Figure 1: Example - Santiago Bernabeu
CatastRo provides access to different API services of the Spanish Cadastre. With CatastRo, you can download official information on addresses, properties, parcels, and buildings.
The OVCCoordenadas service allows retrieving the coordinates of a known cadastral reference (geocoding). It is also possible to retrieve the cadastral references around a specific pair of coordinates (reverse geocoding). CatastRo returns the results in a tibble format. This functionality is described in detail in the corresponding vignette (see vignette("ovcservice", package = "CatastRo")).
The INSPIRE Directive aims to create a European Union spatial data infrastructure for the purposes of EU environmental policies and policies or activities which may have an impact on the environment. This European Spatial Data Infrastructure will enable the sharing of environmental spatial information among public sector organisations, facilitate public access to spatial information across Europe and assist in policy-making across boundaries.
The implementation of the INSPIRE directive on the Spanish Cadastre (see Catastro INSPIRE) allows retrieving spatial objects from the database of the cadastre:
sf objects as provided by the sf package.SpatRaster objects as provided by the terra package.Note that the coverage of this service is 95% of the Spanish territory, excluding the Basque Country and Navarre1, which have their own independent cadastral offices.
There are three types of functions, each one querying a different service:
ATOM service: The ATOM service allows batch downloading vector objects of different cadastral elements for a specific municipality.
WFS service: The WFS service allows downloading vector objects of specific cadastral elements. Note that there are some restrictions on the extent and number of elements to query. For batch downloading the ATOM service is preferred.
WMS service: This service allows downloading georeferenced images of different cadastral elements.
In this example, we will demonstrate some of the main capabilities of the package by recreating a cadastral map of the surroundings of the Santiago Bernabéu Stadium. We will use the WMS and WFS services to get different layers, in order 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)
# Project for tiles
stadium_parcel_pr <- sf::st_transform(stadium_parcel, 25830)
# Extract imagery: Labels of the parcel
labs <- catr_wms_get_layer(
stadium_parcel_pr,
what = "parcel",
styles = "BoundariesOnly",
srs = 25830
)
# Plot
library(ggplot2)
library(tidyterra) # For terra tiles
ggplot() +
geom_spatraster_rgb(data = labs) +
geom_sf(
data = stadium_parcel_pr,
fill = NA, col = "red", linewidth = 2
) +
geom_sf(data = stadium, fill = "red", alpha = .5) +
coord_sf(crs = 25830)Figure 1: Example - Santiago Bernabeu
We can also create thematic maps using the information available on the spatial objects. We will produce a visualization of the urban growth of Granada using CatastRo, replicating the map produced by Dominic Royé (Royé 2019), using the ATOM service.
First, we extract the coordinates of the city center of Granada using mapSpain:
library(dplyr)
library(sf)
library(mapSpain)
# Use mapSpain for getting the coords
city <- esp_get_capimun(munic = "^Granada$")The next step is to extract the buildings using the ATOM service. We will also use the function catr_get_code_from_coords() to identify Granada’s code in the Cadastre and download the buildings with catr_atom_get_buildings().
city_catr_code <- catr_get_code_from_coords(city)
city_catr_code
#> # A tibble: 1 × 12
#> munic catr_to catr_munic catrcode cpro cmun inecode nm cd cmc cp cm
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 GRANADA 18 900 18900 18 087 18087 GRANADA 18 900 18 87
city_bu <- catr_atom_get_buildings(city_catr_code$catrcode)The next step in creating the visualization is to limit the analysis to a circle with a radius of 1.5 km around the city center:
buff <- city |>
# Adjust CRS to 25830: (Buildings)
st_transform(st_crs(city_bu)) |>
# Buffer
st_buffer(1500)
# Cut buildings
dataviz <- st_intersection(city_bu, buff)
ggplot(dataviz) +
geom_sf()
Figure 2: Minimal cadastral map of Granada
Now let’s extract the construction year, available in the column beginning:
# Extract 4 initial positions
year <- substr(dataviz$beginning, 1, 4)
# Replace all entries that do not look like numbers with 0000
year[!(year %in% 0:2500)] <- "0000"
# Convert to numeric
year <- as.integer(year)
# New column
dataviz <- dataviz |>
mutate(year = year)The last step is to create groups based on the year and create the data visualization. Here we use the function cut() to create classes for every decade starting from year 1900:
dataviz <- dataviz |>
mutate(
year_cat = cut(year, breaks = c(0, seq(1900, 2030, by = 10)), dig.lab = 4)
)
ggplot(dataviz) +
geom_sf(aes(fill = year_cat), color = NA, na.rm = TRUE) +
scale_fill_manual(
values = hcl.colors(15, "Spectral"),
na.translate = FALSE
) +
theme_void() +
labs(title = "GRANADA", 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)
)
Figure 2: Granada - Urban growth
The package CatastRoNav provides access to the Cadastre of Navarre, with similar functionalities to CatastRo.↩︎