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.
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.
With the Z7 indexing, it is a very fast operation to query the neighbours, and the neighbours are alsway in a predictable order.
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.
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 (via DuckDB).
Comparing H3 and IGEO7 for Your Use Case
| Use case | H3 sufficient? | IGEO7 advantage |
|---|---|---|
| Nearest-neighbour lookup | ✓ | Marginal |
| Density maps (events/km²) | Biased | Equal area eliminates bias |
| Area statistics (land cover) | Biased | Equal area eliminates bias |
| Scientific reproducibility | Varies by location | Consistent everywhere |
| Very fine resolution (< 1 m) | No (max res 15 ~1 m²) | Res 18-20 reach centimetres |
| Cloud-native data cubes | Limited | Xarray-XDGGS integration |
| OGC API DGGS compliance | Partial | pydggsapi native support |
Further Reading
- Equal Area — why cell area uniformity matters
- Resolution Table — choose the right resolution
- Ecosystem — tools to build IGEO7 workflows