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ORIGINAL RESEARCH article

Front. Public Health
Sec. Environmental Health and Exposome
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1402536
This article is part of the Research Topic Greening Urban Spaces and Human Health, Volume II View all 24 articles

Assessing the Nonlinear Impact of Green Space Exposure on Psychological Stress Perception Using Machine Learning and Street View Images

Provisionally accepted
Tianlin Zhang Tianlin Zhang Lei Wang Lei Wang Yike Hu Yike Hu *Yazhuo Zhang Yazhuo Zhang *Wenzheng Zhang Wenzheng Zhang *
  • Tianjin University, Tianjin, China

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

    Urban green space (GS) exposure is recognized as a nature-based strategy for addressing urban challenges. However, the stress relieving effects and mechanisms of GS exposure are yet to be fully explored. The development of machine learning and street view images offers a method for large-scale measurement and precise empirical analysis. This study focuses on the central area of Shanghai, examining the complex effects of GS exposure on psychological stress perception. By constructing a multidimensional psychological stress perception scale and integrating machine learning algorithms with extensive street view images data, we successfully developed a framework for measuring urban stress perception. Using the scores from the psychological stress perception scale provided by volunteers as labelled data, we predicted the psychological stress perception in Shanghai's central urban area through the Support Vector Machine (SVM) algorithm. Additionally, this study employed the interpretable machine learning model eXtreme Gradient Boosting (XGBoost) algorithm to reveal the nonlinear relationship between GS exposure and residents' psychological stress. Results indicate that the GS exposure in central Shanghai is generally low, with significant spatial heterogeneity. GS exposure has a positive impact on reducing residents' psychological stress. However, this effect has a threshold; when GS exposure exceeds 0.35, its impact on stress perception gradually diminishes. We recommend combining the threshold of stress perception with GS exposure to identify urban spaces, thereby guiding precise strategies for enhancing GS. This research not only demonstrates the complex mitigating effect of GS exposure on psychological stress perception but also emphasizes the importance of considering the "dose-effect" of it in urban planning and construction. Based on opensource data, the framework and methods developed in this study have the potential to be applied in different urban environments, thus providing more comprehensive support for future urban planning.Cities, as vital components of contemporary society, serve as central hubs for social, economic, political, and cultural activities (1,2). Their intricate spatial structures, diverse socio-economic

    Keywords: Urban greening, Baidu street view, Human perception, Health Planning, Sustainable environment

    Received: 17 Mar 2024; Accepted: 13 Aug 2024.

    Copyright: © 2024 Zhang, Wang, Hu, Zhang and Zhang. 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:
    Yike Hu, Tianjin University, Tianjin, China
    Yazhuo Zhang, Tianjin University, Tianjin, China
    Wenzheng Zhang, Tianjin University, Tianjin, 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.