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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