fixes urls

01-introduction.Rmd

@@ -15,7 +15,7 @@ By the end of the book, we hope you will be ready to apply your skills tackle re
 
 Over the last few decades free and open source software for geospatial (FOSS4G\index{FOSS4G}) has progressed at an astonishing rate.
 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](http://qgis.org/en/site/)\index{QGIS}, have made geographic analysis accessible worldwide.
+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)).
 A simplistic comparison between the different approaches is illustrated in Table \@ref(tab:gdsl).
@@ -186,14 +186,14 @@ The use of R code, therefore, enables teaching geocomputation with reference to
 
 R is a powerful language for geocomputation but there are many other options for geographic data analysis providing thousands of geographic functions\index{function}.
 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.
-This section briefly introduces the languages [C++](https://isocpp.org/)\index{C++}, [Java](https://www.oracle.com/java/index.html)\index{Java} and [Python](https://www.python.org/)\index{Python} for geocomputation, in preparation for Chapter \@ref(gis).
+This section briefly introduces the languages [C++](https://isocpp.org/)\index{C++}, [Java](https://www.oracle.com/java/)\index{Java} and [Python](https://www.python.org/)\index{Python} for geocomputation, in preparation for Chapter \@ref(gis).
 
 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):\index{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++\index{C++} and Java\index{Java} tend to run faster (once they have been compiled).
 
-C++\index{C++} provides the basis for many GIS packages such as [QGIS](https://www.qgis.org/)\index{QGIS}, [GRASS](https://grass.osgeo.org/)\index{GRASS} and [SAGA](http://www.saga-gis.org/)\index{SAGA} so it is a sensible starting point.
+C++\index{C++} provides the basis for many GIS packages such as [QGIS](https://www.qgis.org/en/site/)\index{QGIS}, [GRASS](https://grass.osgeo.org/)\index{GRASS} and [SAGA](https://saga-gis.sourceforge.io/)\index{SAGA} so it is a sensible starting point.
 Well-written C++\index{C++} is very fast, making it a good choice for performance-critical applications such as processing large geographic datasets, but is harder to learn than Python or R.
 C++\index{C++} has become more accessible with the **Rcpp** package, which provides a good 'way in' to C\index{C!language} programming for R users.
 Proficiency with such low-level languages opens the possibility of creating new, high-performance 'geoalgorithms' and a better understanding of how GIS software works (see Chapter \@ref(algorithms)).
@@ -237,7 +237,7 @@ Like R, Python also supports geographic data analysis and manipulation with pack
 
 There are many ways to handle geographic data in R, with dozens of packages\index{R-spatial} in the area.^[
 An overview of R's spatial ecosystem can be found in the CRAN\index{CRAN} Task View on the Analysis of Spatial Data
-(see https://cran.r-project.org/web/views/Spatial.html).
+(see https://cran.r-project.org/view=Spatial).
 ]
 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 (Figure \@ref(fig:cranlogs)).
@@ -401,9 +401,9 @@ This includes the **mapsf** package (successor of **cartography**) [@giraud_maps
 
 <!-- spatstat?? -->
 
-In late 2021, the planned retirement of **rgdal**, **rgeos** and **maptools** at the end of 2023 was announced on [the R-sig-Geo mailing list](https://stat.ethz.ch/pipermail/r-sig-geo/2021-September/028760.html) by Roger Bivand.
-This would have a large impact on existing workflows applying these packages, but also will influence the packages that depend on **rgdal**, **rgeos** or **maptools**. 
-Therefore, Bivand's suggestion is to transition to more modern tools, including **sf** and **terra**, as explained in this book's next chapters.
+In late 2021, the planned retirement of **rgdal**, **rgeos** and **maptools** was announced on [the R-sig-Geo mailing list](https://stat.ethz.ch/pipermail/r-sig-geo/2021-September/028760.html) by Roger Bivand.
+This retirement at the end of 2023 has had a large impact on existing workflows applying these packages, but also will influence the packages that depend on them. 
+Therefore, Bivand's suggestion was to transition to more modern tools, including **sf** and **terra**, as explained in this book's next chapters.
 
 ## Exercises
 

02-spatial-data.Rmd

@@ -8,7 +8,7 @@
 This is the first practical chapter of the book, and therefore it comes with some software requirements.
 <!-- toDo: rl-->
 <!-- should we be that specific regarding the r version?-->
-You need access to a computer with a recent version of R installed (R [4.2.0](https://stat.ethz.ch/pipermail/r-announce/2022/000683.html) or a later version).
+You need access to a computer with a recent version of R installed (R [4.3.0](https://stat.ethz.ch/pipermail/r-announce/2022/000683.html) or a later version).
 We recommend not only reading the prose but also *running the code* in each chapter to build your geocomputational skills.
 
