Dorman Learning R for Geospatial Analysis

Leverage the power of R to elegantly manage crucial geospatial analysis tasks
1. Auflage 2025
ISBN: 978-1-78398-437-4
Verlag: De Gruyter
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 364 Seiten

Key FeaturesBook DescriptionThis book is intended for anyone who wants to learn how to efficiently analyze geospatial data with R, including GIS analysts, researchers, educators, and students who work with spatial data and who are interested in expanding their capabilities through programming. The book assumes familiarity with the basic geographic information concepts (such as spatial coordinates), but no prior experience with R and/or programming is required. By focusing on R exclusively, you will not need to depend on any external software—a working installation of R is all that is necessary to begin.What you will learn - Make inferences from tables by joining, reshaping, and aggregating
- Familiarize yourself with the R geospatial data analysis ecosystem
- Prepare reproducible, publicationquality plots and maps
- Efficiently process numeric data, characters, and dates
- Reshape tabular data into the necessary form for the specific task at hand
- Write R scripts to automate the handling of raster and vector spatial layers
- Process elevation rasters and time series visualizations of satellite images
- Perform GIS operations such as overlays and spatial queries between layers
- Spatially interpolate meteorological data to produce climate maps
Who this book is for

Autoren/Hrsg.

Dorman, Michael

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering

Über Autor(innen)

Dorman Michael :

Michael Dorman is currently a PhD candidate at the Department of Geography and Environmental Development, Ben-Gurion University of the Negev. His research explores the response of planted pine forests to changing climate through remote sensing and dendrochronology. He uses R extensively for time series and spatial statistical analyses and visualization. In spring 2013, he prepared and taught a course named Introduction to Programming for Spatial Data Analysis at the Ben-Gurion University of the Negev, introducing R as an environment for spatial data analysis to undergraduate Geography students. The course material served as a foundation for this book. Michael holds a Master's degree in Life Sciences from the Ben-Gurion University of the Negev and a Bachelor's degree in Plant Sciences in Agriculture from The Hebrew University of Jerusalem. He has authored or coauthored eight papers in scientific literature and actively participated in 18 scientific conferences.