Ajout de variables de sites
2025-08-08
library(sf); library(dplyr); library(ggplot2)1 DEFINITION DE LA GRILLE EN REGION BRETAGNE CONTINENTALE
grille_10x10 <- sf::st_read(
paste0(wd$data, "masques/Grille_10x10/Grille_10X10.shp")
)## Reading layer `Grille_10X10' from data source
## `/home/onyxia/work/AnalyseDonneesOpportunisteGMB/data/masques/Grille_10x10/Grille_10X10.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 9625 features and 2 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -280000 ymin: 5880000 xmax: 1310000 ymax: 7170000
## Projected CRS: RGF93 v1 / Lambert-93RegionBretagneConti <- sf::st_read(
paste0(wd$data, "masques/RegionBretagneConti/RegionBretagneConti.shp")
)## Reading layer `RegionBretagneConti' from data source
## `/home/onyxia/work/AnalyseDonneesOpportunisteGMB/data/masques/RegionBretagneConti/RegionBretagneConti.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 1 feature and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 123927.4 ymin: 6650496 xmax: 404382.1 ymax: 6882056
## CRS: NARegionBretagneConti <- RegionBretagneConti%>%
transforme_carte() %>%
sf::st_transform(2154) %>%
sf::st_buffer(dist = 200) %>%
transforme_carte()
grille_10x10 <- grille_10x10 %>%
transforme_carte() %>%
sf::st_intersection(RegionBretagneConti) %>%
dplyr::arrange(CODE_10KM) %>%
dplyr::select(CODE_10KM)## Warning: attribute variables are assumed to be spatially constant throughout all geometriesgrille_10x10 <- grille_10x10 %>%
dplyr::filter(sf::st_is(grille_10x10,c("POLYGON","MULTIPOLYGON")))2 VARIABLES STRUCTURANTES
var_noms <- c("Indice_Diversite_500m", "Densite_Cultures_500m",
"Distance_EcotoneArbore", "Distance_Littoral", "Distance_Eau")
for(var in var_noms) {
raster_path <- paste0(wd$data, "masques/VariablesStructurates/", var, ".tif")
rast <- terra::rast(raster_path)
terra::crs(rast) <- "EPSG:2154"
grille_10x10 <- sf::st_transform(grille_10x10, crs = "EPSG:2154")
vals <- exactextractr::exact_extract(rast, grille_10x10, 'mean')
grille_10x10[[var]] <- vals
}Suppression car hors du territoire -> Ca nous corrige le problème qu’on avait avec les individus dans la partie bluffée du 44.
grille_10x10 <- grille_10x10%>%
dplyr::filter(!is.na(Distance_Littoral))var_noms <- c("Indice_Diversite_500m", "Densite_Cultures_500m",
"Distance_EcotoneArbore", "Distance_Littoral", "Distance_Eau")
for(var in var_noms) {
print(grille_10x10%>%
ggplot()+
geom_sf(aes(fill = .data[[var]]))
)
}
3 FAMILLES DE PAYSAGES
On choisis la famille la plus fréquente dans la cellule?
famille_paysage <- sf::st_read(
paste0(wd$data, "masques/famille_paysage/familles_paysages.shp")
)## Reading layer `familles_paysages' from data source
## `/home/onyxia/work/AnalyseDonneesOpportunisteGMB/data/masques/famille_paysage/familles_paysages.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 39 features and 3 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 99226.49 ymin: 6701141 xmax: 412224.8 ymax: 6881501
## Projected CRS: RGF93 v1 / Lambert-93famille_paysage <- famille_paysage %>%
dplyr::rename(Nom_paysage = NOM,
Famille_paysage = FAMILLE)%>%
dplyr::mutate_if(is.character,as.factor)%>%
dplyr::select(Nom_paysage, Famille_paysage)
jointure <- sf::st_join(grille_10x10, famille_paysage, left = FALSE)
mode_cellule <- jointure %>%
sf::st_drop_geometry() %>%
dplyr::group_by(CODE_10KM) %>%
dplyr::summarise(
Famille_paysage = Mode(Famille_paysage),
Nom_paysage = Mode(Nom_paysage)
)
grille_10x10 <- grille_10x10 %>%
dplyr::left_join(mode_cellule, by = "CODE_10KM")On a une case sans de valeurs, donc on lui attribut la valeur la plus fréquente parmi ses voisins.
