In theory, no other method of grid generation can produce better estimates (in the sense of being unbiased and having minimum error) of the form of a mapped surface than kriging. In practice, the effectiveness of the technique depends on the correct specification of several parameters that describe the semivariogram and the model of the drift. However, because kriging is robust, even with a naive selection of parameters the method will do no worse than conventional grid estimation procedures.
The price that must be paid for optimality in estimation is computational complexity. A large set of simultaneous equations must be solved for every grid node estimated by kriging. Therefore, computer run times will be significantly longer if a map is produced by kriging rather than by conventional gridding. In addition, an extensive prior study of the data must be made to test for stationarity, determine the form of the semivariogram, set the neighborhood size, and select the proper order of the drift if it exists. These properties are not independent, and because the system is underdetermined, trial-and-error experimentation may be necessary to determine the best combination. For this reason, to warrant the additional costs of analysis and processing, kriging probably should be applied in those instances where the best possible estimates of the surface are essential, the data are of reasonably good quality, and estimates of the error are needed.