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Identifying Land Use Functions in Five New First-Tier Cities Based on Multi-Source Big Data

Author

Listed:
  • Wangmin Yang

    (College of Resources, Sichuan Agricultural University, Chengdu 611130, China)

  • Yang Ye

    (College of Resources, Sichuan Agricultural University, Chengdu 611130, China)

  • Bowei Fan

    (College of Resources, Sichuan Agricultural University, Chengdu 611130, China)

  • Shuang Liu

    (Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610299, China)

  • Jingwen Xu

    (College of Resources, Sichuan Agricultural University, Chengdu 611130, China)

Abstract

With the continuous development of big data technology, semantic-rich multi-source big data provides broader prospects for the research of urban land use function recognition. This study relied on POI data and OSM data to select the central urban areas of five new first-tier cities as the study areas. The TF-IDF algorithm was used to identify the land use functional layout of the study area and establish a confusion matrix for accuracy verification. The results show that: (1) The common feature of these five cities is that the total number and area of land parcels for residential land, commercial service land, public management and service land, and green space and open space land all account for over 90%. (2) The Kappa coefficients were all in the range [0.61, 0.80], indicating a high consistency of accuracy evaluation. (3) Chengdu and Tianjin have the highest land use function mixing degree, followed by Xi‘an, Nanjing, and Hangzhou. (4) Among the five new first-tier cities, Hangzhou and Nanjing have the highest similarity in land use function structure layout. This study attempts to reveal the current land use situation of five cities, which will provide a reference for urban development planning and management.

Suggested Citation

  • Wangmin Yang & Yang Ye & Bowei Fan & Shuang Liu & Jingwen Xu, 2024. "Identifying Land Use Functions in Five New First-Tier Cities Based on Multi-Source Big Data," Land, MDPI, vol. 13(3), pages 1-22, February.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:3:p:271-:d:1342845
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    References listed on IDEAS

    as
    1. Tianle Li & Xinqi Zheng & Chunxiao Zhang & Ruiguo Wang & Jiayu Liu, 2022. "Mining Spatial Correlation Patterns of the Urban Functional Areas in Urban Agglomeration: A Case Study of Four Typical Urban Agglomerations in China," Land, MDPI, vol. 11(6), pages 1-18, June.
    2. Krause, Cory M. & Zhang, Lei, 2019. "Short-term travel behavior prediction with GPS, land use, and point of interest data," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 349-361.
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