Week 8: Spatial Autocorrelation¶

Core Questions¶

• When are nearby observations more similar than expected by chance?
• How does spatial dependence affect statistical inference?
• How do neighborhood definitions shape spatial autocorrelation results?

Concepts¶

• Spatial dependence.
• Spatial autocorrelation.
• Moran's I.
• Geary's C.
• Local Moran's I.
• Hotspots.
• Spatial weights and neighborhood definitions.

Python Tools¶

• libpysal.
• esda.
• GeoPandas.

Applied Lab¶

Students calculate global and local Moran's I for an ecological or environmental variable and map spatial clusters, outliers, and hotspots.

Deliverable¶

Submit an autocorrelation report explaining the spatial weights used, the global statistic, local patterns, and ecological interpretation.