Deploy commit: fixes #1135 e024deb

02-spatial-data.md

@@ -882,7 +882,7 @@ To turn off S2 for the entirety of a project, you can create a file called .Rpro
 
 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)\index{raster data model}.
 This data model often refers to so-called regular grids, in which each cell has the same, constant size -- and we will focus on the regular grids in this book only.
-However, several other types of grids exist, including rotated, sheared, rectilinear, and curvilinear grids (see chapter 1 of @pebesmaSpatialDataScience2023 or chapter 2 of @tennekes_elegant_2022).
+However, several other types of grids exist, including rotated, sheared, rectilinear, and curvilinear grids (see chapter 1 of @pebesma_spatial_2023 or chapter 2 of @tennekes_elegant_2022).
 
 The raster data model usually consists of a raster header\index{raster!header}
 and a matrix (with rows and columns) representing equally spaced cells (often also called pixels; Figure \@ref(fig:raster-intro-plot):A).^[
@@ -944,7 +944,7 @@ The **terra** package mostly relies on a large number of built-in functions, whe
 On the other hand, **stars** uses some built-in functions (usually with names starting with `st_`), some existing **dplyr** functions (e.g., `filter()` or `slice()`), and also has its own methods for existing R functions (e.g., `split()` or `aggregate()`).
 
 Importantly, it is straightforward to convert objects from **terra** to **stars** (using `st_as_stars()`) and the other way round (using `rast()`).
-We also encourage you to read @pebesmaSpatialDataScience2023 for the most comprehensive introduction to the **stars** package.
+We also encourage you to read @pebesma_spatial_2023 for the most comprehensive introduction to the **stars** package.
 
 ### Introduction to terra
 

03-attribute-operations.md

@@ -638,7 +638,7 @@ class(world_data)
 ## Manipulating raster objects
 
 In contrast to the vector data model underlying simple features (which represents points, lines and polygons as discrete entities in space), raster data represent continuous surfaces.
-This section shows how raster objects work by creating them *from scratch*, building on Section \@ref(an-introduction-to-terra).
+This section shows how raster objects work by creating them *from scratch*, building on Section \@ref(introduction-to-terra).
 Because of their unique structure, subsetting and other operations on raster datasets work in a different way, as demonstrated in Section \@ref(raster-subsetting).
 
 \index{raster!manipulation}

04-spatial-operations.md

@@ -359,7 +359,7 @@ st_relate(x, y)
 
 Understanding DE-9IM strings allows new binary spatial predicates to be developed.
 The help page `?st_relate` contains function definitions for 'queen' and 'rook' relations in which polygons share a border or only a point, respectively.
-'Queen' relations mean that 'boundary-boundary' relations (the cell in the second column and the second row in Table \@ref(tab:de9emtable), or the 5th element of the DE-9IM string) must not be empty, corresponding to the pattern `F***T****`, while for 'rook' relations, the same element must be 1 (meaning a linear intersection) (see \@ref(queens)).
+'Queen' relations mean that 'boundary-boundary' relations (the cell in the second column and the second row in Table \@ref(tab:de9emtable), or the 5th element of the DE-9IM string) must not be empty, corresponding to the pattern `F***T****`, while for 'rook' relations, the same element must be 1 (meaning a linear intersection) (see Figure \@ref(fig:queens)).
 These are implemented as follows:
 
 

07-reproj.md

@@ -137,7 +137,7 @@ These string representations, built on a key=value form (e.g, `+proj=longlat +da
 \index{CRS!proj-string}
 Recent PROJ versions (6+) still allow use of proj-strings to define coordinate operations, but some proj-string keys (`+nadgrids`, `+towgs84`, `+k`, `+init=epsg:`) are either no longer supported or are discouraged.
 Additionally, only three datums (i.e., WGS84, NAD83, and NAD27) can be directly set in proj-string.
-Longer explanations of the evolution of CRS definitions and the PROJ library can be found in @bivand_progress_2021, chapter 2 of @pebesmaSpatialDataScience2023, and a [blog post by Floris Vanderhaeghe, available at inbo.github.io/tutorials/tutorials/spatial_crs_coding/](https://inbo.github.io/tutorials/tutorials/spatial_crs_coding/).
+Longer explanations of the evolution of CRS definitions and the PROJ library can be found in @bivand_progress_2021, chapter 2 of @pebesma_spatial_2023, and a [blog post by Floris Vanderhaeghe, available at inbo.github.io/tutorials/tutorials/spatial_crs_coding/](https://inbo.github.io/tutorials/tutorials/spatial_crs_coding/).
 Also, as outlined in the [PROJ documentation](https://proj.org/development/reference/cpp/cpp_general.html) there are different versions of the WKT CRS format including WKT1 and two variants of WKT2, the latter of which (WKT2, 2018 specification) corresponds to the ISO 19111:2019 [@opengeospatialconsortium_wellknown_2019].
 
