Disponible con una licencia de Geostatistical Analyst.
Kriging assumes that at least some of the spatial variation observed in natural phenomena can be modeled by random processes with spatial autocorrelation, and require that the spatial autocorrelation be explicitly modeled. Kriging techniques can be used to describe and model spatial patterns, predict values at unmeasured locations, and assess the uncertainty associated with a predicted value at the unmeasured locations.
The Geostatistical Wizard offers several types of kriging, which are suitable for different types of data and have different underlying assumptions:
- Ordinary Kriging
- Simple Kriging
- Universal Kriging
- Indicator Kriging
- Probability Kriging
- Disjunctive Kriging
- Empirical Bayesian Kriging
- Areal Interpolation
These methods can be used to produce the following surfaces:
- Maps of kriging predicted values
- Maps of kriging standard errors associated with predicted values
- Maps of probability, indicating whether or not a predefined critical level was exceeded
- Maps of quantiles for a predetermined probability level
The exceptions to this are:
- Indicator and Probability kriging, which produce the following:
- Maps of probability, indicating whether or not a predefined critical level was exceeded
- Maps of standard errors of indicators
- Areal Interpolation, which produces the following:
- Maps of predicted values
- Maps of standard errors associated with predicted values
There are several components of geostatistical models. The most important are to examine the data through exploratory spatial data analysis (ESDA) and variography, build a kriging model to suit your needs (see what are the different kriging models?), and check that the results are accurate by performing cross validation and validation and comparing alternate models to choose the best one.