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H3 Analytics Overview

When to use H3​

Consider using H3 tiling when:

  • Creating heatmaps of events (populations, counts, etc.)
  • Wanting to compare datasets across resolutions & scale
  • Working with sparse datasets

Example of buildings heatmap based on the Overture Buildings dataset:

Code
common = fused.load("https://github.com/fusedio/udfs/blob/main/public/common/")
@fused.udf
def udf(bounds: fused.types.Bounds = [-122.71963771127753,36.53196328805067,-120.70395948802646,38.082911654639275]):
    res = bounds_to_res(bounds)
    print(res)
    releases = ['2024-02-15-alpha-0', '2024-03-12-alpha-0', '2024-08-20-0', '2024-09-18-0', '2024-10-23-0', '2024-11-13-0', '2024-12-18-0', '2025-01-22-0', '2025-03-19-1', '2025-04-23-0', '2025-05-21-0']
    release1 = releases[-1]
    df1 = common.read_hexfile(bounds, f"s3://fused-users/fused/sina/overture_overview/{release1}/hex{res}.parquet", clip=True)

    return df1

@fused.cache
def bounds_to_res(bounds, res_offset=0, max_res=14, min_res=3):
    z = common.estimate_zoom(bounds)
    return max(min(int(3 + max(0, z - 3) / 1.7 + res_offset), max_res), min_res)

Converting Datasets to H3 tiles​

We've provided a few options for converting datasets to H3 tiles:

TypeData SizeHexagonification TypeH3 OutputConsideration
File to H3Small (< 100MB)On the flyStatic ResolutionEasy to adjust. Can easily change field to hexagonify
Dynamic Tile to H3Small or LargeOn the flyDynamic ResolutionEasy to adjust. Can easily change field to hexagonify
Ingesting Dataset to H3Large (> 100MB)Pre-computedDynamic ResolutionRequires deciding on which variable to hexagonify ahead of time

For choosing the right resolution, see Resolution Guide.

Example UDFs​