Multi-Distance Spatial Cluster Analysis (Ripleys K Function) determines whether feature locations show significant clustering or dispersion. However, unlike the other spatial pattern tools that we've examined in this section, it does not take the value at a location into account. It only determines clustering by the location of the features. This tool is often used in fields like environmental studies, health care, and crime, where you are attempting to determine if one feature attracts another feature. Also, keep in mind that a dataset can be both clustered and dispersed as seen in the line graph on this slide. We'll examine what this means in more detail on the next slide.
In this exercise, you'll use the Multi-Distance Spatial Cluster Analysis tool to analyze the clustering or dispersion of burglaries.
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