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1850Re: [ai-geostats] Regression vs. Kriging vs. Simulation vs. IDW

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  • Gali Sirkis
    Jan 2, 2005

      see few practical points that you may find useful:

      1. kriging vs regression:

      a) kriging honors original data points, while
      regression does not
      b) kriging allows to account for anizotropy
      c) kriging allows to control the influence of the data

      2. Kriging versus other interpolation technics

      a) Kriging allows to decluster data
      b) kriging allows to estimate uncertainty of
      c) kriging allows to use for estimation secondary
      information from another exhaustive dataset

      3. Kriging vs simulations

      a) Kriging produces smoother version than real
      distribution, while simulation gives more details
      b) simulations allow to estimate joint uncertainty,
      for example probability that values in several
      adjacent points are above certain level.
      c) simulation allows to estimate risk of various
      scenarios - while kriging only shows the most probable

      All the best,

      Gali Sirkis.

      > Hello everyone,
      > I apologize if this question is too elementary for
      > this list;
      > I want to understand the key differences between
      > linear regression,
      > kriging, conditional simulation and other
      > interpolation techniques such as
      > IDW or splines in the analyses of spatial data. I
      > would like to know the
      > assumptions, strengths and weaknesses of each
      > method, and when one method
      > should be preferred to another. I browsed the
      > archives and looked at some
      > of the on-line papers, but they are written at a
      > level beyond my own
      > current understanding. It seems to me that this
      > would be a great topic for
      > the first chapter of an introductory spatial
      > analysis textbook. Can anyone
      > recommend any basic textbooks or references on this
      > topic?
      > Any assistance you can offer would be appreciated.
      > Sincerely,
      > Seumas Rogan
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