Spatial scan statistics based on empirical likelihood

Abstract

Spatial cluster is a spatial analysis and mapping technique for cluster identification in spatial phenomena. This work proposes a nonparametric scan method for cluster detection based on the empirical likelihood, and it was compared with the Kulldorff test. The main contribution of this method is that no family of distributions has to be assumed in the analysis. The method is able to deal with the presence of overdispersion, zero inflated and other characteristics that are common in real data. The proposal was evaluated via simulations studies considering data following a zero inflated Poisson distribution. The results show that the proposed method can substantially reduce the probabilities of the error type I for zero inflated data, with low power probabilities for cluster with size less than eight observations. In a case study of measle in São Paulo, Brazil, only the Kulldorff test identified a cluster. It is recommended that a cluster detected by the spatial scan statistic of Kulldorff should be interpreted with caution when it is not confirmed by the empirical likelihood scan statistic.