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giscoR

giscoR downloads global and European geospatial data from Eurostat’s GISCO geodata distribution for use in R.

Key features

  • Retrieve GISCO datasets for country boundaries, NUTS regions, administrative units such as Local Administrative Units (LAU), statistical units, transport networks and basic service locations.
  • For compatible administrative and statistical datasets, select among resolutions 60M, 20M, 10M, 03M and 01M. Choose coordinate reference systems EPSG:4326, EPSG:3035 and EPSG:3857.
  • Use each grid, transport or basic service dataset in its documented format and coordinate reference system.
  • Work with sf objects for spatial analysis.
  • Cache downloaded files for faster repeated access.

Population grids

gisco_get_grid() downloads polygon or cell-center point GeoPackages in EPSG:3035 at resolutions from 1 km to 100 km. Population columns use names such as TOT_P_2021. To calculate people per square kilometer, divide the population count by the cell area in square kilometers (resolution^2). Population variables have year- and country-specific licensing conditions. Consult the official grid documentation before publication or redistribution.

Installation

Install giscoR from CRAN:

Quick example

This example compares the boundaries of the Netherlands at four resolutions.

library(giscoR)
library(sf)
library(dplyr)

# Download Netherlands boundaries at different resolutions.
nl_all <- lapply(c("60", "20", "10", "03"), function(r) {
  gisco_get_countries(country = "Netherlands", year = 2024, resolution = r) |>
    mutate(res = paste0(r, "M"))
}) |>
  bind_rows()

glimpse(nl_all)
#> Rows: 4
#> Columns: 15
#> $ CNTR_ID     <chr> "NL", "NL", "NL", "NL"
#> $ COUNTRY_URI <chr> "NLD", "NLD", "NLD", "NLD"
#> $ CNTR_NAME   <chr> "Netherlands", "Netherlands", "Netherlands", "Netherlands"
#> $ NAME_ENGL   <chr> "Netherlands", "Netherlands", "Netherlands", "Netherlands"
#> $ NAME_FREN   <chr> "Pays-Bas", "Pays-Bas", "Pays-Bas", "Pays-Bas"
#> $ ISO3_CODE   <chr> "NLD", "NLD", "NLD", "NLD"
#> $ SVRG_UN     <chr> "UN Member State", "UN Member State", "UN Member State", "…
#> $ CAPT        <chr> "Amsterdam", "Amsterdam", "Amsterdam", "Amsterdam"
#> $ STAT_CODE   <chr> "OA", "OA", "OA", "OA"
#> $ EU_STAT     <chr> "T", "T", "T", "T"
#> $ EFTA_STAT   <chr> "F", "F", "F", "F"
#> $ CC_STAT     <chr> "F", "F", "F", "F"
#> $ NAME_GERM   <chr> "Niederlande", "Niederlande", "Niederlande", "Niederlande"
#> $ res         <chr> "60M", "20M", "10M", "03M"
#> $ geometry    <MULTIPOLYGON [°]> MULTIPOLYGON (((7.208935 53..., MULTIPOLYGON (((7.202794 5…

# Plot with ggplot2.

library(ggplot2)

ggplot(nl_all) +
  geom_sf(fill = "#AD1D25") +
  facet_wrap(~res) +
  labs(
    title = "Netherlands boundaries at different resolutions",
    subtitle = "Year: 2024",
    caption = gisco_attributions()
  ) +
  theme_minimal()

Netherlands boundaries at different resolutions

Advanced example: thematic maps

This example uses ggplot2 to map 2024 population density across NUTS 3 regions. The workflow follows the work of Milos Popovic.

Download NUTS 3 regions and country boundaries for 2024.

library(giscoR)
library(dplyr)
library(eurostat)
library(ggplot2)

# Retrieve sf objects.
nuts3 <- gisco_get_nuts(
  year = 2024,
  epsg = 3035,
  resolution = 10,
  nuts_level = 3
)

# Get country boundaries at NUTS 0 level.

country_lines <- gisco_get_nuts(
  year = 2024,
  epsg = 3035,
  resolution = 10,
  spatialtype = "BN",
  nuts_level = 0
)

Use eurostat to download the population density data and select 2024.

