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Mapping the results of local statistics

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  • Stephen Matthews

    (Pennsylvania State University)

  • Tse-Chuan Yang

    (University at Albany, State University of New York)

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    Abstract

    The application of geographically weighted regression (GWR) – a local spatial statistical technique used to test for spatial nonstationarity – has grown rapidly in the social, health and demographic sciences. GWR is a useful exploratory analytical tool that generates a set of location-specific parameter estimates which can be mapped and analysed to provide information on spatial nonstationarity in relationships between predictors and the outcome variable. A major challenge to GWR users, however, is how best to map these parameter estimates. This paper introduces a simple mapping technique that combines local parameter estimates and local t-values on one map. The resultant map can facilitate the exploration and interpretation of nonstationarity.

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    File URL: http://www.demographic-research.org/volumes/vol26/6/26-6.pdf
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    Bibliographic Info

    Article provided by Max Planck Institute for Demographic Research, Rostock, Germany in its journal Demographic Research.

    Volume (Year): 26 (2012)
    Issue (Month): 6 (March)
    Pages: 151-166

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    Handle: RePEc:dem:demres:v:26:y:2012:i:6

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    Web page: http://www.demogr.mpg.de/

    Related research

    Keywords: geographically weighted regression; local statistics; mapping; nonstationarity;

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    1. Partridge, Mark D. & Rickman, Dan S., 2005. "Persistent Pockets Of Extreme American Poverty: People Or Place Based?," Working Papers 18907, Oregon State University, Rural Poverty Research Center (RPRC).
    2. Benson, Todd & Chamberlin, Jordan & Rhinehart, Ingrid, 2005. "An investigation of the spatial determinants of the local prevalence of poverty in rural Malawi," Food Policy, Elsevier, vol. 30(5-6), pages 532-550.
    3. C Brunsdon & A S Fotheringham & M Charlton, 1998. "Spatial nonstationarity and autoregressive models," Environment and Planning A, Pion Ltd, London, vol. 30(6), pages 957-973, June.
    4. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, 06.
    5. Kamar Ali & Mark D. Partridge & M. Rose Olfert, 2007. "Can Geographically Weighted Regressions Improve Regional Analysis and Policy Making?," International Regional Science Review, , vol. 30(3), pages 300-329, July.
    6. Dan-Lin Yu, 2006. "Spatially varying development mechanisms in the Greater Beijing Area: a geographically weighted regression investigation," The Annals of Regional Science, Springer, vol. 40(1), pages 173-190, March.
    7. David C Wheeler, 2009. "Simultaneous coefficient penalization and model selection in geographically weighted regression: the geographically weighted lasso," Environment and Planning A, Pion Ltd, London, vol. 41(3), pages 722-742, March.
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