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Big Data-Based Evaluation of Urban Parks: A Chinese Case Study

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  • Zening Xu

    (Institute of Geographic Science and Natural Resources Research, The Key Laboratory of Regional Sustainable Development Analysis and Simulation, CAS, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Xiaolu Gao

    (Institute of Geographic Science and Natural Resources Research, The Key Laboratory of Regional Sustainable Development Analysis and Simulation, CAS, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Zhiqiang Wang

    (Institute of Geographic Science and Natural Resources Research, The Key Laboratory of Regional Sustainable Development Analysis and Simulation, CAS, Beijing 100101, China)

  • Jie Fan

    (Institute of Geographic Science and Natural Resources Research, The Key Laboratory of Regional Sustainable Development Analysis and Simulation, CAS, Beijing 100101, China)

Abstract

Urban parks play a key role in urban sustainable development. This paper proposes a method for the evaluation of public parks from the perspective of accessibility and quality. The method includes the data extraction of urban park locations and the delineation of urban built-up areas. The processing of urban park data not only involves the extraction from digital maps, but also the classification of urban parks using a semi-automated model in ArcGIS. The urban area is identified using the Point of Interest (POI) data in digital maps, taking economic and human activities into consideration. The service area and its overlapped time is included in the evaluation indicators. With a clear definition of park and urban built-up area, the evaluation result of urban parks is of great comparability. Taking China as an example, the quality of urban parks in 273 prefecture-level cities has been evaluated. The results show that the average service coverage of urban parks in Chinese cities is 64.8%, and that there are significant disparities between cities with different population sizes and locations. The results suggest the necessity to improve public parks in small-and-medium sized cities and inland areas to strengthen the coordination of urbanization and regional development.

Suggested Citation

  • Zening Xu & Xiaolu Gao & Zhiqiang Wang & Jie Fan, 2019. "Big Data-Based Evaluation of Urban Parks: A Chinese Case Study," Sustainability, MDPI, vol. 11(7), pages 1-16, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:7:p:2125-:d:221384
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    References listed on IDEAS

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    1. Nils-Bastian Heidenreich & Anja Schindler & Stefan Sperlich, 2013. "Bandwidth selection for kernel density estimation: a review of fully automatic selectors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(4), pages 403-433, October.
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    Cited by:

    1. Yaqi Du & Rong Zhao, 2022. "Research on the Development of Urban Parks Based on the Perception of Tourists: A Case Study of Taihu Park in Beijing," IJERPH, MDPI, vol. 19(9), pages 1-18, April.
    2. Antonella Pietta & Marco Tononi, 2021. "Re-Naturing the City: Linking Urban Political Ecology and Cultural Ecosystem Services," Sustainability, MDPI, vol. 13(4), pages 1-19, February.

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