Σύγκριση μεθόδων

	Μπεϋζιανό Καθολικό Kriging	Παλινδρόμηση Γεωγραφικά Σταθμισμένη (GWR)
Πεδίο	Χωρική Ανάλυση	Χωρική Ανάλυση
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1990s–2000s	2002
Δημιουργός	Diggle, Tawn & Moyeed; Kitanidis; Handcock & Stein	Fotheringham, Brunsdon & Charlton
Τύπος	Bayesian geostatistical interpolation with trend	Local spatial regression
Θεμελιώδης πηγή	Diggle, P. J., & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer. ISBN: 978-0387329079	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Εναλλακτικές ονομασίες	BUK, Bayesian kriging with trend, Bayesian spatial interpolation with covariates, stochastic universal kriging	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Συναφείς	6	5
Σύνοψη	Bayesian 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.