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

Samyukta Bhupathiraju (), Bart Verspagen and Thomas Ziesemer ()
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Samyukta Bhupathiraju: UNU-MERIT/MGSoG
Authors registered in the RePEc Author Service: Samyukta Bhupatiraju

No 2013-011, MERIT Working Papers from United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (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.

Keywords: spatial principal components analysis; spatial canonical correlation analysis; spatial econometrics; Moran coefficients; spatial concentration (search for similar items in EconPapers)
JEL-codes: C10 R10 R15 (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-geo and nep-ure
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Citations: View citations in EconPapers (2)

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