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Modeling Spatial Anisotropic Relationships Using Gradient-Based Geographically Weighted Regression

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成果类型:
期刊论文
作者:
Yan, Jinbiao;Wu, Bo;Duan, Xiaoqi
通讯作者:
Wu, B
作者机构:
[Yan, Jinbiao] Hengyang Normal Univ, Natl Local Joint Engn Lab Digital Preservat & Inno, Hengyang, Peoples R China.
[Wu, Bo; Wu, B; Yan, Jinbiao] Jiangxi Normal Univ, Sch Geog & Environm, Nanchang, Peoples R China.
[Duan, Xiaoqi] Guizhou Univ, Comp Sci & Technol Inst, Guiyang, Peoples R China.
通讯机构:
[Wu, B ] J
Jiangxi Normal Univ, Sch Geog & Environm, Nanchang, Peoples R China.
语种:
英文
关键词:
gradient-based geographically weighted regression;GWR;spatial nonstationarity;spatial relationship gradient field (SRGF);velocity anisotropy;GWR;anisotropia de velocidad;campo de gradiente de la relacion espacial (SRGF);GWR;no estacionalidad espacial
关键词(中文):
空间非平稳性;空间关系梯度场;速度各向异性
期刊:
Annals of the American Association of Geographers
ISSN:
2469-4452
年:
2023
页码:
24
基金类别:
Funding was provided by the Natural Science Foundation of China (Grant No. 42371419, 41961055, 42371254), Natural Science Foundation of Hunan Province, China (2023JJ40100), and Scientific Research Fund of Hunan Provincial Education Department (Grant No.22A0498) and the open fund project of the National-Local Joint Engineering Laboratory on Digital Preservation and Innovative Technologies (Grant No. 2021HSKFJJ015, CTCZ20K01).
机构署名:
本校为第一机构
摘要:
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....

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