Global Kriging
Global Kriging (Global-Neighborhood Ordinary Kriging) · Also known as: global-neighborhood kriging, full-data kriging, exhaustive kriging, non-local kriging
Global Kriging is the ordinary kriging interpolation procedure applied using all available sample points as the neighborhood — no spatial search window limits which data contribute to each prediction. It produces optimal linear unbiased predictions of an unobserved value at any target location, with associated prediction-error variances, by exploiting a fitted variogram model that encodes spatial autocorrelation across the entire dataset.
Read the full method
Sign in with a free account to read this section.
Method map
The neighbourhood of related methods — select a node to explore.
When to use it
Use global kriging when the dataset is small to moderate in size (typically fewer than a few hundred points), the spatial process is stationary (constant mean and variance) across the entire domain, and you need theoretically minimum-variance linear unbiased predictions. It is appropriate when no strong spatial non-stationarity is present and when computational cost is manageable. Do not use global kriging when the dataset is large (thousands of points) — the n x n matrix inversion becomes prohibitive; prefer local kriging with a search neighborhood instead. Also avoid it when the spatial process is non-stationary (use universal kriging or regression-kriging) or when multiple correlated variables are available (use co-kriging).
Strengths & limitations
- Produces optimal (minimum-variance) linear unbiased predictions under stationarity assumptions.
- Uses all available spatial information without arbitrary neighborhood truncation.
- Provides prediction-error variances at every location, enabling spatially explicit uncertainty mapping.
- Theoretically well-grounded in random field theory with a rich geostatistical literature.
- Consistent and reproducible — the same variogram model yields identical results regardless of how prediction locations are ordered.
- Computational cost scales as O(n^3) with sample size — infeasible for large datasets without approximations.
- Requires stationarity of the spatial process; violated when the mean or variance drifts across the study area.
- Prediction quality is highly sensitive to variogram model choice and fitting; poor variogram estimation propagates into biased weights.
- Cannot incorporate non-spatial covariates without extension to regression-kriging or co-kriging.
Frequently asked
How does global kriging differ from local kriging?
Global kriging uses all n sample points to predict each unobserved location, forming and solving one system of size n. Local kriging restricts predictions to only the nearest k points within a search radius, solving a smaller system repeatedly. Global kriging is theoretically optimal under stationarity but computationally expensive; local kriging scales to large datasets by trading some theoretical optimality for practicality.
When is global kriging preferable to ordinary kriging with a local neighborhood?
When the dataset is small enough that the full n x n covariance matrix inversion is tractable (roughly n < 500), and the spatial autocorrelation structure is genuinely stationary across the entire domain, global kriging is preferred because it avoids the arbitrary choices involved in defining a search neighborhood radius and minimum/maximum neighbor counts.
Does global kriging require the data to be normally distributed?
No — kriging is a linear predictor and achieves minimum-variance unbiasedness among linear predictors regardless of distributional assumptions. Normal distribution is needed only if you want the kriging variance to define exact probability intervals rather than just second-moment bounds.
How do I validate a global kriging model?
Use leave-one-out cross-validation (also called jackknifing): remove each sample point in turn, predict its value using the remaining points, and compare predicted to observed. Report RMSE and the standardized error mean and variance; a well-calibrated model has standardized errors near zero mean and unit variance.
Can global kriging handle datasets with trend (non-stationarity)?
Not directly. If a trend exists, global ordinary kriging will produce biased predictions. You should either remove the trend first (universal kriging / kriging with external drift) or use regression-kriging, which models the trend with covariates and kriging on residuals.
Sources
- Cressie, N. A. C. (1993). Statistics for Spatial Data (revised ed.). Wiley-Interscience. ISBN: 978-0471002550
- Isaaks, E. H., & Srivastava, R. M. (1989). An Introduction to Applied Geostatistics. Oxford University Press. ISBN: 978-0195050134
How to cite this page
ScholarGate. (2026, June 3). Global Kriging (Global-Neighborhood Ordinary Kriging). ScholarGate. https://scholargate.app/en/spatial-analysis/global-kriging
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Co-krigingSpatial analysis↔ compare
- Local KrigingSpatial analysis↔ compare
- Ordinary KrigingSpatial analysis↔ compare
- Spatial AutocorrelationSpatial analysis↔ compare
- Universal KrigingSpatial analysis↔ compare