h3-toolkit
A Polars-native toolkit for aggregating and visualizing geospatial data using Uber's H3 spatial indexing system.
Documentation: https://h3-toolkit.readthedocs.io/
Installation
pip install h3-toolkit
# With visualization support (pydeck, mapclassify)
pip install h3-toolkit[vis]
Quick Example
import geopandas as gpd
from h3_toolkit import H3Toolkit
from h3_toolkit.aggregation import Mean, Sum
# Load your vector data (e.g., census blocks, building footprints)
gdf = gpd.read_file("your_data.geojson")
result = (
H3Toolkit()
.process_from_vector(gdf, resolution=9)
.set_aggregation_strategy({
"population": Sum(),
"income": Mean(),
})
.get_result()
)
print(result)
# shape: (n, 3)
# ┌─────────────────┬────────────┬────────┐
# │ cell ┆ population ┆ income │
# │ --- ┆ --- ┆ --- │
# │ u64 ┆ f64 ┆ f64 │
# ╞═════════════════╪════════════╪════════╡
# │ 613194865823… ┆ 1204.0 ┆ 52300. │
# └─────────────────┴────────────┴────────┘
Features
- Polars-native — all aggregations run on Polars, no pandas overhead
- Chainable API — compose
process_from_vector,set_aggregation_strategy, andget_resultin a single pipeline - Multiple input formats — vector (GeoDataFrame), raster, or existing H3 cells
- Pluggable aggregation strategies —
Sum,Mean,Count,EqualSplit,Centroid, and more - Optional visualization — built-in pydeck-based map rendering via the
visextra
Contributing
Contributions are welcome! Please read CONTRIBUTING.md before opening a pull request.
License
MIT © City Science Lab @ TaipeiTech, Syuan-Bo Huang
1 maintainer
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