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Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data.

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This study adapts the Gini coefficient for spatial scan statistics on ordinal data to optimize maximum cluster size detection. The modified Gini coefficient accurately identifies optimal cluster sizes, enhancing spatial cluster pattern discovery.

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Area of Science:

  • Spatial statistics
  • Geographic information systems
  • Ordinal data analysis

Background:

  • Spatial scan statistic is crucial for detecting spatial clusters.
  • Research on scanning window shapes is extensive, but maximum cluster size optimization is less explored.
  • Existing Gini coefficient methods for cluster size optimization are limited to the Poisson model.

Purpose of the Study:

  • To adapt the Gini coefficient for spatial scan statistics on ordinal data.
  • To determine the optimal maximum reported cluster size for ordinal data.
  • To evaluate the performance of the modified Gini coefficient approach.

Main Methods:

  • Adoption and modification of the Gini coefficient for ordinal data.
  • Application of the modified Gini coefficient to spatial scan statistics.
  • Evaluation through simulation studies and a real-world data example.

Main Results:

  • The Gini coefficient was effectively adapted for the ordinal model with modifications.
  • The Gini coefficient accurately identified optimal maximum reported cluster sizes, often matching or underestimating true sizes.
  • The approach demonstrated high accuracy in selecting appropriate cluster sizes.

Conclusions:

  • The modified Gini coefficient is a valuable tool for optimizing maximum reported cluster size in spatial scan statistics for ordinal data.
  • This method allows for more refined cluster detection and informative discovery of spatial patterns.
  • The approach enhances the utility of spatial scan statistics for analyzing ordinal spatial data.