摘要
This paper summarizes a few spatial statistical analysis methods for to measuring spatial autocorrelation and spatial association, discusses the criteria for the identification of spatial association by the use of global Moran Coefficient, Local Moran and Local Geary. Furthermore, a user-friendly statistical module, combining spatial statistical analysis methods with GIS visual techniques, is developed in Arcview using Avenue. An example is also given to show the usefulness of this module in identifying and quantifying the underlying spatial association patterns between economic units.
This paper summarizes a few spatial statistical analysis methods for tomeasuring spatial autocorrelation and spatial association, discusses the criteria for theidentification of spatial association by the use of global Moran Coefficient, Local Moran and LocalGeary. Furthermore, a user-friendly statistical module, combining spatial statistical analysismethods with GIS visual techniques, is developed in Arcview using Avenue. An example is also givento show the usefulness of this module in identifying and quantifying the underlying spatialassociation patterns between economic units.
基金
FundedbytheNationalNaturalScienceFoundationofChina (N0 .40 4 0 1 0 2 1 )andtheNationalSocialScienceFoundationofChina (N0 .0 4CJL0 1 9)
