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AI-GEOSTATS: Help, please with spatial(?) correlation

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  • Jozsef.Fabian@office.ksh.hu
    Dear list members, I am a GIS programmer at Hungarian Central Statistical Office, and trying to make a work about statistical connections between time required
    Message 1 of 2 , Feb 7, 2003
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      Dear list members,

      I am a GIS programmer at Hungarian Central Statistical Office, and
      trying to make a work about statistical connections between time
      required to reach some source (e.g. the capitol, the border crossing
      points, or local civil services) and local social parameters (this first
      time the governmental tax/person - as an indicator of the income).

      I have computed the required time data via network analysis for each
      localities, and have the respond variable. Using R I have made some
      linear fittings between time as predictor and the paid tax/person as
      respondent, but, I suspect, the strong linear correlation I found is an
      outcome from the spatial autocorrelation in the tax data.

      I have mapped the local spatial autocorrelation for these data, and
      found that it shows positive, negative and insignificant spatial
      autocorrelations between the neighbours in large, well separated
      continuous areas. The same areal distribution is typical for the
      residuals from the linear correlation.

      My question is: should I use geostatistical methods based on variogram?
      The argument to support this method is: My predictor is a distance-like
      value - in fact the time which is a function of the available speed on
      the road segments and the distance between localities.
      The argument against this method: My data are not from spatially
      continual variable(s), because there is not living people between
      settlements.

      Or should I include a "spatial lag" - the local average of the data
      weighted by the inverse distance (time) - into the regression?
      I strongly suspect, that this later method is the better solution, but
      can someone direct me to a publication about similar work?

      Please, excuse me for this longish "question".

      Thank you in advance
      Jozsef Fabian
      GIS programmes
      HCSO Hungary

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    • Roger Bivand
      ... This is an interesting analysis, with quite a lot of features. 1) What are the localities? Can their behaviour (as local councils etc.) affect the tax per
      Message 2 of 2 , Feb 7, 2003
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        On Fri, 7 Feb 2003 Jozsef.Fabian@... wrote:

        > Dear list members,
        >
        > I am a GIS programmer at Hungarian Central Statistical Office, and
        > trying to make a work about statistical connections between time
        > required to reach some source (e.g. the capitol, the border crossing
        > points, or local civil services) and local social parameters (this first
        > time the governmental tax/person - as an indicator of the income).
        >
        > I have computed the required time data via network analysis for each
        > localities, and have the respond variable. Using R I have made some
        > linear fittings between time as predictor and the paid tax/person as
        > respondent, but, I suspect, the strong linear correlation I found is an
        > outcome from the spatial autocorrelation in the tax data.

        This is an interesting analysis, with quite a lot of features.

        1) What are the localities? Can their behaviour (as local councils etc.)
        affect the tax per capita? Or is the tax per capita more a result of the
        state of the local economy? Why per capita (tax comes from working people,
        not total population)? My guess would be that local economic conditions
        are the main "driver". How does tax per capita correlate with firm
        formation, unemployment?

        2) Have you tested the residuals of your linear model for spatial
        autocorrelation, or just the response variable?

        3) How many distance variables are you using to measure isolation - the
        most isolated being a long way from a) the capital, b) a border crossing,
        and c) local services?

        4) How many localities are you examining? How are you constructing the
        spatial weights matrix for calculating spatial autocorrelation?

        >
        > I have mapped the local spatial autocorrelation for these data, and
        > found that it shows positive, negative and insignificant spatial
        > autocorrelations between the neighbours in large, well separated
        > continuous areas. The same areal distribution is typical for the
        > residuals from the linear correlation.
        >
        > My question is: should I use geostatistical methods based on variogram?
        > The argument to support this method is: My predictor is a distance-like
        > value - in fact the time which is a function of the available speed on
        > the road segments and the distance between localities.
        > The argument against this method: My data are not from spatially
        > continual variable(s), because there is not living people between
        > settlements.

        I think that using geostatistical methods would be premature while quite a
        lot can still be done treating the data as spatial lattice data. I would
        worry about the different effects of eastern and western borders, and
        population density, across the country.

        >
        > Or should I include a "spatial lag" - the local average of the data
        > weighted by the inverse distance (time) - into the regression?
        > I strongly suspect, that this later method is the better solution, but
        > can someone direct me to a publication about similar work?
        >
        > Please, excuse me for this longish "question".
        >
        Very interesting - please contact me off the list if you prefer.

        Roger Bivand


        > Thank you in advance
        > Jozsef Fabian
        > GIS programmes
        > HCSO Hungary
        >

        --
        Roger Bivand
        Economic Geography Section, Department of Economics, Norwegian School of
        Economics and Business Administration, Breiviksveien 40, N-5045 Bergen,
        Norway. voice: +47 55 95 93 55; fax +47 55 95 93 93
        e-mail: Roger.Bivand@...


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