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Spatial neural network

April 14, 2024

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.[2][3][1]

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]

History edit

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 edit

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 edit

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 edit

There exist case-study applications of SNNs in:

See also edit

References edit

  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. 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. 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. 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. 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. doi:10.1007/s10115-023-01847-0. PMC 9994417. 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. 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.

April 14, 2024
spatial, neural, network, confused, with, spatial, network, other, uses, disambiguation, snns, constitute, supercategory, tailored, neural, networks, representing, predicting, geographic, phenomena, they, generally, improve, both, statistical, accuracy, reliab. 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 2 3 1 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 Contents 1 History 2 Spatial models 3 Categories 4 Applications 5 See also 6 ReferencesHistory editOpenshaw 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 editSpatial 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 editThere 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 editThere exist case study applications of SNNs in energy for predicting the electricity consumption 11 agriculture for classifying the vegetation 12 real estate for appraising the premises 13 1 See also editStatistics Neural networks supercategories Statistical software Quantitative geography Spatial analysis GIS softwareReferences edit 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 doi 10 1080 13658816 2021 1871618 S2CID 233883395 a b Morer I Cardillo A Diaz Guilera A Prignano L Lozano S 2020 Comparing spatial networks a one size fits all efficiency driven approach Physical Review 101 4 042301 doi 10 1103 PhysRevE 101 042301 hdl 2445 161417 PMID 32422764 S2CID 49564277 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 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 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 Openshaw S 1998 Neural network genetic and fuzzy logic models of spatial interaction Environment and Planning 30 10 1857 1872 doi 10 1068 a301857 S2CID 14290821 Anselin L 2017 A local indicator of multivariate spatial association extending Geary s C PDF Report Center for Spatial Data Science p 27 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 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 doi 10 1016 j softx 2022 101291 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 doi 10 1007 s10115 023 01847 0 PMC 9994417 S2CID 257436979 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 doi 10 1088 1742 6596 1402 3 033092 S2CID 237302678 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 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 Retrieved from https en wikipedia org w index php title Spatial neural network amp oldid 1212347542, wikipedia, wiki, book, books, library,

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