The traditional SaTScan algorithm[1],[2] uses the euclidean dis- tance between centroids of the regions in a map to assemble a con- nected (in the sense that two connected regions share a physical border) sets of regions. According to the value of the respective log- arithm of the likelihood ratio (LLR) a connected set of regions can be classified as a statistically significant detected cluster. Considering the study of events like contagious diseases or homicides we con- sider using the flow of people between two regions in order to build up a set of regions (zone) with high incidence of cases of the event. In this sense the regions will be closer as the greater the flow of peo- ple between them. In a cluster of regions formed according to the cri- terion of proximity due to the flow of people, the regions will be not necessarily connected to each other.
Objective
We present a new approach to the circular scan method [1] that uses the flow of people to detect and infer clusters of regions with high incidence of some event randomly distributed in a map. We use a real database of homicides cases in Minas Gerais state, in south- east Brazil to compare our proposed method with the original circu- lar scan method in a study of simulated clusters and the real situation.