 ## Querying and setting coordinate systems {#crs-setting}

16-synthesis.md

@@ -105,7 +105,7 @@ Some topics and themes appear repeatedly, with the aim of building essential ski
 
 We deliberately omitted some topics that are covered in-depth elsewhere.
 Statistical modeling of spatial data such as point pattern analysis\index{point pattern analysis}, spatial interpolation\index{spatial interpolation} (e.g., kriging) and spatial regression\index{spatial regression}, for example, are mentioned in the context of machine learning in Chapter \@ref(spatial-cv) but not covered in detail.
-There are already excellent resources on these methods, including statistically orientated chapters in @pebesmaSpatialDataScience2023 and books on point pattern analysis [@baddeley_spatial_2015], Bayesian techniques applied to spatial data [@gomez-rubio_bayesian_2020; @moraga_spatial_2023], and books focused on particular applications such as health [@moraga_geospatial_2019] and [wildfire severity analysis](https://bookdown.org/mcwimberly/gdswr-book/application---wildfire-severity-analysis.html) [@wimberly_geographic_2023].
+There are already excellent resources on these methods, including statistically orientated chapters in @pebesma_spatial_2023 and books on point pattern analysis [@baddeley_spatial_2015], Bayesian techniques applied to spatial data [@gomez-rubio_bayesian_2020; @moraga_spatial_2023], and books focused on particular applications such as health [@moraga_geospatial_2019] and [wildfire severity analysis](https://bookdown.org/mcwimberly/gdswr-book/application---wildfire-severity-analysis.html) [@wimberly_geographic_2023].
 Other topics which received limited attention were remote sensing and using R alongside (rather than as a bridge to) dedicated GIS software.
 There are many resources on these topics, including a [discussion on remote sensing in R](https://github.com/r-spatial/discuss/issues/56), @wegmann_remote_2016 and the GIS-related teaching materials available from [Marburg University](https://geomoer.github.io/moer-info-page/courses.html).
 

404.html

@@ -129,7 +129,7 @@ <h2>Second Edition</h2>
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   <div class="col-12 col-md-6 mt-3">
-    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2024-10-02.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2024-10-03.</p>
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adv-map.html

@@ -1342,7 +1342,7 @@ <h2>Second Edition</h2>
 <footer class="bg-primary text-light mt-5"><div class="container"><div class="row">
 
   <div class="col-12 col-md-6 mt-3">
-    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2024-10-02.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2024-10-03.</p>
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algorithms.html

@@ -529,7 +529,7 @@ <h2>Second Edition</h2>
 <footer class="bg-primary text-light mt-5"><div class="container"><div class="row">
 
   <div class="col-12 col-md-6 mt-3">
-    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2024-10-02.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2024-10-03.</p>
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attr.html

@@ -608,7 +608,7 @@ <h2>
 <span class="header-section-number">3.3</span> Manipulating raster objects<a class="anchor" aria-label="anchor" href="#manipulating-raster-objects"><i class="fas fa-link"></i></a>
 </h2>
 <p>In contrast to the vector data model underlying simple features (which represents points, lines and polygons as discrete entities in space), raster data represent continuous surfaces.
-This section shows how raster objects work by creating them <em>from scratch</em>, building on Section <a href="#an-introduction-to-terra"><strong>??</strong></a>.
+This section shows how raster objects work by creating them <em>from scratch</em>, building on Section <a href="spatial-class.html#introduction-to-terra">2.3.2</a>.
 Because of their unique structure, subsetting and other operations on raster datasets work in a different way, as demonstrated in Section <a href="attr.html#raster-subsetting">3.3.1</a>.</p>
 <p>
 The following code recreates the raster dataset used in Section <a href="spatial-class.html#raster-classes">2.3.4</a>, the result of which is illustrated in Figure <a href="attr.html#fig:cont-raster">3.2</a>.
@@ -861,7 +861,7 @@ <h2>Second Edition</h2>
 <footer class="bg-primary text-light mt-5"><div class="container"><div class="row">
 
   <div class="col-12 col-md-6 mt-3">
-    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2024-10-02.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2024-10-03.</p>
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conclusion.html

