Summary
This week concludes the review of linear algebra and focused more heavily on applications of matrix operations in the context of spatial analysis and GIScience. We first reviewed how to compute eigen values and eigen vectors using fundamental operations from previous weeks. Next, the object was presented and operations such as row standardization (normalization) described. Spatial regression and more specifically spatially lagged variables were then briefly described with the goal of illustrating the use of matrices in a spatial analysis context. This theme of broad application was also applied to the Moran's I case study where a spatial weights matrix plays a key role.
Additional References
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