h3-toolkit 0.3.16

A toolkit for aggregating and visualizing geospatial data using H3 hexagonal grids

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, and get_result in 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 vis extra

Contributing

Contributions are welcome! Please read CONTRIBUTING.md before opening a pull request.

License

MIT © City Science Lab @ TaipeiTech, Syuan-Bo Huang