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Research on the Equity of Urban Green Park Space Layout Based on Ga2SFCA Optimization Method—Taking the Core Area of Beijing as an Example

Author

Listed:
  • Yilun Cao

    (The Bartlett School of Architecture, University College London, London WC1H 0QB, UK)

  • Yuhan Guo

    (Department of Architecture, Hubei University of Arts And Science, Xiangyang 441053, China)

  • Mingjuan Zhang

    (College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China)

Abstract

(1) Background: The issue of equity in the layout of urban green park spaces is an essential dimension of urban public resource allocation. (2) Objective: To analyze the equity of the distribution of parkland in the core area of Beijing from a quantitative and spatial perspective. By measuring both vehicular and pedestrian transport modes, the study identifies areas with low levels of green space provision and provides strategies for optimization. It is hoped that this study can provide a basis for future green space construction in the core area of Beijing. (3) Methods: In this paper, the Gauss Two-step Floating Catchment Area Method (Ga2SFCA) is used to study the green park space layout in the core area of Beijing. The two modes of 30min-walk and 10min-car-journey were used to measure the fair values of the residential unit scale, the street district scale, and the overall scale, respectively. (4) Results: The study results show that the fair values based on the 30-min walk and the 10-min car journey differ significantly. For the 30-min walk-based travel mode, the proportion of fair (Class IV) and fairer (Class V) areas is approximately 20%, while for the 10 min car-based travel mode, the corresponding class is over 90%. (5) Conclusions: The overall equity of urban parkland in Beijing core area is better for car-based travel modes, while for walking modes, the supply is still insufficient, and the distribution of parkland is polarized.

Suggested Citation

  • Yilun Cao & Yuhan Guo & Mingjuan Zhang, 2022. "Research on the Equity of Urban Green Park Space Layout Based on Ga2SFCA Optimization Method—Taking the Core Area of Beijing as an Example," Land, MDPI, vol. 11(8), pages 1-20, August.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:8:p:1323-:d:889326
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    References listed on IDEAS

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    Cited by:

    1. Yilun Cao & Yuhan Guo & Chang Wang & Yunyuan Li, 2022. "Evaluation and Optimization of Refuge Green Space in the Central Area of Tianjin for Geological Disasters," Sustainability, MDPI, vol. 14(23), pages 1-25, November.
    2. Danli Yang & Jianwei Sun & Shuangyu Xie & Jing Luo & Fangqin Yang, 2025. "Spatial Accessibility Characteristics and Optimization of Multi-Stage Schools in Rural Mountainous Areas in China: A Case Study of Qixingguan District," Sustainability, MDPI, vol. 17(9), pages 1-20, April.
    3. Qidi Dong & Heng Lu & Xiaohong Luo & Pengman He & Di Li & Linjia Wu & Yundi Wei & Xuli Chen, 2023. "Evaluation and Optimization of Green Space Fairness in Urban Built-Up Areas Based on an Improved Supply and Demand Model: A Case Study of Chengdu, China," Sustainability, MDPI, vol. 15(20), pages 1-22, October.
    4. Yukuan Dong & Xi Chen & Dongyang Lv & Qiushi Wang, 2023. "Evaluation of Urban Green Space Supply and Demand Based on Mobile Signal Data: Taking the Central Area of Shenyang City as an Example," Land, MDPI, vol. 12(9), pages 1-20, September.
    5. Chenming Zhang & Bei He & Wei Li & Chunyang Guo, 2023. "Spatial Distribution and Accessibility Evaluation of National Water Parks in China," Sustainability, MDPI, vol. 15(15), pages 1-17, July.
    6. Chang Wang & Siyuan Wang & Yilun Cao & Haojun Yan & Yunyuan Li, 2023. "The Social Equity of Urban Parks in High-Density Urban Areas: A Case Study in the Core Area of Beijing," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
    7. Kang Gu & Jiamei Liu & Di Wang & Yue Dai & Xueyan Li, 2024. "Analyzing the Supply and Demand Dynamics of Urban Green Spaces Across Diverse Transportation Modes: A Case Study of Hefei City’s Built-Up Area," Land, MDPI, vol. 13(11), pages 1-25, November.

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