IDEAS home Printed from https://ideas.repec.org/p/unm/unumer/2013011.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://www.merit.unu.edu/publications/wppdf/2013/wp2013-011.pdf
    Download Restriction: no
    ---><---

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

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sylvie Démurger & Jeffrey D. Sachs & Wing Thye Woo & Shuming Bao & Gene Chang & Andrew Mellinger, 2002. "Geography, Economic Policy, and Regional Development in China," Asian Economic Papers, MIT Press, vol. 1(1), pages 146-197.
    2. Melissa Dell & Benjamin F. Jones & Benjamin A. Olken, 2014. "What Do We Learn from the Weather? The New Climate-Economy Literature," Journal of Economic Literature, American Economic Association, vol. 52(3), pages 740-798, September.
    3. Gaël Raballand, 2003. "Determinants of the Negative Impact of Being Landlocked on Trade: An Empirical Investigation Through the Central Asian Case," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 45(4), pages 520-536, December.
    4. Frederick van der Ploeg & Steven Poelhekke, 2007. "Volatility, Financial Development and the Natural Resource Curse," Economics Working Papers ECO2007/36, European University Institute.
    5. Oasis Kodila-Tedika & Simplice A. Asongu, 2015. "The Effect of Intelligence on Financial Development: A Cross-Country Comparison," Research Africa Network Working Papers 15/002, Research Africa Network (RAN).
    6. Davide Fiaschi & Andrea Mario Lavezzi & Angela Parenti, 2020. "Deep and Proximate Determinants of the World Income Distribution," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(3), pages 677-710, September.
    7. Miren Lafourcade & Jacques-François Thisse, 2011. "New Economic Geography: The Role of Transport Costs," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 4, Edward Elgar Publishing.
    8. Steven N. Durlauf & Andros Kourtellos & Chih Ming Tan, 2008. "Empirics of Growth and Development," Chapters, in: Amitava Krishna Dutt & Jaime Ros (ed.), International Handbook of Development Economics, Volumes 1 & 2, volume 0, chapter 3, Edward Elgar Publishing.
    9. Oleg Badunenko & Daniel Henderson & Romain Houssa, 2014. "Significant drivers of growth in Africa," Journal of Productivity Analysis, Springer, vol. 42(3), pages 339-354, December.
    10. Nancy Birdsall & Liliana Rojas-Suarez (ed.), 2004. "Financing Development: The Power of Regionalism," Peterson Institute Press: All Books, Peterson Institute for International Economics, number 359, October.
    11. Anastasia Litina, 2016. "Natural land productivity, cooperation and comparative development," Journal of Economic Growth, Springer, vol. 21(4), pages 351-408, December.
    12. Priebe, Jan & Rudolf, Robert, 2015. "Does the Chinese Diaspora Speed Up Growth in Host Countries?," World Development, Elsevier, vol. 76(C), pages 249-262.
    13. Michael Breen & Robert Gillanders, 2012. "Corruption, institutions and regulation," Economics of Governance, Springer, vol. 13(3), pages 263-285, September.
    14. Szirmai, Adam & Verspagen, Bart, 2015. "Manufacturing and economic growth in developing countries, 1950–2005," Structural Change and Economic Dynamics, Elsevier, vol. 34(C), pages 46-59.
    15. Richard S. J. Tol, 2021. "The Economic Impact of Climate in the Long Run," World Scientific Book Chapters, in: Anil Markandya & Dirk Rübbelke (ed.), CLIMATE AND DEVELOPMENT, chapter 1, pages 3-36, World Scientific Publishing Co. Pte. Ltd..
    16. Lionel Fontagné & Gianluca Santoni, 2019. "Agglomeration economies and firm-level labor misallocation," Journal of Economic Geography, Oxford University Press, vol. 19(1), pages 251-272.
    17. Timothy Besley & Torsten Persson, 2011. "Pillars of Prosperity: The Political Economics of Development Clusters," Economics Books, Princeton University Press, edition 1, number 9624.
    18. Schünemann, Johannes & Bloom, David E. & Canning, David & Kotschy, Rainer & Prettner, Klaus, 2018. "Health and Economic Growth: Reconciling the Micro and Macro Evidence," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181554, Verein für Socialpolitik / German Economic Association.
    19. Prskawetz, A. & Kogel, T. & Sanderson, W.C. & Scherbov, S., 2007. "The effects of age structure on economic growth: An application of probabilistic forecasting to India," International Journal of Forecasting, Elsevier, vol. 23(4), pages 587-602.
    20. Kahn, Matthew E. & Mohaddes, Kamiar & Ng, Ryan N.C. & Pesaran, M. Hashem & Raissi, Mehdi & Yang, Jui-Chung, 2021. "Long-term macroeconomic effects of climate change: A cross-country analysis," Energy Economics, Elsevier, vol. 104(C).

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:unm:unumer:2013011. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ad Notten (email available below). General contact details of provider: https://edirc.repec.org/data/meritnl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.