Using the Flow of People in Cluster Detection and Inference

This work proposes a cluster detection method that adapts the traditional circular scan method, in the snese the proposed method uses the flow of people as a measure of proximity, interaction between regions of a map to identify a set of regions with a high risk of occurrence of some specific event. The flow of people between two regions is estimated by the gravitational method as proportional to the product of their gross domestic product and inversely proportional to the square of the distance between them. The performance of the proposed method was compared with the traditional circular scan simulating clusters from a database of real cases of homicides and also analyzing the real picture. In all simulated cases the proposed techniques overcame the circular scan with better results of detection power, sensibility and positive predictive value, except for regular shaped simulated clusters. When applied to the real situation of homicides cases the spatial flow scan algorithm presented results quite similar to original spatial scan since the detected cluster was regular. In conclusion we consider that the proposed method is a good alternative for detection of irregular and or non-connected clusters