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

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  • Chaosheng Zhang
    Hi Dave, My concern is that the 25 locations may not be enough to capture the spatial structure of the parameter (which climatic data?) across the UK. As
    Message 1 of 3 , Mar 3, 2005
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      Hi Dave,
       
      My concern is that the 25 locations may not be enough to capture the spatial structure of the parameter (which climatic data?) across the UK. As discussed earlier in this list, you need to perform analyses on spatial correlation. If your data is lack of spatial (auto)correlation, any spatial interpolation is questionable.
       
      Meanwhile, there should be some models in climate. Some time ago, I had to produce a long-term average climate map (precipitation) of Ireland, and I found that it is helpful to use the knowledge of experts in climate, not based on pure mathematical methods, even though they are very attractive. Well, the model itself is still mathematical: precipitation is a function of coordinates (second order) and elevation. Such a model may not be applicable everywhere, but UK should be similar to Ireland, or you may try to find other models. This may also give you an idea of using co-kriging, but there should be a good relationship between your parameter and e.g., elevation. The fact of "rain shadow" obviously destroys such a relationship. What I did was to perform an interpolation on the residuals, and put the residuals back to the modelled results.
       
      Hope this is helpful for you to think about your problem.
       
      Cheers,
       
      Chaosheng
      --------------------------------------------------------------------------
      Dr. Chaosheng Zhang
      Lecturer in GIS
      Department of Geography
      National University of Ireland, Galway
      IRELAND
      Tel: +353-91-524411 x 2375
      Direct Tel: +353-91-49 2375
      Fax: +353-91-525700
      E-mail:
      Chaosheng.Zhang@...
      Web 1: www.nuigalway.ie/geography/zhang.html
      Web 2: www.nuigalway.ie/geography/gis
      ----------------------------------------------------------------------------
       
       
      ----- Original Message -----
      From: "Dave Miller" <Dave.Miller@...>
      Sent: Wednesday, March 02, 2005 5:06 PM
      Subject: [ai-geostats] Interpolation of climatic data thorugh space and time.

      >
      > 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:
      href="mailto:dave.miller@...">dave.miller@...
      > websites: http://www.macaulay.ac.uk
      > http://www.macaulay.ac.uk/LADSS/ladss.shtml
      >
      >


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    • Mahdi Osman
      As an addition to my previous messge............. VarioWin, Vesper etc are easy tools for variography. Variowin offers a simple interactive variogram modelling
      Message 2 of 3 , 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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