@@ -196,7 +196,7 @@ <h2>
 Some topics and themes appear repeatedly, with the aim of building essential skills for geocomputation, and showing you how to go further, into more advanced topics and specific applications.</p>
 <p>We deliberately omitted some topics that are covered in-depth elsewhere.
 Statistical modeling of spatial data such as point pattern analysis, spatial interpolation (e.g., kriging) and spatial regression, for example, are mentioned in the context of machine learning in Chapter <a href="spatial-cv.html#spatial-cv">12</a> but not covered in detail.
-There are already excellent resources on these methods, including statistically orientated chapters in <span class="citation">(<a href="#ref-pebesmaSpatialDataScience2023"><strong>pebesmaSpatialDataScience2023?</strong></a>)</span> and books on point pattern analysis <span class="citation">(<a href="references.html#ref-baddeley_spatial_2015">Baddeley, Rubak, and Turner 2015</a>)</span>, Bayesian techniques applied to spatial data <span class="citation">(<a href="references.html#ref-gomez-rubio_bayesian_2020">Gómez-Rubio 2020</a>; <a href="references.html#ref-moraga_spatial_2023">Moraga 2023</a>)</span>, and books focused on particular applications such as health <span class="citation">(<a href="references.html#ref-moraga_geospatial_2019">Moraga 2019</a>)</span> and <a href="https://bookdown.org/mcwimberly/gdswr-book/application---wildfire-severity-analysis.html">wildfire severity analysis</a> <span class="citation">(<a href="references.html#ref-wimberly_geographic_2023">Wimberly 2023</a>)</span>.
+There are already excellent resources on these methods, including statistically orientated chapters in <span class="citation">Pebesma and Bivand (<a href="references.html#ref-pebesma_spatial_2023">2023b</a>)</span> and books on point pattern analysis <span class="citation">(<a href="references.html#ref-baddeley_spatial_2015">Baddeley, Rubak, and Turner 2015</a>)</span>, Bayesian techniques applied to spatial data <span class="citation">(<a href="references.html#ref-gomez-rubio_bayesian_2020">Gómez-Rubio 2020</a>; <a href="references.html#ref-moraga_spatial_2023">Moraga 2023</a>)</span>, and books focused on particular applications such as health <span class="citation">(<a href="references.html#ref-moraga_geospatial_2019">Moraga 2019</a>)</span> and <a href="https://bookdown.org/mcwimberly/gdswr-book/application---wildfire-severity-analysis.html">wildfire severity analysis</a> <span class="citation">(<a href="references.html#ref-wimberly_geographic_2023">Wimberly 2023</a>)</span>.
 Other topics which received limited attention were remote sensing and using R alongside (rather than as a bridge to) dedicated GIS software.
 There are many resources on these topics, including a <a href="https://github.com/r-spatial/discuss/issues/56">discussion on remote sensing in R</a>, <span class="citation">Wegmann, Leutner, and Dech (<a href="references.html#ref-wegmann_remote_2016">2016</a>)</span> and the GIS-related teaching materials available from <a href="https://geomoer.github.io/moer-info-page/courses.html">Marburg University</a>.</p>
 <p>We focused on machine learning rather than spatial statistical inference in Chapters <a href="spatial-cv.html#spatial-cv">12</a> and <a href="eco.html#eco">15</a> because of the abundance of quality resources on the topic.
@@ -444,7 +444,7 @@ <h2>Second Edition</h2>
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+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2024-10-03.</p>
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eco.html

@@ -628,7 +628,7 @@ <h2>Second Edition</h2>
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-    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2024-10-02.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2024-10-03.</p>
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foreword-1st-edition.html

@@ -157,7 +157,7 @@ <h2>Second Edition</h2>
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foreword-2nd-edition.html

@@ -148,7 +148,7 @@ <h2>Second Edition</h2>
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geometry-operations.html

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gis.html

@@ -1064,7 +1064,7 @@ <h2>Second Edition</h2>
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index.html

@@ -118,7 +118,7 @@ <h1>Welcome<a class="anchor" aria-label="anchor" href="#welcome"><i class="fas f
 <p>The online version of the book is hosted at <a href="https://r.geocompx.org">r.geocompx.org</a> and kept up-to-date by <a href="https://github.com/geocompx/geocompr/actions">GitHub Actions</a>.
 Its current ‘build status’ as follows:</p>
 <p><a href="https://github.com/geocompx/geocompr/actions"><img src="https://github.com/geocompx/geocompr/workflows/Render/badge.svg" alt="Actions"></a></p>
-<p>This version of the book was built on GH Actions on 2024-10-02.</p>
+<p>This version of the book was built on GH Actions on 2024-10-03.</p>
 <p><a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png"></a><br>This book is licensed to you under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License</a>.</p>
 <p><a rel="license" href="https://creativecommons.org/publicdomain/zero/1.0/"><img alt="Creative Commons License" style="border-width:0" src="https://licensebuttons.net/l/zero/1.0/88x31.png"></a><br>The code samples in this book are licensed under <a rel="license" href="https://creativecommons.org/publicdomain/zero/1.0/">Creative Commons CC0 1.0 Universal (CC0 1.0)</a>.</p>
 <div id="how-to-contribute" class="section level2 unnumbered">
@@ -231,7 +231,7 @@ <h2>Second Edition</h2>
 <footer class="bg-primary text-light mt-5"><div class="container"><div class="row">
 
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-    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2024-10-02.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2024-10-03.</p>
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index.md

@@ -1,7 +1,7 @@
 --- 
 title: 'Geocomputation with R'
 author: 'Robin Lovelace, Jakub Nowosad, Jannes Muenchow'
-date: '2024-10-02'
+date: '2024-10-03'
 site: bookdown::bookdown_site
 output: bookdown::bs4_book
 documentclass: krantz
@@ -44,7 +44,7 @@ Its current 'build status' as follows:
 
 [![Actions](https://github.com/geocompx/geocompr/workflows/Render/badge.svg)](https://github.com/geocompx/geocompr/actions)
 
-This version of the book was built on GH Actions on 2024-10-02.
+This version of the book was built on GH Actions on 2024-10-03.
 
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png"/></a><br/>This book is licensed to you under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License</a>.
 

intro.html

@@ -506,7 +506,7 @@ <h2>Second Edition</h2>
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+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2024-10-03.</p>
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