Workshop materials available here:

https://github.com/yohman/workshoppy...

Visual interpretations are meaningful ways to determine spatial trends in our data. However, underlying factors—such as inconsistent geographies, scale, data gaps, overlapping data—have the potential to produce incorrect assumptions, as valuable information may be conveniently hidden from the visual output.

One way to address this issue is to amend your visual output with geostatistical validation. In this workshop, we will use Python to look at one such approach: Spatial Autocorrelation.