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

Format:

Book

Author/Creator:

Bivand, Roger S.

Contributor:

Pebesma, Edzer J.

Gómez-Rubio, Virgilio

George R. Fink Memorial Fund.

Series:

Use R!

Language:

English

Subjects (All):

Spatial analysis (Statistics)--Data processing.

Spatial analysis (Statistics).

R (Computer program language).

Physical Description:

xiv, 374 pages : illustrations ; 24 cm.

Place of Publication:

New York ; London : Springer, 2008.

Summary:

Applied Spatial Data Analysis with R is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website.

This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information systems, the environmental sciences, ecology, public health and disease control, economics, public administration and political science.

The book has a website where coloured figures, complete code examples, data sets, and other support material may be found: http://www.asdar-book.org.

The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.

Contents:

1 Hello World: Introducing Spatial Data 1

1.1 Applied Spatial Data Analysis 1

1.2 Why Do We Use R 2

1.2.1 ... In General? 2

1.2.2 ... for Spatial Data Analysis? 3

1.3 R and GIS 4

1.3.1 What is GIS? 4

1.3.2 Service-Oriented Architectures 6

1.3.3 Further Reading on GIS 6

1.4 Types of Spatial Data 7

1.5 Storage and Display 10

1.6 Applied Spatial Data Analysis 11

1.7 R Spatial Resources 13

Part I Handling Spatial Data in R

2 Classes for Spatial Data in R 21

2.2 Classes and Methods in R 23

2.3 Spatial Objects 28

2.4 SpatialPoints 30

2.4.1 Methods 31

2.4.2 Data Frames for Spatial Point Data 33

2.5 SpatialLines 38

2.6 SpatialPolygons 41

2.6.1 SpatialPolygonsDataFrame Objects 44

2.6.2 Holes and Ring Direction 46

2.7 SpatialGrid and SpatialPixel Objects 47

3 Visualising Spatial Data 57

3.1 The Traditional Plot System 58

3.1.1 Plotting Points, Lines, Polygons, and Grids 58

3.1.2 Axes and Layout Elements 60

3.1.3 Degrees in Axes Labels and Reference Grid 64

3.1.4 Plot Size, Plotting Area, Map Scale, and Multiple Plots 65

3.1.5 Plotting Attributes and Map Legends 66

3.2 Trellis/Lattice Plots with spplot 68

3.2.1 A Straight Trellis Example 68

3.2.2 Plotting Points, Lines, Polygons, and Grids 70

3.2.3 Adding Reference and Layout Elements to Plots 72

3.2.4 Arranging Panel Layout 73

3.3 Interacting with Plots 74

3.3.1 Interacting with Base Graphics 74

3.3.2 Interacting with spplot and Lattice Plots 76

3.4 Colour Palettes and Class Intervals 76

3.4.1 Colour Palettes 76

3.4.2 Class Intervals 77

4 Spatial Data Import and Export 81

4.1 Coordinate Reference Systems 82

4.1.1 Using the EPSG List 83

4.1.2 PROJ.4 CRS Specification 84

4.1.3 Projection and Transformation 85

4.1.4 Degrees, Minutes, and Seconds 87

4.2 Vector File Formats 88

4.2.1 Using OGR Drivers in rgdal 89

4.2.2 Other Import/Export Functions 93

4.3 Raster File Formats 93

4.3.1 Using GDAL Drivers in rgdal 94

4.3.2 Writing a Google Earth Image Overlay 97

4.3.3 Other Import/Export Functions 98

4.4 Grass 99

4.4.1 Broad Street Cholera Data 104

4.5 Other Import/Export Interfaces 106

4.5.1 Analysis and Visualisation Applications 108

4.5.2 TerraLib and aRT 108

4.5.3 Other GIS and Web Mapping Systems 110

4.6 Installing rgdal 111

5 Further Methods for Handling Spatial Data 113

5.1 Support 113

5.2 Overlay 116

5.3 Spatial Sampling 118

5.4 Checking Topologies 120

5.4.1 Dissolving Polygons 121

5.4.2 Checking Hole Status 122

5.5 Combining Spatial Data 123

5.5.1 Combining Positional Data 123

5.5.2 Combining Attribute Data 124

5.6 Auxiliary Functions 126

6 Customising Spatial Data Classes and Methods 127

6.1 Programming with Classes and Methods 127

6.1.1 S3-Style Classes and Methods 129

6.1.2 S4-Style Classes and Methods 130

6.2 Animal Track Data in Package Trip 130

6.2.1 Generic and Constructor Functions 131

6.2.2 Methods for Trip Objects 133

6.3 Multi-Point Data: SpatialMultiPoints 134

6.4 Hexagonal Grids 137

6.5 Spatio-Temporal Grids 140

6.6 Analysing Spatial Monte Carlo Simulations 144

6.7 Processing Massive Grids 146

Part II Analysing Spatial Data

7 Spatial Point Pattern Analysis 155

7.2 Packages for the Analysis of Spatial Point Patterns 156

7.3 Preliminary Analysis of a Point Pattern 160

7.3.1 Complete Spatial Randomness 160

