I am not too sure what you mean by "mysterious kriging output" - your
attached plans appear okay to me, the model appears to reflect the
underlying data reasonably well. The "plaid effect" that you refer to -
seen on the variance map makes sense. There seems to be a general
mis-conception that kriging variance is related to the actual data values -
this is not the case. Kriging variance is related, pure and simply, to data
configuration - in areas where there is a lot of data the kriging variance
is low, where there is little data the variance is high.
Hope this helps.
From: Noemi Barabas barabas@...
Date: Mon, 8 Mar 2004 18:18:55 -0500 (EST)
Subject: AI-GEOSTATS: mysterious kriging output
I am working on a kriging problem of log-PCB concentrations in
river sediments (the coordinates have been "straightened"), using GSLib.
I have strong anisotropy with a ratio of about 1:6 (x:y). I have some
clustered locations as well as some sparsely sampled areas, and several
instances where the high and low concentrations are found very close to
eachother. The distribution is lognormal and I am working with
log-transformed values. The variograms are rather nice in both
directions. Nevertheless, ordinary kriging gives a very peculiar-looking
map (of log-concentrations). It would be too difficult to put into
words, so I have included maps of estimates, variance and local mean as
Does anybody know what causes this "plaid" effect? Looking at the map
of variances, it appears that an estimation location has low variance
if it has a data point directly above and next to it, but intermediate
variance if those same two data points are in a diagonal direction
relative to the axes of anisotropy, even if the new position takes the
estimation point closer to the data points. I would like to undestand the
reason for this effect, as well as whether there is something that can be
done about it.
Could the fact that there are high values embedded in low value locations
be partially responsible for these strange maps?
(I did experiment with octant search, various maximum search radii,
various min and max number of data points for estimation, and this effect
persists. I even reversed the angles of anisotropy, tried different
variogram ranges. The variogram ranges are about 20% of the width/length
of the domain, and the relative nugget effect is about 6% in both
Thanks very much!
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