Deploy commit: Merge pull request #1175 from geocompx/refactor_location

Refactor Chapter 14 to use z22 Census 2022 data 2cb2a1a

Author: jannes-m

14-location.md

@@ -129,7 +129,7 @@ <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 2026-01-28.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2026-01-30.</p>
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404.html

@@ -1343,7 +1343,7 @@ <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 2026-01-28.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2026-01-30.</p>
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adv-map.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 2026-01-28.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2026-01-30.</p>
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algorithms.html

@@ -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 2026-01-28.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2026-01-30.</p>
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attr.html

@@ -144,7 +144,7 @@ <h2>
 <p>Although the classes, attributes and column names of the resulting objects <code>nz_u1</code> to <code>nz_u3</code> differ, their geometries are identical, as verified using the base R function <code><a href="https://rspatial.github.io/terra/reference/identical.html">identical()</a></code>.<a class="footnote-ref" tabindex="0" data-toggle="popover" data-content="&lt;p&gt;
 The first operation, undertaken by the function &lt;code&gt;st_union()&lt;/code&gt;, creates an object of class &lt;code&gt;sfc&lt;/code&gt; (a simple feature column).
 The latter two operations create &lt;code&gt;sf&lt;/code&gt; objects, each of which &lt;em&gt;contains&lt;/em&gt; a simple feature column.
-Therefore, it is the geometries contained in simple feature columns, not the objects themselves, that are identical.&lt;/p&gt;"><sup>104</sup></a>
+Therefore, it is the geometries contained in simple feature columns, not the objects themselves, that are identical.&lt;/p&gt;"><sup>103</sup></a>
 Which to use?
 It depends: the former only processes the geometry data contained in <code>nz</code> so is faster, while the other options performed attribute operations, which may be useful for subsequent steps.
 Whether to use the base R function <code><a href="https://rspatial.github.io/terra/reference/aggregate.html">aggregate()</a></code> or the <strong>dplyr</strong> function <code><a href="https://dplyr.tidyverse.org/reference/summarise.html">summarise()</a></code> is a matter of preference, with the latter being more readable for many.</p>
@@ -326,7 +326,7 @@ <h2>
 <p>Another software-related direction for future learning is <strong>discovering geocomputation with other languages</strong>.
 There are good reasons for learning R as a language for geocomputation, as described in Chapter <a href="intro.html#intro">1</a>, but it is not the only option.<a class="footnote-ref" tabindex="0" data-toggle="popover" data-content="&lt;p&gt;
 R’s strengths are particularly relevant to our definition of geocomputation due to its emphasis on scientific reproducibility, widespread use in academic research and unparalleled support for statistical modeling of geographic data.
-Furthermore, we advocate learning one language for geocomputation in depth before delving into other languages/frameworks because of the costs associated with context switching, and R is an excellent starting point on your geocomputational journey.&lt;/p&gt;"><sup>105</sup></a>
+Furthermore, we advocate learning one language for geocomputation in depth before delving into other languages/frameworks because of the costs associated with context switching, and R is an excellent starting point on your geocomputational journey.&lt;/p&gt;"><sup>104</sup></a>
 It would be possible to study <em>Geocomputation with: Python</em>, <em>C++</em>, <em>JavaScript</em>, <em>Scala</em> or <em>Rust</em> in equal depth.
 Each has evolving geospatial capabilities.
 <a href="https://github.com/rasterio/rasterio"><strong>rasterio</strong></a>, for example, is a Python package with similar functionality as the <strong>terra</strong> package used in this book.
@@ -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 2026-01-28.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2026-01-30.</p>
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conclusion.html

