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Nowosad committed Jan 3, 2025
2 parents d7c6f31 + b848389 commit 68e7081
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6 changes: 3 additions & 3 deletions .github/workflows/main.yaml
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Expand Up @@ -7,15 +7,15 @@ jobs:
bookdown:
name: Render-Book
runs-on: ubuntu-latest
container: geocompr/geocompr:suggests
container: ghcr.io/geocompx/docker:suggests
env:
GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
steps:
- uses: actions/checkout@v2
- name: Render Book
run: |
Rscript -e 'install.packages("remotes")'
Rscript -e 'remotes::install_github("geocompx/geocompkg", dependencies = TRUE, force = TRUE)'
#Rscript -e 'install.packages("remotes")'
#Rscript -e 'remotes::install_github("geocompx/geocompkg", dependencies = TRUE, force = TRUE)'
#Rscript -e 'install.packages("geocompkg", repos = c("https://geocompr.r-universe.dev", "https://cloud.r-project.org"), dependencies = TRUE, force = TRUE)'
Rscript -e 'bookdown::render_book("index.Rmd")'
cp -fvr _redirects _book/
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2 changes: 1 addition & 1 deletion 01-introduction.Rmd
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Expand Up @@ -53,7 +53,7 @@ This may sound simple and easy to achieve (which it is if you carefully maintain

We define geocomputation as

> A field of research, software development and practical application that uses geographic data to solve problems, with a focus on reproducibility, flexibility and tool development.
> Academic research, software development and practical applications that use geographic data to solve problems, with a focus on reproducibility, flexibility and tool development.
Geocomputation\index{geocomputation!definition} is a young term, dating back to the first conference on the subject in 1996.^[
The first 'GeoComputation' conference took place at the University of Leeds, where one of the authors (Robin) is currently based.
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2 changes: 1 addition & 1 deletion 02-spatial-data.Rmd
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Expand Up @@ -37,7 +37,7 @@ Unless you already have these packages installed, the first thing to do is to in
install.packages("sf")
install.packages("terra")
install.packages("spData")
install.packages("spDataLarge", repos = "https://nowosad.r-universe.dev")
install.packages("spDataLarge", repos = "https://geocompr.r-universe.dev")
```

```{r, eval=FALSE, echo=FALSE, message=FALSE, results='hide'}
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2 changes: 1 addition & 1 deletion 04-spatial-operations.Rmd
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Expand Up @@ -148,7 +148,7 @@ waldo::compare(canterbury_height2, canterbury_height4)
At this point, there are three identical (in all but row names) versions of `canterbury_height`, one created using the `[` operator, one created via an intermediary selection object, and another using **sf**'s convenience function `st_filter()`.
<!-- RL: commented out for now as old. Todo: if we ever update that vignette uncomment the next line. -->
<!-- To explore spatial subsetting in more detail, see the supplementary vignettes on `subsetting` and [`tidyverse-pitfalls`](https://geocompr.github.io/geocompkg/articles/) on the [geocompkg website](https://geocompr.github.io/geocompkg/articles/). -->
The next section explores different types of spatial relation, also known as binary predicates, that can be used to identify whether or not two features are spatially related or not.
The next section explores different types of spatial relation, also known as binary predicates, that can be used to identify whether two features are spatially related or not.

### Topological relations

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2 changes: 1 addition & 1 deletion 13-transport.Rmd
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Expand Up @@ -540,7 +540,7 @@ This is done using the publicly available OSRM service with the **stplanr** func

```{r 13-transport-18, message=FALSE}
routes_short = route(l = desire_lines_short, route_fun = route_osrm,
osrm.profile = "bike")
osrm.profile = "car")
```

The output is `routes_short`, an `sf` object representing routes on the transport network\index{network} that are suitable for cycling (according to the OSRM routing engine at least), one for each desire line.
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4 changes: 2 additions & 2 deletions code/03-cont-raster-plot.R
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Expand Up @@ -8,12 +8,12 @@ colfunc2 = c("clay" = "brown", "silt" = "sandybrown", "sand" = "rosybrown")
p1 = tm_shape(elev) +
tm_raster(col.scale = tm_scale_continuous(),
col.legend = tm_legend(title = "",
position = tm_pos_auto_in(),
position = tm_pos_in(),
bg.color = "white"))

p2 = tm_shape(grain) +
tm_raster(col.scale = tm_scale_categorical(values = colfunc2),
col.legend = tm_legend(title = "",
position = tm_pos_auto_in(),
position = tm_pos_in(),
bg.color = "white"))
tmap_arrange(p1, p2, nrow = 1)

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