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Spatial Modelling of Disease Dispersion Using a Local Statistic: The Case of AIDS

  • Arthur Getis
  • J. Keith Ord
Part of the Advanced Studies in Theoretical and Applied Econometrics book series (ASTA, volume 35)

Abstract

In this paper, a statistical test based on a randomization procedure is discussed with regard to the hypothesis that a spatial cluster or hot spot exists. Clusters or “hot spots” represent a greater degree of a specified activity in a region than one would expect by chance. The test can be practically applied in a number of areas where the identification of a cluster may lead to a further understanding of the phenomena’s behavior. In particular, the procedure is well-suited for the identification of disease clusters where sample data may be very large and deviate appreciably from normal form. Paelinck, in his pioneering work on spatial econometrics, made clear that randomization tests possess properties of great power, consistency, and unbiasedness when normal-theory assumptions are true and are statistically efficient for distribution-free situations.

Keywords

Cumulative Incidence Sudden Infant Death Syndrome Critical Distance Spatial Association Spatial Diffusion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Arthur Getis
  • J. Keith Ord

There are no affiliations available

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