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Study on the Spatial Classification of Construction Land Types in Chinese Cities: A Case Study in Zhejiang Province

Author

Listed:
  • Lin Dong

    (Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Jiazi Li

    (Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Yingjun Xu

    (Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China
    Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China
    State Key Laboratory of Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China)

  • Youtian Yang

    (Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Xuemin Li

    (Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Hua Zhang

    (Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China
    Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China
    State Key Laboratory of Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China)

Abstract

Identifying the land-use type and spatial distribution of urban construction land is the basis of studying the degree of exposure and the economic value of disaster-affected bodies, which are of great significance for disaster risk predictions, emergency disaster reductions, and asset allocations. Based on point of interest (POI) data, this study adopts POI spatialization and the density-based spatial clustering of applications with noise (DBSCAN) algorithm to accomplish the spatial classification of construction land. Zhejiang province is selected as a study area, and its construction land is divided into 11 land types using an accurate spatial classification method based on measuring the area of ground items. In the research, the POI dataset, which includes information, such as spatial locations and usage types, was constructed by big data cleaning and visual interpretation and approximately 620,000 pieces in total. The overall accuracy of the confusion matrix is 76.86%, which is greatly improved compared with that constructed with EULUC data (61.2%). In addition, compared with the official statistical data of 11 cities in Zhejiang Province, the differences between the calculated spatial proportions and statistics are not substantial. Meanwhile, the spatial characteristics of the studied land-use types are consistent with the urban planning data but with higher accuracy. The research shows that the construction land in Zhejiang Province has a high degree of land intensity, concentrated assets, and high economic exposure. The approach proposed in this study can provide a reference for city management including urbanization process, risk assessment, emergency management and asset allocation.

Suggested Citation

  • Lin Dong & Jiazi Li & Yingjun Xu & Youtian Yang & Xuemin Li & Hua Zhang, 2021. "Study on the Spatial Classification of Construction Land Types in Chinese Cities: A Case Study in Zhejiang Province," Land, MDPI, vol. 10(5), pages 1-14, May.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:5:p:523-:d:554207
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    References listed on IDEAS

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    4. Hualin Xie, 2017. "Towards Sustainable Land Use in China: A Collection of Empirical Studies," Sustainability, MDPI, vol. 9(11), pages 1-9, November.
    5. Hailing Xu & Jianghong Zhu & Zhanqi Wang, 2019. "Exploring the Spatial Pattern of Urban Block Development Based on POI Analysis: A Case Study in Wuhan, China," Sustainability, MDPI, vol. 11(24), pages 1-25, December.
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    Cited by:

    1. Zhenchao Zhang & Weixin Luan & Chuang Tian & Min Su & Zeyang Li, 2021. "Spatial Distribution Equilibrium and Relationship between Construction Land Expansion and Basic Education Schools in Shanghai Based on POI Data," Land, MDPI, vol. 10(10), pages 1-17, October.
    2. Yirui Han & Qinqin Pan & Yuee Cao & Jianhong Zhang & Jiaxuan Yuan & Borui Li & Saiqiang Li & Renfeng Ma & Xu Luo & Longbin Sha & Xiaodong Yang, 2022. "Estimation of Grain Crop Yields after Returning the Illegal Nurseries and Orchards to Cultivated Land in the Yangtze River Delta Region," Land, MDPI, vol. 11(11), pages 1-19, November.
    3. Youtian Yang & Lin Dong & Jiazi Li & Wenli Li & Dan Sheng & Hua Zhang, 2022. "A refined model of a typhoon near-surface wind field based on CFD," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(1), pages 389-404, October.

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