Dear friends
I appreciate all useful comments.
I have to say my data are electrical conductivity values of soil in a
semiarid area with a high variance and coefficient of variation.
I looked for a trend. But I don not think there is any specific trend in my
data.
I have another misunderstanding.
If log or normal scores transformation does not improve the
semivariogram'parameters (e.g., results in a larger nugget variance), is it
still justified to use the log-kriging or multi-gaussian kriging?
Suzaneh
"sebastiano.trevisani" <[EMAIL PROTECTED]> wrote: Hi again
As Monica says, it could be important to look at the data
taking into account the physical and chemical processes
involved in the phenomenum under study.
Maybe you can try to use some auxiliary variable by means of a kriging with
external drift approach (but it depends from the processes).
Then a rank trasformation (but with all the issues related to the back
transformation) could work.
Sebastiano Trevisani
---------- Initial Header -----------
>From : [EMAIL PROTECTED]
To : "Mailing list Geostatistics" [email protected]
Cc :
Date : Mon, 15 Oct 2007 05:46:44 -0700 (PDT)
Subject : RE: AI-GEOSTATS: Smoothness of indicator kriging over ordinary kriging
> Thank you very much Sebastiano and Piere Goovaerts for your suggestions.
> First of all I did not see any special trend. As Piere Goovaerts said I
> took log of data and semivariogram of logarithm still show a moderate
> spatial correlation however the nugget effect is higher. I have a sparse
> sampling of data values with areas of high values located mostly in the
> north and south of the area. I tried to divide the area to three more
> homogenous sub-areas. But the semivariograms for these sub-areas show less
> spatial correlation than for whole area.
> What can I do now? By the way I already removed a few very suspicious
> values from the data sat.
> Should I stick with ordinary kriging only?
> Regards
> Suzaneh
>
>
> Pierre Goovaerts wrote: Hi Suzanne,
>
> I am surprised that you don't obtain a well-structured indicator
> variogram for the median threshold at least. This might indicate that
> the structure you see in the variogram of raw data is caused by
> a cluster of extreme values. These data are distinguished only
> for extreme quantile thresholds, which should explain why you
> don't see any correlation for middle thresholds.
> I would suspect that taking the log of the data would also reduce
> the structure you see on your variogram.
>
> Hope it helps,
>
> Pierre
>
> Pierre Goovaerts
> Chief Scientist at BioMedware Inc.
> Courtesy Associate Professor, University of Florida
> President of PGeostat LLC
>
> Office address:
> 516 North State Street
> Ann Arbor, MI 48104
> Voice: (734) 913-1098 (ext. 8)
> Fax: (734) 913-2201
> http://home.comcast.net/~goovaerts/
>
> ________________________________
>
> From: [EMAIL PROTECTED] on behalf of Suzanne
> Sent: Mon 10/15/2007 4:03 AM
> To: [email protected]
> Subject: AI-GEOSTATS: Smoothness of indicator kriging over ordinary kriging
>
>
> Dear list
> I have a data set of highly positively skewed.
> I tried to use indicator kriging to improve the estimation accuracy over OK.
> But I found out some difficulties:
> 1- The omnidirectional semivariogram show a strong to moderate spatial
> correlation whereas indicator semivariograms except for 0.1 and 0.8
> quantiles do not show any spatial correlation.
> 2- I tried to use some quantiles, which their indicator kriging show a weak
> spatial correlation. I run IK with 5 possible cutoffs. The estimation
> accuracy goes a little bit higher however the map produced using IK is much
> smoother than OK.
> I do not know why this happen? And what should I do now?
> I really need help. Please let me know your opinion about that.
> Best regards
> Suzaneh
>
>
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