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diff --git a/02-spatial-data.md b/02-spatial-data.md
@@ -247,11 +247,9 @@ world_mini
 #> Dimension:     XY
 #> Bounding box:  xmin: -180 ymin: -18.3 xmax: 180 ymax: -0.95
 #> Geodetic CRS:  WGS 84
-#> # A tibble: 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'!

diff --git a/06-raster-vector.md b/06-raster-vector.md
@@ -397,6 +397,11 @@ grain_poly = as.polygons(grain) |>
   st_as_sf()
 ```
 
+
+```
+#> Some legend items or map compoments do not fit well (e.g. due to the specified font size). They are rescaled Set the tmap option component.autoscale to FALSE to disable this.FALSE
+```
+
 <div class="figure" style="text-align: center">
 <img src="figures/06-raster-vector-40-1.png" alt="Illustration of vectorization of raster (left) into polygons (dissolve = FALSE; center) and aggregated polygons (dissolve = TRUE; right)." width="100%" />
 <p class="caption">(\#fig:06-raster-vector-40)Illustration of vectorization of raster (left) into polygons (dissolve = FALSE; center) and aggregated polygons (dissolve = TRUE; right).</p>

diff --git a/12-spatial-cv.md b/12-spatial-cv.md
@@ -310,25 +310,25 @@ Third, the **resampling** approach assesses the predictive performance of the mo
 
 To use a GLM\index{GLM} in **mlr3**\index{mlr3 (package)}, we must create a **task** containing the landslide data.
 Since the response is binary (two-category variable) and has a spatial dimension, we create a classification\index{classification} task with `as_task_classif_st()` of the **mlr3spatiotempcv** package [@schratz_mlr3spatiotempcv_2021, for non-spatial tasks, use `mlr3::as_task_classif()` or `mlr3::as_task_regr()` for regression\index{regression} tasks, see `?Task` for other task types].^[The **mlr3** ecosystem makes heavily use of **data.table** and **R6** classes. And though you might use **mlr3** without knowing the specifics of **data.table** or **R6**, it might be rather helpful. To learn more about **data.table**, please refer to https://rdatatable.gitlab.io/data.table/. To learn more about **R6**, we recommend [Chapter 14](https://adv-r.hadley.nz/fp.html) of the Advanced R book [@wickham_advanced_2019].]
-The first essential argument of these `as_task_` functions is `backend`.
-`backend` expects that the input data includes the response and predictor variables.
+The first essential argument of these `as_task_` functions is `x`.
+`x` expects that the input data includes the response and predictor variables.
 The `target` argument indicates the name of a response variable (in our case this is `lslpts`) and `positive` determines which of the two factor levels of the response variable indicate the landslide initiation point (in our case this is `TRUE`).
 All other variables of the `lsl` dataset will serve as predictors.
 For spatial CV, we need to provide a few extra arguments.
 The `coordinate_names` argument expects the names of the coordinate columns (see Section \@ref(intro-cv) and Figure \@ref(fig:partitioning)).
-Additionally, we should decide if we want to use the coordinates as predictors in the modeling (`coords_as_features`) and indicate the used CRS (`crs`).
+Additionally, we should indicate the used CRS (`crs`) and decide if we want to use the coordinates as predictors in the modeling (`coords_as_features`).
 
 
 ```r
 # 1. create task
 task = mlr3spatiotempcv::as_task_classif_st(
-  id = "ecuador_lsl",
-  backend = mlr3::as_data_backend(lsl), 
+  mlr3::as_data_backend(lsl), 
   target = "lslpts", 
+  id = "ecuador_lsl",
   positive = "TRUE",
   coordinate_names = c("x", "y"),
-  coords_as_features = FALSE,
-  crs = "EPSG:32717"
+  crs = "EPSG:32717",
+  coords_as_features = FALSE
   )
 ```
 

diff --git a/13-transport.md b/13-transport.md
@@ -569,7 +569,7 @@ 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] 3832
+#> [1] 3828
 ```
 
 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.

