Method for Evaluating Urban Building Renewal Potential Based on Multimachine Learning Integration: A Case Study of Longgang and Longhua Districts in Shenzhen
文献类型：期刊论文
| 作者 | Sun, Dengkuo2; Lu, Yuefeng1,2,4; Qin, Yong2; Lu, Miao3,4; Song, Zhenqi2; Ding, Ziqi2 |
| 刊名 | LAND
 |
| 出版日期 | 2025 |
| 卷号 | 14期号:1页码:15 |
| 关键词 | urban renewal potential assessment multimodel fusion micro-level analysis Shenzhen |
| DOI | 10.3390/land14010015 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | 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%. |
| URL标识 | 查看原文 |
| WOS关键词 | DECISION-SUPPORT ; GENTRIFICATION ; SUSTAINABILITY ; TRANSFORMATION |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001407952600001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/211343]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Lu, Yuefeng |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 2.Shandong Univ Technol, Sch Civil Engn & Geomat, Zibo 255049, Peoples R China; 3.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China 4.Natl Ctr Technol Innovat Comprehens Utilizat Salin, Dongying 257347, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Sun, Dengkuo,Lu, Yuefeng,Qin, Yong,et al. Method for Evaluating Urban Building Renewal Potential Based on Multimachine Learning Integration: A Case Study of Longgang and Longhua Districts in Shenzhen[J]. LAND,2025,14(1):15. |
| APA | Sun, Dengkuo,Lu, Yuefeng,Qin, Yong,Lu, Miao,Song, Zhenqi,&Ding, Ziqi.(2025).Method for Evaluating Urban Building Renewal Potential Based on Multimachine Learning Integration: A Case Study of Longgang and Longhua Districts in Shenzhen.LAND,14(1),15. |
| MLA | Sun, Dengkuo,et al."Method for Evaluating Urban Building Renewal Potential Based on Multimachine Learning Integration: A Case Study of Longgang and Longhua Districts in Shenzhen".LAND 14.1(2025):15. |
入库方式： OAI收割
来源：地理科学与资源研究所
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