1943Re: [ai-geostats] Interpolation of climatic data thorugh space and time.
- Mar 4, 2005As 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
Please check also R packages such as sgeostat, geoR, MASS, etc
> 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.
> Dave Miller
> Dave Miller
> Research Assistant
> GIS & Remote Sensing
> The Macaulay Institute
> 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
Mahdi Osman (PhD)
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