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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.

Suggested Citation

  • Cho, Seong-Hoon & Lambert, Dayton M. & Kim, Seung Gyu & Jung, Suhyun, 2009. "Extreme coefficients in Geographically Weighted Regression and their effects on mapping," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49117, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea09:49117
    DOI: 10.22004/ag.econ.49117
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    Cited by:

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    3. López-Carr, David & Davis, Jason & Jankowska, Marta M. & Grant, Laura & López-Carr, Anna Carla & Clark, Matthew, 2012. "Space versus place in complex human–natural systems: Spatial and multi-level models of tropical land use and cover change (LUCC) in Guatemala," Ecological Modelling, Elsevier, vol. 229(C), pages 64-75.
    4. Yujiao Chen & Zhengbo Luo, 2022. "Hedonic Pricing of Houses in Megacities Pre- and Post-COVID-19: A Case Study of Shanghai, China," Sustainability, MDPI, vol. 14(17), pages 1-21, September.
    5. 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.
    6. Feuillet, T. & Commenges, H. & Menai, M. & Salze, P. & Perchoux, C. & Reuillon, R. & Kesse-Guyot, E. & Enaux, C. & Nazare, J.-A. & Hercberg, S. & Simon, C. & Charreire, H. & Oppert, J.M., 2018. "A massive geographically weighted regression model of walking-environment relationships," Journal of Transport Geography, Elsevier, vol. 68(C), pages 118-129.
    7. Fabián Santos & Valerie Graw & Santiago Bonilla, 2019. "A geographically weighted random forest approach for evaluate forest change drivers in the Northern Ecuadorian Amazon," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-37, December.
    8. 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.
    9. Stephen Matthews & Tse-Chuan Yang, 2012. "Mapping the results of local statistics," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 26(6), pages 151-166.
    10. Sisman, S. & Aydinoglu, A.C., 2022. "A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul," Land Use Policy, Elsevier, vol. 119(C).
    11. 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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