Hi
Well, in general the simulations are useful if you need to evaluate
spatial uncertainty, for
example if you have to apply some transfer function to your data or the need
to evaluate the probability that some neighbor cells overcome
jointly a given threshold.
Then kriging variance is useful if the spatial continuity model
you have calculated is effectively representative of the spatial variability
on the whole spatial domain of study i.e. there are not subdomains
characterized
by strong differences in spatial variability.
Then variance alone is useful in condition of normality... in the other cases
you need the complete distribution i.e. indicator kriging.
I guess that if you have strong positive skewnees you could
have a trend. In this case, if I remember well, the estimation error is also
function of the trend model.
Hope, this might help!
At 12.18 14/01/2008, you wrote:
Hi everyone,
this is my very first post on this mailing-list. I need to produce a
map of soil salinity space variability. I have chosen to compare the
estimation of OK, Regression Kriging and Indicator Kriging reliability
on my data-set. I know that kriging variance depends only on the
geometrical configuration of my samples and not on their actual
values, but I thought the kriging variance could be used as a measure
of the estimation quality. Instead reading Goovaerts (p. 184) I
understand that "the kriging standard deviation cannot be used as a
direct measure of estimation error".
Someone says that simulations would give a better result, but I found
an archive post from Pebesma saying that for many simulations the
error variance tends to the kriging variance...
So, what kriging statistics should I use to assess local estimation precision?
Giovanni
PS: I have strong positive skewness in my sample distribution. I'm
usign normal score tranformation, so I have to assess precision for
backtransformed values.
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