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Testing for Local Spatial Autocorrelation in the Presence of Global Autocorrelation


  • J. Keith Ord
  • Arthur Getis


A fundamental concern of spatial analysts is to find patterns in spatial data that lead to the identification of spatial autocorrelation or association. Further, they seek to identify peculiarities in the data set that signify that something out of the ordinary has occurred in one or more regions. In this paper we provide a statistic that tests for local spatial autocorrelation in the presence of the global autocorrelation that is characteristic of heterogeneous spatial data. After identifying the structure of global autocorrelation, we introduce a new measure that may be used to test for local structure. This new statistic Oi is asymptotically normally distributed and allows for straightforward tests of hypotheses. We provide several numerical examples that illustrate the performance of this statistic and compare it with another measure that does not account for global structure. Copyright 2001 Blackwell Publishers

Suggested Citation

  • J. Keith Ord & Arthur Getis, 2001. "Testing for Local Spatial Autocorrelation in the Presence of Global Autocorrelation," Journal of Regional Science, Wiley Blackwell, vol. 41(3), pages 411-432.
  • Handle: RePEc:bla:jregsc:v:41:y:2001:i:3:p:411-432

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    Cited by:

    1. Elena Kotyrlo, 2013. "Stationarity conditions for the spatial first-order and serial second-order model," Letters in Spatial and Resource Sciences, Springer, vol. 6(1), pages 19-29, March.
    2. Mark D. Partridge & Marlon Boarnet & Steven Brakman & Gianmarco Ottaviano, 2012. "Introduction: Whither Spatial Econometrics?," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 167-171, May.
    3. repec:spr:soinre:v:135:y:2018:i:2:d:10.1007_s11205-016-1497-9 is not listed on IDEAS
    4. Bivand, Roger & Müller, Werner G. & Reder, Markus, 2009. "Power calculations for global and local Moran's," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2859-2872, June.
    5. Yongmei Lu & Jean-Claude Thill, 2008. "Cross-scale analysis of cluster correspondence using different operational neighborhoods," Journal of Geographical Systems, Springer, vol. 10(3), pages 241-261, September.
    6. repec:eee:ecomod:v:209:y:2007:i:2:p:264-276 is not listed on IDEAS
    7. Zhang, Tonglin & Lin, Ge, 2007. "A decomposition of Moran's I for clustering detection," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6123-6137, August.
    8. Herrera Gómez, Marcos, 2013. "Análisis de Estructuras Espaciales Persistentes. Desempleo Departamental en Argentina
      [Persistent Spatial Structure Analysis. Regional Unemployment in Argentina]
      ," MPRA Paper 49407, University Library of Munich, Germany.
    9. Grubesic, Tony H., 2006. "A spatial taxonomy of broadband regions in the United States," Information Economics and Policy, Elsevier, vol. 18(4), pages 423-448, November.
    10. Ron Johnston & Michael Poulsen & James Forrest, 2009. "Using Local Statistics to Portray Ethnic Residential Segregation in London," The Centre for Market and Public Organisation 09/213, Department of Economics, University of Bristol, UK.
    11. Barry Boots, 2006. "Local configuration measures for categorical spatial data: binary regular lattices," Journal of Geographical Systems, Springer, vol. 8(1), pages 1-24, March.
    12. ERTUR, Cem & KOCH, Wilfried, 2004. "Analyse spatiale des disparités régionales dans l'Europe élargie," LEG - Document de travail - Economie 2004-03, LEG, Laboratoire d'Economie et de Gestion, CNRS, Université de Bourgogne.

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