Jump to content

Spatial neural network

Add links
From Wikipedia, the free encyclopedia
This is an old revision of this page, as edited by GeogSage (talk | contribs) at 16:13, 16 March 2023 (→Applications: added banner to note section needs expansion. Feel free to remove once at least a brief explanation of each bullet point is included.). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.
Not to be confused with Spatial network.
For other uses, see SNN (disambiguation).

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.[1][2][3]

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, 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][3][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.[1].
  • 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.[2] 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[3][9]. Like the SVANNs, they do not consistently handle spatial heterogeneity at multiple scales.[3]

Applications

There exist case-study applications of SNNs in:

See also

References

  1. ^ 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). doi:10.1103/PhysRevE.101.042301.
  2. ^ 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.
  3. ^ a b c d Hagenauer J, Helbich M (2022). "A geographically weighted artificial neural network". International journal of geographical information science. 36 (2): 215–235. doi:10.1080/13658816.2021.1871618.
  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. 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. 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). doi:10.1068/a301857.
  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. doi:10.1080/19475683.2021.1903996.
  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". SoftwareX. 21. 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. doi:10.1007/s10115-023-01847-0.
  11. ^ 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.
  12. ^ 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. doi:10.1016/j.techfore.2021.121067.
Retrieved from "https://en.wikipedia.org/w/index.php?title=Spatial_neural_network&oldid=1144981087"