 To keep track of your learning journey, it may be worth starting by creating a new folder on your computer to save your R scripts, outputs and other things related to Geocomputation with R as you go.
@@ -285,7 +285,7 @@ There are many reasons (linked to the advantages of the simple features model):
 - Enhanced plotting performance
 - **sf** objects can be treated as data frames in most operations
 - **sf** function names are relatively consistent and intuitive (all begin with `st_`)
-- **sf** functions can be combined with the `|>` operator and works well with the [tidyverse](http://tidyverse.org/) collection of R packages\index{tidyverse}.
+- **sf** functions can be combined with the `|>` operator and works well with the [tidyverse](https://www.tidyverse.org/) collection of R packages\index{tidyverse}.
 
 **sf**'s support for **tidyverse** packages is exemplified by `read_sf()`, a function for importing geographic vector data covered in detail in Section \@ref(iovec).
 Unlike the function `st_read()`, which returns attributes stored in a base R `data.frame` (and which emits verbose messages, not shown in the code chunk below), `read_sf()` silently returns data as a **tidyverse** `tibble`.
@@ -817,7 +817,7 @@ Benchmarks, in the package's [documentation](https://dcooley.github.io/sfheaders
 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].
+Another example is the [H3](https://h3geo.org/) 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).
 

03-attribute-operations.Rmd

@@ -322,7 +322,7 @@ knitr::kable(tibble(Symbol = operators, Name = operators_exp),
 ### Chaining commands with pipes
 
 \index{pipe operator}
-Key to workflows using **dplyr** functions is the ['pipe'](http://r4ds.had.co.nz/pipes.html) operator `%>%` (or since R `4.1.0` the native pipe `|>`), which takes its name from the Unix pipe `|` [@grolemund_r_2016].
+Key to workflows using **dplyr** functions is the ['pipe'](https://r4ds.had.co.nz/pipes.html) operator `%>%` (or since R `4.1.0` the native pipe `|>`), which takes its name from the Unix pipe `|` [@grolemund_r_2016].
 Pipes enable expressive code: the output of a previous function becomes the first argument of the next function, enabling *chaining*.
 This is illustrated below, in which only countries from Asia are filtered from the `world` dataset, next the object is subset by columns (`name_long` and `continent`) and the first five rows (result not shown).
 
@@ -435,15 +435,15 @@ knitr::kable(
 ```
 
 ```{block2 03-attribute-operations-31, type='rmdnote'}
-More details are provided in the help pages (which can be accessed via `?summarize` and `vignette(package = "dplyr")` and Chapter 5 of [R for Data Science](http://r4ds.had.co.nz/transform.html#grouped-summaries-with-summarize). 
+More details are provided in the help pages (which can be accessed via `?summarize` and `vignette(package = "dplyr")` and Chapter 5 of [R for Data Science](https://r4ds.had.co.nz/transform.html#grouped-summaries-with-summarize). 
 ```
 
 ###  Vector attribute joining
 
 Combining data from different sources is a common task in data preparation. 
 Joins do this by combining tables based on a shared 'key' variable.
 **dplyr** has multiple join functions including `left_join()` and `inner_join()` --- see `vignette("two-table")` for a full list.
-These function names follow conventions used in the database language [SQL](http://r4ds.had.co.nz/relational-data.html) [@grolemund_r_2016, Chapter 13]; using them to join non-spatial datasets to `sf` objects is the focus of this section.
+These function names follow conventions used in the database language [SQL](https://r4ds.had.co.nz/relational-data.html) [@grolemund_r_2016, Chapter 13]; using them to join non-spatial datasets to `sf` objects is the focus of this section.
 **dplyr** join functions work the same on data frames and `sf` objects, the only important difference being the `geometry` list column.
 The result of data joins can be either an `sf` or `data.frame` object.
 The most common type of attribute join on spatial data takes an `sf` object as the first argument and adds columns to it from a `data.frame` specified as the second argument.

04-spatial-operations.Rmd

@@ -157,7 +157,7 @@ Notice that each geometry pair has a "DE-9IM" string such as FF2F11212, describe
 \index{topological relations}
 
 ```{r relations, echo=FALSE, fig.cap="Topological relations between vector geometries, inspired by Figures 1 and 2 in Egenhofer and Herring (1990). The relations for which the function(x, y) is true are printed for each geometry pair, with x represented in pink and y represented in blue. The nature of the spatial relationship for each pair is described by the Dimensionally Extended 9-Intersection Model string.", fig.show='hold', message=FALSE, fig.asp=0.66, warning=FALSE}
-# source("https://github.com/geocompx/geocompr/raw/c4-v2-updates-rl/code/de_9im.R")
+# source("https://github.com/geocompx/geocompr/raw/main/code/de_9im.R")
 source("code/de_9im.R")
 library(sf)
 xy2sfc = function(x, y) st_sfc(st_polygon(list(cbind(x, y))))

