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AI-GEOSTATS: re: sampling

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  • Jan-Willem van Groenigen
    I agree with both Marcel and Don that the first question, before any sampling strategy can be chosen, should be what the data is going to be used for, i.e.
    Message 1 of 2 , Aug 29, 2001
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      I agree with both Marcel and Don that the first question, before any
      sampling strategy can be chosen, should be what the data is going to be
      used for, i.e. what is the sampling objective. Of course, Marcel was
      talking from a mining perspective, I am talking from a soil science
      perspective. In my sampling optimization software, I tried to include as
      many different optimization criteria as possible. There are at least three
      fundamentallydifferent objectives for spatial studies that I have come across:

      1) to describe spatial variability. Sometimes finding certain variogram
      parameters can be an aim by itself (e.g. to detect periodicity or
      anisotropy). In my opinion, this might be one of the most difficult
      optimization criteria to formulate (although some people definitely tried,
      Don among others in a 1987 paper).

      2) to optimize spatial interpolation. In my case, this would be important
      in precision agriculture, in order to produce high quality maps of
      soil/crop parameters and use those for remedial action. My previous e-mail
      was mainly focussed on this - minimizing the kriging variance is one of the
      optimization criteria you could try for this case. I gave this a shot in my
      Geoderma papers that I referred to earlier.

      3) to detect hot-spots or low-spots. In my case, this is very important in
      soil pollution studies, where your very precisely want to delineate
      polluted areas (because remediation costs money, and there are health risks
      involved), but you are not very interested in the areas that are well below
      the environmental threshold. I suspect that this is quite often the case in
      minin studies. I tried to tackle this sort of optimization criterion in my
      environmetrics paper.

      Of course, one cannot always go without the other. In order to optimize
      spatial interpolation, you probably need at least an indication of the
      nature of the spatial variability, and preferably a variogram. I agree with
      Don that a phased approach is probably best for such cases. However, I
      don't think I would go for a purely random approach. In my case, I would
      probably in the first stage lay out a coarse grid over the whole area, and
      include some short-distance observations (either randomly selected, or
      somehow clustered). This should give me an idea about the nature of the
      spatial variability, which I could use to optimize my second stage,
      additional sampling scheme for minimal kriging variance. Also, the spatial
      simulated annealing algorithm would allow me to make full use of the first
      stage samples.

      Hope this helps,

      JW.






      ******************************************
      Jan Willem van Groenigen
      University of California - Davis
      Dept. of Agronomy and Range Science
      1 Shield Avenue
      Davis, CA 95616 - 8515, U.S.A.
      ------------------------------
      e-mail: groenigen@...
      http://agronomy.ucdavis.edu/groenigen
      tel. (530) 752-3457
      fax. (530) 752-4361
      *****************************************


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    • Isobel Clark
      Hi folks Numerous apologies to anyone who downloaded Krigame over the last two days. The file got corrupted and isn t actually kriging!! New version now up.
      Message 2 of 2 , Aug 29, 2001
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        Hi folks

        Numerous apologies to anyone who downloaded Krigame
        over the last two days. The file got corrupted and
        isn't actually kriging!!

        New version now up.

        Sorry sorry sorry
        Isobel Clark

        PS: on Mark Burnett's sampling thing. In South African
        gold mining, they have 100 years of back sampling in
        similar reefs (or parts of reefs). This helps a lot
        for designing the 'coarse' sampling suggested by Jan
        Willem and then developing reliable local models.

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