here is a summary of the replies I got to my question on standardised versus

general relative semivariograms. The original question is posted at the end of

this mail. The least I can say is that there is not much work written about it

even if these are frequently used in environmental sciences !

Thanks a lot to Digby Millikan, Nicolas Jeannee, Ulrich Leopold and Yetta

Jager for their kind help.

Digby Millikan suggests the following reading:

M. David "Geostatistical Ore Reserve Estimation" discusses the General

Relative Variogram which was developed to demonstrate the existence of the

proportional effect which he found to exist in ore bodies. He generated

separate variograms of low and high grade regions and found that when

compensated by the mean where found to be identical, showing a direct relation

between correlation and mean.

In the text are graphs of standard deviation vs. mean scatter plots.

While the proportional effect displayed variograms for different regions,

Multiple Indicator Kriging is used where different variabilities for

different grade ranges co-exist in the same regions. Variograms are generated

for separate grade ranges which are then used in the Multiple Indicator

Kriging algorithm to produce the estimates.

Nicolas Jeannee, who works like me on pollutants which typically have highly

skewed distributions, underlines that a problem with these semivariograms is

their explicit estimation of mean and variance, which are non robust

statistics in the case of positively skewed variables, and require

stationarity...

He suggests the use of the weighted variograms presented in

Rivoirard (2000). Weighted Variograms. In: �Geostats 2000�,

W. Kleingeld and D. Krige (Eds.).

Proocedings available on cd-rom only for the moment..

Nicolas and Ulrich Leopold suggested another reference:

Srivastava R. M & H. M. Parker. 1989. Robust measures of spatial continuity.

In: Geostatistics, M. Armstrong (Editor). Kluwer, Dordrecht.

Vol. I, pages 295-308

While my posting was focusing more on the use of these semivariograms for

descriptive analyses, Yetta Jager anticipated already further questions:

�I can see why one would want to use weights inversely related to variance to

account for differences in variance (heteroscedasticity) in estimating

variogram parameters, but I'm not clear how a variogram that is standardized

to means fits into the kriging model. How would you be able to estimate at

points with unknown values if the autocorrelation with neighbors depended on

the unknown estimate?

In my experience with indicator kriging, the closer you get to the median, the

higher the variance (sill) is (p=0.5==>var=0.25), and the more room there is

to observe spatial autocorrelation as a difference between nugget and sill.

As you get to the extremes, the sill drops and it is hard to detect

autocorrelation. I suppose in theory it shouldn't matter, but in practice

that's what I've observed."

Thanks again for all the replies !

Best whishes,

Gregoire

--------------------------------------------------------------------------

Original question:

>Dear all,

mean

>

>can anyone point the relative advantages and drawbacks of

>

>- standardised semivariograms (See Pannatier, 1996: Variowin. Software

>for spatial data analysis in 2 D, page 39) where gamma(h) is divided by the

>variance for each lag;

>

>and the

>

>- general relative semivariogram (Isaaks & Srivastava, 1989: An introduction

>to applied geostatistics, page 164) where gamma(h) is standardised by the

>of each lag.

relative

>

>I'm currently analysing the spatial structure of radioactive deposition for

>different levels with the help of indicators. Standardised & general

>semivariograms describe very well the structures while the semivariogram is

general

>not really appropriate for such a highly

>skewed variable. For low values of the chosen thresholds, the standardised

>semivariograms shows me a stronger spatial correlation compared to the

>relative semivariogram.

show

>

>For higher threshold values, the opposite situation appears.

>

>Would this mean that low values show strong fluctuations but that the mean

>value remains quite constant in space while high levels of radioactivity

>less fluctuations but the mean values change more in space ?. Has anyone

Gregoire Dubois

>experienced similar observations with other variables ?

>

>Apparently, there has not been much published on these functions, even if

>these are frequently used.

>

Institute of Mineralogy and Petrography

Dept. of Earth Sciences

University of Lausanne

Switzerland

http://www.ai-geostats.org

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