diff --git a/14-location.md b/14-location.md
@@ -422,7 +422,7 @@ For instance, a score greater than 9 might be a suitable threshold indicating ra
 
 ```{=html}
 <div class="leaflet html-widget html-fill-item" id="htmlwidget-841de324d41155df19a0" style="width:100%;height:389.34px;"></div>
-<script type="application/json" data-for="htmlwidget-841de324d41155df19a0">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"&copy; <a href=\"https://openstreetmap.org/copyright/\">OpenStreetMap<\/a>,  <a href=\"https://opendatacommons.org/licenses/odbl/\">ODbL<\/a>"}]},{"method":"addRasterImage","args":["data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACgAAAAoCAYAAACM/rhtAAAAM0lEQVRYhe3SMQ0AMAwEsQcb/hTSuQSaVLIR3HAJAADfqfR0wm1dEC9VevcCq+MAAIBhB20vBvBK3JZrAAAAAElFTkSuQmCC",[[52.69660085729197,13.08200261479863],[52.32107408861835,13.69815913809763]],0.8,null,null,null]},{"method":"addLegend","args":[{"colors":["darkgreen"],"labels":["potential locations"],"na_color":null,"na_label":"NA","opacity":0.5,"position":"bottomright","type":"unknown","title":"Legend","extra":null,"layerId":null,"className":"info legend","group":null}]}],"limits":{"lat":[52.32107408861835,52.69660085729197],"lng":[13.08200261479863,13.69815913809763]}},"evals":[],"jsHooks":[]}</script>
+<script type="application/json" data-for="htmlwidget-841de324d41155df19a0">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"&copy; <a href=\"https://openstreetmap.org/copyright/\">OpenStreetMap<\/a>,  <a href=\"https://opendatacommons.org/licenses/odbl/\">ODbL<\/a>"}]},{"method":"addRasterImage","args":["data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACgAAAAoCAYAAACM/rhtAAAAM0lEQVRYhe3SMQ0AMAwEsQcb/hTSuQSaVLIR3HAJAADfqfR0wm1dEC9VevcCq+MAAIBhB20vBvBK3JZrAAAAAElFTkSuQmCC",[[52.69660085729197,13.08200261479863],[52.32107408861835,13.69815913809763]],null,null,{"tileSize":256,"zIndex":1,"minZoom":0,"opacity":0.8}]},{"method":"addLegend","args":[{"colors":["darkgreen"],"labels":["potential locations"],"na_color":null,"na_label":"NA","opacity":0.5,"position":"bottomright","type":"unknown","title":"Legend","extra":null,"layerId":null,"className":"info legend","group":null}]}],"limits":{"lat":[52.32107408861835,52.69660085729197],"lng":[13.08200261479863,13.69815913809763]}},"evals":[],"jsHooks":[]}</script>
 ```
 
 <p class="caption">(\#fig:bikeshop-berlin)Suitable areas (i.e., raster cells with a score > 9) in accordance with our hypothetical survey for bike stores in Berlin.</p>

diff --git a/15-eco.md b/15-eco.md
@@ -380,8 +380,11 @@ For specifying a spatial task, we use again the **mlr3spatiotempcv** package [@s
 
 ```r
 # create task
-task = mlr3spatiotempcv::as_task_regr_st(select(rp, -id, -spri),
-  id = "mongon", target = "sc")
+task = mlr3spatiotempcv::as_task_regr_st(
+  select(rp, -id, -spri),
+  target = "sc",
+  id = "mongon"
+)
 ```
 
 Using an `sf` object as the backend automatically provides the geometry information needed for the spatial partitioning later on.

diff --git a/404.html b/404.html
diff --git a/_redirects b/_redirects
@@ -2,6 +2,6 @@
 http://geocompr.robinlovelace.net/* http://r.geocompx.org/:splat 301!
 https://geocompr.robinlovelace.net/* https://r.geocompx.org/:splat 301!
 
- /solutions/* https://r-geocomp-solutions.netlify.app/:splat 200
+ /solutions/* https://geocompx.github.io/solutions/:splat 200
  /es/* https://r-geocomp-es.netlify.app/:splat 200
  /fr/* https://r-geocomp-fr.netlify.app/:splat 200