Spatial neural network

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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) GWNN and GWR prediction differences.jpg
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)

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]

Contents

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.

Applications

There exist case-study applications of SNNs in:

See also

References

  1. 1 2 3 4 5 6 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. 1 2 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. 1 2 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. 1 2 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. 1 2 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. 1 2 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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