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A general framework for estimation and inference of geographically weighted regression models: 2. Spatial association and model specification tests

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  • Antonio Páez
  • Takashi Uchida
  • Kazuaki Miyamoto

Abstract

Spatial association effects, perhaps the most important concern in the analysis of spatial data, have been amply studied from a global perspective in the exploratory and modeling domains, and more recently also from a local perspective in the realm of exploratory data analysis. In a local modeling framework, however, the issue of how to detect and model spatial association by using geographically weighted regression (GWR) remains largely unresolved. In this paper we exploit a recent development that casts GWR as a model of locational heterogeneity, to formulate a general model of spatial effects that includes as special cases GWR with a spatially lagged objective variable and GWR with spatial error autocorrelation. The approach also permits the derivation of formal tests against several forms of model misspecification, including locational heterogeneity in global models, and spatial error autocorrelation in GWR models. Application of these results is exemplified with a case study.

Suggested Citation

  • Antonio Páez & Takashi Uchida & Kazuaki Miyamoto, 2002. "A general framework for estimation and inference of geographically weighted regression models: 2. Spatial association and model specification tests," Environment and Planning A, Pion Ltd, London, vol. 34(5), pages 883-904, May.
  • Handle: RePEc:pio:envira:v:34:y:2002:i:5:p:883-904
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    Cited by:

    1. Wei, Chuan-Hua & Qi, Fei, 2012. "On the estimation and testing of mixed geographically weighted regression models," Economic Modelling, Elsevier, vol. 29(6), pages 2615-2620.
    2. Li, Hengyun & Chen, Jason Li & Li, Gang & Goh, Carey, 2016. "Tourism and regional income inequality: Evidence from China," Annals of Tourism Research, Elsevier, vol. 58(C), pages 81-99.
    3. Cho, Seong-Hoon & Kim, Seung Gyu & Roberts, Roland K. & Jung, Suhyun, 2009. "Amenity values of spatial configurations of forest landscapes over space and time in the Southern Appalachian Highlands," Ecological Economics, Elsevier, vol. 68(10), pages 2646-2657, August.
    4. Roberto Benedetti & Monica Pratesi & Nicola Salvati, 2013. "Local stationarity in small area estimation models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(1), pages 81-95, March.
    5. Shaoming Cheng & Huaqun Li, 2011. "Spatially Varying Relationships of New Firm Formation in the United States," Regional Studies, Taylor & Francis Journals, vol. 45(6), pages 773-789.
    6. Bo Pieter Johannes Andree & Francisco Blasques & Eric Koomen, 2017. "Smooth Transition Spatial Autoregressive Models," Tinbergen Institute Discussion Papers 17-050/III, Tinbergen Institute.

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