Microproduct data guideintermediate

WorldPop Population Estimates

Country-level high-resolution population grids for building catchment, coverage, and demographic-denominator tools.

Start the guide Open official source

At a glance

Difficulty
Intermediate — some data preparation helps
Size
Large · 0.01–20 GB
Formats
JSON, GeoTIFF
Access
API or Download
API key
Not required
Provider
WorldPop, University of Southampton
Updates
Occasional
Data terms
Creative Commons Attribution 4.0

From source to product signal

Test a product idea in four steps

WorldPop REST metadata lists per-country population rasters. Start with one ISO3 code and the unconstrained 2000-2020 series, not the global mosaic. Grid values are modeled estimates, not a census count, and some derived layers use a different share-alike licence.

1

Check the setup

  • Python 3.10 or newer
  • A notebook environment such as Jupyter or Google Colab
  • An internet connection
2

Access the data

  1. 1.Read the REST API basics and the CC BY 4.0 licence text.
  2. 2.Request metadata for one country instead of downloading a continental mosaic.
  3. 3.Keep ISO3, year, DOI, and the published file URL.
Open official source
3

Run the Python example

Install the packages, then run the notebook cell.

python -m pip install pandas requests

import pandas as pd
import requests

response = requests.get(
    "https://www.worldpop.org/rest/data/pop/wpgp",
    params={"iso3": "LUX"},
    timeout=30,
)
response.raise_for_status()
layers = pd.DataFrame(response.json()["data"])
sample = layers[layers["popyear"].astype(str) == "2020"]
print(sample[["iso3", "popyear", "doi", "data_format", "files"]].head())
4

Test a useful signal

List Luxembourg's 2020 WorldPop raster metadata

Test whether REST metadata can locate one country unconstrained population file.

  1. 01Keep ISO3, year, DOI, and the published GeoTIFF path for 2020.
  2. 02Confirm the file is a country raster rather than a global mosaic.
  3. 03Cite WorldPop under CC BY 4.0 and explain that pixel counts are modeled estimates, not census enumerations.

Dataset details

WorldPop, University of Southampton is an academic source. Last verified 2026-08-18. Temporal coverage: 2000-2020 global per-country estimates.

Theme

Demographics & Development

Domains

DemographicsPopulationGeospatial

Data types

GeospatialGridded Data

Tasks

Population EstimationGeographic AnalysisCoverage Analysis

Geography

Global

Formats

JSONGeoTIFF

Provider

WorldPop, University of Southampton

Data terms

Creative Commons Attribution 4.0