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Negative Spatial Autocorrelation: One of the Most Neglected Concepts in Spatial Statistics

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  • Daniel A. Griffith

    (School of Economic, Political, and Policy Sciences, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080, USA)

Abstract

Negative spatial autocorrelation is one of the most neglected concepts in quantitative geography, regional science, and spatial statistics/econometrics in general. This paper focuses on and contributes to the literature in terms of the following three reasons why this neglect exists: Existing spatial autocorrelation quantification, the popular form of georeferenced variables studied, and the presence of both hidden negative spatial autocorrelation, and mixtures of positive and negative spatial autocorrelation in georeferenced variables. This paper also presents details and insights by furnishing concrete empirical examples of negative spatial autocorrelation. These examples include: Multi-locational chain store market areas, the shrinking city of Detroit, Dallas-Fort Worth journey-to-work flows, and county crime data. This paper concludes by enumerating a number of future research topics that would help increase the literature profile of negative spatial autocorrelation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jstats:v:2:y:2019:i:3:p:27-415:d:258088
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    References listed on IDEAS

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    1. E Saliby & R J Paul, 2009. "A farewell to the use of antithetic variates in Monte Carlo simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(7), pages 1026-1035, July.
    2. Daniel Griffith, 2006. "Hidden negative spatial autocorrelation," Journal of Geographical Systems, Springer, vol. 8(4), pages 335-355, October.
    3. 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.
    4. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    5. 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.
    6. Daniel A. Griffith, 2013. "Better Articulating Normal Curve Theory for Introductory Mathematical Statistics Students: Power Transformations and Their Back-Transformations," The American Statistician, Taylor & Francis Journals, vol. 67(3), pages 157-169, August.
    7. Daniel A. Griffith & David W. S. Wong & Thomas Whitfield, 2003. "Exploring Relationships Between the Global and Regional Measures of Spatial Autocorrelation," Journal of Regional Science, Wiley Blackwell, vol. 43(4), pages 683-710, November.
    8. Hodges, James S. & Reich, Brian J., 2010. "Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love," The American Statistician, American Statistical Association, vol. 64(4), pages 325-334.
    9. Schelling, Thomas C, 1969. "Models of Segregation," American Economic Review, American Economic Association, vol. 59(2), pages 488-493, May.
    10. Daniel A. Griffith, 2003. "Spatial Autocorrelation and Spatial Filtering," Advances in Spatial Science, Springer, number 978-3-540-24806-4, Fall.
    11. Yongwan Chun & Daniel 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.
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    Cited by:

    1. Daniel A. Griffith & Yongwan Chun, 2022. "Some useful details about the Moran coefficient, the Geary ratio, and the join count indices of spatial autocorrelation," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-30, December.
    2. Uğur Ursavaş & Carlos Mendez, 2023. "Regional income convergence and conditioning factors in Turkey: revisiting the role of spatial dependence and neighbor effects," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 71(2), pages 363-389, October.
    3. Daniel A. Griffith, 2020. "A Family of Correlated Observations: From Independent to Strongly Interrelated Ones," Stats, MDPI, vol. 3(3), pages 1-19, June.

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