# 1 Introduction

This book is about harnessing the power of modern 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 spatial 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, by removing constraints on your creativity imposed by software. By the end of the book you should be able to create new tools for geocomputation in the form of shareable R scripts and functions.

Over the last few decades free and open source software for geospatial data (FOSS4G) has progressed at an astonishing rate. Thanks to organisations such as FOSS4G and the wider open source movement geospatial analysis is no longer the preserve of those with expensive hardware and software: anyone can now download high performance spatial libraries on their computer. Open source Geographic Information Systems (GIS) such as QGIS have greatly reduced the ‘barrier to entry’. Furthermore many GIS programs provide a command-line interface, either from the system command line (the Unix terminal on Linux and Mac or Windows Powershell) via application programming interfaces (APIs) (see Chapter 9) and console windows (such as the Python Console in QGIS) to supplement the main graphical user interface (GUI). Still, many GIS programs and teaching materials focus on the GUI, which can have the unintended consequence of discouraging reproducibility (see Table 1.1). Motivated by the importance of reproducibility for scientific research and other advantages of typing commands rather than pointing and clicking, this book focuses on R’s Command Line Interface (CLI) for GIS operations. The reproducible and ‘computational’ workflows enabled by R’s CLI can also help unleash its statistical capabilities on geographic data (see section 1.2).

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

Reproducibility is a major advantage of command-line interfaces, but what does it mean in practice? We define it as follows:

A process is reproducible only if 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, Nüst, and Bivand 2012).

A major aim of this book is to make geographic data analysis more accessible as part of a reproducible workflow. R is a flexible language that allows access to many spatial software libraries (see section 1.2). Before going into the details of the software, however, it is worth taking a step back and thinking about what we mean by geocomputation.

## 1.1 What is geocomputation?

Geocomputation is a relatively young field with a ~30 year history, dating back to the first conference on the subject in 1996.1 What distinguishes geocomputation from the older quantitative geography, is its emphasis on “creative and experimental” GIS applications (Longley et al. 1998). Additionally, it is also about developing new, research-driven 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 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 in one important way, however. At the turn of the 21st 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 geocompuation on publicly accessible datasets, which are more widely available than ever before (as we will see in Chapter 7).2 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 the fledgling Geographic Data Science (GDS). Each term shares an emphasis on a ‘scientific’ (implying reproducible and falsifiable) approach building on GIS software, although their origins and main fields of application differ. GDS, for example, emphasizes ‘data science’ skills and large datasets, and can be used as a synonym for Geocomputation. A distinguishing feature of Geocomputation, as advocated in this book, is its focus on applied geographic analysis and the application and development of new methods.

Geocomputation is young, but it builds on older fields. 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, Pebesma, and Gómez-Rubio 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 create your own innovative solutions to geographic data problems.

## 1.2 Why Geocomputation with R?

Early geographers used a variety of tools including barometers, compasses and sextants to advance knowledge about the world (Wulf 2015). It was only with the invention of the marine chronometer in 1761 that it became possible to calculate longitude at sea, enabling ships to take more direct routes and accurate maps of the world.

Nowadays such lack of geographic data is hard to imagine. Every smartphone has a global positioning (GPS) receiver and a multitude of sensors on devides ranging from satellites and semi-autonomous vehicles to citizen scientists incessantly measure every part of the world. The rate of data produced is overwhelming. An autonomous vehicle, for example, can generate 100 GB of data per day (The Economist 2016). Remote sensing data from satellites has become too large to analyze the corresponding data with a single computer, leading to initiatives such as OpenEO.

This ‘geodata revolution’ drives demand for high performance computer hardware and efficent, scalable software to handle and extract signal from the noise to understand and perhaps change the world. ‘Geodatabases’ enable storage and generation of manageble subsets from the vast geographic data stores, making interfaces for gaining knowledge from them vital tools for the future. R is one such tool, with advanced analysis, modelling and visualization capabilities. In this context the focus of the book is not on the language itself (see Wickham 2014). Instead we use R as a ‘tool for the trade’ for understanding the world, similar to Humboldt’s use of tools to gain a deep understanding of nature in all its complexity and interconnections Wulf (2015). Although programming can seem like a reductionist activity, the aim is to teach Geocomputation with R not only for fun, but for understanding the world.

