Spatial Autocorrelation

A Fundamental Property of Geospatial Phenomena
Erscheinungsjahr 2025
ISBN: 978-0-443-41743-6
Verlag: Elsevier Science

Buch, Englisch, 340 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 450 g

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.

Autoren/Hrsg.

Griffith, Daniel

Li, Bin

Fachgebiete

• Geowissenschaften Geologie Geologie
• Geowissenschaften Geographie | Raumplanung Geodäsie, Kartographie, GIS, Fernerkundung
• Geowissenschaften Geologie GIS, Geoinformatik

Inhaltsverzeichnis

1. What Is Spatial Autocorrelation? A Conceptualization
2. Spatial Autocorrelation Is Everywhere
3. Quantifying Spatial Autocorrelation: An Intuitive Approach with Few Equations
4. Reflections on Spatial Autocorrelation Model Specifications for Beginners
5. Geographic Distributions: Univariate Spatial Autocorrelation
6. Areal Associations: Multivariate Spatial Autocorrelation
7. Spatial Autocorrelation and Spatial Interaction
8. Some Spatial Autocorrelation Final Frontiers: A Partial Future Research Agenda
9. Summary and Concluding Remarks

Über Autor(innen)

Li, Bin
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.

Griffith, Daniel
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.