NASA SEDAC Population Density

Kyle Messier, with assistance from GitHub Copilot

2026-05-20

This article demonstrates a compact workflow for the NASA SEDAC population-density rasters. These downloads require a NASA EarthData token.

This vignette runs its live workflow when rendered locally. The heavy download, processing, extraction, and plotting chunks are skipped automatically on CI, CRAN checks, and pkgdown builds; set AMADEUS_RUN_VIGNETTES=true to force live execution in those environments.

Available inputs and data availability

download_data(dataset_name = "sedac_population", ...) wraps download_population().

• Inputs are global GPW population-density rasters keyed by year; request a single year or year = "all" for the all-years product.
• data_resolution supports "30 second", "2.5 minute", "15 minute", "30 minute", and "60 minute". The all-years product is not available at 30-second resolution, so amadeus automatically falls back to 2.5-minute resolution in that case.
• data_format accepts "GeoTIFF", "ASCII", or "netCDF". The all-years product is only available as netCDF and will be downloaded that way even if another format is requested.
• Downloads are global rather than extent-based; crop to your study area during processing.
• Files arrive as zip archives, with unzip and remove_zip controlling archive handling. Access may require NASA EarthData authentication, and amadeus will use NASA_EARTHDATA_TOKEN when needed.

Download representative requests

directory_to_save <- file.path(tempdir(), "population_workflow")
download_data(
  dataset_name = "sedac_population",
  year = "2020",
  data_format = "GeoTIFF",
  data_resolution = "15 minute",
  directory_to_save = directory_to_save,
  acknowledgement = TRUE,
  unzip = TRUE,
  remove_zip = FALSE
)

Process one workflow-ready data product

processed_data <- process_covariates(
  covariate = "population",
  path = list.files(
    directory_to_save,
    pattern = "\\.tif$",
    recursive = TRUE,
    full.names = TRUE
  )[1]
)
terra::plot(log10(processed_data))

Calculate covariates at points

df <- data.frame(
  site_id = c("site_1", "site_2", "site_3","site_4"),
  lon = c(-157.8583, -155.5319, -155.5828,-159.5),
  lat = c(21.3069, 19.5, 19.8968, 22.0)
)
example_points_sf <- sf::st_as_sf(
  df,
  coords = c("lon", "lat"),
  crs = 4326
)


point_values <- calculate_covariates(
  covariate = "population",
  from = processed_data,
  locs = example_points_sf,
  locs_id = "site_id",
  radius = 1000,
  fun = "mean",
  geom = "sf"
)

print(point_values)

Calculate covariates at polygons and demonstrate the weighted_mean function

Here we calculate and compare a simple mean and a population-weighted mean for the same set of polygons. The weighted_mean function uses the population density values as weights, so it will give more influence to areas with higher population density within the specified radius.

nc <- st_read(system.file("shape/nc.shp", package="sf"))
directory_prism <- file.path(tempdir(), "population_workflow","prism")

# Get prism data 
download_data(
  dataset_name = "prism",
  time = "201005",
  element = "tmax",
  data_type = "ts",
  directory_to_save = directory_prism,
  acknowledgement = TRUE,
  unzip = TRUE,
  remove_zip = FALSE
)

processed_prism <- process_covariates(
  covariate = "prism",
  path = list.files(
    paste0(directory_prism,"/data_files/"),
    pattern = ".nc",
    recursive = TRUE,
    full.names = TRUE
  )[1],
  element = "tmax",
  time = "201005"
)

pop_weighted_values <- calculate_covariates(
  covariate = "prism",
  from = processed_prism,
  locs = nc,
  locs_id = "FIPS",
  weights = processed_data,
  geom = "sf"
)

print(pop_weighted_values)

area_weighted_values <- calculate_covariates(
  covariate = "prism",
  from = processed_prism,
  locs = nc,
  locs_id = "FIPS",
  geom = "sf"
)

print(area_weighted_values)