Salīdzināt metodes

	Beijesiskā universālā kriginga metode	Ģeogrāfiski svērtā regresija (GWR)
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1990s–2000s	2002
Autors	Diggle, Tawn & Moyeed; Kitanidis; Handcock & Stein	Fotheringham, Brunsdon & Charlton
Tips	Bayesian geostatistical interpolation with trend	Local spatial regression
Pirmavots	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
Citi nosaukumi	BUK, Bayesian kriging with trend, Bayesian spatial interpolation with covariates, stochastic universal kriging	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Saistītās	6	5
Kopsavilkums	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.