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SPATCLUS: an R package for arbitrarily shaped multiple spatial cluster detection for case event data.

Christophe Demattei1, Nicolas Molinari, Jean-Pierre Daurès

  • 1Laboratoire de biostatistique, d'Epidémiologie et de Santé Publique, UFR Médecine Site Nord IURC, 641 Avenue du Doyen Gaston Giraud, 34093 Montpellier Cedex 5, France. demattei@iurc.montp.inserm.fr

Computer Methods and Programs in Biomedicine
|September 12, 2006
PubMed
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This study introduces SPATCLUS, an R package for spatial cluster detection in case event data. It identifies multiple clusters of any shape using a novel trajectory-based data transformation and advanced statistical methods.

Area of Science:

  • Spatial statistics
  • Computational epidemiology
  • Geographic information systems

Background:

  • Accurate spatial cluster detection is crucial for understanding disease patterns and resource allocation.
  • Existing methods may struggle with detecting multiple clusters or clusters of arbitrary shapes.

Purpose of the Study:

  • To introduce the SPATCLUS R package for spatial cluster detection.
  • To implement a novel method for identifying spatial clusters in case event data.
  • To enable the detection of multiple clusters irrespective of their shape.

Main Methods:

  • A trajectory-based data transformation is employed, assigning selection order and nearest neighbor distances to each point.
  • Distances are weighted by expected values under a uniform distribution hypothesis.

Related Experiment Videos

  • Multiple structural change models, a dynamic programming algorithm, and the double maximum test are used for cluster identification.
  • Monte Carlo simulations assess the statistical significance of detected clusters.

    • Main Results:

    • The SPATCLUS package successfully implements the proposed spatial cluster detection method.
    • The method demonstrates the capability to detect multiple spatial clusters.
    • The approach is effective for clusters of any shape, overcoming limitations of traditional methods.

    Conclusions:

    • SPATCLUS provides a robust tool for spatial cluster detection in public health and epidemiology.
    • The trajectory-based method enhances the ability to find complex spatial patterns in event data.
    • This approach offers a significant advancement in identifying geographically localized disease occurrences.