Deploy commit: Merge pull request #1180 from geocompx/copilot/trigger-github-workflow-re-run

Add scheduled workflow to detect upstream failures; set weekly cadence 734d547

Author: Robinlovelace

02-spatial-data.md

@@ -248,11 +248,9 @@ world_mini
 #> Dimension:     XY
 #> Bounding box:  xmin: -180 ymin: -18.3 xmax: 180 ymax: -0.95
 #> Geodetic CRS:  WGS 84
-#> # A data frame: 2 × 4
-#>   iso_a2 name_long continent                                                geom
-#> * <chr>  <chr>     <chr>                                      <MULTIPOLYGON [°]>
-#> 1 FJ     Fiji      Oceania   (((-180 -16.6, -180 -16.5, -180 -16, -180 -16.1, -…
-#> 2 TZ     Tanzania  Africa    (((33.9 -0.95, 31.9 -1.03, 30.8 -1.01, 30.4 -1.13,…
+#>   iso_a2 name_long continent                           geom
+#> 1     FJ      Fiji   Oceania MULTIPOLYGON (((-180 -16.6,...
+#> 2     TZ  Tanzania    Africa MULTIPOLYGON (((33.9 -0.95,...
 ```
 
 All this may seem rather complex, especially for a class system that is supposed to be 'simple'!
@@ -982,14 +980,14 @@ Typing the name of the raster into the console, will print out the raster header
 
 ``` r
 my_rast
-#> class       : SpatRaster 
+#> class       : SpatRaster
 #> size        : 457, 465, 1  (nrow, ncol, nlyr)
-#> resolution  : 0.000833, 0.000833  (x, y)
-#> extent      : -113, -113, 37.1, 37.5  (xmin, xmax, ymin, ymax)
-#> coord. ref. : lon/lat WGS 84 (EPSG:4326) 
-#> source      : srtm.tif 
-#> name        : srtm 
-#> min value   : 1024 
+#> resolution  : 0.0008333333, 0.0008333333  (x, y)
+#> extent      : -113.2396, -112.8521, 37.13208, 37.51292  (xmin, xmax, ymin, ymax)
+#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
+#> source      : srtm.tif
+#> name        : srtm
+#> min value   : 1024
 #> max value   : 2892
 ```
 
@@ -1062,14 +1060,14 @@ The `SpatRaster` class also handles multiple layers, which typically correspond
 multi_raster_file = system.file("raster/landsat.tif", package = "spDataLarge")
 multi_rast = rast(multi_raster_file)
 multi_rast
-#> class       : SpatRaster 
+#> class       : SpatRaster
 #> size        : 1428, 1128, 4  (nrow, ncol, nlyr)
 #> resolution  : 30, 30  (x, y)
 #> extent      : 301905, 335745, 4111245, 4154085  (xmin, xmax, ymin, ymax)
-#> coord. ref. : WGS 84 / UTM zone 12N (EPSG:32612) 
-#> source      : landsat.tif 
-#> names       : landsat_1, landsat_2, landsat_3, landsat_4 
-#> min values  :      7550,      6404,      5678,      5252 
+#> coord. ref. : WGS 84 / UTM zone 12N (EPSG:32612)
+#> source      : landsat.tif
+#> names       : landsat_1, landsat_2, landsat_3, landsat_4
+#> min values  :      7550,      6404,      5678,      5252
 #> max values  :     19071,     22051,     25780,     31961
 ```
 

