Spatial neural network: Difference between revisions

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{{distinguish|Spatial network}}

{{other uses|SNN (disambiguation)}}

[[File:GWNN and GWR prediction differences.jpg |thumb |upright=1.50 |top |Difference in predicted house prices within the states of Austria, from a GWR and a GWNN whose the weighting metrics respectively use the Euclidean distance (ED) and travel time distance (TTD).<ref name="Hagenauer et al. (2022)" />]]

<!-- 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'; please read the webpages: 'Wikipedia:INCLUDEONLY' and 'Wikipedia:PARTRANS', for understanding the properties and purposes of the used HTML tags --><onlyinclude><noinclude>'''Spatial neural networks''' ('''SNNs''')</noinclude><includeonly>Spatial neural networks (SNNs)</includeonly> 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]]<!-- if you transform 'geo-spatial' into 'geospatial' or conversely, please apply the transformation everywhere -->, 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 |page=042301 |doi=10.1103/PhysRevE.101.042301|pmid=32422764 |bibcode=2020PhRvE.101d2301M |hdl=2445/161417 |s2cid=49564277 |hdl-access=free |arxiv=1807.00565 }}</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&ndash;21 |doi=10.1145/3466688|s2cid=244786699 }}</ref><ref name="Hagenauer et al. (2022)">{{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&ndash;235 |doi=10.1080/13658816.2021.1871618|s2cid=233883395 |doi-access=free |bibcode=2022IJGIS..36..215H }}</ref><includeonly> Examples of SNNs are the OSFA spatial neural networks, SVANNs and GWNNs.</includeonly></onlyinclude>

==History==

[[Stan Openshaw|Openshaw]] (1993) and Hewitson et al. (1994) started investigating the applications of the a-spatial/classic NNs to geographic phenomena.<ref name="Openshaw (1993)">{{cite book |vauthors=Openshaw S |date=1993 |chapter=Modelling spatial interaction using a neural net |title=Geographic information systems, spatial modelling and policy evaluation |pages=147–164 |veditors=Fischer M, Nijkamp P |publisher=Springer |___location=Berlin |isbn=978-3-642-77500-0 |doi=10.1007/978-3-642-77500-0_10}}</ref><ref name="Hewitson et al. (1994)">{{cite book |vauthors= Hewitson B, Crane R |date=1994 |title=Neural nets: applications in geography |series=The GeoJournal Library |volume=29 |pages=196 |publisher=Springer |___location=Berlin |isbn=978-94-011-1122-5 |doi=10.1007/978-94-011-1122-5}}</ref> They observed that a-spatial/classic NNs outperform the other extensively applied a-spatial/classic statistical models (e.g. regression models, clustering algorithms, maximum likelihood classifications) in [[geography]], especially when there exist non-linear [[Relation (mathematics)|relations]] between the geo-spatial datasets' [[Variable and attribute (research)|variables]].<ref name="Openshaw (1993)"/><ref name="Hewitson et al. (1994)"/> Thereafter, Openshaw (1998) also compared these a-spatial/classic NNs with other modern and original a-spatial statistical models at that time (i.e. fuzzy logic models, genetic algorithm models); he concluded that the a-spatial/classic NNs are statistically competitive.<ref name="Openshaw (1998)">{{cite journal |vauthors=Openshaw S |date=1998 |title=Neural network, genetic, and fuzzy logic models of spatial interaction |journal=Environment and Planning |volume=30 |issue=10 |pages=1857–1872 |doi=10.1068/a301857|bibcode=1998EnPlA..30.1857O |s2cid=14290821 }}</ref> Thereafter scientists developed several categories of SNNs &ndash; see below.

==Spatial models==

Spatial statistical models (aka geographically weighted models, or merely spatial models) like the geographically weighted regressions (GWRs), SNNs, 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 |volume=28 |pages=5–14 |doi=10.1080/19475683.2021.1903996|s2cid=233574813 |doi-access=free }}</ref><ref name="Hagenauer et al. (2022)"/><ref name="Lu et al. (2023)"/>

==Categories==

There exist several categories of methods/approaches for designing and applying SNNs.

*'''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 |volume=65 |issue=6 |pages=2699–2729 |doi=10.1007/s10115-023-01847-0|pmid=37035130 |s2cid=257436979 |pmc=9994417 |bibcode=2023KIS....65.2699X }}</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. (2022)"/><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 |page=101291 |doi=10.1016/j.softx.2022.101291|bibcode=2023SoftX..2101291L |url=https://eprints.whiterose.ac.uk/194864/7/1-s2.0-S2352711022002096-main.pdf }}</ref> Like the SVANNs, they do not consistently handle spatial heterogeneity at multiple scales.<ref name="Hagenauer et al. (2022)"/>

==Applications==

<!-- thematic section within a methodological entry should remain a list, so that readers can quickly find examples of applications in their thematic fields/subfields; illustrative syntheses of case studies should appear within a separate/new Wikipedia’s entry (e.g. on 'applications of spatial models'); a section: 'software' reviewing the 'application software' and 'libraries/packages' with snippets of source codes would be really informative -->

There exist [[case-study]] applications of SNNs in:

* [[energy industry|energy]] for predicting the [[Electric energy consumption|electricity consumption]];<ref name="Rif'an et al. (2019)">{{cite journal |vauthors= Rif'an M, Daryanto D, Agung A |date=2019 |title=Spatial neural network for forecasting energy consumption of Palembang area |journal=Journal of Physics: Conference Series |volume=1402 |issue=3 |page=033092 |doi=10.1088/1742-6596/1402/3/033092|s2cid=237302678 |doi-access=free |bibcode=2019JPhCS1402c3092R }}</ref>

* [[agriculture]] for classifying the [[vegetation]];<ref name="Podlipnov et al. (2023)">{{cite conference |vauthors=Podlipnov V, Firsov N, Ivliev N, Mashkov S, Ishkin P, Skidanov R, Nikonorov A |date=2023 |title=Spectral-spatial neural network classification of hyperspectral vegetation images |book-title= IOP conference series: earth and environmental science |volume=1138 |doi=10.1088/1755-1315/1138/1/012040|doi-access=free }}</ref>

* [[real estate]] for appraising the [[premises]].<ref name="Lin et al. (2021)">{{cite journal |vauthors=Lin R, Ou C, Tseng K, Bowen D, Yung K, Ip W |date=2021 |title=The Spatial neural network model with disruptive technology for property appraisal in real estate industry |journal=Technological Forecasting and Social Change |volume=177 |page=121067 |doi=10.1016/j.techfore.2021.121067}}</ref><ref name="Hagenauer et al. (2022)" />

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{{reflist}}

[[Category:Artificial neural networks]]

[[Category:Neural network architectures]]

[[Category:Spatial analysis]]