Merge remote-tracking branch 'refs/remotes/origin/main'

02-spatial-data.Rmd

@@ -821,14 +821,14 @@ library(tmap)
 tm1 = tm_shape(india_buffer_with_s2) +
   tm_fill(fill = hcl.colors(4, palette = "purple green")[2], lwd = 0.01) +
   tm_shape(india) +
-  tm_fill(fill = "grey95") +
+  tm_fill(fill = "gray95") +
   tm_title("st_buffer() with dist = 1") +
   tm_title("s2 switched on (default)", position = tm_pos_in("right", "bottom"), size = 1)
 
 tm2 = tm_shape(india_buffer_without_s2) +
   tm_fill(fill = hcl.colors(4, palette = "purple green")[3], lwd = 0.01) +
   tm_shape(india) +
-  tm_fill(fill = "grey95") +
+  tm_fill(fill = "gray95") +
   tm_title(" ") +
   tm_title("s2 switched off", position = tm_pos_in("right", "bottom"), size = 1)
 

04-spatial-operations.Rmd

@@ -552,7 +552,7 @@ tm_shape(grid_sf) +
   tm_fill(fill = c("queens", "rooks"), 
           fill.scale = tm_scale(values = c("white", "black"))) +
   tm_shape(grid_sf) +
-  tm_borders(col = "grey", lwd = 2) +
+  tm_borders(col = "gray", lwd = 2) +
   tm_layout(frame = FALSE, legend.show = FALSE,
             panel.labels = c("queen", "rook"))
 ```

09-mapping.Rmd

@@ -1012,9 +1012,9 @@ Building on this basic example and knowing where to find help (see `?shiny`), th
 The recommended next step is to open the previously mentioned [`CycleHireApp/app.R`](https://github.com/geocompx/geocompr/blob/main/apps/CycleHireApp/app.R) script in an integrated development environment (IDE) of choice, modify it and re-run it repeatedly.
 The example contains some of the components of a web mapping application implemented in **shiny** and should 'shine' a light on how they behave.
 
-The `CycleHireApp/app.R` script contains **shiny** functions that go beyond those demonstrated in the simple 'lifeApp' example (Figure \@ref(fig:CycleHireApp-html)).
-These include `reactive()` and `observe()` (for creating outputs that respond to the user interface --- see `?reactive`) and `leafletProxy()` (for modifying a `leaflet` object that has already been created).
-Such elements are critical to the creation of web mapping applications implemented in **shiny**.
+The `CycleHireApp/app.R` script contains **shiny** functions that go beyond those demonstrated in the simple 'lifeApp' example, deployed at [shiny.robinlovelace.net/CycleHireApp](https://shiny.robinlovelace.net/CycleHireApp).
+These include `reactive()` and `observe()`, (for creating outputs that respond to the user interface, see `?reactive`) and `leafletProxy()` (for modifying a `leaflet` object that has already been created).
+Such elements enable web mapping applications implemented in **shiny** [@lovelace_propensity_2017].
 A range of 'events' can be programmed including advanced functionality such as drawing new layers or subsetting data, as described in the shiny section of RStudio's **leaflet** [website](https://rstudio.github.io/leaflet/shiny.html).
 
 ```{block2 shinynote, type='rmdnote'}
@@ -1040,11 +1040,9 @@ For that reason, despite advocating **shiny**, we recommend starting with the lo
 This way your prototype web applications should be limited not by technical considerations, but by your motivations and imagination.
 
 ```{r CycleHireApp-html, echo=FALSE, message=FALSE, fig.cap="CycleHireApp, a simple web mapping application for finding the closest cycle hiring station based on your location and requirement of cycles. Interactive version available online at: r.geocompx.org.",fig.scap="Cycle Hire App, a simple web mapping application.", eval=knitr::is_html_output(), out.width="690"}
-knitr::include_app("https://shiny.robinlovelace.net/CycleHireApp/")
-```
-
-```{r CycleHireApp-latex, echo=FALSE, message=FALSE, fig.cap="CycleHireApp, a simple web mapping application for finding the closest cycle hiring station based on your location and requirement of cycles. Interactive version available online at r.geocompx.org.", fig.scap="coffeeApp, a simple web mapping application.", eval=knitr::is_latex_output()}
-knitr::include_graphics("images/09_cycle_hire_app.png")
+if (knitr::is_html_output()){
+  knitr::include_url("https://shiny.robinlovelace.net/CycleHireApp/")
+}
 ```
 
