Loading ...
Sorry, an error occurred while loading the content.

AI-GEOSTATS: SUM: standardised versus general relative semivariograms

Expand Messages
  • Gregoire Dubois
    Dear all, 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
    Message 1 of 1 , Apr 6, 2001
    • 0 Attachment
      Dear all,

      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,
      >
      >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
      mean
      >of each lag.
      >
      >I'm currently analysing the spatial structure of radioactive deposition for
      >different levels with the help of indicators. Standardised & general
      relative
      >semivariograms describe very well the structures while the semivariogram is
      >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
      general
      >relative semivariogram.
      >
      >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
      show
      >less fluctuations but the mean values change more in space ?. Has anyone
      >experienced similar observations with other variables ?
      >
      >Apparently, there has not been much published on these functions, even if
      >these are frequently used.
      >




      Gregoire Dubois
      Institute of Mineralogy and Petrography
      Dept. of Earth Sciences
      University of Lausanne
      Switzerland

      http://www.ai-geostats.org

      ____________________________________________________________________
      Get free email and a permanent address at http://www.netaddress.com/?N=1

      --
      * To post a message to the list, send it to ai-geostats@...
      * As a general service to the users, please remember to post a summary of any useful responses to your questions.
      * To unsubscribe, send an email to majordomo@... with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list
      * Support to the list is provided at http://www.ai-geostats.org
    Your message has been successfully submitted and would be delivered to recipients shortly.