CatastRo provides access to different API services of the Spanish Cadastre. With CatastRo, you can download official information on addresses, properties, parcels and buildings.
OVCCoordenadas service
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")).
INSPIRE services
The INSPIRE Directive aims to create a European Union Spatial Data Infrastructure (SDI) for the purposes of EU environmental policies and policies or activities which may have an impact on the environment. This European Spatial Data Infrastructure enables 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 in the Spanish Cadastre (see Catastro INSPIRE) allows retrieval of spatial objects from the cadastral database:
-
Vector objects: Parcels, addresses, buildings, cadastral zones and more. CatastRo provides these objects as
sfobjects, using the sf package. -
Imagery: Image layers representing the same information as the vector objects. CatastRo provides these objects as
SpatRasterobjects, using 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 restrictions on the extent and number of elements that can be queried. For batch downloading, the ATOM service is preferred.
WMS service: This service allows downloading georeferenced images of different cadastral elements.
Examples
Working with layers
In this example, we demonstrate some of the main capabilities of the package by recreating a cadastral map of the surroundings of the Santiago Bernabéu Stadium. We use the WMS and WFS services to get different layers.
# Extract buildings 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
)
# Extract cadastral parcels using spatial objects in the query.
stadium_parcel <- catr_wfs_get_parcels_bbox(stadium)
# Project for tiles.
stadium_parcel_pr <- sf::st_transform(stadium_parcel, 25830)
# Extract parcel label imagery.
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: Santiago Bernabeu example
Thematic maps
We can also create thematic maps using the information available on the spatial objects. We 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 Granada’s city center using mapSpain:
library(dplyr)
library(sf)
library(mapSpain)
# Use mapSpain to get the coordinates.
city <- esp_get_capimun(munic = "^Granada$")The next step is to extract the buildings using the ATOM service. We also use catr_get_code_from_coords() to identify Granada’s code in the Cadastre and download the buildings with catr_atom_get_buildings().
city_catr_code <- catr_get_code_from_coords(city)
city_catr_code
#> # A tibble: 1 × 12
#> munic catr_to catr_munic catrcode cpro cmun inecode nm cd cmc cp cm
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 GRANADA 18 900 18900 18 087 18087 GRANADA 18 900 18 87
city_bu <- catr_atom_get_buildings(city_catr_code$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 for 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 the first four positions.
year <- substr(dataviz$beginning, 1, 4)
# Replace entries that do not look like numbers with 0000.
year[!(year %in% 0:2500)] <- "0000"
# Convert to numeric.
year <- as.integer(year)
# Add a 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