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SCALE: The original paper tested up to 500 vector layers; our default configuration tests [5, 10, 20, 50, 100] layers but supports scaling to 500. 2. RASTER GENERATION: The paper used NLMpy mid-point displacement algorithm. Our implementation uses NLMpy when available, with Gaussian filter fallback. 3. RANDOM MISALIGNMENT: The paper mentions "jittering the origin point by up to one pixel" for raster alignment - this feature is not implemented in our reproduction. 4. ADDITIONAL COMPARISON: We added xdggs as an alternative DGGS implementation not present in the original study, extending the work from pure reproduction to include replication with different tools. 5. PRE-INDEXED SCENARIO: The paper's raster benchmark used pre-indexed data in Apache Parquet queried with Polars. Our benchmark includes both on-the-fly indexing and pre-indexed scenarios to enable direct comparison. https://w3id.org/np/RADCSRkRrlaOzRZ-lkPh1dnvthkhqFz55eGr1wtpW03vk/dggs-replication-2026 https://w3id.org/sciencelive/o/terms/hasDiscipline http://www.wikidata.org/entity/Q8008 https://w3id.org/np/RADCSRkRrlaOzRZ-lkPh1dnvthkhqFz55eGr1wtpW03vk/dggs-replication-2026 https://w3id.org/sciencelive/o/terms/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 https://w3id.org/np/RADCSRkRrlaOzRZ-lkPh1dnvthkhqFz55eGr1wtpW03vk/dggs-replication-2026 https://w3id.org/sciencelive/o/terms/hasScopeDescription This study aims to reproduce and replicate the computational benchmark experiments from Law & Ardo (2024) "Using a discrete global grid system for a scalable, interoperable, and reproducible system of landuse mapping" (DOI: 10.1080/20964471.2024.2429847). Specifically: 1. VECTOR BENCHMARK (Figure 6): Reproduces the comparison between traditional vector overlay operations and DGGS-based methods using H3 polyfilling, testing scalability across 5-500 input layers. 2. 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