Global Population Projections (H3-indexed)

WorldPop SSP 1km population projections (2025–2100) aggregated to H3 hexagonal cells in optimized Parquet format. Eight H3 resolutions (1–8), one file per resolution, sorted by h3_index. v2 (recommended) uses BIGINT h3_index with 17 columns; v1 (legacy) uses VARCHAR h3_index with geometry/lat/lon/area_km2.

Quick Start

1-- DuckDB (v2, recommended)
2INSTALL h3 FROM community; LOAD h3;
3INSTALL httpfs;             LOAD httpfs;
4SET s3_region = 'us-west-2';
5
6SELECT h3_index,
7       h3_h3_to_string(h3_index) AS h3_hex,
8       h3_cell_to_lat(h3_index) AS lat,
9       h3_cell_to_lng(h3_index) AS lng,
10       pop_2025, pop_2050, pop_2100,
11       (pop_2100 / pop_2025)::FLOAT AS growth_ratio
12FROM read_parquet('s3://us-west-2.opendata.source.coop/walkthru-earth/indices/population/v2/scenario=SSP2/h3_res=5/data.parquet')
13ORDER BY pop_2025 DESC
14LIMIT 20;
1-- DuckDB (v2, recommended)
2INSTALL h3 FROM community; LOAD h3;
3INSTALL httpfs;             LOAD httpfs;
4SET s3_region = 'us-west-2';
5
6SELECT h3_index,
7       h3_h3_to_string(h3_index) AS h3_hex,
8       h3_cell_to_lat(h3_index) AS lat,
9       h3_cell_to_lng(h3_index) AS lng,
10       pop_2025, pop_2050, pop_2100,
11       (pop_2100 / pop_2025)::FLOAT AS growth_ratio
12FROM read_parquet('s3://us-west-2.opendata.source.coop/walkthru-earth/indices/population/v2/scenario=SSP2/h3_res=5/data.parquet')
13ORDER BY pop_2025 DESC
14LIMIT 20;

1# Python (v2)
2import duckdb
3
4con = duckdb.connect()
5con.install_extension("httpfs"); con.load_extension("httpfs")
6con.execute("INSTALL h3 FROM community"); con.load_extension("h3")
7con.sql("SET s3_region = 'us-west-2'")
8
9df = con.sql("""
10    SELECT h3_index,
11           h3_cell_to_lat(h3_index) AS lat,
12           h3_cell_to_lng(h3_index) AS lng,
13           pop_2025, pop_2050, pop_2100
14    FROM read_parquet(
15        's3://us-west-2.opendata.source.coop/walkthru-earth/indices/population/v2/scenario=SSP2/h3_res=5/data.parquet'
16    ) WHERE h3_cell_to_lat(h3_index) BETWEEN 20 AND 35
17        AND h3_cell_to_lng(h3_index) BETWEEN 68 AND 90
18""").fetchdf()
1# Python (v2)
2import duckdb
3
4con = duckdb.connect()
5con.install_extension("httpfs"); con.load_extension("httpfs")
6con.execute("INSTALL h3 FROM community"); con.load_extension("h3")
7con.sql("SET s3_region = 'us-west-2'")
8
9df = con.sql("""
10    SELECT h3_index,
11           h3_cell_to_lat(h3_index) AS lat,
12           h3_cell_to_lng(h3_index) AS lng,
13           pop_2025, pop_2050, pop_2100
14    FROM read_parquet(
15        's3://us-west-2.opendata.source.coop/walkthru-earth/indices/population/v2/scenario=SSP2/h3_res=5/data.parquet'
16    ) WHERE h3_cell_to_lat(h3_index) BETWEEN 20 AND 35
17        AND h3_cell_to_lng(h3_index) BETWEEN 68 AND 90
18""").fetchdf()