05-geometry-operations.md

@@ -634,15 +634,15 @@ elev = rast(system.file("raster/elev.tif", package = "spData"))
 clip = rast(xmin = 0.9, xmax = 1.8, ymin = -0.45, ymax = 0.45,
             resolution = 0.3, vals = rep(1, 9))
 elev[clip, drop = FALSE]
-#> class       : SpatRaster 
+#> class       : SpatRaster
 #> size        : 2, 1, 1  (nrow, ncol, nlyr)
 #> resolution  : 0.5, 0.5  (x, y)
 #> extent      : 1, 1.5, -0.5, 0.5  (xmin, xmax, ymin, ymax)
-#> coord. ref. : lon/lat WGS 84 (EPSG:4326) 
+#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
 #> source(s)   : memory
-#> varname     : elev 
-#> name        : elev 
-#> min value   :   18 
+#> varname     : elev
+#> name        : elev
+#> min value   :   18
 #> max value   :   24
 ```
 

08-read-write-plot.md

@@ -409,6 +409,7 @@ multilayer_rast = rast(multilayer_filepath)
 All of the previous examples read spatial information from files stored on your hard drive. 
 However, GDAL also allows reading data directly from online resources, such as HTTP/HTTPS/FTP web resources.
 The only thing we need to do is to add a `/vsicurl/` prefix before the path to the file.
+This tells GDAL to use its virtual file system for network resources, which uses **HTTP Range requests** to fetch only the specific byte ranges needed for an operation rather than downloading the entire file.
 Let's try it by connecting to the global monthly snow probability at 500-m resolution for the period 2000-2012.
 Snow probability for December is stored as a Cloud Optimized GeoTIFF (COG) file (see Section \@ref(file-formats)) at [zenodo.org](https://zenodo.org/record/5774954/files/clm_snow.prob_esacci.dec_p.90_500m_s0..0cm_2000..2012_v2.0.tif).
 To read an online file, we just need to provide its URL together with the `/vsicurl/` prefix.
@@ -419,18 +420,18 @@ myurl = paste0("/vsicurl/https://zenodo.org/record/5774954/files/",
                "clm_snow.prob_esacci.dec_p.90_500m_s0..0cm_2000..2012_v2.0.tif")
 snow = rast(myurl)
 snow
-#> class       : SpatRaster 
+#> class       : SpatRaster
 #> size        : 35849, 86400, 1  (nrow, ncol, nlyr)
-#> resolution  : 0.00417, 0.00417  (x, y)
-#> extent      : -180, 180, -62, 87.4  (xmin, xmax, ymin, ymax)
-#> coord. ref. : lon/lat WGS 84 (EPSG:4326) 
-#> source      : clm_snow.prob_esacci.dec_p.90_500m_s0..0cm_2000..2012_v2.0.tif 
+#> resolution  : 0.004166667, 0.004166667  (x, y)
+#> extent      : -180, 180, -62.00083, 87.37  (xmin, xmax, ymin, ymax)
+#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
+#> source      : clm_snow.prob_esacci.dec_p.90_500m_s0..0cm_2000..2012_v2.0.tif
 #> name        : clm_snow.prob_esacci.dec_p.90_500m_s0..0cm_2000..2012_v2.0
 ```
 
 \index{COG} 
-Due to the fact that the input data is COG, we are actually not reading this file to our RAM, but rather creating a connection to it without obtaining any values.
-Its values will be read if we apply any value-based operation (e.g., `crop()` or `extract()`).
+When combined with the `/vsicurl/` prefix, COG files enable highly efficient remote data access.
+Instead of reading the whole file into RAM, GDAL creates a connection that **selectively fetches** only the byte ranges needed for subsequent operations (e.g., `crop()` or `extract()`), making it possible to work with multi-gigabyte files over the internet almost as if they were local.
 This allows us also to just read a tiny portion of the data without downloading the entire file.
 For example, we can get the snow probability for December in Reykjavik (70%) by specifying its coordinates and applying the `extract()` function:
 

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] 3284
+#> [1] 3141
 ```
 
 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.

14-location.md

@@ -140,15 +140,15 @@ input_ras = rast(input_tidy, type = "xyz", crs = "EPSG:3035")
 
