R语言实战：空间数据分析入门与案例解析

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"《应用空间数据分析与R》（Applied Spatial Data Analysis with R）是一本由Roger S. Bivand、Edzer Pebesma和Virgilio Gómez-Rubio合著的第二版书籍，它专注于介绍如何使用R语言进行空间数据分析。该书属于著名的UseR!系列，由Robert Gentleman、Kurt Hornik和Giovanni G. Parmigiani共同编辑。此系列致力于提供实用的R编程教程，适合初学者和专业人员深入理解并掌握数据处理技术。本书内容涵盖了空间数据的处理、可视化和建模，通过丰富的案例研究让读者能够轻松理解和实践。作者们在书中详细阐述了如何利用R语言中的spatial packages（如sf, raster,和rgdal等），处理地理空间数据，包括数据读取、数据清理、空间操作、空间统计分析，以及高级主题如空间插值和GIS功能。此外，书中还涉及了如何在R中构建地图、执行空间聚类和网络分析，以及如何将分析结果应用于实际问题。《应用空间数据分析与R》的特点在于其理论与实践相结合的方式，确保读者不仅能学习理论知识，还能通过实例操作加深理解。对于那些对地理信息系统（GIS）、生态学、经济学、地理学或城市规划等领域感兴趣的专业人士，这是一本不可或缺的参考书。此外，该书也适用于R语言学习者，特别是希望扩展其在空间数据分析领域的技能的程序员和数据科学家。该书的ISBN号和DOI号分别为978-1-4614-7617-7（印刷版）和978-1-4614-7618-4（电子版），版权归属于Springer Science+Business Media。版权信息强调所有权利保留，包括翻译、重印、再利用图解、朗诵、广播、微缩复制或其他任何形式的传输，都受到保护。《应用空间数据分析与R》是一本全面且实用的指南，它展示了如何利用R的强大功能进行空间数据处理，对于想要在这个领域深化专业知识的读者来说，无疑是一次宝贵的探索和学习之旅。"

