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Spatial partitioning using multivariate cluster analysis and a contiguity algorithm

M S Carvalho1, O G Cruz, F F Nobre

  • 1Departamento de Epidemiologia, Escola Nacional de Saúde Pública, Fundacao Oswaldo Cruz, Rua Leopoldo Bulhões, Rio de Janeiro, Brazil.

Statistics in Medicine
|September 15, 1996
PubMed
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This study introduces a novel method for combining small geographical units to stabilize mortality rates. By analyzing socioeconomic indicators, it creates spatially homogeneous areas for reliable epidemiological analysis.

Area of Science:

  • Epidemiology
  • Spatial Analysis
  • Geographic Information Systems (GIS)

Background:

  • Spatial analysis of epidemiological data aids in identifying disease patterns and informing public health strategies.
  • Using small, homogeneous areal units offers high spatial resolution but can lead to spurious variability in disease indicators due to small populations.
  • Stabilizing mortality rates requires methods to pool information across areas with similar risk profiles.

Purpose of the Study:

  • To present an approach for combining small geographical units to stabilize mortality rates.
  • To classify small areas based on socioeconomic indicators to define risk profiles.
  • To create a new partition of geographical regions with internally homogeneous socioeconomic characteristics.

Main Methods:

Related Experiment Videos

  • Principal component analysis (PCA) was employed to analyze socioeconomic indicators.
  • Cluster analysis was used to classify small areas based on their socioeconomic profiles.
  • A custom algorithm aggregated neighboring areas with similar profiles until a target population size was reached.

    • Main Results:

    • The method successfully classified census tracts into four distinct socioeconomic categories in Rio de Janeiro.
    • The aggregation algorithm generated a new set of areal units with preserved internal socioeconomic homogeneity.
    • The approach demonstrated the feasibility of creating stable mortality rates from small, heterogeneous units.

    Conclusions:

    • The described method effectively addresses the challenge of spurious variability in epidemiological indicators from small areas.
    • This approach provides a robust tool for spatial epidemiological analysis, enhancing the reliability of disease pattern identification and control strategies.
    • The developed algorithm facilitates the creation of spatially meaningful and socioeconomically homogeneous units for public health research.