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In spatial statistics, eigenvector spatial filtering refers to removing spatial autocorrelation from regression by adding selected eigenvectors of a spatial weights matrix as synthetic covariates that capture map patterns. This meaning comes from Tiefelsdorf (2007) in Environment and Planning A: Economy and Space.[1]
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