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Re: [ai-geostats] Frightened of Spatial Autocorrelation

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  • Viktoras Didziulis
    Hello, Kevin ! Predictive interpolation is a very interesting field. You may be interested in GIS applications based on Dempster-Shafer Theory. There are some
    Message 1 of 10 , Sep 3, 2004
      Hello, Kevin !

      Predictive interpolation is a very interesting field.

      You may be interested in GIS applications based on Dempster-Shafer Theory.
      There are some online material also linked to archeology and prediction of
      archaeological sites at http://gis.esri
      com/library/userconf/proc99/proceed/papers/pap295/p295.htm
      and http://websrv5.sdu.dk/ejstrud/forskning.html

      Also IDRISI GIS has a collection of useful modeling tools for multicriteria
      analysis. It is described in manual which can be downloaded from IDRISI web
      site...

      Another subject of interest might be various AI techniques. I personally am
      experimenting with Case Based Reasoning (spatial predictions of community
      structure). There are some references and an experimental module online at
      http://www.alleco.fi/allmaps
      Currently I am rewriting the code (for the third time already :)). Still, at
      least for me it looks promising. Although currently I came to a decision
      that these 'explicit' GIS modeling techniques must be suplemented with the
      implicit' ones based on cellular automation, nearest neighbourhood or
      variograms and krigging. The reason to think so is that those 'inteligent'
      methods predict ecological niches. But in real world those niches may remain
      unoccupied. So we need an interaction of explicit top-down influence in form
      of 'niches' and implicit bottom-up influence in the form of 'growth from a
      seed'.

      Best regards
      Viktoras

      -------Original Message-------

      From: Kevin M. Curtin
      Date: 2004 m. rugs�jis 02 d. 12:44:03
      To: ai-geostats@...
      Subject: [ai-geostats] Frightened of Spatial Autocorrelation

      Hello All,
      I�m not sure if this is the correct forum for this�but a colleague has asked
      a question I�d like to address.

      This fellow wants to predict the location of archaeological sites based on
      factors such as soil type, proximity to a water source, slope, AND proximity
      to other archaeological sites.

      On proposing such a predictive model he has been challenged with, �How are
      you going to deal with Spatial Autocorrelation�. We�re not sure why SA
      should be a problem since we are suggesting that spatial proximity is a
      factor in settlement location.

      So, do we need to test for SA and why?

      Thanks in advance,
      Kevin
    • Niels Chr. Nielsen
      Isobel, Kevin and others I would be very interested as well, since my collegue (an archaeologist) and myself (geographer) are doing work on Bronze Age Denmark,
      Message 2 of 10 , Sep 3, 2004
        Isobel, Kevin and others

        I would be very interested as well, since my collegue (an archaeologist) and myself (geographer) are doing work on Bronze Age Denmark, in the landscape of Vendsyssel. See web site http://websrv5.sdu.dk/ejstrud/forskning.html
        with paper http://websrv5.sdu.dk/ejstrud/forskning/GIS/ejstrud_wunsdorf_2001.pdf
        and http://www.humaniora.sdu.dk/kulturmiljoe (mostly in Danish)

        Niels


        Isobel Clark wrote:
        Kevin
        
        Sounds like an ideal case for Geographically Weighted
        Regression. 
        
        You could use semi-variograms or spatial
        auto-correlation to determine exactly how proximity
        defines relationship. My only current beef with GWR is
        the seemingly pre-defined distance weighting
        functions. Not had much time to get into this yet, so
        don't dump on me all you experts out there.
        
        I would be interested in any published results on this
        as one of my business partners is doing similar work
        on bronze age denmark.
        
        Isobel
        http://uk.geocities.com/drisobelclark
        
        
        	
        	
        		
        ___________________________________________________________ALL-NEW Yahoo! Messenger - all new features - even more fun!  http://uk.messenger.yahoo.com
        
          

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      • Rajive Ganguli
        With ref. to the posting below (AI techniques and predictive work), we have recently done a lot of work comparing kriging and neural network performance. As
        Message 3 of 10 , Sep 3, 2004
          With ref. to the posting below (AI techniques and predictive work), we
          have recently done a lot of work comparing kriging and neural network
          performance. As an example, one of those papers (soon to show up in
          Jo. Exp. Geol) is posted at
          http://www.faculty.uaf.edu/ffrg/papers/nomepap_modi_revise.zip.

          To summarize our findings, I would say there hasn't been much
          difference in prediction performance. One or the other is marginally
          better on any given case. An indication of the absolute performance
          of either methods can be easily obtained from the semi-variogram:
          lower the nugget, better both methods perform, while higher the nugget
          the worse they both perform.

          In the paper that is posted, both methods performed very well with NN
          having a prediction performance (R_sq) upwards of 0.98 with very low
          bias and kriging being 0.95.

