Modeling Spatial Anisotropic Relationships Using Gradient-Based Geographically Weighted Regression.
Distance and direction play crucial roles in modeling the spatial nonstationarity relationship. Because Euclidean distance ignores the effect of direction, several modified geographically weighted regression (GWR) attempts have been made to model anisotropic relationships using various non-Euclidean...
| Publicado en: | Annals of the American Association of Geographers Vol. 114; no. 4; pp. 697 - 719 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=176845495&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 176845495 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 24694452 JRMH jtl: Annals of the American Association of Geographers issn: 24694452 maglogo: N pubinfo: dt: 2024 vid: 114 iid: 4 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 176845495 10.1080/24694452.2023.2295391 ppf: 697 ppct: 22 formats: tig: atl: Modeling Spatial Anisotropic Relationships Using Gradient-Based Geographically Weighted Regression. aug: au: Yan, Jinbiao Wu, Bo Duan, Xiaoqi affil: National-Local Joint Engineering Laboratory on Digital Preservation and Innovative Technologies for the Culture of Traditional Villages and Towns, Hengyang Normal University, China, and School of Geography and Environment, Jiangxi Normal University, China School of Geography and Environment, Jiangxi Normal University, China Computer Science and Technology Institute, Guizhou University, China su: Euclidean distance Distances Velocity Anisotropy Gradient coils sug: subj: Euclidean distance Distances Velocity Anisotropy Gradient coils keyword: gradient-based geographically weighted regression GWR spatial nonstationarity spatial relationship gradient field (SRGF) velocity anisotropy anisotropía de velocidad campo de gradiente de la relación espacial (SRGF) no estacionalidad espacial 空间关系梯度场 空间非平稳性 速度各向异性 gradient-based geographically weighted regression GWR spatial nonstationarity spatial relationship gradient field (SRGF) velocity anisotropy anisotropía de velocidad campo de gradiente de la relación espacial (SRGF) no estacionalidad espacial 空间关系梯度场 空间非平稳性 速度各向异性 ab: Distance and direction play crucial roles in modeling the spatial nonstationarity relationship. Because Euclidean distance ignores the effect of direction, several modified geographically weighted regression (GWR) attempts have been made to model anisotropic relationships using various non-Euclidean distance metrics. These methods, however, adopt uniform parameters to define the non-Euclidean metrics over the whole study area, neglecting the varying numerical features existing in different regions. As a result, they fail to accurately depict spatial anisotropic relationships between variables. To address this issue, we propose a novel method called gradient-based geographically weighted regression (GGWR) that integrates the gradient of spatial relationships into GWR. Additionally, we introduce an l-norm regularization technique to achieve the parameter estimation of GGWR. Both simulated and actual data sets were used to validate the proposed method, and the experimental results demonstrate that the gradient field of the spatial relationship obtained by GGWR can effectively characterize the direction and intensity of variable relationships at various locations. Moreover, GGWR outperforms other models, including GWR, directional geographically weighted regression, and Minkowski distance-based geographically weighted regression, in terms of fitting accuracy, coefficient estimation accuracy, and interpretation of coefficient symbols. These findings indicate that the GGWR can be a valuable tool for modeling spatial anisotropic relationships by leveraging the spatial relationship gradient field. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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