Summarizing large spatial datasets: Spatial principal components and spatial canonical correlation
AbstractWe 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.
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Bibliographic InfoPaper provided by United Nations University, Maastricht Economic and social Research and training centre on Innovation and Technology in its series UNU-MERIT Working Paper Series with number 011.
Date of creation: 2013
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Web page: http://www.merit.unu.edu
spatial principal components analysis; spatial canonical correlation analysis; spatial econometrics; Moran coefficients; spatial concentration;
Other versions of this item:
- Bhupathiraju, Samyukta & Verspagen, Bart & Ziesemer, Thomas, 2013. "Summarizing large spatial datasets: Spatial principal components and spatial canonical correlation," MERIT Working Papers 011, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
- R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General
- R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-03-23 (All new papers)
- NEP-ECM-2013-03-23 (Econometrics)
- NEP-GEO-2013-03-23 (Economic Geography)
- NEP-NEU-2013-03-23 (Neuroeconomics)
- NEP-URE-2013-03-23 (Urban & Real Estate Economics)
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- Bhupatiraju, Samyukta & Verspagen, Bart, 2013. "Economic development, growth, institutions and geography," MERIT Working Papers 056, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
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