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Spatial Regression Analysis Using Eigenvector Spatial Filtering
Explains how eigenvector spatial filtering (ESF) provides solutions for linear and non-linear regression model specifications
Daniel Griffith (Author), Yongwan Chun (Author), Bin Li (Author)
9780128150436, Elsevier Science
Paperback, published 14 September 2019
286 pages
22.9 x 15.1 x 1.9 cm, 0.45 kg
"Provides an overview of traditional linear multivariate statistics applied to geospatial data, with an emphasis on SA, its data analytic impacts, and its representation by eigenvector spatial filters. " --Journal of Economic Literature
Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering (MESF) technique. MESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in their georeferenced data analyses. Its appeal is in its simplicity, yet its implementation drawbacks include serious complexities associated with constructing an eigenvector spatial filter. This book discusses MESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, space-time models, and spatial interaction models. Spatial Regression Analysis Using Eigenvector Spatial Filtering is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre.
1. Spatial autocorrelation2. An introduction to spectral analysis3. MESF and linear regression4. Software implementation for constructing an ESF, with special reference to linear regression5. MESF and generalized linear regression6. Modeling spatial heterogeneity with MESF7. Spatial interaction modeling 8. Space-time modeling9. MESF and multivariate statistical analysis10. Concluding comments: Toy dataset implementation demonstrations
