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 Semivariogram 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 |

The 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 E-M Algorithm |

Eigenvector Spatial Filtering: Another Spatial E-M 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 |