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The performance of the prospective irregular shaped space-time permutation scan statistics, named mlink-space and mlink space-time, is promising. The proposed methods provide clusters detection in which the shape of the cluster can be irregular in space, and equally important, its spatial extent may shift over time. Another contribution of our proposed methods is its capacity to integrate a wide variety of information during the clustering process. The surveillance analysis can be adapted to specific aspects of the event under study such as social networks, transportation routes, and landscape information. By doing so, it is expected that an Euclidian based surveillance system and our proposed methods may detect different clusters, and both types may be used in parallel.
We first examine a Poisson time point process (TPP) to detect the hot spot and to determine its intensity and then the permanent cluster. We consider a cluster to be permanent if the connected component of the cluster has an area larger than 1.5 times the average point area. We also propose an approximation of the permutation probability of the perimeter of the point cloud. We show that the area of the point cloud obtained with our approach can be different from the real point cloud but still detects the permanent point clusters. d2c66b5586