Spatial Statistics and Geostatistics - Chun, Yongwan

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Résumé :
About the Authors
Preface
Introduction
Spatial Statistics and Geostatistics
R Basics
Spatial Autocorrelation
Indices Measuring Spatial Dependency
Important Properties of MC
Relationships Between MC And GR, and MC and Join Count Statistics
Graphic Portrayals: The Moran Scatterplot and the Semi-variogram Plot
Impacts of Spatial Autocorrelation
Testing for Spatial Autocorrelation in Regression Residuals
R Code for Concept Implementations
Spatial Sampling
Selected Spatial Sampling Designs
Puerto Rico DEM Data
Properties of the Selected Sampling Designs: Simulation Experiment Results
Sampling Simulation Experiments On A Unit Square Landscape
Sampling Simulation Experiments On A Hexagonal Landscape Structure
Resampling Techniques: Reusing Sampled Data
The Bootstrap
The Jackknife
Spatial Autocorrelation and Effective Sample Size
R Code for Concept Implementations
Spatial Composition and Configuration
Spatial Heterogeneity: Mean and Variance
ANOVA
Testing for Heterogeneity Over a Plane: Regional Supra-Partitionings
Establishing a Relationship to the Superpopulation
A Null Hypothesis Rejection Case With Heterogeneity
Testing for Heterogeneity Over a Plane: Directional Supra-Partitionings
Covariates Across a Geographic Landscape
Spatial Weights Matrices
Weights Matrices for Geographic Distributions
Weights Matrices for Geographic Flows
Spatial Heterogeneity: Spatial Autocorrelation
Regional Differences
Directional Differences: Anisotropy
R Code for Concept Implementations
Spatially Adjusted Regression And Related Spatial Econometrics
Linear Regression
Nonlinear Regression
Binomial/Logistic Regression
Poisson/Negative Binomial Regression
Geographic Distributions
Geographic Flows: A Journey-To-Work Example
R Code for Concept Implementations
Local Statistics: Hot And Cold Spots
Multiple Testing with Positively Correlated Data
Local Indices of Spatial Association
Getis-Ord Statistics
Spatially Varying Coefficients
R Code For Concept Implementations
Analyzing Spatial Variance And Covariance With Geostatistics And Related Techniques
Semi-variogram Models
Co-kriging
DEM Elevation as a Covariate
Landsat 7 ETM+ Data as a Covariate
Spatial Linear Operators
Multivariate Geographic Data
Eigenvector Spatial Filtering: Correlation Coefficient Decomposition
R Code for Concept Implementations
Methods For Spatial Interpolation In Two Dimensions
Kriging: An Algebraic Basis
The EM Algorithm
Spatial Autoregression: A Spatial EM Algorithm
Eigenvector Spatial Filtering: Another Spatial EM Algorithm
R Code for Concept Implementations
More Advanced Topics In Spatial Statistics
Bayesian Methods for Spatial Data
Markov Chain Monte Carlo Techniques
Selected Puerto Rico Examples
Designing Monte Carlo Simulation Experiments
A Monte Carlo Experiment Investigating Eigenvector Selection when Constructing a Spatial Filter
A Monte Carlo Experiment Investigating Eigenvector Selection from a Restricted Candidate Set of Vectors
Spatial Error: A Contributor to Uncertainty
R Code for Concept Implementations
References
Index
...

Biographie:
About the Authors
Preface
Introduction
Spatial Statistics and Geostatistics
R Basics
Spatial Autocorrelation
Indices Measuring Spatial Dependency
Important Properties of MC
Relationships Between MC And GR, and MC and Join Count Statistics
Graphic Portrayals: The Moran Scatterplot and the Semi-variogram Plot
Impacts of Spatial Autocorrelation
Testing for Spatial Autocorrelation in Regression Residuals
R Code for Concept Implementations
Spatial Sampling
Selected Spatial Sampling Designs
Puerto Rico DEM Data
Properties of the Selected Sampling Designs: Simulation Experiment Results
Sampling Simulation Experiments On A Unit Square Landscape
Sampling Simulation Experiments On A Hexagonal Landscape Structure
Resampling Techniques: Reusing Sampled Data
The Bootstrap
The Jackknife
Spatial Autocorrelation and Effective Sample Size
R Code for Concept Implementations
Spatial Composition and Configuration
Spatial Heterogeneity: Mean and Variance
ANOVA
Testing for Heterogeneity Over a Plane: Regional Supra-Partitionings
Establishing a Relationship to the Superpopulation
A Null Hypothesis Rejection Case With Heterogeneity
Testing for Heterogeneity Over a Plane: Directional Supra-Partitionings
Covariates Across a Geographic Landscape
Spatial Weights Matrices
Weights Matrices for Geographic Distributions
Weights Matrices for Geographic Flows
Spatial Heterogeneity: Spatial Autocorrelation
Regional Differences
Directional Differences: Anisotropy
R Code for Concept Implementations
Spatially Adjusted Regression And Related Spatial Econometrics
Linear Regression
Nonlinear Regression
Binomial/Logistic Regression
Poisson/Negative Binomial Regression
Geographic Distributions
Geographic Flows: A Journey-To-Work Example
R Code for Concept Implementations
Local Statistics: Hot And Cold Spots
Multiple Testing with Positively Correlated Data
Local Indices of Spatial Association
Getis-Ord Statistics
Spatially Varying Coefficients
R Code For Concept Implementations
Analyzing Spatial Variance And Covariance With Geostatistics And Related Techniques
Semi-variogram Models
Co-kriging
DEM Elevation as a Covariate
Landsat 7 ETM+ Data as a Covariate
Spatial Linear Operators
Multivariate Geographic Data
Eigenvector Spatial Filtering: Correlation Coefficient Decomposition
R Code for Concept Implementations
Methods For Spatial Interpolation In Two Dimensions
Kriging: An Algebraic Basis
The EM Algorithm
Spatial Autoregression: A Spatial EM Algorithm
Eigenvector Spatial Filtering: Another Spatial EM Algorithm
R Code for Concept Implementations
More Advanced Topics In Spatial Statistics
Bayesian Methods for Spatial Data
Markov Chain Monte Carlo Techniques
Selected Puerto Rico Examples
Designing Monte Carlo Simulation Experiments
A Monte Carlo Experiment Investigating Eigenvector Selection when Constructing a Spatial Filter
A Monte Carlo Experiment Investigating Eigenvector Selection from a Restricted Candidate Set of Vectors
Spatial Error: A Contributor to Uncertainty
R Code for Concept Implementations
References
Index
...

