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Reads the GHSL GHS Population Grid data

Source: R/data_ghsl.R
read_ghsl_population_grid.Rd

Downloads and processes GHSL (Global Human Settlement Layer) GHS Population Grid data, which provides global population estimates at high spatial resolution. The function extracts multi-temporal raster files and formats them for compatibility with PRIO-GRID temporal structure.

Usage

read_ghsl_population_grid()

Value

A SpatRaster object

Details

GHSL GHS Population Grid provides population count estimates at 1 km spatial resolution for multiple time periods globally. This function:

  • Downloads zipped GHSL population 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 population data at 5-year intervals (typically 1975-2030)

Note

  • Files are automatically extracted from zip archives and cached locally

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

  • Population estimates include both observed and modeled/projected values

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

References

Schiavina M, Freire S, MacManus K (2023). “GHS-POP R2023A - GHS Population Grid Multitemporal (1975-2030).” doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE , 2024-09-27.

Examples

if (FALSE) { # \dontrun{
# Read GHSL population grid data
# Warning: This involves large file downloads and processing
ghsl_population <- read_ghsl_population_grid()

# Examine the structure
print(ghsl_population)

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

# Plot population for specific year
terra::plot(ghsl_population[["2020-12-31"]],
            main = "Global Population Distribution 2020")

# Compare population change over time
pop_1990 <- ghsl_population[["1990-12-31"]]
pop_2020 <- ghsl_population[["2020-12-31"]]
pop_change <- pop_2020 - pop_1990
terra::plot(pop_change, main = "Population Change 1990-2020")

# Extract population for specific region (example: crop to extent)
# europe_extent <- terra::ext(-10, 40, 35, 70)
# europe_pop <- terra::crop(ghsl_population, europe_extent)
# terra::plot(europe_pop[[nlyr(europe_pop)]], main = "Europe Population")

# Calculate total global population by year
global_pop_totals <- terra::global(ghsl_population, "sum", na.rm = TRUE)
years <- as.numeric(substr(names(ghsl_population), 1, 4))
plot(years, global_pop_totals$sum / 1e9,
     type = "l", main = "Global Population Trends",
     xlab = "Year", ylab = "Population (Billions)")
} # }