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GEOSTATS: Multivariate spatial autocorrelation and Mantel's tests

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  • Justin_Quirouette@pch.gc.ca
    Hello all: Of all the listservs I m subscribed to, this one seemed the most appropriate to post. I m looking for a way to calculate spatial autocorrelation for
    Message 1 of 1 , Feb 22, 1999
      Hello all:

      Of all the listservs I'm subscribed to, this one seemed the most appropriate to
      post.

      I'm looking for a way to calculate spatial autocorrelation for a multivariate
      datatset. What I have is a table containing X, Y, Z1, Z2, Z3...Zn information
      that was derived from a point-in-polygon
      analysis of species distribution points over categorical GIS data layers (i.e.
      Soil type, Forest type, Topography, etc.) and I'm looking for the lag distance
      that best minimizes the effects of spatial autocorrelation.

      I have some apps that calculate either global stats (Moran's) or variograms, but
      they are univariate! What is the best approach? Should I go through the long
      process of calculating the best lag distance for
      each individual layer and use the average best lag? Alternatively, does anyone
      have a pre-compiled app for Windows or DOS that will perform the appropriate
      calcs for this type of multivariate data?

      My readings have also pointed me towards "Partial Mantel's test on multiple
      predictor variables".
      Of course I have several questions concerning this:

      1: How do I derive a distance matrix for the categorical GIS layers (predictor
      variables)?
      2: How do I derive a distance matrix for geographic location (predictor
      variable)?
      3: How do I derive a distance matrix for the species distribution data in
      question (response variable)?
      4: Where can I find an app that performs these calcs?

      I have access to most GIS apps (except ArcINFO) and several stats apps (mainly
      SPSS 7.5.3).



      Thank you for your time and help with these (confusing newbie) questions.
      Justin



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