Feature: H3 Aggregation

See the Big Picture and the Details—With One Hexagonal Grid

Aggregate millions of data points into meaningful spatial clusters that automatically adjust resolution as you zoom. Built on Uber's H3 system for fast, accurate spatial analysis.

Problem

Point data at scale becomes visual noise—millions of overlapping markers that hide patterns instead of revealing them. Traditional heatmaps look pretty but lose precision. Square grids introduce distortion. And pre-aggregating data at fixed resolutions means you're always stuck with someone else's choices about what to show.

Solution

Honeycomb Maps uses the H3 spatial indexing system to group your data into hexagonal cells that automatically change resolution as you zoom. See high-level regional patterns when zoomed out, then drill into neighborhood-level detail without switching views or re-running queries. Metrics like counts, sums, and averages are calculated on-the-fly for each cell.

Key Benefits

Automatic resolution scaling

H3 layers change resolution on-the-fly based on zoom level. Metrics for each cell are recalculated instantly—no manual configuration required.

Works with existing H3 data

Already have H3 indexes in your Snowflake tables? Import them directly. Great for pre-calculated metrics or data that's already been spatially indexed.

Automatic indexing for points

Don't have H3 indexes yet? No problem. Honeycomb automatically calculates H3 cells for any point data with latitude and longitude coordinates.

Drill down with filters

Filter your data for a specific H3 cell and automatically include all child cells. Perfect for investigating anomalies or understanding what's happening in a specific area.

How It Works

1
Add your dataConnect a Snowflake table with point data (lat/lng) or existing H3 index columns.
2
Configure metricsChoose what to calculate for each cell—count, sum, average, or custom aggregations.
3
Explore at any scaleZoom in and out to see patterns at different resolutions. Click any cell to drill down further.

Example Use Case

A ride-sharing company needed to understand pickup density across their entire service area—from regional patterns down to individual street corners. Using H3 aggregation, their operations team can see citywide hotspots at a glance, then zoom into a busy intersection to understand exactly which blocks are generating the most demand. When they spot an anomaly, they click the hexagon to filter down to just those trips and investigate what's driving the pattern.