Contents xv

4.6.2 TerraLib and aRT ................................ 123

4.6.3 Other GIS Systems ............................... 124

4.7 Installing rgdal ......................................... 125

5 Further Methods for Handling Spatial Data .............. 127

5.1 Support ............................................... 127

5.2 Handling and Combining Features ........................ 130

5.2.1 The rgeos Package ................................ 130

5.2.2 Using rgeos ...................................... 132

5.3 Map Overlay or Spatial Join ............................. 140

5.3.1 Spatial Aggregation .............................. 142

5.3.2 Using the raster Package for Extract Operations ..... 145

5.3.3 Spatial Sampling ................................. 146

5.4 Auxiliary Functions ..................................... 149

6 Spatio-Temporal Data .................................... 151

6.1 Introduction ........................................... 151

6.2 Types of Spatio-Temporal Data .......................... 151

6.2.1 Spatial Point or Area, Time Instance or Interval...... 152

6.2.2 Are Space and Time of Primary Interest? ........... 152

6.2.3 Regularity of Space-Time Layouts .................. 152

6.2.4 Do Objects Change Location? ..................... 153

6.3 Classes in spacetime .................................... 154

6.4 Handling Time Series Data with xts ...................... 155

6.5 Construction of ST Objects............................... 156

6.6 Selection, Addition, and Replacement of Attributes ......... 158

6.7 Overlay and Aggregation ................................ 159

6.8 Visualisation ........................................... 161

6.8.1 Multi-panel Plots................................. 161

6.8.2 Space-Time Plots ................................ 162

6.8.3 Animated Plots .................................. 163

6.8.4 Time Series Plots ................................ 164

6.9 Further Packages ....................................... 164

6.9.1 Handling Spatio-Temporal Data .................... 165

6.9.2 Analysing Spatio-Temporal Data ................... 165

6.10 Outlook ............................................... 165

Part II Analysing Spatial Data

7 Spatial P oint Pattern Analysis ............................ 173

7.1 Introduction ........................................... 173

7.2 Packages for the Analysis of Spatial Point Patterns ......... 174

7.3 Preliminary Analysis of a Point Pattern ................... 178

7.3.1 Complete Spatial Randomness ..................... 179

7.3.2 G Function: Distance to the Nearest Event .......... 179

xvi Contents

7.3.3 F Function: Distance from a Point

to the Nearest Event .............................. 181

7.4 Statistical Analysis of Spatial Point Processes .............. 182

7.4.1 Homogeneous Poisson Processes .................... 183

7.4.2 Inhomogeneous Poisson Processes .................. 184

7.4.3 Estimation of the Intensity ........................ 184

7.4.4 Likelihood of an Inhomogeneous Poisson Process ..... 187

7.4.5 Second-Order Properties .......................... 190

7.5 Some Applications in Spatial Epidemiology ................ 192

7.5.1 Case–Control Studies ............................. 193

7.5.2 Binary Regression Estimator ....................... 198

7.5.3 Binary Regression Using Generalised

Additive Models ................................. 199

7.5.4 Point Source Pollution ............................ 202

7.5.5 Accounting for Confounding and Covariates ......... 206

7.6 Further Methods for the Analysis

of Point Patterns ....................................... 210

8 Interp olation and Geostatistics ........................... 213

8.1 Introduction ........................................... 213

8.2 Exploratory Data Analysis .............................. 214

8.3 Non-geostatistical Interpolation Methods .................. 215

8.3.1 Inverse Distance Weighted Interpolation ............. 215

8.3.2 Linear Regression ................................ 216

8.4 Estimating Spatial Correlation: The Variogram ............. 217

8.4.1 Exploratory Variogram Analysis .................... 219

8.4.2 Cutoﬀ, Lag Width, Direction Dependence ........... 222

8.4.3 Variogram Modelling ............................. 224

8.4.4 Anisotropy ...................................... 228

8.4.5 Multivariable Variogram Modelling ................. 229

8.4.6 Residual Variogram Modelling ..................... 230

8.5 Spatial Prediction ...................................... 232

8.5.1 Universal, Ordinary, and Simple Kriging ............ 233

8.5.2 Multivariable Prediction: Cokriging ................. 233

8.5.3 Collocated Cokriging ............................. 236

8.5.4 Cokriging Contrasts .............................. 237

8.5.5 Kriging in a Local Neighbourhood .................. 237

8.5.6 Change of Support: Block Kriging .................. 238

8.5.7 Stratifying the Domain............................ 240

8.5.8 Trend Functions and Their Coeﬃcients.............. 241

8.5.9 Non-linear Transforms of the Response Variable ...... 242

8.5.10 Singular Matrix Errors ............................ 243

8.6 Kriging, Filtering, Smoothing ............................ 245

8.7 Model Diagnostics ...................................... 247

8.7.1 Cross Validation Residuals ........................ 247

8.7.2 Cross Validation z-Scores .......................... 249

Contents xvii

8.7.3 Multivariable Cross Validation ..................... 250

8.7.4 Limitations to Cross Validation .................... 250

8.8 Geostatistical Simulation ................................ 252

8.8.1 Sequential Simulation ............................. 252

8.8.2 Non-linear Spatial Aggregation and Block Averages . . . 254

8.8.3 Multivariable and Indicator Simulation .............. 255

8.9 Model-Based Geostatistics and Bayesian Approaches ........ 256

8.10 Monitoring Network Optimisation ........................ 256

8.11 Other R Packages for Interpolation and Geostatistics ........ 258

8.11.1 Non-geostatistical Interpolation .................... 258

8.11.2 Spatial .......................................... 259

8.11.3 RandomFields ................................... 259

8.11.4 geoR and geoRglm ............................... 259

8.11.5 Fields ........................................... 260

8.11.6 spBayes ......................................... 260

8.12 Spatio-Temporal Prediction .............................. 260

9 Mo delling Areal Data .................................... 263

9.1 Introduction ........................................... 263

9.2 Spatial Neighbours and Spatial Weights ................... 266

9.2.1 Neighbour Objects ............................... 266

9.2.2 Spatial Weights Objects ........................... 269

9.2.3 Handling Spatial Weights Objects .................. 273

9.2.4 Using Weights to Simulate Spatial Autocorrelation ... 274

9.3 Testing for Spatial Autocorrelation ....................... 275

9.3.1 Global Tests ..................................... 278

9.3.2 Local Tests ...................................... 284

9.4 Fitting Models of Areal Data ............................ 288

9.4.1 Spatial Statistics Approaches ...................... 290

9.4.2 Spatial Econometrics Approaches ................... 303

9.4.3 Other Methods ................................... 314

10 Disease Mapping ......................................... 319

10.1 Introduction ........................................... 320

10.2 Statistical Models ...................................... 322

10.2.1 Poisson-Gamma Model ............................ 323

10.2.2 Log-Normal Model ............................... 325

10.2.3 Marshall’s Global EB Estimator .................... 326

10.3 Spatially Structured Statistical Models .................... 328

10.4 Bayesian Hierarchical Models ............................ 330

10.4.1 The Poisson-Gamma Model Revisited ............... 332

10.4.2 Spatial Models ................................... 336

10.5 Geoadditive Models ..................................... 345

10.6 Detection of Clusters of Disease .......................... 347

10.6.1 Testing the Homogeneity of the Relative Risks ....... 348

10.6.2 Moran’s I Test of Spatial Autocorrelation ........... 350

10.6.3 Tango’s Test of General Clustering ................. 351

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