
Spatial Regression Analysis Using Eigenvector Spatial Filtering
- 1st Edition - September 14, 2019
- Authors: Daniel Griffith, Yongwan Chun, Bin Li
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 5 0 4 3 - 6
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 1 5 6 9 2 - 6
Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering… Read more

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 models
- Includes computer code and template datasets for further modeling
- Provides comprehensive coverage of related concepts in spatial data analysis and spatial statistics
Graduate students and researchers worldwide working in spatial econometrics, spatial statistics, urban and regional economics, spatial data analysis, and more broadly from geography, GIS science, ecology, regional science, epidemiology and public health, economics, demography, applied statistics, remote sensing, urban and regional planning, transportation, and crime mapping
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
- Edition: 1
- Published: September 14, 2019
- Language: English
DG
Daniel Griffith
YC
Yongwan Chun
BL