Environmental and Ecological Statistics

, Volume 25, Issue 2, pp 257–275 | Cite as

Multiple source spatial cluster detection via multi-criteria analysis

  • Alexandre C. L. Almeida
  • Luiz H. Duczmal
  • André L. F. CançadoEmail author
  • Fabio R. da Silva


Multiple data sources are essential to provide reliable information regarding the emergence of potential health threats, compared to single source methods. Spatial Scan Statistics have been adapted to analyze multivariate data sources, but only ad hoc procedures have been devised to address the problem of selecting the most likely cluster and computing its significance. In this work, information from multiple data sources of disease surveillance is incorporated to achieve more coherent spatial cluster detection using tools from multi-criteria analysis. The best cluster solutions are found by maximizing two objective functions simultaneously, based on the concept of dominance. To evaluate the statistical significance of solutions, a statistical approach based on the concept of attainment function is used. The multi-criteria approach has several advantages: the representation of the evaluation function for each data source is clear, and does not suffer from an artificial, and possibly confusing mixture with the other data source evaluations; it is possible to attribute, in a rigorous way, the statistical significance of each candidate cluster; and it is possible to analyze and pick-up the best cluster solutions, as given naturally by the non-dominated set. The methodology is illustrated with real datasets.


Attainment surface Multi-criteria Multiple data sources Spatial scan statistic 



The authors are deeply indebted to CNPq (Project 311710/2016-6), CAPES and FAPEMIG (PPM-00596-17), Brazil, for financial support.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Alexandre C. L. Almeida
    • 1
  • Luiz H. Duczmal
    • 2
  • André L. F. Cançado
    • 3
    Email author
  • Fabio R. da Silva
    • 4
  1. 1.Department of Physics and MathematicsUniversidade Federal de São João del ReiSão João del ReiBrazil
  2. 2.Department of StatisticsUniversidade Federal de Minas GeraisBelo HorizonteBrazil
  3. 3.Department of StatisticsUniversidade de BrasíliaBrasíliaBrazil
  4. 4.Department of Computer ScienceCentro Federal de Educação Tecnológica de Minas GeraisBelo HorizonteBrazil

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