Sommaire:
About the Authors
Preface
Introduction
Spatial Statistics and Geostatistics
R Basics
Spatial Autocorrelation
Indices Measuring Spatial Dependency
Important Properties of MC
Relationships Between MC And GR, and MC and Join Count Statistics
Graphic Portrayals: The Moran Scatterplot and the Semi-variogram Plot
Impacts of Spatial Autocorrelation
Testing for Spatial Autocorrelation in Regression Residuals
R Code for Concept Implementations
Spatial Sampling
Selected Spatial Sampling Designs
Puerto Rico DEM Data
Properties of the Selected Sampling Designs: Simulation Experiment Results
Sampling Simulation Experiments On A Unit Square Landscape
Sampling Simulation Experiments On A Hexagonal Landscape Structure
Resampling Techniques: Reusing Sampled Data
The Bootstrap
The Jackknife
Spatial Autocorrelation and Effective Sample Size
R Code for Concept Implementations
Spatial Composition and Configuration
Spatial Heterogeneity: Mean and Variance
ANOVA
Testing for Heterogeneity Over a Plane: Regional Supra-Partitionings
Establishing a Relationship to the Superpopulation
A Null Hypothesis Rejection Case With Heterogeneity
Testing for Heterogeneity Over a Plane: Directional Supra-Partitionings
Covariates Across a Geographic Landscape
Spatial Weights Matrices
Weights Matrices for Geographic Distributions
Weights Matrices for Geographic Flows
Spatial Heterogeneity: Spatial Autocorrelation
Regional Differences
Directional Differences: Anisotropy
R Code for Concept Implementations
Spatially Adjusted Regression And Related Spatial Econometrics
Linear Regression
Nonlinear Regression
Binomial/Logistic Regression
Poisson/Negative Binomial Regression
Geographic Distributions
Geographic Flows: A Journey-To-Work Example
R Code for Concept Implementations
Local Statistics: Hot And Cold Spots
Multiple Testing with Positively Correlated Data
Local Indices of Spatial Association
Getis-Ord Statistics
Spatially Varying Coefficients
R Code For Concept Implementations
Analyzing Spatial Variance And Covariance With Geostatistics And Related Techniques
Semi-variogram Models
Co-kriging
DEM Elevation as a Covariate
Landsat 7 ETM+ Data as a Covariate
Spatial Linear Operators
Multivariate Geographic Data
Eigenvector Spatial Filtering: Correlation Coefficient Decomposition
R Code for Concept Implementations
Methods For Spatial Interpolation In Two Dimensions
Kriging: An Algebraic Basis
The EM Algorithm
Spatial Autoregression: A Spatial EM Algorithm
Eigenvector Spatial Filtering: Another Spatial EM Algorithm
R Code for Concept Implementations
More Advanced Topics In Spatial Statistics
Bayesian Methods for Spatial Data
Markov Chain Monte Carlo Techniques
Selected Puerto Rico Examples
Designing Monte Carlo Simulation Experiments
A Monte Carlo Experiment Investigating Eigenvector Selection when Constructing a Spatial Filter
A Monte Carlo Experiment Investigating Eigenvector Selection from a Restricted Candidate Set of Vectors
Spatial Error: A Contributor to Uncertainty
R Code for Concept Implementations
References
Index
...

SAGE has a long tradition of publishing accessible texts explaining key concepts in statistics. This book is in my opinion very useful. I particularly like the choice of statistical problems, the focus on one region to explain a series of problems and the availability of R code, which makes it easy for the reader to reproduce the analysis.