# Retrieve Eurostat data.
popdens <- get_eurostat("demo_r_d3dens", verbose = FALSE) |>
  filter(TIME_PERIOD == "2024-01-01")

Join the population density data to the NUTS 3 geometries, then group the values into intervals for the map.

# Merge data.
nuts3_sf <- nuts3 |>
  left_join(popdens, by = "geo")

# Create breaks and labels.
br <- c(0, 25, 50, 100, 200, 500, 1000, 2500, 5000, 10000, 30000)
labs <- prettyNum(br[-1], big.mark = ",")

# Label missing values in the plot.
labeller_plot <- function(x) {
  ifelse(is.na(x), "No data", x)
}
nuts3_sf <- nuts3_sf |>
  # Cut with labels.
  mutate(values_cut = cut(values, br, labels = labs))

# Create palette.
pal <- hcl.colors(length(labs), "Lajolla")

# Create plot.
ggplot(nuts3_sf) +
  geom_sf(aes(fill = values_cut), linewidth = 0, color = NA, alpha = 0.9) +
  geom_sf(data = country_lines, col = "black", linewidth = 0.1) +
  # Center on Europe with EPSG 3035.
  coord_sf(
    xlim = c(2377294, 7453440),
    ylim = c(1313597, 5628510)
  ) +
  # Configure legends.
  scale_fill_manual(
    values = pal,
    # Label missing values.
    labels = labeller_plot,
    drop = FALSE,
    guide = guide_legend(direction = "horizontal", nrow = 1)
  ) +
  theme_void() +
  # Configure the theme.
  theme(
    plot.title = element_text(
      color = rev(pal)[2],
      size = rel(1.5),
      hjust = 0.5,
      vjust = -6
    ),
    plot.subtitle = element_text(
      color = rev(pal)[2],
      size = rel(1.25),
      hjust = 0.5,
      vjust = -10,
      face = "bold"
    ),
    plot.caption = element_text(color = "grey60", hjust = 0.5, vjust = 0),
    legend.text = element_text(color = "grey20", hjust = 0.5),
    legend.title = element_text(color = "grey20", hjust = 0.5),
    legend.position = "bottom",
    legend.title.position = "top",
    legend.text.position = "bottom",
    legend.key.height = unit(0.5, "line"),
    legend.key.width = unit(2.5, "line")
  ) +
  # Add labels.
  labs(
    title = "Population density in 2024",
    subtitle = "NUTS 3 level",
    fill = "people per square kilometer",
    caption = paste0(
      "Source: Eurostat, ",
      gisco_attributions(),
      "\nBased on Milos Popovic's work"
    )
  )

Population density in 2024

Caching

LAU and other large datasets can exceed 50 MB. Set a cache directory with gisco_set_cache_dir() to reuse downloaded files on subsequent requests.

gisco_set_cache_dir("./path/to/location")

Contributing

See the GitHub repository for source code.

Contributions are welcome.

Citation

To cite ‘giscoR’ in publications use:

Hernangómez D (2026). giscoR: Download Eurostat GISCO Spatial Data. doi:10.32614/CRAN.package.giscoR https://doi.org/10.32614/CRAN.package.giscoR. https://ropengov.github.io/giscoR/.

A BibTeX entry for LaTeX users is:

@Manual{R-giscoR,
  title = {{giscoR}: Download Eurostat GISCO Spatial Data},
  doi = {10.32614/CRAN.package.giscoR},
  author = {Diego Hernangómez},
  year = {2026},
  version = {1.3.1},
  url = {https://ropengov.github.io/giscoR/},
  abstract = {Tools to download global and European spatial data from the Eurostat GISCO (Geographic Information System of the Commission) data distribution <https://ec.europa.eu/eurostat/web/gisco>. The package provides helpers for country boundaries, Nomenclature of Territorial Units for Statistics (NUTS) regions, administrative units, statistical units, transport networks, basic service locations and other GISCO datasets. This package is neither affiliated with nor endorsed by Eurostat.},
}

Eurostat’s general copyright notice and license policy applies. Some datasets have additional download and usage provisions. The download and use of these data are subject to acceptance of those provisions. See the administrative units and statistical units, grids and basic services for more details, including source-specific conditions.

Source: https://ec.europa.eu/eurostat/web/gisco/geodata

Disclaimer

This package is neither affiliated with nor endorsed by Eurostat. The authors are not responsible for any misuse of the data.