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GEOSTATS: Non-colocated disease datasets - HELP?!

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  • Jonathan Reynolds
    I m an ecologist with an interest in wildlife disease epidemiology. I have two unique datasets representing indices of occurrence of the same disease in two
    Message 1 of 3 , Sep 8, 2000
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      I'm an ecologist with an interest in wildlife disease epidemiology. I have
      two unique datasets representing indices of occurrence of the same disease
      in two species, both highly mobile terrestrial mammals. Visually (in
      postings) the two maps are convincingly similar - i.e. these data are
      ecologically very interesting indeed! I want to test the spatial
      correlation between the two datasets, because it's likely that one species
      is the reservoir infecting the other.

      Leaving aside my inadequacies as a mathematician (which cripple me as a
      reader of geostats texts), my problems fall into two categories:

      (1) Spatial autocorrelation

      A logical first step would seem to be to test whether each dataset is
      spatially autocorrelated. This seems likely when one examines the postings,
      but semivariograms suggest that a sill is very quickly reached (at about 20
      km), a distance that seems improbably small as we are dealing with highly
      mobile mammals and epidemics that look to be 100-200 km across. In both
      datasets, the variogram value is thereafter highly variable with increasing
      distance, and there is some suggestion of an oscillating 'hole effect'.
      However, as distance increases the variogram is clearly being influenced by
      the shape of mainland Britain, and the timid faith I have in the variogram
      at small h falls away rapidly as h increases. For instance, a location in
      south-west Wales is very close to north Cornwall for a bird (by Euclidean
      distance), but quite far away for a terrestrial mammal that must travel by
      land around the Severn estuary.

      A further consideration, if I have correctly understood the meaning of
      stationarity, is that both datasets have an underlying trend, with values
      increasing from west to east and north to south. Despite having read at
      length in the AI-Geostats archives, I am still unsure how to deal with this
      in practical terms.

      For each species alone, the variogram is theoretically of tremendous
      interest. How local are the epidemics? How close must an epidemic be
      before a given animal is at risk? Is there genuinely a rippling effect
      surrounding a disease epicentre? Unfortunately, from the outset there seems
      to be a discrepancy between the variogram and my eye. I am a sworn disciple
      of objectivity, but I'm not yet convinced that my variogram is doing the
      right thing.

      (2) Sampling locations and correlation between the datasets

      Both datasets cover the whole UK (including some islands, which are easily
      and logically excluded), but originate from two populations of people
      (hunters and veterinarians) with necessarily different geographical
      distributions - i.e. they are not colocated. I could convert both datasets
      to a common regular grid, but this involves interpolation, a number of
      assumptions, and the creation of quite a few new grid locations that have NO
      data from one or both dataset(s). If I did convert to a common grid, I am
      then at a loss to know how to proceed further. The two datasets do not have
      similar underlying distributions. One is an incidence (count of diseased
      animals per unit effort), and is easily normalised by a log transformation.
      The other is a measure of prevalence, with many essential (meaningful) zeros
      that make transformation awkward and perhaps undesirable; these prevalence
      data can also be weighted by the sample size on which each is based.

      Please can anyone suggest a route forward? I have read (all the easy words
      in) quite a number of textbooks. So far as I can judge (I pull up all too
      soon), most books stop short of problems like this because no
      self-respecting miner would burden himself by collecting data so awkwardly.
      For me, this is a crude pilot study, hence a stratified sampling programme
      to test a hypothesis will be the next stage IF I can formalise the
      correlation that looks so blindingly obvious to the naked eye. So please
      don't suggest I do my sampling differently.

      Jonathan Reynolds


      Dr Jonathan C Reynolds
      The Game Conservancy Trust
      Fordingbridge
      Hampshire SP6 1EF
      UK

      tel: +44 (0)1425 652381
      FAX: +44 (0)1425 651026
      email: jreynolds@...
      website: www.gct.org.uk/index.html

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    • Roger Bivand
      ... Are the data in fact two sets of points of occurrences? Could the hypothesised dependence between them be tested rather using point pattern analysis than
      Message 2 of 3 , Sep 9, 2000
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        On Fri, 8 Sep 2000, Jonathan Reynolds wrote:

        > I'm an ecologist with an interest in wildlife disease epidemiology. I have
        > two unique datasets representing indices of occurrence of the same disease
        > in two species, both highly mobile terrestrial mammals. Visually (in
        > postings) the two maps are convincingly similar - i.e. these data are
        > ecologically very interesting indeed! I want to test the spatial
        > correlation between the two datasets, because it's likely that one species
        > is the reservoir infecting the other.
        >

        Are the data in fact two sets of points of occurrences? Could the
        hypothesised dependence between them be tested rather using point pattern
        analysis than geostatistics, say by the Ripley K12-function? Or are they
        some other number on some spatial basis?

        Just curious,

        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@...
        and: Department of Geography and Regional Development, University of
        Gdansk, al. Mar. J. Pilsudskiego 46, PL-81 378 Gdynia, Poland.

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      • Shane Hornibrook
        I am contemplating the purchase of ESRI s ResearchAnalyst, and I would appreciate feedback from anyone who has used it. Is it stable? Worth purchasing? (BTW:
        Message 3 of 3 , Sep 9, 2000
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          I am contemplating the purchase of ESRI's ResearchAnalyst, and I would
          appreciate feedback from anyone who has used it. Is it stable? Worth
          purchasing? (BTW: We have AV 3.2 w/ spatial analyst FWIW)

          Thanks in advance.
          --Shane
          Shane Hornibrook, (http://www.geologist.net/)

          GIS Statistician/Programmer/Analyst
          Department of Community Health and Epidemiology
          Clinical Research Centre
          5849 University Avenue, Halifax, Nova Scotia, B3H 4H7

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