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Re: AI-GEOSTATS: cokriging and ked

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  • Pierre Goovaerts
    Hello, It is my experience that, for a given data set, the impact of secondary information is usually more pronounced when using KED (kriging with external
    Message 1 of 2 , Jan 16, 2003

      It is my experience that, for a given data set,
      the impact of secondary information is usually
      more pronounced when using KED (kriging with
      external drift) or SKLM (simple kriging with
      local means) instead of cokriging.
      Several factors will control the relative influence of
      secondary information in cokriging, namely
      the correlation coefficient, sampling intensity,
      and relative nugget effect of primary versus
      secondary variables. As I showed in my book, if
      the primary variable has a much larger nugget
      effect than the secondary variable and the two are
      well correlated, the secondary data may screen the
      influence of primary data. Try to play with
      these parameters and see what would be the impact on
      the final map. Although a detailed map might appear
      more desirable or "better" at first glance, beware
      that the impact of your DEM can be overestimated
      by some techniques and you might end up getting better
      re-estimation results for the smooth map.

      KED could be performed using the program kt3d in Gslib.

      Hope it helps,



      Dr. Pierre Goovaerts
      Consultant in (Geo)statistics
      President of PGeostat, LLC
      and Senior Chief Scientist with Biomedware Inc.
      710 Ridgemont Lane
      Ann Arbor, Michigan, 48103-1535, U.S.A.

      E-mail: goovaert@...
      Phone: (734) 668-9900
      Fax: (734) 668-7788


      On Thu, 16 Jan 2003, Alvaro Silva wrote:

      > Hello
      > Thanks to all that give me some help on the cokriging (Tom, Donald, Susan
      > and Tomislav). By reading Goovaerts (hello thanks for your course in Lisbon
      > last November) paper on precipitation estimation including elevation, I
      > notice that cokriging presents smooth surfaces, but I think this isn't
      > always true. I have tested it for the temperature estimation on Madeira
      > Island, and the maps show clearly a relation with altitude although for
      > Portugal mainland, i could not achieve yet this detail. I have tested the
      > Neural Networks with the same purpose and the resulting map for the annual
      > mean air temperature is very good, it captures fine details and presents
      > variability very well, also the r between observed and estimated values
      > with an independent dataset was very good (0.98). When I decided to test
      > also the cokriging to compare the results I was disapointed, because NN
      > presents a much better map. Now i try to understand why the CK doesn't give
      > results as good as I thought it could give.
      > I also would like to test kriging with external drift, does anyone know
      > where can I find a friendly and free software to do so, preferencially with
      > a tutorial.
      > Thanks once again and with my best regards,
      > Álvaro
      > ----------
      > José Álvaro Mendes Pimpão Alves Silva
      > Geógrafo - Técnico de SIG Geographer - GIS Technician
      > Departamento de Clima e Ambiente Atmosférico Climate Department
      > Instituto de Meteorologia Portuguese Meteorological Institute
      > Rua C do Aeroporto
      > 1749 - 047 Lisboa
      > Portugal
      > Tel: (+351) 218483961
      > Fax: (+351) 218402370
      > Email: alvaro.silva@...
      > ----------

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