My own take differs a bit from Edzer's. He is of course correct that the use 
of conditional simulation to identify uncertainty at a location is at best an 
inefficient way to accomplish this. I've reviewed articles that did this. I 
don't know if it's ignorance or just the trendy nature of simulation.

However, if one's application is concerned with joint uncertainties, for 
example, the joint probability that an entire region is above a certain 
height, than simulation appears to be necessary.

Yours,

Ashton Shortridge

On Friday 29 September 2006 11:12 am, Edzer J. Pebesma wrote:
> Dear Kerry and Gustavo,
>
> The kriging variance is a perfect measure for estimation uncertainty as
> long as a second order stationary model is a good representation of the
> data under study. Obviously, if variability and/or spatial correlation
> varies over the field of interest and you have sufficient data to
> characterize this, or e.g. do a non-linear transform such as a
> log-transform to correct for a proportional effect, than you can and
> will do better when taking this into account.
>
> In my opinion papers such as those by Journel and Rossi have
> over-shouted their point, and have ignored that for many cases a second
> order stationary random field is a suitable model, if not the only
> possible.
>
> The argument that after rejecting the kriging variance, conditional
> simulation is suddenly needed as the solution get some measure of
> uncertainty is invalid: if you create a large enough set of conditional
> Gaussian simulations, their mean value equals the kriging mean and their
> variance equals the kriging variance. Nothing is gained, only an
> expensive approximation of something rather cheap is obtained.
>
> You will not find many papers that make this point, as the only point is
> that someone else is wrong. Not many people like to write such stuff.
> Below is a reference that may be hard to get (but you can google for the
> first author). I for instance didn't enjoy writing this email.
>
> Best regards,
> --
> Edzer
>
> Heuvelink, G.B.M. and E.J. Pebesma, 2002, Is the ordinary kriging
> variance a proper measure of interpolation error? In: Proceedings of the
> fifth International Symposium on Spatial Accuracy Assessment in Natural
> Resources and Environmental Sciences (eds. G. Hunter and K. Lowell).
> Melbourne: RMIT University, 179-186.
>
> Gustavo G. Pilger wrote:
> > Hi,
> >
> > Indeed the kriging variance is only semi-variogram and spatial data
> > configuration dependent. The kriging variance is calculated taking
> > into account only the geometry of the samples, i.e. their spatial
> > arrangement and the semi-variogram. Basically, kriging variance do not
> > take into account the value of the samples, but only their location
> > (and the semi-variogram), consequently ignoring the local variability.
> > Therefore this parameter is not appropriate to measure uncertainty.
> > For this purpose you should consider the use of conditional simulation
> > methods.
> >
> > I wrote some papers about this subject some years ago. For exemple:
> >
> > PILGER, Gustavo G.; COSTA, Joao Felipe Coimbra Leite; KOPPE, Jair
> > Carlos, 2001.  Additional Samples: Where they Should be Located?.
> > Natural Resources Research, New York, v. 10, n. 3, p. 197-207.
> >
> > I can send you a copy if you wish.
> >
> > I hope this helps you.
> >
> > Cheers.
> > <><><><><><><><><><><><><><><><><><><><><><><>
> > Gustavo G. Pilger, Mining Engineer, MSc, PhD
> > Senior Geostatistician
> > MBR - Brazil
> > <><><><><><><><><><><><><><><><><><><><><><><>
> >
> >> Hi.  I just read through Journel and Rossi's 1999 paper, "When do we
> >> need a trend model in Kriging".  In the appendix they say "A kriging
> >> variance is but a variogram-model dependent ranking of data
> >> configurations; being data-value  independent, it is generally not a
> >> measure of local accuracy...This fact is unfortunately not yet fully
> >> appreciated by some practitioners".  Can someone explain the
> >> implications of this in terms of determining cost-efficiency analysis
> >> for sample designs?  Specifically, can we use kriging variance
> >> estimates across potential sampling grids, (from modeled variograms
> >> estimated from say a pilot study) to estimate the variability
> >> associated with different sampling densities/configurations.  In
> >> addition, can someone provide some references that address this topic.
> >>
> >> Thanks,
> >> Kerry
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-- 
Ashton Shortridge
Assistant Professor                     [EMAIL PROTECTED]
Dept of Geography                       http://www.msu.edu/~ashton
235 Geography Building                  ph (517) 432-3561
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