IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i24p16602-d999443.html
   My bibliography  Save this article

Nonlinear Effects of the Neighborhood Environments on Residents’ Mental Health

Author

Listed:
  • Lin Zhang

    (Institute of Studies for the Greater Bay Area (Guangdong, Hong Kong, Macau), Guangdong University of Foreign Studies, Guangzhou 510006, China)

  • Suhong Zhou

    (School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China
    Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China)

  • Lanlan Qi

    (School of Management, Guangdong Industry Polytechnic, Guangzhou 510300, China)

  • Yue Deng

    (School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China)

Abstract

In the context of rapid urbanization and the “Healthy China” strategy, neighborhood environments play an important role in improving mental health among urban residents. While an increasing number of studies have explored the linear relationships between neighborhood environments and mental health, much remains to be revealed about the nonlinear health effects of neighborhood environments, the thresholds of various environmental factors, and the optimal environmental exposure levels for residents. To fill these gaps, this paper collected survey data from 1003 adult residents in Guangzhou, China, and measured the built and social environments within the neighborhoods. The random forest model was then employed to examine the nonlinear effects of neighborhood environments on mental health, evaluate the importance of each environmental variable, as well as identify the thresholds and optimal levels of various environmental factors. The results indicated that there are differences in the importance of diverse neighborhood environmental factors affecting mental health, and the more critical environmental factors included greenness, neighborhood communication, and fitness facility density. The nonlinear effects were shown to be universal and varied among neighborhood environmental factors, which could be classified into two categories: (i) higher exposure levels of some environmental factors (e.g., greenness, neighborhood communication, and neighborhood safety) were associated with better mental health; (ii) appropriate exposure levels of some environmental factors (e.g., medical, fitness, and entertainment facilities, and public transport stations) had positive effects on mental health, whereas a much higher or lower exposure level exerted a negative impact. Additionally, this study identified the exact thresholds and optimal exposure levels of neighborhood environmental factors, such as the threshold (22.00%) and optimal exposure level (>22.00%) of greenness and the threshold (3.80 number/km 2 ) and optimal exposure level (3.80 number/km 2 ) of fitness facility density.

