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Re: [ai-geostats] Software for Automatic Semivariogram Estimation

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  • Edzer J. Pebesma
    ... Some. I did have a look at your data, and at the ArcGIS fit window you sent me. Clearly, we do not fully agree on what is to be considered a good job.
    Message 1 of 3 , Feb 28 8:29 AM
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      Mach Nife wrote:

      >I'm hunting for a software (freeware/openSource if
      >possible), that would help estimating the best
      >possible semivariogram curve in a non-interactive way.
      >As an example, ArcGis Geostatistical Analyst does a
      >pretty good job at this when we accept the defaults.
      >It does some automatic calculations for the parameters
      >of the selected model. I've tried Gstat "Fit" method
      >(in the command-line version), but the results aren't
      >what I expected. What I need is a command line
      >software or one that can be controlled by programming.
      >Any ideas?

      Some. I did have a look at your data, and at the
      ArcGIS fit window you sent me. Clearly, we do not
      fully agree on what is to be considered a "good" job.
      ArcGIS calculates semivariances up to the largest
      distances present in your data set; afaik the general
      recommendation is not to look further than half the
      longest distance (compare acf computation in time
      series); the gstat default is one third the diagonal
      of the area spanned. Have you tried modifying any
      of these defaults? Interval widths?

      When looking at the fit, it seems that ArcGIS shows
      a couple (4?) directional variograms in a single
      plot, but apart from that, the sample variogram suggests
      a linear model. It is obvious that fitting three parameters
      (exponential model with nugget) to something that
      tends to be linear will lead to problems -- an infinite
      set of solutions, for instance. When you insist on
      having an exponential model, you could for
      instance force the range to a certain (large) value.
      I suspect ArcGIS stops adjusting the range of the
      exponential model when it exceeds the data extent
      (Constantin, are you with us?), but should that be
      considered good practice?

      My experience with automatic, general-purpose
      automatic variogram fitting are not very positive;
      if it were, gstat would probably have such a function.

      Are there other ai-geostats readers who have positive or
      negative experiences with, or who routinely trust,
      automatically fitted variograms? Which software?

      Looking forward to a heated debate,

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    • Isobel Clark
      Hi All It is difficult to have an automatic best fit semi-variogram until you define what you mean by best fit . Noel Cressie s goodness of fit statistic goes
      Message 2 of 3 , Feb 28 8:48 AM
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        Hi All
        It is difficult to have an automatic best fit semi-variogram until you define what you mean by "best fit". Noel Cressie's goodness of fit statistic goes a long way towards the ideal, but is very insensitive to changes in nugget effect and pretty insensitive to fairly large changes in the ranges. Optimal Cressie fits aren't always optimal visually, either.
        None of the automated methods I've heard of will choose the type of semi-variogram model and/or the number of nested components. Or anisotropy for the most part.
        As Ed says, if we knew the criteria we'd all write software for it (and retire!).
        I also look forward to varied opinions. Semi-variogram fitting is one of the most subjective stages of a geostatistical analysis.
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