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Re: GEOSTATS: RE: Model Comparison....Help anyone?

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  • Jennifer Dungan
    ... One appropriate method would be the practice used in comparison of thematic maps generated using remotely sensed data (usually used with the raster, or
    Message 1 of 3 , Jul 14 2:33 PM
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      jchristensen@... wrote:

      > My question is this: What is the most appropriate method to test whether or not
      > my model results do a reasonable job of predicting or identifying prime oyster
      > habitat? The model results in 37,000 grid cells (for the entire bay); however,
      > I have only 113 existing oyster reef points. The model output is categorical
      > (optimum, high, medium, low, unsuitable), and my oyster point data is merely
      > presence/absence (0/1).

      One appropriate method would be the practice used in comparison of thematic
      maps generated using remotely sensed data (usually used with the raster, or
      grid cell, data model). See pages 388-395 in Campbell (1996) Introduction to
      Remote Sensing for a clear exposition. The summary statistic is called kappa,
      and it distills the measure of agreement between the two maps adjusted for
      chance agreement. First, a contingency table or error matrix must be created.

      > Visually, the distribution of oyster points in my model output indicate that the
      > model worked well (87% of points fell in optimum category, 13% in high, and no
      > points in the remaining suitability categories). Even so, because there are so
      > many resulting grid cells in the model, most of the optimum and high grid cell
      > contain NO oysters. Is there a method to investigate whether or not the model
      > is statistically significant?

      The error matrix would then have

      Number of model cells Number of model cell
      with optimal (and/or high) oysters with low suit. of oysters
      Number of cells
      with existing oysters 98 (or 113) 0

      Number of cells
      without existing oysters x1 x2

      If your 113 existing oyster locations represent
      an exhaustive survey and are all the points you think are in the bay, x1 and x2 will be
      large numbers. If not, and you will only test the quality of your model at the 113
      survey locations, x1=15 (or 0) and x2=0 (or 15). You will need to decide what cutoff
      in terms of suitability to use to evaulate the results (the optimal only or optimal+high
      classes). See the example in Campbell for how to calculate kappa. You could also use
      alternative methods as described in his text.

      This is of course an aspatial summary of your results. Also valuable would
      be a simple error map, showing *where* the errors of commission and ommission
      are.

      Sincerely,

      Jennifer

      Jennifer Dungan | MS 242-4
      Research Scientist, JCWS, Inc. | NASA Ames Research Center
      Tel: 415-604-3618 FAX: 415-604-4680 | Moffett Field, CA 94035-1000
      email: jdungan@... | USA
      URL: http://geo.arc.nasa.gov |

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    • J. Felipe Costa
      Dear John, Since the problem you posed consist in checking discrete (very dense) block model interpolated from a sparsely sampled data set, I would try a cross
      Message 2 of 3 , Jul 15 12:56 AM
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        Dear John,

        Since the problem you posed consist in checking discrete (very dense)
        block model interpolated from a sparsely sampled data set, I would try a
        cross validation or jacknifing method.

        As far as I understood, the only information you have are the 137 sampled
        points and it is against them you can check your assumptions used in
        interpolating the entire grid.

        Unless you have further information to validate your model, the input
        sample data set seems to be the only reasonable info to be cross checked.

        Check in the geostat literature (Isaaks and Sirvastava 1989 for example)
        for the details in cross validation.

        cheers

        ------------------------------------------------------------------------
        J. Felipe Costa, PhD candidate Phone:
        University of Queensland national: (07)3365.3473
        Dept. of Earth Sciences international: (61)(7)3365-3473
        Brisbane, Qld 4072 Fax: (07) 3365-1277
        E-mail: costa@... home: (07) 3878.6475
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