Simone Not so banal a question. 34 years ago my supervisor gave me some papers to read which said exactly that. Even with a Master's in applied statistics, I could not make head-nor-tail of the explanation. So I went on a three week short course at Fontainebleau and they explained it around the middle of the third week!
A very simplistic explanation: when you calculate an ordinary covariance you have two columns of figures, say variable g and f. The covariance between g and f is calculated (in practice) by multiplying the two columns together, summing the results and then subtracting the product of the two means. Difficult to do in a text email but: Sum(g x f)/n - mean(g) x mean(f) This is exactly equivalent to: Sum (g-mean(g)x(f-mean(f))/n {leave out the whole n or (n-1) debate at this point) In a geostatistical context, g would be the value of a sample (any sample). f would be the value of another sample a specified distance away. That is, specify one particular distance (h), find pairs of samples that distance apart, first sample in pair is g (first column), second sample in pair is f (second column). Calculate covariance as above with the modification that the mean of g and the mean of f will be the same. Repeat for many different distances and you end up with a graph of how the covariance of the values varies with the distance between the samples. I, personally, prefer the semi-variogram approach because it is a lot easier to explain! Also, you do not need to know (or estimate) the mean. More explanations in free downloadable Practical Geostatistics 1979, http://uk.geocities.com/drisobelclark/practica.htm. If you have second order stationarity, the covariance function is simply the sample value variance minus the semi-variogram. Does this help? Isobel
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