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436RE: AI-GEOSTATS: Declustering

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  • Isobel Clark
    Nov 16, 2001
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      > I am slightly confused:
      > ... if sampling is clustered preferentially in
      > e.g. > higher values
      > areas, would this not bias the semi-variogram for
      > the first few lags?...at
      > least if, as it can happen, the variance is related
      > to the mean.
      The semi-variogram is calculated on the difference
      between the two sample values. If the basic
      assumptions for semi-variogram construction are
      correct, differences are unrelated to the actual value
      of each sample or to the actual absolute location of
      the pair. Therefore, clustering does not influence the
      semi-variogram.

      If you have a situation where variance is related to
      the mean, e.g. with highly skewed data, you need to
      transform these values in some way before constructing
      a semi-variogram. This is true whether or not you have
      clustered sampling. Absolutely regular sampling will
      not give you a valid semi-variogram if you violate the
      assumptions upon which it is based.

      > What about the effects of the possible
      > over-estimation of the global mean due to
      clustering?
      If you are estimating the global mean based on a
      distribution model, you need to decluster. If you are
      estimating the global meaning on the basis of a kriged
      grid, you do not need to decluster as the kriging
      system does that for you.

      You can experiment with these questions using our
      totally free unlimited kriging game. This can be found
      in my 'briefcase' at
      http://uk.geocities.com/drisobelclark/briefcase.html

      Does this help?
      Isobel Clark

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