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1471Re: AI-GEOSTATS: mysterious kriging output

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  • Pierre Goovaerts
    Mar 9, 2004
      Hello,

      I agree that in many environmental datasets we could question the
      assumption of existence of a single population. Although there are
      ways to split the data into several populations, the key issue is
      that the study area needs also to be stratified into several populations.
      In some fields, such as geology, geological maps could provide
      a stratification of the study area and helps delineating the boundaries
      between populations. This is far less obvious for environmental
      data sets.

      Looking at Noemi's maps, I would agree with Richard's comment that
      nothing seems to be out of the ordinary. Of course, when dealing with
      streams the data configuration is far from optimal and screening effects
      abound. Also, the strong anisotropy ratio means that we deal with
      a "zonal-like" anisotopy which might cause sudden changes of covariance
      for slight difference of angles. In particular, this covariance model
      could lead to very small correlations off the two main axes of anisotropy,
      which could explain the larger kriging variance observed along the
      diagonal directions.

      Pierre

      <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

      Dr. Pierre Goovaerts
      President of PGeostat, LLC
      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
      http://alumni.engin.umich.edu/~goovaert/

      <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

      On Tue, 9 Mar 2004, Monica Palaseanu-Lovejoy wrote:

      > Hi,
      >
      > I am working myself with pollution data in soils and i have very high
      > values very close to very low values, and highly skewed
      > distribution. I am more and more concerned with doing kriging on
      > transformed data. This simply means we believe the data came
      > from only one population. But what if it comes from 2 different
      > populations representing 2 different polluting processes? Much
      > more if we do believe there are no gross error measurements. The
      > fact that high values are very close to low values would tell me that
      > the spatial autocorrelation is violated locally. I would try first to see
      > if the outliers (local and global) represent a different population, if
      > these values cluster or not, how significant is the association high-
      > low values, and if the global Moran's I increases if i eliminate the
      > "outliers". Maybe the majority of the data which have a higher
      > spatial autocorrelation belong to a "better expressed" diffusive
      > process, (maybe an older one) while the rest of the data which
      > were identified as outliers before, represent a more patch-y or point
      > source pollution process which didn't have time to diffuse over the
      > entire study area (a younger process, maybe?).
      >
      > Of course if you have proof that the data came from only one
      > population then .... it is a different story.
      >
      > I will really appreciate to hear other opinions about these thoughts.
      >
      > Thanks,
      >
      > Monica
      >
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