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Using survival analysis to study spatial point patterns in geographical epidemiology.

S Reader1

  • 1University of South Florida, Department of Geography, College of Arts and Sciences, Tampa 33620, USA. streader@luna.cas.usf.edu

Social Science & Medicine (1982)
|March 14, 2000
PubMed
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This study introduces survival analysis as a novel method for analyzing spatial point patterns, offering a valuable alternative to the traditional K-function. This approach overcomes K-function limitations, enhancing the investigation of spatial clustering and random labeling hypotheses.

Area of Science:

  • Spatial statistics
  • Point pattern analysis
  • Survival analysis

Background:

  • The spatial K-function is widely used for detecting spatial clustering in point patterns.
  • K-functions analyze patterns across multiple spatial scales but suffer from data aggregation and information loss.
  • Cumulative counting in K-functions can compromise pattern analysis at different scales due to inter-dependencies.

Purpose of the Study:

  • To propose and evaluate survival analysis as a new method for spatial point pattern analysis.
  • To address the limitations of the K-function, particularly data aggregation and scale dependency.
  • To investigate the 'random labeling' hypothesis using survival analysis on inter-event distances.

Main Methods:

  • Application of survival analysis to inter-event distances in bivariate spatial point patterns.

Related Experiment Videos

  • Utilizing survival analysis, typically used in temporal domains, for spatial data.
  • Comparison with the established K-function approach.

    • Main Results:

    • Survival analysis effectively analyzes spatial point patterns without the data aggregation inherent in K-functions.
    • The proposed method demonstrates utility in controlled data and epidemiological applications.
    • Survival analysis proves to be a valuable complement to K-function analysis.

    Conclusions:

    • Survival analysis offers a robust alternative for spatial point pattern analysis.
    • This method overcomes key limitations of the K-function, improving data interpretation.
    • Survival analysis provides a powerful tool for investigating spatial clustering and hypotheses like random labeling.