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    Geographically weighted Poisson regression for disease association mapping


    Nakaya, T. and Fotheringham, Stewart and Brunsdon, Chris and Charlton, Martin (2005) Geographically weighted Poisson regression for disease association mapping. Statistics in Medicine, 24 (17). pp. 2695-2717. ISSN 0277-6715

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    Abstract

    This paper describes geographically weighted Poisson regression (GWPR) and its semi-parametric variant as a new statistical tool for analysing disease maps arising from spatially non-stationary processes. The method is a type of conditional kernel regression which uses a spatial weighting function to estimate spatial variations in Poisson regression parameters. It enables us to draw surfaces of local parameter estimates which depict spatial variations in the relationships between disease rates and socio-economic characteristics. The method therefore can be used to test the general assumption made, often without question, in the global modelling of spatial data that the processes being modelled are stationary over space. Equally, it can be used to identify parts of the study region in which ‘interesting’ relationships might be occurring and where further investigation might be warranted. Such exceptions can easily be missed in traditional global modelling and therefore GWPR provides disease analysts with an important new set of statistical tools. We demonstrate the GWPR approach applied to a dataset of working-age deaths in the Tokyo metropolitan area, Japan. The results indicate that there are signifcant spatial variations (that is, variation beyond that expected from random sampling) in the relationships between working-age mortality and occupational segregation and between working-age mortality and unemployment throughout the Tokyo metropolitan area and that, consequently, the application of traditional ‘global’ models would yield misleading results.

    Item Type: Article
    Keywords: Poisson regression; geographically weighted regression; kernel mapping; spatial analysis; Tokyo;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
    Item ID: 5955
    Identification Number: https://doi.org/10.1002/sim.2129
    Depositing User: Martin Charlton
    Date Deposited: 12 Mar 2015 12:16
    Journal or Publication Title: Statistics in Medicine
    Publisher: John Wiley & Sons, Inc
    Refereed: Yes
    URI:

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