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An Evaluation and Region Division Method for Ecosystem Service Supply and Demand Based on Land Use and POI Data

Author

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  • Xiumei Tang

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    Department of Resources and Environment, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China
    Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China)

  • Yu Liu

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    Department of Resources and Environment, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China
    Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China)

  • Yuchun Pan

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    Department of Resources and Environment, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China
    Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China)

Abstract

Mastering the regional spatial differences of ecosystem service supply and ecosystem service demand is of great significance to scientifically planning the development and utilization of national land and maintaining healthy development of ecosystems. Based on the relationship analysis of ecosystem service supply and ecosystem service demand, this study explored the regional ecosystem service supply by ecosystem service value based on grid data and constructed an ecosystem service demand evaluation model that integrated the construction land ecosystem service demand equivalent for static aspects and the point of interest (POI) kernel density estimation for dynamic aspects on the basis of land use and POI data. In the end, it put forward a region division method for ecosystem service supply and ecosystem service demand and conducted an empirical analysis of Haidian District, Beijing. The following results were concluded: (1) the ecosystem service value of different grids in Haidian District was between RMB (Chinese monetary unit, Yuan) 0 and RMB 2.4787 million. In terms of spatial distribution, the ecosystem service supply took on an obvious trend of gradual decrease from the northwest to the southeast, with major ecosystem service supply coming from the northwest. (2) The construction land ecosystem service demand equivalent of Haidian District was characterized by a multicenter cluster: the high equivalent area was in the southeast, while the equivalent of the northwest was relatively low. POI kernel density estimation demonstrated cluster distribution, with a high kernel density estimation in the southeast, a lower kernel density estimation in the central part, and the lowest kernel density estimation in the northwest. The ecosystem service demand index also showed cluster distribution: high index in the southeast, low index in the northwest, and prominent sudden changes from the central part to the south. (3) The bivariate local spatial autocorrelation cluster diagram method was used to divide five types of ecosystem service supply and ecosystem service demand, namely non-significant correlation region, high ecosystem service supply and high ecosystem service demand region, high ecosystem service supply and low ecosystem service demand region, low ecosystem service supply and high ecosystem service demand region, low ecosystem service supply and low ecosystem service demand region. Grids with the highest ratio belonged to the non-significant correlation region; the distribution of low ecosystem service supply and high ecosystem service demand region had the greatest concentration, mainly in the southeast; the grids of high ecosystem service supply and low ecosystem service demand region were mainly present in the northwest and in a continuous way; the grids of low ecosystem service supply and low ecosystem service demand region, and high ecosystem service supply and high ecosystem service demand region were extremely few, with sporadic distribution in the central part. The research results could provide a basis for the adjustment and fine management of regional land use structure.

Suggested Citation

  • Xiumei Tang & Yu Liu & Yuchun Pan, 2020. "An Evaluation and Region Division Method for Ecosystem Service Supply and Demand Based on Land Use and POI Data," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2524-:d:336096
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    References listed on IDEAS

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    1. Yang, Qing & Liu, Gengyuan & Casazza, Marco & Campbell, Elliot T. & Giannetti, Biagio F. & Brown, Mark T., 2018. "Development of a new framework for non-monetary accounting on ecosystem services valuation," Ecosystem Services, Elsevier, vol. 34(PA), pages 37-54.
    2. Li, Mengya & Kwan, Mei-Po & Wang, Fahui & Wang, Jun, 2018. "Using points-of-interest data to estimate commuting patterns in central Shanghai, China," Journal of Transport Geography, Elsevier, vol. 72(C), pages 201-210.
    3. Jie Bao & Chengcheng Xu & Pan Liu & Wei Wang, 2017. "Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests," Networks and Spatial Economics, Springer, vol. 17(4), pages 1231-1253, December.
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    1. Wang, Zhuangzhuang & Fu, Bojie & Zhang, Liwei & Wu, Xutong & Li, Yingjie, 2022. "Ecosystem service assessments across cascade levels: typology and an evidence map," Ecosystem Services, Elsevier, vol. 57(C).

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