Arbitrarily shaped multiple spatial cluster detection for case event data.

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. Po-tential clusters are located by modelling the multiple structural change of the dis-tances 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