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Detection of temporal changes in the spatial distribution of cancer rates using local Moran’s I and geostatistically simulated spatial neutral models


  • Pierre Goovaerts


  • Geoffrey M. Jacquez



This paper presents the first application of spatially correlated neutral models to the detection of changes in mortality rates across space and time using the local Moran’s I statistic. Sequential Gaussian simulation is used to generate realizations of the spatial distribution of mortality rates under increasingly stringent conditions: 1) reproduction of the sample histogram, 2) reproduction of the pattern of spatial autocorrelation modeled from the data, 3) incorporation of regional background obtained by geostatistical smoothing of observed mortality rates, and 4) incorporation of smooth regional background observed at a prior time interval. The simulated neutral models are then processed using two new spatio-temporal variants of the Moran’s I statistic, which allow one to identify significant changes in mortality rates above and beyond past spatial patterns. Last, the results are displayed using an original classification of clusters/outliers tailored to the space-time nature of the data. Using this new methodology the space-time distribution of cervix cancer mortality rates recorded over all US State Economic Areas (SEA) is explored for 9 time periods of 5 years each. Incorporation of spatial autocorrelation leads to fewer significant SEA units than obtained under the traditional assumption of spatial independence, confirming earlier claims that Type I errors may increase when tests using the assumption of independence are applied to spatially correlated data. Integration of regional background into the neutral models yields substantially different spatial clusters and outliers, highlighting local patterns which were blurred when local Moran’s I was applied under the null hypothesis of constant risk. Copyright Springer-Verlag Berlin Heidelberg 2005

Suggested Citation

  • Pierre Goovaerts & Geoffrey M. Jacquez, 2005. "Detection of temporal changes in the spatial distribution of cancer rates using local Moran’s I and geostatistically simulated spatial neutral models," Journal of Geographical Systems, Springer, vol. 7(1), pages 137-159, October.
  • Handle: RePEc:kap:jgeosy:v:7:y:2005:i:1:p:137-159
    DOI: 10.1007/s10109-005-0154-7

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    References listed on IDEAS

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    7. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
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    9. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
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    Cited by:

    1. 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.
    2. Nica, M., 2010. "Small Business Clusters in Oklahoma: MAR or Jacobs Effects?," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 10(2).
    3. Qiang Cai & Gerard Rushton & Budhendra Bhaduri, 2012. "Validation tests of an improved kernel density estimation method for identifying disease clusters," Journal of Geographical Systems, Springer, vol. 14(3), pages 243-264, July.

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