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Verkkopohjainen tilastollinen analyysi×Spatiaalinen autokorrelaatio×
TieteenalaSpatiaalianalyysiSpatiaalianalyysi
MenetelmäperheRegression modelRegression model
Syntyvuosi1990s–2000s1950
KehittäjäAtsuyuki Okabe and colleaguesP. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)
TyyppiSpatial network modelSpatial statistic / exploratory spatial data analysis
AlkuperäislähdeOkabe, A., Satoh, T., Furuta, T., Sugihara, K., & Okano, K. (2006). Generalized network Voronoi diagrams: Concepts, computational methods, and applications. International Journal of Geographical Information Science, 22(9), 965–994. DOI ↗Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗
Rinnakkaisnimetnetwork spatial analysis, network-constrained spatial analysis, spatial network analysis, NBSAspatial dependence, geographic autocorrelation, spatial clustering measure, SA
Liittyvät35
TiivistelmäNetwork-based spatial analysis (NBSA) analyzes the distribution and interaction of spatial phenomena constrained to a network structure — such as roads, railways, or rivers — using network distance rather than straight-line (Euclidean) distance. It is the appropriate framework whenever movement, proximity, or risk is governed by the underlying network topology rather than open space.Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal clusters and outliers at the level of individual observations.
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ScholarGateVertaile menetelmiä: Network-Based Spatial Analysis · Spatial Autocorrelation. Haettu 2026-06-15 osoitteesta https://scholargate.app/fi/compare