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.

• | Author: Daniel Griffith, Yongwan Chun, Bin Li
• | Publisher: Academic Press
• | Publication Date: Sep 14, 2019
• | Number of Pages: 286 pages
• | Language: English
• | Binding: Paperback
• | ISBN-10: 0128150432
• | ISBN-13: 9780128150436