Uncovering drivers of community-level house price dynamics through multiscale geographically weighted regression: A case study of Wuhan, China
文献类型：期刊论文
| 作者 | Lu, Binbin1,2; Ge, Yong3; Shi, Yilin2; Zheng, Jianghua1; Harris, Paul4 |
| 刊名 | SPATIAL STATISTICS
 |
| 出版日期 | 2023-03-01 |
| 卷号 | 53页码:15 |
| ISSN号 | 2211-6753 |
| 关键词 | Spatial heterogeneity Temporal dynamics Multi-scale Real estate market Urban planning |
| DOI | 10.1016/j.spasta.2022.100723 |
| 通讯作者 | Ge, Yong(gey@lreis.ac.cn) |
| 英文摘要 | For buyers, investors and urban policy, understanding drivers of community-level house prices across space and across time, are important for urban management and economic planning. In this study, we interrogated two housing market datasets, one from 2015, the other from 2019, for Wuhan, China, in order to uncover diversities and similarities in the spatial relationships between house price and contextual data; and in the context of increasingly volatile markets. A non-stationary approach was adopted with basic geographically weighted regression (GWR) and multiscale GWR (MGWR), where only the latter enables relationships to vary at their own spatial scale. In terms of model fit, both MGWR (adj. R2: 0.94 and 0.97, for 2015 and 2019, respectively) and GWR (adj. R2: 0.87 and 0.81) represented an improvement over the usual linear regression (adj. R2: 0.11 and 0.09) and the spatial lag mode (adj. R2: 0.21 and 0.27). Similarly marked improvements for GWR and for MGWR were found using corrected Akaike Information Criterion (AICc) based fit diagnostics. However, of more importance and via MGWR, the spatially varying drivers of house price were found to operate at a range of spatial scales, that in turn changed in strength and significance between the two study years. Such insights allow for spatially-and temporally-aware decision-and policy-making for housing price control and urban planning, given China's housing markets can be increasing prone to strong growth coupled with severe depressions. (c) 2022 Elsevier B.V. All rights reserved. |
| WOS关键词 | MARKET DYNAMICS ; SPATIAL VARIATION ; MODEL ; HETEROGENEITY ; MACHINE ; CITIES ; SIZE ; TIME |
| 资助项目 | National Natural Science Foundation of China[NSFC: 41725006] ; National Natural Science Foundation of China[42071368] ; National Natural Science Foundation of China[U2033216] ; Hubei Provincial Key Research and Development Program[2021BAA185] ; Zhizhuo Research Fund on Spatial-Temporal Artificial Intelligence[ZZJJ202203] |
| WOS研究方向 | Geology ; Mathematics ; Remote Sensing |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:000913589100001 |
| 资助机构 | National Natural Science Foundation of China ; Hubei Provincial Key Research and Development Program ; Zhizhuo Research Fund on Spatial-Temporal Artificial Intelligence |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/189692]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Ge, Yong |
| 作者单位 | 1.Xinjiang Univ, Coll Geog & Remote Sensing Sci, Urumqi 830000, Peoples R China 2.Wuhan Univ, Sch Remote Sensing & Informat Engn, 129 Luoyu Rd, Wuhan 430079, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 4.Rothamsted Res, Sustainable Agr Sci North Wyke, Okehampton, England |
| 推荐引用方式 GB/T 7714 | Lu, Binbin,Ge, Yong,Shi, Yilin,et al. Uncovering drivers of community-level house price dynamics through multiscale geographically weighted regression: A case study of Wuhan, China[J]. SPATIAL STATISTICS,2023,53:15. |
| APA | Lu, Binbin,Ge, Yong,Shi, Yilin,Zheng, Jianghua,&Harris, Paul.(2023).Uncovering drivers of community-level house price dynamics through multiscale geographically weighted regression: A case study of Wuhan, China.SPATIAL STATISTICS,53,15. |
| MLA | Lu, Binbin,et al."Uncovering drivers of community-level house price dynamics through multiscale geographically weighted regression: A case study of Wuhan, China".SPATIAL STATISTICS 53(2023):15. |
入库方式： OAI收割
来源：地理科学与资源研究所
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