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

Dengkuo Sun, Yuefeng Lu (), Yong Qin, Miao Lu (), Zhenqi Song and Ziqi Ding
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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
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
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

Land, 2024, vol. 14, issue 1, 1-23

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%.

Keywords: urban renewal; potential assessment; multimodel fusion; micro-level analysis; Shenzhen (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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