 ## Other mapping packages

10-gis.Rmd

@@ -212,11 +212,11 @@ aggzone_wgs = st_transform(aggregating_zones, "EPSG:4326")
 #| results: hide
 library(tmap)
 tm_shape(incongr_wgs) +
-  tm_polygons(col = "grey5") +
+  tm_polygons(col = "gray5") +
   tm_shape(aggzone_wgs) +
   tm_borders(col_alpha = 0.5, col = "red") +
   tm_add_legend(type = "lines", labels = c("incongr_wgs", "aggzone_wgs"),
-                col = c("grey5", "red"), lwd = 3, position = tm_pos_in("right", "top")) +
+                col = c("gray5", "red"), lwd = 3, position = tm_pos_in("right", "top")) +
   tm_scalebar(position = c("left", "bottom"), breaks = c(0, 0.5, 1)) +
   tm_layout(frame = FALSE, legend.text.size = 1,
             inner.margins = c(0.02, 0.02, 0.03, 0.02))

12-spatial-cv.Rmd

@@ -26,7 +26,7 @@ library(lgr)                # logging framework for R
 library(mlr3)               # unified interface to machine learning algorithms
 library(mlr3learners)       # most important machine learning algorithms
 library(mlr3extralearners)  # access to even more learning algorithms
-library(mlr3proba)          # make probabilistic predictions, here only needed for mlr3extralearners::list_learners()
+library(mlr3proba)          # here needed for mlr3extralearners::list_learners()
 library(mlr3spatiotempcv)   # spatiotemporal resampling strategies
 library(mlr3tuning)         # hyperparameter tuning
 library(mlr3viz)            # plotting functions for mlr3 objects
@@ -214,7 +214,7 @@ However, it is possible to include spatial autocorrelation\index{autocorrelation
 Though, this is beyond the scope of this book, we give the interested reader some pointers where to look it up:
 
 1. The predictions of regression kriging combines the predictions of a regression with the kriging of the regression's residuals [@goovaerts_geostatistics_1997; @hengl_practical_2007; @bivand_applied_2013]. 
-2. One can also add a spatial correlation (dependency) structure to a generalized least squares model  (`nlme::gls()`; @zuur_mixed_2009; @zuur_beginners_2017).  
+2. One can also add a spatial correlation (dependency) structure to a generalized least squares model  [`nlme::gls()`, @zuur_mixed_2009; @zuur_beginners_2017].  
 3. One can also use mixed-effect modeling approaches.
 Basically, a random effect imposes a dependency structure on the response variable which in turn allows for observations of one class to be more similar to each other than to those of another class [@zuur_mixed_2009]. 
 Classes can be, for example, bee hives, owl nests, vegetation transects or an altitudinal stratification.
@@ -444,7 +444,7 @@ mean(score_spcv_glm$classif.auc) |>
   round(2)
 ```
 
-To put these results in perspective, let us compare them with AUROC\index{AUROC} values from a 100-repeated 5-fold non-spatial cross-validation (Figure \@ref(fig:boxplot-cv); the code for the non-spatial cross-validation\index{cross-validation} is not shown here but will be explored in the exercise section).
+To put these results in perspective, let us compare them with AUROC\index{AUROC} values from a 100-repeated 5-fold non-spatial cross-validation (Figure \@ref(fig:boxplot-cv); the code for the non-spatial cross-validation\index{cross-validation} is not shown here but will be explored in the Exercise section).
 As expected (see Section \@ref(intro-cv)), the spatially cross-validated result yields lower AUROC values on average than the conventional cross-validation approach, underlining the over-optimistic predictive performance of the latter due to its spatial autocorrelation\index{autocorrelation!spatial}.
 
 ```{r boxplot-cv, echo=FALSE, message=FALSE, out.width="75%", fig.cap="Boxplot showing the difference in GLM AUROC values on spatial and conventional 100-repeated 5-fold cross-validation.", fig.scap="Boxplot showing AUROC values."}
@@ -625,7 +625,7 @@ score_spcv_svm = dplyr::filter(score, learner_id == "classif.ksvm.tuned",
                                resampling_id == "repeated_spcv_coords")
 ```
 
-Let us have a look at the final AUROC\index{AUROC}: the model's ability to discriminate the two classes. 
+Let's have a look at the final AUROC\index{AUROC}: the model's ability to discriminate the two classes. 
 
 ```{r 12-spatial-cv-33}
 # final mean AUROC