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Description: Brief introduction to CatastRo package.

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 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.

Source: INSPIRE Knowledge Base

The implementation of the INSPIRE directive on the Spanish Cadastre (see Catastro INSPIRE) allows retrieval of spatial objects from the cadastral database:

- Vector objects: Parcels, addresses, buildings, cadastral zones and more. These objects are provided by CatastRo as `sf` objects, using the sf package.
- Imagery: Image layers representing the same information as the vector objects. These objects are provided by CatastRo as `SpatRaster` objects, 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:

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 restrictions on the extent and number of elements that can be queried. 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 use the WMS and WFS services to get different layers, in order to show some of the capabilities of the package:

    # 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
    )

    # 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

### 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 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

## References

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

1. The package CatastRoNav provides access to the Cadastre of Navarre, with similar functionalities to CatastRo.↩︎