Spatial neural network
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:
- energy for predicting the electricity consumption;[11]
- agriculture for classifying the vegetation;[12]
- real estate for appraising the premises.[13][1]
See also
- Statistics
- Neural networks' supercategories
- Statistical software
- Quantitative geography
- Spatial analysis
- GIS software
References
- ↑ 1.0 1.1 1.2 1.3 1.4 1.5 "A geographically weighted artificial neural network". International Journal of Geographical Information Science 36 (2): 215–235. 2022. doi:10.1080/13658816.2021.1871618.
- ↑ 2.0 2.1 "Comparing spatial networks: a one-size-fits-all efficiency-driven approach". Physical Review 101 (4): 042301. 2020. doi:10.1103/PhysRevE.101.042301. PMID 32422764.
- ↑ 3.0 3.1 "Spatial variability aware deep neural networks (SVANN): a general approach". ACM Transactions on Intelligent Systems and Technology 12 (6): 1–21. 2021. doi:10.1145/3466688.
- ↑ 4.0 4.1 "Modelling spatial interaction using a neural net". Geographic information systems, spatial modelling and policy evaluation. Berlin: Springer. 1993. pp. 147–164. doi:10.1007/978-3-642-77500-0_10. ISBN 978-3-642-77500-0.
- ↑ 5.0 5.1 Neural nets: applications in geography. The GeoJournal Library. 29. Berlin: Springer. 1994. pp. 196. doi:10.1007/978-94-011-1122-5. ISBN 978-94-011-1122-5.
- ↑ "Neural network, genetic, and fuzzy logic models of spatial interaction". Environment and Planning 30 (10): 1857–1872. 1998. doi:10.1068/a301857.
- ↑ Anselin L (2017). A local indicator of multivariate spatial association: extending Geary's C (Report). Center for Spatial Data Science. pp. 27. https://geodacenter.github.io/docs/LA_multivariateGeary1.pdf.
- ↑ "Modelling spatial processes in quantitative human geography". Annals of GIS 28: 5–14. 2021. doi:10.1080/19475683.2021.1903996.
- ↑ 9.0 9.1 "GWmodelS: A software for geographically weighted models". SoftwareX 21: 101291. 2023. doi:10.1016/j.softx.2022.101291. https://eprints.whiterose.ac.uk/194864/7/1-s2.0-S2352711022002096-main.pdf.
- ↑ "Harnessing heterogeneity in space with statistically guided meta-learning". Knowledge and Information Systems 65 (6): 2699–2729. 2023. doi:10.1007/s10115-023-01847-0.
- ↑ "Spatial neural network for forecasting energy consumption of Palembang area". Journal of Physics: Conference Series 1402 (3): 033092. 2019. doi:10.1088/1742-6596/1402/3/033092.
- ↑ "Spectral-spatial neural network classification of hyperspectral vegetation images". 1138. 2023. doi:10.1088/1755-1315/1138/1/012040.
- ↑ "The Spatial neural network model with disruptive technology for property appraisal in real estate industry". Technological Forecasting and Social Change 177: 121067. 2021. doi:10.1016/j.techfore.2021.121067.
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