随机梯度地理加权回归(sgGWR):用于地理加权回归的可扩展带宽优化

IF 4.3 1区地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Geographical Information Science Pub Date : 2023-11-24 DOI:10.1080/13658816.2023.2285471

Hayato Nishi, Yasushi Asami

引用次数: 0

摘要

地理加权回归(GWR)是一种被广泛接受的空间依赖回归方法。由于GWR的标定需要大量的计算量，因此有一些有效的标定方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Stochastic gradient geographical weighted regression (sgGWR): scalable bandwidth optimization for geographically weighted regression

GWR (Geographical Weighted Regression) is a widely accepted regression method under spatial dependency. Since the calibration of GWR is computationally intensive, some efficient methods for calibra...

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来源期刊

International Journal of Geographical Information Science 地学-计算机：信息系统

CiteScore

11.00

自引率

7.00%

发文量

审稿时长

9 months

期刊介绍： International Journal of Geographical Information Science provides a forum for the exchange of original ideas, approaches, methods and experiences in the rapidly growing field of geographical information science (GIScience). It is intended to interest those who research fundamental and computational issues of geographic information, as well as issues related to the design, implementation and use of geographical information for monitoring, prediction and decision making. Published research covers innovations in GIScience and novel applications of GIScience in natural resources, social systems and the built environment, as well as relevant developments in computer science, cartography, surveying, geography and engineering in both developed and developing countries.