WorldPop just released a new Global Demographic Data covering 2015 and 2030. This gridded population estimate at 1-km resolution is produced using a Random Forest disaggregation model, which is trained using updated covariates and census data to provide high-resolution, contemporary, and future population distributions essential for applications like disaster response, urban planning, and health resource allocation.

The primary data, including the gridded population estimates (2015 and 2030 at 100-km resolution), can be directly downloaded from the official WorldPop website and the Humanitarian Data Exchange (HDX), making them readily available for general use and humanitarian applications.

The total population dataset at the enhanced 100-meter resolution is accessible via the Google Earth Engine (GEE) platform. This is hosted as a curated public catalog by Samapriya Roy, allowing researchers to leverage GEE's cloud-based computing power for large-scale analysis without needing to download massive files.

Along with that, total population and age-sex structure at 1-km resolution are also available on GEE and ready for use.

var year = "2020";
// total population at 100-m resolution
var total_100m = ee.ImageCollection("projects/sat-io/open-datasets/WORLDPOP/pop")
	.filterDate(year + "-01-01", year + "-12-31");

// total population at 1-km resolution
var total_1km = ee.ImageCollection('projects/wpgp-global2/assets/pop_1km_ua')
  .filterDate(year+'-01-01', year+'-12-31').mosaic();

// age-sex structure at 1-km resolution
var agesex_1km = ee.ImageCollection('projects/wpgp-global2/assets/agesex_1km_ua')
  .filterDate(year+'-01-01', year+'-12-31').mosaic();

In this example, we calculate and display global dependency ratio in 2025. Dependency ratio is a demographic measure that compares the number of non-working individuals (children and the elderly) to the number of working-aged individuals in a population. In this case, non-working individuals fall between 0-15 and 65+ age groups.

var year = '2025';
var get_bands = function(bands){
  bands = ee.List(bands);
  var f = bands.map(function(b){return ee.String('f_').cat(ee.Number(b).format('%02d'))});
  var m = bands.map(function(b){return ee.String('m_').cat(ee.Number(b).format('%02d'))});
  return ee.List(f).cat(m);
};

var bands_young = get_bands([0,1,5,10]);
var bands_prod = get_bands(ee.List.sequence(15,60,5));
var bands_old = get_bands([65,70,75,80,85,90]);

var agesex = ee.ImageCollection('projects/wpgp-global2/assets/agesex_1km_ua')
  .filterDate(year+'-01-01', year+'-12-31').mosaic();

var pop_young = agesex.select(bands_young).reduce('sum');
var pop_prod = agesex.select(bands_prod).reduce('sum');
var pop_old = agesex.select(bands_old).reduce('sum');
var dependency = pop_young.add(pop_old).divide(pop_prod);

var palettes = require('users/gena/packages:palettes');
var palette = palettes.colorbrewer.Spectral[9];

Map.addLayer(dependency, {min:0.2, max:1, palette:palette}, 'dependency');

Bondarenko M., Priyatikanto R., Tejedor-Garavito N., Zhang W., McKeen T., Cunningham A., Woods T., Hilton J., Cihan D., Nosatiuk B., Brinkhoff T., Tatem A., Sorichetta A.. 2025. The spatial distribution of population in 2015-2030 at a resolution of 30 arc (approximately 1km at the equator) R2025A version v1. Global Demographic Data Project - Funded by The Bill and Melinda Gates Foundation (INV-045237). WorldPop - School of Geography and Environmental Science, University of Southampton. DOI:10.5258/SOTON/WP00845.