H3: Uber's Hexagonal Hierarchical Spatial Index¶
Brodsky, I. (2018). H3: Uber's Hexagonal Hierarchical Spatial Index. Uber Engineering Blog.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Spatial Indexing
- In ride-hailing, the item set is the fleet of available drivers; the space is the road network, often approximated by a geohash grid laid over latitude-longitude; the embedding maps each driver to a grid cell from their live GPS.
This sourceDescribes Uber's production hexagonal grid spatial index, which encodes each cell as a 64-bit id at one of multiple resolutions; rider and driver GPS positions are mapped to H3 cells so that finding nearby drivers becomes a same-cell / neighboring-cell lookup rather than a scan of every driver — the geohash-grid-over-GPS dispatch use case for real-time driver matching.
Supported in partVerified against the work's full text
Uber's H3 post shows latitude-longitude points, such as cars, being bucketed into grid cells, but it describes a hexagonal H3 grid, not geohash, and never mentions live driver GPS or a road network.
“The basic functions of the H3 library are for indexing locations, which transforms latitude and longitude pairs to a 64-bit H3 index , identifying a grid cell.”
- In ride-hailing, the item set is the fleet of available drivers; the space is the road network, often approximated by a geohash grid laid over latitude-longitude; the embedding maps each driver to a grid cell from their live GPS.
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