Files

1walkthru-earth/indices/population/
2  v2/scenario=SSP2/                              # recommended
3    h3_res=1/data.parquet        430 cells
4    h3_res=2/data.parquet      2,222 cells
5    h3_res=3/data.parquet     12,310 cells
6    h3_res=4/data.parquet     71,552 cells
7    h3_res=5/data.parquet    414,388 cells
8    h3_res=6/data.parquet  2,288,660 cells
9    h3_res=7/data.parquet 11,421,958 cells
10    h3_res=8/data.parquet 44,888,216 cells
11    _metadata.json
12  v1/scenario=SSP2/                              # legacy (VARCHAR h3_index, has geometry/lat/lon/area_km2)
13    h3_res=1/data.parquet .. h3_res=8/data.parquet
1walkthru-earth/indices/population/
2  v2/scenario=SSP2/                              # recommended
3    h3_res=1/data.parquet        430 cells
4    h3_res=2/data.parquet      2,222 cells
5    h3_res=3/data.parquet     12,310 cells
6    h3_res=4/data.parquet     71,552 cells
7    h3_res=5/data.parquet    414,388 cells
8    h3_res=6/data.parquet  2,288,660 cells
9    h3_res=7/data.parquet 11,421,958 cells
10    h3_res=8/data.parquet 44,888,216 cells
11    _metadata.json
12  v1/scenario=SSP2/                              # legacy (VARCHAR h3_index, has geometry/lat/lon/area_km2)
13    h3_res=1/data.parquet .. h3_res=8/data.parquet

Compression: ZSTD level 3. Row groups: 1,000,000 rows.

Schema (v2, recommended)

17 columns total. Geometry, lat/lon, and area_km2 from v1 are derivable from h3_index via the DuckDB h3 extension (e.g., h3_cell_to_lat(h3_index), h3_cell_to_lng(h3_index)).

Sample values (res 5, most populated cells, SSP2 2025):

How It Works

1. WorldPop 1 km rasters are read in 5° × 5° geographic windows
2. Each pixel is assigned to an H3 cell via h3.latlng_to_cell()
3. Population counts are summed per H3 cell (correct for count data)
4. Overlapping window-boundary cells are deduplicated via GROUP BY h3_index, SUM()
5. Final Parquet is sorted by h3_index (v2 stores BIGINT h3_index only; v1 legacy includes native GEOMETRY via DuckDB spatial)

All resolutions produce consistent world totals (~8.19 billion for 2025).

More Examples

1-- Population growth hotspots (2025 → 2100)
2SELECT h3_index,
3       h3_cell_to_lat(h3_index) AS lat,
4       h3_cell_to_lng(h3_index) AS lng,
5       pop_2025, pop_2100,
6       (pop_2100 / pop_2025)::FLOAT AS growth_ratio
7FROM read_parquet('s3://us-west-2.opendata.source.coop/walkthru-earth/indices/population/v2/scenario=SSP2/h3_res=5/data.parquet')
8WHERE pop_2025 > 100000
9ORDER BY growth_ratio DESC
10LIMIT 20;
11
12-- Continental totals
13SELECT CASE
14         WHEN h3_cell_to_lat(h3_index) BETWEEN -35 AND 37 AND h3_cell_to_lng(h3_index) BETWEEN -20 AND 55 THEN 'Africa'
15         WHEN h3_cell_to_lat(h3_index) BETWEEN 5 AND 55 AND h3_cell_to_lng(h3_index) BETWEEN 60 AND 150 THEN 'Asia'
16         WHEN h3_cell_to_lat(h3_index) BETWEEN 35 AND 72 AND h3_cell_to_lng(h3_index) BETWEEN -12 AND 45 THEN 'Europe'
17         WHEN h3_cell_to_lat(h3_index) BETWEEN -56 AND 15 AND h3_cell_to_lng(h3_index) BETWEEN -82 AND -34 THEN 'South America'
18         WHEN h3_cell_to_lat(h3_index) BETWEEN 15 AND 72 AND h3_cell_to_lng(h3_index) BETWEEN -170 AND -50 THEN 'North America'
19         ELSE 'Other'
20       END AS continent,
21       SUM(pop_2025)::BIGINT AS pop_2025,
22       SUM(pop_2050)::BIGINT AS pop_2050,
23       SUM(pop_2100)::BIGINT AS pop_2100
24FROM read_parquet('s3://us-west-2.opendata.source.coop/walkthru-earth/indices/population/v2/scenario=SSP2/h3_res=5/data.parquet')
25GROUP BY continent
26ORDER BY pop_2025 DESC;
27
28-- DuckDB-WASM (browser) — use HTTPS URL
29SELECT h3_index, pop_2025, pop_2050
30FROM read_parquet(
31    'https://data.source.coop/walkthru-earth/indices/population/v2/scenario=SSP2/h3_res=5/data.parquet'
32)
33WHERE h3_cell_to_lat(h3_index) BETWEEN 35 AND 45
34  AND h3_cell_to_lng(h3_index) BETWEEN -10 AND 5
35LIMIT 100;
1-- Population growth hotspots (2025 → 2100)
2SELECT h3_index,
3       h3_cell_to_lat(h3_index) AS lat,
4       h3_cell_to_lng(h3_index) AS lng,
5       pop_2025, pop_2100,
6       (pop_2100 / pop_2025)::FLOAT AS growth_ratio
7FROM read_parquet('s3://us-west-2.opendata.source.coop/walkthru-earth/indices/population/v2/scenario=SSP2/h3_res=5/data.parquet')
8WHERE pop_2025 > 100000
9ORDER BY growth_ratio DESC
10LIMIT 20;
11
12-- Continental totals
13SELECT CASE
14         WHEN h3_cell_to_lat(h3_index) BETWEEN -35 AND 37 AND h3_cell_to_lng(h3_index) BETWEEN -20 AND 55 THEN 'Africa'
15         WHEN h3_cell_to_lat(h3_index) BETWEEN 5 AND 55 AND h3_cell_to_lng(h3_index) BETWEEN 60 AND 150 THEN 'Asia'
16         WHEN h3_cell_to_lat(h3_index) BETWEEN 35 AND 72 AND h3_cell_to_lng(h3_index) BETWEEN -12 AND 45 THEN 'Europe'
17         WHEN h3_cell_to_lat(h3_index) BETWEEN -56 AND 15 AND h3_cell_to_lng(h3_index) BETWEEN -82 AND -34 THEN 'South America'
18         WHEN h3_cell_to_lat(h3_index) BETWEEN 15 AND 72 AND h3_cell_to_lng(h3_index) BETWEEN -170 AND -50 THEN 'North America'
19         ELSE 'Other'
20       END AS continent,
21       SUM(pop_2025)::BIGINT AS pop_2025,
22       SUM(pop_2050)::BIGINT AS pop_2050,
23       SUM(pop_2100)::BIGINT AS pop_2100
24FROM read_parquet('s3://us-west-2.opendata.source.coop/walkthru-earth/indices/population/v2/scenario=SSP2/h3_res=5/data.parquet')
25GROUP BY continent
26ORDER BY pop_2025 DESC;
27
28-- DuckDB-WASM (browser) — use HTTPS URL
29SELECT h3_index, pop_2025, pop_2050
30FROM read_parquet(
31    'https://data.source.coop/walkthru-earth/indices/population/v2/scenario=SSP2/h3_res=5/data.parquet'
32)
33WHERE h3_cell_to_lat(h3_index) BETWEEN 35 AND 45
34  AND h3_cell_to_lng(h3_index) BETWEEN -10 AND 5
35LIMIT 100;

