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Spatial Autocorrelation
A Fundamental Property of Geospatial Sciences
- 1st Edition - June 1, 2025
- Authors: Daniel Griffith, Bin Li
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 4 1 7 4 3 - 6
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 4 1 7 4 4 - 3
Spatial Autocorrelation: A Fundamental Property of Geospatial Phenomena offers a comprehensive exploration of one of the most critical concepts in spatial analysis. Beginning with… Read more
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Request a sales quoteSpatial Autocorrelation: A Fundamental Property of Geospatial Phenomena offers a comprehensive exploration of one of the most critical concepts in spatial analysis. Beginning with foundational theories and clear definitions, this book thoroughly sets out the concepts and theory of spatial autocorrelation through detailed conceptualisation and practical examples. The detailed case studies illustrate the pervasive influence of spatial patterns in scientific inquiry, with an eye toward future research and innovative techniques. It provides practical methodologies for quantifying spatial autocorrelation, complete with step-by-step guidance and real-world applications. Spatial Autocorrelation equips graduate students, researchers, and professionals with the knowledge and tools to confidently navigate and apply spatial analysis in their respective domains, making it a vital addition to a number of disciplines that utilise spatial analysis.
• Explores a fundamental geospatial concept through a blend of multidisciplinary topics
• Provides an educational focus on spatial autocorrelation in a conceptually-friendly manner
• Offers a progressive, iterative layout spanning basic to advanced concepts
• Emphasizes novel as well as benchmark empirical examples and simulation experiments
• Covers in-depth spatial autocorrelation in various disciplines, being nascent in some of them
• Provides an educational focus on spatial autocorrelation in a conceptually-friendly manner
• Offers a progressive, iterative layout spanning basic to advanced concepts
• Emphasizes novel as well as benchmark empirical examples and simulation experiments
• Covers in-depth spatial autocorrelation in various disciplines, being nascent in some of them
Geospatial students, scientists and professionals, as well as any scholars involved in spatial autocorrelation as a fundamental property of georeferenced data
Chapter 1: What Is Spatial Autocorrelation? A Conceptualization
Chapter 2: Spatial Autocorrelation Is Everywhere
Chapter 3: Quantifying Spatial Autocorrelation: An Intuitive Approach with Few Equations
Chapter 4: Reflections on Spatial Autocorrelation Model Specifications for Beginners
Chapter 5: Geographic Distributions: Univariate Spatial Autocorrelation
Chapter 6: Areal Associations: Multivariate Spatial Autocorrelation
Chapter 7: Spatial Autocorrelation and Spatial Interaction
Chapter 8: Some Spatial Autocorrelation Final Frontiers: A Partial Future Research Agenda
Chapter 9: Summary and Concluding Remarks
Chapter 2: Spatial Autocorrelation Is Everywhere
Chapter 3: Quantifying Spatial Autocorrelation: An Intuitive Approach with Few Equations
Chapter 4: Reflections on Spatial Autocorrelation Model Specifications for Beginners
Chapter 5: Geographic Distributions: Univariate Spatial Autocorrelation
Chapter 6: Areal Associations: Multivariate Spatial Autocorrelation
Chapter 7: Spatial Autocorrelation and Spatial Interaction
Chapter 8: Some Spatial Autocorrelation Final Frontiers: A Partial Future Research Agenda
Chapter 9: Summary and Concluding Remarks
- No. of pages: 340
- Language: English
- Edition: 1
- Published: June 1, 2025
- Imprint: Elsevier
- Paperback ISBN: 9780443417436
- eBook ISBN: 9780443417443
DG
Daniel Griffith
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.
Affiliations and expertise
University of Texas at Dallas, Texas, USABL
Bin Li
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.