Spatial Regression Analysis Using Eigenvector Spatial Filtering 1st edition by Daniel Griffith, Yongwan Chun, Bin Li ISBN 0128150432 978-0128150436
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ISBN 10: 0128150432
ISBN 13: 978-0128150436
Author: Daniel Griffith, Yongwan Chun, Bin Li
Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering (MESF) technique. MESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in their georeferenced data analyses. Its appeal is in its simplicity, yet its implementation drawbacks include serious complexities associated with constructing an eigenvector spatial filter.
This book discusses MESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, space-time models, and spatial interaction models. Spatial Regression Analysis Using Eigenvector Spatial Filtering is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre.
- Reviews the uses of ESF across linear regression, generalized linear regression, spatial autocorrelation measurement, and spatially varying coefficient models
- Includes computer code and template datasets for further modeling
- Provides comprehensive coverage of related concepts in spatial data analysis and spatial statistics
Spatial Regression Analysis Using Eigenvector Spatial Filtering 1st Table of contents:
Chapter 1: Spatial autocorrelation
Abstract
1.1 Defining SA
1.2 Impacts of SA on attribute statistical distributions
1.3 Summary
Appendix 1.A The mean and variance of the MC for linear regression residuals
Chapter 2: An introduction to spectral analysis
Abstract
2.1 Representing SA in the spectral domain
2.2 The spectral analysis of one-dimensional data
2.3 The spectral analysis of two-dimensional data
2.4 The spectral analysis of three-dimensional data
2.5 Summary
Appendix 2.A The spectral decomposition of a SWM
Chapter 3: MESF and linear regression
Abstract
3.1 A theoretical foundation for ESFs
3.2 Estimating an ESF as an OLS problem: An illustrative linear regression example
3.3 Simulation experiments based upon ESFs
3.4 ESF prediction with linear regression
3.5 Summary
Chapter 4: Software implementation for constructing an ESF, with special reference to linear regression
Abstract
4.1 Software implementation
4.2 Geographic scale and resolution issues for ESFs
4.3 Determining the candidate set of eigenvectors
4.4 Extensions to large georeferenced datasets: Implications for big spatial data
4.5 Summary
Appendix 4.A Frequency distributions of the 10,000 NDVI values for the validation analysis, with superimposed theoretical normal curves
Chapter 5: MESF and generalized linear regression
Abstract
5.1 The logistic regression model specification
5.2 The binomial regression model specification
5.3 The Poisson regression model specification
5.4 The negative binomial regression model specification
5.5 The selection of eigenvectors to construct an ESF for GLMs
5.6 ESF prediction with generalized linear regression
5.7 Summary
Chapter 6: Modeling spatial heterogeneity with MESF
Abstract
6.1 Spatially varying coefficients
6.2 An ESF expansion of regression coefficients
6.3 Multicollinearity in spatially varying coefficients
6.4 Local SA ESFs
6.5 Summary
Appendix 6.A Bonferroni adjustment simulation experiment results
Chapter 7: Spatial interaction modeling
Abstract
7.1 Initial spatial interaction descriptions of internal Texas migration
7.2 Spatially autocorrelated origin and destination variables
7.3 Network autocorrelation in migration flows
7.4 Spatial and network autocorrelation in journey-to-work flows: A reconnaissance
7.5 A toy example: Exemplifying the necessary data structures
7.6 Summary
Appendix 7.A A Corpus Christi toy spatial interaction dataset R code
Appendix 7.B The functions.R code
Chapter 8: Space–time modeling
Abstract
8.1 Estimating a SURE term
8.2 Space–time data structures: Eigenvector space–time filters
8.3 A toy example: Exemplifying the necessary data structures
8.4 Summary
Appendix 8.A A Corpus Christi toy space–time dataset R code
Chapter 9: MESF and multivariate statistical analysis
Abstract
9.1 PCA, FA, and MESF
9.2 MANOVA and MESF
9.3 DFA and MESF
9.4 CCA and MESF
9.5 CA and MESF
9.6 Summary
Appendix 9.A A dendogram from Ward’s algorithm for original attribute data
Appendix 9.B Multivariate statistical analysis R code
Chapter 10: Concluding comments: Toy dataset implementation demonstrations
Abstract
10.1 The toy example: A Dallas–Fort Worth metroplex county geographic resolution dataset
10.2 The setup
10.3 Moran scatterplots
10.4 Normal approximation regression: The spatial linear regression specification
10.5 Poisson regression: The MESF specification
10.6 Binomial regression: The MESF specification
10.7 Spatially varying coefficients: The MESF specification
10.8 Summary
Epilogue
Index
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Tags: Daniel Griffith, Yongwan Chun, Bin Li, Spatial Regression, Spatial Filtering