This comprehensive guide reveals how to harness the power of R’s modern geospatial ecosystem. It starts with the fundamentals—understanding vector and raster data types, choosing appropriate coordinate reference systems and measuring distances and areas accurately. You’ll learn why packages such as sf (which implements the OGC Simple Features standard and relies on PROJ, GEOS and GDAL for geometry operations) and terra (a faster, simpler successor to the raster package) form the backbone of spatial analysis in R.
The book shows you how to obtain geographic data from shapefiles, APIs and remote sensing sources; manipulate, clean and combine datasets using tidyverse tools; and create publication‑quality maps with ggplot2, tmap (which can switch between static and interactive modes) and leaflet. It then guides you through advanced methods: building spatial weight matrices and testing for autocorrelation with spdep; analysing point patterns with spatstat; performing interpolation and kriging with gstat; handling spatio‑temporal arrays using stars; fitting spatial regression models; and integrating interactive dashboards with Shiny.
Rich case studies demonstrate how to map demographic change, model environmental phenomena and investigate disease outbreaks. Throughout the extended edition, you’ll find expanded explanations, clear code listings and reproducible workflows to ensure you can apply each technique in your own work. This book is ideal for data scientists, GIS analysts, researchers and students seeking to master geospatial analysis in R—from simple maps to sophisticated spatial statistics.
Pages: 41
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