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Re: AI-GEOSTATS: relatively high semivariances at first lags

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  • Chaosheng Zhang
    Ercan, There might be two problems in your data set. (1) The sample number is too small. (2) The are some high value outliers in your data set. I understand
    Message 1 of 2 , Apr 15 6:29 AM
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      Ercan,

      There might be two problems in your data set.
      (1) The sample number is too small.
      (2) The are some high value outliers in your data set.

      I understand that it is hard for you to improve the first problem. However,
      the second problem can be partly solved by (1) excluding the outliers in the
      variogram calculation; (2) a better transformation (e.g., Box-Cox); (3) use
      of median values instead of the average in the variogram calculation. The
      highest value at the first lag is caused by some pairs of significant
      variance at very short distances (one of the values in these pairs should be
      regarded as a spatial outlier).

      Good luck.

      Chaosheng Zhang
      =================================================
      Dr. Chaosheng Zhang
      Lecturer in GIS
      Department of Geography
      National University of Ireland
      Galway
      IRELAND

      Tel: +353-91-524411 ext. 2375
      Fax: +353-91-525700
      Email: Chaosheng.Zhang@...
      ChaoshengZhang@...
      Web: http://www.nuigalway.ie/geography/zhang.html
      =================================================

      ----- Original Message -----
      From: "Ercan Yesilirmak" <ercanyesilirmak@...>
      To: <ai-geostats@...>
      Sent: Monday, April 15, 2002 8:59 AM
      Subject: AI-GEOSTATS: relatively high semivariances at first lags


      > Dear list members
      >
      > My question is as follows:
      >
      > In my exercise, semivariance value is at near zero
      > high at first lag, then in second lag jumps to the
      > highest semivariance value, and then decreases
      > gradually to global variance and fluactuates around
      > it. These data is logtransformed form of a data with a
      > skewness of 3.9. Variogram is omnidirectional. Data is
      > composed of 34 samples.
      >
      > Where is the problem? How to solve this?
      >
      > Regards
      > Ercan
      >
      > __________________________________________________
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