---
title: "Getting started with happign"
author: "Paul Carteron"
date: "2024-05-05"
output: rmarkdown::html_vignette
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%\VignetteIndexEntry{Getting started with happign}
%\VignetteEngine{knitr::rmarkdown}
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---
# Before starting
We can load the `happign` package, and some additional packages we will need (`sf` to manipulate spatial data and `tmap` to create maps)
```r
library(happign)
library(sf)
library(tmap);tmap_mode("plot")
#> tmap mode set to plotting
```
# WFS, WMS and WMTS service
`happign` use three web service from IGN :
* WMS raster : data in raster format e.g. images (.jpg, .png, .tif, ...)
* WMTS : same as WMS raster but images are precalculated
* WFS : data in vector format (.shp, ...).
More detailed information are available [here](https://www.ogc.org/standard/wms/) for WMS, [here](https://www.ogc.org/standard/wmts/) for WMTS and [here](https://www.ogc.org/standard/wfs/) for WFS.
To download data from IGN web services at least two elements are needed :
* A layer name ;
* An input shape read by [`sf`]( https://CRAN.R-project.org/package=sf) package.
## Layer name
It is possible to find the names of available layers from the IGN website. For example, the first layer name in **WFS format** for ["Administratif" category](https://geoservices.ign.fr/services-web-experts-administratif) is *"ADMINEXPRESS-COG-CARTO.LATEST:arrondissement"*
All layer's name can be accessed from R with the `get_layers_metadata()` function. This one connects directly to the IGN site which allows to have the last updated resources. It can be used for WMS and WFS :
```r
administratif_wfs <- get_layers_metadata(data_type = "wfs")
administratif_wms <- get_layers_metadata(data_type = "wms-r")
administratif_wms <- get_layers_metadata(data_type = "wmts")
head(administratif_wfs)
#> Name
#> 1 OCS-GERS_BDD_LAMB93_2016:oscge_gers_32_2016
#> 2 OCS-GERS_BDD_LAMB93_2019:oscge_gers_32_2019
#> 3 ADMINEXPRESS-COG.LATEST:arrondissement
#> 4 ADMINEXPRESS-COG.LATEST:arrondissement_municipal
#> 5 ADMINEXPRESS-COG.LATEST:canton
#> 6 ADMINEXPRESS-COG.LATEST:chflieu_arrondissement_municipal
#> Title Abstract
#> 1 OCSGE Gers 2016 OCSGE Gers 2016
#> 2 OCSGE Gers 2019 OCSGE Gers 2019
#> 3 ADMINEXPRESS COG 2023 édition 2023
#> 4 ADMINEXPRESS COG 2023 édition 2023
#> 5 ADMINEXPRESS COG 2023 édition 2023
#> 6 ADMINEXPRESS COG 2023 édition 2023
```
You can specify an apikey to focus on specific category. API keys can be directly retrieved on the [IGN website from the expert web services](https://geoservices.ign.fr/services-web-experts) or with `get_apikeys()` function.
```r
get_apikeys()
#> [1] "administratif" "adresse" "agriculture"
#> [4] "altimetrie" "cartes" "cartovecto"
#> [7] "clc" "economie" "enr"
#> [10] "environnement" "geodesie" "lambert93"
#> [13] "ocsge" "ortho" "orthohisto"
#> [16] "parcellaire" "satellite" "sol"
#> [19] "topographie" "transports"
administratif_wmts <- get_layers_metadata("wmts", "administratif")
head(administratif_wmts)
#> Title
#> 1 ADMINEXPRESS COG CARTO
#> 2 ADMINEXPRESS COG
#> 3 Limites administratives mises à jour en continu.
#> Abstract
#> 1 Limites administratives Express COG code officiel géographique 2023
#> 2 Limites administratives Express COG code officiel géographique. 2023
#> 3 Limites administratives mises à jour en continu ; Edition : 2024-03-25
#> Identifier
#> 1 ADMINEXPRESS-COG-CARTO.LATEST
#> 2 ADMINEXPRESS-COG.LATEST
#> 3 LIMITES_ADMINISTRATIVES_EXPRESS.LATEST
```
## Downloading the data
Now that we know how to get a layer name, it only takes a few lines to get plethora of resources. For the example we will look at the beautiful town of Penmarch in France. A part of this town is stored as a shape in happign.
