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Operational Local Join Count Statistics for Cluster Detection.

Luc Anselin1, Xun Li1

  • 1Center for Spatial Data Science, The University of Chicago, Chicago, IL 60637.

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Summary
This summary is machine-generated.

This study introduces a new spatial analysis method for binary data, offering a conditional local join count statistic. This technique maps clusters of binary variables and their co-locations, useful when all event locations are known.

Keywords:
LISAjoin count statisticmultivariate spatial associationspatial clustersspatial data science

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Area of Science:

  • Spatial statistics
  • Geographic Information Systems (GIS)
  • Urban analytics

Background:

  • Traditional spatial association measures often struggle with binary data.
  • Existing point pattern analysis methods require specific event data, not comprehensive location data.

Purpose of the Study:

  • To operationalize a Local Indicator of Spatial Association (LISA) for binary variables.
  • To develop a conditional local join count statistic for spatial clustering analysis.
  • To extend the statistic for bivariate and multivariate co-location analysis.

Main Methods:

  • Development of a conditional local join count statistic.
  • Extension to bivariate and multivariate contexts, including co-location.
  • Implementation in open-source GeoDa software for spatial data analysis.

Main Results:

  • The method effectively identifies local clusters of binary variables.
  • It successfully maps co-location clusters of multiple binary variables.
  • Empirical examples demonstrate its application in real estate and urban design.

Conclusions:

  • The conditional local join count statistic is a valuable alternative to point pattern methods for binary spatial data.
  • The approach facilitates the mapping of spatial clusters and co-locations.
  • GeoDa software provides a practical tool for implementing these advanced spatial analyses.