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 ).²Unlike 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 .

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 ), which emerged in the 1960s (Coppock and Rhind 1991).

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 ).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.