[jandrewrogers]

Discrete Global Grid Systems (DGGS), which is what this touches on, are a deceptively complex topic. You are optimizing for many performance dimensions, and some things that intuitively seem like they should be optimized for don't really matter, and other things people tend to ignore (like congruency) matter quite a bit.

For example, "equal area" subdivision of the surface is not a particularly useful property. This seems like it is wrong on its face such that most people try to achieve it but you have to remember that equal area only matters if your data is uniformly and predictably distributed. Geospatial data models are neither in a pretty severe way as a rule, which means you'll have to deal with data load asymmetry regardless via some other mechanism. If you can ignore equal area because it is handled by some other mechanism, it opens up other possible surface decompositions that have much stronger properties in other ways.

Compactness of representation and cost of computing relationships on the representation has a large impact on real-world index performance that is frequently ignored. The poorness of lat/lon grids in this regard is often significantly underestimated. A singularity-free 3D DGGS can have an indexing structure that is an order of magnitude smaller than a basic 2D lat/long grid for addressing, and the storage on disk of records encoded in these DGGS are also significantly smaller. This all adds up to a lot of performance.

Hexagonal grids tend to work particularly well for visualization. However, they do have their own significant weaknesses e.g. it is typically not a good representation for join operations and they are relatively expensive to search at large scales relative to some other DGGS tessellation systems.

[espeed]

Hi Andrew - You went to school for chemistry and chemical engineering. I'm still experimenting with ideas for binary codes of succinct/implicit geometric representations for similarity graph embeddings of non-spatial property data like text and numbers. I keep bumping into the crystallography literature -- the traditional lattice structures and also quasicrystal / fibonacci chain representations. How much of your chemical engineering background have influenced your designs?

[jandrewrogers]

Almost no influence as related to spatial structure; I tend to view spatial structure as an information theoretic manifestation.

On the other hand, chemical engineering had a big influence on how I reason about distributed systems. That discipline is essentially about the design of complex, continuous flow, coordination-free distributed computation systems that are robustly stable in an efficient equilibrium. It maps directly to computer science but has a concept of the problem space that I think is much more refined than what you commonly see in computer science though it is never expressed in computer science terms. But it makes sense, it is chemical engineering’s One Job.

[javajosh]

>robustly stable in an efficient equilibrium

I would like to read more about what this means and how it applies to distributed systems. I have a physics background, but I sense that chemists tend to have a much more intricate (and interesting) conception of "stability".

[refset]

I found it interesting to discover that there's a significant amount of cutting-edge HPC development along these lines for meteorology and climate modeling: https://www.hpcwire.com/2017/06/22/gpus-power9-figure-promin...