R is a multi-platform, open source language and environment for statistical computing and graphics (r-project.org/). With a wide range of packages R also supports advanced geospatial statistics, modeling and visualization. New integrated development environments (IDEs) such as RStudio have made R more user friendly for many, easing map making with a panel dedicated to interactive visualization.

At its core R is an object-oriented, functional programming language (Wickham 2014), and was specifically designed as an interactive interface to other software (Chambers 2016). The latter also includes many ‘bridges’ to a treasure trove of GIS software, ‘geolibraries’ and functions (see Chapter 9). It is thus ideal for quickly creating ‘geo-tools’, without needing to master lower level languages (compared to R) such as C, FORTRAN or Java (see section 1.3). This can feel like breaking free from the metaphorical ‘glass ceiling’ imposed by GUI-based or proprietary geographic information systems (see Table 1.1 for a definition of GUI). Furthermore R facilitates access to other languages: the packages Rcpp and reticulate enable access to C++ and Python code, for example. This means R can be used as a ‘bridge’ to a wide range of geospatial programs (see section 1.3).

An example showing R’s flexibility and evolving geographic capabilities is leaflet (Cheng, Karambelkar, and Xie 2018). We’ll see in Chapter 8 how this interactive mapping package has been extended: there are now many ways to generate interactive geographic data visualizations from the R command line. Thanks to these developments the statement that R has “limited interactive [plotting] facilities” (Bivand, Pebesma, and Gómez-Rubio 2013) is no longer true. This is demonstrated by the following code chunk (which creates Figure 1.1).

library(leaflet)
popup = c("Robin", "Jakub", "Jannes")
leaflet() %>%
lat = c(52, 53, 49),
popup = popup)

Figure 1.1: Where the authors are from. The basemap is a tiled image of the Earth at Night provided by NASA. Interact with the online version at robinlovelace.net/geocompr, for example by zooming-in and clicking on the popups.

It would have been difficult to produce Figure 1.1 using R a few years ago, let alone as an interactive map. This illustrates R’s flexibility and how, thanks to developments such as knitr and leaflet, it can be used as an interface to other software, a theme that will recur throughout this book. The use of R code, therefore, enables teaching geocomputation with reference to reproducible examples such as that provided in 1.1 rather than abstract concepts.

## 1.3 Software for geocomputation

R is a powerful language for geocomputation but there are many other options for spatial data analysis providing thousands of geographic functions. Awareness of other languages for geocomputation will help decide when a different tool may be more appropriate for a specific task, and place R in the wider geospatial ecosystem. Various R interfaces or ‘bridges’ to dedicated GIS software have been created (see Chapter 9) so it’s worth knowing what is out there even from an R spatial perspective: there may already be an existing (or in-development) bridge enabling functions implemented in other languages to be run from the R command line. This section briefly introduces the languages C++, Java and Python for geocomputation, in preparation for Chapter 9.

An important feature of R (and Python) is that it is an interpreted language. This is advantageous because it enables interactive programming in a Read–Eval–Print Loop (REPL): code entered into the console is immediately executed and the result is printed, rather than waiting for the intermediate stage of compilation. On the other hand compiled languages such as C++, Java and Rust tend to run faster (after they have been compiled, which takes computer and programmer time).

C++ provides the basis for many GIS packages such as QGIS, GRASS and SAGA so is a sensible starting point. Well-written C++ is very fast, making it a good choice for performance-critical applications such as the processing of large spatial data but is harder to learn than Python or R. C++ has become more accessible through the Rcpp package, which provides a good ‘way in’ to C programming for R users. Proficiency with C++ opens the possibility of creating new, high-performance ‘geoalgorithms’ and a better understanding of the code underlying dedicated open-source GIS software such as GEOS (see Chapter 10).

