Geocomputation with R

ISBN-13: 9781138304512 / Angielski / Twarda / 2019 / 335 str.

Robin Lovelace; Jakub Nowosad; Jannes Muenchow

Opis

Presents a vision for an introductory and accessible textbook on geographic data analysis, modelling, and visualisation with R. The discipline of Geography captures each of these things, as well as content to get R beginners up-to-speed with R in general.

Spis treści

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 operationsIntroduction 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 functionsIntroductionScriptsGeometric 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