# H3 — Hexagonal Hierarchical Geospatial Indexing System

> A hierarchical geospatial indexing system using hexagonal cells, developed by Uber for spatial analysis at scale.

## Install

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# H3 — Hexagonal Hierarchical Geospatial Indexing System

## Quick Use
```bash
pip install h3
```
```python
import h3

lat, lng = 37.7749, -122.4194
h3_index = h3.latlng_to_cell(lat, lng, res=9)
print(h3_index)  # e.g. "8928308280fffff"

neighbors = h3.grid_disk(h3_index, 1)
boundary = h3.cell_to_boundary(h3_index)
```

## Introduction
H3 is a geospatial indexing system that partitions the globe into hierarchical hexagonal cells at 16 resolution levels. Originally built at Uber for ride pricing, ETAs, and supply modeling, it provides a uniform grid that avoids the distortion artifacts of square grids near the poles.

## What H3 Does
- Indexes any latitude/longitude coordinate into a hexagonal cell at a chosen resolution (0-15)
- Traverses the hierarchy: parent, children, and compact/uncompact operations across resolutions
- Computes neighborhoods: k-ring (grid disk), grid distance, and grid path between cells
- Converts between H3 indexes and boundary polygons for rendering on maps
- Supports polyfill to find all H3 cells covering a given polygon geometry

## Architecture Overview
The core library is written in C for performance and portability. Bindings exist for Python (h3-py), JavaScript (h3-js), Java, Go, and Rust. H3 uses an icosahedral face-centered projection, subdividing each face into hexagons (with 12 pentagons at vertices). Each cell is identified by a 64-bit integer encoding its resolution and position, enabling fast bitwise operations for traversal.

## Self-Hosting & Configuration
- Install language-specific bindings: pip install h3, npm install h3-js, or use the C library directly
- No server or API key needed; H3 is a pure computation library
- Choose resolution based on your use case: res 9 (~0.1 km²) for city-level, res 7 (~5 km²) for regional
- Use with PostGIS via the h3-pg extension for spatial SQL queries on hexagonal grids
- Integrate with deck.gl H3HexagonLayer or Kepler.gl for browser-based hex visualization

## Key Features
- Uniform cell areas at each resolution for unbiased spatial aggregation
- 16 resolution levels from continental (res 0, ~4M km²) to sub-meter (res 15)
- Hierarchical containment: every child cell nests within its parent
- Edge and vertex indexing for modeling adjacency and connectivity
- Compact representation reduces storage by replacing groups of children with parents

## Comparison with Similar Tools
- **S2 Geometry (Google)** — uses square cells on a sphere; H3 hexagons provide more uniform neighbor distances
- **Geohash** — rectangular cells with edge-sharing artifacts; H3 hexagons share edges uniformly
- **PostGIS grid functions** — arbitrary grids on projected data; H3 provides a standardized global hierarchy
- **Uber's Placekey** — built on H3 for POI identification; H3 is the lower-level spatial index underneath
- **Open Location Code (Plus Codes)** — encodes locations as short strings; H3 is optimized for spatial analytics

## FAQ
**Q: Why hexagons instead of squares?**
A: Hexagons have uniform adjacency (every neighbor shares an edge, not just a corner), which eliminates directional bias in distance calculations and spatial smoothing.

**Q: How do I visualize H3 cells on a map?**
A: Convert cell indexes to GeoJSON boundary polygons with cell_to_boundary, then render with Leaflet, Mapbox GL, or deck.gl's H3HexagonLayer.

**Q: Can H3 be used in SQL databases?**
A: Yes. The h3-pg extension brings H3 functions to PostgreSQL/PostGIS. DuckDB and ClickHouse also have H3 extensions.

**Q: What is the performance like for large datasets?**
A: H3 operations are O(1) per cell using bitwise index arithmetic. Polyfilling a polygon scales with the number of cells it covers, which is efficient at appropriate resolutions.

## Sources
- https://github.com/uber/h3
- https://h3geo.org/

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Source: https://tokrepo.com/en/workflows/asset-dafc4225
Author: AI Open Source