IDEAS home Printed from https://ideas.repec.org/a/kap/jgeosy/v18y2016i1d10.1007_s10109-015-0225-3.html
   My bibliography  Save this article

Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters

Author

Listed:
  • Yongwan Chun

    (The University of Texas at Dallas)

  • Daniel A. Griffith

    (The University of Texas at Dallas)

  • Monghyeon Lee

    (The University of Texas at Dallas)

  • Parmanand Sinha

    (The University of Tennessee)

Abstract

Because eigenvector spatial filtering (ESF) provides a relatively simple and successful method to account for spatial autocorrelation in regression, increasingly it has been adopted in various fields. Although ESF can be easily implemented with a stepwise procedure, such as traditional stepwise regression, its computational efficiency can be further improved. Two major computational components in ESF are extracting eigenvectors and identifying a subset of these eigenvectors. This paper focuses on how a subset of eigenvectors can be efficiently and effectively identified. A simulation experiment summarized in this paper shows that, with a well-prepared candidate eigenvector set, ESF can effectively account for spatial autocorrelation and achieve computational efficiency. This paper further proposes a nonlinear equation for constructing an ideal candidate eigenvector set based on the results of the simulation experiment.

Suggested Citation

  • Yongwan Chun & Daniel A. Griffith & Monghyeon Lee & Parmanand Sinha, 2016. "Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters," Journal of Geographical Systems, Springer, vol. 18(1), pages 67-85, January.
    • Handle: RePEc:kap:jgeosy:v:18:y:2016:i:1:d:10.1007_s10109-015-0225-3
    DOI: 10.1007/s10109-015-0225-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10109-015-0225-3
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10109-015-0225-3?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. Daniel A Griffith, 2004. "A Spatial Filtering Specification for the Autologistic Model," Environment and Planning A, , vol. 36(10), pages 1791-1811, October.
    2. Jesús Crespo Cuaresma & Martin Feldkircher, 2013. "Spatial Filtering, Model Uncertainty And The Speed Of Income Convergence In Europe," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(4), pages 720-741, June.
    3. Aurélien Fichet de Clairfontaine & Manfred Fischer & Rafael Lata & Manfred Paier, 2015. "Barriers to cross-region research and development collaborations in Europe: evidence from the fifth European Framework Programme," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(2), pages 577-590, March.
    4. Christoph Grimpe & Roberto Patuelli, 2011. "Regional knowledge production in nanomaterials: a spatial filtering approach," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 46(3), pages 519-541, June.
    5. Yongwan Chun, 2008. "Modeling network autocorrelation within migration flows by eigenvector spatial filtering," Journal of Geographical Systems, Springer, vol. 10(4), pages 317-344, December.
    6. Wang, Yiyi & Kockelman, Kara M. & Wang, Xiaokun (Cara), 2013. "Understanding spatial filtering for analysis of land use-transport data," Journal of Transport Geography, Elsevier, vol. 31(C), pages 123-131.
    7. R. Kelley Pace & James P. Lesage & Shuang Zhu, 2013. "Interpretation and Computation of Estimates from Regression Models using Spatial Filtering," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 352-369, September.
    8. John Hughes & Murali Haran, 2013. "Dimension reduction and alleviation of confounding for spatial generalized linear mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 139-159, January.
    9. Michael Tiefelsdorf & Daniel A Griffith, 2007. "Semiparametric Filtering of Spatial Autocorrelation: The Eigenvector Approach," Environment and Planning A, , vol. 39(5), pages 1193-1221, May.
    10. repec:rim:rimwps:29-08 is not listed on IDEAS
    11. Daniel A. Griffith, 2003. "Spatial Autocorrelation and Spatial Filtering," Advances in Spatial Science, Springer, number 978-3-540-24806-4, Fall.
    12. Joachim Möller & Rüdiger Soltwedel, 2007. "Recent Developments of Regional Labor Market Analysis Using Spatial Econometrics: Introduction," International Regional Science Review, , vol. 30(2), pages 95-99, April.
    13. Tony Smith & Ka Lee, 2012. "The effects of spatial autoregressive dependencies on inference in ordinary least squares: a geometric approach," Journal of Geographical Systems, Springer, vol. 14(1), pages 91-124, January.
    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. Rodolfo Metulini & Roberto Patuelli & Daniel A. Griffith, 2018. "A Spatial-Filtering Zero-Inflated Approach to the Estimation of the Gravity Model of Trade," Econometrics, MDPI, vol. 6(1), pages 1-15, February.
    2. Lan Hu & Yongwan Chun & Daniel A. Griffith, 2020. "Uncovering a positive and negative spatial autocorrelation mixture pattern: a spatial analysis of breast cancer incidences in Broward County, Florida, 2000–2010," Journal of Geographical Systems, Springer, vol. 22(3), pages 291-308, July.
    3. Jingyi Zhang & Bin Li & Yumin Chen & Meijie Chen & Tao Fang & Yongfeng Liu, 2018. "Eigenvector Spatial Filtering Regression Modeling of Ground PM 2.5 Concentrations Using Remotely Sensed Data," IJERPH, MDPI, vol. 15(6), pages 1-24, June.
    4. Oshan, Taylor M., 2020. "The spatial structure debate in spatial interaction modeling: 50 years on," OSF Preprints 42vxn, Center for Open Science.
    5. Lan Hu & Daniel A. Griffith & Yongwan Chun, 2018. "Space-Time Statistical Insights about Geographic Variation in Lung Cancer Incidence Rates: Florida, USA, 2000–2011," IJERPH, MDPI, vol. 15(11), pages 1-18, October.
    6. Donegan, Connor & Chun, Yongwan & Hughes, Amy E., 2020. "Bayesian estimation of spatial filters with Moran's eigenvectors and hierarchical shrinkage priors," OSF Preprints fah3z, Center for Open Science.
    7. Wenwen Sun & Daisuke Murakami & Xin Hu & Zhuoran Li & Akari Nakai Kidd & Chunlu Liu, 2023. "Supply–Demand Imbalance in School Land: An Eigenvector Spatial Filtering Approach," Sustainability, MDPI, vol. 15(17), pages 1-14, August.
    8. Daniel A. Griffith & Yongwan Chun & Jan Hauke, 2022. "A Moran eigenvector spatial filtering specification of entropy measures," Papers in Regional Science, Wiley Blackwell, vol. 101(1), pages 259-279, February.
    9. Daniel A. Griffith, 2019. "Negative Spatial Autocorrelation: One of the Most Neglected Concepts in Spatial Statistics," Stats, MDPI, vol. 2(3), pages 1-28, August.

