A Route Map for Successful Applications of Geographically Weighted Regression

doi:10.1111/gean.12316

	A Route Map for Successful Applications of Geographically Weighted Regression
	Comber, Alexis 10; Brunsdon, Christopher 9; Charlton, Martin 9; Dong, Guanpeng 8; Harris, Richard 7; Lu, Binbin 6; Lu, Yihe 5; Murakami, Daisuke 4; Nakaya, Tomoki 3; Wang, Yunqiang 2
刊名	GEOGRAPHICAL ANALYSIS
	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.
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关键词	SPATIALLY VARYING RELATIONSHIPS ; AUTOCORRELATION ; HETEROGENEITY ; SELECTION ; MODELS ; REGULARIZATION
WOS研究方向	Geography
语种	英语
出版者	WILEY
WOS记录号	WOS:000740628900001
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
内容类型	期刊论文
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.