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

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

Expand Messages
  • 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, 2006
    • 0 Attachment
      Mach Nife wrote:

      >Hi,
      >
      >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,
      --
      Edzer

      >machnife
      >
      >__________________________________________________
      >Do You Yahoo!?
      >Tired of spam? Yahoo! Mail has the best spam protection around
      >http://mail.yahoo.com
      >
      >
      >
      >------------------------------------------------------------------------
      >
      >* By using the ai-geostats mailing list you agree to follow its rules
      >( see http://www.ai-geostats.org/help_ai-geostats.htm )
      >
      >* To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to sympa@...
      >
      >Signoff ai-geostats
      >
    • 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, 2006
      • 0 Attachment
        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.
         
        Isobel 
        http://www.kriging.com
      • Pierre Goovaerts
        Hello, I have been using a modified version of VARFIT that is available on the Computer and Geosciences website. Pardo-Iguzquiza E: VARFIT: a Fortran-77
        Message 3 of 3 , Feb 28, 2006
        • 0 Attachment
          Hello,

          I have been using a modified version of VARFIT that is available on the
          Computer and Geosciences website.

          Pardo-Iguzquiza E: VARFIT: a Fortran-77 program for fitting variogram models by weighted least squares. Computers and Geosciences 1999, 25:251-261.

          I agree that choosing a set of weighting factors for the fit can be frustrating
          and it's why in the program I provided with my latest IJHG paper, I allowed
          the user to choose among 5 options for the weights. For the weights N(h)/gamma(h),
          I used the experimental semivariogram values, which eliminates the problem of
          weights that change during the iterative fitting procedure and also attenuates
          the impact of unreliable semivariogram values computed for the first lags
          (i.e. impact of preferential sampling of high values as in my WRR paper on
          groundwater arsenic concentrations).

          I have increasingly used automatic fitting procedures, followed by a visual
          assessment of the fit (and yes the fitting of variogram clouds in Arcview is
          one of the many ESRI blunders). It has proven convenient in several situations:
          1. implementation of Poisson kriging to be used by epidemiologists, who
          have no idea what a semivariogram is, for removing noise from rate data,
          2. use of indicator kriging for automatic mapping procedures (SIC 2004 paper
          that is available on my webpage).
          3. testing of new algorithms using hundreds of simulations. The common use
          of only one jackknife or simulation to compare the prediction performances of
          various algorithms is very hazardous since the ranking can drastically change
          would another subset of data be used for validation.

          Cheers,

          Pierre



          Pierre Goovaerts
          Chief Scientist at BioMedware
          516 North State Street
          Ann Arbor, MI 48104
          Voice: (734) 913-1098 (ext. 8)
          Fax: (734) 913-2201
          http://home.comcast.net/~goovaerts/

          ________________________________

          From: Mach Nife [mailto:machnife@...]
          Sent: Tue 2/28/2006 10:16 AM
          To: ai-geostats
          Subject: [ai-geostats] Software for Automatic Semivariogram Estimation



          Hi,

          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?
          machnife

          __________________________________________________
          Do You Yahoo!?
          Tired of spam? Yahoo! Mail has the best spam protection around
          http://mail.yahoo.com
        Your message has been successfully submitted and would be delivered to recipients shortly.