You are currently viewing an archived version of Topic Point Pattern Analysis.
If updates or revisions have been published you can find them at Point Pattern Analysis.
Learning Objectives:
List the conditions that make point pattern analysis a suitable process
Identify the various ways point patterns may be described
Identify various types of K-function analysis
Describe how Independent Random Process/Chi-Squared Result (IRP/CSR) may be used to make statistical statements about point patterns
Outline measures of pattern based on first and second order properties such as the mean center and standard distance, quadrat counts, nearest neighbor distance, and the more modern G, F, and K functions
Outline the basis of classic critiques of spatial statistical analysis in the context of point pattern analysis
Explain how distance-based methods of point pattern measurement can be derived from a distance matrix
Explain how proximity polygons (e.g., Thiessen polygons) may be used to describe point patterns
Explain how the K function provides a scale-dependent measure of dispersion
Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics
You are currently viewing an archived version of Topic Point Pattern Analysis. If updates or revisions have been published you can find them at Point Pattern Analysis.
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