Exploring Urban Crime Through the Lens of an Adaptive Region Partitioning Technique and Built Environment Features
In the realm of urban studies, the choice of regional partitioning schemes can significantly influence analytical outcomes, thereby affecting the precision and dependability of research findings. This paper proposes a new region partitioning method with multi-density distribution, namely the Adaptiv...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10824789/ |
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| author | Yuchen Yan Hua Wang Wei Quan Yuxin Wang |
| author_facet | Yuchen Yan Hua Wang Wei Quan Yuxin Wang |
| author_sort | Yuchen Yan |
| collection | DOAJ |
| description | In the realm of urban studies, the choice of regional partitioning schemes can significantly influence analytical outcomes, thereby affecting the precision and dependability of research findings. This paper proposes a new region partitioning method with multi-density distribution, namely the Adaptive Region Partitioning (ARP) method, and explores the impact of built environment features under different region partitioning methods on crime analysis. Compared with the traditional partitioning method, the research unit under the ARP method exhibits improved intra-regional homogeneity and inter-regional heterogeneity characteristics, more accurately maps the spatial distribution characteristics of the urban data, effectively mitigates the risk of multicollinearity, improves the accuracy of the regression model, and provides a new and reliable regional partitioning scheme for crime research. Finally, by applying the SHAP (SHapley Additive exPlanations) method, this study further reveals the extent to which different built environment features influence crime, providing important data support for urban planning and the formulation of crime prevention strategies. |
| format | Article |
| id | doaj-art-2d7477e2d6ec4d7cb5b5d2254d68c1e3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2d7477e2d6ec4d7cb5b5d2254d68c1e32025-01-24T00:02:03ZengIEEEIEEE Access2169-35362025-01-0113115321154310.1109/ACCESS.2025.352566010824789Exploring Urban Crime Through the Lens of an Adaptive Region Partitioning Technique and Built Environment FeaturesYuchen Yan0https://orcid.org/0000-0002-0245-2241Hua Wang1https://orcid.org/0000-0001-7360-8648Wei Quan2https://orcid.org/0000-0001-5341-9268Yuxin Wang3School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, ChinaSchool of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, ChinaSchool of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, ChinaSchool of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, ChinaIn the realm of urban studies, the choice of regional partitioning schemes can significantly influence analytical outcomes, thereby affecting the precision and dependability of research findings. This paper proposes a new region partitioning method with multi-density distribution, namely the Adaptive Region Partitioning (ARP) method, and explores the impact of built environment features under different region partitioning methods on crime analysis. Compared with the traditional partitioning method, the research unit under the ARP method exhibits improved intra-regional homogeneity and inter-regional heterogeneity characteristics, more accurately maps the spatial distribution characteristics of the urban data, effectively mitigates the risk of multicollinearity, improves the accuracy of the regression model, and provides a new and reliable regional partitioning scheme for crime research. Finally, by applying the SHAP (SHapley Additive exPlanations) method, this study further reveals the extent to which different built environment features influence crime, providing important data support for urban planning and the formulation of crime prevention strategies.https://ieeexplore.ieee.org/document/10824789/Urban crimebuilt environmentregion partitioningmulti-density dataSHAP |
| spellingShingle | Yuchen Yan Hua Wang Wei Quan Yuxin Wang Exploring Urban Crime Through the Lens of an Adaptive Region Partitioning Technique and Built Environment Features IEEE Access Urban crime built environment region partitioning multi-density data SHAP |
| title | Exploring Urban Crime Through the Lens of an Adaptive Region Partitioning Technique and Built Environment Features |
| title_full | Exploring Urban Crime Through the Lens of an Adaptive Region Partitioning Technique and Built Environment Features |
| title_fullStr | Exploring Urban Crime Through the Lens of an Adaptive Region Partitioning Technique and Built Environment Features |
| title_full_unstemmed | Exploring Urban Crime Through the Lens of an Adaptive Region Partitioning Technique and Built Environment Features |
| title_short | Exploring Urban Crime Through the Lens of an Adaptive Region Partitioning Technique and Built Environment Features |
| title_sort | exploring urban crime through the lens of an adaptive region partitioning technique and built environment features |
| topic | Urban crime built environment region partitioning multi-density data SHAP |
| url | https://ieeexplore.ieee.org/document/10824789/ |
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