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1943Re: [ai-geostats] Interpolation of climatic data thorugh space and time.

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  • Mahdi Osman
    Mar 4, 2005
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      As an addition to my previous messge.............


      VarioWin, Vesper etc are easy tools for variography. Variowin offers a
      simple interactive variogram modelling interface. Vesper, developed by
      Australian centre of precision agriclture is very interesting, it is based
      on ml iteration methods. Try for more information. I have been doing
      variography using ARCMAM (8.3). It was not interactive at all, but plenty of
      colours, mate.

      Please check also R packages such as sgeostat, geoR, MASS, etc

      Cheers

      Mahdi

      >

      > Dear all,
      >
      > Please bear with me on this. A first submission to the list from a
      > perplexed and increasingly stressed research assistant (I'm sure you've
      > all been there once upon a time, or here)! My queries relate principally
      > to the comparison of interpolation methods.
      >
      > I have a dataset of 25 locations across the UK of empirically derived
      > values based on cloud cover. Each station has a varying length of record
      > between 10 years and 42 years between the years 1952 - 2000.
      >
      > To test which was the most appropriate technique to use for
      > interpolation between locations for the mean value for each site, I have
      > tested a variety of functions available in ArcGIS Geostatistical Analyst
      > (version 8.3) including Inverse Distance Weighting, the five radial
      > basis functions (completely regularised spline, spline with tension,
      > thin plate spline, multiquadratic, inverse multiquadratic) and ordinary
      > kriging (spherical semivariogram, no nugget, search neighbourhood
      > equalling the range of the variogram). Because of the limited number of
      > locations for which data was available, I have used cross-validation to
      > generate RMSE, MAE, MSE and G-measures for each interpolation method. My
      > first question is: Is the surface with the lowest resulting error
      > measures, be they RMSE, MAE or MSE necessarily a feasible way to select
      > the best interpolation method? If so the Inverse Multiqudratic function
      > appears to yield the best surface.
      >
      > Secondly, since data is available on a year-to-year basis, I'd like to
      > be able to analyse the variability between years. The problem is that
      > data isn't necessarily available for each year for each site! As a
      > result the 'best' interpolation method (as measured by RMSE at least)
      > varies between years. Nice. By ranking the methods for each year and
      > summing the ranks for each interpolation method, it seems that overall
      > the Inverse Multiquadratic function marginally outperforms the spline
      > with tension.
      >
      > If you've got this far then thanks for reading, and if anyone can
      > suggest any tips on where I might go from here with my analysis (or
      > where I need to go back to!) I'd be very happy to hear from you.
      >
      > Regards,
      >
      > Dave Miller
      >
      > ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
      > Dave Miller
      > Research Assistant
      > GIS & Remote Sensing
      > The Macaulay Institute
      > Craigiebuckler
      > Aberdeen
      > AB15 8QH
      >
      > tel: +44 (0) 1224 498200 (switchboard) ext. 2261
      > fax +44 (0) 1224 311556
      > e-mail: dave.miller@...
      > websites: http://www.macaulay.ac.uk
      > http://www.macaulay.ac.uk/LADSS/ladss.shtml
      >
      >

      --
      -----------------------------------
      Mahdi Osman (PhD)
      E-mail: m_osm@...
      -----------------------------------

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