Suggested Citation

  • Lin Zhang & Suhong Zhou & Lanlan Qi & Yue Deng, 2022. "Nonlinear Effects of the Neighborhood Environments on Residents’ Mental Health," IJERPH, MDPI, vol. 19(24), pages 1-17, December.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16602-:d:999443
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/24/16602/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/24/16602/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peng Zang & Hualong Qiu & Fei Xian & Linchuan Yang & Yanan Qiu & Hongxu Guo, 2022. "Nonlinear Effects of the Built Environment on Light Physical Activity among Older Adults: The Case of Lanzhou, China," IJERPH, MDPI, vol. 19(14), pages 1-15, July.
    2. Yingzhi Qiu & Yuqi Liu & Yi Liu & Zhigang Li, 2019. "Exploring the Linkage between the Neighborhood Environment and Mental Health in Guangzhou, China," IJERPH, MDPI, vol. 16(17), pages 1-15, September.
    3. Lin Zhang & Suhong Zhou & Mei-Po Kwan & Fei Chen & Rongping Lin, 2018. "Impacts of Individual Daily Greenspace Exposure on Health Based on Individual Activity Space and Structural Equation Modeling," IJERPH, MDPI, vol. 15(10), pages 1-18, October.
    4. Lingling Su & Suhong Zhou & Mei-Po Kwan & Yanwei Chai & Xue Zhang, 2022. "The impact of immediate urban environments on people’s momentary happiness," Urban Studies, Urban Studies Journal Limited, vol. 59(1), pages 140-160, January.
    5. Giulia Melis & Elena Gelormino & Giulia Marra & Elisa Ferracin & Giuseppe Costa, 2015. "The Effects of the Urban Built Environment on Mental Health: A Cohort Study in a Large Northern Italian City," IJERPH, MDPI, vol. 12(11), pages 1-18, November.
    6. Yang, Haoran & Zhang, Qinran & Helbich, Marco & Lu, Yi & He, Dongsheng & Ettema, Dick & Chen, Long, 2022. "Examining non-linear associations between built environments around workplace and adults’ walking behaviour in Shanghai, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 234-246.
    7. Tao, Tao & Wang, Jueyu & Cao, Xinyu, 2020. "Exploring the non-linear associations between spatial attributes and walking distance to transit," Journal of Transport Geography, Elsevier, vol. 82(C).
    8. Sun, Bindong & Yan, Hong & Zhang, Tinglin, 2017. "Built environmental impacts on individual mode choice and BMI: Evidence from China," Journal of Transport Geography, Elsevier, vol. 63(C), pages 11-21.
    9. Eric Morris & Erick Guerra, 2015. "Mood and mode: does how we travel affect how we feel?," Transportation, Springer, vol. 42(1), pages 25-43, January.
    10. Wang, Xiaoge & Liu, Ye & Zhu, Chunwu & Yao, Yao & Helbich, Marco, 2022. "Associations between the streetscape built environment and walking to school among primary schoolchildren in Beijing, China," Journal of Transport Geography, Elsevier, vol. 99(C).
    11. Da-Hye Yim & Youngsang Kwon, 2021. "Does Young Adults’ Neighborhood Environment Affect Their Depressive Mood? Insights from the 2019 Korean Community Health Survey," IJERPH, MDPI, vol. 18(3), pages 1-15, January.
    12. Md. Kamruzzaman & Simon Washington & Douglas Baker & Wendy Brown & Billie Giles-Corti & Gavin Turrell, 2016. "Built environment impacts on walking for transport in Brisbane, Australia," Transportation, Springer, vol. 43(1), pages 53-77, January.
    13. Md. Kamruzzaman & Simon Washington & Douglas Baker & Wendy Brown & Billie Giles-Corti & Gavin Turrell, 2016. "Built environment impacts on walking for transport in Brisbane, Australia," Transportation, Springer, vol. 43(1), pages 53-77, January.
    14. China Mills, 2018. "From ‘Invisible Problem’ to Global Priority: The Inclusion of Mental Health in the Sustainable Development Goals," Development and Change, International Institute of Social Studies, vol. 49(3), pages 843-866, May.
    15. Qingyuan Zhao & Trevor Hastie, 2021. "Causal Interpretations of Black-Box Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 272-281, January.
    16. Ding, Chuan & Cao, Xinyu & Liu, Chao, 2019. "How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds," Journal of Transport Geography, Elsevier, vol. 77(C), pages 70-78.
    17. Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2020. "Using machine learning for direct demand modeling of ridesourcing services in Chicago," Journal of Transport Geography, Elsevier, vol. 83(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xue Zhang & Suhong Zhou, 2023. "Building a City with Low Noise Pollution: Exploring the Mental Health Effect Thresholds of Spatiotemporal Environmental Noise Exposure and Urban Planning Solution," IJERPH, MDPI, vol. 20(5), pages 1-21, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Hongtai & Zheng, Rong & Li, Xuan & Huo, Jinghai & Yang, Linchuan & Zhu, Tong, 2022. "Nonlinear and threshold effects of the built environment on e-scooter sharing ridership," Journal of Transport Geography, Elsevier, vol. 104(C).
