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Spatial Distribution Characteristics of Japan’s Forest Therapy Bases and Their Influencing Factors

Author

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  • Hui Li

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
    Beijing Laboratory of Urban and Rural Ecological Environment, Beijing 100083, China)

  • Mingrui Xu

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Jianzhe Li

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Zhenyu Li

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Ziyao Wang

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Weijie Zhuang

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Chunyi Li

    (Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry, Beijing 100091, China)

Abstract

Forest therapy bases are essential ecological recreational locations to promote mental and physical health while at the same time allowing people to enjoy and appreciate the forest. The article took Japan, where the development of forest therapy is in a mature stage, as the research object. Using multi-data and the methodologies of Kernel Density Analysis in ArcGIS and GeoDetector, the spatial distribution characteristics of Japan’s forest therapy bases were investigated, as well as the influencing factors. The results reveal that the spatial distribution of forest therapy bases in Japan is unbalanced, with an aggregated distribution in economically developed and densely populated areas. The spatial density of natural landscape resources, Gross Domestic Product (GDP), the spatial density of population, distance from three major metropolitan areas, rail transit ridership, and spatial density of tourism resources are factors influencing the distribution of forest therapy bases in Japan. The factors interact with each other, forming the spatial distribution pattern. Among these factors, GDP has the greatest explanatory power for the spatial distribution of forest therapy bases in Japan, followed by the distance from Japan’s three major metropolitan areas and spatial density of tourism resources, while the spatial density of population, spatial density of natural landscape resources, and rail transit ridership have a relatively weaker influence on forest therapy bases in Japan. The findings provide some insight into the macroscopic layout of forest therapy bases in various regions of different countries, demonstrating that excellent transportation facilities and good natural resources are the fundamental considerations for the location of forest therapy bases and that densely populated urban areas with a strong economic foundation are key areas for the development of forest therapy bases. Additionally, to take advantage of industrial agglomeration and synergize regional development, considerations for merging with existing resources, such as national parks, forest parks, and recreation forests, should be made.

Suggested Citation

  • Hui Li & Mingrui Xu & Jianzhe Li & Zhenyu Li & Ziyao Wang & Weijie Zhuang & Chunyi Li, 2022. "Spatial Distribution Characteristics of Japan’s Forest Therapy Bases and Their Influencing Factors," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15156-:d:973805
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    References listed on IDEAS

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    1. Insook Lee & Heeseung Choi & Kyung-Sook Bang & Sungjae Kim & MinKyung Song & Buhyun Lee, 2017. "Effects of Forest Therapy on Depressive Symptoms among Adults: A Systematic Review," IJERPH, MDPI, vol. 14(3), pages 1-18, March.
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    Cited by:

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    2. Chuanchuan Yuan & Mu Jiang, 2023. "Migration and Land Exploitation from Yuan to Qing Dynasties: Insights from 252 Traditional Villages in Hunan, China," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
    3. Guanglei Yang & Lixin Wu & Liang Xie & Zhezheng Liu & Zhe Li, 2023. "Study on the Distribution Characteristics and Influencing Factors of Traditional Villages in the Yunnan, Guangxi, and Guizhou Rocky Desertification Area," Sustainability, MDPI, vol. 15(20), pages 1-23, October.
    4. Xiaogang Feng & Moqing Hu & Sekhar Somenahalli & Xinyuan Bian & Meng Li & Zaihui Zhou & Fengxia Li & Yuan Wang, 2023. "A Study of Spatio-Temporal Differentiation Characteristics and Driving Factors of Shaanxi Province’s Traditional Heritage Villages," Sustainability, MDPI, vol. 15(10), pages 1-18, May.
    5. Shiying Li & Yuhong Song & Hua Xu & Yijiao Li & Shaokun Zhou, 2023. "Spatial Distribution Characteristics and Driving Factors for Traditional Villages in Areas of China Based on GWR Modeling and Geodetector: A Case Study of the Awa Mountain Area," Sustainability, MDPI, vol. 15(4), pages 1-19, February.

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