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Bayesovské univerzální krigování×Prostorová autokorelace×
OborProstorová analýzaProstorová analýza
RodinaRegression modelRegression model
Rok vzniku1990s–2000s1950
TvůrceDiggle, Tawn & Moyeed; Kitanidis; Handcock & SteinP. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)
TypBayesian geostatistical interpolation with trendSpatial statistic / exploratory spatial data analysis
Původní zdrojDiggle, P. J., & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer. ISBN: 978-0387329079Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗
Další názvyBUK, Bayesian kriging with trend, Bayesian spatial interpolation with covariates, stochastic universal krigingspatial dependence, geographic autocorrelation, spatial clustering measure, SA
Příbuzné65
ShrnutíBayesian Universal Kriging (BUK) extends classical universal kriging by placing prior distributions on trend coefficients and spatial covariance parameters, then propagating full posterior uncertainty into predictions. It interpolates spatially referenced continuous data while simultaneously estimating large-scale deterministic trends driven by covariates and small-scale stochastic spatial dependence, yielding prediction intervals that honestly account for both parameter and interpolation uncertainty.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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ScholarGatePorovnat metody: Bayesian Universal Kriging · Spatial Autocorrelation. Získáno 2026-06-17 z https://scholargate.app/cs/compare