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Hidden negative spatial autocorrelation

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  • Daniel Griffith

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Suggested Citation

  • Daniel Griffith, 2006. "Hidden negative spatial autocorrelation," Journal of Geographical Systems, Springer, vol. 8(4), pages 335-355, October.
    • Handle: RePEc:kap:jgeosy:v:8:y:2006:i:4:p:335-355
    DOI: 10.1007/s10109-006-0034-9
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    References listed on IDEAS

    as
    1. 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.
    2. Stirböck, Claudia, 2002. "Explaining the Level of Relative Investment Specialisation: A Spatial Econometric Analysis of EU Regions," ZEW Discussion Papers 02-49, ZEW - Leibniz Centre for European Economic Research.
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    Citations

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    Cited by:

    1. 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.
    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. 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.
    4. Rene Westerholt & Enrico Steiger & Bernd Resch & Alexander Zipf, 2016. "Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-31, September.
    5. Kirillov, Andrew, 2021. "A study on spatial autocorrelation: Case of Russian regional inflation," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 5-22.
    6. 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.
    7. Sauquet, Alexandre & Marchand, Sébastien & Féres, José Gustavo, 2014. "Protected areas, local governments, and strategic interactions: The case of the ICMS-Ecológico in the Brazilian state of Paraná," Ecological Economics, Elsevier, vol. 107(C), pages 249-258.
    8. Millo, Giovanni, 2017. "A simple randomization test for spatial correlation in the presence of common factors and serial correlation," Regional Science and Urban Economics, Elsevier, vol. 66(C), pages 28-38.
    9. Fernando A. López & Pedro J. Martínez-Ortiz & Juan-Gabriel Cegarra-Navarro, 2017. "Spatial spillovers in public expenditure on a municipal level in Spain," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(1), pages 39-65, January.
    10. 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.
    11. Andrea Vaona, 2010. "Spatial autocorrelation and the sensitivity of RESET: a simulation study," Journal of Geographical Systems, Springer, vol. 12(1), pages 89-103, March.
    12. 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.
    13. 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.
    14. Jørgen Lauridsen & Reinhold Kosfeld, 2007. "Spatial cointegration and heteroscedasticity," Journal of Geographical Systems, Springer, vol. 9(3), pages 253-265, September.

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    More about this item

    Keywords

    Eigenvector; Hidden negative spatial autocorrelation; Negative spatial autocorrelation; Spatial autoregressive model; Spatial filter model; C21; R23;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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