Exploring nonlinear and interaction effects of urban campus built environments on exercise walking using crowdsourced data
IntroductionUniversity campuses, with their abundant natural resources and sports facilities, are essential in promoting walking activities among students, faculty, and nearby communities. However, the mechanisms through which campus environments influence walking activities remain insufficiently un...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1549786/full |
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| author | Bo Lu Bo Lu Qingyun Liu Hao Liu Tianxiang Long Tianxiang Long |
| author_facet | Bo Lu Bo Lu Qingyun Liu Hao Liu Tianxiang Long Tianxiang Long |
| author_sort | Bo Lu |
| collection | DOAJ |
| description | IntroductionUniversity campuses, with their abundant natural resources and sports facilities, are essential in promoting walking activities among students, faculty, and nearby communities. However, the mechanisms through which campus environments influence walking activities remain insufficiently understood. This study examines universities in Wuhan, China, using crowdsourced data and machine learning methods to analyze the nonlinear and interactive effects of campus built environments on exercise walking.MethodsThis study utilized crowdsourced exercise walking data and incorporated diverse campus characteristics to construct a multidimensional variable system. By applying the XGBoost algorithm and SHAP (SHapley Additive exPlanations), an explainable machine learning framework was established to evaluate the importance of various factors, explore the nonlinear relationships between variables and walking activity, and analyze the interaction effects among these variables.ResultsThe findings underscore the significant impact of several key factors, including the proportion of sports land, proximity to water bodies, and Normalized Difference Vegetation Index NDVI, alongside the notable influence of six distinct campus area types. The analysis of nonlinear effects revealed distinct thresholds and patterns of influence that differ from other urban environments, with some variables exhibiting fluctuated or U-shaped effects. Additionally, strong interactions were identified among variable combinations, highlighting the synergistic impact of elements like sports facilities, green spaces, and waterfront areas when strategically integrated.ConclusionThis research contributes to the understanding of how campus built environments affect walking activities, offering targeted recommendations for campus planning and design. Recommendations include optimizing the spatial configuration of sports facilities, green spaces, and water bodies to maximize their synergistic impacts on walking activity. These insights can foster the development of inclusive, health-promoting, and sustainable campuses. |
| format | Article |
| id | doaj-art-ba17f34ceede48d88ddb599cf5a2da7a |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Public Health |
| spelling | doaj-art-ba17f34ceede48d88ddb599cf5a2da7a2025-01-30T06:22:09ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-01-011310.3389/fpubh.2025.15497861549786Exploring nonlinear and interaction effects of urban campus built environments on exercise walking using crowdsourced dataBo Lu0Bo Lu1Qingyun Liu2Hao Liu3Tianxiang Long4Tianxiang Long5School of Architecture and Art, Central South University, Changsha, ChinaKey Laboratory of Urban Planning Information Technology of Hunan Provincial Universities, Hunan City University, Yiyang, ChinaSchool of Architecture and Art, Central South University, Changsha, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha, ChinaCollege of Architecture and Urban Planning, Hunan City University, Yiyang, ChinaKey Laboratory of Digital Urban and Rural Spatial Planning of Hunan Province, Hunan City University, Yiyang, ChinaIntroductionUniversity campuses, with their abundant natural resources and sports facilities, are essential in promoting walking activities among students, faculty, and nearby communities. However, the mechanisms through which campus environments influence walking activities remain insufficiently understood. This study examines universities in Wuhan, China, using crowdsourced data and machine learning methods to analyze the nonlinear and interactive effects of campus built environments on exercise walking.MethodsThis study utilized crowdsourced exercise walking data and incorporated diverse campus characteristics to construct a multidimensional variable system. By applying the XGBoost algorithm and SHAP (SHapley Additive exPlanations), an explainable machine learning framework was established to evaluate the importance of various factors, explore the nonlinear relationships between variables and walking activity, and analyze the interaction effects among these variables.ResultsThe findings underscore the significant impact of several key factors, including the proportion of sports land, proximity to water bodies, and Normalized Difference Vegetation Index NDVI, alongside the notable influence of six distinct campus area types. The analysis of nonlinear effects revealed distinct thresholds and patterns of influence that differ from other urban environments, with some variables exhibiting fluctuated or U-shaped effects. Additionally, strong interactions were identified among variable combinations, highlighting the synergistic impact of elements like sports facilities, green spaces, and waterfront areas when strategically integrated.ConclusionThis research contributes to the understanding of how campus built environments affect walking activities, offering targeted recommendations for campus planning and design. Recommendations include optimizing the spatial configuration of sports facilities, green spaces, and water bodies to maximize their synergistic impacts on walking activity. These insights can foster the development of inclusive, health-promoting, and sustainable campuses.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1549786/fullexercise walkinguniversity campusmachine learningnonlinear relationshipsinteraction effects |
| spellingShingle | Bo Lu Bo Lu Qingyun Liu Hao Liu Tianxiang Long Tianxiang Long Exploring nonlinear and interaction effects of urban campus built environments on exercise walking using crowdsourced data Frontiers in Public Health exercise walking university campus machine learning nonlinear relationships interaction effects |
| title | Exploring nonlinear and interaction effects of urban campus built environments on exercise walking using crowdsourced data |
| title_full | Exploring nonlinear and interaction effects of urban campus built environments on exercise walking using crowdsourced data |
| title_fullStr | Exploring nonlinear and interaction effects of urban campus built environments on exercise walking using crowdsourced data |
| title_full_unstemmed | Exploring nonlinear and interaction effects of urban campus built environments on exercise walking using crowdsourced data |
| title_short | Exploring nonlinear and interaction effects of urban campus built environments on exercise walking using crowdsourced data |
| title_sort | exploring nonlinear and interaction effects of urban campus built environments on exercise walking using crowdsourced data |
| topic | exercise walking university campus machine learning nonlinear relationships interaction effects |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1549786/full |
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