Bandingkan kaedah

	Bayesian Universal Kriging	Autokorelasi Ruang
Bidang	Analisis Reruang	Analisis Reruang
Keluarga	Regression model	Regression model
Tahun asal	1990s–2000s	1950
Pengasas	Diggle, Tawn & Moyeed; Kitanidis; Handcock & Stein	P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)
Jenis	Bayesian geostatistical interpolation with trend	Spatial statistic / exploratory spatial data analysis
Sumber perintis	Diggle, P. J., & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer. ISBN: 978-0387329079	Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.
Alias	BUK, Bayesian kriging with trend, Bayesian spatial interpolation with covariates, stochastic universal kriging	spatial dependence, geographic autocorrelation, spatial clustering measure, SA
Berkaitan	6	5
Ringkasan	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.	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.