You are currently viewing an archived version of Topic Global Measures of Spatial Association.
If updates or revisions have been published you can find them at Global Measures of Spatial Association.
Learning Objectives:
Describe the effect of the assumption of stationarity on global measures of spatial association
Justify, compute, and test the significance of the join count statistic for a pattern of objects
Compute the K function
Explain how a statistic that is based on combining all the spatial data and returning a single summary value or two can be useful in understanding broad spatial trends
Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics
Compute Moran’s I and Geary’s c for patterns of attribute data measured on interval/ratio scales
Explain how the K function provides a scale-dependent measure of dispersion
You are currently viewing an archived version of Topic Global Measures of Spatial Association. If updates or revisions have been published you can find them at Global Measures of Spatial Association.
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