Geographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid.
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%0 Journal Article
%1 journals/gis/DaiWWZZHD22
%A Dai, Zhen
%A Wu, Sensen
%A Wang, Yuanyuan
%A Zhou, Hongye
%A Zhang, Feng
%A Huang, Bo
%A Du, Zhenhong
%D 2022
%J Int. J. Geogr. Inf. Sci.
%K dblp
%N 11
%P 2248-2269
%T Geographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid.
%U http://dblp.uni-trier.de/db/journals/gis/gis36.html#DaiWWZZHD22
%V 36
@article{journals/gis/DaiWWZZHD22,
added-at = {2022-12-05T00:00:00.000+0100},
author = {Dai, Zhen and Wu, Sensen and Wang, Yuanyuan and Zhou, Hongye and Zhang, Feng and Huang, Bo and Du, Zhenhong},
biburl = {https://www.bibsonomy.org/bibtex/26e37636a2b3793a388bc851ed313147d/dblp},
ee = {https://doi.org/10.1080/13658816.2022.2100892},
interhash = {82ba9d4436e450e91507ca5bbb2d7a2b},
intrahash = {6e37636a2b3793a388bc851ed313147d},
journal = {Int. J. Geogr. Inf. Sci.},
keywords = {dblp},
number = 11,
pages = {2248-2269},
timestamp = {2024-04-08T19:28:28.000+0200},
title = {Geographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid.},
url = {http://dblp.uni-trier.de/db/journals/gis/gis36.html#DaiWWZZHD22},
volume = 36,
year = 2022
}
