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Identification of Inefficient Urban Land for Urban Regeneration Considering Land Use Differentiation

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  • Rui Jin

    (School of Architecture and Planning, Hunan University, Changsha 410082, China
    Hunan Provincial Key Laboratory of Human Settlements in Hilly Regions, Changsha 410082, China)

  • Chunyuan Huang

    (School of Architecture and Planning, Hunan University, Changsha 410082, China)

  • Pei Wang

    (School of Architecture and Planning, Hunan University, Changsha 410082, China)

  • Junyong Ma

    (School of Architecture and Planning, Hunan University, Changsha 410082, China)

  • Yiliang Wan

    (School of Geographical Sciences, Hunan Normal University, Changsha 410081, China
    Geography Key Laboratory of Spatial Big Data Mining and Application of Hunan Province, Changsha 410081, China)

Abstract

Accurately identifying inefficient urban land is essential for urban regeneration and mining underutilized assets. Previous studies have primarily focused on examining the overall efficiency of land use without adequately considering the heterogeneity of urban land use types and comprehensive characteristics of urban quality. As a result, the spatial accuracy and precision of research findings have been relatively low. To address this gap, we developed a comprehensive method to identify inefficient urban lands for residential, commercial, and industrial use. The method integrated multi-source geographic data to quantitatively characterize the efficiency of different land use types considering six key dimensions, including building attribute, urban service, transportation condition, environmental quality, business performance, and production efficiency, utilized principal component analysis to reduce the multicollinearity and the dimensionality of the data, and identified land clusters with similar features that were inefficiently used by means of hierarchical clustering. By applying the method to Changsha, China, we validated its effectiveness. The results demonstrate that the method can accurately identify inefficient residential, commercial, and industrial land, with kappa coefficients of 0.71, 0.77, and 0.68, respectively. The identification results reveal the spatial distribution patterns of different types of inefficient land. Inefficient residential land is concentrated towards the city center, particularly in central areas. Inefficient commercial land is relatively evenly distributed, mainly outside the core commercial regions. Inefficient industrial land clusters towards the periphery, forming several agglomeration areas centered around industrial parks. By precisely identifying inefficient urban land and focusing on the key influencing factors, the proposed method enables the site selection of urban regeneration, site redevelopment evaluation, and optimization of urban resources.

Suggested Citation

  • Rui Jin & Chunyuan Huang & Pei Wang & Junyong Ma & Yiliang Wan, 2023. "Identification of Inefficient Urban Land for Urban Regeneration Considering Land Use Differentiation," Land, MDPI, vol. 12(10), pages 1-24, October.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:10:p:1957-:d:1265604
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    References listed on IDEAS

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    1. Guoqing Cui & Wenlong Zheng & Siliang Chen & Yue Dong & Tingyu Huang, 2022. "Study on the Spatial Pattern Characteristics and Influencing Factors of Inefficient Urban Land Use in the Yellow River Basin," Land, MDPI, vol. 11(9), pages 1-24, September.
    2. Maurice Roux, 2018. "A Comparative Study of Divisive and Agglomerative Hierarchical Clustering Algorithms," Journal of Classification, Springer;The Classification Society, vol. 35(2), pages 345-366, July.
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    4. Tao Zhou & Yulin Zhou & Guiwen Liu, 2017. "Key Variables for Decision-Making on Urban Renewal in China: A Case Study of Chongqing," Sustainability, MDPI, vol. 9(3), pages 1-19, March.
    5. Martin Heidenreich, 2015. "The New Museum Folkwang in Essen. A Contribution to the Cultural and Economic Regeneration of the Ruhr Area?," European Planning Studies, Taylor & Francis Journals, vol. 23(8), pages 1529-1547, August.
    6. Ginevra Balletto & Mara Ladu & Federico Camerin & Emilio Ghiani & Jacopo Torriti, 2022. "More Circular City in the Energy and Ecological Transition: A Methodological Approach to Sustainable Urban Regeneration," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    7. Richards, Daniel R. & Tunçer, Bige, 2018. "Using image recognition to automate assessment of cultural ecosystem services from social media photographs," Ecosystem Services, Elsevier, vol. 31(PC), pages 318-325.
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