@@ -172,7 +172,7 @@ <h2>
 <span><span class="va">dem</span> <span class="op">=</span> <span class="fu"><a href="https://rspatial.github.io/terra/reference/rast.html">rast</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/system.file.html">system.file</a></span><span class="op">(</span><span class="st">"raster/dem.tif"</span>, package <span class="op">=</span> <span class="st">"spDataLarge"</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">ndvi</span> <span class="op">=</span> <span class="fu"><a href="https://rspatial.github.io/terra/reference/rast.html">rast</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/system.file.html">system.file</a></span><span class="op">(</span><span class="st">"raster/ndvi.tif"</span>, package <span class="op">=</span> <span class="st">"spDataLarge"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p><code>study_area</code> is a polygon representing the outline of the study area, and <code>random_points</code> is an <code>sf</code> object containing the 100 randomly chosen sites.
-<code>comm</code> is a community matrix of the wide data format <span class="citation">(<a href="references.html#ref-wickham_tidy_2014">Wickham 2014</a>)</span> where the rows represent the visited sites in the field and the columns the observed species.<a class="footnote-ref" tabindex="0" data-toggle="popover" data-content="&lt;p&gt;In statistics, this is also called a contingency table or cross-table.&lt;/p&gt;"><sup>101</sup></a></p>
+<code>comm</code> is a community matrix of the wide data format <span class="citation">(<a href="references.html#ref-wickham_tidy_2014">Wickham 2014</a>)</span> where the rows represent the visited sites in the field and the columns the observed species.<a class="footnote-ref" tabindex="0" data-toggle="popover" data-content="&lt;p&gt;In statistics, this is also called a contingency table or cross-table.&lt;/p&gt;"><sup>100</sup></a></p>
 <div class="sourceCode" id="cb491"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># sites 35 to 40 and corresponding occurrences of the first five species in the</span></span>
 <span><span class="co"># community matrix</span></span>
@@ -198,7 +198,7 @@ <h2>
 <p>The next step is to compute variables which are not only needed for the modeling and predictive mapping (see Section <a href="eco.html#predictive-mapping">15.4.2</a>) but also for aligning the non-metric multidimensional scaling (NMDS) axes with the main gradient in the study area, altitude and humidity, respectively (see Section <a href="eco.html#nmds">15.3</a>).</p>
 <p>Specifically, we compute catchment slope and catchment area from a digital elevation model using R-GIS bridges (see Chapter <a href="gis.html#gis">10</a>).
 Curvatures might also represent valuable predictors, and in the Exercise section you can find out how they would impact the modeling result.</p>
-<p>To compute catchment area and catchment slope, we can make use of the <code>sagang:sagawetnessindex</code> function.<a class="footnote-ref" tabindex="0" data-toggle="popover" data-content="&lt;p&gt;Admittedly, it is a bit unsatisfying that the only way of knowing that &lt;code&gt;sagawetnessindex&lt;/code&gt; computes the desired terrain attributes is to be familiar with SAGA.&lt;/p&gt;"><sup>102</sup></a>
+<p>To compute catchment area and catchment slope, we can make use of the <code>sagang:sagawetnessindex</code> function.<a class="footnote-ref" tabindex="0" data-toggle="popover" data-content="&lt;p&gt;Admittedly, it is a bit unsatisfying that the only way of knowing that &lt;code&gt;sagawetnessindex&lt;/code&gt; computes the desired terrain attributes is to be familiar with SAGA.&lt;/p&gt;"><sup>101</sup></a>
 <code><a href="https://r-spatial.github.io/qgisprocess/reference/qgis_show_help.html">qgis_show_help()</a></code> returns all function parameters and default values of a specific geoalgorithm.
 Here, we present only a selection of the complete output.</p>
 <div class="sourceCode" id="cb492"><pre class="downlit sourceCode r">
@@ -306,7 +306,7 @@ <h2>
 <span><span class="va">pa</span> <span class="op">=</span> <span class="va">pa</span><span class="op">[</span><span class="fu"><a href="https://rspatial.github.io/terra/reference/rowSums.html">rowSums</a></span><span class="op">(</span><span class="va">pa</span><span class="op">)</span> <span class="op">!=</span> <span class="fl">0</span>, <span class="op">]</span>  <span class="co"># 84 rows, 69 columns</span></span></code></pre></div>
 <p>The resulting matrix serves as input for the NMDS.
 <code>k</code> specifies the number of output axes, here, set to 4.<a class="footnote-ref" tabindex="0" data-toggle="popover" data-content='&lt;p&gt;
-One way of choosing &lt;code&gt;k&lt;/code&gt; is to try &lt;code&gt;k&lt;/code&gt; values between 1 and 6 and then using the result which yields the best stress value &lt;span class="citation"&gt;(&lt;a href="references.html#ref-mccune_analysis_2002"&gt;McCune et al. 2002&lt;/a&gt;)&lt;/span&gt;.&lt;/p&gt;'><sup>103</sup></a>
+One way of choosing &lt;code&gt;k&lt;/code&gt; is to try &lt;code&gt;k&lt;/code&gt; values between 1 and 6 and then using the result which yields the best stress value &lt;span class="citation"&gt;(&lt;a href="references.html#ref-mccune_analysis_2002"&gt;McCune et al. 2002&lt;/a&gt;)&lt;/span&gt;.&lt;/p&gt;'><sup>102</sup></a>
 NMDS is an iterative procedure trying to make the ordinated space more similar to the input matrix in each step.
 To make sure that the algorithm converges, we set the number of steps to 500 using the <code>try</code> parameter.</p>
 <div class="sourceCode" id="cb499"><pre class="downlit sourceCode r">
@@ -633,7 +633,7 @@ <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 2026-01-28.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2026-01-30.</p>
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eco.html

@@ -157,7 +157,7 @@ <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 2026-01-28.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2026-01-30.</p>
   </div>
 
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