WebGIS | GeoAI | EO

Harnessing Uber’s H3 Spatial Indexing System

H3, developed by Uber, is a spatial indexing system designed to efficiently manage, analyze, and visualize spatial data. This open-source project provides a unique way to partition the globe into a set of hexagonal tiles, addressing many of the challenges faced by traditional spatial indexing systems.

How H3 Works

Hexagonal Grid System

H3 divides the Earth’s surface into hexagonal cells. The choice of hexagons over other shapes, like squares or triangles, is due to several advantageous properties:

  • Uniformity: Hexagons have a more consistent distance between cell centres, leading to a more uniform sampling of areas.
  • Edge Adjacency: Each hexagon shares edges with six neighbours, providing more natural adjacency relationships.
  • Efficient Sampling: Hexagons are better at approximating circles, which are often used in spatial queries.

Hierarchical Structure

H3’s hierarchical nature means that each hexagon can be recursively subdivided into smaller hexagons. This allows for multiple levels of resolution, from very coarse to very fine, enabling users to zoom in and out seamlessly. The hierarchy is represented through a unique indexing system where each hexagon at a higher resolution can be directly traced back to its parent hexagon at a lower resolution.

  • Index Representation: Each hexagonal cell is identified by a unique 64-bit integer, known as an H3 index. This index encodes the cell’s position and resolution level.
  • Geospatial Operations: H3 provides a suite of functions to perform common geospatial operations, including finding the nearest neighbours, converting between H3 indices and geographic coordinates (latitude and longitude), and calculating the area or distance of cells.
  • Resolution Levels: H3 supports sixteen resolutions, levels range from 0 to 15, with 0 being the coarsest and 15 the finest. At resolution 0, the globe is covered by 122 hexagons, while at resolution 15, each hexagon is approximately 1 square meter in area.


How it helped us in mobile network optimisation 

In the fast-evolving world of telecommunications, optimizing mobile networks for efficiency, coverage, and performance is critical. Our project leverages Uber’s H3, along with advanced data technologies like HyperLogLog and TimescaleDB, to create a comprehensive solution for automatic mobile network problem detection and optimization. The solution, integrated into a fast and scalable GIS platform, addresses critical network issues such as poor coverage, handover failures, and throughput bottlenecks. It provides real-time situational awareness and supports pre-emptive measures to enhance network performance.

Key Features of the Solution

  1. Problem Detection:

  1. GIS Platform Capabilities:

Fast and Scalable GIS Platform: Our solution leverages H3 for its ability to partition the globe into hexagonal cells, providing a highly efficient way to index and manage spatial data. This enables rapid processing and visualization of network performance metrics across different geographical areas and scales.

  1. Real-Time Knowledge and Insights:

Implementation Details

  • Uber’s H3

H3 was utilized to partition the network coverage area into hexagonal grids, offering several advantages: Uniform Coverage, Hierarchical Structure and Efficient Neighboring Cell Analysis.

  • HyperLogLog

HyperLogLog was employed for efficient cardinality estimation.

  • TimescaleDB

TimescaleDB, an extension of PostgreSQL, was used to manage time-series data.

Our project showcases the integration of cutting-edge technologies to solve complex mobile network optimization problems. By leveraging Uber’s H3 for spatial indexing, HyperLogLog for efficient data analysis, and TimescaleDB for managing time-series data, we have created a powerful, real-time GIS platform. The platform tailored for telecommunication needs, provides deep insights into network performance, enabling proactive optimization and enhancing overall service quality.


Other Applications of H3

Urban Planning

Urban planners can use H3 to analyze and visualize spatial data across different resolutions. For instance, they can aggregate population data at a coarse resolution to understand city-wide trends and then zoom into finer resolutions to study neighbourhood-level details.

Ride-Sharing and Logistics

For companies like Uber, H3 is instrumental in optimizing routes, managing fleets, and balancing supply and demand. By breaking down cities into hexagonal grids, we can efficiently allocate resources, predict ride requests, and plan dynamic pricing strategies.

Environmental Monitoring

H3 is also valuable in environmental science for monitoring and managing natural resources. It can be used to track deforestation, manage wildlife habitats, and monitor climate change impacts by providing a consistent and scalable way to handle spatial data.

Disaster Response

During natural disasters, emergency responders can utilize H3 to quickly assess and respond to affected areas. The hierarchical nature of H3 allows for rapid aggregation and disaggregation of data, facilitating efficient resource allocation and decision-making.



H3 represents a significant advancement in spatial indexing, offering a robust and flexible solution for handling geospatial data. Its hexagonal tiling, hierarchical structure, and versatile applications make it a powerful tool for various industries, from urban planning and ride-sharing to environmental monitoring and disaster response. As spatial data continues to grow in importance, H3 provides a scalable and efficient framework for leveraging this information.

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