Examples
Working with layers
On this example we would demonstrate some of the main capabilities of the package by recreating a cadastral map of the surroundings of the Santiago Bernabéu Stadium. We would make use of the WMS and WFS services to get different layers in order to show some of the capabilities of the package:
# Extract building by bounding box
# Check https://boundingbox.klokantech.com/
library(CatastRo)
stadium <- catr_wfs_get_buildings_bbox(
c(-3.6891446916, 40.4523311971, -3.687462138, 40.4538643165),
srs = 4326
)
# Now extract cadastral parcels. We can use spatial objects on the query
stadium_parcel <- catr_wfs_get_parcels_bbox(stadium)
# Extract imagery: Labels of the parcel
labs <- catr_wms_get_layer(stadium_parcel,
what = "parcel",
styles = "BoundariesOnly"
)
# Plot
library(ggplot2)
library(tidyterra) # For terra tiles
ggplot() +
geom_spatraster_rgb(data = labs) +
geom_sf(data = stadium_parcel, fill = NA, col = "red", size = 2) +
geom_sf(data = stadium, fill = "red", alpha = .5)
Thematic maps
We can create also thematic maps using the information available on the spatial objects. We would produce a visualization of the urban growth of Valencia using CatastRo, replicating the map produced by Dominic Royé on his post Visualize urban growth, using the ATOM services.
In first place, we extract the coordinates of the city center of Valencia using mapSpain:
library(dplyr)
library(sf)
library(mapSpain)
# Use mapSpain for getting the coords
val <- esp_get_capimun(munic = "^València$")Next step consists on extracting the buildings using the ATOM
service. We would use also the function
catr_get_code_from_coords() to identify the code of
Valencia in the Cadastre, and we would download the buildings with
catr_atom_get_buildings().
val_catr_code <- catr_get_code_from_coords(val)
val_catr_code
#> # A tibble: 1 × 12
#> munic catr_to catr_munic catrcode cpro cmun inecode nm cd cmc cp cm
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 VALENCIA 46 900 46900 46 250 46250 VALENCIA 46 900 46 250
valencia_bu <- catr_atom_get_buildings(val_catr_code$catrcode)Next step for creating the visualization is to limit the analysis to a circle of radius 2.5 km around the city center:
buff <- val %>%
# Adjust CRS to 25830: (Buildings)
st_transform(st_crs(valencia_bu)) %>%
# Buffer
st_buffer(2500)
# Cut buildings
dataviz <- st_intersection(valencia_bu, buff)
ggplot(dataviz) +
geom_sf()
Let’s extract now the construction year, available in the column
beginning:
# Extract 4 initial positions
year <- substr(dataviz$beginning, 1, 4)
# Replace all that doesn't look as a number with 0000
year[!(year %in% 0:2500)] <- "0000"
# To numeric
year <- as.integer(year)
# New column
dataviz <- dataviz %>%
mutate(year = year)Last step is to create groups based on the year and create the data
visualization. We use here the function
ggplot2::cut_number() to create 15 different classes:
dataviz <- dataviz %>%
mutate(year_cat = ggplot2::cut_number(year,
n = 15
))
ggplot(dataviz) +
geom_sf(aes(fill = year_cat), color = NA) +
scale_fill_manual(values = hcl.colors(15, "Spectral")) +
theme_void() +
labs(title = "VALÈNCIA", fill = "") +
theme(
panel.background = element_rect(fill = "black"),
plot.background = element_rect(fill = "black"),
legend.justification = .5,
legend.text = element_text(
colour = "white",
size = 12
),
plot.title = element_text(
colour = "white", hjust = .5,
margin = margin(t = 30),
size = 30
),
plot.caption = element_text(
colour = "white",
margin = margin(b = 20), hjust = .5
),
plot.margin = margin(r = 40, l = 40)
)