Geocomputation with R

Description

Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), "bridges" to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/. Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds, where he has taught R for geographic research over many years, with a focus on transport systems. Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan, where his focus is on the analysis of large datasets to understand environmental processes. Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena, where he develops and teaches a range of geographic methods, with a focus on ecological modeling, statistical geocomputing, and predictive mapping. All three are active developers and work on a number of R packages, including stplanr, sabre, and RQGIS. Cover Half Title Title Page Copyright Page Dedication Table of Contents Foreword Preface 1: Introduction 1.1 What is geocomputation? 1.2 Why use R for geocomputation? 1.3 Software for geocomputation 1.4 R’s spatial ecosystem 1.5 The history of R-spatial 1.6 Exercises I: Foundations 2: Geographic data in R 2.1 Introduction 2.2 Vector data 2.2.1 An introduction to simple features 2.2.2 Why simple features? 2.2.3 Basic map making 2.2.4 Base plot arguments 2.2.5 Geometry types 2.2.6 Simple feature geometries (sfg) 2.2.7 Simple feature columns (sfc) 2.2.8 The sf class 2.3 Raster data 2.3.1 An introduction to raster 2.3.2 Basic map making 2.3.3 Raster classes 2.4 Coordinate Reference Systems 2.4.1 Geographic coordinate systems 2.4.2 Projected coordinate reference systems 2.4.3 CRSs in R 2.5 Units 2.6 Exercises 3: Attribute data operations 3.1 Introduction 3.2 Vector attribute manipulation 3.2.1 Vector attribute subsetting 3.2.2 Vector attribute aggregation 3.2.3 Vector attribute joining 3.2.4 Creating attributes and removing spatial information 3.3 Manipulating raster objects 3.3.1 Raster subsetting 3.3.2 Summarizing raster objects 3.4 Exercises 4: Spatial data operations 4.1 Introduction 4.2 Spatial operations on vector data 4.2.1 Spatial subsetting 4.2.2 Topological relations 4.2.3 Spatial joining 4.2.4 Non-overlapping joins 4.2.5 Spatial data aggregation 4.2.6 Distance relations 4.3 Spatial operations on raster data 4.3.1 Spatial subsetting 4.3.2 Map algebra 4.3.3 Local operations 4.3.4 Focal operations 4.3.5 Zonal operations 4.3.6 Global operations and distances 4.3.7 Merging rasters 4.4 Exercises 5: Geometry operations 5.1 Introduction 5.2 Geometric operations on vector data 5.2.1 Simplification 5.2.2 Centroids 5.2.3 Buffers 5.2.4 Affine transformations 5.2.5 Clipping 5.2.6 Geometry unions 5.2.7 Type transformations 5.3 Geometric operations on raster data 5.3.1 Geometric intersections 5.3.2 Extent and origin 5.3.3 Aggregation and disaggregation 5.4 Raster-vector interactions 5.4.1 Raster cropping 5.4.2 Raster extraction 5.4.3 Rasterization 5.4.4 Spatial vectorization 5.5 Exercises 6: Reprojecting geographic data 6.1 Introduction 6.2 When to reproject? 6.3 Which CRS to use? 6.4 Reprojecting vector geometries 6.5 Modifying map projections 6.6 Reprojecting raster geometries 6.7 Exercises 7: Geographic data I/O 7.1 Introduction 7.2 Retrieving open data 7.3 Geographic data packages 7.4 Geographic web services 7.5 File formats 7.6 Data input (I) 7.6.1 Vector data 7.6.2 Raster data 7.7 Data output (O) 7.7.1 Vector data 7.7.2 Raster data 7.8 Visual outputs 7.9 Exercises II: Extensions 8: Making maps with R 8.1 Introduction 8.2 Static maps 8.2.1 tmap basics 8.2.2 Map objects 8.2.3 Aesthetics 8.2.4 Color settings 8.2.5 Layouts 8.2.6 Faceted maps 8.2.7 Inset maps 8.3 Animated maps 8.4 Interactive maps 8.5 Mapping applications 8.6 Other mapping packages 8.7 Exercises 9: Bridges to GIS software 9.1 Introduction 9.2 (R)QGIS 9.3 (R)SAGA 9.4 GRASS through rgrass7 9.5 When to use what? 9.6 Other bridges 9.6.1 Bridges to GDAL 9.6.2 Bridges to spatial databases 9.7 Exercises 10: Scripts, algorithms and functions 10.1 Introduction 10.2 Scripts 10.3 Geometric algorithms 10.4 Functions 10.5 Programming 10.6 Exercises 11: Statistical learning 11.1 Introduction 11.2 Case study: Landslide susceptibility 11.3 Conventional modeling approach in R 11.4 Introduction to (spatial) cross-validation 11.5 Spatial CV with mlr 11.5.1 Generalized linear model 11.5.2 Spatial tuning of machine-learning hyperparameters 11.6 Conclusions 11.7 Exercises III: Applications 12: Transportation 12.1 Introduction 12.2 A case study of Bristol 12.3 Transport zones 12.4 Desire lines 12.5 Routes 12.6 Nodes 12.7 Route networks 12.8 Prioritizing new infrastructure 12.9 Future directions of travel 12.10 Exercises 13: Geomarketing 13.1 Introduction 13.2 Case study: bike shops in Germany 13.3 Tidy the input data 13.4 Create census rasters 13.5 Define metropolitan areas 13.6 Points of interest 13.7 Identifying suitable locations 13.8 Discussion and next steps 13.9 Exercises 14: Ecology 14.1 Introduction 14.2 Data and data preparation 14.3 Reducing dimensionality 14.4 Modeling the floristic gradient 14.4.1 mlr building blocks 14.4.2 Predictive mapping 14.5 Conclusions 14.6 Exercises 15: Conclusion 15.1 Introduction 15.2 Package choice 15.3 Gaps and overlaps 15.4 Where to go next? 15.5 The open source approach Bibliography Index