Geometry / Coordinate Derivation

v2 files do not include geometry, lat, lon, or area_km2 columns — these are derivable from the BIGINT h3_index using the DuckDB h3 community extension:

1INSTALL h3 FROM community; LOAD h3;
2
3SELECT h3_index,
4       h3_h3_to_string(h3_index)  AS h3_hex,
5       h3_cell_to_lat(h3_index)   AS lat,
6       h3_cell_to_lng(h3_index)   AS lng,
7       h3_cell_area(h3_index, 'km^2') AS area_km2
8FROM read_parquet('s3://us-west-2.opendata.source.coop/walkthru-earth/indices/population/v2/scenario=SSP2/h3_res=5/data.parquet')
9LIMIT 5;
1INSTALL h3 FROM community; LOAD h3;
2
3SELECT h3_index,
4       h3_h3_to_string(h3_index)  AS h3_hex,
5       h3_cell_to_lat(h3_index)   AS lat,
6       h3_cell_to_lng(h3_index)   AS lng,
7       h3_cell_area(h3_index, 'km^2') AS area_km2
8FROM read_parquet('s3://us-west-2.opendata.source.coop/walkthru-earth/indices/population/v2/scenario=SSP2/h3_res=5/data.parquet')
9LIMIT 5;

v1 (legacy) files retain the original schema with VARCHAR h3_index, native Parquet 2.11+ GEOMETRY, lat, lon, and area_km2 columns. v1 is located at indices/population/v1/scenario=SSP2/h3_res={1-8}/data.parquet.

Source

WorldPop Global SSP Projections v0.2 — 30 arc-second (~1 km) gridded population projections for 2025–2100 under five Shared Socioeconomic Pathways (SSP1–SSP5). Float32, LZW-compressed GeoTIFF, EPSG:4326, coverage 60°S–84°N.

WorldPop. (2018). Global 1km-grid population projections, v0.2. University of Southampton. doi:10.5258/SOTON/WP00849

License

This dataset is licensed under CC BY 4.0 by walkthru.earth. The source WorldPop SSP Projections is licensed under CC BY 4.0 by the University of Southampton.