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Generate GHS-WUP-DEGURBA urban proportion grid

Source: R/data_ghs_wup.R
gen_ghs_wup_degurba_urban.Rd

Generates PRIO-GRID level urbanization data using the GHS-WUP-DEGURBA (Degree of Urbanisation) classification with a standard urban definition. This function provides the proportion of urban area within each PRIO-GRID cell for all available 5-year intervals (1975–2030).

Usage

gen_ghs_wup_degurba_urban()

Value

A SpatRaster object with values ranging from 0 to 1

Details

This function uses a predefined urban definition that includes:

  • 30: Urban centres (cities)

  • 23: Dense urban cluster

  • 22: Semi-dense urban cluster

  • 21: Suburban or peri-urban

Areas classified as rural (codes 10, 11, 12, 13) are considered non-urban. The resulting raster provides values between 0 and 1, representing the proportion of each PRIO-GRID cell that is classified as urban.

This operation can be computationally intensive and may take time depending on system performance and the size of the underlying rasters.

A slight nearest neighbor resampling was applied to get the exact PRIO-GRID extent.

Note

  • Aggregation uses mean to calculate urban proportion per cell

  • Nearest-neighbor resampling is applied for spatial alignment

  • Large rasters may require significant memory and processing time

  • For custom urban definitions, use ghs_wup_degurba directly

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.

See also

ghs_wup_degurba for custom urban definitions

Examples

if (FALSE) { # \dontrun{
# Generate PRIO-GRID level urbanization rasters
urban_pg <- gen_ghs_wup_degurba_urban()

# Inspect structure
print(urban_pg)

# Plot urbanization for 2020
terra::plot(urban_pg[["2020-12-31"]],
            main = "PRIO-GRID Urban Proportion 2020")

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

# Identify highly urbanized cells (>75% urban)
highly_urban <- urban_pg[["2020-12-31"]] > 0.75
terra::plot(highly_urban, main = "Highly Urbanized Areas")

# Calculate global urban area trends
urban_stats <- terra::global(urban_pg, "mean", na.rm = TRUE)
years <- as.numeric(substr(names(urban_pg), 1, 4))
plot(years, urban_stats$mean,
     type = "l", main = "Global Urbanization Trends",
     xlab = "Year", ylab = "Mean Urban Proportion")
} # }