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Method for Evaluating Urban Building Renewal Potential Based on Multimachine Learning Integration: A Case Study of Longgang and Longhua Districts in Shenzhen

Author

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  • Dengkuo Sun

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, China)

  • Yuefeng Lu

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, China
    State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    National Center of Technology Innovation for Comprehensive Utilization of Saline-Alkali Land, Dongying 257347, China)

  • Yong Qin

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, China)

  • Miao Lu

    (National Center of Technology Innovation for Comprehensive Utilization of Saline-Alkali Land, Dongying 257347, China
    State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, The Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China)

  • Zhenqi Song

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, China)

  • Ziqi Ding

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, China)

Abstract

With the continuous advancement of urbanization, urban renewal has become a vital means of enhancing urban functionality and improving living environments. Traditional urban renewal research primarily focuses on the macro level, analyzing regions or units, with limited studies targeting individual buildings. Consequently, the unique characteristics and specific requirements of individual buildings during urban renewal have often been overlooked. This study first identified individual buildings undergoing urban renewal in the Longgang and Longhua Districts of Shenzhen, China, from 2018 to 2023 using multisource data such as the 2018 Shenzhen Building Census. A regression analysis based on building characteristics and locational factors was conducted using a stacking ensemble machine learning model. In addition, buildings were categorized into residential, industrial, and commercial types based on their usage, enabling both overall- and category-specific predictions of building renewal. The results show the following: (1) Using the prediction results of multilayer perceptron (MLP) and eXtreme Gradient Boosting (XGBoost) base models as inputs and fusing them with an AdaBoost classifier as the final metamodel, the goodness of fit of the overall building renewal regression model increased by 2.19%. (2) The regression model achieved an overall urban renewal prediction accuracy of 89.41%. Categorizing urban renewal projects improved the goodness of fit for residential and industrial building renewal by 0.14% and 6.13%, respectively. (3) Compared with traditional macro-level evaluation methods, the experimental results of this study improved by 8.41%, and compared with single-model approaches based on planning permit data, the accuracy improved by 29.11%.

Suggested Citation

  • Dengkuo Sun & Yuefeng Lu & Yong Qin & Miao Lu & Zhenqi Song & Ziqi Ding, 2024. "Method for Evaluating Urban Building Renewal Potential Based on Multimachine Learning Integration: A Case Study of Longgang and Longhua Districts in Shenzhen," Land, MDPI, vol. 14(1), pages 1-23, December.
    • Handle: RePEc:gam:jlands:v:14:y:2024:i:1:p:15-:d:1553118
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    References listed on IDEAS

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