The defining feature of spatial data analysis is the reference, within the data being analyzed, to locations on the surface of the earth. This is a very broad subject encompassing distinct areas of expertise such as spatial statistics, geometric computation, and image processing.

In practice, spatial data is commonly stored, viewed, and analyzed in Geographic Information System (GIS) software, of which the most well-known example is ArcGIS. However, most often, menu-based interfaces of GIS software are too narrow in scope to meet with specialized demands or too inflexible to feasibly accomplish customized, repetitive tasks. Writing scripts rather than using menus or working in combination with external software are two commonly used paths to solve such problems. However, what if we can use a single environment, combining the advantages of programming and spatial data analysis capabilities with a comprehensive ecosystem of computational tools that are readily implementable in customized procedures?

This book will demonstrate that the R programming language is indeed such an environment and teach you how to use it in order to perform various spatial data analysis tasks.

(For more resources related to this topic, see here.)

What you will learn

This book covers the basic concepts related to writing R code. You will also learn how to work with vectors, time series, tables, rasters, points, lines, and polygons. The book also covers several advanced themes associated with raster data analysis in R. Demonstrations on how rasters and vector layers can be combined in a single analysis are shown. Transformation between raster and vector data structures as well as data extraction from a raster based on vector layers are covered in this book. Moreover, we will also learn how spatial interpolation can be carried out in R through examples of interpolating meteorological point measurements to create annual temperature maps of Spain. You will also explore some of the most useful methods for advanced visualization of spatial data in R, using the ggplot2, ggmap, and lattice packages.

How the book differs

Most currently available books on this subject are focused on advanced applications such as spatial statistics, assuming you have prior knowledge of R and the respective scientific domains. Yet, introductory material on R from the point of view of a spatial data analyst, which is focused on introductory topics such as spatial data handling, computation, and visualization, is scarce. This book aims to fill the gap. Thus, this book is intended for anyone who wants to learn how to efficiently analyze geospatial data with R. No prior experience with R and/or programming is required; only you need to be familiar with basic geographic information concepts (such as spatial coordinates).

Required skills

To follow through the examples in this book, all you need to do is install R (which is available for free) and download the example datasets from the book’s website. Some of the examples also require you to have an Internet connection to download additional datasets and R packages from the R environment.

Summary

This book is composed of step-by-step tutorials, starting with the language basics before proceeding to cover the main GIS operations and data types. Visualization of spatial data is vital either during the various analysis steps and/or as the final product, and this book shows you how to get the most out of R’s visualization capabilities. The book culminates with examples of cutting-edge applications utilizing R’s strengths as a statistical and graphical tool.

Resources for Article:

 Further resources on this subject:

• Data visualization [article]
• Machine Learning in Bioinformatics [article]
• Specialized Machine Learning Topics [article]