Refactor description of sf interfaces, big it up

01-introduction.Rmd

@@ -168,7 +168,7 @@ if(knitr::is_latex_output()){
 
 It would have been difficult to produce Figure \@ref(fig:interactive) 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 Figure \@ref(fig:interactive) rather than abstract concepts.
+The use of R code, therefore, enables teaching geocomputation with reference to reproducible examples representing real world phenomena, rather than just abstract concepts.
 
 ## Software for geocomputation
 <!--rl-->

02-spatial-data.Rmd

@@ -8,6 +8,7 @@ This is the first practical chapter of the book, and therefore it comes with som
 We assume that you have an up-to-date version of R installed and that you are comfortable using software with a command-line interface such as the integrated development environment (IDE) RStudio.
 <!--or VSCode?-->
 
+<!-- Should we update these references to more up-to-date resources? -->
 If you are new to R, we recommend reading Chapter 2 of the online book *Efficient R Programming* by @gillespie_efficient_2016 and learning the basics of the language with reference to resources such as @grolemund_r_2016.
 Organize your work (e.g., with RStudio projects) and give scripts sensible names such as `02-chapter.R` to document the code you write as you learn.
 \index{R!pre-requisites}
@@ -107,8 +108,16 @@ source("https://github.com/Robinlovelace/geocompr/raw/main/code/02-vectorplots.R
 knitr::include_graphics(c("figures/vector_lonlat.png", "figures/vector_projected.png"))
 ```
 
-**sf** is a package providing classes for geographic vector data.
-Not only does **sf** supersede **sp**, it also provides a consistent command-line interface to GEOS\index{GEOS} and GDAL\index{GDAL}, superseding **rgeos** and **rgdal** (described in Section \@ref(the-history-of-r-spatial)).
+The **sf** package provides classes for geographic vector data and a consistent command-line interface to important low level libraries for geocomputation:
+
+- PROJ, a powerful library for coordinate system transformations, which underlies the content covered in Chapter \@ref(reproj-geo-data)
+- GDAL\index{GDAL}, for reading, writing and manipulating a wide range of geographic data formats, covered in Chapter \@ref(read-write)
+- GEOS\index{GEOS}, for geometry operations such as calculating buffers and centroids, covered in Chapter \@ref(geometric-operations)
+- [S2](https://s2geometry.io/), a spherical geometry engine written in C++ developed by Google, via the [**s2**](https://r-spatial.github.io/s2/) package, covered in Section \@ref(s2) below and in Chapter \@ref(reproj-geo-data)
+<!-- - [liblwgeom](https://github.com/postgis/postgis/tree/master/liblwgeom), a geometry engine used by PostGIS, via the [**lwgeom**](https://r-spatial.github.io/lwgeom/) package -->
+
+Information about these interfaces is provided by **sf** the first time the package is loaded, explaining the meaning of the text `Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1; sf_use_s2() is TRUE` that appears below the `library(sf)` command at the beginning of this chapter.
+At the time of writing (2022), **sf** is the only package/system *in any language* that provides a unified interface to this wide range of high performance functionality from an interactive command-line environment for reproducible research.
 This section introduces **sf** classes in preparation for subsequent chapters (Chapters \@ref(geometric-operations) and \@ref(read-write) cover the GEOS and GDAL interface, respectively).
 
 ### An introduction to simple features {#intro-sf}
@@ -258,7 +267,6 @@ class(nc_dfr)
 class(nc_tbl)
 ```
 
-
 As described in Chapter \@ref(attr), which shows how to manipulate `sf` objects with **tidyverse** functions, **sf** is now the go-to package for analysis of spatial vector data in R (not withstanding the **spatstat** package ecosystem which provides numerous functions for spatial statistics).
 Many popular packages build on **sf**, as shown by the rise in its popularity in terms of number of downloads per day, as shown in Section \@ref(r-ecosystem) in the previous chapter.
 Transitioning established packages and workflows away from legacy packages **rgeos** and **rgdal** takes time [@bivand_progress_2021], but the process was given a sense of urgency by messages printed when they were loaded, which state that they "will be retired by the end of 2023".
@@ -774,6 +782,42 @@ sf::st_crs(df_sf) = 4326
 It is fast and reliable at 'casting' geometry columns to different types, a topic covered in Chapter \@ref(geometric-operations).
 Benchmarks, in the package's [documentation](https://dcooley.github.io/sfheaders/articles/examples.html#performance) and in test code developed for this book, show it is much faster than the `sf` package for such operations.
 
