spb/tools-spboucher-ai

1{2  "slug": "h3",3  "name": "H3",4  "aliases": [5    "Uber H3",6    "h3-py",7    "h3-js"8  ],9  "vendor": {10    "name": "Uber Technologies (open-source project)",11    "slug": "uber",12    "website": "https://h3geo.org"13  },14  "homepage_url": "https://h3geo.org/",15  "docs_url": "https://h3geo.org/docs/",16  "repo_url": "https://github.com/uber/h3",17  "license_url": "https://github.com/uber/h3/blob/master/LICENSE",18  "product_type": "library",19  "product_status": "active",20  "summary": "Hexagonal hierarchical geospatial indexing system from Uber with a C core and bindings for Python, JavaScript, Java and SQL engines",21  "description": "H3 is an open-source geospatial indexing system created at Uber that partitions the globe into hexagonal cells at sixteen resolutions, each identified by a 64-bit integer. The C library converts coordinates to cell indexes and back, traverses parents and children across resolutions, finds neighbours and rings, fills polygons with cells and computes distances, which makes aggregation, binning and spatial joins fast and uniform compared with irregular administrative units. Official and community bindings exist for Python, JavaScript, Java, Go, Rust, R and database engines such as DuckDB, PostgreSQL (h3-pg) and BigQuery. Typical uses are density maps, ride or listing demand grids, and feature engineering for spatial models. Hexagons cannot nest exactly, so hierarchy is approximate, cell areas vary slightly across the globe, and H3 is an index rather than a geometry library, so it complements rather than replaces PostGIS or GeoPandas.",22  "capabilities": [23    "Point to cell indexing at 16 resolutions",24    "Hierarchy traversal (parent, children, compact/uncompact)",25    "Neighbour rings and grid distance",26    "Polygon fill and cell boundary geometry",27    "Bindings for Python, JavaScript, Java, Go, Rust, R and SQL engines",28    "Compact 64-bit identifiers suited to joins and aggregation"29  ],30  "workflows": [31    "Bin property listings into resolution-9 hexagons and map median prices",32    "Join two datasets on H3 cells instead of running polygon intersections"33  ],34  "advantages": [35    "Apache-2.0 and dependency-free C core",36    "Uniform neighbourhoods simplify spatial statistics",37    "Wide language and database support"38  ],39  "limitations": [40    "Approximate hierarchy; children do not nest exactly",41    "Cell area and shape distortion vary by location",42    "Not a geometry or projection library"43  ],44  "prerequisites": [45    "A binding for your language or database"46  ],47  "audiences": [48    "Data scientists doing spatial aggregation",49    "Geospatial engineers building grids and indexes"50  ],51  "alternatives": [52    "postgis",53    "geopandas",54    "duckdb"55  ],56  "comparison_notes": "PostGIS and GeoPandas operate on exact geometries and are needed for precise overlays; H3 trades exactness for fast, uniform aggregation and joins, and both DuckDB and PostGIS have H3 extensions so the approaches combine.",57  "categories": [58    {59      "slug": "gis-mapping",60      "primary": true61    }62  ],63  "tags": [64    "gis",65    "spatial-index",66    "hexagons",67    "open-source",68    "python",69    "c"70  ],71  "use_cases": [72    "map-geodata",73    "aggregate-spatial-data"74  ],75  "operating_systems": [76    "macos",77    "linux",78    "windows"79  ],80  "execution_modes": [81    "local"82  ],83  "interfaces": [84    "library"85  ],86  "deployment_models": [87    "embedded"88  ],89  "integrations": [90    "DuckDB",91    "PostgreSQL (h3-pg)",92    "BigQuery",93    "Spark",94    "GeoPandas",95    "deck.gl"96  ],97  "data_formats": [98    "64-bit cell indexes",99    "GeoJSON boundaries"100  ],101  "languages": [102    "C",103    "Python",104    "JavaScript",105    "Java",106    "Go",107    "Rust",108    "R"109  ],110  "source_model": "open-source",111  "license_spdx": "Apache-2.0",112  "pricing_model": [113    "open-source",114    "free"115  ],116  "self_hostable": true,117  "sources": [118    {119      "url": "https://h3geo.org/",120      "type": "official_site",121      "supports": [122        "name",123        "summary"124      ],125      "checked_at": "2026-11-02",126      "title": "H3: Hexagonal hierarchical geospatial indexing system"127    },128    {129      "url": "https://h3geo.org/docs/",130      "type": "official_docs",131      "supports": [132        "capabilities",133        "limitations"134      ],135      "checked_at": "2026-11-02",136      "title": "H3 documentation"137    },138    {139      "url": "https://github.com/uber/h3/blob/master/LICENSE",140      "type": "license_page",141      "supports": [142        "license_spdx",143        "source_model"144      ],145      "checked_at": "2026-11-02",146      "title": "LICENSE (Apache 2.0)"147    },148    {149      "url": "https://h3geo.org/docs/community/bindings/",150      "type": "official_docs",151      "supports": [152        "languages",153        "integrations"154      ],155      "checked_at": "2026-11-02",156      "title": "H3 bindings"157    }158  ],159  "confidence": "high",160  "depth": "standard",161  "featured": false,162  "discovery_method": "editorial-research",163  "editorial_status": "reviewed",164  "visibility": "public",165  "author": "batch-F"166}167