Spatial Autocorrelation
Content for Monday, October 30, 2023
Now that we’ve learned about the power of spatial autocorrelation for interpolation from point data, it’s time to explore methods for spatial autocorrelation with areal data. We’ll have to define neighbors because distance is a little more ambiguous here and then look at some global and local measures of autocorrelation.
Resources
Ch. 7: Spatial Neighborhood Matrices in from Paula Moraga’s new book Spatial Statistics for Data Science: Theory and Practice with
Rgives a little gentler introduction to spatial neighbors specifically in the context of statistical models.Chapter 14 Proximity and Areal Data in Spatial Data Science by Edzer Pebesma and Roger Bivand provides explanations of how the
spdeppackage can be used to construct neighborhood weights.
Spatial Autocorrelation in R provides some easy code for working through neighbors with areal data and calculating spatial autocorrelation measures.
Objectives
By the end of today you should be able to:
Use the
spdeppackage to identify the neighbors of a given polygon based on proximity, distance, and minimum numberUnderstand the underlying mechanics of Moran’s I and calculate it for various neighbors
Distinguish between global and local measures of spatial autocorrelation
Visualize neighbors and clusters