```r
penmarch <- read_sf(system.file("extdata/penmarch.shp", package = "happign"))
```
### WFS
`get_wfs` can be used to download borders :
```r
penmarch_borders <- get_wfs(x = penmarch,
layer = "LIMITES_ADMINISTRATIVES_EXPRESS.LATEST:commune")
#> Features downloaded : 1
# Checking result
tm_shape(penmarch_borders)+
tm_polygons(alpha = 0, lwd = 2)+
tm_shape(penmarch)+
tm_polygons(col = "red")+
tm_add_legend(type = "fill", border.col = "black", border.lwd =2,
col = NA, labels = "border from get_wfs")+
tm_add_legend(type = "fill", col = "red", labels = "penmarch shape from happign package")+
tm_layout(main.title = "Penmarch borders from IGN",
main.title.position = "center",
legend.position = c(0.7, -0.1),
outer.margins = c(0.1, 0,0,0),
frame = FALSE)
#> Legend labels were too wide. The labels have been resized to 0.61. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.
```
It's as simple as that!
Now you have to rely on your curiosity to explore the multiple possibilities that IGN offers. For example, who has never wondered how many hedges for biodiversity there are in Penmarch?
*Spoiler : there are 436 of them !*
```r
hedges <- get_wfs(x = penmarch_borders,
layer = "BDTOPO_V3:haie",
spatial_filter = "intersects")
#> Features downloaded : 436
# Checking result
tm_shape(penmarch_borders) + # Borders of penmarch
tm_borders(lwd = 2) +
tm_shape(hedges) + # Point use to retrieve data
tm_lines(col = "red", size = 0.3) +
tm_add_legend(type = "line", label = "Hedges", col = "red") +
tm_layout(main.title = "Hedges recorded by the IGN in Penmarch",
main.title.position = "center",
legend.position = c("right", "bottom"),
frame = FALSE)
```
### WMS raster
For raster, the process is the same, but with the function `get_wms_raster()`, but you need to specify the resolution (note that it must be in the same coordinate system as the crs parameter). There's plenty of elevation resources inside ["altimetrie" category](https://geoservices.ign.fr/services-web-experts-altimetrie). A basic one is the Digital Elevation Model (DEM or MNT in French). Borders of Penmarch are used to download the DEM. Note that for DEM, we don't want an RGB image but values of each pixels. That why `rgb=FALSE` is used below.
```r
layers_metadata <- get_layers_metadata("wms-r", "altimetrie")
dem_layer <- layers_metadata[2, 1] #LEVATION.ELEVATIONGRIDCOVERAGE
mnt <- get_wms_raster(x = penmarch_borders,
layer = dem_layer,
res = 25,
crs = 2154,
rgb = FALSE)
#> 0...10...20...30...40...50...60...70...80...90...100 - done.
#> Raster is saved at : C:\Users\PaulCarteron\AppData\Local\Temp\Rtmp63I4Tv\filea98465250a.tif
mnt[mnt < 0] <- NA # remove negative values in case of singularity
tm_shape(mnt) +
tm_raster(title = "Elevation [m]") +
tm_shape(penmarch_borders)+
tm_borders(lwd = 2)+
tm_layout(main.title = "DEM of Penmarch",
main.title.position = "center",
legend.position = c("right", "bottom"),
legend.bg.color = "white", legend.bg.alpha = 0.7)
```
__*Rq :*__
* *Raster from `get_wms_raster()` are `SpatRaster` object from the `terra` package. To learn more about conversion between other raster type in R go [check this out](https://geocompx.org/post/2021/spatial-classes-conversion/).*
### WMTS
For WMTS, no resolution is needed beacause images are precalculated but a zoom level is needed. The higher the zoom level is, the more precis image is. If you only need visualisation, i recommend to use WMTS instead of WMS.
```r
layers_metadata <- get_layers_metadata("wmts", "ortho")
ortho_layer <- layers_metadata[1, 3] #HR.ORTHOIMAGERY.ORTHOPHOTOS
hr_ortho <- get_wmts(x = penmarch_borders,
layer = ortho_layer,
zoom = 14)
#> 0...10...20...30...40...50...60...70...80...90...100 - done.
tm_shape(hr_ortho) +
tm_rgb(title = "Orthophoto Hight Resolution") +
tm_shape(penmarch_borders)+
tm_borders(lwd = 2)+
tm_layout(main.title = "Orthophoto Hight Resolution",
main.title.position = "center",
legend.position = c("right", "bottom"),
legend.bg.color = "white", legend.bg.alpha = 0.7)
```
