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Spatial neural network

In this article we are going to explore Spatial neural network and everything you need to know about it. From its origin to its impact on society, through its different applications and advances in research, we will delve into the fascinating world of Spatial neural network. We will learn about its benefits, its challenges and the future perspectives that open around it. Through this article, we aim to offer you a complete and updated overview of Spatial neural network, so that you can understand its importance and how it can affect your daily life. Get ready to discover everything about Spatial neural network and immerse yourself in a journey of knowledge and learning!

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Not to be confused with Spatial network.
For other uses, see SNN (disambiguation).
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)[1]

Spatial neural networks (SNNs) constitute a supercategory of tailored neural networks (NNs) for representing and predicting geographic phenomena. They generally improve both the statistical accuracy and reliability of the a-spatial/classic NNs whenever they handle geo-spatial datasets, and also of the other spatial (statistical) models (e.g. spatial regression models) whenever the geo-spatial datasets' variables depict non-linear relations.[2][3][1]

History

Openshaw (1993) and Hewitson et al. (1994) started investigating the applications of the a-spatial/classic NNs to geographic phenomena.[4][5] 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 relations between the geo-spatial datasets' variables.[4][5] 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.[6] Thereafter scientists developed several categories of SNNs – 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 dependence/autocorrelation, spatial heterogeneity, spatial association/cross-correlation) from the geo-locations of the geo-spatial datasets' (statistical) individuals/units.[7][8][1][9]

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 weights and designing a spatial structure from the originally a-spatial/classic neural networks.[2]
  • 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.[3] They generally outperform the OSFA spatial neural networks, but they do not consistently handle the spatial heterogeneity at multiple scales.[10]
  • 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.[1][9] Like the SVANNs, they do not consistently handle spatial heterogeneity at multiple scales.[1]

Applications

There exist case-study applications of SNNs in:

See also

References

  1. ^ a b c d e f Hagenauer J, Helbich M (2022). "A geographically weighted artificial neural network". International Journal of Geographical Information Science. 36 (2): 215–235. Bibcode:2022IJGIS..36..215H. doi:10.1080/13658816.2021.1871618. S2CID 233883395.
  2. ^ a b Morer I, Cardillo A, Díaz-Guilera A, Prignano L, Lozano S (2020). "Comparing spatial networks: a one-size-fits-all efficiency-driven approach". Physical Review. 101 (4) 042301. arXiv:1807.00565. Bibcode:2020PhRvE.101d2301M. doi:10.1103/PhysRevE.101.042301. hdl:2445/161417. PMID 32422764. S2CID 49564277.
  3. ^ a b Gupta J, Molnar C, Xie Y, Knight J, Shekhar S (2021). "Spatial variability aware deep neural networks (SVANN): a general approach". ACM Transactions on Intelligent Systems and Technology. 12 (6): 1–21. doi:10.1145/3466688. S2CID 244786699.
  4. ^ a b Openshaw S (1993). "Modelling spatial interaction using a neural net". In Fischer M, Nijkamp P (eds.). Geographic information systems, spatial modelling and policy evaluation. Berlin: Springer. pp. 147–164. doi:10.1007/978-3-642-77500-0_10. ISBN 978-3-642-77500-0.
  5. ^ a b Hewitson B, Crane R (1994). Neural nets: applications in geography. The GeoJournal Library. Vol. 29. Berlin: Springer. p. 196. doi:10.1007/978-94-011-1122-5. ISBN 978-94-011-1122-5.
  6. ^ Openshaw S (1998). "Neural network, genetic, and fuzzy logic models of spatial interaction". Environment and Planning. 30 (10): 1857–1872. Bibcode:1998EnPlA..30.1857O. doi:10.1068/a301857. S2CID 14290821.
  7. ^ Anselin L (2017). A local indicator of multivariate spatial association: extending Geary's C (PDF) (Report). Center for Spatial Data Science. p. 27.
  8. ^ Fotheringham S, Sachdeva M (2021). "Modelling spatial processes in quantitative human geography". Annals of GIS. 28: 5–14. doi:10.1080/19475683.2021.1903996. S2CID 233574813.
  9. ^ a b Lu B, Hu Y, Yang D, Liu Y, Liao L, Yin Z, Xia T, Dong Z, Harris P, Brunsdon C, Comber A, Dong G (2023). "GWmodelS: A software for geographically weighted models" (PDF). SoftwareX. 21 101291. Bibcode:2023SoftX..2101291L. doi:10.1016/j.softx.2022.101291.
  10. ^ Xie Y, Chen W, He E, Jia X, Bao H, Zhou X, Ghosh E, Ravirathinam P (2023). "Harnessing heterogeneity in space with statistically guided meta-learning". Knowledge and Information Systems. 65 (6): 2699–2729. Bibcode:2023KIS....65.2699X. doi:10.1007/s10115-023-01847-0. PMC 9994417. PMID 37035130. S2CID 257436979.
  11. ^ Rif'an M, Daryanto D, Agung A (2019). "Spatial neural network for forecasting energy consumption of Palembang area". Journal of Physics: Conference Series. 1402 (3) 033092. Bibcode:2019JPhCS1402c3092R. doi:10.1088/1742-6596/1402/3/033092. S2CID 237302678.
  12. ^ Podlipnov V, Firsov N, Ivliev N, Mashkov S, Ishkin P, Skidanov R, Nikonorov A (2023). "Spectral-spatial neural network classification of hyperspectral vegetation images". IOP conference series: earth and environmental science. Vol. 1138. doi:10.1088/1755-1315/1138/1/012040.
  13. ^ Lin R, Ou C, Tseng K, Bowen D, Yung K, Ip W (2021). "The Spatial neural network model with disruptive technology for property appraisal in real estate industry". Technological Forecasting and Social Change. 177 121067. doi:10.1016/j.techfore.2021.121067.
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