The scan statistic is widely used in spatial cluster detection applications of inhomogeneous Poisson processes. The most popular variant of the spatial scan is the circular scan. However, such approach has several limitations, in particular, the circular window is not suitable to make the correct description of irregularly shaped and/or unconnected clusters. Additionally, such methodology does not incorporate the tools needed for quantifying the uncertainty in the description of the most likely cluster in the analysis. In the present work we build upon the previously proposed methodology called intensity function a more efficient and accurate way of defining the uncertainty in the identification of spatial clusters using Item Response Theory ideas. Using simulated data we show that the proposed method can correctly identify primary, secondary and irregular clusters.
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The authors are deeply indebted to CAPES and to CNPq, Brazil, for financial support via Projects PROCAD-NF 2008 and 459535/2014-5, respectively. Cibele Q. da-Silva and Luiz Duczmal were supported by CNPq-Brazil, BPPesq.
Handling Editor: Pierre Dutilleul.
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Cançado, A.L.F., Gomes, A.E., da-Silva, C.Q. et al. An Item Response Theory approach to spatial cluster estimation and visualization. Environ Ecol Stat 23, 435–451 (2016). https://doi.org/10.1007/s10651-016-0347-x
- Inhomogeneous Poisson process
- Irregularly shaped spatial clusters
- Item Response Theory
- Scan statistic