Overview
Spatial Autocorrelation: A Fundamental Property of Geospatial Sciences is an in-depth guide to understanding a crucial aspect of spatial analysis. The book begins with theories and clear definitions, laying a solid foundation for the reader. Through detailed explanations and practical examples, it delves into the concept and theory of spatial autocorrelation, illustrating the significance of spatial patterns in scientific research. The book includes comprehensive case studies that highlight the impact of spatial patterns on research and suggests innovative techniques for future studies. Additionally, it offers practical methodologies for quantifying spatial autocorrelation, complete with step-by-step guidance and real-world applications. This makes it an essential resource for graduate students, researchers, and professionals, providing them with the necessary tools to effectively apply spatial analysis in various fields.
Full Product Details
Author: Daniel Griffith (Professor of Geospatial Information Sciences, University of Texas at Dallas, Texas, USA) ,
Bin Li (Louisiana State University, USA)
Publisher: Elsevier - Health Sciences Division
Imprint: Elsevier - Health Sciences Division
Weight: 0.450kg
ISBN: 9780443417436
ISBN 10: 0443417431
Pages: 340
Publication Date: 01 August 2025
Audience:
Professional and scholarly
,
College/higher education
,
Professional & Vocational
,
Postgraduate, Research & Scholarly
Format: Paperback
Publisher's Status: Forthcoming
Availability: Not yet available
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.
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. Dr Li is a Professor at Central Michigan U. in the US, where he was the former chair of the Department of Geography and Environmental Studies. His previous position was at U. of Miami. He specializes in Geographic Information Science with research and teaching experiences in Spatial Statistics, Geographic Information Services, and Cartography. His recent journal publications and presentations focus on information redundancy in big data, visualization of spatial structures, and regression modeling with large spatial data sets. He authored three books on spatial statistics, and edited several books in GIScience. He serves on editorial boards of several academic journals, including the Annals of AAG and Geospatial Information Science.
