Daniel Griffith, Yongwan Chun, Bin Li

Spatial Regression Analysis Using Eigenvector Spatial Filtering

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Academic Press / Elsevier Science & Technology
• Artikelnr. des Verlages: C2017-0-01015-7
• Englisch
• Abmessung: 13mm x 152mm x 229mm
• Gewicht: 450g
• ISBN-13: 9780128150436
• Artikelnr.: 57148962

Autorenporträt

Daniel A. Griffith is an Ashbel Smith Professor of Geospatial Information Sciences at the University of Texas at Dallas, affiliated professor in the College of Public Health at the University of South Florida, and adjunct professor in the Department of Resource Economics and Environmental Sociology at the University of Alberta. He holds degrees in Mathematics, Statistics, and Geography, and arguably is the inventor of Moran eigenvector spatial filtering. He is a two-time Fulbright Senior Specialist, an AAG Distinguished Research Honors awardee, and an elected fellow of the Royal Society of Canada, UCGIS, AAG, American Association for the Advancement of Science, American Statistical Association, Regional Science Association International, and Spatial Econometrics Association.

Inhaltsangabe

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

Rezensionen

"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