卢宾宾, 葛咏, 秦昆, 郑江华. 地理加权回归分析技术综述[J]. 武汉大学学报 ( 信息科学版), 2020, 45(9): 1356-1366. DOI: 10.13203/j.whugis20190346
引用本文: 卢宾宾, 葛咏, 秦昆, 郑江华. 地理加权回归分析技术综述[J]. 武汉大学学报 ( 信息科学版), 2020, 45(9): 1356-1366. DOI: 10.13203/j.whugis20190346
LU Binbin, GE Yong, QIN Kun, ZHENG Jianghua. A Review on Geographically Weighted Regression[J]. Geomatics and Information Science of Wuhan University, 2020, 45(9): 1356-1366. DOI: 10.13203/j.whugis20190346
Citation: LU Binbin, GE Yong, QIN Kun, ZHENG Jianghua. A Review on Geographically Weighted Regression[J]. Geomatics and Information Science of Wuhan University, 2020, 45(9): 1356-1366. DOI: 10.13203/j.whugis20190346

地理加权回归分析技术综述

A Review on Geographically Weighted Regression

  • 摘要: 空间数据关系中的异质性或非平稳性特征是近期空间统计或相关应用领域的研究热点之一,而局部空间统计分析技术的提出与发展是其关键环节。地理加权回归分析技术(geographically weighted regression,GWR)通过关于位置的局部加权回归分析模型求解,以随着空间位置不同而变化的参数估计结果,量化反映空间数据关系中的异质性或非平稳性特征。GWR技术已在众多领域内广泛应用,逐渐成为重要的空间关系异质性建模工具之一。针对GWR模型解算、结果解读、模型检验等基础技术环节进行了系统总结,分别分析回顾了其对应的相关研究进展以及应用过程中存在的问题。同时,系统梳理了近年来GWR技术的主要拓展与延伸,重点阐述了其在采用灵活的距离度量选择、参数的多尺度估计以及时空数据建模方面的GWR技术扩展研究。此外,还简要介绍了现有的主要GWR技术软件工具,以期为读者和用户提供相对全面的GWR技术信息参考与知识总结。

     

    Abstract: 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.

     

/

返回文章
返回