Spatial Statistics and Geostatistics : Theory and Applications for Geographic Information Science and Technology (Paperback)

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