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A simple test for spatial pattern in regional health data

S D Walter1

  • 1Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada.

Statistics in Medicine
|May 30, 1994
PubMed
Summary
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The rank adjacency statistic D effectively measures spatial autocorrelation in health data. A new normal approximation simplifies significance testing, removing the need for simulations.

Area of Science:

  • Spatial statistics
  • Biostatistics
  • Geographic epidemiology

Background:

  • Spatial autocorrelation is crucial for understanding regional health patterns.
  • The rank adjacency statistic D is a key measure for spatial autocorrelation.
  • Previous methods for testing D's significance required complex simulations.

Purpose of the Study:

  • To derive the mean and approximate variance for the rank adjacency statistic D.
  • To develop a normal approximation for testing the significance of D.
  • To validate the statistic and its approximation using real-world health data.

Main Methods:

  • Derivation of mean and variance for statistic D with general and binary weights.
  • Application of a normal approximation for significance testing.

Related Experiment Videos

  • Empirical analysis using cancer maps and urban/rural residence data.

    • Main Results:

    • The mean and approximate variance of D were successfully derived.
    • A normal approximation demonstrated excellent properties for testing D's significance.
    • The normal approximation eliminates the need for computationally intensive simulations.

    Conclusions:

    • The derived formulas and normal approximation provide a robust method for analyzing spatial autocorrelation in regional health data.
    • This approach simplifies significance testing, making spatial analysis more accessible.
    • The method is validated by its application to diverse datasets, including cancer incidence and population distribution.