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Re: GEOSTATS: SUMMARY: Non-colocated disease datasets. Further help sought!

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  • Isobel Clark
    Jonathan I haven t had time to go through your extensive e-mail in detail, but here are a couple of thoughts to be going on with: ========================
    Message 1 of 2 , Oct 28, 2000
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      I haven't had time to go through your extensive e-mail
      in detail, but here are a couple of thoughts to be
      going on with:


      simple correlation: if you calculate the
      non-co-located semi-variogram (or covariance function)
      the apparent nugget effect is a direct estimate of
      what the correlation between the two variables would
      be if you had them co-located. This allows for the
      spatial 'auto-correlation' as well as statistical
      correlation. I (personally) favour a rank transform as
      this is a well established way of finding a
      correlation for non-Normal data.

      With a rank transform, your zeroes would be given
      arbitrary (randomised) ranks and you could do a few
      repeats to see if this makes a lot of difference.

      References for MUCK can be found at


      Some packages (including EcoSSe) define 'distance' in
      a specified module which is used by all of the
      routines. This module can be replaced to allow the use
      of an algebraic function of look up table for
      distances. All routines then use that definition
      instead of Euclidean distance.


      Technically constructing a semi-variogram or other
      spatial dependence analysis on the residuals from a
      GLM (trend) surface is incorrect. However, we have all
      been doing it very successfully for almost 30 years,
      so I wouldn't worry about it over muchly. cf.
      Practical Geostatistics 2000, Chapter 12.

      Isobel Clark

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