7.3.2 G Function: Distance to the Nearest Event 161

7.3.3 F Function: Distance from a Point to the Nearest Event 162

7.4 Statistical Analysis of Spatial Point Processes 163

7.4.1 Homogeneous Poisson Processes 164

7.4.2 Inhomogeneous Poisson Processes 165

7.4.3 Estimation of the Intensity 165

7.4.4 Likelihood of an Inhomogeneous Poisson Process 168

7.4.5 Second-Order Properties 171

7.5 Some Applications in Spatial Epidemiology 172

7.5.1 Case-Control Studies 173

7.5.2 Binary Regression Estimator 178

7.5.3 Binary Regression Using Generalised Additive Models 180

7.5.4 Point Source Pollution 182

7.5.5 Accounting for Confounding and Covariates 186

7.6 Further Methods for the Analysis of Point Patterns 190

8 Interpolation and Geostatistics 191

8.2 Exploratory Data Analysis 192

8.3 Non-Geostatistical Interpolation Methods 193

8.3.1 Inverse Distance Weighted Interpolation 193

8.3.2 Linear Regression 194

8.4 Estimating Spatial Correlation: The Variogram 195

8.4.1 Exploratory Variogram Analysis 196

8.4.2 Cutoff, Lag Width, Direction Dependence 200

8.4.3 Variogram Modelling 201

8.4.4 Anisotropy 205

8.4.5 Multivariable Variogram Modelling 206

8.4.6 Residual Variogram Modelling 208

8.5 Spatial Prediction 209

8.5.1 Universal, Ordinary, and Simple Kriging 209

8.5.2 Multivariable Prediction: Cokriging 210

8.5.3 Collocated Cokriging 212

8.5.4 Cokriging Contrasts 213

8.5.5 Kriging in a Local Neighbourhood 213

8.5.6 Change of Support: Block Kriging 215

8.5.7 Stratifying the Domain 216

8.5.8 Trend Functions and their Coefficients 217

8.5.9 Non-Linear Transforms of the Response Variable 218

8.5.10 Singular Matrix Errors 220

8.6 Model Diagnostics 221

8.6.1 Cross Validation Residuals 222

8.6.2 Cross Validation z-Scores 223

8.6.3 Multivariable Cross Validation 225

8.6.4 Limitations to Cross Validation 225

8.7 Geostatistical Simulation 226

8.7.1 Sequential Simulation 227

8.7.2 Non-Linear Spatial Aggregation and Block Averages 229

8.7.3 Multivariable and Indicator Simulation 230

8.8 Model-Based Geostatistics and Bayesian Approaches 230

8.9 Monitoring Network Optimization 231

8.10 Other R Packages for Interpolation and Geostatistics 233

8.10.1 Non-Geostatistical Interpolation 233

8.10.2 Spatial 233

8.10.3 RandomFields 234

8.10.4 geoR and geoRglm 235

8.10.5 Fields 235

9 Areal Data and Spatial Autocorrelation 237

9.2 Spatial Neighbours 239

9.2.1 Neighbour Objects 240

9.2.2 Creating Contiguity Neighbours 242

9.2.3 Creating Graph-Based Neighbours 244

9.2.4 Distance-Based Neighbours 246

9.2.5 Higher-Order Neighbours 249

9.2.6 Grid Neighbours 250

9.3 Spatial Weights 251

9.3.1 Spatial Weights Styles 251

9.3.2 General Spatial Weights 253

9.3.3 Importing, Converting, and Exporting Spatial Neighbours and Weights 255

9.3.4 Using Weights to Simulate Spatial Autocorrelation 257

9.3.5 Manipulating Spatial Weights 258

9.4 Spatial Autocorrelation: Tests 258

9.4.1 Global Tests 261

9.4.2 Local Tests 268

10 Modelling Areal Data 273

10.2 Spatial Statistics Approaches 274

10.2.1 Simultaneous Autoregressive Models 277

10.2.2 Conditional Autoregressive Models 282

10.2.3 Fitting Spatial Regression Models 284

10.3 Mixed-Effects Models 287

10.4 Spatial Econometrics Approaches 289

10.5 Other Methods 296

10.5.1 GAM, GEE, GLMM 297

10.5.2 Moran Eigenvectors 302

10.5.3 Geographically Weighted Regression 305

11 Disease Mapping 311

11.2 Statistical Models 314

11.2.1 Poisson-Gamma Model 315

11.2.2 Log-Normal Model 316

11.2.3 Marshall's Global EB Estimator 318

11.3 Spatially Structured Statistical Models 319

11.4 Bayesian Hierarchical Models 321

11.4.1 The Poisson-Gamma Model Revisited 322

11.4.2 Spatial Models 325

11.5 Detection of Clusters of Disease 332

11.5.1 Testing the Homogeneity of the Relative Risks 333

11.5.2 Moran's I Test of Spatial Autocorrelation 335

11.5.3 Tango's Test of General Clustering 335

11.5.4 Detection of the Location of a Cluster 337

11.5.5 Geographical Analysis Machine 337

11.5.6 Kulldorff's Statistic 338

11.5.7 Stone's Test for Localised Clusters 340

11.6 Other Topics in Disease Mapping 341

R and Package Versions Used 344

Data Sets Used 344.

Local Notes:

Acquired for the Penn Libraries with assistance from the George R. Fink Memorial Fund.

ISBN:

9780387781709

0387781706

OCLC:

226974722