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Geocomputation with R (Chapman & Hall/CRC


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Table of Contents

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

I Foundations
2. Geographic data in R
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

3. Attribute data operations
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

4. Spatial data operations
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

5. Geometry operations
Geometric operations on vector data
Affine transformations
Geometry unions
Type transformations
Geometric operations on raster data
Geometric intersections
Extent and origin
Aggregation and disaggregation
Raster-vector interactions
Raster cropping
Raster extraction
Spatial vectorization

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

7. Geographic data I/O
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

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

9. Bridges to GIS software
GRASS through rgrass
When to use what?
Other bridges
Bridges to GDAL
Bridges to spatial databases

10. Scripts, algorithms and functions
Geometric algorithms

11. Statistical learning
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

III Applications
12. Transportation

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

13. Geomarketing
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

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

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

About the Author

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.


"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

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