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 for WMS, here for WMTS and here for WFS.
To download data from IGN web services at least two elements are needed :
- A layer name ;
- An input shape read by
sfpackage.
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 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 :
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 wfs_sup:acte_sup
#> 4 aeag-rwbody-sdage2022:rwbody_ag_sdage2022
#> 5 LIMITES_ADMINISTRATIVES_EXPRESS.LATEST:arrondissement
#> 6 ADMINEXPRESS-COG.LATEST:arrondissement
#> Title
#> 1 OCSGE Gers 2016
#> 2 OCSGE Gers 2019
#> 3 Acte de servitude d'utilité publique
#> 4 Adour Garonne - Masses d'eau rivières - SDAGE 2022
#> 5 Arrondissement
#> 6 Arrondissement COG
#> Abstract
#> 1 OCSGE Gers 2016
#> 2 OCSGE Gers 2019
#> 3 Liste des actes instituant une servitude d'utilité publique
#> 4 Adour Garonne - Masses d'eau rivières - SDAGE 2022
#> 5 Limites administrative mises à jour en continu
#> 6 Arrondissement ADMINEXPRESS COGYou can specify an apikey to focus on specific category. API keys can
be directly retrieved on the IGN website from
the expert web services or with get_apikeys()
function.
get_apikeys()
#> [1] "administratif" "adresse"
#> [3] "agriculture" "altimetrie"
#> [5] "cartes" "cartovecto"
#> [7] "clc" "economie"
#> [9] "enr" "environnement"
#> [11] "geodesie" "lambert93"
#> [13] "ocsge" "ortho"
#> [15] "orthohisto" "parcellaire"
#> [17] "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 mises à jour en continu ; Edition : 2024-02-22
#> 2 Limites administratives mises à jour en continu ; Edition : 2024-02-22
#> 3 Limites administratives mises à jour en continu ; Edition : 2024-12-18
#> Identifier
#> 1 ADMINEXPRESS-COG-CARTO.LATEST
#> 2 ADMINEXPRESS-COG.LATEST
#> 3 LIMITES_ADMINISTRATIVES_EXPRESS.LATESTDownloading 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.
WFS
get_wfs can be used to download borders :
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)
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 !
hedges <- get_wfs(x = penmarch_borders,
layer = "BDTOPO_V3:haie",
spatial_filter = "intersects")
#> Features downloaded : 434
# 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. 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.
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\Rtmpueo2T1\file514477f61a4e.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()areSpatRasterobject from theterrapackage. To learn more about conversion between other raster type in R go check this out.
WMTS
For WMTS, no resolution is needed because 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.
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)
#> Warning in CPL_gdalwarp(source, destination,
#> options, oo, doo, config_options, : GDAL
#> Error 1: Cannot find TileMatrix of zoom
#> level 14 in TileMatrixSet 'PM_6_19'
#> Error: Check that `layer` is valid
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)
#> Error in eval(expr, envir, enclos): objet 'hr_ortho' introuvable