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]
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- ^ 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. Bibcode:2020PhRvE.101d2301M. doi:10.1103/PhysRevE.101.042301. hdl:2445/161417. PMID 32422764. S2CID 49564277.
- ^ 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.
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