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Replication of DGGS Benchmark Using HEALPix-geo with Sphere and WGS84 Ellipsoid
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- Uses HEALPix (healpix-geo) instead of H3 -- this is the replication variable
- Tests both sphere and WGS84 ellipsoid (extension beyond original)
- Default testing up to 50 vector layers and 500 raster layers
- Comparison against H3 uses pre-computed reference results from the companion H3 replication study (DOI: 10.5281/zenodo.18904498)
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Implements polygon polyfill via healpix_geo.nested.polygon_coverage() and raster indexing via healpix_geo.nested.lonlat_to_healpix() at HEALPix depth 9 (~1 km² cells, matching H3 resolution 9). Vector benchmark: Voronoi polygons, dissolve by value, polyfill to HEALPix cells, join on cell ID, 7-bit classification. Raster benchmark: Gaussian-smoothed layers, pre-indexed pixel-to-cell mapping, aggregate and classify. Ellipsoid analysis: compares sphere vs WGS84 cell assignments across equatorial (0°), Mediterranean (+48°), Scandinavian (+62°), and Arctic (+78°) regions. Environment: Python 3.11, healpix-geo 0.1.1, Docker containerized, seeded RNG (seed=42).
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Replicates both vector and raster benchmarks from Law & Ardo (2024) using HEALPix indexing via the healpix-geo library (Rust-based) instead of H3. Extends the analysis with a geodetic comparison: all benchmarks are run twice -- once with sphere-based indexing, once with WGS84 ellipsoid indexing -- across four latitude bands (equatorial, mid-latitude, high-latitude, arctic).
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Anne Fouilloux
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