Full Product Details
Author: 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)
Publisher: Elsevier Science Publishing Co Inc
Imprint: Academic Press Inc
Weight: 0.450kg
ISBN: 9780128150436
ISBN 10: 0128150432
Pages: 286
Publication Date: 14 September 2019
Audience:
Professional and scholarly
,
Professional & Vocational
Format: Paperback
Publisher's Status: Active
Availability: Manufactured on demand
We will order this item for you from a manufactured on demand supplier.
Reviews
""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
"""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"
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
Author Information
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.
