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In 1938 Daniel Gerhardus "Danie" Krige obtained an undergraduate degree in mining engineering and started a brilliant career centered on analyzing the gold and uranium mines in the Witwatersrand conglomerates of South Africa. He became interested in the disharmony between the poor reliability of reserve estimation reports and the magnitude of the economic decisions that were based on these studies. Back at the University of Witwatersrand, he wrote a master's thesis that began a revolution in mining evaluation methods. Krige was not alone in his research. Another mining engineer, Georges Matheron, a Frenchman, thought space data analysis belonged in a separate discipline, just as geophysics is a separate branch from physics. He named the new field geostatistics. Kriging is the name given in geostatistics to a collection of generalized linear regression techniques for the estimation of spatial phenomena. Pierre Carlier, another Frenchman, coined the term krigeage in the late 1950s to honor Krige's seminal work. Matheron anglicized the term to kriging when he published a paper for English-speaking readers. France dominated the development and application of geostatistics for several years. However, geostatistics in general, and kriging in particular, are employed by few and are regarded with apprehension by many. One of the possible applications of kriging is in computer mapping. Computer contouring methods can be grouped into two families: triangulation and gridding. The former is a direct procedure in which the contour lines are computed straight from the data by partitioning the sampling area into triangles with one observation per vertex. Kriging belongs in the gridding family. A grid is a regular arrangement of locations or nodes. In the gridding method the isolines are determined from interpolated values at the nodes. The difference between kriging and other weighting methods is in the calculation of the weights. Even for the simplest form of kriging, the calculations are more demanding. The kriging system of equations differs from classical regression in that the observations are allowed to be correlated and that neither the estimate nor the observations are necessarily points - they may have a volume, shape, and orientation. The mean square error is the average of the squares of the differences between the true and the estimated values. Simple kriging, the most basic form of kriging in that the system of equations has the fewest terms, requires the phenomena to have a constant and known mean. The next step up, ordinary kriging, does not require knowledge of the population mean. The external drift method, universal kriging, and intrinsic kriging go even further by allowing fluctuations in the mean. In practice, estimation by kriging is not as difficult to handle as it may look at first glance. In these days of high technology, all the details in the procedure are coded into computer programs. When properly used, kriging has several appealing attributes, the most important being that it does the work more accurately. By design, kriging provides the weights that result in the minimum mean square error. And yes, there have been people who have tested its superiority with real data. Practice has consistently confirmed theory. Kriging is also robust. Within reasonable limits, kriging tends to persist in yielding correct estimates even when the user selects the wrong model, misspecifies parameters, or both. This property should be an incentive for the novice to try the method. Gross misuse of kriging, though, can lead to poor results, worse even than those produced by alternative methods. Kriging has evolved and continues to expand to accommodate the estimation of increasingly demanding realities. Conclusions Theory and practice show that computer contour maps generated using kriging have the least mean square estimation error. In addition, the method provides information to assess the reliability of the maps.