Contents
Foreword xiii
Preface xv
1 Introduction 1
1.1 What is geocomputation? 2
1.2 Why use R for geocomputation? 4
1.3 Software for geocomputation 6
1.4 R's spatial ecosystem 8
1.5 The history of R-spatial 10
1.6 Exercises 13
I Foundations 15
2 Geographic data in R 17
2.1 Introduction 18
2.2 Vector data 19
2.2.1 An introduction to simple features 20
2.2.2 Why simple features? 24
2.2.3 Basic map making 25
2.2.4 Base plot arguments 26
2.2.5 Geometry types 28
2.2.6 Simple feature geometries (sfg) 29
2.2.7 Simple feature columns (sfc) 32
2.2.8 The sf class 34
2.3 Raster data 35
2.3.1 An introduction to raster 36
2.3.2 Basic map making 37
2.3.3 Raster classes 38
2.4 Coordinate Reference Systems 40
2.4.1 Geographic coordinate systems 41
2.4.2 Projected coordinate reference systems 42
2.4.3 CRSs in R 44
2.5 Units 44
2.6 Exercises 46
3 Attribute data operations 47
3.1 Introduction 47
3.2 Vector attribute manipulation 48
3.2.1 Vector attribute subsetting 50
3.2.2 Vector attribute aggregation 54
3.2.3 Vector attribute joining 55
3.2.4 Creating attributes and removing spatial information 59
3.3 Manipulating raster objects 60
3.3.1 Raster subsetting 62
3.3.2 Summarizing raster objects 64
3.4 Exercises 65
4 Spatial data operations 67
4.1 Introduction 67
4.2 Spatial operations on vector data 68
4.2.1 Spatial subsetting 68
4.2.2 Topological relations 71
4.2.3 Spatial joining 73
4.2.4 Non-overlapping joins 75
4.2.5 Spatial data aggregation 77
4.2.6 Distance relations 80
4.3 Spatial operations on raster data 81
4.3.1 Spatial subsetting 81
4.3.2 Map algebra 83
4.3.3 Local operations 84
4.3.4 Focal operations 85
4.3.5 Zonal operations 86
4.3.6 Global operations and distances 87
4.3.7 Merging rasters 88
4.4 Exercises 88
5 Geometry operations 91
5.1 Introduction 91
5.2 Geometric operations on vector data 92
5.2.1 Simplification 92
5.2.2 Centroids 94
5.2.3 Buffers 96
5.2.4 Affine transformations 97
5.2.5 Clipping 99
5.2.6 Geometry unions 101
5.2.7 Type transformations 102
5.3 Geometric operations on raster data 106
5.3.1 Geometric intersections 107
5.3.2 Extent and origin 107
5.3.3 Aggregation and disaggregation 109
5.4 Raster-vector interactions 111
5.4.1 Raster cropping 112
5.4.2 Raster extraction 113
5.4.3 Rasterization 117
5.4.4 Spatial vectorization 120
5.5 Exercises 123
6 Reprojecting geographic data 127
6.1 Introduction 127
6.2 When to reproject? 130
6.3 Which CRS to use? 131
6.4 Reprojecting vector geometries 134
6.5 Modifying map projections 135
6.6 Reprojecting raster geometries 138
6.7 Exercises 141
7 Geographic data I/O 143
7.1 Introduction 143
7.2 Retrieving open data 144
7.3 Geographic data packages 145
7.4 Geographic web services 147
7.5 File formats 149
7.6 Data input (I) 151
7.6.1 Vector data 151
7.6.2 Raster data 154
7.7 Data output (O) 154
7.7.1 Vector data 154
7.7.2 Raster data 156
7.8 Visual outputs 157
7.9 Exercises 158
II Extensions 159
8 Making maps with R 161
8.1 Introduction 161
8.2 Static maps 162
8.2.1 tmap basics 163
8.2.2 Map objects 165
8.2.3 Aesthetics 167
8.2.4 Color settings 168
8.2.5 Layouts 172
8.2.6 Faceted maps 175
8.2.7 Inset maps 177
8.3 Animated maps 179
8.4 Interactive maps 181
8.5 Mapping applications 188
8.6 Other mapping packages 192
8.7 Exercises 197
9 Bridges to GIS software 199
9.1 Introduction 199
9.2 (R)QGIS 202
9.3 (R)SAGA 206
9.4 GRASS through rgrass7 209
9.5 When to use what? 214
9.6 Other bridges 215
9.6.1 Bridges to GDAL 215
9.6.2 Bridges to spatial databases 217
9.7 Exercises 220
10 Scripts, algorithms and functions 221
10.1 Introduction 221
10.2 Scripts 222
10.3 Geometric algorithms 224
10.4 Functions 229
10.5 Programming 232
10.6 Exercises 233
11 Statistical learning 235
11.1 Introduction 235
11.2 Case study: Landslide susceptibility 237
11.3 Conventional modeling approach in R 239
11.4 Introduction to (spatial) cross-validation 242
11.5 Spatial CV with mlr 242
11.5.1 Generalized linear model 244
11.5.2 Spatial tuning of machine-learning hyperparameters 247
11.6 Conclusions 253
11.7 Exercises 254
III Applications 257
12 Transportation 259
12.1 Introduction 259
12.2 A case study of Bristol 261
12.3 Transport zones 263
12.4 Desire lines 267
12.5 Routes 270
12.6 Nodes 272
12.7 Route networks 274
12.8 Prioritizing new infrastructure 275
12.9 Future directions of travel 277
12.10 Exercises 278
13 Geomarketing 281
13.1 Introduction 281
13.2 Case study: bike shops in Germany 282
13.3 Tidy the input data 283
13.4 Create census rasters 283
13.5 Define metropolitan areas 286
13.6 Points of interest 289
13.7 Identifying suitable locations 291
13.8 Discussion and next steps 293
13.9 Exercises 294
14 Ecology 295
14.1 Introduction 295
14.2 Data and data preparation 297
14.3 Reducing dimensionality 300
14.4 Modeling the floristic gradient 303
14.4.1 mlr building blocks 305
14.4.2 Predictive mapping 307
14.5 Conclusions 309
14.6 Exercises 310
15 Conclusion 313
15.1 Introduction 313
15.2 Package choice 314
15.3 Gaps and overlaps 316
15.4 Where to go next? 317
15.5 The open source approach 319
Bibliography 321
Index 331