hextraj

Hex labelling of trajectory data.

Hex region	OD connectivity
hex_aggregation.ipynb	hex_conn_dask.ipynb

Maps lon/lat positions to a projected hexagonal grid and provides tools for aggregation and connectivity analysis.

• hex_counts — heat maps and density aggregation
• hex_connectivity — origin-destination matrices
• hex_connectivity_power — multi-generation transport probabilities
• hex_connectivity_dask — lazy dask-native connectivity for large datasets
• Full dask support throughout

Getting started

Explore the notebooks:

• hex_conn.ipynb — Label NW Shelf trajectories, compute OD connectivity, visualise choropleth + edge overlay.
• hex_aggregation.ipynb — Grid construction, choropleth aggregation, and weighted edges.
• hex_grid_construction.ipynb — Rectangle and region hex grids.
• hex_analysis.ipynb — Analysis functions (hex_counts, hex_connectivity, hex_connectivity_power).
• hex_conn_dask.ipynb — Dask-native connectivity for large datasets.

Installation

pip install hextraj

For full support, including the optional secondary dependencies:

pip install hextraj[full]

Or from source:

pip install git+https://github.com/willirath/hextraj.git@main

Quick example

from hextraj import HexProj

hp = HexProj(lon_origin=-3.0, lat_origin=54.0, hex_size_meters=50_000)

# Label positions → int64 hex IDs
hex_ids = hp.label(lon, lat)

# Build a GeoDataFrame with Polygon geometries
gdf = hp.to_geodataframe(hp.region_of_hexes(region_polygon))
gdf["count"] = counts.reindex(gdf.index).fillna(0)
gdf.plot(column="count", cmap="YlOrRd")

Citing

Cite hextraj through its DOI. 10.5281/zenodo.22040740 is the concept DOI, and it resolves to the most recent release. Zenodo mints a separate DOI for each release, so use that one to pin an exact version.

CITATION.cff holds the same metadata. GitHub renders it as BibTeX or APA under "Cite this repository".