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Exploring Relationships Between the Global and Regional Measures of Spatial Autocorrelation

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  • Daniel A. Griffith
  • David W. S. Wong
  • Thomas Whitfield

Abstract

The objective of this research is to investigate dimensions of geographic variation in spatial dependency contained within large multilevel data sets. We calculate 1990 population density by census block group, county, and state for the 48 coterminous states and the District of Columbia of the United States, calculations of interest to a wide variety of spatial scientists. We explore relations between these levels and their variation across the nation. The empirical findings generated by this work furnish implications concerning the Modifiable Areal Unit Problem (MAUP), spatial autocorrelation statistics, scale effects, and resolution.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jregsc:v:43:y:2003:i:4:p:683-710
    DOI: 10.1111/j.0022-4146.2003.00316.x
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    Cited by:

    1. Giuseppe Arbia & Francesca Petrarca, 2011. "Effects of MAUP on spatial econometric models," Letters in Spatial and Resource Sciences, Springer, vol. 4(3), pages 173-185, October.
    2. Behr, Andreas & Schiwy, Christoph & Hong, Lucy, 2022. "Impact of Agglomeration Economies on Regional Performance in Germany," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 52(1), May.
    3. Richard Henry Rijnks & Sierdjan Koster & Philip McCann, 2019. "The Neighbour’s Effect on well‐Being: How Local Relative Income Differentials Affect Resident's Subjective Well‐Being," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 110(5), pages 605-621, December.
    4. Athukorala, Wasantha & Martin, Wade & Wilson, Clevo & Rajapaksa, Darshana, 2019. "Valuing bushfire risk to homeowners: Hedonic property values study in Queensland, Australia," Economic Analysis and Policy, Elsevier, vol. 63(C), pages 44-56.
    5. Wasantha Athukorala & Wade Martin & Prasad Neelawala & Darshana Rajapaksa & Clevo Wilson, 2016. "Impact Of Wildfires And Floods On Property Values: A Before And After Analysis," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 61(01), pages 1-23, March.
    6. Joseph Karanja & Lawrence M. Kiage, 2022. "Scale implications and evolution of a social vulnerability index in Atlanta, Georgia, USA," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(1), pages 789-812, August.
    7. H. S. Chang & T. L. Chen & H. T. Cheng, 2018. "Comparing the spatial patterns of earthquake disaster probability and individual risk perception: a case study of Yongkang Township in Tainan, Taiwan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(3), pages 1589-1610, September.
    8. Daniel Griffith & David Wong, 2007. "Modeling population density across major US cities: a polycentric spatial regression approach," Journal of Geographical Systems, Springer, vol. 9(1), pages 53-75, April.
    9. Daniel Griffith, 2006. "Hidden negative spatial autocorrelation," Journal of Geographical Systems, Springer, vol. 8(4), pages 335-355, October.
    10. 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.

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