Geocomputation with R

Robin Lovelace, Jakub Nowosad, Jannes Muenchow

• Publisher: Chapman and Hall/CRC
• Year: 2019
• ISBN:
  9781351396899
• Edition: 1
• Topic: Matematica
• Subtopic: Probabilità e statistica

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/.

1

Introduction

This book is about using the power of computers to do things with geographic data. It teaches a range of spatial skills, including: reading, writing and manipulating geographic data; making static and interactive maps; applying geocomputation to solve real-world problems; and modeling geographic phenomena. By demonstrating how various geographic operations can be linked, in reproducible ‘code chunks’ that intersperse the prose, the book also teaches a transparent and thus scientific workflow. Learning how to use the wealth of geospatial tools available from the R command line can be exciting, but creating new ones can be truly liberating. Using the command-line driven approach taught throughout, and programming techniques covered in Chapter 10, can help remove constraints on your creativity imposed by software. After reading the book and completing the exercises, you should therefore feel empowered with a strong understanding of the possibilities opened up by R’s impressive geographic capabilities, new skills to solve real-world problems with geographic data, and the ability to communicate your work with maps and reproducible code.

Over the last few decades free and open source software for geospatial (FOSS4G) has progressed at an astonishing rate. Thanks to organizations such as OSGeo, geographic data analysis is no longer the preserve of those with expensive hardware and software: anyone can now download and run high-performance spatial libraries. Open source Geographic Information Systems (GIS) such as QGIS¹ have made geographic analysis accessible worldwide. GIS programs tend to emphasize graphical user interfaces (GUIs), with the unintended consequence of discouraging reproducibility (although many can be used from the command line as we’ll see in Chapter 9). R, by contrast, emphasizes the command line interface (CLI). A simplistic comparison between the different approaches is illustrated in Table 1.1.

TABLE 1.1: Differences in emphasis between software packages (Graphical User Interface (GUI) of Geographic Information Systems (GIS) and R).

Attribute	Desktop GIS (GUI)	R
Home disciplines	Geography	Computing, Statistics
Software focus	Graphical User Interface	Command line
Reproducibility	Minimal	Maximal

This book is motivated by the importance of reproducibility for scientific research (see the note below). It aims to make reproducible geographic data analysis workflows more accessible, and demonstrate the power of open geospatial software available from the command-line. “Interfaces to other software are part of R” (Eddelbuettel and Balamuta, 2018). This means that in addition to outstanding ‘in house’ capabilities, R allows access to many other spatial software libraries, explained in Section 1.2 and demonstrated in Chapter 9. Before going into the details of the software, however, it is worth taking a step back and thinking about what we mean by geocomputation.

Reproducibility is a major advantage of command-line interfaces, but what does it mean in practice? We define it as follows: “A process in which the same results can be generated by others using publicly accessible code.”

This may sound simple and easy to achieve (which it is if you carefully maintain your R code in script files), but has profound implications for teaching and the scientific process (Pebesma et al., 2012).

1.1 What is geocomputation?

Geocomputation is a young term, dating back to the first conference on the subject in 1996.² What distinguished geocomputation from the (at the time) commonly used term ‘quantitative geography’, its early advocates proposed, was its emphasis on “creative and experimental” applications (Longley et al., 1998) and the development of new tools and methods (Openshaw and Abrahart, 2000): “GeoComputation is about using the various different types of geodata and about developing relevant geo-tools within the overall context of a ‘scientific’ approach.” This book aims to go beyond teaching methods and code; by the end of it you should be able to use your geocomputational skills, to do “practical work that is beneficial or useful” (Openshaw and Abrahart, 2000).

Our approach differs from early adopters such as Stan Openshaw, however, in its emphasis on reproducibility and collaboration. At the turn of the 21^st Century, it was unrealistic to expect readers to be able to reproduce code examples, due to barriers preventing access to the necessary hardware, software and data. Fast-forward two decades and things have progressed rapidly. Anyone with access to a laptop with ~4GB RAM can realistically expect to be able to install and run software for geocomputation on publicly accessible datasets, which are more widely available than ever before (as we will see in Chapter 7).³ Unlike early works in the field, all the work presented in this book is reproducible using code and example data supplied alongside the book, in R packages such as spData, the installation of which is covered in Chapter 2.

Geocomputation is closely related to other terms including: Geographic Information Science (GIScience); Geomatics; Geoinformatics; Spatial Information Science; Geoinformation Engineering (Longley, 2015); and Geographic Data Science (GDS). Each term shares an emphasis on a ‘scientific’ (implying reproducible and falsifiable) approach influenced by GIS, although their origins and main fields of application differ. GDS, for example, emphasizes ‘data science’ skills and large datasets, while Geoinformatics tends to focus on data structures. But the overlaps between the terms are larger than the differences between them and we use geocomputation as a rough synonym encapsulating all of them: they all seek to use geographic data for applied scientific work. Unlike early users of the term, however, we do not seek to imply that there is any cohesive academic field called ‘Geocomputation’ (or ‘GeoComputation’ as Stan Openshaw called it). Instead, we define the term as follows: working with geographic data in a computational way, focusing on code, reproducibility and modularity.

Geocomputation is a recent term but is influenced by old ideas. It can be seen as a part of Geography, which has a 2000+ year history (Talbert, 2014); and an extension of Geographic Information Systems (GIS) (Neteler and Mitasova, 2008), which emerged in the 1960s (Coppock and Rhind, 1991).

Geography has played an important role in explaining and influencing humanity’s relationship with the natural world long before the invention of the computer, however. Alexander von Humboldt’s travels to South America in the early 1800s illustrates this role: not only did the resulting observations lay the foundations for the traditions of physical and plant geography, they also paved the way towards policies to protect the natural world (Wulf, 2015). This book aims to contribute to the ‘Geographic Tradition’ (Livingstone, 1992) by harnessing the power of modern computers and open source software.

The book’s links to older disciplines were reflected in suggested titles for the book: Geography with R and R for GIS. Each has advantages. The former conveys the message that it comprises much more than just spatial data: non-spatial attribute data are inevitably interwoven with geometry data, and Geography is about more than where something is on the map. The latter communicates that this is a book about using R as a GIS, to perform spatial operations on geographic data (Bivand et al., 2013). However, the term GIS conveys some connotations (see Table 1.1) which simply fail to communicate one of R’s greatest strengths: its console-based ability to seamlessly switch between geographic and non-geographic data processing, modeling and visualization tasks. By contrast, the term geocomputation implies reproducible and creative programming. Of course, (geocomputational) algorithms are powerful tools that can become highly complex. However, all algorithms are composed of smaller parts. By teaching you its foundations and underlying structure, we aim to empower you to c...