Spatial neural network: Difference between revisions

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==Spatial models==
Spatial statistical models (aka geographically weighted models, or merely spatial models) like the geographically weighted regressions, SNNs/GWNNs, etc., are spatially tailored (a-spatial/classic) statistical models, so to learn and model the deterministic components of the [[spatial variability]] (i.e. [[Spatial analysis#Spatial dependence|spatial dependence/autocorrelation]], [[spatial heterogeneity]], [[Spatial analysis#Spatial association|spatial association/cross-correlation]]) from the geo-locations of the geo-spatial datasets’ [[Statistical unit|(statistical) individuals/units]].<ref name="Anselin (2017)">{{cite report |author=Anselin L |date=2017 |title=A local indicator of multivariate spatial association: extending Geary’s C |publisher=Center for Spatial Data Science |pages=27 |url=https://geodacenter.github.io/docs/LA_multivariateGeary1.pdf}}</ref><ref name="Fotheringham et al. (2021)">{{cite journal |vauthors=Fotheringham S, Sachdeva M |date=2021 |title=Modelling spatial processes in quantitative human geography |journal=Annals of GIS |doi=10.1080/19475683.2021.1903996}}</ref><ref name="Hagenauer et al. (2021)"/><ref name="Lu et al. (2023)"/>
 
==Categories==