Java is another important and versatile language for geocomputation. GIS packages gvSig, OpenJump and uDig are all written in Java. There are many GIS libraries written in Java, including GeoTools and JTS, the Java Topology Suite (GEOS is a C++ port of JTS). Furthermore, many map server applications use Java including Geoserver/Geonode, deegree and 52°North WPS.

Java’s object-oriented syntax is similar to that of C++. A major advantage of Java is that it is platform-independent (which is unusual for a compiled language) and is highly scalable, making it suitable language for IDEs such as RStudio, with which this book was written. Java has fewer tools for statistical modeling and visualization than Python or R, although it can be used for data science (Brzustowicz 2017).

Python is an important language for geocomputation. Like R, it is a popular tool for data science, although it more general purpose than R. Both languages are object-oriented, and have many areas of overlap, leading to initiatives such as the reticulate package that facilitates access to Python from R and the Ursa Labs initiative to support portable libraries of benefit to the entire open source data science ecosystem.

In practice both languages have their strengths and to some extent which you use is less important than the domain of application and communication of results. Learning either will provide a head-start in learning the other. However, there are major advantages of R over Python for geocomputation which explains its prominence in this book. R has unparalleled support for statistics, including spatial statistics, with hundreds of packages (unmatched by Python) supporting thousands of statistical methods.

The major advantage of Python is that it is a general-purpose programming language. It is used in many domains, including desktop software, computer games, websites and data science. R, by contrast, was originally developed for statistics. It has been extended in many directions including, for example, web application development (see 8), but is still primarily used for statistics and data science, explaining its smaller (but rapidly growing) user base. Python is often the only shared language between different (geocomputation) communities and can be seen as the ‘glue’ that holds many GIS programs together. Many geoalgorithms, including those in QGIS and ArcMap, can be accessed from the Python command line, making it well-suited as a starter language for command-line GIS.3

For spatial statistics and predictive modeling, however, R is second-to-none. This does not mean you must chose either R or Python: Python supports most common statistical techniques (though R tends to support new developments in spatial statistics earlier) and many concepts learned from Python can be applied to the R world. Like R Python also supports spatial data analysis and manipulation with packages such as osgeo, Shapely, NumPy and PyGeoProcessing (Garrard 2016).

## 1.4 R’s spatial ecosystem

There are many ways to handle spatial data in R, with dozens of packages in the area.4 In this book we endeavor to teach the state-of-the-art in the field whilst ensuring that the methods are future-proof. Like many areas of software development, R’s spatial ecosystem is rapidly evolving. Because R is open source, these developments can easily build on previous work, by ‘standing on the shoulders of giants’, as Isaac Newton put it in 1675. This approach is advantageous because it encourages collaboration and avoids ‘reinventing the wheel’. The package sf (covered in Chapter 2), for example, builds on its predecessor sp.

A surge in development time (and interest) in ‘R-Geo’ has followed the award of a grant by the R Consortium for the development of support for Simple Features, an open-source standard and model to store and access vector geometries. This resulted in the sf package (covered in 2.1.1). Multiple places reflect the immense interest in sf. This is especially true for the R-sig-Geo Archives, a long-standing open access email list containing much R-spatial wisdom accumulated over the years.

It is noteworthy that shifts in the wider R community, as exemplified by the data processing package dplyr (released in 2014) influenced shifts in R’s spatial ecosystem. Alongside other packages that have a shared style and emphasis on ‘tidy data’ (including e.g., ggplot2), dplyr was placed in the tidyverse ‘metapackage’ in late 2016. The tidyverse approach, with its focus on long-form data and fast intuitively named functions, has become immensely popular. This has led to a demand for ‘tidy spatial data’ which has been partly met by sf and other approaches such as tabularaster. An obvious feature of the tidyverse is the tendency for packages to work in harmony. There is no equivalent geoverse but there are attempts at harmonization between packages hosted on in the r-spatial organisation and a growing number of packages use sf (Table 1.2).

