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[ai-geostats] spatial relationships

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  • Isobel Clark
    Dear oh Dear, I am failing to communicate (again). As far as I know, I didn t say you could not use geostatistics when a trend is present! I regularly use
    Message 1 of 3 , Sep 2, 2004
      Dear oh Dear, I am failing to communicate (again).

      As far as I know, I didn't say you could not use
      geostatistics when a trend is present! I regularly use
      Universal Kriging for data with a trend and kriging
      with an external drift when the trend is governed by
      an outside factor (see free tutorial at website).

      The question originally posed what how does one decide
      that geostatistics is not appriate. The answer
      Gregoire and myself gave was "when you cannot get a
      semi-variogam graph" after trying all possible
      variations of transforms, interpretation and
      de-trending.

      I recently worked with an orange grove in Florida
      (bugs on oranges) which showed no decent
      semi-variogram even though rough inverse distance maps
      looked reasonable. It turned out they had two
      different kinds of tree in the orchard. Separating the
      'rootstocks' yielded a vastly improved semi-variogram
      and decent geostatistical analysis.

      My additional point was that failure to obtain a
      semi-variogram model simply means that there is no
      'distance related' structure. It does NOT mean there
      is NO spatial structure.

      Isobel
      http://geoecosse.bizland.com/softwares





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    • Chaosheng Zhang
      Hi all, The interesting story given by Tim Glover is a good example of spatial outliers . The dumptruck loads of polluted soils are too different from the
      Message 2 of 3 , Sep 3, 2004
        Hi all,
         
        The interesting story given by Tim Glover is a good example of "spatial outliers". The dumptruck loads of polluted soils are too different from the neighbourhood, and thus they should be regarded as spatial outliers. When one cannot get a decent variogram after trying all possible data transformations and robust calculations, another way worth trying is to detect such spatial outliers (e.g., using local Moran's I). 
         
        About two years ago, I dealt with a dataset of soil organic carbon in Ireland, and found that exclusion of spatial outliers significantly improves the structure of a variogram. When doing kriging, the excluded values may be put back to preserve information of raw data.
         
        This may be an explanation to the conflict between failed variogram and spatial structure. Spatial outliers may destroy spatial structures and thus result in failed variograms.
         
        Cheers,
         
        Chaosheng
        --------------------------------------------------------------------------
        Dr. Chaosheng Zhang
        Lecturer in GIS
        Department of Geography
        National University of Ireland, Galway
        IRELAND
        Tel: +353-91-524411 x 2375
        Fax: +353-91-525700
        E-mail:
        Chaosheng.Zhang@...
        Web 1: www.nuigalway.ie/geography/zhang.html
        Web 2: www.nuigalway.ie/geography/gis/index.htm
        ----------------------------------------------------------------------------
         
        ----- Original Message -----
        From: "Isobel Clark" <drisobelclark@...>
        Sent: Thursday, September 02, 2004 3:30 PM
        Subject: [ai-geostats] spatial relationships

        > Dear oh Dear, I am failing to communicate (again).
        >
        >
        As far as I know, I didn't say you could not use
        > geostatistics when a
        trend is present! I regularly use
        > Universal Kriging for data with a
        trend and kriging
        > with an external drift when the trend is governed
        by
        > an outside factor (see free tutorial at website).
        >
        >
        The question originally posed what how does one decide
        > that
        geostatistics is not appriate. The answer
        > Gregoire and myself gave was
        "when you cannot get a
        > semi-variogam graph" after trying all
        possible
        > variations of transforms, interpretation and
        >
        de-trending.
        >
        > I recently worked with an orange grove in
        Florida
        > (bugs on oranges) which showed no decent
        > semi-variogram
        even though rough inverse distance maps
        > looked reasonable. It turned out
        they had two
        > different kinds of tree in the orchard. Separating
        the
        > 'rootstocks' yielded a vastly improved semi-variogram
        > and
        decent geostatistical analysis.
        >
        > My additional point was that
        failure to obtain a
        > semi-variogram model simply means that there is
        no
        > 'distance related' structure. It does NOT mean there
        > is NO
        spatial structure.
        >
        > Isobel
        >
        href="http://geoecosse.bizland.com/softwares">http://geoecosse.bizland.com/softwares
        >
        >
        >
        >
        >
        >
        ___________________________________________________________ALL-NEW Yahoo! Messenger - all new features - even more fun! 
        http://uk.messenger.yahoo.com
        >
        >


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