Comparing H3 and S2: Grid Systems for Aggregating and Visualizing Data on Maps

By Dan McCarey

When it comes to aggregating spatial data and visualizing it effectively on maps, two grid systems stand out: H3 and S2. Both systems provide powerful tools for working with geospatial data, but their design philosophies and use cases differ in meaningful ways. This blog explores these differences and evaluates how each system performs for data aggregation and visualization.

What Are H3 and S2?

H3 (Hexagonal Hierarchical Spatial Index) is a grid system developed by Uber. It partitions the globe into hexagonal cells, providing a uniform spatial index at multiple resolutions. The hexagonal shape is particularly well-suited for spatial analytics due to its uniform distance to neighbors and reduced edge effects compared to squares.

S2 is a spatial indexing system developed by Google. It represents the Earth as a cube, projected onto a sphere, and divides it into quadrilateral cells. S2 focuses on maintaining an efficient hierarchical structure and precise spherical geometry calculations.

Key Features and Differences

Aggregating Data with H3 and S2

Both H3 and S2 support hierarchical aggregation, but the choice between them can affect the outcome of your analysis.

• H3 Aggregation:

  • The hexagonal cells enable smooth aggregation of spatial data due to their uniform adjacency and consistent area.
  • Aggregating data at different resolutions is straightforward, as each hexagon at a higher resolution nests perfectly within larger hexagons.
  • Ideal for applications requiring even sampling or minimal distortion in neighborhood relationships, such as heatmaps and clustering.

• S2 Aggregation:

  • The quad-tree hierarchy provides a flexible mechanism for aggregation, allowing for efficient calculations at various levels of detail.
  • However, the quadrilateral cells may introduce slight distortions in area and shape at the poles, making them less ideal for global visualizations.
  • Best for tasks requiring integration with Google’s services or high-precision geometrical operations.

Visualizing Data on Maps

The cell shapes and hierarchy in H3 and S2 directly influence how well they lend themselves to visualization.

• H3 Visualization:

  • Hexagons create visually appealing maps with fewer edge effects and better representation of proximity relationships.
  • Heatmaps and density plots benefit from hexagonal grids due to their uniformity.
  • The consistent size and shape of hexagons across resolutions simplify rendering.

• S2 Visualization:

  • The quadrilateral cells are slightly less intuitive for visualization compared to hexagons, especially when dealing with irregular patterns.
  • That said, S2’s integration with Google Maps and its precision in representing spherical geometry make it a strong choice for high-detail maps and web-based visualizations.

Choosing the Right System

The choice between H3 and S2 depends on the specific requirements of your project:

• Choose H3 if:

  • Your analysis benefits from hexagonal grids (e.g., proximity analysis, heatmaps).
  • Uniform cell area and shape are critical.
  • You need a straightforward mechanism for hierarchical aggregation.

• Choose S2 if:

  • You prioritize integration with Google’s geospatial tools.
  • High-precision spherical geometry calculations are necessary.
  • Your project involves complex hierarchical relationships or irregularly shaped areas.

H3 and S2 are both great tools for managing spatial data, and their strengths cater to different use cases. While H3 excels in uniformity and simplicity, S2 shines in precision and flexibility. By understanding their differences, you can choose the right system to aggregate and visualize data effectively, tailoring your approach to the demands of your project.