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Geocomputation with R
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.
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.
- GenresNonfiction
354 pages, Hardcover
First published March 21, 2019
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Displaying 1 - 5 of 5 reviews
May 28, 2020
Right now this is probably the frontier when it comes to automated spatial tasks and visualisation in R. The reason I make this claim is, inter alia, because it is fully relying on Haldey Wickham's tidyverse principles, and it is using the new sf-class of spatial objects in R (mostly developed by Edzer Pebesma) instead of the older and much clumsier sp class. Make sure to check out the free online version of the book (plus exercises and additional material): https://geocompr.github.io/.
June 4, 2019
Geocomputation with R is an invaluable resource for anyone who wants to learn spatial analysis techniques with R. The book is written in a very approachable manner that is sure to satisfy R beginners, but provides best practices, examples, online resources, and further applications/extensions which allow more advanced users to delve deeper. From working with spatial datasets, to formatting maps, to creating interactive/animated visuals, Geocomputation with R provides a solid foundation on which to build up spatial analysis and presentation skills. As a fairly new R user who previously only used GIS for spatial analysis, I can credit this book with making the switch to a (nearly) fully R workflow much simpler than it could have been. Highly recommended!
September 16, 2022
Bit too much word salad and there are definitely sections of the book that could be skipped (by the authors own admission, but he calls these people that do this 'impatient'). Anyway, overall it covers what it needs to, and was sufficient for my uses.
September 2, 2020
Very good resource for teaching/learning spatial analysis in R.
February 18, 2024
Complete guidance for learning geospatial using R
Displaying 1 - 5 of 5 reviews