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Detailed Assessment of the Spatial Distribution of Urban Parks According to Day and Travel Mode Based on Web Mapping API: A Case Study of Main Parks in Wuhan

Author

Listed:
  • Qiang Niu

    (School of Urban Design, Wuhan University, 299 Bayi Road, Wuchang District, Wuhan 420072, China)

  • Ye Wang

    (Guangzhou Urban Planning & Design Survey Research Institute, 10-12 Jianshe Road, Yuexiu District, Guangzhou 510060, China)

  • Yuan Xia

    (School of Urban Design, Wuhan University, 299 Bayi Road, Wuchang District, Wuhan 420072, China)

  • Hao Wu

    (School of Urban Design, Wuhan University, 299 Bayi Road, Wuchang District, Wuhan 420072, China)

  • Xi Tang

    (School of Urban Design, Wuhan University, 299 Bayi Road, Wuchang District, Wuhan 420072, China)

Abstract

This article employs two indexes—accessibility and effective service ratio (ESR)—to assess the spatial distribution of urban parks with the consideration of both equity and efficiency. Traditional approaches to calculate these two indexes are often based on the shortest distance to the park within its service radius by network analysis. Such approaches cannot reflect the actual travel behaviors of urban residents and require extensive data collection of road networks and complex parameter setting. To avoid these defects, this study directly acquires travel time data for various travel modes in a specific time period to the park through web mapping API (Application Program Interface), then calculates the accessibility and ESR of urban parks based on these detailed data. This method gets closer to actual park usage situation and avoids the cumbersome process of road network model building. At last, a case study is conducted on the assessment of spatial distribution of main parks in Wuhan, finding that districts with poor park accessibility in Wuhan can be divided into three types: districts with an absence of parks, districts with an insufficiency with parks, and districts separated from parks by traffic. Then, pertinent improvement suggestions are proposed, which provide some bases for decisions on future park planning and construction.

Suggested Citation

  • Qiang Niu & Ye Wang & Yuan Xia & Hao Wu & Xi Tang, 2018. "Detailed Assessment of the Spatial Distribution of Urban Parks According to Day and Travel Mode Based on Web Mapping API: A Case Study of Main Parks in Wuhan," IJERPH, MDPI, vol. 15(8), pages 1-14, August.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:8:p:1725-:d:163274
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    References listed on IDEAS

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    2. Votsis, Athanasios, 2017. "Planning for green infrastructure: The spatial effects of parks, forests, and fields on Helsinki's apartment prices," Ecological Economics, Elsevier, vol. 132(C), pages 279-289.
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    Cited by:

    1. 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.
    2. Liu, Yang & Kwan, Mei-Po & Wong, Man Sing & Yu, Changda, 2023. "Current methods for evaluating people's exposure to green space: A scoping review," Social Science & Medicine, Elsevier, vol. 338(C).
    3. Jie Chang & Qiuju Deng & Piaopiao Hu & Zhao Yang & Moning Guo & Feng Lu & Yuwei Su & Jiayi Sun & Yue Qi & Ying Long & Jing Liu, 2023. "Driving Time to the Nearest Percutaneous Coronary Intervention-Capable Hospital and the Risk of Case Fatality in Patients with Acute Myocardial Infarction in Beijing," IJERPH, MDPI, vol. 20(4), pages 1-12, February.
    4. Linlin Liu & Bohong Zheng & Chen Luo & Komi Bernard Bedra & Francis Masrabaye, 2022. "Access to City Center: Automobile vs. Public Transit," IJERPH, MDPI, vol. 19(9), pages 1-16, May.

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