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CatastRo provide access to different API services of the Spanish Cadastre. With CatastRo it is possible to download official information of addresses, properties, parcels and buildings.

OVCCoordenadas Service

The OVCCoordenadas service allows to retrieve 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 on a tibble format (see vignette("tibble", package = "tibble")). This functionality is described in detail in the corresponding vignette (see vignette("ovcservice")).

INSPIRE Services

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.

From https://inspire.ec.europa.eu/about-inspire/563

The implementation of the INSPIRE directive on the Spanish Cadastre (see Catastro INSPIRE) allows to retrieve spatial objects from the database of the cadastre:

  • Vector objects: Parcels, addresses, buildings, cadastral zones and more. These objects are provided by CatastRo as sf objects (see ?sf).

  • Imagery: Image layers representing the same information than the vector objects. These objects are provided by CatastRo as SpatRaster objects (see ?terra).

Note that the coverage of this service is 95% of the Spanish territory, excluding Basque Country and Navarre that have their own independent cadastral offices.

There are three types of functions, each one querying a different service:

  1. ATOM service: The ATOM service allows to batch-download vector objects of different cadastral elements for a specific municipality.

  2. WFS service: The WFS service allows to download vector objects of specific cadastral elements.Note that there are some restrictions on the extension and number of elements to query. For batch-downloading the ATOM service is preferred.

  3. WMS service: This service allows to download georeferenced images of different cadastral elements.

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", linewidth = 2) +
  geom_sf(data = stadium, fill = "red", alpha = .5)

plot of chunk santbernabeu

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_mu…¹ catrc…² cpro  cmun  inecode nm    cd    cmc   cp   
#>   <chr>    <chr>   <chr>     <chr>   <chr> <chr> <chr>   <chr> <chr> <chr> <chr>
#> 1 VALENCIA 46      900       46900   46    250   46250   VALE… 46    900   46   
#> # … with 1 more variable: cm <chr>, and abbreviated variable names ¹​catr_munic,
#> #   ²​catrcode

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()

plot of chunk minimal

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)
  )

plot of chunk dataviz

References

Royé, Dominic. 2019. “Visualize Urban Growth.” Dr. Dominic Royé. https://dominicroye.github.io/en/2019/visualize-urban-growth/.