Unraveling the influence of income-based ambient population heterogeneity on theft spatial patterns: insights from mobile phone big data analysis
Abstract While previous research has underscored the profound influence of the ambient population distribution on the spatial dynamics of crime, the exploration regarding the impact of heterogeneity within the ambient population, such as different income groups, on crime is still in its infancy. Wit...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2024-01-01
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| Series: | Humanities & Social Sciences Communications |
| Online Access: | https://doi.org/10.1057/s41599-024-02610-8 |
| _version_ | 1797350078152704000 |
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| author | Chong Xu Zhenhao He Guangwen Song Debao Chen |
| author_facet | Chong Xu Zhenhao He Guangwen Song Debao Chen |
| author_sort | Chong Xu |
| collection | DOAJ |
| description | Abstract While previous research has underscored the profound influence of the ambient population distribution on the spatial dynamics of crime, the exploration regarding the impact of heterogeneity within the ambient population, such as different income groups, on crime is still in its infancy. With the support of mobile phone big data, this study constructs an index of ambient population heterogeneity to represent the complexity of the social environment. After controlling for the effects of total ambient population, nonlocal rate, transportation accessibility, crime attractors, and crime generators, this study employs a negative binomial regression model to examine the influence of ambient population heterogeneity and different income groups on the spatial manifestations of thefts. The findings indicate that ambient population heterogeneity significantly escalates the incidence of thefts, with middle and upper-middle-income groups acting as more attractive targets, whereas the higher-income group exerts a deterrent effect. The interaction analysis shows that increased population heterogeneity contributes to social disorder, thereby amplifying the attractiveness of the ambient population to perpetrators. These conclusions highlight the crucial role of ambient population heterogeneity in explaining crime dynamics and therefore enrich the routine activity theory. |
| first_indexed | 2024-03-08T12:39:39Z |
| format | Article |
| id | doaj.art-a21790f21e9d42dcb5fbc4beb67c947c |
| institution | Directory Open Access Journal |
| issn | 2662-9992 |
| language | English |
| last_indexed | 2024-03-08T12:39:39Z |
| publishDate | 2024-01-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Humanities & Social Sciences Communications |
| spelling | doaj.art-a21790f21e9d42dcb5fbc4beb67c947c2024-01-21T12:14:14ZengSpringer NatureHumanities & Social Sciences Communications2662-99922024-01-0111111110.1057/s41599-024-02610-8Unraveling the influence of income-based ambient population heterogeneity on theft spatial patterns: insights from mobile phone big data analysisChong Xu0Zhenhao He1Guangwen Song2Debao Chen3Center of GeoInformatics for Public Security, School of Geography and Remote Sensing, Guangzhou UniversityCenter of GeoInformatics for Public Security, School of Geography and Remote Sensing, Guangzhou UniversityCenter of GeoInformatics for Public Security, School of Geography and Remote Sensing, Guangzhou UniversityDepartment of Geography, University of CincinnatiAbstract While previous research has underscored the profound influence of the ambient population distribution on the spatial dynamics of crime, the exploration regarding the impact of heterogeneity within the ambient population, such as different income groups, on crime is still in its infancy. With the support of mobile phone big data, this study constructs an index of ambient population heterogeneity to represent the complexity of the social environment. After controlling for the effects of total ambient population, nonlocal rate, transportation accessibility, crime attractors, and crime generators, this study employs a negative binomial regression model to examine the influence of ambient population heterogeneity and different income groups on the spatial manifestations of thefts. The findings indicate that ambient population heterogeneity significantly escalates the incidence of thefts, with middle and upper-middle-income groups acting as more attractive targets, whereas the higher-income group exerts a deterrent effect. The interaction analysis shows that increased population heterogeneity contributes to social disorder, thereby amplifying the attractiveness of the ambient population to perpetrators. These conclusions highlight the crucial role of ambient population heterogeneity in explaining crime dynamics and therefore enrich the routine activity theory.https://doi.org/10.1057/s41599-024-02610-8 |
| spellingShingle | Chong Xu Zhenhao He Guangwen Song Debao Chen Unraveling the influence of income-based ambient population heterogeneity on theft spatial patterns: insights from mobile phone big data analysis Humanities & Social Sciences Communications |
| title | Unraveling the influence of income-based ambient population heterogeneity on theft spatial patterns: insights from mobile phone big data analysis |
| title_full | Unraveling the influence of income-based ambient population heterogeneity on theft spatial patterns: insights from mobile phone big data analysis |
| title_fullStr | Unraveling the influence of income-based ambient population heterogeneity on theft spatial patterns: insights from mobile phone big data analysis |
| title_full_unstemmed | Unraveling the influence of income-based ambient population heterogeneity on theft spatial patterns: insights from mobile phone big data analysis |
| title_short | Unraveling the influence of income-based ambient population heterogeneity on theft spatial patterns: insights from mobile phone big data analysis |
| title_sort | unraveling the influence of income based ambient population heterogeneity on theft spatial patterns insights from mobile phone big data analysis |
| url | https://doi.org/10.1057/s41599-024-02610-8 |
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