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Spatial Identification and Change Analysis of Production-Living-Ecological Space Using Multi-Source Geospatial Data: A Case Study in Jiaodong Peninsula, China

Author

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  • Mingyan Ni

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Yindi Zhao

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Caihong Ma

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Wenzhi Jiang

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Yanmei Xie

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Xiaolin Hou

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

Abstract

The significant heterogeneity in the spatial distribution of point of interest (POI) data, the absence of human socio-economic activity information in remote sensing images (RSI), and the high cost of land use (LU) data acquisition restrict their application in PLES spatial identification. Utilizing easily accessible data for detailed spatial identification of PLES remains an urgent challenge, especially when selecting a study area that encompasses both urban built-up areas (UBUA) and non-urban built-up areas (NUBUA). To address this issue, we proposed a PLES spatial identification method that combines POI data and land cover (LC) data in this paper. The proposed method first classified spatial analysis units (SAUs) into agricultural production space (APS), ecological space (ES), and ambiguous space (AS) based on the rich surface physical information from LC data. Subsequently, the AS was further classified into living space (LS) and non-agricultural production space (NAPS) based on the rich human socioeconomic information from POI data. For the AS that contains no POI, a simple rule was established to differentiate it into LS or NAPS. The effectiveness of the method was verified by accuracy evaluation and visual comparison. Applying the method to the Jiaodong Peninsula, we identified the PLES of the Jiaodong Peninsula for 2018 and 2022, further explored their spatial distribution characteristics, and analyzed their changes. Finally, we conducted a discussion on the real-world situations and driving mechanisms of the PLES changes and proposed several policy insights. The results indicated that both the spatial distribution characteristics of PLES and PLES change in the Jiaodong Peninsula were obvious and showed significant differentiation between UBUA and NUBUA. Climatic and natural resource conditions, geographic location, macro-policies, and governmental behaviors drove the PLES changes.

Suggested Citation

  • Mingyan Ni & Yindi Zhao & Caihong Ma & Wenzhi Jiang & Yanmei Xie & Xiaolin Hou, 2023. "Spatial Identification and Change Analysis of Production-Living-Ecological Space Using Multi-Source Geospatial Data: A Case Study in Jiaodong Peninsula, China," Land, MDPI, vol. 12(9), pages 1-27, September.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:9:p:1748-:d:1235816
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    References listed on IDEAS

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