    2. Yin, Chun & Cao, Jason & Sun, Bindong & Liu, Jiahang, 2023. "Exploring built environment correlates of walking for different purposes: Evidence for substitution," Journal of Transport Geography, Elsevier, vol. 106(C).
    3. Tao, Sui & Cheng, Long & He, Sylvia & Witlox, Frank, 2023. "Examining the non-linear effects of transit accessibility on daily trip duration: A focus on the low-income population," Journal of Transport Geography, Elsevier, vol. 109(C).
    4. Wu, Fangning & Li, Wenjing & Qiu, Waishan, 2023. "Examining non-linear relationship between streetscape features and propensity of walking to school in Hong Kong using machine learning techniques," Journal of Transport Geography, Elsevier, vol. 113(C).
    5. Du, Qiang & Zhou, Yuqing & Huang, Youdan & Wang, Yalei & Bai, Libiao, 2022. "Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership," Journal of Transport Geography, Elsevier, vol. 102(C).
    6. Xu, Yiming & Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2021. "Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 170-188.
    7. Eric T. H. Chan & Tim Schwanen & David Banister, 2021. "The role of perceived environment, neighbourhood characteristics, and attitudes in walking behaviour: evidence from a rapidly developing city in China," Transportation, Springer, vol. 48(1), pages 431-454, February.
    8. Kajosaari, Anna & Hasanzadeh, Kamyar & Kyttä, Marketta, 2019. "Residential dissonance and walking for transport," Journal of Transport Geography, Elsevier, vol. 74(C), pages 134-144.
    9. Tong, Zhaomin & Zhang, Ziyi & An, Rui & Liu, Yaolin & Chen, Huiting & Xu, Jiwei & Fu, Shihang, 2024. "Detecting anomalous commuting patterns: Mismatch between urban land attractiveness and commuting activities," Journal of Transport Geography, Elsevier, vol. 116(C).
    10. Lin, Tao & Wang, Donggen & Guan, Xiaodong, 2017. "The built environment, travel attitude, and travel behavior: Residential self-selection or residential determination?," Journal of Transport Geography, Elsevier, vol. 65(C), pages 111-122.
    11. Cheng, Long & Shi, Kunbo & De Vos, Jonas & Cao, Mengqiu & Witlox, Frank, 2021. "Examining the spatially heterogeneous effects of the built environment on walking among older adults," Transport Policy, Elsevier, vol. 100(C), pages 21-30.
    12. Sun, Shan & Guo, Liang & Yang, Shuo & Cao, Jason, 2024. "Exploring the contributions of Ebike ownership, transit access, and the built environment to car ownership in a developing city," Journal of Transport Geography, Elsevier, vol. 116(C).
    13. Laviolette, Jérôme & Morency, Catherine & Waygood, E.O.D., 2022. "A kilometer or a mile? Does buffer size matter when it comes to car ownership?," Journal of Transport Geography, Elsevier, vol. 104(C).
    14. Phani Kumar, P. & Ravi Sekhar, Ch. & Parida, Manoranjan, 2018. "Residential dissonance in TOD neighborhoods," Journal of Transport Geography, Elsevier, vol. 72(C), pages 166-177.
    15. Li, Zekun & Chen, Zhenhua, 2023. "Predicting the future development scale of high-speed rail through the urban scaling law," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    16. Kamruzzaman, Md. & Aston, Laura & Baker, Douglas & Braun, Boris & Shatu, Farjana, 2021. "Changes in land use typology of global airports: An empirical investigation with implications for the aerotropolis concept," Journal of Transport Geography, Elsevier, vol. 97(C).
    17. Hania Rahimi-Ardabili & Thomas Astell-Burt & Phi-Yen Nguyen & Juan Zhang & Yu Jiang & Guang-Hui Dong & Xiaoqi Feng, 2021. "Green Space and Health in Mainland China: A Systematic Review," IJERPH, MDPI, vol. 18(18), pages 1-22, September.
    18. Zhang, Xiaojian & Zhou, Zhengze & Xu, Yiming & Zhao, Xilei, 2024. "Analyzing spatial heterogeneity of ridesourcing usage determinants using explainable machine learning," Journal of Transport Geography, Elsevier, vol. 114(C).
    19. Rongrong Zhang & Song Liu & Ming Li & Xiong He & Chunshan Zhou, 2021. "The Effect of High-Density Built Environments on Elderly Individuals’ Physical Health: A Cross-Sectional Study in Guangzhou, China," IJERPH, MDPI, vol. 18(19), pages 1-22, September.
    20. Faan Chen & Adriano Borges Costa, 2024. "Exploring the causal effects of the built environment on travel behavior: a unique randomized experiment in Shanghai," Transportation, Springer, vol. 51(1), pages 215-245, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16602-:d:999443. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.