GEOCOMPUTATION WITH R

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

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/.

Authors
• 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.