Visualize urban growth

The General Directorate for the Cadastre of Spain has spatial information of the all buildings except for the Basque Country and Navarra. This data set is part of the implementation of INSPIRE, the Space Information Infrastructure in Europe. More information can be found here. We will use the links (urls) in ATOM format, which is an RSS type for web feeds, allowing us to obtain the download link for each municipality. Since 2022, a package to access the API is directly available CatastRo.

Packages

Package	Description
tidyverse	Collection of packages (visualization, manipulation): ggplot2, dplyr, purrr, etc.
sf	Simple Feature: import, export and manipulate vector data
CatastRo	Catastro Spain API
tmap	Easy creation of thematic maps
classInt	Create univariate class intervals

# install the packages if necessary

if (!require("tidyverse")) install.packages("tidyverse")
if (!require("CatastRo")) install.packages("CatastRo")
if (!require("sf")) install.packages("sf")
if (!require("tmap")) install.packages("tmap")
if (!require("classInt")) install.packages("classInt")

# load packages

library(sf)
library(CatastRo)
library(tidyverse)
library(classInt)
library(tmap)

Access the data

To import the building data we use the catr_atom_get_buildings() function.

buildings_val <- catr_atom_get_buildings("Valencia", to = "Valencia")

buildings_val[,1:5]

Data preparation

We only have to convert the column of the construction year (beginning) into a Date class. The date column contains some dates in --01-01 format, which does not correspond to any recognizable date. Therefore, we replace the first - with 0000.

buildings_val <- mutate(buildings_val,
  beginning = str_replace(beginning, "^-", "0000") |>
    ymd_hms() |> as_date()
)

Distribution chart

Before creating the maps of the construction years, which will reflect urban growth, we will make a graph of distribution of the beginning variable. We can clearly identify periods of urban expansion. We will use the ggplot2 package with the geometry of geom_density() for this purpose.

# limit the period after 1750
filter(buildings_val, beginning >= "1750-01-01") |>
  ggplot(aes(beginning)) +
  geom_density(fill = "#2166ac", alpha = 0.7) +
  scale_x_date(
    date_breaks = "20 year",
    date_labels = "%Y"
  ) +
  labs(y = NULL, x = NULL, title = "Evolution of urban development") +
  theme_minimal(base_family = "Montserrat") +
  theme(panel.grid.minor = element_blank())

Buffer of 2,5 km for Valencia

To visualize better the distribution of urban growth, we limit the map to a radius of 2.5 km from the city center. Therefore, we use the geocode_OSM() function of the tmaptools package to obtain the coordinates of Valencia in class sf. Then we project the points to the system we use for the buildings (EPSG: 25830). The st_crs() function returns the coordinate system of a spatial object sf. Finally, we create with the function st_buffer() a buffer with 2500 m and the intersection with our building data. It is also possible to create a buffer in the form of a rectangle indicating the style with the argument endCapStyle =" SQUARE ".

# get the coordinates of Valencia
ciudad_point <- tmaptools::geocode_OSM("Valencia", as.sf = TRUE)

#  project the points
ciudad_point <- st_transform(ciudad_point, st_crs(buildings_val))

# create the buffer
point_bf <- st_buffer(ciudad_point, 2500) # radius of 2500 m


# get the intersection between the buffer and the building
buildings_val25 <- st_intersection(buildings_val, point_bf)

Prepare data for mapping

We categorize the year into 15 groups using quartiles. It is also possible to modify the number of classes or the applied method (eg jenks, fisher, etc), you can find more details in the help ?classIntervals.

