Content deleted Content added
m improve: meaningfulness – i.e. add a white space at the beginning of the invisible text between the HTML tags: <includeonly> ... </onlyinclude> for correctly transcluding this text |
improve: wholesomeness – i.e. add an HTLM comment for specifying to be cautious in altering the lead/introduction, while highlighting the Wikipedia's entries/articles within which it transcludes and the Wikipedia's pages about the used HTML tags for transclusion purpose |
||
Line 1:
{{Short description|Category of tailored neural networks}}
{{distinguish|Spatial network}}{{other uses|SNN (disambiguation)}}
<!-- Please be cautious in revising the lead/introduction since its visible and invisible texts transclude in the entry/article on: 'spatial analysis' and 'types of artificial neural networks'; read WP:INCLUDEONLY and WP:PARTRANS for understanding the properties and purposes of the HTML tags--><onlyinclude>'''Spatial neural networks''' ('''SNNs''') constitute a supercategory of tailored [[artificial neural networks|neural networks (NNs)]] for representing and predicting geographic phenomena. They generally improve both the statistical [[Accuracy and precision|accuracy]] and [[Statistical reliability|reliability]] of the a-spatial/classic NNs whenever they handle [[Geographic data and information|geo-spatial datasets]], and also of the other spatial [[Statistical model|(statistical) models]] (e.g. spatial regression models) whenever the geo-spatial [[data set|datasets]]' variables depict [[Nonlinear system|non-linear relations]].<ref name="Morer et al. (2020)">{{cite journal |vauthors=Morer I, Cardillo A, Díaz-Guilera A, Prignano L, Lozano S |date=2020 |title=Comparing spatial networks: a one-size-fits-all efficiency-driven approach |journal=Physical review |volume=101 |issue=4 |doi=10.1103/PhysRevE.101.042301}}</ref><ref name="Gupta et al. (2021)">{{cite journal |vauthors=Gupta J, Molnar C, Xie Y, Knight J, Shekhar S |date=2021 |title=Spatial variability aware deep neural networks (SVANN): a general approach |journal=ACM Transactions on intelligent systems and technology |volume=12 |issue=6 |pages=1–21 |doi=10.1145/3466688}}</ref><ref name="Hagenauer et al. (2021)">{{cite journal |vauthors=Hagenauer J, Helbich M |date=2022 |title=A geographically weighted artificial neural network |journal=International journal of geographical information science |volume=36 |issue=2 |pages=215–235 |doi=10.1080/13658816.2021.1871618}}</ref><includeonly> Examples of SNNs are the OSFA spatial neural networks, SVANNs and GWNNs.</includeonly></onlyinclude>
==History==
| |||