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Modeling spatial variations in household disposable income with Geographically Weighted Regression

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  • Chasco, Coro
  • García, Isabel
  • Vicéns, José

Abstract

The purpose of this paper is to analyze the spatially varying impacts of some classical regressors on per capita household income in Spanish provinces. The authors model this distribution following both a traditional global regression and a local analysis with Geographically Weighted Regression (GWR). Several specifications are compared, being the adaptive bisquare weighting function the more efficient in terms of goodness-of-fit. We test for global and local spatial instability using some F-tests and other statistical measures. We find some evidence of spatial instability in the distribution of this variable in relation to some explanatory variables, which cannot be totally solved by spatial dependence specifications. GWR has revealed as a better specification to model per capita household income. It highlights some facets of the relationship completely hidden in the global results and forces us to ask about questions we would otherwise not have asked. Moreover, the application of GWR can also be of help to further exercises of micro-data spatial prediction.

Suggested Citation

  • Chasco, Coro & García, Isabel & Vicéns, José, 2007. "Modeling spatial variations in household disposable income with Geographically Weighted Regression," MPRA Paper 1682, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:1682
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    File URL: https://mpra.ub.uni-muenchen.de/9581/1/MPRA_paper_9581.pdf
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    References listed on IDEAS

    as
    1. Griffith, Daniel A., 1992. "A spatially adjusted N-way ANOVA model," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 347-369, September.
    2. A. Stewart Fotheringham & Martin Charlton & Chris Brunsdon, 1997. "Measuring Spatial Variations in Relationships with Geographically Weighted Regression," Advances in Spatial Science, in: Manfred M. Fischer & Arthur Getis (ed.), Recent Developments in Spatial Analysis, chapter 4, pages 60-82, Springer.
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    Cited by:

    1. Tara A. Smith & J. S. Onésimo Sandoval, 2019. "Examining the Local Spatial Variability of Robberies in Saint Louis Using a Multi-Scale Methodology," Social Sciences, MDPI, vol. 8(2), pages 1-25, February.

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    More about this item

    Keywords

    Geographically Weighted Regression (GWR); spatial non-stationarity; spatial prediction; income; Spanish provinces;
    All these keywords.

    JEL classification:

    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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