> My research is on heavy metal pollution in water
> bodies.........
Hi, some thoughts (your numbering):
(1) One of the things I have found successful is the
following:
construct your semi-variogram using ALL of your
data but not allowing pairs between samples in
different water bodies;
use cross validation on each water body separately
to see if the 'generic' model works for all of them or
whether some are more variable or harder to predict
than others;
use the generic model for kriging with a
variance/sill scaled for each water body.
> Is there any consistent tested way to approach such
> 'not-enough-data' situations?
Not really, but I have found this works if the
'deposition' is similar in the various bodies.
(2) 'Hot spots' are (a) erratic highs due to
distribution being skewed or (b) true outliers
(inhomogeneities). Which? Tackle accordingly. Cross
validation will pick up outliers but not work properly
if data is severely skewed.
> Could it be possible to effectively fit variograms,
> when the hot spots are present?
Try calculating semi-variograms with and without 'hot
spots' and see what happens.
Kriging is based on an assumption of homogeneity and
it is a little unfair to expect it to come back and
say "that's a daft thing to do" ;-)
> [ For most of the cases I tried with such suspected
> hotspot data, my results show that
> the linear interpolation works better than the
> krigged distribution based on the 'fitted'
> variograms]
I find this statement interesting. How do you define
"better" -- prettier? nicer? easier to interpret? less
polluted?
Isobel Clark
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