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1619Re: [ai-geostats] variograms

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  • Isobel Clark
    Jul 8, 2004

      If your data is irregularly spaced, then you need to
      experiment with 'lag' intervals to balance between

      (a) getting enough points to see the shape of the
      graph and

      (b) getting enough pairs in each point to have some
      confidence in it.

      Remember that each point on your graph is an estimate
      of a variance. Some books give hard-and-fast rules
      like "you have to have 25 pairs in each point" but,
      personally, I think this is fatuous. The real
      situation is a bit circular -- if you have a regular
      phenomenon, you can get the shape with few samples and
      few points; if you have an erratic phenomenon you need
      many samples and lots of points.

      Over the years, I have found the folowing useful:

      i) look at the 'nearest neighbour' or inter-sample
      distances to see what the 'natural' spacing in your
      data is.

      ii) Use that to guide your first choice for lag
      interval and experiment around that distance.

      iii) Use the Cressie goodness of fit statistic to help
      you judge the fit of your model.

      iv) Use cross validation to help you judge the fit of
      the model and the behaviour of the kriging errors.

      If your data is on a grid, life is a lot easier, just
      use 1/5th of the grid spacing as your lag interval.

      The usual rule of thumb on number of lags is not to go
      more than half the extent of your study area. That is,
      if your study area is 1km on a side, construct your
      semi-variogram to a maximum of 500 metres.

      Hope this helps


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