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Using geostatistical techniques, you can create surfaces incorporating the statistical properties of the measured data. Because geostatistics is based on statistics, these techniques produce not only prediction surfaces but also error or uncertainty surfaces, giving you an indication of how good the predictions are. Understanding output surface types explains the types of prediction maps, which geostatistical technique you can use to create each map, and whether or not it contains standard error.
Many methods are associated with geostatistics, but they are all in the kriging family. Ordinary, simple, universal, probability, indicator, and disjunctive kriging, along with their counterparts in cokriging, are available in Geostatistical Analyst. Not only do these kriging methods create prediction and error surfaces, they can also produce probability and quantile output maps depending on your needs.
Kriging is divided into two distinct tasks: quantifying the spatial structure of the data and producing a prediction. Quantifying the structure, known as variography, is where you fit a spatial-dependence model to your data. To make a prediction for an unknown value for a specific location, kriging will use the fitted model from variography, the spatial data configuration, and the values of the measured sample points around the prediction location. Geostatistical Analyst has many tools to help you determine which parameters to use and also provides reliable defaults that you can use to make a surface quickly.