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Reads the GHS-WUP-DEGURBA data

Source: R/data_ghs_wup.R
read_ghs_wup_degurba.Rd

Downloads and processes GHS-WUP-DEGURBA (World Urbanization Prospects - Degree of Urbanisation) data, which provides global urbanization levels classified according to the Degree of Urbanisation methodology. The function extracts multi-temporal raster files and formats them for compatibility with PRIO-GRID temporal structure.

Usage

read_ghs_wup_degurba()

Value

A SpatRaster object with DEGURBA classification codes

Details

GHS-WUP-DEGURBA provides urbanization classification at 1 km spatial resolution for multiple time periods globally. This function:

  • Downloads zipped GHS-WUP-DEGURBA raster files from the data repository

  • Extracts TIF files from zip archives (cached to avoid repeated extraction)

  • Loads and combines multiple raster layers into a single multi-temporal raster

  • Standardizes layer names using PRIO-GRID date format for consistency

  • Provides urbanization classification data at 5-year intervals (typically 1975-2030)

The DEGURBA classification uses the following codes:

  • 30: Urban centres (cities)

  • 23: Dense urban cluster

  • 22: Semi-dense urban cluster

  • 21: Suburban or peri-urban

  • 13: Rural cluster

  • 12: Low density rural

  • 11: Very low density rural

  • 10: Water bodies or uninhabited areas

Note

  • Files are automatically extracted from zip archives and cached locally

  • The function handles large files and may take time for initial download

  • Classification includes both observed and modeled/projected values

  • Temporal alignment uses PRIO-GRID month/day conventions for consistency

References

Schiavina M, Melchiorri M, Pesaresi M, Jacobs-Crisioni C, Dijkstra L (2025). “GHS-WUP-DEGURBA R2025A – GHS-WUP DEGURBA Settlement Layers, Application of the Degree of Urbanisation Methodology (Stage I) to GHS-WUP-POP R2025A, Multitemporal (1975-2100).” doi:10.2905/1c049178-ab00-4bbc-b638-3e3c19daaacb .

European Commission, Statistical Office of the European Union (2021). “Applying the Degree of Urbanisation — A Methodological Manual to Define Cities, Towns and Rural Areas for International Comparisons.” doi:10.2785/706535 .

Jacobs-Crisioni C, Schiavina M, Alessandrini A, Dijkstra L (2025). “Population by Degree of Urbanization and by Urban Agglomeration from 1950 to 2100.” https://publications.jrc.ec.europa.eu/repository/handle/JRC144219. doi:10.2760/1419546 , 2025-12-18.

Examples

if (FALSE) { # \dontrun{
# Read GHS-WUP-DEGURBA data
# Warning: This involves large file downloads and processing
degurba <- read_ghs_wup_degurba()

# Examine the structure
print(degurba)

# View available time periods
time_periods <- names(degurba)
print(time_periods)

# Plot urbanization classification for specific year
terra::plot(degurba[["2020-12-31"]],
            main = "Global Urbanization Classification 2020")

# Compare urbanization change over time
urban_1990 <- degurba[["1990-12-31"]]
urban_2020 <- degurba[["2020-12-31"]]
terra::plot(urban_2020 - urban_1990,
            main = "Urbanization Change 1990-2020")

# Extract urbanization for specific region (example: crop to extent)
# asia_extent <- terra::ext(60, 140, -10, 50)
# asia_urban <- terra::crop(degurba, asia_extent)
# terra::plot(asia_urban[[nlyr(asia_urban)]], main = "Asia Urbanization")

# Calculate frequency of each urbanization class
freq_2020 <- terra::freq(degurba[["2020-12-31"]])
print(freq_2020)
} # }