A Review on Geographically Weighted Regression

被引：0
|
作者：
Lu B. [1 ,3 ]
Ge Y. [2 ]
Qin K. [1 ]
Zheng J. [3 ]
机构：
[1] School of Remote Sensing and Information Engineering, Wuhan University, Wuhan
[2] Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing
[3] College of Resources and Environment Sciences, Xinjiang University, Urumqi
来源：
| 1600年 / Editorial Board of Medical Journal of Wuhan University卷 / 45期
基金：
中国国家自然科学基金;
关键词：
Geographically weighted models; Spatial analysis; Spatial heterogeneity; Spatial non-stationarity; Spatial statistics;
D O I：
10.13203/j.whugis20190346
中图分类号：
学科分类号：
摘要：
Spatial heterogeneity or non-stationarity in data relationships is one of the hot topics in spatial statistics or relative application fields, while the development of local techniques forms an essential part for the relative studies. Geographically weighted regression (GWR) provides spatially varying coefficient estimates via location-specific weighted regression model calibrations, to explore spatial heterogeneities or non-stationarities, quantitatively. It has been widely used in a number of fields, and become one of the most important tools for exploring spatial heterogeneities in data relationships. We summarized the GWR basics in model calibration, result interpretation, model diagnostics, reviewed its research progress and problems in its applications, respectively. Meanwhile, we sorted out the important extensions of the basic GWR technique, particularly in applying flexible distance metric choices in GWR model calibration, multiscale parameter estimates and spatiotemporal data modeling. In addition, we also introduced the main GWR tools or software accordingly to provide the users or readers comprehensive reference and knowledge on the GWR technique. © 2020, Editorial Board of Geomatics and Information Science of Wuhan University. All right reserved.
引用
收藏
页码：1356 / 1366
页数：10
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