 ``` r
 input_ras
-#> class       : SpatRaster 
+#> class       : SpatRaster
 #> size        : 859, 641, 4  (nrow, ncol, nlyr)
 #> resolution  : 1000, 1000  (x, y)
 #> extent      : 4031000, 4672000, 2689000, 3548000  (xmin, xmax, ymin, ymax)
-#> coord. ref. : ETRS89-extended / LAEA Europe (EPSG:3035) 
+#> coord. ref. : ETRS89-extended / LAEA Europe (EPSG:3035)
 #> source(s)   : memory
-#> names       :   pop, women, mean_age, hh_size 
-#> min values  :     3,     0,      8.5,       1 
-#> max values  : 24164,   100,    104.4,     144
+#> names       :   pop, women, mean_age, hh_size
+#> min values  :     3,     0,      8.5,       1
+#> max values  : 24164,   100,   104.43,  143.88
 ```
 
 \BeginKnitrBlock{rmdnote}<div class="rmdnote">Note that we are using an equal-area projection (EPSG:3035; Lambert Equal Area Europe), i.e., a projected CRS\index{CRS!projected} where each grid cell has the same area, here 1000 * 1000 square meters. 

404.html

@@ -22,7 +22,7 @@
 <script src="libs/bootstrap-4.6.0/bootstrap.bundle.min.js"></script><link href="libs/Lato-0.4.10/font.css" rel="stylesheet">
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+<script src="libs/bs3compat-0.10.0/transition.js"></script><script src="libs/bs3compat-0.10.0/tabs.js"></script><script src="libs/bs3compat-0.10.0/bs3compat.js"></script><link href="libs/bs4_book-1.0.0/bs4_book.css" rel="stylesheet">
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@@ -129,7 +129,7 @@ <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 2026-01-30.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2026-05-12.</p>
   </div>
 
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adv-map.html

@@ -22,7 +22,7 @@
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+<script src="libs/bs3compat-0.10.0/transition.js"></script><script src="libs/bs3compat-0.10.0/tabs.js"></script><script src="libs/bs3compat-0.10.0/bs3compat.js"></script><link href="libs/bs4_book-1.0.0/bs4_book.css" rel="stylesheet">
 <script src="libs/bs4_book-1.0.0/bs4_book.js"></script><meta name="citation_title" content="Chapter 9 Making maps with R | Geocomputation with R">
 <meta name="citation_author" content="Robin Lovelace">
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@@ -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 2026-01-30.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2026-05-12.</p>
   </div>
 
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algorithms.html

@@ -22,7 +22,7 @@
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+<script src="libs/bs3compat-0.10.0/transition.js"></script><script src="libs/bs3compat-0.10.0/tabs.js"></script><script src="libs/bs3compat-0.10.0/bs3compat.js"></script><link href="libs/bs4_book-1.0.0/bs4_book.css" rel="stylesheet">
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 <meta name="citation_author" content="Robin Lovelace">
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@@ -529,7 +529,7 @@ <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 2026-01-30.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2026-05-12.</p>
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attr.html

@@ -22,7 +22,7 @@
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+<script src="libs/bs3compat-0.10.0/transition.js"></script><script src="libs/bs3compat-0.10.0/tabs.js"></script><script src="libs/bs3compat-0.10.0/bs3compat.js"></script><link href="libs/bs4_book-1.0.0/bs4_book.css" rel="stylesheet">
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@@ -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 2026-01-30.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2026-05-12.</p>
   </div>
 
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conclusion.html

@@ -22,7 +22,7 @@
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+<script src="libs/bs3compat-0.10.0/transition.js"></script><script src="libs/bs3compat-0.10.0/tabs.js"></script><script src="libs/bs3compat-0.10.0/bs3compat.js"></script><link href="libs/bs4_book-1.0.0/bs4_book.css" rel="stylesheet">
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@@ -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 2026-01-30.</p>
+    <p>"<strong>Geocomputation with R</strong>" was written by Robin Lovelace, Jakub Nowosad, Jannes Muenchow. It was last built on 2026-05-12.</p>
   </div>
 
   <div class="col-12 col-md-6 mt-3">