{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"}],"enrichment":{"capability":"Provides PySpark bindings for H3, enabling hexagonal geospatial indexing operations on distributed DataFrames\u2014converting coordinates to H3 cells, indexing geometries, and performing spatial joins at scale.","skillfed_tags":["geospatial","distributed-computing"],"use_cases":["Index geographic features (buildings, roads, regions) into H3 cells for distributed spatial bucketing and clustering.","Perform approximate spatial joins between two large datasets by indexing both on H3 and joining on cell identity.","Generate buffered spatial indexes around geometries using k-ring operations for distance-based queries.","Organize geospatial data into hierarchical hexagonal grids for efficient map visualization and aggregation.","Implement distance joins by combining H3 indexing with secondary distance validation using a UDF."],"what_it_does":"h3-pyspark wraps Uber's H3 hierarchical hexagonal indexing system for use in PySpark DataFrames, enabling you to convert latitude/longitude coordinates into H3 cell identifiers and index complex geometries (points, polygons, multipolygons) as sets of H3 cells at a chosen resolution. It extends the vanilla H3 library with PySpark-native operations for spatial indexing, k-ring buffering, and spatial joins\u2014allowing you to bucket and cluster geometries efficiently across a distributed cluster.\n\nThe package assumes GeoJSON representation of geometries and H3 cells as string columns, making it a natural fit for pipelines that already work with GeoJSON. It is most useful for approximate spatial joins and distance-based bucketing, though results are candidates rather than exact matches and should be validated with a secondary distance check if precision is required.","worth_installing":"Yes, if you are already using PySpark and need distributed H3 indexing\u2014it has no runtime dependencies, low install friction, and a permissive license. However, be aware that the package is dormant; verify compatibility with your PySpark and H3 versions before committing to production. No known security vulnerabilities."},"id":"h3-pyspark","links":{"html":"https://skillfed.io/packages/h3-pyspark","md":"https://skillfed.io/packages/h3-pyspark.md","pypi":"https://pypi.org/project/h3-pyspark/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2022-03-10","license_spdx":null,"license_treatment":"permissive","name":"h3-pyspark","python_support":"supports_current","summary":"PySpark bindings for H3, a hierarchical hexagonal geospatial indexing system"},"popularity":{"monthly_downloads":225618,"position":9218,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.2.6"}