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{{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|
==History==
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*'''One-Size-Fits-all (OSFA) spatial neural networks''', use the OSFA method/approach for globally computing the spatial [[Weighting|weights]] and designing a spatial [[structure]] from the originally a-spatial/classic neural networks.<ref name="Morer et al. (2020)"/>.
*'''Spatial Variability Aware Neural Networks''' ('''SVANNs''') use an enhanced OSFA method/approach that locally recomputes the spatial weights and redesigns the spatial structure of the originally a-spatial/classic NNs, at each geo-___location of the (statistical) individuals/units' attributes' values<ref name="Gupta et al. (2021)"/> They generally outperform the OSFA spatial neural networks, but they do not consistently handle the spatial heterogeneity at multiple scales.<ref name="Xie et al. (2023)">{{cite journal |vauthors=Xie Y, Chen W, He E, Jia X, Bao H, Zhou X, Ghosh E, Ravirathinam P |date=2023 |title=Harnessing heterogeneity in space with statistically guided meta-learning |journal=Knowledge and information systems |doi=10.1007/s10115-023-01847-0}}</ref>
*'''Geographically Weighted Neural Networks''' ('''GWNNs''') are similar to the SVANNs but they use the so-called Geographically Weighted Model (GWM) method/approach by Lu et al. (2023), so to locally recompute the spatial weights and redesign the spatial structure of the originally a-spatial/classic neural networks<ref name="Hagenauer et al. (2021)"/><ref name="Lu et al. (2023)">{{cite journal |vauthors=Lu B, Hu Y, Yang D, Liu Y, Liao L, Yin Z, Xia T, Dong Z, Harris P, Brunsdon C, Comber A, Dong G |date=2023 |title=GWmodelS: A software for geographically weighted models |journal=SoftwareX |volume=21 |doi=10.1016/j.softx.2022.101291}}</ref>. Like the SVANNs, they do not consistently handle
==Applications==
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