          Thanks,


          Rajive Ganguli, Ph.D., P.E., C.O.I
          Associate Professor of Mining Engineering
          University of Alaska Fairbanks
          ================================
          Office: 317 Duckering Building
          Mailing Add: Box 755800, Fairbanks, AK 99775
          ph: 907-474-7631, fax: 907-474-6635
          web: http://www.faculty.uaf.edu/ffrg/
          -------------------------------------------
          "He uses statistics as a drunken man uses lamp-posts... for support rather
          than illumination." - Andrew Lang (1844-1912)




          -----Original Message-----
          From: Viktoras Didziulis [mailto:viktoras.didziulis@...]
          Sent: Friday, September 03, 2004 12:12 AM
          To: ai-geostats@...
          Subject: Re: [ai-geostats] Frightened of Spatial Autocorrelation

          Hello, Kevin ! Predictive interpolation is a very interesting field. You
          may be interested in GIS applications based on Dempster-Shafer Theory.
          There are some online material also linked to archeology and prediction of
          archaeological sites at http://gis.esri
          com/library/userconf/proc99/proceed/papers/pap295/p295.htm and
          http://websrv5.sdu.dk/ejstrud/forskning.html Also IDRISI GIS has a
          collection of useful modeling tools for multicriteria
          analysis. It is described in manual which can be downloaded from IDRISI web
          site... Another subject of interest might be various AI techniques. I
          personally am
          experimenting with Case Based Reasoning (spatial predictions of community
          structure). There are some references and an experimental module online at
          http://www.alleco.fi/allmaps Currently I am rewriting the code (for the
          third time already :)). Still, at
          least for me it looks promising. Although currently I came to a decision
          that these 'explicit' GIS modeling techniques must be suplemented with the
          implicit' ones based on cellular automation, nearest neighbourhood or
          variograms and krigging. The reason to think so is that those 'inteligent'
          methods predict ecological niches. But in real world those niches may remain
          unoccupied. So we need an interaction of explicit top-down influence in form
          of 'niches' and implicit bottom-up influence in the form of 'growth from a
          seed'. Best regards Viktoras -------Original Message------- From:
          Kevin M. Curtin Date: 2004 m. rugsëjis 02 d. 12:44:03 To:
          ai-geostats@... Subject: [ai-geostats] Frightened of Spatial
          Autocorrelation Hello All, I'm not sure if this is the correct forum for
          this…but a colleague has asked
          a question I'd like to address. This fellow wants to predict the location
          of archaeological sites based on
          factors such as soil type, proximity to a water source, slope, AND proximity
          to other archaeological sites. On proposing such a predictive model he has
          been challenged with, "How are
          you going to deal with Spatial Autocorrelation". We're not sure why SA
          should be a problem since we are suggesting that spatial proximity is a
          factor in settlement location. So, do we need to test for SA and why?
          Thanks in advance, Kevin
        • Volker Bahn
          Hi Kevin, I work in the field of distribution modeling of birds and somewhat come from the other direction than most geostatistics people here on the list. In
          Message 4 of 10 , Sep 7, 2004
            Hi Kevin,

            I work in the field of distribution modeling of birds and somewhat come from
            the other direction than most geostatistics people here on the list. In
            ecology, we first only predicted by local conditions and habitat, then were
            pointed to the problems of spatial autocorrelation in such an approach, then
            tried to compensate for autocorrelation problems in statistics and only
            lately recognized that autocorrelation is actually additional information
            that could improve prediction. Steve already posted the most current papers
            regarding this issue in Ecography, which helped me much
            (http://www.nceas.ucsb.edu/~liebhold/ecography/). I would add the following
            paper to the list:

            Lichstein J. W., T. R. Simons, S. A. Shriner, and K. E. Franzreb. 2002.
            Spatial autocorrelation and autoregressive models in ecology. Ecological
            Monographs 72(3):445-463.

            For birds it has been well documented that autocorrelation in distributions
            are caused by autocorrelation in underlying resources. Thus, in theory, if
            you cover ALL important predictors in your model (let's say a regular
            regression or any other "non-spatial" method), the spatial structure in the
            distribution is modeled implicitly by being contained in the predictors.
            However, if you miss a predictor (which in practice will always be the
            case), you will miss its spatial structure and the residuals of your
            analysis will reflect this structure rendering these approaches ineffective
            and statistically flawed. In addition, I'm trying to show in my research
            that dispersal of individuals (meaning leaving either the birthplace or the
            last breeding place permanently to breed elsewhere) also leads to
            autocorrelation in distributions. This could also be the case for
            archeological sites as there was undoubtedly some contact and exchange among
            neighbors and this contact would have been more intense with close neighbors
            as travel comes at a cost. Thus I would expect autocorrelation in the
            spatial distribution of archeological sites above and beyond the
            autocorrelation in the underlying conditions predicting archeological sites.
            I use conditional autoregressive regression models (CAR) in Splus to model
            bird distributions.

            I hope this helps

            Volker
            _______________________________
            Volker Bahn
            Dept. of Wildlife Ecology - Rm. 210
            University of Maine
            5755 Nutting Hall
            Orono, Maine
            04469-5755, USA
            Tel. (207) 581 2799
            Fax: (207) 581 2858
            volker.bahn@...
            http://www.wle.umaine.edu/used_text%20files/Volker%20Bahn/home.htm


            ----- Original Message -----
            From: Kevin M. Curtin
            To: ai-geostats@...
            Sent: Thursday, September 02, 2004 15:43
            Subject: [ai-geostats] Frightened of Spatial Autocorrelation


            Hello All,
            I'm not sure if this is the correct forum for this.but a colleague has asked
            a question I'd like to address.

            This fellow wants to predict the location of archaeological sites based on
            factors such as soil type, proximity to a water source, slope, AND proximity
            to other archaeological sites.

            On proposing such a predictive model he has been challenged with, "How are
            you going to deal with Spatial Autocorrelation". We're not sure why SA
            should be a problem since we are suggesting that spatial proximity is a
            factor in settlement location.

            So, do we need to test for SA and why?

            Thanks in advance,
            Kevin




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