Spatial Regression Analysis Using Eigenvector Spatial Filtering



de

, ,

Éditeur :

Academic Press


Paru le : 2019-09-14



eBook Téléchargement ebook sans DRM
Lecture en ligne (streaming)
138,20

Téléchargement immédiat
Dès validation de votre commande
Ajouter à ma liste d'envies
Image Louise Reader présentation

Louise Reader

Lisez ce titre sur l'application Louise Reader.

Description
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
Pages
286 pages
Collection
n.c
Parution
2019-09-14
Marque
Academic Press
EAN papier
9780128150436
EAN EPUB SANS DRM
9780128156926

Prix
138,20 €

Dr. Daniel A. Griffith is an Ashbel Smith Professor Emeritus of Geospatial Information Sciences atthe University of Texas at Dallas, United States; a past affiliated Professor in the College of PublicHealth at the University of South Florida, United States; and an Adjunct Professor in the Departmentof Resource Economics and Environmental Sociology at the University of Alberta, Canada. Hespecializes in spatial statistics, quantitative-urban-economic geography, and urban public health.Yongwan Chun is an Associate Professor of Geospatial Information Sciences at the University of Texas at Dallas. His research interests lie in spatial statistics and GIS, focusing on urban issues, including population movement, environment, health, and crime. His research has been supported by the US National Science Foundation, and the US National Institutes of Health, among others. He has over 50 publications, including books, journal articles, book chapters, and conference proceedings.Today, Dr. Li's research is focused on statistics and machine learning. He has published >75 peer reviewed research papers with >1,300 citations of his work.

Suggestions personnalisées