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Description

This 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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Amazon verified reviews

R N Thornton Jan 02, 2017

This book is clearly written and comprehensive, containing many tips about use of R in addition to its core spatial content. Code is available through the internet - essential really. I can hardly recommend this book too much for developing skills in spatial analysis.

Amazon Verified review

DARKO KRALIK Aug 10, 2016

This book is an extraordinary introduction to geospatial analysis.At first You could get feeling that this book is so good mostly because of inate expressiveness of R.But real truth is that it's excellent selection of examples and it's methodical approach reveals R, it's geo libraries, and geospatial analysis as such in clear and simple way.And I am saying that after browsing thru dozens and dozens other geospatial books and environments.

Alvaro Neto Oct 28, 2018

Very straightforward. Although I had a rudimentary understanding of R beforehand, I believe anyone with interest in the topic can pick this up.

Kim G Mar 31, 2015

This book will likely be a great resource for new users to R who hope to perform geospatial analyses. The first three chapters of the book provide a very basic level introduction to the R language and environment that will be useful to new readers, but may be found extraneous and unnecessary for those familiar with using R and only seeking further information on geospatial analysis. There are small instances of when the code supplied in the text is specific to a Windows PC environment, so new users on a different operating system will be left on their own to figure this step out; though it is not a difficult step. Advanced R users may also find the repeated instances throughout the chapters of how to load and look at your data to be tedious. Though I have not assessed this book in its entirety, I found the discussion on working with layers of data very useful to tasks I have tried implementing in the past. The book does a good job of describing many of the existing mapping tools that R is capable of, and should be useful to future researchers and data scientists.

Christian S. Mar 19, 2015

This is a great intro for geospatial analysis. The book is self contained and gives you a brief practical intro to R. The first three chapters give you the basics of the data structures and libraries you need to understand to follow the remaining chapters of the book.In chapter 4 is where the fun part starts. The intro to each chapter is clear and concise and the examples are nice to work with. The visualizations are great.The ebook has the images in full color, not sure about the printed version.Even though the author uses Windows file paths in their examples, it was easy to adjust them to work on a Mac using RStudio. The book provides a complete set of images and raster files as part of the source code.I wish the author had included examples of least-cost paths. It was just mentioned briefly.In summary I would recommend this book as a great intro to R and geospatial analysis and visualization.

Learning R for Geospatial Analysis: Leverage the power of R to elegantly manage crucial geospatial analysis tasks

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