Deploy commit: use latest mlr3 package for 15-tune.rds, adjust prediction code and mention mlr3spatial b0cba2b

Author: jannes-m

05-geometry-operations.md

@@ -674,7 +674,8 @@ Performing an algebraic operation on two objects with differing extents in R, th
 
 ``` r
 elev_3 = elev + elev_2
-#> Error: [+] extents do not match
+#> Error:
+#> ! [+] extents do not match
 ```
 
 However, we can align the extent of two rasters with `extend()`. 

09-mapping.md

@@ -796,7 +796,8 @@ Switch **tmap** back to plotting mode with the same function:
 
 ``` r
 tmap_mode("plot")
-#> ℹ tmap mode set to "plot".
+#> ℹ tmap modes "plot" - "view"
+#> ℹ toggle with `tmap::ttm()`
 ```
 
 If you are not proficient with **tmap**, the quickest way to create interactive maps in R may be with **mapview**\index{mapview (package)}.

13-transport.md

@@ -570,7 +570,7 @@ routes_short_scenario = routes_short |>
   mutate(bicycle = bicycle + car_driver * uptake,
          car_driver = car_driver * (1 - uptake))
 sum(routes_short_scenario$bicycle) - sum(routes_short$bicycle)
-#> [1] 3242
+#> [1] 3241
 ```
 
 Having created a scenario in which approximately 4000 trips have switched from driving to cycling, we can now model where this updated modeled cycling activity will take place.
@@ -643,13 +643,6 @@ ways_centrality = ways_sfn |>
   mutate(betweenness = tidygraph::centrality_edge_betweenness(lengths)) 
 ```
 
-
-```
-#> [plot mode] legend/component: Some components or legends are too "wide" and are
-#> therefore rescaled.
-#> ℹ Set the tmap option `component.autoscale = FALSE` to disable rescaling.
-```
-
 <div class="figure" style="text-align: center">
 <img src="figures/wayssln-1.png" alt="Route network datasets. The gray lines represent a simplified road network, with segment thickness proportional to betweenness. The green lines represent potential cycling flows (one way) calculated with the code above." width="100%" />
 <p class="caption">(\#fig:wayssln)Route network datasets. The gray lines represent a simplified road network, with segment thickness proportional to betweenness. The green lines represent potential cycling flows (one way) calculated with the code above.</p>

15-eco.md

@@ -7,7 +7,7 @@
 <title>Chapter 16 Conclusion | Geocomputation with R</title>
 <meta name="author" content="Robin Lovelace, Jakub Nowosad, Jannes Muenchow">
 <meta name="description" content="16.1 Introduction Like the introduction, this concluding chapter contains a few code chunks. The aim is to synthesize the contents of the book, with reference to recurring themes/concepts, and to...">
-<meta name="generator" content="bookdown 0.43 with bs4_book()">
+<meta name="generator" content="bookdown 0.45 with bs4_book()">
 <meta property="og:title" content="Chapter 16 Conclusion | Geocomputation with R">
 <meta property="og:type" content="book">
 <meta property="og:url" content="https://r.geocompx.org/conclusion.html">
@@ -33,7 +33,7 @@
 <script src="libs/htmlwidgets-1.6.4/htmlwidgets.js"></script><link href="libs/leaflet-1.3.1/leaflet.css" rel="stylesheet">
 <script src="libs/leaflet-1.3.1/leaflet.js"></script><link href="libs/leafletfix-1.0.0/leafletfix.css" rel="stylesheet">
 <script src="libs/proj4-2.6.2/proj4.min.js"></script><script src="libs/Proj4Leaflet-1.0.1/proj4leaflet.js"></script><link href="libs/rstudio_leaflet-1.3.1/rstudio_leaflet.css" rel="stylesheet">
-<script src="libs/leaflet-binding-2.2.2/leaflet.js"></script><script src="libs/kePrint-0.0.1/kePrint.js"></script><link href="libs/lightable-0.0.1/lightable.css" rel="stylesheet">
+<script src="libs/leaflet-binding-2.2.3/leaflet.js"></script><script src="libs/kePrint-0.0.1/kePrint.js"></script><link href="libs/lightable-0.0.1/lightable.css" rel="stylesheet">
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 <link rel="icon" type="image/png" sizes="16x16" href="images/favicon-16x16.png">
 <script>
@@ -196,11 +196,11 @@ <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">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>.
+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 et al. 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>
+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 et al. (<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.
-These resources include <span class="citation">A. Zuur et al. (<a href="references.html#ref-zuur_mixed_2009">2009</a>)</span>, <span class="citation">A. F. Zuur et al. (<a href="references.html#ref-zuur_beginners_2017">2017</a>)</span> which focus on ecological use cases, and freely available teaching material and code on <em>Geostatistics &amp; Open-source Statistical Computing</em> hosted at <a href="https://css.cornell.edu/faculty/dgr2/teach/">css.cornell.edu/faculty/dgr2</a>.
+These resources include <span class="citation">Zuur et al. (<a href="references.html#ref-zuur_mixed_2009">2009</a>)</span>, <span class="citation">Zuur et al. (<a href="references.html#ref-zuur_beginners_2017">2017</a>)</span> which focus on ecological use cases, and freely available teaching material and code on <em>Geostatistics &amp; Open-source Statistical Computing</em> hosted at <a href="https://css.cornell.edu/faculty/dgr2/teach/">css.cornell.edu/faculty/dgr2</a>.
 <a href="https://sdesabbata.github.io/r-for-geographic-data-science/"><em>R for Geographic Data Science</em></a> provides an introduction to R for geographic data science and modeling.</p>
 <p>We have largely omitted geocomputation on ‘big data’ by which we mean datasets that do not fit on a high-spec laptop.
 This decision is justified by the fact that the majority of geographic datasets that are needed for common research or policy applications <em>do</em> fit on consumer hardware, large high-resolution remote sensing datasets being a notable exception (see Section <a href="gis.html#cloud">10.8</a>).
@@ -444,7 +444,7 @@ <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 2025-08-28.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2026-01-19.</p>
   </div>
 
   <div class="col-12 col-md-6 mt-3">