code supplement for DGGS geometry comparisons

Assumptions are that the respective libraries are installed with their Python bindings:

• Google S2 and https://github.com/google/s2geometry/tree/master/src/python
• Uber H3 and https://github.com/uber/h3-py
• OpenEaggr DGGS
• rHEALPix
• DGGRID and dggrid4py and https://github.com/allixender/dggrid4py

generate cells to parquet

• S2 can currently only be built on Linux (potentially also MacOS/Unix)
• OpenEAGGR and DGGRID are possible to build on Windows as well
• rHEALPix and H3 are available as multiplatform Python Pypi packages
• the experiment was run on a 2-CPU Intel XEON (each 8 cores*2 threads) server with 32 GB RAM and Ubuntu 18.04.3

creates (cell_id, wkt_geom) for the defined DGGS types and resolutions defined in config.json

#> python generate_cells_parquet.py -action cells -config config.json -cpu 16 -dggrid $(which dggrid) -out results_gen

caveats

• EAGGR doesn't seem to have a predefined logic of hierarchical cell resolutions for ISEA3H
• EAGGR doesn't seem to have a region filling algorithm available, neither for ISEA4T nor ISEA3H
• rHEALPix is pure Python (with Numpy/Scipy support), but cell generation/conversion is slower than the other C/C++ based implementations
• DGGRID is a commandline tool and can predominantly only be used to generate a grid and fill with sampling data, the Python API is only a wrapper

future potentials

• implement high-performance API in compiled language with C-API (C, C++, Rust, Fortran or similar) for rHEALPix
• implement region coverer/filler algorithm and fixed resolutions API for OpenEAGGR
• implement module that provides access DGGRID internal APIs for cell-wise logic (like dggridR or pydggrid), but as C compatible library, build on top and add spatial analysis functions like S2 or H3