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Featured Articles
Deep dives into the technologies, architectures, and ideas shaping the future of geospatial intelligence.
cuSpatial: GPU-Accelerated Spatial Analytics with RAPIDS
cuSpatial brings GPU acceleration to spatial operations — point-in-polygon, nearest-neighbour, trajectory analysis, and more — via the RAPIDS ecosystem. When CPU-based tools hit their ceiling on very large datasets, cuSpatial can deliver 10–100x additional speedups.
Python PerformancePostGIS, GeoPandas, and DuckDB: A Decision Framework for Spatial Analytics
Three tools dominate Python spatial analytics: PostGIS for indexed server queries, GeoPandas for in-memory Python-native analysis, and DuckDB for serverless SQL on files. Understanding which to reach for — and when to combine them — is the difference between a fast pipeline and a slow one.
Python PerformanceDask-GeoPandas: Parallel Spatial Processing Across CPU Cores
Dask-GeoPandas partitions a GeoDataFrame across CPU cores for parallel spatial operations. When Shapely's vectorised API saturates a single core, Dask-GeoPandas scales across all cores. Understand partitioning strategy, operation compatibility, and a real 50-million-parcel workflow.
Python PerformanceH3 Hierarchical Smoothing: Continuous Spatial Surfaces from Sparse Data
Sparse point data creates noisy hexbin maps with many empty cells. H3's K-ring neighbourhood and parent-child hierarchy enable spatial smoothing that fills gaps and creates continuous surfaces — without interpolation libraries or complex kernels.
Python PerformanceH3 as a Spatial Join Accelerator: Approximate Joins at Scale
Exact spatial joins via STRtree are precise but can be slow for very large datasets. H3 cell assignment reduces spatial joins to fast hash joins — trading a controlled approximation for orders-of-magnitude speed improvements. When this tradeoff makes sense and how to implement it.
Python PerformanceH3 Parent Rollup: Multi-Scale Aggregation Without Re-aggregating
H3's strict parent-child hierarchy enables pre-computing aggregations at fine resolution and rolling them up to coarser scales in microseconds. Build a multi-resolution data cube that answers queries at any resolution without reprocessing the source data.
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