Earlier quoted context omitted.
I just want to make it clear that there's a real reason behind H3, not just "invent something really cool." I don't work at Uber, anymore, but I did work on the team that originated H3 and worked on H3. To condense it down to a few bullet points, we realized: 1. We needed to move from geofences to a grid for both data science reasons and scaling reasons to perform realtime aggregated and anonymized activity analysis…
excellent -- reading carefully; this one is fun .. https://github.com/uber/h3-py/blob/master/docs/UnifiedDataLa...
I personally feel the unidirectional edges[1] are a real differentiator. If you have these space-time buckets and compare counts between two points in time, you can see the change in density, but you don't know how active it is. Tracking transitions between hexagons in an anonymous, aggregated fashion lets you see if the "stagnant" areas of your map are stagnant because the actors actually aren't moving, or if there's a large "mixing" of the actors but you've reached a quasi-static-equilibrium in the system.
It also lets you see which regions are more connected to each other if, for instance, hexagon A always flows back and forth with hexagon B which also flows back and forth with hexagon C bordering both but no such flow between C and A, so from data you can spot potential rivers, deduce roadblocks/accidents, that certain businesses must be closed, etc.
[1](https://uber.github.io/h3/#/documentation/api-reference/unid...)