Geospatial Indexing Explained: A Comparison of Geohash, S2, and H3
a day ago
- #data-science
- #geospatial-indexing
- #geocoding
- Geospatial indexing maps latitude-longitude pairs to geographical subdivisions, useful for analytics, feature engineering, and database indexing.
- Geohash (2008) uses a Z-order curve and equirectangular projection, but has issues with proximity preservation and size variability; it is simple and widely available.
- Google S2 (2017) improves on Geohash with a Hilbert curve and unfolded cube projection, better preserving locality and reducing cell size discrepancies.
- Uber H3 (2018) uses hexagons and an icosahedron projection, offering equidistant neighbors and consistent cell sizes, but sacrifices strict spatial hierarchy.
- Choice among tools depends on use case: H3 for uniform cell sizes, S2 for locality-sensitive hashing, and Geohash for simplicity in small areas.