The downsides are the characteristics that make h3 or s2 useful. For h3, the single neighbor type means it is well suited to flow analysis and S2 having exact cell subdivision means it is great for simplifying geometry.
However, there a number of use cases where choosing a spatial index is a more stylistic choice, like for visualization.
The aim of A5 is not to replace S2/H3 but rather to offer an alternative that has different strengths and weaknesses compared to existing solutions
Also check S2: http://s2geometry.io/, created at Google before H3, which uses squares and underpins the fast indexing in BigQuery amongst many other things
The ones that seem obvious:
- You need very high resolution. H3 is also 64 bit I think, but it seems like A5 highest resolution is about 4 orders of magnitude higher.
- Equal cell size: are the cells exactly equal in size (in m2)? H3 they vary by up to ~2x.
What are the downsides? The shapes are irregular, distances between centroids are not uniform...
The downsides are the characteristics that make h3 or s2 useful. For h3, the single neighbor type means it is well suited to flow analysis and S2 having exact cell subdivision means it is great for simplifying geometry.
However, there a number of use cases where choosing a spatial index is a more stylistic choice, like for visualization.
The aim of A5 is not to replace S2/H3 but rather to offer an alternative that has different strengths and weaknesses compared to existing solutions
H3: Uber’s Hexagonal Hierarchical Spatial Index https://www.uber.com/en-DE/blog/h3/