+### Spherical geometry operations with S2 {#s2}
+
+Spherical geometry engines are based on the fact that world is round while simple mathematical procedures for geocomputation, such as calculating a straight line between two points or the area enclosed by a polygon, assume planar (projected) geometries.
+Since **sf** version 1.0.0, R supports spherical geometry operations 'out of the box', thanks to its interface to Google's S2 spherical geometry engine via the **s2** interface package.
+S2 is perhaps best known as an example of a Discrete Global Grid System (DGGS).
+Another example is the [H3](https://eng.uber.com/h3/) global hexagonal hierarchical spatial index  [@bondaruk_assessing_2020].
+
+Although potentially useful for describing locations anywhere on Earth using character strings such as [e66ef376f790adf8a5af7fca9e6e422c03c9143f](https://developers.google.com/maps/documentation/gaming/concepts_playable_locations), the main benefit of **sf**'s interface to S2 is its provision of drop-in functions for calculations such as distance, buffer, and area calculations, as described in **sf**'s built in documentation which can be opened with the command [`vignette("sf7")`](https://r-spatial.github.io/sf/articles/sf7.html).
+
+**sf** can run in two modes with respect to S2: on and off.
+By default the S2 geometry engine is turned on, as can be verified with the following command:
+
+```{r}
+sf_use_s2()
+```
+
+An example of the consequences of turning the geometry engine off is shown below, by creating buffers around the `india` object created earlier in the chapter (note the warnings emitted when S2 is turned off):
+
+```{r}
+india_buffer_with_s2 = st_buffer(india, 1)
+sf_use_s2(FALSE)
+india_buffer_without_s2 = st_buffer(india, 1)
+```
+
+```{r s2example, fig.show='hold', out.width="60%", echo=FALSE, fig.cap="Example of the consequences of turning off the S2 geometry engine. Both representations of a buffer around India were created with the same command but the light green polygon object was created with S2 switched on, resulting in a buffer of 1 m. The larger red polygon was created with S2 switched off, resulting in a buffer with inaccurate units of degrees longitude/latitude."}
+plot(st_geometry(india_buffer_without_s2), expandBB = c(0, 0.2, 0.1, 1), col = "red")
+plot(st_geometry(india_buffer_with_s2), expandBB = c(0, 0.2, 0.1, 1), col = "lightgreen", add = TRUE)
+```
+
+Throughout this book we will assume that S2 is turned on, unless explicitly stated.
+Turn it on again with the following command.
+
+```{r}
+sf_use_s2(TRUE)
+```
+
 ## Raster data
 
 The spatial raster data model represents the world with the continuous grid of cells (often also called pixels; Figure \@ref(fig:raster-intro-plot):A).
@@ -1028,7 +1072,6 @@ For now, it is sufficient to know:
 - Knowing which CRS your data is in, and whether it is in geographic (lon/lat) or projected (typically meters), is important and has consequences for how R handles spatial and geometry operations
 - CRSs of `sf` objects can be queried with the function `st_crs()`, CRSs of `terra` objects can be queried with the function `crs()`
 
-
 ```{r vector-crs, echo=FALSE, fig.cap="Examples of geographic (WGS 84; left) and projected (NAD83 / UTM zone 12N; right) coordinate systems for a vector data type.", message=FALSE, fig.asp=0.56, fig.scap="Examples of geographic and projected CRSs (vector data)."}
 # source("https://github.com/Robinlovelace/geocompr/raw/main/code/02-vector-crs.R")
 knitr::include_graphics("figures/02_vector_crs.png")

_01-ex.Rmd

@@ -3,7 +3,7 @@ E1. Think about the terms 'GIS'\index{GIS}, 'GDS' and 'geocomputation' described
 
 E2. Provide three reasons for using a scriptable language such as R for geocomputation instead of using a graphical user interface (GUI) based GIS such as QGIS\index{QGIS}.
 
-E3. Name two advantages and two disadvantages of using mature vs recent packages for geographic data analysis\index{geographic data analysis} (for example **sp** vs **sf**\index{sf}, or **raster** vs **terra**).
+E3. Think about real world problems you would like to solve with help from geographic data. Try sketching a workflow, with rectangles representing datsets and ovals representing methods/processes to transform them, with a pen and paper or a digital sketching tool such as Excalidraw.
 
 <!--toDo: rl -->
 <!--add solutions!-->

geocompr.bib

@@ -225,6 +225,22 @@ @book{blangiardo_spatial_2015
   keywords = {\#nosource}
 }
 
+@article{bondaruk_assessing_2020,
+  title = {Assessing the State of the Art in {{Discrete Global Grid Systems}}: {{OGC}} Criteria and Present Functionality},
+  shorttitle = {Assessing the State of the Art in {{Discrete Global Grid Systems}}},
+  author = {Bondaruk, Ben and Roberts, Steven A. and Robertson, Colin},
+  date = {2020-03-01},
+  journaltitle = {Geomatica},
+  volume = {74},
+  number = {1},
+  pages = {9--30},
+  publisher = {{NRC Research Press}},
+  issn = {1195-1036},
+  doi = {10.1139/geomat-2019-0015},
+  url = {https://cdnsciencepub.com/doi/abs/10.1139/geomat-2019-0015},
+  urldate = {2021-08-12}
+}
+
 @book{borcard_numerical_2011,
   title = {Numerical Ecology with {{R}}},
   author = {Borcard, Daniel and Gillet, François and Legendre, Pierre},