The spatial scan statistic is the usual measure of strength of a cluster [1]. Another important measure is its geometric regularity [2]. A genetic multiobjective algorithm was developed elsewhere to identify irregularly shaped clusters [3]. A search is executed aiming to maximize two objectives, namely the scan statistic and the regularity of shape (using the compactness concept). The solution presented is a Pareto-set, consisting of all the clusters found which are not simultaneously worse in both objectives. A significance evaluation is conducted in parallel for all clusters in the Pareto-set through Monte Carlo simulation, then finding the most likely cluster. \
Objective
Situations where a disease cluster does not have a regular shape are fairly common. Moreover, maps with multiple clustering, when there is not a clearly dominating primary cluster, also occur frequently. We would like to develop a method to analyze more thoroughly the several levels of clustering that arise naturally in a disease map divided into m regions.