Spatial neural network: Difference between revisions

Revision as of 11:32, 31 March 2023

Revision as of 09:42, 17 April 2023

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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|s2cid=257436979 }}</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|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)"/>

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<!-- 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 }}</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}}</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)" />