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

Author

Listed:
  • Bhupathiraju, Samyukta

    (UNU-MERIT/MGSoG)

  • Verspagen, Bart

    (UNU-MERIT/MGSoG, and Maastricht University)

  • Ziesemer, Thomas

    (UNU-MERIT/MGSoG, and Maastricht University)

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.

Suggested Citation

  • Bhupathiraju, Samyukta & Verspagen, Bart & Ziesemer, Thomas, 2013. "Summarizing large spatial datasets: Spatial principal components and spatial canonical correlation," MERIT Working Papers 2013-011, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    • Handle: RePEc:unm:unumer:2013011
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    File URL: https://www.merit.unu.edu/publications/wppdf/2013/wp2013-011.pdf
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    References listed on IDEAS

    as
    1. John Luke Gallup & Jeffrey D. Sachs & Andrew Mellinger, 1999. "Geography and Economic Development," CID Working Papers 1, Center for International Development at Harvard University.
    2. Gallup, John L. & Sachs, Jeffrey D. & Mellinger, Andrew, "undated". "Geography and Economic Development," Instructional Stata datasets for econometrics geodata, Boston College Department of Economics.
    3. Gallup, John & Sachs, Jeffrey, 1999. "Geography and Economic Development," Harvard Institute for International Development (HIID) Papers 294434, Harvard University, Kennedy School of Government.
    4. Gallup, J.L. & Sachs, J.D. & Mullinger, A., 1999. "Geography and Economic Development," Papers 1, Chicago - Graduate School of Business.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Bhupatiraju S., 2014. "The geographic dimensions of institutions," MERIT Working Papers 2014-086, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    2. Bhupatiraju S. & Verspagen B., 2013. "Economic development, growth, institutions and geography," MERIT Working Papers 2013-056, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).

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    More about this item

    Keywords

    spatial principal components analysis; spatial canonical correlation analysis; spatial econometrics; Moran coefficients; spatial concentration;
    All these keywords.

    JEL classification:

    • 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

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