Table 1.2: The top 5 most downloaded packages that depend on sf, in terms of average number of downloads per day over the previous month. As of 2018-06-14 there are 80 packages which import sf.
plotly 1888
raster 1725
spData 1429
leaflet 976
spdep 892

## 1.5 The history of R-spatial

There are many benefits of using recent spatial packages such as sf, but it also important to be aware of the history of R’s spatial capabilities: many functions, use-cases and teaching material are contained in older packages. These can still be useful today, provided you know where to look.

R’s spatial capabilities originated in early spatial packages in the S language (Bivand and Gebhardt 2000). The 1990s saw the development of numerous S scripts and a handful of packages for spatial statistics. R packages arose from these and by 2000 there were R packages for various spatial methods “point pattern analysis, geostatistics, exploratory spatial data analysis and spatial econometrics”, according to an article presented at GeoComputation 2000 (Bivand and Neteler 2000) Some of these, notably spatial, sgeostat and splancs are still available on CRAN (Rowlingson and Diggle 1993, 2017; Venables and Ripley 2002; Majure and Gebhardt 2016).

A subsequent article in R News (the predecessor of The R Journal) contained an overview of spatial statistical software in R at the time, much of which was based on previous code written for S/S-PLUS (Ripley 2001). This overview described packages for spatial smoothing and interpolation, including akima and geoR (Akima and Gebhardt 2016; Jr and Diggle 2016), and point pattern analysis, including splancs (Rowlingson and Diggle 2017) and spatstat, which remains dominant in the field of spatial point pattern analysis (Baddeley, Rubak, and Turner 2015).

The following R News issue (Volume 1/3) put spatial packages in the spotlight again, with an introduction to splancs and a commentary on future prospects regarding spatial statistics (Bivand 2001). Additionally, the issue introduced two packages for testing spatial autocorrelation that eventually became part of spdep (Bivand 2017). Notably, the commentary mentions the need for standardization of spatial interfaces, efficient mechanisms for exchanging data with GIS, and handling of spatial metadata such as coordinate reference systems (CRS).

maptools (written by Nicholas Lewin-Koh; Bivand and Lewin-Koh 2017) is another important package from this time. Initially maptools just contained a wrapper around shapelib and permitted the reading of ESRI Shapefiles into geometry nested lists. The corresponding and nowadays obsolete S3 class called “Map” stored this list alongside an attribute data frame. The work on the “Map” class representation was nevertheless important since it directly fed into sp prior to its publication on CRAN.

In 2003, Bivand (2003) published an extended review of spatial packages. Around this time the development of R’s spatial capabilities increasingly supported interfaces to external libraries, especially to GDAL and PROJ. These interfaces facilitated geographic data I/O (meaning input output and covered in chapter 7) and CRS transformations, respectively. Bivand (2003) proposed a spatial data class system, including support for points, lines, polygons and grids based on GDAL’s support for a wide range of spatial data formats. All these ideas contributed to the packages rgdal and sp, which became the foundational packages for spatial data analysis with R (Bivand, Pebesma, and Gómez-Rubio 2013).

rgdal, released in 2003, provided GDAL bindings for R which greatly enhanced its ability to import data from previously unavailable geographic data formats. The initial release supported only raster drivers but subsequent enhancements provided support for coordinate reference systems (via the PROJ library), reprojections and import of vector file formats (see Chapter 7 for more on file formats). Many of these additional capabilities were developed by Barry Rowlingson and released in the rgdal codebase in 2006 (see Rowlingson et al. 2003 and the R-help email list for context).

sp, released in 2005, overcame R’s inability to distinguish spatial and non-spatial objects (Pebesma and Bivand 2005). sp grew from a workshop in Vienna in 2003 and was hosted at sourceforge before migrating to R-Forge. Prior to 2005, geographic coordinates were generally treated like any other number. sp changed this with its classes and generic methods supporting points, lines, polygons and grids, and attribute data.

sp stores information such as bounding box, coordinate reference system and attributes in slots in Spatial objects using the S4 class system, enabling data operations to work on geographic data (see section 2.1.2). Further, sp provides generic methods such as summary() and plot() for geographic data. In the following decade, sp classes rapidly became the go-to standard for spatial data in R and the number of packages that depended on it increased from around 20 in 2008 and over 100 in 2013 (Bivand, Pebesma, and Gómez-Rubio 2013). As of 2018 almost 500 packages rely on sp, making it an important part of the R ecosystem. Prominent R packages using sp include: gstat, for spatial and spatio-temporal geostatistics; geosphere, for spherical trigonometry; and adehabitat used for the analysis of habitat selection by animals (E. Pebesma and Graeler 2018; Calenge 2006; Hijmans 2016).

