Navegando por Autor "Moura, Flávio dos Reis"

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Using the flow of people in cluster detection and inference.
(2013) Ferreira, Sabino José; Oliveira, Francisco S.; Tavares, Ricardo; Moura, Flávio dos Reis
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.
A weighted non-connectivity penalty for detection and inference of irregularly shaped clusters.
(2017) Duarte, Anderson Ribeiro; Silva, Spencer Barbosa da; Oliveira, Fernando Luiz Pereira de; Ribeiro, Marcelo Carlos; Cançado, André Luiz Fernandes; Moura, Flávio dos Reis
Methods for the detection and inference of irregularly shaped geographic clusters with count data are important tools in disease surveillance and epidemiology. Recently, several methods were developed which combine Kulldorff’s Spatial Scan Statistic with some penalty function to control the excessive freedom of shape of spatial clusters. Different penalty functions were conceived based on the cluster geometric shape or on the adjacency structure and non-connectivity of the cluster associated graph. Those penalty function were also implemented using the framework of multi-objective optimization methods. In particular, the non-connectivity penalty was shown to be very effective in cluster detection. Basically, the non-connectivity penalty function relies on the adjacency structure of the cluster’s associated graph but it does not take into account the population distribution within the cluster. Here we introduce a modification of the non-connectivity penalty function, introducing weights in the components of the penalty function according to the cluster population distribution. Our methods is able to identify multiple clusters in the study area. We show through numerical simulations that our weighted non-connectivity penalty function outperforms the original non-connectivity function in terms of power of detection, sensitivity and positive predictive value, also being computationally fast. Both single-objective and multi-objective versions of the algorithm are implemented and compared.