Spatial Regression Analysis Using Eigenvector Spatial Filtering - Softcover

Griffith, Daniel; Chun, Yongwan; Li, Bin

Softcover

ISBN 10: 0128150432 ISBN 13: 9780128150436

Verlag: Academic Press, 2019

Inhaltsangabe

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

Über diesen Titel

Über die Autorinnen und Autoren

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.

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.

Von der hinteren Coverseite

Spatial Regression Analysis Using Eigenvector Spatial Filtering provides both the 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 their georeferenced data analyses. Its appeal is its simplicity. With its flexible structure, ESF can be easily applied to generalized linear regression models as well as linear regression models. Spatial Regression Analysis Using Eigenvector Spatial Filtering discusses ESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, and spatial interaction models. It provides a tutorial for ESF model specification and interfaces with user-friendly software developed by the authors for implementing ESF.

Details zur Ausgabe

• Verlag: Academic Press
• Erscheinungsdatum: 2019
• Sprache: Englisch
• ISBN 10: 0128150432
• ISBN 13: 9780128150436
• Einband: Tapa blanda
• Auflage: 1
• Anzahl der Seiten: 288
• Kontakt zum Hersteller: Nicht verfügbar
• Verantwortliche Person: Yeepin GmbH; 492000000000; sunshine12232@outlook.com; ; Alleestr.11; Kaiserslautern; Rheinland-Pfalz; 67655; Deutschland