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Extreme coefficients in Geographically Weighted Regression and their effects on mapping

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  • Cho, Seong-Hoon
  • Lambert, Dayton M.
  • Kim, Seung Gyu
  • Jung, Suhyun

Abstract

This study deals with the issue of extreme coefficients in geographically weighted regression (GWR) and their effects on mapping coefficients using three datasets with different spatial resolutions. We found that although GWR yields extreme coefficients regardless of the resolution of the dataset or types of kernel function, 1) the GWR tends to generate extreme coefficients for less spatially dense datasets, 2) coefficient maps based on polygon data representing aggregated areal units are more sensitive to extreme coefficients, and 3) coefficient maps using bandwidths generated by a fixed calibration procedure are more vulnerable to the extreme coefficients than adaptive calibration.

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File URL: http://purl.umn.edu/49117
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Bibliographic Info

Paper provided by Agricultural and Applied Economics Association in its series 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin with number 49117.

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Date of creation: 2009
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Handle: RePEc:ags:aaea09:49117

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Related research

Keywords: extreme coefficient; fixed and adaptive calibrations; geographically weighted regression; Mapping; Research Methods/ Statistical Methods;

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  1. Lambert, Dayton M. & McNamara, Kevin T. & Garrett, Megan I., 2006. "An Application of Spatial Poisson Models to Manufacturing Investment Location Analysis," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 38(01), April.
  2. 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.
  3. Luc Anselin, 2001. "Spatial Effects in Econometric Practice in Environmental and Resource Economics," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 705-710.
  4. Partridge, Mark D. & Rickman, Dan S., 2007. "Persistent Pockets of Extreme American Poverty and Job Growth: Is There a Place-Based Policy Role?," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 32(01), April.
  5. Deller, Steven C. & Lledo, Victor, 2007. "Amenities and Rural Appalachia Economic Growth," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 36(1), April.
  6. McMillen, Daniel P., 1996. "One Hundred Fifty Years of Land Values in Chicago: A Nonparametric Approach," Journal of Urban Economics, Elsevier, vol. 40(1), pages 100-124, July.
  7. 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.
  8. Steven Farber & Antonio Páez, 2007. "A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations," Journal of Geographical Systems, Springer, vol. 9(4), pages 371-396, December.
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Cited by:
  1. Stephen Matthews & Daniel M. Parker, 2013. "Progress in Spatial Demography," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(10), pages 271-312, February.
  2. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, . "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Tinbergen Institute Discussion Papers 13-171/VIII, Tinbergen Institute.
  3. Hans Koster & Jos van Ommeren & Piet Rietveld, 2011. "Geographic Concentration of Business Services Firms: A Poisson Sorting Model," ERSA conference papers ersa11p750, European Regional Science Association.
  4. Koster, Hans R.A. & van Ommeren, Jos & Rietveld, Piet, 2014. "Estimation of semiparametric sorting models: Explaining geographical concentration of business services," Regional Science and Urban Economics, Elsevier, vol. 44(C), pages 14-28.

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