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"Ideal for anyone who wishes to gain a practical understanding of spatial statistics and geostatistics. Difficult concepts are well explained and supported by excellent examples in R code, allowing readers to see how each of the methods is implemented in practice" - Professor Tao Cheng, University College London Focusing specifically on spatial statistics and including components for ArcGIS, R, SAS and WinBUGS, this book illustrates the use of basic spatial statistics and geostatistics, as well as the spatial filtering techniques used in all relevant programs and software. It explains and…mehr
"Ideal for anyone who wishes to gain a practical understanding of spatial statistics and geostatistics. Difficult concepts are well explained and supported by excellent examples in R code, allowing readers to see how each of the methods is implemented in practice" - Professor Tao Cheng, University College London Focusing specifically on spatial statistics and including components for ArcGIS, R, SAS and WinBUGS, this book illustrates the use of basic spatial statistics and geostatistics, as well as the spatial filtering techniques used in all relevant programs and software. It explains and demonstrates techniques in:
spatial sampling
spatial autocorrelation
local statistics
spatial interpolation in two-dimensions
advanced topics including Bayesian methods, Monte Carlo simulation, error and uncertainty.
It is a systematic overview of the fundamental spatial statistical methods used by applied researchers in geography, environmental science, health and epidemiology, population and demography, and planning. A companion website includes digital R code for implementing the analyses in specific chapters and relevant data sets to run the R codes.
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Inhaltsangabe
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
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
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
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
Rezensionen
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.
This book is ideal for anyone who wishes to gain a practical understanding of spatial statistics and geostatistics. Difficult concepts are well explained and supported by excellent examples in R code, allowing readers to see how each of the methods is implemented in practice. Professor Tao Cheng University College London
This text is a remarkable roadmap to the methods of spatial statistics and in particular, the technique of spatial filtering. The included case studies and computer code make the book extraordinarily interactive and will benefit both students and applied researchers across many disciplines. W. Ryan Davis PhD Candidate in Economics, University of Texas at Dallas
This is a valuable and enjoyable addition to applied spatial statistics, particularly because the reader, or rather user, of the book can see exactly what the authors are doing, and so may reproduce all the analyses using the code provided. Professor Roger S. Bivand Norges Handelshøyskole Norwegian School of Economics
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