---
title: "Get started"
description: Access Spanish Cadastre data with CatastRo.
vignette: >
%\VignetteIndexEntry{Get started}
%\VignetteEngine{quarto::html}
%\VignetteEncoding{UTF-8}
bibliography: REFERENCES.bib
link-citations: true
---
**CatastRo** provides access to services from the [Spanish
Cadastre](https://www.sedecatastro.gob.es/). With **CatastRo**, you can
retrieve addresses, buildings, cadastral parcels and georeferenced map images.
## OVCCoordenadas service
The
[**OVCCoordenadas**](https://ovc.catastro.meh.es/ovcservweb/OVCSWLocalizacionRC/OVCCoordenadas.asmx)
service retrieves the coordinates of a known cadastral reference
(geocoding). It can also retrieve cadastral references near a pair of
coordinates (reverse geocoding). **CatastRo** returns the results as tibbles.
These operations are 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 organizations, facilitates public access to spatial
information across Europe and assists in policy-making across boundaries.
```{=html}
Source: INSPIRE Knowledge Base
```
The Spanish Cadastre implementation of the INSPIRE Directive (see [Spanish
Cadastre INSPIRE
services](https://www.catastro.hacienda.gob.es/webinspire/index.html)) provides
access to spatial objects from the cadastral database:
- **Vector objects:** Parcels, addresses, buildings, cadastral zones and more.
**CatastRo** returns these as `sf` objects from the **sf** package.
- **Imagery:** Image layers representing the same information as the vector
objects. **CatastRo** returns these as `SpatRaster` objects from the
**terra** package.
These services cover 95% of the Spanish territory, excluding the Basque Country
and Navarre[^1], which have their own independent cadastral offices.
[^1]: The package [**CatastRoNav**](https://ropenspain.github.io/CatastRoNav/)
provides access to the Cadastre of Navarre, with similar functionality to
**CatastRo**.
There are three INSPIRE services:
1. **ATOM service:** The ATOM service downloads complete municipal datasets
for different cadastral elements.
2. **WFS service:** The WFS service downloads vector objects for specific
cadastral elements. Note that there are restrictions on the extent and
number of elements that can be queried. For full municipal downloads,
prefer the ATOM service.
3. **WMS service:** The WMS service downloads georeferenced map images for
different cadastral elements.
## Examples
### Working with layers
This example demonstrates some of the main capabilities of the package by
recreating a cadastral map of the surroundings of the [Santiago Bernabéu
Stadium](https://en.wikipedia.org/wiki/Santiago_Bernab%C3%A9u_Stadium). We use
the WMS and WFS services to retrieve different layers.
``` r
# 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 a spatial object as the query input.
stadium_parcel <- catr_wfs_get_parcels_bbox(stadium)
# Transform to the tile CRS.
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) # Plot SpatRaster layers.
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 Bernabéu example
### Thematic maps
We can also create thematic maps from attributes in the spatial objects. This
example visualizes the urban growth of Granada with **CatastRo**, reproducing a
map by [Dominic Royé](https://dominicroye.github.io) [@roye19] with the ATOM
service.
First, we retrieve the geometry of Granada's city center with **mapSpain**:
``` r
library(dplyr)
library(sf)
library(mapSpain)
# Use esp_get_capimun() to get the city geometry.
city <- esp_get_capimun(munic = "^Granada$")
```
Next, we use `catr_get_code_from_coords()` to identify Granada's cadastral
municipality code, then download its buildings with
`catr_atom_get_buildings()`.
``` r
city_catr_code <- catr_get_code_from_coords(city)
city_catr_code
#> # A tibble: 1 × 12
#> munic catr_to catr_munic catrcode cpro cmun inecode nm cd cmc cp cm
#>
#> 1 GRANADA 18 900 18900 18 087 18087 GRANADA 18 900 18 87
city_bu <- catr_atom_get_buildings(city_catr_code$catrcode)
```
Next, we limit the analysis to a circle with a radius of 1.5 km around the city
center:
``` r
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
Next, we extract the construction year from the `beginning` column:
``` r
# Extract the first four characters.
year <- substr(dataviz$beginning, 1, 4)
# Replace entries that do not look like years with 0000.
year[!(year %in% 0:2500)] <- "0000"
# Convert to integers.
year <- as.integer(year)
# Add a new column.
dataviz <- dataviz |>
mutate(year = year)
```
Finally, we group the data by construction year and create the visualization.
The `cut()` function creates classes for each decade from 1900 onward:
``` r
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)
)
```