# find 15 classes
br <- classIntervals(year(buildings_val25$beginning), 15, "quantile")

# create labels
lab <- names(print(br, under = "<", over = ">", cutlabels = FALSE))

# categorize the year
buildings_val25 <- mutate(buildings_val25,
  yr_cl = cut(year(beginning),
    br$brks,
    labels = lab,
    include.lowest = TRUE
  )
)

Map of Valencia

For the mapping, we will use the tmap package. It is an interesting alternative to ggplot2. It is a package of functions specialized in creating thematic maps. The philosophy of the package follows the same as in ggplot2, creating multiple layers with different functions, which always start with tm_*and combine with +. Building a map with tmap always starts with tm_shape(), where the data, we want to draw, is defined. Then we add the corresponding geometry to the data type (tm_polygon(), tm_border(), tm_dots() or even tm_raster()). The tm_layout() function help us to configure the map style.

When we need more colors than the maximum allowed by RColorBrewer, we can pass the colors to the colorRampPalette() function. This function interpolates a set of given colors.

# colours
col_spec <- RColorBrewer::brewer.pal(11, "Spectral")

# colour ramp function
col_spec_fun <- colorRampPalette(col_spec)

# create the final map
tm_shape(buildings_val25) +
  tm_polygons("yr_cl",
    border.col = "transparent",
    palette = col_spec_fun(15), # adapt to the number of classes
    textNA = "Without data",
    title = ""
  ) +
  tm_layout(
    bg.color = "black",
    outer.bg.color = "black",
    legend.outside = TRUE,
    legend.text.color = "white",
    legend.text.fontfamily = "Montserrat",
    panel.label.fontfamily = "Montserrat",
    panel.label.color = "white",
    panel.label.bg.color = "black",
    panel.label.size = 5,
    panel.label.fontface = "bold"
  )

We can export our map using the function tmap_save("name.png", dpi = 300). I recommend using the dpi = 300 argument for a good image quality.

An alternative way to the tmap package is ggplot2.

# create the final map
ggplot(buildings_val25) +
  geom_sf(aes(fill = yr_cl), colour = NA) +
  scale_fill_manual(values = col_spec_fun(15), na.translate = FALSE) + # adapt to the number of classes
  labs(title = "VALÈNCIA", fill = NULL) +
  theme_void(base_family = "Montserrat") +
  theme(
    panel.background = element_rect(fill = "black"),
    plot.background = element_rect(fill = "black"),
    legend.justification = .5,
    legend.text = element_text(colour = "white", size = 12),
    legend.key.height = unit(3, "lines"),
    legend.key.width = unit(.5, "lines"),
    plot.title = element_text(
      colour = "white", hjust = .5, size = 60,
      margin = margin(t = 30)
    ),
    plot.caption = element_text(
      colour = "white",
      margin = margin(b = 20), hjust = .5, size = 16
    ),
    plot.margin = margin(r = 40, l = 40)
  )

# create the final map
ggplot(buildings_val25) +
  geom_sf(aes(fill = yr_cl), colour = NA) +
  scale_fill_manual(values = col_spec_fun(15), na.translate = FALSE) + # adapt to the number of classes
  labs(title = "VALÈNCIA", fill = NULL) +
  theme_void(base_family = "Montserrat") +
  theme(
    panel.background = element_rect(fill = "black"),
    plot.background = element_rect(fill = "black"),
    legend.justification = .5,
    legend.text = element_text(colour = "white", size = 12),
    legend.key.height = unit(3, "lines"),
    legend.key.width = unit(.5, "lines"),
    plot.title = element_text(
      colour = "white", hjust = .5, size = 60,
      margin = margin(t = 30)
    ),
    plot.caption = element_text(
      colour = "white",
      margin = margin(b = 20), hjust = .5, size = 16
    ),
    plot.margin = margin(r = 40, l = 40)
  )

To export the result of ggplot we can use the function ggsave("name.png").

Dynamic map with leaflet

A very interesting advantage is the tmap_leaflet() function of the tmap package to easily pass a map created in the same frame to leaflet.

# tmap object
m <- tm_shape(buildings_val25) +
  tm_polygons("yr_cl",
    border.col = "transparent",
    palette = col_spec_fun(15), # adapt to the number of classes
    textNA = "Without data",
    title = ""
  )


# dynamic map
tmap_leaflet(m)