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. 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.