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GEOSTATS: Assessing Aggregation Algorithms

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  • Michael J. Plastino
    I am relatively new to the field of geo-statistics and would like to request more expert advice on the topic of assessing aggregation algorithms for watershed
    Message 1 of 1 , Oct 8, 1998
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      I am relatively new to the field of geo-statistics and would like to
      request more expert advice on the topic of assessing aggregation
      algorithms for watershed health rankings.

      I am currently embarking on thesis work centering on the Environmental
      Protection Agency's (EPA) Index of Watershed Indicators (IWI). The IWI
      ranks the 2,108 watersheds (USGS 8-digit hyrologic unit code "Cataloging
      Unit") in the U.S for 15 different categories plus an overall score that
      combines the 15 categories.

      I will analyze the effectiveness of various aggregation algorithms in
      determining the overall score (combination of the 15 categories). I am
      trying to establish a reference or method for gaging effectiveness of
      algorithms. I have 2 preliminary ideas:

      1) Compare corelations of overall rankings with proximity of neighboring
      watersheds.

      I am thinking that the closer together the centroids of watersheds are,
      the closer their overall rankings would be. The degree of corelation of
      these variables could serve as a measure of an aggregation algorithm's
      effectiveness.

      2) Compare overall rankings with other national rankings of the health of
      water in watersheds.

      The problem with this method is the lack of any other such national
      rankings, to my knowledge. [Does anyone else know of other similar
      watershed rankings?]

      A possible alternative to this approach could be to survey some experts in
      the field as to their epxert evaluation of watershed health for a few
      sample watersheds. These expert evaluations could be used to gage the
      effectiveness of various aggregation algorithms.

      Input on any of the above topics is greatly appreciated.
      Thank you very much. Sincerely,
      Michael Plastino


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