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Use Cases

IGEO7 is particularly well suited to applications where equal area matters and where hierarchical aggregation is part of the workflow.

Environmental Monitoring​

Biodiversity and species distribution modelling requires area-accurate grid cells so that species richness per unit area is comparable across regions. IGEO7's equal-area guarantee means a cell at resolution 9 in Estonia covers the same 1.264 km² as a cell in Brazil.

# Index species observations to IGEO7 resolution 9
gdf_obs = dggrid.cells_for_geo_points(
    geodf_points_wgs84=observations,
    cell_ids_only=True,
    dggs_type="IGEO7",
    resolution=9,
)
# Count per cell — directly comparable across the globe
counts = gdf_obs.groupby("name").size()

Land cover and habitat area — aggregate raster data (Sentinel-2, Copernicus Land Cover) into IGEO7 cells for consistent area-weighted statistics. Resolution 14 (~9.8 m) matches Sentinel-2's 10 m native resolution.

Earth Observation Data Cubes​

IGEO7 integrates with Xarray-XDGGS for cloud-native analysis of gridded data:

import xarray as xr
import xdggs

ds = xr.open_zarr("s3://my-bucket/sentinel2.zarr")
ds_igeo7 = ds.dggs.assign_igeo7(resolution=14)
monthly_ndvi = ds_igeo7.groupby("time.month").mean()

The uniform cell size makes spatial aggregation and resampling mathematically correct — no area-weighting corrections needed.

Hydrology and Water Resources​

Stream networks and catchment delineation benefit from IGEO7's multi-resolution hierarchy. Resolutions 8–10 (~3–1.3 km) match typical hydrological modelling scales; resolution 12 (~68 m) suits fine-scale flow routing.

Because cells are equal area, runoff volumes computed per cell are directly summable up the hierarchy without correction factors.

Urban Analytics and Planning​

Resolution 11 (~181 m) and 12 (~68 m) provide natural scales for neighbourhood-level urban analysis — matching typical census block sizes and street-grid granularity. The hexagonal topology (all 6 neighbours equidistant) avoids the directional bias of square grids.

# Generate IGEO7 grid over a city at 181m resolution
city_bbox = shapely.geometry.box(24.5, 59.3, 25.2, 59.6)  # Tallinn

grid = dggrid.grid_cell_polygons_for_extent(
    dggs_type="IGEO7",
    resolution=11,
    clip_geom=city_bbox,
    output_address_type="Z7_STRING",
)

OGC API DGGS Services​

pydggsapi implements the OGC API – DGGS standard with IGEO7 as a supported reference system. This allows IGEO7-indexed data to be served via standardised REST endpoints, consumable by any OGC-compliant client.

Supported backends: Clickhouse (columnar analytics), Zarr (cloud-native arrays), Parquet.

See pydggsapi ecosystem page.

Comparing H3 and IGEO7 for Your Use Case​

Use caseH3 sufficient?IGEO7 advantage
Nearest-neighbour lookup✓Marginal
Density maps (events/km²)BiasedEqual area eliminates bias
Area statistics (land cover)BiasedEqual area eliminates bias
Scientific reproducibilityVaries by locationConsistent everywhere
Very fine resolution (< 1 m)No (max res 15 ~1 m²)Res 18–20 reach centimetres
Cloud-native data cubesLimitedXarray-XDGGS integration
OGC API DGGS compliancePartialpydggsapi native support

Further Reading​