Spatial Regression Analysis Using Eigenvector Spatial Filtering

— —

Daniel A. Griffith (University of Texas at Dallas, Texas, USA) Yongwan Chun (University of Texas at Dallas, Texas, USA)

Spatial Regression Analysis Using Eigenvector Spatial Filtering

Daniel A. Griffith (University of Texas at Dallas, Texas, USA), Yongwan Chun (University of Texas at Dallas, Texas, USA), Bin Li (Department of Experimental Statistics Louisiana State University Baton Rouge, Louisiana, USA)

$247.95

Paperback

Not in-store but you can order this
How long will it take?

Availability Information

We source books from suppliers in Australia and overseas. For books we don't currently have in stock, the time it takes to get them from our suppliers can vary widely - from a few days to a few months - so we check each book with each supplier to determine the expected time it will take to be supplied to us.

We then advise you accordingly. If the time taken to get any book is too long for you, you can let us know and we will cancel or adjust your order, and refund as required.

To find out the anticipated arrival time for specific items prior to ordering, please contact us by phone or email:

Phone +61 2 9264 3111, or 1800 4 BOOKS (1800 4 26657) if outside Sydney:
option 1 Abbey's Bookshop (Crime, History, Science, Kids & more) • info@abbeys.com.au
option 2 Language Book Centre (ESL & Foreign Languages) • language@abbeys.com.au
option 3 Galaxy Bookshop (Sci-fi, Fantasy, Romance, Graphic Novels) • sf@galaxybooks.com.au

QTY:

English

Academic Press Inc
14 September 2019

Data analysis: general; Urban economics; Geographical information systems (GIS) & remote sensing; Mathematical & statistical software

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.

By: Daniel A. Griffith (University of Texas at Dallas Texas USA), Yongwan Chun (University of Texas at Dallas, Texas, USA), Bin Li (Department of Experimental Statistics Louisiana State University Baton Rouge, Louisiana, USA)
Imprint: Academic Press Inc
Country of Publication: United States
Dimensions: Height: 229mm, Width: 151mm,
Weight: 450g
ISBN: 9780128150436
ISBN 10: 0128150432
Pages: 286
Publication Date: 14 September 2019
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Daniel A. Griffith is an Ashbel Smith Professor of Geospatial Information Sciences at the University of Texas at Dallas, affiliated professor in the College of Public Health at the University of South Florida, and adjunct professor in the Department of Resource Economics and Environmental Sociology at the University of Alberta. He holds degrees in Mathematics, Statistics, and Geography, and arguably is the inventor of Moran eigenvector spatial filtering. He is a two-time Fulbright Senior Specialist, an AAG Distinguished Research Honors awardee, and an elected fellow of the Royal Society of Canada, UCGIS, AAG, American Association for the Advancement of Science, American Statistical Association, Regional Science Association International, and Spatial Econometrics Association. 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.

Reviews for Spatial Regression Analysis Using Eigenvector Spatial Filtering

"""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"