Arbitrarily shaped multiple spatial cluster detection for case event data

Authors:
Christophe Dematteı;Nicolas Molinari;Jean-Pierre Daurès
Affiliations:
Laboratoire de biostatistique, d'épidémiologie et de santé publique, UFR Médecine Site NORD UPM/IURC, 640 avenue du Doyen Gaston Giraud, 34295 Montpellier Cedex 5, France;Laboratoire de biostatistique, d'épidémiologie et de santé publique, UFR Médecine Site NORD UPM/IURC, 640 avenue du Doyen Gaston Giraud, 34295 Montpellier Cedex 5, France;Laboratoire de biostatistique, d'épidémiologie et de santé publique, UFR Médecine Site NORD UPM/IURC, 640 avenue du Doyen Gaston Giraud, 34295 Montpellier Cedex 5, France
Venue:
Computational Statistics & Data Analysis
Year:
2007

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Abstract

An original method is proposed for spatial cluster detection of case event data. A selection order and the distance from the nearest neighbour are attributed to each point, once pre-selected points have been taken into account. This distance is weighted by the expected distance under the uniform distribution hypothesis. Potential clusters are located by modelling the multiple structural change of the distances on the selection order and the best model (containing one or several potential clusters) is selected using the double maximum test. Finally a p-value is obtained for each potential cluster. With this method multiple clusters of any shape can be detected.