While rgdal and sp solved many spatial issues, R was still lacked the ability to do geometric operations (see chapter 5). Colin Rundel addressed this issue by developing rgeos, an R interface to the open-source geometry library (GEOS) during a Google Summer of Coding project in 2010 (Bivand and Rundel 2017). rgeos enabled GEOS to manipulate sp objects, with functions such as gIntersection().

Another limitation of sp — its limited support for raster data — was overcome by raster, first released in 2010 (Hijmans 2017). Its class system and functions support a range raster operations as outlined in section 2.2. A key feature of raster is its ability to work with datasets that are too large to fit into the main memory (RAM), thereby overcoming one of R’s major limitations with respect to spatial data. raster also supports map algebra (see section 4.3.2).

In parallel with these developments of class systems and methods came the support for R as an interface to dedicated GIS software. GRASS (R. S. Bivand 2000) and follow-on packages spgrass6 and rgrass7 (for GRASS GIS 6 and 7, respectively) were prominent examples in this direction (Bivand 2016b, 2016a). Other examples of bridges between R and GIS include RSAGA (Brenning, Bangs, and Becker 2018, first published in 2008), ArcGIS (A. Brenning 2012a, first published in 2008), and RQGIS (Muenchow, Schratz, and Brenning 2017, first published in 2016) (see Chapter 9).

Visualization was not a focus initially, with the bulk of R-spatial development focussed on analysis and geographic operations. sp provided methods for map making using both the base and lattice plotting system but demand was growing for advanced map making capabilities, especially after the release of ggplot2 in 2007. ggmap extended ggplot2’s spatial capabilities (Kahle and Wickham 2013), by facilitating access to ‘basemap’ tiles from online services such as Google Maps. Though ggmap facilitated map-making with ggplot2, its utility was limited by the need to fortify spatial objects, which means converting them into long data frame. While this works well for points it is computationally inefficient for lines and polygons, since each coordinate (vertex) is converted into a row, leading to huge data frames to represent complex geometries. Although geographic visualization tended to focus on vector data, raster visualisation is supported in raster and received a boost with the release of rasterVis (Lamigueiro 2014). As of 2018 map making in R is a hot topic with dedicated packages such as tmap, leaflet and mapview all supporting the class system provided by sf, the focus of the next chapter (see Chapter 8 for more on vizualization).

## 1.6 Exercises

1. Think about the terms ‘GIS’, ‘GDS’ and ‘Geocomputation’ described above. Which is your favorite, and why?

2. Provide three reasons for using a scriptable language such as R for geocomputation instead of using an established GIS program such as QGIS.

3. Name two advantages and two disadvantages of using mature packages compared with ‘cutting edge’ packages for spatial data (for example sp vs sf).

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1. The conference took place at the University of Leeds, where one of the authors (Robin) is currently based and where the 21st GeoComputation was hosted in 2017 (see http://www.geocomputation.org/).

2. A laptop with 4GB running a modern operating system such as Ubuntu 16.04 onwards should also be able to reproduce the contents of this book. A laptop with this specification or above can be acquired second-hand for ~US\$100 in many countries nowadays, reducing the financial/hardware barrier to geocomputation far below the levels in operation in the early 2000s, when high-performance computers were unaffordable for most people.

3. Python modules providing access to geoalgorithms include grass.script for GRASS, saga-python for SAGA-GIS, processing for QGIS and arcpy for ArcGIS.

4. An overview of R’s spatial ecosystem can be found in the CRAN Task View on the Analysis of Spatial Data (see https://cran.r-project.org/web/views/Spatial.html).