Loading ...
Sorry, an error occurred while loading the content.

[ai-geostats] spatial relationships

Expand Messages
  • Isobel Clark
    Mark I could not agree more with Gregoire (with one proviso, see below). Both geostatistics and any weighted average estimators are based on the same
    Message 1 of 3 , Sep 1, 2004
    • 0 Attachment
      Mark

      I could not agree more with Gregoire (with one
      proviso, see below).

      Both geostatistics and any weighted average estimators
      are based on the same assumptions -- that relationship
      between values at two locations depends on the
      distance between them and (possibly) their relative
      orientation. If you cannot get a decent semi-variogram
      after trying every type of graph [normal, robust,
      relative] and every transformation and/or
      interpretation of your data [logarithm, indicator,
      rank transforms, Normal scores, mixed populations],
      you do not have a distance-based relationship. This
      conclusion also rules out: inverse distance weighting
      of any kind; Delaunay triangles; Thiessen polygons and
      so on.

      My proviso: there are other forms of spatial
      relationship than pure distance/direction types. The
      simplest example of this is data with a trend, where
      the value at a specified point will depend on its
      absolute position. There may be an added component for
      the 'residuals' which turns out to be
      distance/direction based. There are also many examples
      where, for example, flow characteristics, connectivity
      and so on play a large part in the structure of your
      variable.

      In short: no decent semi-variogram does NOT mean no
      spatial relationship. It means no simple second-order
      stationary geostatistical type spatial relationship.

      Isobel
      http://geoecosse.bizland.com/whatsnew.htm





      ___________________________________________________________ALL-NEW Yahoo! Messenger - all new features - even more fun! http://uk.messenger.yahoo.com
    • 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 2 of 3 , Sep 2, 2004
      • 0 Attachment
        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





        ___________________________________________________________ALL-NEW Yahoo! Messenger - all new features - even more fun! http://uk.messenger.yahoo.com
      • 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 3 of 3 , Sep 3, 2004
        • 0 Attachment
          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
          >
          >


          > * By using the ai-geostats mailing list you agree to follow its rules
          > ( see
          href="http://www.ai-geostats.org/help_ai-geostats.htm">http://www.ai-geostats.org/help_ai-geostats.htm )
          >
          > * To unsubscribe to ai-geostats, send the
          following in the subject or in the body (plain text format) of an email message to
          sympa@...
          >
          > Signoff
          ai-geostats
          >
        Your message has been successfully submitted and would be delivered to recipients shortly.