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 |
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
# 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]
Simple feature collection with 36346 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 720570.9 ymin: 4351286 xmax: 734981.9 ymax: 4382906
Projected CRS: ETRS89 / UTM zone 30N
First 10 features:
gml_id lowerCorner upperCorner
1 ES.SDGC.BU.000100100YJ27F 725589.5805 4377132.9425 725605.5005 4377146.6725
2 ES.SDGC.BU.000100100YJ28A 720570.9393 4382157.3445 720646.359 4382259.3929
3 ES.SDGC.BU.000100100YJ36A 730582.5135 4362033.4165 730589.793 4362043.9665
4 ES.SDGC.BU.000100200YJ27F 725711.36 4377093.8715 725745.63 4377124.46
5 ES.SDGC.BU.000100200YJ28A 720851.8475 4381937.212 720866.847 4381955.6425
6 ES.SDGC.BU.000100300YJ27F 725336.89 4376989.78 725386.83 4377084.18
7 ES.SDGC.BU.000100300YJ36C 729967.6535 4364320.2035 729979.9435 4364324.2535
8 ES.SDGC.BU.000100400YJ27F 724956.7325 4376895.1345 724971.625 4376909.0335
9 ES.SDGC.BU.000100400YJ36C 729953.5525 4364259.855 730039.851 4364319.6035
10 ES.SDGC.BU.000200100YJ26F 726653.43835 4367240.6706 726673.662 4367258.1575
beginLifespanVersion conditionOfConstruction geometry
1 2008-10-20T00:00:00 functional MULTIPOLYGON (((725594.9 43...
2 2022-03-10T00:00:00 functional MULTIPOLYGON (((720582.3 43...
3 2006-01-18T00:00:00 functional MULTIPOLYGON (((730586.1 43...
4 2016-10-04T00:00:00 functional MULTIPOLYGON (((725718.3 43...
5 2008-10-20T00:00:00 functional MULTIPOLYGON (((720860.3 43...
6 2016-03-15T00:00:00 functional MULTIPOLYGON (((725376.5 43...
7 2008-10-20T00:00:00 ruin MULTIPOLYGON (((729967.7 43...
8 2016-03-15T00:00:00 functional MULTIPOLYGON (((724958.6 43...
9 2008-10-20T00:00:00 functional MULTIPOLYGON (((729984.8 43...
10 2017-01-31T00:00:00 functional MULTIPOLYGON (((726661.4 43...
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()
)
Warning: There was 1 warning in `stopifnot()`.
ℹ In argument: `beginning = as_date(ymd_hms(str_replace(beginning, "^-",
"0000")))`.
Caused by warning:
! 6 failed to parse.
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())
Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
not found in Windows font database
Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
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)
Warning: attribute variables are assumed to be spatially constant throughout
all geometries
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$beginning), 15, "quantile")
Warning in classIntervals(year(buildings_val25$beginning), 15, "quantile"): var
has missing values, omitted in finding classes
style: quantile
< 1890 1890 - 1913 1913 - 1926 1926 - 1930 1930 - 1940 1940 - 1950
929 1368 1145 356 1687 1041
1950 - 1958 1958 - 1962 1962 - 1966 1966 - 1970 1970 - 1973 1973 - 1978
1447 1025 1220 1156 1155 1190
1978 - 1989 1989 - 2000 > 2000
1234 1132 1216
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"
)
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
We can export our map using the function tmap_save("name.png", dpi = 300)
. I recommend using the dpi = 300
argument for a good image quality.
An alternative way to the tmap
package is ggplot2
.
# create the final map
ggplot(buildings_val25) +
geom_sf(aes(fill = yr_cl), colour = NA) +
scale_fill_manual(values = col_spec_fun(15), na.translate = FALSE) + # adapt to the number of classes
labs(title = "VALÈNCIA", fill = NULL) +
theme_void(base_family = "Montserrat") +
theme(
panel.background = element_rect(fill = "black"),
plot.background = element_rect(fill = "black"),
legend.justification = .5,
legend.text = element_text(colour = "white", size = 12),
legend.key.height = unit(3, "lines"),
legend.key.width = unit(.5, "lines"),
plot.title = element_text(
colour = "white", hjust = .5, size = 60,
margin = margin(t = 30)
),
plot.caption = element_text(
colour = "white",
margin = margin(b = 20), hjust = .5, size = 16
),
plot.margin = margin(r = 40, l = 40)
)
Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
not found in Windows font database
Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
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)