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- dggs-replication-2026 hasMethodologyDescription "REPRODUCTION METHODOLOGY: - Vector benchmark: Implemented H3 polyfilling algorithm via h3-py library to convert Voronoi polygons to H3 cells at resolution 14, matching the paper's approach - Raster benchmark: Used H3 Python loop (h3.latlng_to_cell) to index raster pixels to H3 cells, replicating the paper's indexing method - Classification: Implemented all 7 number-theoretic classification functions (prime, perfect, triangular, square, pentagonal, hexagonal, Fibonacci) as described in the paper - Data generation: Created synthetic Voronoi polygons and NLM raster landscapes following the paper's specifications REPLICATION METHODOLOGY: - Raster benchmark: Replaced H3 Python loop with xdggs library (xdggs.H3Info.geographic2cell_ids) for vectorized coordinate-to-cell conversion - This tests whether alternative DGGS implementations affect the benchmark conclusions COMPUTATIONAL ENVIRONMENT: - Python 3.11 with h3 4.x, xdggs, NumPy, GeoPandas, Polars - Docker container for reproducibility - Benchmarks run on standardized hardware with multiple iterations" assertion.