1st Edition

Geocomputation with R




ISBN 9781138304512
Published March 25, 2019 by Chapman and Hall/CRC
335 Pages

USD $99.95

Prices & shipping based on shipping country


Preview

Book 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.

Table of Contents

1. Introduction 
  What is geocomputation?                    
  Why geocomputation with R?                 
  Software for geocomputation                  
  R’s spatial ecosystem                      
  The history of R-spatial                     
  Exercises                             

I Foundations
2. Geographic data in R
  Introduction                           
  Vector data                            
  An introduction to simple features           
  Why simple features?                  
  Basic map making                    
  Base plot arguments                   
  Geometry types                      
  Simple feature geometries (sfg)             
  Simple feature columns (sfc)              
  The sf class                        
  Raster data                            
  An introduction to raster                
  Basic map making                    
  Raster classes                       
  Coordinate Reference Systems                 
  Geographic coordinate systems             
  Projected coordinate systems              
  CRSs in R                         
  Units                               
  Exercises                             

3. Attribute data operations 
  Introduction                           
  Vector attribute manipulation                 
  Vector attribute subsetting               
  Vector attribute aggregation              
  Vector attribute joining                 
  Creating attributes and removing spatial information 
  Manipulating raster objects                   
  Raster subsetting                     
  Summarizing raster objects               
  Exercises                             

4. Spatial data operations
  Introduction                           
  Spatial operations on vector data               
  Spatial subsetting                    
  Topological relations                   
  Spatial joining                      
  Non-overlapping joins                  
  Spatial data aggregation                 
  Distance relations                    
  Spatial operations on raster data                
  Spatial subsetting                    
  Map algebra                       
  Local operations                     
  Focal operations                     
  Zonal operations                     
  Global operations and distances             
  Merging rasters                      
  Exercises                             

5. Geometry operations
  Introduction                           
  Geometric operations on vector data              
  Simplification                       
  Centroids                         
  Buffers                           
  Affine transformations                  
  Clipping                          
  Geometry unions                     
  Type transformations                  
  Geometric operations on raster data              
  Geometric intersections                 
  Extent and origin                     
  Aggregation and disaggregation             
  Raster-vector interactions                    
  Raster cropping                      
  Raster extraction                     
  Rasterization                       
  Spatial vectorization                   
  Exercises                             

6. Reprojecting geographic data
  Introduction                           
  When to reproject?                       
  Which CRS to use?                       
  Reprojecting vector geometries                 
  Modifying map projections                   
  Reprojecting raster geometries                 
  Exercises                             

7. Geographic data I/O
  Introduction                           
  Retrieving open data                      
  Geographic data packages                    
  Geographic web services                     
  File formats                           
  Data Input (I)                          
  Vector data                        
  Raster data                        
  Data output (O)                         
  Vector data                        
  Raster data                        
  Visual outputs                          
  Exercises                             

II Extensions
8. Making maps with R
  Introduction                           
  Static maps                            
  tmap basics                        
  Map objects                        
  Aesthetics                         
  Color settings                       
  Layouts                          
  Faceted maps                       
  Inset maps                        
  Animated maps                         
  Interactive maps                         
  Mapping applications                      
  Other mapping packages                    
  Exercises                             

9. Bridges to GIS software
  Introduction                           
  (R)QGIS                             
  (R)SAGA                             
  GRASS through rgrass                    
  When to use what?                       
  Other bridges                          
  Bridges to GDAL                     
  Bridges to spatial databases               
  Exercises                             

10. Scripts, algorithms and functions 
  Introduction                            
  Scripts                               
  Geometric algorithms                      
  Functions                             
  Programming                           
  Exercises                             

11. Statistical learning
  Introduction                           
  Case study: Landslide susceptibility              
  Conventional modeling approach in R             
  Introduction to (spatial) cross-validation           
  Spatial CV with mlr                      
  Generalized linear model                 
  Spatial tuning of machine-learning hyperparameters 
  Conclusions                            
  Exercises                             

III Applications
12. Transportation

  Introduction                           
  A case study of Bristol                     
  Transport zones                         
  Desire lines                            
  Routes                              
  Nodes                               
  Route networks                         
  Prioritizing new infrastructure                 
  Future directions of travel                    
 Exercises                             

13. Geomarketing
  Introduction                           
  Case study: bike shops in Germany              
  Tidy the input data                       
  Create census rasters                      
  Define metropolitan areas                    
  Points of interest                         
  Identifying suitable locations                  
  Discussion and next steps                    
  Exercises                             

14. Ecology
  Introduction                           
  Data and data preparation                   
  Reducing dimensionality                    
  Modeling the floristic gradient                 
  mlr building blocks                   
  Predictive mapping                    
  Conclusions                            
  Exercises                             

15. Conclusion
  Introduction                           
  Package choice                          
  Gaps and overlaps                        
  Where next?                           
  The open source approach                   

...
View More

Author(s)

Biography

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.

Reviews

"Geocomputation with R offers several advantages. Firstly, it uses up-to-date packages, mainly the 'sf' package for vector processing which was not available at the time the previous books were written. 'sf’' is truly a game-changer in the field of working with spatial data in R. I believe this alone makes writing the new book worthwhile. Secondly, the book offers a very broad overview, trying—and in my opinion succeeding—to encompass all non-statistical themes involved in geo-computation, including subjects such as location and transport modeling in R (chapters 7-8) which were never published before. Thirdly, the book offers a lot of illustrations and clearly demonstrates key concepts in GIS and geo-computation from the R point of view. I believe these characteristics will give the book an advantage and quite possibly make it the most popular choice in the category of spatial analysis in R for several years to come…The book can be used both as reference and as a textbook…The present book will definitely become the main textbook for this course once published."
~Michael Dorman, Ben-Gurion University of the Negev