Border analysis for spatial clusters.
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Date
2018
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Abstract
Background: The spatial scan statistic is widely used by public health professionals in the detection of spatial clusters
in inhomogeneous point process. The most popular version of the spatial scan statistic uses a circular-shaped scanning
window. Several other variants, using other parametric or non-parametric shapes, are also available. However,
none of them offer information about the uncertainty on the borders of the detected clusters.
Method: We propose a new method to evaluate uncertainty on the boundaries of spatial clusters identified through
the spatial scan statistic for Poisson data. For each spatial data location i, a function F(i) is calculated. While not a probability,
this function takes values in the [0, 1] interval, with a higher value indicating more evidence that the location
belongs to the true cluster.
Results: Through a set of simulation studies, we show that the F function provides a way to define, measure and
visualize the certainty or uncertainty of each specific location belonging to the true cluster. The method can be
applied whether there are one or multiple detected clusters on the map. We illustrate the new method on a data set
concerning Chagas disease in Minas Gerais, Brazil.
Conclusions: The higher the intensity given to an area, the higher the plausibility of that particular area to belong
to the true cluster in case it exists. This way, the F function provides information from which the public health practitioner
can perform a border analysis of the detected spatial scan statistic clusters. We have implemented and illustrated
the border analysis F function in the context of the circular spatial scan statistic for spatially aggregated Poisson
data. The definition is clearly independent of both the shape of the scanning window and the probability model
under which the data is generated. To make the new method widely available to users, it has been implemented in
the freely available SaTScanTM software www.satscan.org.
Description
Keywords
Spatial scan statistic, Cluster delineation, Disease surveillance, Disease mapping
Citation
OLIVEIRA, F. L. P. et al. Border analysis for spatial clusters. International Journal of Health Geographics, v. 17, n. 5, p. 1-10, 2018. Disponível em: <https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-018-0124-1>. Acesso em: 16 jun. 2018.