From Street Form to Spatial Justice: Explaining Urban Exercise Inequality via a Triadic SHAP-Informed Framework
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912971618254848 |
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| author | Zhao, Minwei Yang, Guosheng Zhang, Zhuoni Biljecki, Filip Zu, Hanzhi Wu, Cai |
| author_facet | Zhao, Minwei Yang, Guosheng Zhang, Zhuoni Biljecki, Filip Zu, Hanzhi Wu, Cai |
| contents | Urban streets are essential everyday health infrastructure, yet their capacity to support physical activity is unevenly distributed. This study develops a theory-informed and explainable framework to diagnose street-level exercise deprivation by integrating Lefebvre's spatial triad with multi-source urban data and SHAP-based analysis. Using Shenzhen as a case study, we show that while conceived spatial attributes have the strongest overall influence on exercise intensity, local deprivation mechanisms vary substantially across contexts. We identify a seven-mode typology of deprivation and locate high-demand but low-support street segments as priority areas for intervention. The study offers both a theory-grounded analytical framework and a practical diagnostic tool for promoting spatial justice in everyday physical activity. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_03570 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Street Form to Spatial Justice: Explaining Urban Exercise Inequality via a Triadic SHAP-Informed Framework Zhao, Minwei Yang, Guosheng Zhang, Zhuoni Biljecki, Filip Zu, Hanzhi Wu, Cai Computers and Society Information Theory Machine Learning 62H30, 91D10, 68T05 I.2.6; I.5.2; H.2.8; J.4 Urban streets are essential everyday health infrastructure, yet their capacity to support physical activity is unevenly distributed. This study develops a theory-informed and explainable framework to diagnose street-level exercise deprivation by integrating Lefebvre's spatial triad with multi-source urban data and SHAP-based analysis. Using Shenzhen as a case study, we show that while conceived spatial attributes have the strongest overall influence on exercise intensity, local deprivation mechanisms vary substantially across contexts. We identify a seven-mode typology of deprivation and locate high-demand but low-support street segments as priority areas for intervention. The study offers both a theory-grounded analytical framework and a practical diagnostic tool for promoting spatial justice in everyday physical activity. |
| title | From Street Form to Spatial Justice: Explaining Urban Exercise Inequality via a Triadic SHAP-Informed Framework |
| topic | Computers and Society Information Theory Machine Learning 62H30, 91D10, 68T05 I.2.6; I.5.2; H.2.8; J.4 |
| url | https://arxiv.org/abs/2507.03570 |