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[ai-geostats] Spatial analysis

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  • jd92@leicester.ac.uk
    Dear All, I am looking for some advice on the best type of spatial analysis to use for some soil plot data I have. I was initially going to compare variograms
    Message 1 of 5 , Aug 6, 2004
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      Dear All,

      I am looking for some advice on the best type of spatial analysis to use for some soil plot data I have. I was initially going to compare variograms but I've been advised that they may not be appropriate bacause of the small sample size!

      I'm looking at the spatial distribution of soil properties across different vegetation types. I've sampled at three different nested spatial scales and have a total of 11 plots.

      Each plot was 60mx60m and contained 108 random sample points. This plot was quatered and two of the 30m x 30m cells contained 9 sample points. The two remaining quarters were then split into 10m x 10m cells. 6 of the 10m x10m cells contained 9 samples each. Finally, 4 other 10m x 10m cells each containing a 1.5m x 1.5m quadrat was created with again 9 sample points within it. This produces a total of 108 randomly sampled points (A diagram can be e-mailed if needed).

      Is there a way I can characterise the variability at each spatial scale that would allow me to compare both between scales for each plot and between the plots?

      Thanks,
      Jen

      Jennifer Dickie
      Post-graduate Researcher
      Department of Geography
      University of Leicester
      University Road
      Leicester
      LE1 7RH

      Tel: 01162525148
      Fax: 01162523854
      E-mail: jd92@...
    • Koen Hufkens
      ... A figure would be nice to interpretate the situation. Put it on some website, this makes it easier for people to access it if they want to and links are
      Message 2 of 5 , Aug 6, 2004
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        >Is there a way I can characterise the variability at each spatial scale that would allow me to compare both between scales for each plot and between the plots?
        >
        >
        A figure would be nice to interpretate the situation. Put it on some
        website, this makes it easier for people to access it if they want to
        and links are accepted by the maillinglist server.

        I had a similar problem and I used a Moran's I index, as far as I know
        the most simple test you can do and it can me automated. You could
        calculate the index for every scale and compare scales in and between
        plots and you can calculate them for every plot to compare plots.

        This is the quick and easy approach to the problem I would use... but
        who am I...

        Good luck with your analysis,
        Koen.
      • Monica Palaseanu-Lovejoy
        Hi, It is true that the general consensus is that you need at least 50 to 100 points for a stable semi-variogram, but myself i bent this rule a little and i
        Message 3 of 5 , Aug 6, 2004
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          Hi,

          It is true that the general consensus is that you need at least 50 to
          100 points for a stable semi-variogram, but myself i bent this rule a
          little and i worked with 35 points ....

          But you can use LISA (Local Indicators of Spatial Association -
          LISA, 1995, by Luc Anselin, geographical Analysis 27 (2), pp 93 -
          115). LISA is implemented in GeoDA freely available from
          http://sal.agecon.uiuc.edu/default.php

          GeoDa is very friendly and easy to use, but you need to be at least
          a little familiar with ESRI products (ArcView or ArcGIS) and
          especially shape files.

          You can also use sdep package from R (http://cran.r-project.org/) -
          which is a free statistical software - very powerful but with a very
          steep curve of learning.

          The idea is to use local indicators such as local Moran's I and local
          Gearry c for investigating local spatial instabilities (homogeneity
          versus heterogeneity).

          I hope this helps,

          Monica


          Monica Palaseanu-Lovejoy
          University of Manchester
          School of Geography
          Mansfield Cooper Bld. 3.21
          Oxford Road
          Manchester M13 9PL
          England, UK
          Tel: +44 (0) 275 8689
          Email: monica.palaseanu-lovejoy@...
        • Harris, Allan
          Hello! I would suggest using spatial autocorrelation such as the Moran s I or Geary s Ratio. Both methods would allow you to measure both the proximity of
          Message 4 of 5 , Aug 6, 2004
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            Hello!

            I would suggest using spatial autocorrelation such as the Moran's I or Geary's Ratio. Both methods would allow you to measure both the proximity of locations and the similarities of the characteristics at these locations.

            Regards,

            Allan C. Harris - QA/QC & Environmental Engineer
            Operations Assurance Services (OAS)
            US Department of Energy - Fernald Closure Project
            P.O. Box 538705 MS45
            Cincinnati, Ohio 45253-8705
            work 513.648.3184; fax 513.648.3076
            Email Allan.Harris@...

            "The Ultimate Inspiration Is The Deadline!"

            -----Original Message-----
            From: Monica Palaseanu-Lovejoy
            [mailto:monica.palaseanu-lovejoy@...]
            Sent: Friday, August 06, 2004 12:23 PM
            To: jd92@...; ai-geostats@...
            Subject: Re: [ai-geostats] Spatial analysis


            Hi,

            It is true that the general consensus is that you need at least 50 to
            100 points for a stable semi-variogram, but myself i bent this rule a
            little and i worked with 35 points ....

            But you can use LISA (Local Indicators of Spatial Association -
            LISA, 1995, by Luc Anselin, geographical Analysis 27 (2), pp 93 -
            115). LISA is implemented in GeoDA freely available from
            http://sal.agecon.uiuc.edu/default.php

            GeoDa is very friendly and easy to use, but you need to be at least
            a little familiar with ESRI products (ArcView or ArcGIS) and
            especially shape files.

            You can also use sdep package from R (http://cran.r-project.org/) -
            which is a free statistical software - very powerful but with a very
            steep curve of learning.

            The idea is to use local indicators such as local Moran's I and local
            Gearry c for investigating local spatial instabilities (homogeneity
            versus heterogeneity).

            I hope this helps,

            Monica


            Monica Palaseanu-Lovejoy
            University of Manchester
            School of Geography
            Mansfield Cooper Bld. 3.21
            Oxford Road
            Manchester M13 9PL
            England, UK
            Tel: +44 (0) 275 8689
            Email: monica.palaseanu-lovejoy@...



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          • Gregoire Dubois
            Good day everyone ! It is true that the general consensus is that you need at least 50 to 100 points for a stable semi-variogram, but myself i bent this rule
            Message 5 of 5 , Aug 7, 2004
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              Good day everyone !

              "It is true that the general consensus is that you need at least 50 to
              100 points for a stable semi-variogram, but myself i bent this rule a
              little and i worked with 35 points .... "

              .... and I even worked with 9 or 10 points recently (how shocking !).

              It is true that one of the favourite comments made by reviewers (those
              unexperienced in geostatistics) is "how many points do you need/did you
              use" and the favourite reply of the authors is almost systematically
              "there is a rule of thumb about this matter... 30 pairs of points is a
              minimum" followed by a reference to Cressie's book. Such a reply is a
              convenient way to get around the discussion. I must admit, I also used
              the same reference to avoid wasting time and space with discussions that
              would distract the reader from the main argument treated.

              Practically, the number of points you need to describe a spatial
              correlation is very much depending on your experience with the
              phenomenon you study. If you expect a strong correlation (because of
              previous case studies, litterature) and see such a correlation, even if
              calculated on very few observations, why not trust your results? If you
              don't see anything, than you could consider increasing your sample size.
              Few observations will obviously not lead you to results that are
              statistically robust but they may be enough to confirm your assumptions.
              I believe everyone who has a bit of experience in geostatistics has been
              impressed by the large amount of silly semi-variograms published in peer
              reviewed papers: even if these semi-variograms were calculated on
              thousands of points, many are showing pure noise. Nevertheless, some
              dare fitting a model on these semi-variograms and derive conclusions
              and/or generate maps.

              My 2 cents contribution for the week ...

              Cheers,

              Gregoire
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