Geographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid-Reference-Cited by-同舟云学术

Geographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid

Published:2022-09-27 Issue:11 Volume:36 Page:2248-2269
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Dai Zhen¹,Wu Sensen¹²^ORCID,Wang Yuanyuan²³,Zhou Hongye¹,Zhang Feng¹²^ORCID,Huang Bo⁴⁵,Du Zhenhong¹²^ORCID

Affiliation:

1. School of Earth Sciences, Zhejiang University, Hangzhou, China

2. Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, China

3. Ocean Academy, Zhejiang University, Zhoushan, China

4. Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, NT, Hong Kong

5. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, NT, Hong Kong

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Provincial Key R&D Program of Zhejiang

Publisher

Informa UK Limited

Subject

Library and Information Sciences,Geography, Planning and Development,Information Systems

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2022.2100892

Reference32 articles.

1. Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity

2. Statistical Analysis of Spatial and Spatio-Temporal Point Patterns

3. Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity

4. Multiscale Geographically Weighted Regression (MGWR)

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