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.
“...randomization tests appear to be superior or equal to their parametric counterparts in most cases.”
Jean H. P. Paelinck, Leo H. Klassen, Spatial Econometric, 1979, p. 87
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Getis, A., Ord, J.K. (1998). Spatial Modelling of Disease Dispersion Using a Local Statistic: The Case of AIDS. In: Griffith, D.A., Amrhein, C.G., Huriot, JM. (eds) Econometric Advances in Spatial Modelling and Methodology. Advanced Studies in Theoretical and Applied Econometrics, vol 35. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-2899-6_12
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