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AI-GEOSTATS: global vs local ordinary kriging

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  • Ulrich Leopold
    Dear list, What would you consider the most reliable ordinary kriging estimate? To use a local search neighbourhood (slightly bigger than the effective range)
    Message 1 of 5 , Jul 8, 2003
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      Dear list,

      What would you consider the most reliable ordinary kriging estimate? To
      use a local search neighbourhood (slightly bigger than the effective
      range) or set to global to include *all* data locations?


      Ulrich


      --
      __________________________________________________

      Ulrich Leopold MSc.

      Department of Physical Geography
      Institute for Biodiversity and Ecosystem Dynamics
      Faculty of Science
      University of Amsterdam
      Nieuwe Achtergracht 166
      NL-1018WV Amsterdam

      Phone: +31-(0)20-525-7456 (7451 Secretary)
      Fax: +31-(0)20-525-7431
      Email: uleopold@...
      http://www.frw.uva.nl/soil/Welcome.html

      Check us also out at:
      Netherlands Centre for Geo-ecological Research
      http://www.frw.uva.nl/icg




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    • Edzer J. Pebesma
      ... Depending on the strength of spatial correlaton and block size. If blocks are small and spatial correlation strong, the difference may be negligible. If
      Message 2 of 5 , Jul 8, 2003
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        Ulrich Leopold wrote:

        >Dear list,
        >
        >What would you consider the most reliable ordinary kriging estimate? To
        >use a local search neighbourhood (slightly bigger than the effective
        >range) or set to global to include *all* data locations?
        >
        >
        >Ulrich
        >
        >
        >
        >
        Depending on the strength of spatial correlaton and block size. If
        blocks are small and
        spatial correlation strong, the difference may be negligible. If blocks
        are large, relative
        to the domain, I would choose large neighbourhoods. In case of weak
        spatial correlation,
        you're basically estimating a local or global mean; so it depends on
        what you want
        for result: local or global mean values.
        --
        Edzer



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      • Isobel Clark
        Ulrich Depends how powerful your computer is, what algorithm you use to solve equations and how many data you have. Isobel
        Message 3 of 5 , Jul 8, 2003
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          Ulrich

          Depends how powerful your computer is, what algorithm
          you use to solve equations and how many data you have.

          Isobel
          http://geoecosse.bizland.com/0toKriging.htm

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        • Pierre Goovaerts
          Hi Ulrich, It s not an easy question. First note that the search strategy includes not only the size of the search window but also the maximum number of
          Message 4 of 5 , Jul 8, 2003
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            Hi Ulrich,

            It's not an easy question. First note that the search strategy
            includes not only the size of the search window but also the maximum
            number of observations. In may occasions, I set the search radius
            to a very large distance and use the number of observations as
            the controling parameter. Using too many observations or too large
            search windows may lead to oversmoothing, while estimates based on
            low number of observations (say less than 8 in 2D) might not be
            very reliable. Of course it depends also on the relative nugget effect.
            If it is large, even further away observations will receive a
            significant weight.

            In practice, global search windows are seldom used because:
            (1) no reliable semivariogram values are available
            for so large distances, (2) the size of the kriging system is
            likely very large, and (3) the stationarity assumption
            within the search window might become questionable.

            The best way to proceed would be to do some cross validation
            using various search strategies and investigate their impact
            on re-estimation scores.

            Regards,

            Pierre Goovaerts

            <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

            Dr. Pierre Goovaerts
            President of PGeostat, LLC
            Chief Scientist with Biomedware Inc.
            710 Ridgemont Lane
            Ann Arbor, Michigan, 48103-1535, U.S.A.

            E-mail: goovaert@...
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            Fax: (734) 668-7788
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            <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

            On 8 Jul 2003, Ulrich Leopold wrote:

            > Dear list,
            >
            > What would you consider the most reliable ordinary kriging estimate? To
            > use a local search neighbourhood (slightly bigger than the effective
            > range) or set to global to include *all* data locations?
            >
            >
            > Ulrich
            >
            >
            > --
            > __________________________________________________
            >
            > Ulrich Leopold MSc.
            >
            > Department of Physical Geography
            > Institute for Biodiversity and Ecosystem Dynamics
            > Faculty of Science
            > University of Amsterdam
            > Nieuwe Achtergracht 166
            > NL-1018WV Amsterdam
            >
            > Phone: +31-(0)20-525-7456 (7451 Secretary)
            > Fax: +31-(0)20-525-7431
            > Email: uleopold@...
            > http://www.frw.uva.nl/soil/Welcome.html
            >
            > Check us also out at:
            > Netherlands Centre for Geo-ecological Research
            > http://www.frw.uva.nl/icg
            >
            >
            >
            >
            > --
            > * To post a message to the list, send it to ai-geostats@...
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            >


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          • Juliann Aukema
            Hi, I have a sampling question. I am planning a study to look at plant diversity and plant distribution patterns in a karstic area. The topography is a series
            Message 5 of 5 , Jul 8, 2003
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              Hi,
              I have a sampling question. I am planning a study to
              look at plant diversity and plant distribution
              patterns in a karstic area. The topography is a series
              of hills and valleys in all directions
              (http://www.cdk-pr.org/index.htm for a picture). I
              have a dataset that contains about 50 sample locations
              distributed through the region (basically a 75
              kilometer transect, 1-15 kilometers wide). At each
              location, plant community data was taken at three
              topographic positions hilltop, hillside, and valley.
              Previous studies have demonstrated a difference in
              plant communities between these three positions,
              therefore I don�t think that this dataset can be
              considered to have 150 points. Seven pairs of points
              are separated by between 45 and 1000 meters, the rest
              are separated by greater distances. I would like to
              expand the sampling so that I have enough points for a
              spatial analysis and to encompass a finer scale (for
              example, neighboring hills). One possibility I am
              considering is taking say 10 evenly distributed
              sampled points and sampling hill, slope, valley along
              a transect for 5 additional hills. My questions are:
              do I need more points for a reasonable spatial
              analysis? how many points would you all recommend �
              minimum number, ideal number? and do you have any
              sampling suggestions other than my transect idea for
              encompassing the finer scale?

              Thanks a lot for suggestions,

              Juliann
              aukemaj@...


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