IDEAS home Printed from https://ideas.repec.org/a/taf/specan/v10y2015i2p160-180.html
   My bibliography  Save this article

An Application of Principal Component Analysis on Multivariate Time-stationary Spatio-temporal Data

Author

Listed:
  • Stephan Stahlschmidt
  • Wolfgang K. H�rdle
  • Helmut Thome

Abstract

Principal component analysis (PCA) denotes a popular algorithmic technique to dimension reduction and factor extraction. Spatial variants have been proposed to account for the particularities of spatial data, namely spatial heterogeneity and spatial autocorrelation, and we present a novel approach which transfers PCA into the spatio-temporal realm. Our approach, named spatio-temporal principal component analysis (stPCA), allows for dimension reduction in the attribute space while striving to preserve much of the data's variance and maintaining the data's original structure in the spatio-temporal domain. Additionally to spatial autocorrelation stPCA exploits any serial correlation present in the data and consequently takes advantage of all particular features of spatial-temporal data. A simulation study underlines the superior performance of stPCA if compared to the original PCA or its spatial variants and an application on indicators of economic deprivation and urbanism demonstrates its suitability for practical use.

Suggested Citation

  • Stephan Stahlschmidt & Wolfgang K. H�rdle & Helmut Thome, 2015. "An Application of Principal Component Analysis on Multivariate Time-stationary Spatio-temporal Data," Spatial Economic Analysis, Taylor & Francis Journals, vol. 10(2), pages 160-180, June.
    • Handle: RePEc:taf:specan:v:10:y:2015:i:2:p:160-180
    DOI: 10.1080/17421772.2015.1023339
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/17421772.2015.1023339
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/17421772.2015.1023339?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hogan J.W. & Tchernis R., 2004. "Bayesian Factor Analysis for Spatially Correlated Data, With Application to Summarizing Area-Level Material Deprivation From Census Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 314-324, January.
    Full references (including those not matched with items on IDEAS)

    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. Ivaldi, Enrico, 2013. "Proposal of a country risk index based on a factorial analysis - Una proposta di indice di rischio paese basato sull’analisi fattoriale: una applicazione ai paesi del sud del Mediterraneo e ai paesi d," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 66(2), pages 231-249.
    2. Will Davis & Alexander Gordan & Rusty Tchernis, 2021. "Measuring the spatial distribution of health rankings in the United States," Health Economics, John Wiley & Sons, Ltd., vol. 30(11), pages 2921-2936, November.
    3. Marta Santagata & Enrico Ivaldi & Riccardo Soliani, 2019. "Development and Governance in the Ex-Soviet Union: An Empirical Inquiry," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 157-190, January.
    4. Courtemanche, Charles & Soneji, Samir & Tchernis, Rusty, 2013. "Modeling Area-Level Health Rankings," IZA Discussion Papers 7631, Institute of Labor Economics (IZA).
    5. Kim, Hea-Jung & Choi, Taeryon & Jo, Seongil, 2016. "Bayesian factor analysis with uncertain functional constraints about factor loadings," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 110-128.
    6. Peter Congdon, 2011. "The Spatial Pattern of Suicide in the US in Relation to Deprivation, Fragmentation and Rurality," Urban Studies, Urban Studies Journal Limited, vol. 48(10), pages 2101-2122, August.
    7. Congdon, Peter, 2009. "Modelling the impact of socioeconomic structure on spatial health outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3047-3056, June.
    8. Robert Sandy & Gilbert Liu & John Ottensmann & Rusty Tchernis & Jeff Wilson & O. T. Ford, 2011. "Studying the Child Obesity Epidemic with Natural Experiments," NBER Chapters, in: Economic Aspects of Obesity, pages 181-221, National Bureau of Economic Research, Inc.
    9. Hoshino, Takahiro, 2008. "A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1413-1429, January.
    10. Stephan Stahlschmidt & Wolfgang Karl Härdle & Helmut Thome, 2014. "An Application of Principal Component Analysis on Multivariate Time-Stationary Spatio-Temporal Data," SFB 649 Discussion Papers SFB649DP2014-016, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    11. Philip A. White & Alan E. Gelfand, 2021. "Multivariate functional data modeling with time-varying clustering," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 586-602, September.
    12. Terrance Savitsky & Daniel McCaffrey, 2014. "Bayesian Hierarchical Multivariate Formulation with Factor Analysis for Nested Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 79(2), pages 275-302, April.
    13. Rubiana Chamarbagwala & Rusty Tchernis, 2006. "The Role of Social Norms in Child Labor and Schooling in India," CAEPR Working Papers 2006-016, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    14. Maura Mezzetti, 2012. "Bayesian factor analysis for spatially correlated data: application to cancer incidence data in Scotland," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 49-74, March.
    15. Riccardo Soliani & Alessia Di Gennaro & Enrico Ivaldi, 2012. "An Index of the Quality of Life for European Countries: Evidence of Deprivation from EU-SILC Data," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 1-14, May.
    16. Phuong T. Vu & Timothy V. Larson & Adam A. Szpiro, 2020. "Probabilistic predictive principal component analysis for spatially misaligned and high‐dimensional air pollution data with missing observations," Environmetrics, John Wiley & Sons, Ltd., vol. 31(4), June.
    17. Peter Congdon, 2008. "The need for psychiatric care in England: a spatial factor methodology," Journal of Geographical Systems, Springer, vol. 10(3), pages 217-239, September.
    18. Qiu, Qihua & Sung, Jaesang & Davis, Will & Tchernis, Rusty, 2018. "Using spatial factor analysis to measure human development," Journal of Development Economics, Elsevier, vol. 132(C), pages 130-149.
    19. Song, Xin-Yuan & Tang, Nian-Sheng & Chow, Sy-Miin, 2012. "A Bayesian approach for generalized random coefficient structural equation models for longitudinal data with adjacent time effects," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4190-4203.
    20. Xiang Luo & Jingjing Qin & Qing Wan & Gui Jin, 2023. "Spatial Human Development Index in China: Measurement and Interpretation Based on Bayesian Estimation," IJERPH, MDPI, vol. 20(1), pages 1-18, January.

    More about this item

    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:taf:specan:v:10:y:2015:i:2:p:160-180. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RSEA20 .

    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.