Spatial Regression Analysis Using Eigenvector Spatial Filtering

Spatial Regression Analysis Using Eigenvector Spatial Filtering provides both theoretical foundations and guidance on practical implementation for the eigenvector spatial filtering (ESF) technique. ESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in georeferenced data analyses. With its flexible structure, ESF can be easily applied to generalized linear regression models. The book discusses ESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, and spatial interaction models. In addition, it provides a tutorial for ESF model specification and interfaces, including author developed, user-friendly software. 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 INDICE: 1. Spatial Autocorrelation 2. An Introduction to Spectral Analysis 3. Eigenvector Spatial Filteirng and Linear Regression 4. Software Implementation for Constructing ESFs 5. Eigenvector Spatial Filtering and Generalized Linear Regression 6. Modeling Spatial Heterogeneoyu with an ESF 7. Local Spatial Autocorrelation 8. Space-time Modeling

• ISBN: 978-0-12-815043-6
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 432
• Fecha Publicación: 01/08/2019
• Nº Volúmenes: 1
• Idioma: Inglés