Summarizing large spatial datasets: Spatial principal components and spatial canonical correlation
Samyukta Bhupatiraju, Bart Verspagen & Thomas Ziesemer
#2013-011
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
JEL Classification: R10, R15, C10