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Spatial Cluster Estimation and Visualization Using Item Response Theory

In: Handbook of Scan Statistics

Author

Listed:
  • André L. F. Cançado

    (Universidade de Brasília, Statistics Department)

  • Antonio E. Gomes

    (Universidade de Brasília, Statistics Department)

  • Cibele Q. da-Silva

    (Universidade de Brasília, Statistics Department)

  • Fernando L. P. Oliveira

    (Universidade Federal de Ouro Preto, Department of Statistics)

  • Luiz H. Duczmal

    (Universidade Federal de Minas Gerais, Campus Pampulha, Department of Statistics)

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.

Suggested Citation

  • André L. F. Cançado & Antonio E. Gomes & Cibele Q. da-Silva & Fernando L. P. Oliveira & Luiz H. Duczmal, 2024. "Spatial Cluster Estimation and Visualization Using Item Response Theory," Springer Books, in: Joseph Glaz & Markos V. Koutras (ed.), Handbook of Scan Statistics, chapter 31, pages 609-627, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4614-8033-4_38
    DOI: 10.1007/978-1-4614-8033-4_38
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