Applied spatial data analysis with R / Roger S. Bivand, Edzer J. Pebesma, Virgilio Gómez Rubio

Incluye bibliografía: páginas 347-360 e índice: páginas 361-374

Preface.. 1 Hello World: Introducing Spatial Data.. 1.1 Applied Spatial Data Analysis.. 1.2 Why Do We Use R.. 1.2.1 ___ In General?.. 1.2.2 ___ for Spatial Data Analysis?.. 1.3 R and GIS.. 1.3.1 What is GIS?.. 1.3.2 Service-Oriented Architectures.. 1.3.3 Further Reading on GIS.. 1.4 Types of Spatial Data.. 1.5 Storage and Display.. 1.6 Applied Spatial Data Analysis.. 1.7 R Spatial Resources.. 1.7.1 Online Resources.. 1.7.2 Layout of the Book.. Part I Handling Spatial Data in R.. 2 Classes for Spatial Data in R.. 2.1 Introduction.. 2.2 Classes and Methods in R.. 2.3 Spatial Objects.. 2.4 SpatialPoints.. 2.4.1 Methods.. 2.4.2 Data Frames for Spatial Point Data.. 2.5 SpatialLines.. 2.6 SpatialPolygons.. 2.6.1 SpatialPolygonsDataFrame Objects.. 2.6.2 Holes and Ring Direction.. 2.7 SpatialGrid and SpatialPixel Objects.. 3 Visualising Spatial Data.. 3.1 The Traditional Plot System.. 3.1.1 Plotting Points, Lines, Polygons, and Grids.. 3.1.2 Axes and Layout Elements.. 3.1.3 Degrees in Axes Labels and Reference Grid.. 3.1.4 Plot Size, Plotting Area, Map Scale, and Multiple Plots.. 3.1.5 Plotting Attributes and Map Legends.. 3.2 Trellis/Lattice Plots with spplot.. 3.2.1 A Straight Trellis Example.. 3.2.2 Plotting Points, Lines, Polygons, and Grids.. 3.2.3 Adding Reference and Layout Elements to Plots.. 3.2.4 Arranging Panel Layout.. 3.3 Interacting with Plots.. 3.3.1 Interacting with Base Graphics. . 3.3.2 Interacting with spplot and Lattice Plots.. 3.4 Colour Palettes and Class Intervals.. 3.4.1 Colour Palettes.. 3.4.2 Class Intervals.. 4 Spatial Data Import and Export.. 4.1 Coordinate Reference Systems.. 4.1.1 Using the EPSG List.. 4.1.2 PROJ.4 CRS Specification.. 4.1.3 Projection and Transformation.. 4.1.4 Degrees, Minutes, and Seconds.. 4.2 Vector File Formats. . 4.2.1 Using OGR Drivers in rgdal.. 4.2.2 Other Import/Export Functions.. 4.3 Raster File Formats.. 4.3.1 Using GDAL Drivers in rgdal

4.3.2 Writing a Google Earth™ Image Overlay.. 4.3.3 Other Import/Export Functions.. 4.4 Grass.. 4.4.1 Broad Street Cholera Data.. 4.5 Other Import/Export Interfaces.. 4.5.1 Analysis and Visualisation Applications.. 4.5.2 TerraLib and aRT.. 4.5.3 Other GIS and Web Mapping Systems.. 4.6 Installing rgdal.. 5 Further Methods for Handling Spatial Data.. 5.1 Support.. 5.2 Overlay.. 5.3 Spatial Sampling.. 5.4 Checking Topologies.. 5.4.1 Dissolving Polygons.. 5.4.2 Checking Hole Status.. 5.5 Combining Spatial Data.. 5.5.1 Combining Positional Data.. 5.5.2 Combining Attribute Data.. 5.6 Auxiliary Functions.. 6 Customising Spatial Data Classes and Methods.. 6.1 Programming with Classes and Methods.. 6.1.1 S3-Style Classes and Methods.. 6.1.2 S4-Style Classes and Methods.. 6.2 Animal Track Data in Package Trip.. 6.2.1 Generic and Constructor Functions.. 6.2.2 Methods for Trip Objects.. 6.3 Multi-Point Data: SpatialMultiPoints.. 6.4 Hexagonal Grids.. 6.5 Spatio-Temporal Grids.. 6.6 Analysing Spatial Monte Carlo Simulations.. 6.7 Processing Massive Grids.. Part II Analysing Spatial Data.. 7 Spatial Point Pattern Analysis.. 7.1 Introduction.. 7.2 Packages for the Analysis of Spatial Point Patterns.. 7.3 Preliminary Analysis of a Point Pattern.. 7.3.1 Complete Spatial Randomness.. 7.3.2 G Function: Distance to the Nearest Event.. 7.3.3 F Function: Distance from a Point to the Nearest Event.. 7.4 Statistical Analysis of Spatial Point Processes.. 7.4.1 Homogeneous Poisson Processes.. 7.4.2 Inhomogeneous Poisson Processes.. 7.4.3 Estimation of the Intensity.. 7.4.4 Likelihood of an Inhomogeneous Poisson Process.. 7.4.5 Second-Order Properties.. 7.5 Some Applications in Spatial Epidemiology.. 7.5.1 Case-Control Studies.. 7.5.2 Binary Regression Estimator.. 7.5.3 Binary Regression Using Generalised Additive Models.. 7.5.4 Point Source Pollution.. 7.5.5 Accounting for Confounding and Covariates

