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Before starting

We can load the happign package, and some additional packages we will need.

WFS and WMS service

happign use two data type from IGN web service :

  • WMS raster : data in raster format which are images (.jpg, .png, .tif)
  • WFS : data in shape format (.shp).

More detailed information are available here for WMS and here for WFS.

To download data you need :

  • API key corresponding to the layer you want ;
  • The layer’s name ;
  • A shape read by sf package

API keys

API keys can be directly retrieved on the IGN website in the expert web services (I recommend you at this point to go and have a look).

For example, if I take the first category “Administratif”, I see that the API key is “administratif”.

With happign, there is no need to go through the website because all 18 api keys can be retrieved by running the function get_apikeys() :

get_apikeys()
#>  [1] "administratif" "adresse"       "agriculture"   "altimetrie"   
#>  [5] "cartes"        "cartovecto"    "clc"           "economie"     
#>  [9] "environnement" "geodesie"      "lambert93"     "ortho"        
#> [13] "orthohisto"    "parcellaire"   "satellite"     "sol"          
#> [17] "topographie"   "transports"

Layer name

As for API key, it is possible to find the names of available layers from the expert web services of the IGN. If I continue to explore the “Administratif” category, the first layer name in WFS format is “ADMINEXPRESS-COG.LATEST:arrondissement”.

Again, all layer’s name and other metadata (abstract, style, keywords, defaultcrs, …) 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 :

apikey <- get_apikeys()[1]
get_layers_metadata(apikey = apikey, data_type = "wfs")
get_layers_metadata(apikey = apikey, data_type = "wms")

Downloading the data

Now that we know how to get an API key and 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.

WFS

First, we are going to get borders of the town which is a shape resources so get_wfs() will be used. This function need a shape to work. A single point is take inside Penmarch so that the function detects all the shape that intersect this point :

penmarch_point <- st_sfc(st_point(c(-4.370, 47.800)), crs = 4326)
penmarch_borders <- get_wfs(shape = penmarch_point,
                            apikey = "administratif",
                            layer_name = "LIMITES_ADMINISTRATIVES_EXPRESS.LATEST:commune")
#> 1/1 downloaded

# Checking result
tm_shape(penmarch_borders) + # Borders of penmarch
   tm_polygons(alpha = 0, lwd = 2) +
tm_shape(penmarch_point) + # Point use to retrieve data
   tm_dots(col = "red", size = 2) +
   tm_add_legend(type = "symbol", label = "lat : -4.370, long : 47.800",
                 col = "red", size = 1) +
   tm_layout(main.title = "Penmarch borders from IGN",
             main.title.position = "center",
             legend.position = c("right", "bottom"),
             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 road junctions there are in Penmarch?

Spoiler : there are 192 of them

dikes <- get_wfs(shape = penmarch_borders,
                 apikey = get_apikeys()[6],
                 layer_name = "BDCARTO_BDD_WLD_WGS84G:noeud_routier")
#> 1/1 downloaded

dikes <- st_intersection(penmarch_borders, dikes)
#> Warning: attribute variables are assumed to be spatially constant throughout all
#> geometries

# Checking result
tm_shape(penmarch_borders) + # Borders of penmarch
   tm_borders(lwd = 2) +
tm_shape(dikes) + # Point use to retrieve data
   tm_dots(col = "red") +
   tm_add_legend(type = "symbol", label = "Road junction", col = "red") +
   tm_layout(main.title = "Road nodes 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(). 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 as shape for downloading the DEM.

apikey <- get_apikeys()[4]
layers_metadata <- get_layers_metadata(apikey, "wms")
dem_layer_name <- layers_metadata[2, "name"]

mnt <- get_wms_raster(shape = penmarch_borders,
                      apikey = apikey,
                      layer_name = dem_layer_name,
                      resolution = 25,
                      filename = "best_raster_name")
#> 1/1 downloaded
file.remove("best_raster_name_25m.tif") # raster are download to disk but I don't want to keep it
#> [1] TRUE

mnt[mnt < 0] <- NA # remove negative values in case of singularity
names(mnt) <- "Elevation [m]" # Rename raster ie the title legend

tm_shape(mnt) +
   tm_raster(colorNA = NULL) +
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,
          frame = FALSE)

Rq :

  • Raster from get_wms_raster() are stars object from the stars package. To learn more about conversion between other raster type in R go check this out.