Spatial Statistics and Geostatistics

• About the AuthorsPrefaceIntroductionSpatial Statistics and GeostatisticsR BasicsSpatial AutocorrelationIndices Measuring Spatial DependencyImportant Properties of MCRelationships Between MC And GR, and MC and Join Count StatisticsGraphic Portrayals: The Moran Scatterplot and the Semi-variogram PlotImpacts of Spatial AutocorrelationTesting for Spatial Autocorrelation in Regression ResidualsR Code for Concept ImplementationsSpatial SamplingSelected Spatial Sampling DesignsPuerto Rico DEM DataProperties of the Selected Sampling Designs: Simulation Experiment ResultsSampling Simulation Experiments On A Unit Square LandscapeSampling Simulation Experiments On A Hexagonal Landscape StructureResampling Techniques: Reusing Sampled DataThe BootstrapThe JackknifeSpatial Autocorrelation and Effective Sample SizeR Code for Concept ImplementationsSpatial Composition and ConfigurationSpatial Heterogeneity: Mean and VarianceANOVATesting for Heterogeneity Over a Plane: Regional Supra-PartitioningsEstablishing a Relationship to the SuperpopulationA Null Hypothesis Rejection Case With HeterogeneityTesting for Heterogeneity Over a Plane: Directional Supra-PartitioningsCovariates Across a Geographic LandscapeSpatial Weights MatricesWeights Matrices for Geographic DistributionsWeights Matrices for Geographic FlowsSpatial Heterogeneity: Spatial AutocorrelationRegional DifferencesDirectional Differences: AnisotropyR Code for Concept ImplementationsSpatially Adjusted Regression And Related Spatial EconometricsLinear RegressionNonlinear RegressionBinomial/Logistic RegressionPoisson/Negative Binomial RegressionGeographic DistributionsGeographic Flows: A Journey-To-Work ExampleR Code for Concept ImplementationsLocal Statistics: Hot And Cold SpotsMultiple Testing with Positively Correlated DataLocal Indices of Spatial Association Getis-Ord StatisticsSpatially Varying CoefficientsR Code For Concept ImplementationsAnalyzing Spatial Variance And Covariance With Geostatistics And Related TechniquesSemi-variogram ModelsCo-krigingDEM Elevation as a CovariateLandsat 7 ETM+ Data as a CovariateSpatial Linear OperatorsMultivariate Geographic DataEigenvector Spatial Filtering: Correlation Coefficient DecompositionR Code for Concept ImplementationsMethods For Spatial Interpolation In Two DimensionsKriging: An Algebraic BasisThe EM AlgorithmSpatial Autoregression: A Spatial EM AlgorithmEigenvector Spatial Filtering: Another Spatial EM AlgorithmR Code for Concept ImplementationsMore Advanced Topics In Spatial StatisticsBayesian Methods for Spatial DataMarkov Chain Monte Carlo TechniquesSelected Puerto Rico ExamplesDesigning Monte Carlo Simulation ExperimentsA Monte Carlo Experiment Investigating Eigenvector Selection when Constructing a Spatial FilterA Monte Carlo Experiment Investigating Eigenvector Selection from a Restricted Candidate Set of VectorsSpatial Error: A Contributor to UncertaintyR Code for Concept ImplementationsReferences Index

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 ChengUniversity 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 DavisPhD 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. BivandNorges Handelshøyskole Norwegian School of Economics