Spatial Regression Analysis Using Eigenvector Spatial Filtering

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.

Reviews the uses of ESF across linear regression, generalized linear regression, spatial autocorrelation measurement, and spatially varying coefficient modelsIncludes computer code and template datasets for further modelingProvides comprehensive coverage of related concepts in spatial data analysis and spatial statistics

1. Spatial autocorrelation

2. An introduction to spectral analysis

3. MESF and linear regression

4. Software implementation for constructing an ESF, with special reference to linear regression

5. MESF and generalized linear regression

6. Modeling spatial heterogeneity with MESF

7. Spatial interaction modeling

8. Space-time modeling

9. MESF and multivariate statistical analysis

10. Concluding comments: Toy dataset implementation demonstrations