Learning R for Geospatial Analysis
Leverage the power of R to elegantly manage crucial geospatial analysis tasks
Published on 7. October 2025
364 pages
978-1-78398-437-4 (ISBN)
Description
Key Features
Book 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
Book 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
All prices
More details
Edition
Digital original
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Digital original
File size
9,27 MB
ISBN-13
978-1-78398-437-4 (9781783984374)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Michael Dorman
Learning R for Geospatial Analysis
Leverage the power of R to elegantly manage crucial geospatial analysis tasks
Book
12/2014
Packt Publishing
€63.60
Shipment within 3-4 weeks
Person
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.
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.
Content
Table of Contents
The R environment
Working with vectors and time series
Working with tabular data
Working with rasters
Working with points, lines and polygons
Advanced operations on rasters
Combining raster and vector datasets
Interpolating point data in space
Advanced visualization of spatial data
Appendix A: External Datasets Used in Examples
Appendix B: Cited References
The R environment
Working with vectors and time series
Working with tabular data
Working with rasters
Working with points, lines and polygons
Advanced operations on rasters
Combining raster and vector datasets
Interpolating point data in space
Advanced visualization of spatial data
Appendix A: External Datasets Used in Examples
Appendix B: Cited References