7.6 Further Methods for the Analysis of Point Patterns.. 8 Interpolation and Geostatistics.. 8.1 Introduction.. 8.2 Exploratory Data Analysis.. 8.3 Non-Geostatistical Interpolation Methods.. 8.3.1 Inverse Distance Weighted Interpolation.. 8.3.2 Linear Regression.. 8.4 Estimating Spatial Correlation: The Variogram.. 8.4.1 Exploratory Variogram Analysis.. 8.4.2 Cutoff, Lag Width, Direction Dependence.. 8.4.3 Variogram Modelling.. 8.4.4 Anisotropy.. 8.4.5 Multivariable Variogram Modelling.. 8.4.6 Residual Variogram Modelling.. 8.5 Spatial Prediction.. 8.5.1 Universal, Ordinary, and Simple Kriging.. 8.5.2 Multivariable Prediction: Cokriging.. 8.5.3 Collocated Cokriging.. 8.5.4 Cokriging Contrasts.. 8.5.5 Kriging in a Local Neighbourhood.. 8.5.6 Change of Support: Block Kriging.. 8.5.7 Stratifying the Domain.. 8.5.8 Trend Functions and their Coefficients.. 8.5.9 Non-Linear Transforms of the Response Variable.. 8.5.10 Singular Matrix Errors.. 8.6 Model Diagnostics.. 8.6.1 Cross Validation Residuals.. 8.6.2 Cross Validation z-Scores.. 8.6.3 Multivariable Cross Validation.. 8.6.4 Limitations to Cross Validation.. 8.7 Geostatistical Simulation.. 8.7.1 Sequential Simulation.. 8.7.2 Non-Linear Spatial Aggregation and Block Averages.. 8.7.3 Multivariable and Indicator Simulation.. 8.8 Model-Based Geostatistics and Bayesian Approaches.. 8.9 Monitoring Network Optimization.. 8.10 Other R Packages for Interpolation and Geostatistics.. 8.10.1 Non-Geostatistical Interpolation.. 8.10.2 spatial.. 8.10.3 RandomFields.. 8.10.4 geoR and geoRglm.. 8.10.5 fields.. 9 Areal Data and Spatial Autocorrelation.. 9.1 Introduction.. 9.2 Spatial Neighbours.. 9.2.1 Neighbour Objects.. 9.2.2 Creating Contiguity Neighbours.. 9.2.3 Creating Graph-Based Neighbours.. 9.2.4 Distance-Based Neighbours.. 9.2.5 Higher-Order Neighbours.. 9.2.6 Grid Neighbours.. 9.3 Spatial Weights.. 9.3.1 Spatial Weights Styles.. 9.3.2 General Spatial Weights

9.3.3 Importing, Converting, and Exporting Spatial Neighbours and Weights.. 9.3.4 Using Weights to Simulate Spatial Autocorrelation.. 9.3.5 Manipulating Spatial Weights.. 9.4 Spatial Autocorrelation: Tests.. 9.4.1 Global Tests.. 9.4.2 Local Tests.. 10 Modelling Areal Data.. 10.1 Introduction.. 10.2 Spatial Statistics Approaches.. 10.2.1 Simultaneous Autoregressive Models.. 10.2.2 Conditional Autoregressive Models.. 10.2.3 Fitting Spatial Regression Models.. 10.3 Mixed-Effects Models.. 10.4 Spatial Econometrics Approaches.. 10.5 Other Methods.. 10.5.1 GAM, GEE, GLMM.. 10.5.2 Moran Eigenvectors.. 10.5.3 Geographically Weighted Regression.. 11 Disease Mapping.. 11.1 Introduction.. 11.2 Statistical Models.. 11.2.1 Poisson-Gamma Model.. 11.2.2 Log-Normal Model.. 11.2.3 Marshall's Global EB Estimator.. 11.3 Spatially Structured Statistical Models.. 11.4 Bayesian Hierarchical Models.. 11.4.1 The Poisson-Gamma Model Revisited.. 11.4.2 Spatial Models.. 11.5 Detection of Clusters of Disease.. 11.5.1 Testing the Homogeneity of the Relative Risks.. 11.5.2 Moran's I Test of Spatial Autocorrelation.. 11.5.3 Tango's Test of General Clustering.. 11.5.4 Detection of the Location of a Cluster.. 11.5.5 Geographical Analysis Machine.. 11.5.6 Kulldorff's Statistic.. 11.5.7 Stone's Test for Localised Clusters.. 11.6 Other Topics in Disease Mapping.. Afterword.. R and Package Versions Used.. Data Sets Used.. References.. Subject Index.. Functions Index