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GEOSTATS: Regression, aggregation and spatial autocorrelation : advice needed!

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  • Andrew Davidson
    Dear all - I am hoping that some of the subscribers to this group will be kind enough to give me some (geo)statistical advice which is related to a project I
    Message 1 of 1 , Jun 16, 1999
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      Dear all -

      I am hoping that some of the subscribers to this group will be kind enough
      to give me some (geo)statistical advice which is related to a project I am
      currently working on. I have only a little background in geostats, so the
      simpler the explanations the better :)

      My research is based on using remote sensing information to predict the
      occurrence of C4 species in a grassland. More specifically, I propose that
      the ratio of early to late season productivity (E/L) as derived from
      remote sensing information will correlate with C4 abundance. I also
      propose that the nature of this relationship will be scale (resolution)
      dependent.

      In order to test this theory, I defined 3 field sites. Each was a 1ha grid
      in which 72 points are spatially nested (see Webster; Webster and Oliver;
      Belleheumer and Legendre; etc for nested sampling). At each of these 72
      plots (0.5m resolution), E/L and C4 abundance were measured. I then ran a
      simple linear regression model of E/L (X) on %C4 (Y) in order to describe
      the functional relationship between the variables. I then aggregated these
      samples (averaging) in order to look at these relationships at coarser
      resolutions (2.5m, 10m and 50m). Thus, at coarser resolutions I have a
      smaller number of points which represent a larger area than the original
      points. At each of these resolutions, I then re-ran the regression
      analysis on the "new" data points. My results indicate that estimates of
      slope and intercept change with resolution, and R^2 values increase
      nonlinearly, increasing from 0.5m to 2.5m and 10m, where they remain
      constant to 50m.

      However, here is my problem : I read a chapter by Bian in "Scaling remote
      sensing and GIS", who got similar results to my study (although they used
      elevation and biomass as variables). I would like to interpret my data (as
      they did) as the results being from finer-resolution variations being
      filtered out at coarser resolutions, giving increasing covariation between
      variables. BUT, they also mention that spatial autocorrelation can
      significantly affect R^2 values. They do not expand on this. I have read a
      number of texts (Ripley, Cressie, Cliff and Ord) which were a little heavy
      on the theory and papers on aggregation, but they did not help......

      Given this, my question is simply "What can I infer wrt my results"?
      Should I :

      (a) not use regression techniques because of the problem of spl autocrln,

      (b) use the techniques and acknowledge their limitations (given that at
      present I cannot model the pattern of autocorrelation, (see below)), or

      (c) do something else?

      If I could find the "real" R^2 values that would be a big help. I have
      analyzed my data in order to get a sense of spatial autocorrelation.
      Variogram estimation is inconclusive as to whether a pattern exists, and
      nested ANOVA suggests that "patchiness" exists in both datasets, but not
      to a great degree (few statistically significant jumps in variance between
      resolutions). I have scoured the ecological literature, and I find very
      few references to this problem. Certainly, many people must have been
      posed with this dilemma?????

      Any help would be appreciated.

      Andrew Davidson

      Department of Geography, Tel : (416) 978 5070
      University of Toronto, email : andy.davidson@...
      100 St George Street,
      Toronto, ON http://eratos.erin.utoronto.ca/grad/
      Canada, M5S 3G3




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