05-geometry-operations.Rmd

@@ -364,7 +364,7 @@ plot(x_and_y, col = "lightgrey", border = "grey", add = TRUE) # intersecting are
 par(op)
 ```
 
-The subsequent code chunk demonstrates how this works for all combinations of the 'Venn' diagram representing `x` and `y`, inspired by [Figure 5.1](http://r4ds.had.co.nz/transform.html#logical-operators) of the book *R for Data Science* [@grolemund_r_2016].
+The subsequent code chunk demonstrates how this works for all combinations of the 'Venn' diagram representing `x` and `y`, inspired by [Figure 5.1](https://r4ds.had.co.nz/transform.html#logical-operators) of the book *R for Data Science* [@grolemund_r_2016].
 
 ```{r venn-clip, echo=FALSE, fig.cap="Spatial equivalents of logical operators.", warning=FALSE}
 source("code/05-venn-clip.R")

07-reproj.Rmd

@@ -66,7 +66,7 @@ st_crs("EPSG:4326")
 \index{CRS!SRID}
 The output of the command shows how the CRS identifier (also known as a Spatial Reference Identifier or [SRID](https://postgis.net/workshops/postgis-intro/projection.html)) works: it is simply a look-up, providing a unique identifier associated with a more complete WKT representation of the CRS.
 This raises the question: what happens if there is a mismatch between the identifier and the longer WKT representation of a CRS?
-On this point @opengeospatialconsortium_wellknown_2019 is clear, the verbose WKT representation takes precedence over the [identifier](https://docs.opengeospatial.org/is/18-010r7/18-010r7.html#37): 
+On this point @opengeospatialconsortium_wellknown_2019 is clear, the verbose WKT representation takes precedence over the [identifier](https://docs.ogc.org/is/18-010r7/18-010r7.html#37): 
 
 > Should any attributes or values given in the cited identifier be in conflict with attributes or values given explicitly in the WKT description, the WKT values shall prevail. 
 
@@ -454,7 +454,7 @@ The example of London was easy to answer because (a) the British National Grid (
 \index{UTM} 
 A commonly used default is Universal Transverse Mercator ([UTM](https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system)), a set of CRSs that divides the Earth into 60 longitudinal wedges and 20 latitudinal segments.
 The transverse Mercator projection used by UTM CRSs is conformal but distorts areas and distances with increasing severity with distance from the center of the UTM zone.
-Documentation from the GIS software Manifold therefore suggests restricting the longitudinal extent of projects using UTM zones to 6 degrees from the central meridian (source: [manifold.net](http://www.manifold.net/doc/mfd9/universal_transverse_mercator_projection.htm)).
+Documentation from the GIS software Manifold therefore suggests restricting the longitudinal extent of projects using UTM zones to 6 degrees from the central meridian (source: [manifold.net](https://manifold.net/doc/mfd9/universal_transverse_mercator_projection.htm)).
 Therefore, we recommend using UTM only when your focus is on preserving angles for relatively small area!
 
 Almost every place on Earth has a UTM code, such as "60H" which refers to northern New Zealand where R was invented.
@@ -689,7 +689,7 @@ unique(cat_raster)
 When reprojecting categorical rasters, the estimated values must be the same as those of the original.
 This could be done using the nearest neighbor method (`near`), which sets each new cell value to the value of the nearest cell (center) of the input raster.
 An example is reprojecting `cat_raster` to WGS84, a geographic CRS well suited for web mapping.
-The first step is to obtain the PROJ definition of this CRS, which can be done, for example using the [http://spatialreference.org](http://spatialreference.org/ref/epsg/wgs-84/) webpage. 
+The first step is to obtain the PROJ definition of this CRS, which can be done, for example using the [https://spatialreference.org/](https://spatialreference.org/ref/epsg/wgs-84/) webpage. 
 The final step is to reproject the raster with the `project()` function which, in the case of categorical data, uses the nearest neighbor method (`near`):
 
 ```{r 06-reproj-31}
@@ -841,7 +841,7 @@ tm_shape(world_mollweide_gr) +
 ```
 
 It is often desirable to minimize distortion for all spatial properties (area, direction, distance) when mapping the world.
-One of the most popular projections to achieve this is [Winkel tripel](http://www.winkel.org/other/Winkel%20Tripel%20Projections.htm), illustrated in Figure \@ref(fig:wintriproj).^[
+One of the most popular projections to achieve this is [Winkel tripel](https://www.winkel.org/other/Winkel%20Tripel%20Projections.htm), illustrated in Figure \@ref(fig:wintriproj).^[
 This projection is used, among others, by the National Geographic Society.
 ]
 The result was created with the following command: