Spatial Autocorrelation
A Fundamental Property of Geospatial Phenomena
- 1st Edition - August 1, 2025
- Imprint: Elsevier
- 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. Beginn… Read more
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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.
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 via a blend of multidisciplinary topics
- Provides an educational focus in a conceptually friendly manner
- Offers a progressive, iterative layout, spanning basic to advanced concepts
- Emphasizes novel as well as benchmark empirical and simulation examples
- Covers 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
1.1 The jigsaw puzzle metaphor: an accessible pedagogic tool
1.2 A formal definition and meaning of spatial autocorrelation
1.3 Zero spatial autocorrelation
1.4 Positive spatial autocorrelation
1.5 Negative spatial autocorrelation
1.6 Positive–negative spatial autocorrelation mixtures: an innovative perspective
1.7 Dénouement of this chapter
References
Chapter 2
Spatial autocorrelation is everywhere!
2.1 Beyond the jigsaw puzzle: other imperfect spatial autocorrelation metaphors
2.2 Spatial autocorrelation in the popular media: common and widespread occurrences
2.3 Spatial autocorrelation in nature: spatially correlated phenomena in the wild
2.4 Spatial autocorrelation in academia: the natural and social sciences
2.5 Spatial autocorrelation in the medical sciences: epidemiology and beyond
2.6 Spatial autocorrelation in engineering: integrating technology and self-correlation
2.7 Spatial autocorrelation in real estate: everyday spatial dependencies–doing the awkward math!
2.8 Spatial autocorrelation in art and music: a cultural emergence
2.9 Spatial autocorrelation implicit in government regulations: unlikely partners
2.10 Geography: the foundation of spatial autocorrelation
2.11 Urban planning: a natural spatial autocorrelation receptacle
2.12 Dénouement of this chapter
References
Chapter 3
Quantifying spatial autocorrelation: an intuitive approach with few equations
3.1 From Pearson correlation to spatial autocorrelation
3.2 Formal quantifications of spatial autocorrelation: Moran coefficient, Geary ratio, join counts, and their interrelationships
3.3 Spatial weights matrix eigenfunctions: a deeper dive
3.4 Semivariogram models: an elementary overview
3.5 Local spatial autocorrelation statistics: an introduction
3.6 Dénouement of this chapter
References
Chapter 4
Reflections on spatial autocorrelation model specifications for beginners
4.1 Revisiting spatial weights matrix and geostatistical model specifications
4.2 A critical review of auto-model specifications
4.3 Sui-models: a new mechanism for embedding spatial autocorrelation in probability functions
4.4 Dénouement of this chapter
Appendix 4A: An implicit overdispersion in Bernoulli random variables
References
Chapter 5
Geographic distributions: univariate spatial autocorrelation
5.1 Spatial autocorrelation: catalyzing geography’s first quantitative revolution
5.2 Normal versus nonnormal georeferenced data: assessing the validity of assumptions
5.3 Variance in georeferenced random variables: homogeneity, stationarity, and stability
5.4 Heterogeneous spatial autocorrelation: from global patterns to local clusters
5.5 Impacts of spatial autocorrelation on random variable properties
5.6 Dénouement of this chapter
References
Chapter 6
Areal associations: multivariate spatial autocorrelation
6.1 Spatial autocorrelation and covarying geographic distributions
6.2 Spatial autocorrelation, multicollinearity, and dimensions of geographic distributions: principal components and factor analysis
6.3 Spatial autocorrelation, multicollinearity, and areal differentiation: multivariate analysis of variance and discriminant function analysis
6.4 Spatial autocorrelation and geographic gradients: canonical correlation
6.5 Spatial autocorrelation and regionalization: cluster analysis
6.6 Dénouement of this chapter
Appendix 6A: A Moran eigenvector spatial filtering linear correlation coefficient decomposition example
Appendix 6B: Evaluating an enhanced vegetation spectral index
Appendix 6C: Simultaneous autoregressive model parameter estimation with the rook’s adjacency definition
References
Chapter 7
Spatial autocorrelation and spatial interaction
7.1 Transitioning from inertia to movement in geospatial academic studies
7.2 A brief historical overview
7.3 A metaanalysis of spatial autocorrelation in journey-to-work flows
7.4 An empirical example: Chicago journey-to-work flows
7.5 Pathways from intractable to tractable solutions
7.6 Dénouement of this chapter
Appendix 7A: Tallies of 2020 census tracts for the largest 36 metropolitan statistical areas
References
Chapter 8
Some spatial autocorrelation final frontiers: a partial future research agenda
8.1 Spatial autocorrelation and art
8.2 Spatial autocorrelation and spatial optimization
8.3 Dénouement of this chapter and a consolidated research agenda
References
Chapter 9
Summary and concluding remarks
9.1 Conceptual foundation constituents
9.2 Conclusions
9.3 Dénouement of this chapter
What is spatial autocorrelation? A conceptualization
1.1 The jigsaw puzzle metaphor: an accessible pedagogic tool
1.2 A formal definition and meaning of spatial autocorrelation
1.3 Zero spatial autocorrelation
1.4 Positive spatial autocorrelation
1.5 Negative spatial autocorrelation
1.6 Positive–negative spatial autocorrelation mixtures: an innovative perspective
1.7 Dénouement of this chapter
References
Chapter 2
Spatial autocorrelation is everywhere!
2.1 Beyond the jigsaw puzzle: other imperfect spatial autocorrelation metaphors
2.2 Spatial autocorrelation in the popular media: common and widespread occurrences
2.3 Spatial autocorrelation in nature: spatially correlated phenomena in the wild
2.4 Spatial autocorrelation in academia: the natural and social sciences
2.5 Spatial autocorrelation in the medical sciences: epidemiology and beyond
2.6 Spatial autocorrelation in engineering: integrating technology and self-correlation
2.7 Spatial autocorrelation in real estate: everyday spatial dependencies–doing the awkward math!
2.8 Spatial autocorrelation in art and music: a cultural emergence
2.9 Spatial autocorrelation implicit in government regulations: unlikely partners
2.10 Geography: the foundation of spatial autocorrelation
2.11 Urban planning: a natural spatial autocorrelation receptacle
2.12 Dénouement of this chapter
References
Chapter 3
Quantifying spatial autocorrelation: an intuitive approach with few equations
3.1 From Pearson correlation to spatial autocorrelation
3.2 Formal quantifications of spatial autocorrelation: Moran coefficient, Geary ratio, join counts, and their interrelationships
3.3 Spatial weights matrix eigenfunctions: a deeper dive
3.4 Semivariogram models: an elementary overview
3.5 Local spatial autocorrelation statistics: an introduction
3.6 Dénouement of this chapter
References
Chapter 4
Reflections on spatial autocorrelation model specifications for beginners
4.1 Revisiting spatial weights matrix and geostatistical model specifications
4.2 A critical review of auto-model specifications
4.3 Sui-models: a new mechanism for embedding spatial autocorrelation in probability functions
4.4 Dénouement of this chapter
Appendix 4A: An implicit overdispersion in Bernoulli random variables
References
Chapter 5
Geographic distributions: univariate spatial autocorrelation
5.1 Spatial autocorrelation: catalyzing geography’s first quantitative revolution
5.2 Normal versus nonnormal georeferenced data: assessing the validity of assumptions
5.3 Variance in georeferenced random variables: homogeneity, stationarity, and stability
5.4 Heterogeneous spatial autocorrelation: from global patterns to local clusters
5.5 Impacts of spatial autocorrelation on random variable properties
5.6 Dénouement of this chapter
References
Chapter 6
Areal associations: multivariate spatial autocorrelation
6.1 Spatial autocorrelation and covarying geographic distributions
6.2 Spatial autocorrelation, multicollinearity, and dimensions of geographic distributions: principal components and factor analysis
6.3 Spatial autocorrelation, multicollinearity, and areal differentiation: multivariate analysis of variance and discriminant function analysis
6.4 Spatial autocorrelation and geographic gradients: canonical correlation
6.5 Spatial autocorrelation and regionalization: cluster analysis
6.6 Dénouement of this chapter
Appendix 6A: A Moran eigenvector spatial filtering linear correlation coefficient decomposition example
Appendix 6B: Evaluating an enhanced vegetation spectral index
Appendix 6C: Simultaneous autoregressive model parameter estimation with the rook’s adjacency definition
References
Chapter 7
Spatial autocorrelation and spatial interaction
7.1 Transitioning from inertia to movement in geospatial academic studies
7.2 A brief historical overview
7.3 A metaanalysis of spatial autocorrelation in journey-to-work flows
7.4 An empirical example: Chicago journey-to-work flows
7.5 Pathways from intractable to tractable solutions
7.6 Dénouement of this chapter
Appendix 7A: Tallies of 2020 census tracts for the largest 36 metropolitan statistical areas
References
Chapter 8
Some spatial autocorrelation final frontiers: a partial future research agenda
8.1 Spatial autocorrelation and art
8.2 Spatial autocorrelation and spatial optimization
8.3 Dénouement of this chapter and a consolidated research agenda
References
Chapter 9
Summary and concluding remarks
9.1 Conceptual foundation constituents
9.2 Conclusions
9.3 Dénouement of this chapter
- Edition: 1
- Published: August 1, 2025
- Imprint: Elsevier
- Language: English
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
Ashbel Smith Professor EmeritusBL
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.
Affiliations and expertise
Central Michigan University, United States