Description
Taylor and Francis Geocomputation With R 1St Edition 2019 Edition by Robin Lovelace, Jakub Nowosad, Jannes Muenchow
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. 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