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Bayesianisches Universelles Kriging×Geographisch gewichtete Regression (GWR)×
FachgebietRäumliche AnalyseRäumliche Analyse
FamilieRegression modelRegression model
Entstehungsjahr1990s–2000s2002
UrheberDiggle, Tawn & Moyeed; Kitanidis; Handcock & SteinFotheringham, Brunsdon & Charlton
TypBayesian geostatistical interpolation with trendLocal spatial regression
Wegweisende QuelleDiggle, P. J., & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer. ISBN: 978-0387329079Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
AliasnamenBUK, Bayesian kriging with trend, Bayesian spatial interpolation with covariates, stochastic universal krigingGWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Verwandt65
ZusammenfassungBayesian 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.Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships.
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ScholarGateMethoden vergleichen: Bayesian Universal Kriging · Geographically Weighted Regression. Abgerufen am 2026-06-18 von https://scholargate.app/de/compare