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GEOSTATS: guided interpolation

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  • Ali_Alaa@seo.state.nm.us
    This is an inherent problem in spatial, and temporal, data analysis where we don t have enough information from the out side of the boundary to avoid a
    Message 1 of 2 , Mar 29, 1997
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      This is an inherent problem in spatial, and temporal, data analysis
      where we don't have enough information from the "out" side of the
      boundary to avoid a biased estimate. From my experience, this problem
      will always have an effect on your solution, no matter what you do.
      Unfortunately, Kriging does not treat this problem thoroughly. In
      non-parametric statistics, for example Kernel Estimators, several
      techniques were adopted. For example the reflection method may be used
      to generate fictitious data points in the "out" side of the boundary as
      mirror image of the real data points which exist in the "in" side of the
      boundary. This technique is reasonable only if the spatial, or the
      temporal, derivative of the function is very small near this boundary.
      Other techniques used with Kernel Estimators are "cut and normalize",
      and "boundary Kernels". I will be more than happy to provide references
      for those who are interested.

      best regards,
      Alaa
      _________________________________________________________
      Alaa I. Ali, Ph.D., P.E.
      New Mexico State Engineer Office
      P.O.Box 25102, Santa Fe, NM 87504
      e-mails: aali@...; or aali@...
      Web: http://www.engineering.usu.edu/Departments/cee/Faculty/ulall/
      Phone: 505-827-6125 Fax: 505-827-6188
      _________________________________________________________

      > contours at the edges (edge effects). Recently, I talked to someone
      who
      > gets around this problem by constraining the analysis as averages over
      what
      > was expected to be homogeneous areas (polygons). Of course, the
      homogeneous
      > areas were subject to modeling and mapping errors.
      >
      > I'm interested in hearing how people deal with edges during surface
      > modeling (short of cropping out the edges, designating homogeneous
      polygons
      > or making assumptions about outliers). [i.e. assume you need to
      interpolate
      > a surface over a given area, but you only have data within that area]

      >
      > Also, I've heard of constraining/tying rainfall to elevation during
      > interpolation (Hutchinson's work). Have others used ancillary data to
      > constrain their surfaces?
      >
      > Maribeth
      > milner@...
      >
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