title: Measuring the spatial accuracy of the spatial scan statistic.
abstract: The spatial scan statistic is well established in spatial epidemiology. However, studies of its spatial accuracy are infrequent and vary in approach, often using multiple measures which complicate the objective ranking of different implementations of the statistic. We address this with three novel contributions. Firstly, a modular framework into which different definitions of spatial accuracy can be compared and hybridised. Secondly, we derive a new single measure, Ω, which takes account of all true and detected clusters, without the need for arbitrary weightings and irrespective of any chosen significance threshold. Thirdly, we demonstrate the new measure, alongside existing ones, in a study of the six output filter options provided by SaTScan™. The study suggests filtering overlapping detected clusters tends to reduce spatial accuracy, and visualising overlapping clusters may be better than filtering them out. Although we only address spatial accuracy, the framework and Ω may be extendible to spatio-temporal accuracy.
authors: Information School, University of Sheffield, Sheffield S1 4DP, UK. simon.read@sheffield.ac.uk
authors: Simon
authors: S
authors: Read
authors: Peter
authors: P
authors: Bath
authors: Peter
authors: P
authors: Willett
authors: Ravi
authors: R
authors: Maheswaran
mesh_terms: D016000:Cluster Analysis; D003198:Computer Simulation / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D062665:Epidemiological Monitoring; D062306:Geography, Medical / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software / Q000592:standards*; D062206:Spatial Analysis; D006113:United Kingdom / Q000453:epidemiology