Uber H3 Hexagonal Modeling
Last updated
Last updated
Uber H3 is an open-source geospatial system created by Uber Technologies . H3 provides a hierarchical grid system that divides the Earth's surface into hexagons of varying sizes, allowing for easy location-based indexing, search, and analysis.
Hexagons can be created at a single scale, for instance to fill an arbitrary polygon at one resolution (see below). They can also be used to generate a much-smaller number of hexagons at multiple scales. In general, operating on h3 hexagons is much faster than on raw arbitrary geometries, at a cost of some precision. Because each hexagon is exactly the same size, this is particularly advantageous for GPU-accelerated workflows.
A principal advantage of the system is that for a given scale, hexagons are approximately-equal area. This stands in contrast to other subdivision schemes based on longitudes and latitudes or web Mercator map projections.
A second advantage is that with hexagons, neighbors in all directions are equidistant. This is not true for rectangular subdivisions like pixels, whose 8 neighbors are at different distances.
The exact amount of precision lost can be tightly bounded, with the smallest sized hexagons supported being about 1m2. That’s more accurate than most current available data sources, short of survey data.
There are some disadvantages to be aware of. The first is that the world can not actually be divided up completely cleanly into hexagons. It turns out that a few pentagons are needed, and this introduces discontinuities. However the system has cleverly placed those pentagons far away from any land masses, so this is only practically a concern for specific maritime operations.
The second issue is that hexagons at adjacent scales do not nest exactly:
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This doesn’t much affect practical operations at any single given scale. But if you look carefully at the California multiscale plot above you will discover tiny discontinuities in the form of gaps or overlaps. These don’t amount to a large percentage of the total area, but nonetheless mean this method is not appropriate when exact counts are required.
Encodes columnar point geometry into a globally-unique h3 cell ID for the specified h3_scale. Scales run from 0 to 15 inclusive, where 0 represents the coarsest resolution and 15 the finest. For details on h3 scales, please see the base library documentation.
This can be applied to literal values:
Or to columnar geographic points:
Note that if you have geographic point data rather than columnar latitude and longitude, you can use the ST_X and ST_Y functions. Also, if you wish to encode the centroids of polygons, such as for building footprints, you can combine this with the ST_CENTROID function.
To retrieve geometric coordinates from an H3 code, two functions are available.
h3ToLat and h3ToLon extract the latitude and longitude respectively, for example:
Given an H3 code, the function h3ToParent is available to find cells above that cell at any hierarchical level. This means that once codes are computed at high resolution, they can be compared to codes at other scales.
Uber's h3 python library provides a wider range of functions than those available above (although at significantly slower performance). The library defaults to generating h3 codes as hexadecimal strings, but can be configured to support BIGINT codes. Please see Uber's documentation for details.
H3 codes can be used in regular joins, including joins in Immerse. They can also be used as aggregators, such as in Immerse custom dimensions. For points which are exactly aligned, such as imports from raster data bands of the same source, aggregating on H3 codes is faster than the exact geographic overlaps function ST_EQUALS
