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Re: [ai-geostats] spatial relationships

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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 1 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.
      Dr. Chaosheng Zhang
      Lecturer in GIS
      Department of Geography
      National University of Ireland, Galway
      Tel: +353-91-524411 x 2375
      Fax: +353-91-525700
      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
      > 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
      > variations of transforms, interpretation and
      > I recently worked with an orange grove in
      > (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
      > '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
      > 'distance related' structure. It does NOT mean there
      > is NO
      spatial structure.
      > Isobel
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