A Route Map for Successful Applications of Geographically Weighted Regression
文献类型：期刊论文
| 作者 | Comber, Alexis10; Brunsdon, Christopher9; Charlton, Martin9; Dong, Guanpeng8; Harris, Richard7; Lu, Binbin6; Lu, Yihe5; Murakami, Daisuke4; Nakaya, Tomoki3; Wang, Yunqiang2 |
| 刊名 | GEOGRAPHICAL ANALYSIS
 |
| 出版日期 | 2022-01-09 |
| 页码 | 24 |
| ISSN号 | 0016-7363 |
| DOI | 10.1111/gean.12316 |
| 通讯作者 | Comber, Alexis(a.comber@leeds.ac.uk) ; Lu, Binbin(binbinlu@whu.edu.cn) |
| 英文摘要 | Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows spatial heterogeneities in processes and relationships to be investigated through a series of local regression models rather than a single global one. Standard GWR assumes that relationships between the response and predictor variables operate at the same spatial scale, which is frequently not the case. To address this, several GWR variants have been proposed. This paper describes a route map to decide whether to use a GWR model or not, and if so which of three core variants to apply: a standard GWR, a mixed GWR or a multiscale GWR (MS-GWR). The route map comprises 3 primary steps that should always be undertaken: (1) a basic linear regression, (2) a MS-GWR, and (3) investigations of the results of these in order to decide whether to use a GWR approach, and if so for determining the appropriate GWR variant. The paper also highlights the importance of investigating a number of secondary issues at global and local scales including collinearity, the influence of outliers, and dependent error terms. Code and data for the case study used to illustrate the route map are provided. |
| WOS关键词 | SPATIALLY VARYING RELATIONSHIPS ; AUTOCORRELATION ; HETEROGENEITY ; SELECTION ; MODELS ; REGULARIZATION |
| 资助项目 | Natural Environment Research Council Newton Fund Grant[NE/N007433/1] ; Natural Environment Research Council Newton Fund Grant[NE/S009124/1] ; Biotechnology and Biological Sciences Research Council[BBS/E/C/000J0100] ; Biotechnology and Biological Sciences Research Council[BBS/E/C/000I0320] ; Biotechnology and Biological Sciences Research Council[BBS/E/C/000I0330] ; National Natural Science Foundation of China[41571130083] ; National Natural Science Foundation of China[42071368] ; National Key Research and Development Program of China[2016YFC0501601] |
| WOS研究方向 | Geography |
| 语种 | 英语 |
| WOS记录号 | WOS:000740628900001 |
| 出版者 | WILEY |
| 资助机构 | Natural Environment Research Council Newton Fund Grant ; Biotechnology and Biological Sciences Research Council ; National Natural Science Foundation of China ; National Key Research and Development Program of China |
| 源URL | [http://ir.ieecas.cn/handle/361006/17365]  |
| 专题 | 地球环境研究所_生态环境研究室 |
| 通讯作者 | Comber, Alexis; Lu, Binbin |
| 作者单位 | 1.Rothamsted Res, Sustainable Agr Sci, North Wyke, Okehampton, England 2.Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian, Peoples R China 3.Tohoku Univ, Grad Sch Environm Studies, Sendai, Miyagi, Japan 4.Inst Stat Math, Dept Stat Data Sci, Tachikawa, Tokyo, Japan 5.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, Joint Ctr Global Change Studies, State Key Lab Urban & Reg Ecol, Beijing, Peoples R China 6.Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430072, Hubei, Peoples R China 7.Univ Bristol, Sch Geog Sci, Bristol, Avon, England 8.Henan Univ, Key Res Inst Yellow River Civilizat & Sustainable, Kaifeng, Peoples R China 9.Maynooth Univ, Natl Ctr Geocomputat, Maynooth, Kildare, Ireland 10.Univ Leeds, Sch Geog, Woodhouse Lane, Leeds LS2 9JT, W Yorkshire, England |
| 推荐引用方式 GB/T 7714 | Comber, Alexis,Brunsdon, Christopher,Charlton, Martin,et al. A Route Map for Successful Applications of Geographically Weighted Regression[J]. GEOGRAPHICAL ANALYSIS,2022:24. |
| APA | Comber, Alexis.,Brunsdon, Christopher.,Charlton, Martin.,Dong, Guanpeng.,Harris, Richard.,...&Harris, Paul.(2022).A Route Map for Successful Applications of Geographically Weighted Regression.GEOGRAPHICAL ANALYSIS,24. |
| MLA | Comber, Alexis,et al."A Route Map for Successful Applications of Geographically Weighted Regression".GEOGRAPHICAL ANALYSIS (2022):24. |
入库方式： OAI收割
来源：地球环境研究所
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