Spatial 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.
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
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.
