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A Family of Geographically Weighted Regression Models

In: Advances in Spatial Econometrics

Author

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  • James P. LeSage

    (University of Toledo)

Abstract

A Bayesian approach to locally linear regression methods introduced in McMillen (1996) and labeled geographically weighted regressions (GWR) in Brunsdon et al. (1996) is set forth in this chapter. The main contribution of the GWR methodology is use of distance weighted sub-samples of the data to produce locally linear regression estimates for every point in space. Each set of parameter estimates is based on a distance-weighted sub-sample of “neighboring observations,” which has a great deal of intuitive appeal in spatial econometrics. While this approach has a definite appeal, it also presents some problems. The Bayesian method introduced here can resolve some difficulties that arise in GWR models when the sample observations contain outliers or non-constant variance.

Suggested Citation

  • James P. LeSage, 2004. "A Family of Geographically Weighted Regression Models," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 11, pages 241-264, Springer.
    • Handle: RePEc:spr:adspcp:978-3-662-05617-2_11
    DOI: 10.1007/978-3-662-05617-2_11
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    Cited by:

    1. Junming Li & Meijun Jin & Honglin Li, 2019. "Exploring Spatial Influence of Remotely Sensed PM 2.5 Concentration Using a Developed Deep Convolutional Neural Network Model," IJERPH, MDPI, vol. 16(3), pages 1-11, February.
    2. Pede, Valerien O. & Florax, Raymond J.G.M. & Holt, Matthew T., 2009. "A Spatial Econometric Star Model With An Application To U.S. County Economic Growth, 1969–2003," Working papers 48117, Purdue University, Department of Agricultural Economics.
    3. Mur, Jesús & Angulo, Ana, 2009. "Model selection strategies in a spatial setting: Some additional results," Regional Science and Urban Economics, Elsevier, vol. 39(2), pages 200-213, March.
    4. Anna Gloria Billé, 2013. "Computational Issues in the Estimation of the Spatial Probit Model: A Comparison of Various Estimators," The Review of Regional Studies, Southern Regional Science Association, vol. 43(2,3), pages 131-154, Winter.
    5. Jianmin Liu & Xiaomei Hu & Jinguang Wu, 2017. "Fiscal decentralization, financial efficiency and upgrading the industrial structure: an empirical analysis of a spatial heterogeneity model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(1), pages 181-196, January.
    6. Cem Ertur & Julie Le Gallo, 2008. "Regional Growth and Convergence: Heterogenous reaction versus interaction in spatial econometric approaches," Working Papers hal-00463274, HAL.
    7. Haiyong Zhang & Xinyu Wang, 2017. "Combined asymmetric spatial weights matrix with application to housing prices," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2337-2353, October.
    8. Rubén Ferrer Velasco & Margret Köthke & Melvin Lippe & Sven Günter, 2020. "Scale and context dependency of deforestation drivers: Insights from spatial econometrics in the tropics," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-32, January.
    9. Kevin McNamara, 2005. "Analysis of Manufacturing Growth in Indiana," ERSA conference papers ersa05p827, European Regional Science Association.
    10. Fan Yang & Fan Ding & Xu Qu & Bin Ran, 2019. "Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data," Sustainability, MDPI, vol. 11(11), pages 1-14, June.
    11. Yerema, Coulibaly Thierry & Wakamatsu, Mihoko & Islam, Moinul & Fukai, Hiroki & Managi, Shunsuke & Zhang, Bingqi, 2020. "Differences in Water Policy Efficacy across South African Water Management Areas," Ecological Economics, Elsevier, vol. 175(C).
    12. Biao Sun & Shan Yang, 2020. "Asymmetric and Spatial Non-Stationary Effects of Particulate Air Pollution on Urban Housing Prices in Chinese Cities," IJERPH, MDPI, vol. 17(20), pages 1-23, October.
    13. Zhong, Haotian & Li, Wei, 2016. "Rail transit investment and property values: An old tale retold," Transport Policy, Elsevier, vol. 51(C), pages 33-48.
    14. Kathleen P. Bell & Timothy J. Dalton, 2007. "Spatial Economic Analysis in Data‐Rich Environments," Journal of Agricultural Economics, Wiley Blackwell, vol. 58(3), pages 487-501, September.
    15. repec:rre:publsh:v:40:y:2010:i:1:p:71-97 is not listed on IDEAS
    16. Li, Wei & Sun, Wen & Li, Guomin & Jin, Baihui & Wu, Wen & Cui, Pengfei & Zhao, Guohao, 2018. "Transmission mechanism between energy prices and carbon emissions using geographically weighted regression," Energy Policy, Elsevier, vol. 115(C), pages 434-442.
    17. Yuan Zhang & Yiguo Sun & Thanasis Stengos, 2019. "Spatial Dependence in the Residential Canadian Housing Market," The Journal of Real Estate Finance and Economics, Springer, vol. 58(2), pages 223-263, February.
    18. Oscar Martinez Ibañez & Miguel Manjón Antolín & Josep-Maria Arauzo-Carod, 2013. "The Geographical Scope of Industrial Location Determinants: An Alternative Approach," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 104(2), pages 194-214, April.
    19. Kim, Sunghoon & DeSarbo, Wayne S. & Chang, Won, 2021. "Note: A new approach to the modeling of spatially dependent and heterogeneous geographical regions," International Journal of Research in Marketing, Elsevier, vol. 38(3), pages 792-803.
    20. Bischoff, Thore Sören & Hipp, Ann & Runst, Petrik, 2022. "Firm innovation and generalized trust as a regional resource," ifh Working Papers 32/2022, Volkswirtschaftliches Institut für Mittelstand und Handwerk an der Universität Göttingen (ifh).
    21. Mühlematter, Dominik J. & Wiedemann, Nina & Xin, Yanan & Raubal, Martin, 2024. "Spatially-aware station based car-sharing demand prediction," Journal of Transport Geography, Elsevier, vol. 114(C).
    22. Fan, Ruguo & Luo, Ming & Zhang, Pengfei, 2016. "A study on evolution of energy intensity in China with heterogeneity and rebound effect," Energy, Elsevier, vol. 99(C), pages 159-169.

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