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ORIGINAL RESEARCH article

Front. Public Health
Sec. Environmental Health and Exposome
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1549786

Exploring nonlinear and interaction effects of urban campus built environments on exercise walking using crowdsourced data

Provisionally accepted
BO LU BO LU 1Qingyun Liu Qingyun Liu 1Hao Liu Hao Liu 1*Tianxiang Long Tianxiang Long 2
  • 1 Central South University, Changsha, China
  • 2 Hunan City University, Yiyang, China

The final, formatted version of the article will be published soon.

    University campuses, with their abundant natural resources and sports facilities, play a vital role 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 an explainable machine learning framework to analyze the nonlinear and interactive effects of campus built environments on exercise walking. The findings underscore the significant impact of several key factors, including 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. This research contributes to the understanding of how campus built environments affect walking activities, offering targeted recommendations for campus planning and design. These insights can foster the development of inclusive, health-promoting, and sustainable campuses.

    Keywords: exercise walking1, university campus2, Machine Learning3, nonlinear relationships4, interaction effects5

    Received: 22 Dec 2024; Accepted: 15 Jan 2025.

    Copyright: © 2025 LU, Liu, Liu and Long. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Hao Liu, Central South University, Changsha, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.