    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. Daisuke Murakami & Daniel Griffith, 2015. "Random effects specifications in eigenvector spatial filtering: a simulation study," Journal of Geographical Systems, Springer, vol. 17(4), pages 311-331, October.
    2. Oshan, Taylor M., 2020. "The spatial structure debate in spatial interaction modeling: 50 years on," OSF Preprints 42vxn, Center for Open Science.
    3. Donegan, Connor & Chun, Yongwan & Hughes, Amy E., 2020. "Bayesian estimation of spatial filters with Moran's eigenvectors and hierarchical shrinkage priors," OSF Preprints fah3z, Center for Open Science.
    4. Paula Margaretic & Christine Thomas-Agnan & Romain Doucet, 2017. "Spatial dependence in (origin-destination) air passenger flows," Papers in Regional Science, Wiley Blackwell, vol. 96(2), pages 357-380, June.
    5. Rafael Lata & Sidonia Proff & Thomas Brenner, 2018. "The influence of distance types on co-patenting and co-publishing in the USA and Europe over time," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 61(1), pages 49-71, July.
    6. Moniruzzaman, Md & Páez, Antonio, 2012. "Accessibility to transit, by transit, and mode share: application of a logistic model with spatial filters," Journal of Transport Geography, Elsevier, vol. 24(C), pages 198-205.
    7. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    8. Clément Gorin, 2016. "Patterns and determinants of inventors' mobility across European urban areas," Working Papers halshs-01313086, HAL.
    9. Christoph Hammer & Aurélien Fichet de Clairfontaine, 2016. "Trade Costs and Income in European Regions," Department of Economics Working Papers wuwp220, Vienna University of Economics and Business, Department of Economics.
    10. Doris A. Oberdabernig & Stefan Humer & Jesus Crespo Cuaresma, 2018. "Democracy, Geography and Model Uncertainty," Scottish Journal of Political Economy, Scottish Economic Society, vol. 65(2), pages 154-185, May.
    11. Daniel A. Griffith & Manfred M. Fischer, 2016. "Constrained Variants of the Gravity Model and Spatial Dependence: Model Specification and Estimation Issues," Advances in Spatial Science, in: Roberto Patuelli & Giuseppe Arbia (ed.), Spatial Econometric Interaction Modelling, chapter 0, pages 37-66, Springer.
    12. Aurélien Fichet de Clairfontaine & Manfred Fischer & Rafael Lata & Manfred Paier, 2015. "Barriers to cross-region research and development collaborations in Europe: evidence from the fifth European Framework Programme," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(2), pages 577-590, March.
    13. Mitze, Timo & Strotebeck, Falk, 2017. "Modeling interregional research collaborations in German biotechnology using industry directory data: A quantitative social network analysis," MPRA Paper 83392, University Library of Munich, Germany.
    14. Qing Luo & Daniel A. Griffith & Huayi Wu, 2019. "Spatial autocorrelation for massive spatial data: verification of efficiency and statistical power asymptotics," Journal of Geographical Systems, Springer, vol. 21(2), pages 237-269, June.
    15. Oshan, Taylor M., 2022. "Spatial Interaction Modeling," OSF Preprints m3ah8, Center for Open Science.
    16. Dieter von Fintel & Eldridge Moses, 2017. "Migration and gender in South Africa: following bright lights and the fortunes of others?," Working Papers 09/2017, Stellenbosch University, Department of Economics, revised 2018.
    17. Mitze, Timo & Strotebeck, Falk, 2019. "Determining factors of interregional research collaboration in Germany's biotech network: Capacity, proximity, policy?," Technovation, Elsevier, vol. 80, pages 40-53.
    18. Daniele Fabbri & Silvana Robone, 2010. "The geography of hospital admission in a national health service with patient choice," Health Economics, John Wiley & Sons, Ltd., vol. 19(9), pages 1029-1047, September.
    19. Christoph Grimpe & Roberto Patuelli, 2011. "Regional knowledge production in nanomaterials: a spatial filtering approach," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 46(3), pages 519-541, June.
    20. Giuseppe Ricciardo Lamonica & Barbara Zagaglia, 2013. "The determinants of internal mobility in Italy, 1995-2006," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(16), pages 407-440.

    More about this item

    Keywords

    Spatial filtering; Spatial autocorrelation; Eigenvectors; Spatial autoregressive regression;
    All these keywords.

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

    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:kap:jgeosy:v:18:y:2016:i:1:d:10.1007_s10109-015-0225-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.