updates index, makes small cleaning, updates mlr3 syntax

01-introduction.Rmd

@@ -21,7 +21,7 @@ Over the last few decades free and open source software for geospatial (FOSS4G\i
 Thanks to organizations such as OSGeo, advanced geographic techniques are no longer the preserve of those with expensive hardware and software: anyone can now download and run high-performance software for geocomputation.
 Open source Geographic Information Systems (GIS\index{GIS}), such as [QGIS](https://qgis.org/en/site/)\index{QGIS}, have made geographic analysis accessible worldwide.
 GIS software products are powerful, but tend to emphasize a graphical user interface\index{graphical user interface} (GUI) approach over the command-line interface (CLI) approach advocated in this book.
-The 'GUI-focus' of many GIS products has unintended consequence of disabling many users from making their work full reproducible\index{reproducibility}, a problem that can be overcome by calling 'geoalgorithms' contained in GIS software from the command line, as we'll see in Chapter \@ref(gis)).
+The 'GUI-focus' of many GIS products has unintended consequence of disabling many users from making their work full reproducible\index{reproducibility}, a problem that can be overcome by calling 'geoalgorithms' contained in GIS software from the command line, as we'll see in Chapter \@ref(gis).
 A simplistic comparison between the different approaches is illustrated in Table \@ref(tab:gdsl).
 
 ```{r gdsl, echo=FALSE, message=FALSE}
@@ -35,7 +35,7 @@ knitr::kable(x = d,
 ```
 
 R is not the only language providing a CLI for geocomputation.
-Other command environments with powerful geographic capabilities exist, including Python, Julia, and JavaScript.
+Other command environments with powerful geographic capabilities exist, including Python\index{Python}, Julia, and JavaScript.
 We encourage curious readers to give them a try and to reproduce the examples in this book in other languages, perhaps with reference to the book [Geocomputation with Python](https://py.geocompx.org/), the open access version of which is also hosted on [geocompx.org](https://geocompx.org/).
 However, R has some advantages that make it well-suited to geocomputation.
 The 'R-spatial stack' is easy to install, has comprehensive and well-maintained core packages meaning less 'context switching' and more focus on techniques rather than getting the code working.
@@ -57,7 +57,7 @@ This may sound simple and easy to achieve (which it is if you carefully maintain
 
 ## What is geocomputation?
 
-Geocomputation is the application and development of computational methods for geographic data processing, analysis, modeling and visualization with command-line tools and scripts, focused on performance, reproducibility and modularity.
+Geocomputation\index{geocomputation} is the application and development of computational methods for geographic data processing, analysis, modeling and visualization with command-line tools and scripts, focused on performance, reproducibility and modularity.
 This definition encapsulates many of the key ideas in this book, building on the short history of the word, dating back to the first conference on the subject in 1996 when it entered the lexicon.^[
 The first 'GeoComputation' conference took place at the University of Leeds, where one of the authors (Robin) is currently based.
 In 2017 the GeoComputation conference returned to University of Leeds, providing a chance for us to work on and present the book (see www.geocomputation.org for more on the conference series, and papers/presentations spanning more than two decades).
@@ -67,7 +67,7 @@ What distinguished geocomputation from the (at the time) commonly used term 'qua
 In the words of Stan Openshaw, a pioneer in the field who was an advocate (and possibly originator) of the term, "GeoComputation is about using the various different types of geodata and about developing relevant geo-tools within the overall context of a 'scientific' approach" [@openshaw_geocomputation_2000].
 Building on this early definition, *Geocomputation with R* goes beyond data analysis and modeling to include the development of new tools and methods for work that is not just interesting academically but beneficial.
 
-Our approach differs from early definitions of geocomputation in one important way, however: in its emphasis on reproducibility and collaboration.
+Our approach differs from early definitions of geocomputation in one important way, however: in its emphasis on reproducibility\index{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 sufficient RAM (at least 8 GB recommended) can install and run software for geocomputation, and reproduce the contents of this book.
@@ -89,7 +89,7 @@ Geocomputation is a recent term but is influenced by old ideas.
 It can be seen as a part of Geography\index{Geography}, which has a 2000+ year history [@talbert_ancient_2014];
 and an extension of *Geographic Information Systems* (GIS\index{GIS}) [@neteler_open_2008], which emerged in the 1960s [@coppock_history_1991].
 
-Geography\index{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.
+Geography\index{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\index{von Humboldt} 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_invention_2015].
 This book aims to contribute to the 'Geographic Tradition' [@livingstone_geographical_1992] by harnessing the power of modern computers and open source software.
@@ -246,8 +246,6 @@ knitr::include_graphics("figures/01-cranlogs.png")
 
 It is noteworthy that shifts in the wider R community, as exemplified by the data processing package **dplyr** (released in [2014](https://cran.r-project.org/src/contrib/Archive/dplyr/)) 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'\index{tidyverse (package)} in late [2016](https://cran.r-project.org/src/contrib/Archive/tidyverse/).
-<!--rl:toDo-->
-<!-- add reference -->
 The **tidyverse**\index{tidyverse (package)} 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 geographic data' which has been partly met by **sf**.
 An obvious feature of the **tidyverse** is the tendency for packages to work in harmony.
@@ -307,6 +305,7 @@ interfaces to GDAL\index{GDAL} and PROJ\index{PROJ}, for example, still power R'
 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 \@ref(read-write) 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_rasp:_2003 and the [R-help](https://stat.ethz.ch/pipermail/r-help/2003-January/028413.html) email list for context).
 
+\index{sp (package)}
 **sp**, released in 2005, overcame R's inability to distinguish spatial and non-spatial objects [@pebesma_classes_2005].
 **sp** grew from a workshop in Vienna in 2003 and was hosted at SourceForge before migrating to R-Forge, and then to GitHub.
 Prior to 2005, geographic coordinates were generally treated like any other number. 
@@ -330,6 +329,7 @@ length(revdep_sf) # 739 # 2023-11-16
 While **rgdal** and **sp** solved many spatial issues, it was not until **rgeos** was developed during a Google Summer of Code project in 2010 [@R-rgeos] that geometry operations could be undertaken on **sp** objects.
 Functions such as `gIntersection()` enabled users to find spatial relationships between geographic objects and to modify their geometries (see Chapter \@ref(geometry-operations) for details on geometric operations with **sf**).
 
+\index{raster (package)}
 A limitation of the **sp** ecosystem was its limited support for raster data.
 This was overcome by **raster**\index{raster}, first released in 2010 [@R-raster].
 **raster**'s class system and functions enabled a range of raster operations, capabilities now implemented in the **terra** package, which supersedes **raster**, as outlined in Section \@ref(raster-data).
@@ -351,7 +351,7 @@ Although geographic visualization tended to focus on vector data, raster visuali
 Since then map making in R has become a hot topic, with dedicated packages such as **tmap**, **leaflet**, **rayshader** and **mapview** gaining popularity, as highlighted in Chapter \@ref(adv-map).
 
 Since 2018, when the First Edition of Geocomputation with R was published, the development of geographic R packages has accelerated. 
-\index{terra (package)}
+\index{terra (package)}\index{raster (package)}
 **terra**, a successor of the **raster** package, was firstly released in 2020 [@R-terra], bringing several benefits to R users working with raster datasets: it  is faster and has more a straightforward user interface than its predecessor, as described in Section \@ref(raster-data).
 
 In mid-2021, a substantial (and in some cases breaking) change was made to the **sf** package by incorporating spherical geometry calculations.