Daniel Griffith, Yongwan Chun, Bin Li

Spatial Regression Analysis Using Eigenvector Spatial Filtering (eBook, ePUB)

• Format: ePub

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. - 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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 286
• Erscheinungstermin: 14. September 2019
• Englisch
• ISBN-13: 9780128156926
• Artikelnr.: 61689541

Autorenporträt

Dr. Daniel A. Griffith is an Ashbel Smith Professor Emeritus of Geospatial Information Sciences at
the University of Texas at Dallas, United States; a past affiliated Professor in the College of Public
Health at the University of South Florida, United States; and an Adjunct Professor in the Department
of Resource Economics and Environmental Sociology at the University of Alberta, Canada. He
specializes in spatial statistics, quantitative-urban-economic geography, and urban public health.

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