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Scan Statistics pp 153–177Cite as

Extensions of the Scan Statistic for the Detection and Inference of SpatialClusters

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Part of the book series: Statistics for Industry and Technology ((SIT))

Abstract

Naus’s early 1965 paper [Naus (1965)] on spatial scan statistics paved the way for a considerable amount of research on geographic-based statistical analysis, inspiring intensive work in the most diverse contexts and applications, including epidemiology, syndromic surveillance, criminality and environmental sciences. Following one line of work, several methods for the detection of irregularly shaped clusters were developed. New tools were devised in order to account for the spatial mobility of individuals, the study of the spatial distribution of individuals according to their survival times and multiple data streams from different sources of syndromic counts. Several algorithms explored the utilization of parametric models other than the Poisson or Bernoulli distributions, and also non-parametric and learning models. We expect that this strong trend of application-driven methodologies in spatial scan statistics should continue in the foreseeable future.

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Authors and Affiliations

  1. Statistics Department, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Luiz Duczmal & Anderson Ribeiro Duarte

  2. Department of Mathematics, Universidade Federal de Ouro Preto, Ouro Preto, Brazil

    Ricardo Tavares

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  1. Luiz Duczmal
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  2. Anderson Ribeiro Duarte
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Editors and Affiliations

  1. Dept. Statistics, University of Connecticut, Glenbrook Rd. 215, Storrs, 06269-3120, U.S.A.

    Joseph Glaz

  2. Dept. Statistics, University of Connecticut, Glenbrook Road 215, Storrs, 06269-4120, U.S.A.

    Vladimir Pozdnyakov

  3. Center for Biomathematical Sciences, Mount Sinai School of Medicine, Gustave L Levy Place 1, New York, 10029, U.S.A.

    Sylvan Wallenstein

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© 2009 Birkhäuser Boston, a part of Springer Science+Business Media, LLC

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Duczmal, L., Duarte, A., Tavares, R. (2009). Extensions of the Scan Statistic for the Detection and Inference of SpatialClusters. In: Glaz, J., Pozdnyakov, V., Wallenstein, S. (eds) Scan Statistics. Statistics for Industry and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4749-0_7

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