| gisco_get_unit | R Documentation |
Download datasets of single spatial units from GISCO to the
cache_dir.
Unlike gisco_get_countries(), gisco_get_nuts() or
gisco_get_urban_audit() (that downloads a full dataset and applies
filters), these functions download a single per unit, reducing the time
of downloading and reading into your R session.
gisco_get_unit_country(
unit = "ES",
year = 2024,
epsg = c(4326, 3857, 3035),
cache = TRUE,
update_cache = FALSE,
cache_dir = NULL,
verbose = FALSE,
resolution = c(1, 3, 10, 20, 60),
spatialtype = c("RG", "LB")
)
gisco_get_unit_nuts(
unit = "ES416",
year = 2024,
epsg = c(4326, 3857, 3035),
cache = TRUE,
update_cache = FALSE,
cache_dir = NULL,
verbose = FALSE,
resolution = c(1, 3, 10, 20, 60),
spatialtype = c("RG", "LB")
)
gisco_get_unit_urban_audit(
unit = "ES001F",
year = 2021,
epsg = c(4326, 3857, 3035),
cache = TRUE,
update_cache = FALSE,
cache_dir = NULL,
verbose = FALSE,
spatialtype = c("RG", "LB")
)
unit |
character vector of unit ids to be downloaded. See Details. |
year |
character string or number. Release year of the file. |
epsg |
character string or number. Projection of the map: 4-digit EPSG code. One of:
|
cache |
logical. Whether to do caching. Default is |
update_cache |
logical. Should the cached file be refreshed?. Default
is |
cache_dir |
character string. A path to a cache directory. See
Caching strategies section in |
verbose |
logical. If |
resolution |
character string or number. Resolution of the geospatial data. One of:
|
spatialtype |
character string. Type of geometry to be returned. Options available are:
|
Check the available unit ids with the required
combination of arguments with gisco_get_metadata().
A sf object.
Please check the download and usage provisions on gisco_attributions().
https://gisco-services.ec.europa.eu/distribution/v2/
All the source files are .geojson files.
gisco_get_metadata(), gisco_get_countries(),
gisco_get_nuts(), gisco_get_urban_audit().
See gisco_id_api to download via GISCO ID service
API.
Additional utils for downloading datasets:
gisco_bulk_download()
# Get metadata
cities <- gisco_get_metadata("urban_audit", 2020)
# Valencia, Spain
valencia <- cities[grep("Valencia", cities$URAU_NAME), ]
valencia
library(dplyr)
# Now get the sf objects and order by AREA_SQM
valencia_sf <- gisco_get_unit_urban_audit(
unit = valencia$URAU_CODE,
year = "2020",
) |>
arrange(desc(AREA_SQM))
# Plot
library(ggplot2)
ggplot(valencia_sf) +
geom_sf(aes(fill = URAU_CATG)) +
scale_fill_viridis_d() +
labs(
title = "Valencia",
subtitle = "Urban Audit 2020",
fill = "Category"
)
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