Prior research has overlooked the heterogeneity of factors influencing the spatial distribution of burglaries across different types of urban communities. To fill this gap, this study makes an in-depth investigation by taking ZG City, a coastal city in southeast China, as an example and differentiating communities as Local Communities (LCs), Migrant Communities (MGCs), and Mixed Communities (MXCs) based on the proportion of migrant population in a community. The results show that the spatial distribution of burglaries in ZG City is affected by factors including the presence of potential perpetrators, social and built environment, and housing types and numbers. In MGCs, indicators of housing types have no significant impact on burglaries. In LCs, Internet bars, bus stops, and rental housing ratio are strong predictors of burglaries, while formerly public housing and housing with high rent significantly reduce burglaries. Notably, the influences of these factors vary due to the variation in the social and physical environment of communities. There are distinct demographic characteristics and built environment features among different communities, leading to varying levels of informal social control and anonymity, which in turn influence the occurrence of burglaries. This study informs context-specific strategies to prevent and control burglaries and for directions of urban renewal.

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Publication Details

Journal
Humanities and Social Sciences Communications
Published
2025-05-05
DOI
https://doi.org/10.1057/s41599-025-04899-5
Primary Topic
Crime Patterns and Interventions
Type
article
Field-Weighted Citation Impact
0.00

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article

Investigating the differences in influencing factors on burglaries in migrant communities, local communities, and mixed communities: a case study of ZG City

Guangwen Song, Xiaoxue Li, Jingxi Liu, J. Anthony Long
1 links
Humanities and Social Sciences Communications
Crime Patterns and Interventions
article

Investigating the differences in influencing factors on burglaries in migrant communities, local communities, and mixed communities: a case study of ZG City

Guangwen Song, Xiaoxue Li, Jingxi Liu, J. Anthony Long
article en

Abstract

Burglaries have detrimental effects on urban residents’ life quality and property safety. Prior research has overlooked the heterogeneity of factors influencing the spatial distribution of burglaries across different types of urban communities. To fill this gap, this study makes an in-depth investigation by taking ZG City, a coastal city in southeast China, as an example and differentiating communities as Local Communities (LCs), Migrant Communities (MGCs), and Mixed Communities (MXCs) based on the proportion of migrant population in a community. The results show that the spatial distribution of burglaries in ZG City is affected by factors including the presence of potential perpetrators, social and built environment, and housing types and numbers. In MGCs, indicators of housing types have no significant impact on burglaries. In LCs, Internet bars, bus stops, and rental housing ratio are strong predictors of burglaries, while formerly public housing and housing with high rent significantly reduce burglaries. Notably, the influences of these factors vary due to the variation in the social and physical environment of communities. There are distinct demographic characteristics and built environment features among different communities, leading to varying levels of informal social control and anonymity, which in turn influence the occurrence of burglaries. This study informs context-specific strategies to prevent and control burglaries and for directions of urban renewal.

Humanities and Social Sciences CommunicationsVol. 12(1)
Guangzhou Institute of Geography (CN)
Dartmouth College, National Natural Science Foundation of China
Reduced inequalities
Openalex Percentile: Top 88%
Crime Patterns and Interventions
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