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| Krigaggio Universale Bayesiano× | Regressione Geograficamente Ponderata (GWR)× | |
|---|---|---|
| Campo | Analisi spaziale | Analisi spaziale |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1990s–2000s | 2002 |
| Ideatore≠ | Diggle, Tawn & Moyeed; Kitanidis; Handcock & Stein | Fotheringham, Brunsdon & Charlton |
| Tipo≠ | Bayesian geostatistical interpolation with trend | Local spatial regression |
| Fonte seminale≠ | 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 |
| Alias | BUK, Bayesian kriging with trend, Bayesian spatial interpolation with covariates, stochastic universal kriging | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| Correlati≠ | 6 | 5 |
| Sintesi≠ | 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. |
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