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Source: vignettes/CatastRo.Rmd
CatastRo.Rmd

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 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://knowledge-base.inspire.ec.europa.eu/overview_en

The implementation of the INSPIRE directive on the Spanish Cadastre (see Catastro INSPIRE) allows retrieving 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 as provided by the sf package.
  • Imagery: Image layers representing the same information as the vector objects. These objects are provided by CatastRo as 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 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 batch-downloading vector objects of different cadastral elements for a specific municipality.

  2. 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.

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

Examples

Working with layers

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 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)

# 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)
Example: Santiago Bernabeu

Example: Santiago Bernabeu

Thematic maps

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 consists of extracting the buildings using the ATOM service. We will also use the function catr_get_code_from_coords() to identify the code of Granada in the Cadastre, and we will 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   
#>   <chr>  <chr>   <chr>      <chr>    <chr> <chr> <chr>   <chr> <chr> <chr> <chr>
#> 1 GRANA… 18      900        18900    18    087   18087   GRAN… 18    900   18   
#> # ℹ 1 more variable: cm <chr>

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 of radius 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()
Minimal cadastral map of Granada

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 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)

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)
  )
Granada: Urban growth

Granada: Urban growth

References

Royé, Dominique. 2019. Visualize Urban Growth. https://dominicroye.github.io/blog/visualize-urban-growth/.