In order to help researchers and practitioners use eigenvector spatial filtering (ESF), which makes spatial statistics more accessible to very many users, Chun and Griffith (both geospatial information sciences, U. of Texas-Dallas) compare geostatistics, spatial autoregression, and ESF analyses. They also include code in the R open-source statistics software for implementing the analysis. The topics include spatial autocorrelation, spatial composition and configuration, spatially adjusted regression and related spatial econometrics, analyzing spatial variance and covariance with geostatistics and related techniques, and more advanced topics in spatial statistics. Annotation ©2013 Book News, Inc., Portland, OR (booknews.com)