index <- sf::st_touches(grille_10x10, grille_10x10)
attribut_val_voisine <- function(val, voisins) {
if (is.na(val)) {
val_voisins <- valeur[voisins]
val_voisins <- val_voisins[!is.na(val_voisins)]
if (length(val_voisins) == 0) {
return(NA)
} else {
return(Mode(val_voisins))
}
} else {
return(val)
}
}
for (col in c("Famille_paysage", "Nom_paysage")) {
valeur <- grille_10x10[[col]]
nvx_valeur <- purrr::map2(valeur, index, attribut_val_voisine)
grille_10x10[[col]] <- factor(unlist(nvx_valeur),
levels = levels(valeur))
}grille_10x10 %>%
ggplot() + geom_sf(aes(fill=Famille_paysage))
4 TAILLE DE LA CELLULE
grille_10x10 <- grille_10x10 %>%
sf::st_transform(2154) %>%
dplyr::mutate(aire_km2 = as.numeric(sf::st_area(grille_10x10)/1000000)) %>%
transforme_carte()%>%
dplyr::filter(aire_km2 > 2)grille_10x10%>%
ggplot()+geom_sf(aes(fill=aire_km2))
5 COORDONNEES DES CENTROIDS
centroids <- sf::st_centroid(grille_10x10)## Warning: st_centroid assumes attributes are constant over geometriescentroid_coords <- sf::st_coordinates(centroids)
grille_10x10_centroids <- grille_10x10 %>%
dplyr::mutate(
X_10km = centroid_coords[, "X"],
Y_10km = centroid_coords[, "Y"]
) %>%
dplyr::select(CODE_10KM, X_10km, Y_10km)
grille_10x10 <- grille_10x10 %>%
dplyr::left_join(
as.data.frame(grille_10x10_centroids),
by = "CODE_10KM",
relationship = "many-to-many"
)
grille_10x10 <- grille_10x10%>%
dplyr::rename(Code_10km = CODE_10KM,
Ind_Diversite = Indice_Diversite_500m,
Dnst_Cultures = Densite_Cultures_500m,
Dist_EcotoneArbore = Distance_EcotoneArbore,
Dist_Littoral = Distance_Littoral,
Dist_Eau = Distance_Eau)6 EXPORTS
summary(grille_10x10)## Code_10km Ind_Diversite Dnst_Cultures Dist_EcotoneArbore Dist_Littoral
## Length:328 Min. :1.445 Min. :0.007704 Min. : 23.80 Min. : 0.2196
## Class :character 1st Qu.:1.923 1st Qu.:0.288156 1st Qu.: 40.38 1st Qu.: 2.7277
## Mode :character Median :2.051 Median :0.388762 Median : 48.53 Median :15.1991
## Mean :2.076 Mean :0.376411 Mean : 58.12 Mean :20.9589
## 3rd Qu.:2.218 3rd Qu.:0.466718 3rd Qu.: 59.67 3rd Qu.:34.7019
## Max. :3.061 Max. :0.694237 Max. :766.34 Max. :79.8731
##
## Dist_Eau Famille_paysage Nom_paysage
## Min. : 77.86 Paysage cultive a ragosses :93 Reliefs des Landes de Lanvaux: 36
## 1st Qu.: 204.78 Paysage boise et de bosquets :57 Plateau de Penthievre : 22
## Median : 237.46 Paysage cultive avec talus :56 Armor morbihannais : 19
## Mean : 257.46 Paysage de bocage a maille elargie :41 Bassin de Pontivy-Loudeac : 18
## 3rd Qu.: 293.17 Paysage de bocage dense sur collines:34 Bassin de Rennes : 17
## Max. :1399.09 Paysage de littoral urbanise :29 Cornouaille interieure : 16
## (Other) :18 (Other) :200
## aire_km2 X_10km Y_10km geometry.x geometry.y
## Min. : 2.294 Min. :-4.752 Min. :47.41 MULTIPOLYGON : 21 MULTIPOLYGON : 21
## 1st Qu.: 78.606 1st Qu.:-3.668 1st Qu.:47.92 POLYGON :307 POLYGON :307
## Median :100.000 Median :-2.907 Median :48.18 epsg:4326 : 0 epsg:4326 : 0
## Mean : 84.662 Mean :-2.896 Mean :48.18 +proj=long...: 0 +proj=long...: 0
## 3rd Qu.:100.000 3rd Qu.:-2.140 3rd Qu.:48.45
## Max. :100.000 Max. :-1.021 Max. :48.87
## Export sous forme shapefile
grille_10x10%>%
dplyr::select(-geometry.y, -geometry.x)%>%
sf::st_write(paste0(wd$data, "derived/VariablesSite.shp"),
delete_dsn = TRUE)## Warning in abbreviate_shapefile_names(obj): Field names abbreviated for ESRI Shapefile driver## Deleting source `/home/onyxia/work/AnalyseDonneesOpportunisteGMB/data/derived/VariablesSite.shp' using driver `ESRI Shapefile'
## Writing layer `VariablesSite' to data source
## `/home/onyxia/work/AnalyseDonneesOpportunisteGMB/data/derived/VariablesSite.shp' using driver `ESRI Shapefile'
## Writing 328 features with 11 fields and geometry type Unknown (any).Export sous forme csv
grille_10x10%>%
dplyr::select(-geometry.y)%>%
sf::st_drop_geometry()%>%
utils::write.csv(paste0(wd$data, "derived/VariablesSite.csv"))