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      A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations

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      Journal of Geographical Systems

      Springer Nature

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          Most cited references 9

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          Spatial Econometrics: Methods and Models

           Luc Anselin (1988)
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            Multicollinearity and correlation among local regression coefficients in geographically weighted regression

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              Geographically weighted Poisson regression for disease association mapping.

              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 data set of working-age deaths in the Tokyo metropolitan area, Japan. The results indicate that there are significant 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. Copyright (c) 2005 John Wiley & Sons, Ltd.
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                Author and article information

                Journal
                Journal of Geographical Systems
                J Geograph Syst
                Springer Nature
                1435-5930
                1435-5949
                October 31 2007
                August 23 2007
                : 9
                : 4
                : 371-396
                Article
                10.1007/s10109-007-0051-3
                © 2007
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