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Screening For Spatial Dependence In Regression Analysis

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  • Arthur Getis

Abstract

ABSTRACT A technique of analysis is presented that is designed to circumvent the problem of finding wasy to estimate parameters of spatially stochastic independent variables. It is based on 1) a type of second‐order analysis that describes the spatial association among weighted observations, and 2) a screening procedure that removes most of the spatial dependence in the dependent variable. The approach is illustrated by a study of the incidence of certain crimes in 49 districts of Columbus, Ohio. It is concluded that spatial justaposition of observations plays a large role in regression analyses that are based on spatial series.

Suggested Citation

  • Arthur Getis, 1990. "Screening For Spatial Dependence In Regression Analysis," Papers in Regional Science, Wiley Blackwell, vol. 69(1), pages 69-81, January.
    • Handle: RePEc:bla:presci:v:69:y:1990:i:1:p:69-81
    DOI: 10.1111/j.1435-5597.1990.tb01204.x
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    Cited by:

    1. Reinhold Kosfeld & Christian Dreger & Hans-Friedrich Eckey, 2008. "On the stability of the German Beveridge curve: a spatial econometric perspective," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(4), pages 967-986, December.
    2. Roberto Patuelli & Norbert Schanne & Daniel A. Griffith & Peter Nijkamp, 2012. "Persistence Of Regional Unemployment: Application Of A Spatial Filtering Approach To Local Labor Markets In Germany," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 300-323, May.
    3. Olivier Thomas, 2001. "A few evidences about the current growth of French cities," ERSA conference papers ersa01p4, European Regional Science Association.
    4. 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.
    5. Julie Le Gallo, 2000. "Spatial econometrics (1, Spatial autocorrelation) [Econométrie spatiale (1, Autocorrélation spatiale)]," Working Papers hal-01527290, HAL.
    6. Harald Badinger & Thomas Url, 2002. "Determinants of regional unemployment: some evidence from Austria," Regional Studies, Taylor & Francis Journals, vol. 36(9), pages 977-988.
    7. Jiwattanakulpaisarn, Piyapong & Noland, Robert B. & Graham, Daniel J., 2010. "Causal linkages between highways and sector-level employment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(4), pages 265-280, May.
    8. Sergio Rey & Brett Montouri, 1999. "US Regional Income Convergence: A Spatial Econometric Perspective," Regional Studies, Taylor & Francis Journals, vol. 33(2), pages 143-156.
    9. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2006. "The Use of Spatial Filtering Techniques: The Spatial and Space-time Structure of German Unemployment Data," Tinbergen Institute Discussion Papers 06-049/3, Tinbergen Institute.
    10. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2011. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," International Regional Science Review, , vol. 34(2), pages 253-280, April.
    11. Philip Salesses & Katja Schechtner & César A Hidalgo, 2013. "The Collaborative Image of The City: Mapping the Inequality of Urban Perception," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-12, July.
    12. 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.
    13. LE GALLO, Julie, 2000. "Econométrie spatiale 1 -Autocorrélation spatiale," LATEC - Document de travail - Economie (1991-2003) 2000-05, LATEC, Laboratoire d'Analyse et des Techniques EConomiques, CNRS UMR 5118, Université de Bourgogne.
    14. repec:asg:wpaper:1013 is not listed on IDEAS
    15. Buendía Azorín, José Daniel & Sánchez de la Vega, María del Mar, 2017. "Output growth thresholds for the creation of employment and the reduction of unemployment: A spatial analysis with panel data from the Spanish provinces, 2000–2011," Regional Science and Urban Economics, Elsevier, vol. 67(C), pages 42-49.
    16. Postiglione, Paolo & Benedetti, Roberto & Lafratta, Giovanni, 2010. "A regression tree algorithm for the identification of convergence clubs," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2776-2785, November.
    17. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    18. 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.

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