Summarizing large spatial datasets: Spatial principal components and spatial canonical correlation

Bhupatiraju, Samyukta, Verspagen, Bart and Ziesemer, Thomas (2013). Summarizing large spatial datasets: Spatial principal components and spatial canonical correlation. UNU-MERIT.

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  • Author Bhupatiraju, Samyukta
    Verspagen, Bart
    Ziesemer, Thomas
    Title Summarizing large spatial datasets: Spatial principal components and spatial canonical correlation
    Publication Date 2013
    Publisher UNU-MERIT
    Abstract We propose a method for spatial principal components analysis that has two important advantages over the method that Wartenberg (1985) proposed. The first advantage is that, contrary to Wartenberg's method, our method has a clear and exact interpretation: it produces a summary measure (component) that itself has maximum spatial correlation. Second, an easy and intuitive link can be made to canonical correlation analysis. Our spatial canonical correlation analysis produces summary measures of two datasets (e.g., each measuring a different phenomenon), and these summary measures maximize the spatial correlation between themselves. This provides an alternative weighting scheme as compared to spatial principal components analysis. We provide example applications of the methods and show that our variant of spatial canonical correlation analysis may produce rather different results than spatial principal components analysis using Wartenberg's method. We also illustrate how spatial canonical correlation analysis may produce different results than spatial principal components analysis.
    Keyword Spatial principal components analysis
    Spatial canonical correlation analysis
    Spatial econometrics
    Moran coefficients
    Spatial concentration
    JEL R10
    R15
    C10
    Copyright Holder UNU-MERIT
    Copyright Year 2013
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    Created: Wed, 11 Dec 2013, 17:14:42 JST