Thanks Digby, You answered more to the question I asked. In this case I assume that you define the overall variance of a random field to be the variance of data spaced beyond the variogram range-- which I can buy, but not quite sure if this definition is practical in all cases-- and that's why I asked this question about estimating variance initially. In my point of view, expected variance for samples with CSR would be a better definition for the overall variance. That's some personal preference, however.
And since you mentioned declustering, I do know a few declustering approaches that will solve the problem of data clusters, but it is doubtful whether these approaches removes all effect of correlation between point data. I'm sure I understand all points of the replies to my question. I think I'm just trying to make sure the definition of variance applies to all cases of application. Meng-ying On Wed, 8 Dec 2004, Digby Millikan wrote: > Meng, > > You wan't to have an evenly spaced sample pattern for you estimation > of the variance, if you use samples within range of each others then these > are clusters of samples which will overweight that area, hence by removing > samples below the range, you remove "clusters" of samples. A common > method of performing statistics on spatial data is first to perform data > declustering, than calculate your statistics, however as Isobel points > out a fast way to do this is remove samples below the range. > > Digby > > > >
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