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Apply urban classification to GHS-WUP-DEGURBA data

Source: R/data_ghs_wup.R
ghs_wup_degurba.Rd

Reclassifies the GHS-WUP-DEGURBA data into binary urban/non-urban categories based on a user-defined set of DEGURBA classification codes. The function aggregates the resulting binary classification to PRIO-GRID resolution, providing the proportion of urban area within each PRIO-GRID cell.

Usage

ghs_wup_degurba(urban_definition)

Arguments

urban_definition

Numeric vector of DEGURBA codes to classify as urban. Valid codes are: 10, 11, 12, 13, 21, 22, 23, 30. See Details for code meanings.

Value

A SpatRaster object with values ranging from 0 to 1, representing the proportion of urban area within each PRIO-GRID cell according to the specified urban definition.

Details

The DEGURBA classification codes represent:

  • 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

The function performs the following operations:

  • Reads the high-resolution GHS-WUP-DEGURBA data

  • Reclassifies cells to 1 (urban) if they match urban_definition, 0 otherwise

  • Aggregates to PRIO-GRID resolution using mean (proportion of urban area)

  • Applies nearest-neighbor resampling for exact PRIO-GRID alignment

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

Note

  • This operation can be computationally intensive for large rasters

  • Temporary files are created during processing

  • The aggregation uses mean to calculate urban proportion per cell

  • Different urban definitions produce different urbanization estimates

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{
# Use only urban centres as urban definition
urban_strict <- ghs_wup_degurba(urban_definition = 30)

# Use broader urban definition (all urban and suburban areas)
urban_broad <- ghs_wup_degurba(urban_definition = c(21, 22, 23, 30))

# Plot comparison
terra::plot(urban_strict[["2020-12-31"]],
            main = "Strict Urban Definition (Centres Only)")
terra::plot(urban_broad[["2020-12-31"]],
            main = "Broad Urban Definition")

# Custom definition: exclude suburban areas
urban_custom <- ghs_wup_degurba(urban_definition = c(22, 23, 30))
} # }