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Spatial Cluster Estimation and Visualization using Item Response Theory

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Handbook of Scan Statistics

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Abstract

In recent years Kulldorff’s circular scan statistic has become the most popular tool for detecting spatial clusters. However, window-imposed limitation may not be appropriate to detect the true cluster. To work around this problem we usually use complex tools that allow the detection of clusters with arbitrary format, but at the expense of an increase in computational effort. In this chapter we describe a methodology that assists the detection of unconnected and arbitrarily shaped clusters and that provides a measure of uncertainty in the design of such clusters.

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Author information

Authors and Affiliations

  1. Statistics Department, Universidade de Brasília, Campus Darcy Ribeiro, 70910-900, Brasília, DF, Brazil

    André L. F. Cançado, Antonio E. Gomes & Cibele Q. da-Silva

  2. Statistics Department, Universidade Federal de Ouro Preto, Campus Morro do Cruzeiro, 35400-000, Ouro Preto, MG, Brazil

    Fernando L. P. Oliveira

  3. Statistics Department, Campus Pampulha, Universidade Federal de Minhas Gerais, Belo Horizonte, MG, Brazil

    Luiz H. Duczmal

Authors
  1. André L. F. Cançado
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  2. Antonio E. Gomes
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  3. Cibele Q. da-Silva
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  4. Fernando L. P. Oliveira
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  5. Luiz H. Duczmal
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Corresponding author

Correspondence to André L. F. Cançado .

Editor information

Editors and Affiliations

  1. University of Connecticut Dept. Statistics, Storrs, Connecticut, USA

    Joseph Glaz

  2. University of Pireaus Dept. Statistics & Insurance Sciences, Piraeus, Greece

    Markos V. Koutras

Section Editor information

  1. Department of Statistics, University of Connecticut, 215 Glenbrook Road, U-4120, 06269-4120, Storrs, CT, USA

    Joseph Glaz

  2. Dept. of Statistics and Insurance Science, University of Piraeus, 18534, Piraeus, Greece

    Markos V. Koutras

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Cançado, A.L.F., Gomes, A.E., da-Silva, C.Q., Oliveira, F.L.P., Duczmal, L.H. (2017). Spatial Cluster Estimation and Visualization using Item Response Theory. In: Glaz, J., Koutras, M. (eds) Handbook of Scan Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8414-1_38-1

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  • DOI: https://doi.org/10.1007/978-1-4614-8414-1_38-1

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-8414-1

  • Online ISBN: 978-1-4614-8414-1

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