Articles
Long-form analysis covering spatial data intelligence, geospatial architecture, cloud-native GIS, open source tools, and the future of location analytics.
Architecture
Real-Time Spatial Streaming: Kafka, Flink, and Live Geofencing
Batch spatial tooling breaks down on moving objects. How to build a streaming geofence pipeline with Redpanda and Apache Flink, using H3 cell IDs as the partition key so containment becomes a hash lookup rather than a geometry test.
ArchitectureBuilding a Geospatial Digital Twin: Imagery, Processing, and Live Data
A complete digital twin built from open source components only — drone photogrammetry with OpenDroneMap, LiDAR classification with PDAL, 3D Tiles and COPC delivery, and live sensor binding via MQTT and SensorThings. With real costs and honest limitations.
ArchitectureLow-Cost, High-Flexibility Spatial Architecture Patterns
Design patterns and technology choices that let you build powerful geospatial systems without enterprise licensing fees — leveraging open data, serverless computing, and cloud-native tools to slash costs by 80–95%.
ArchitectureCloud-Orchestrated Geospatial Workflows: AWS, GCP, and Azure
How to architect scalable, cost-effective geospatial processing pipelines on the major cloud platforms — covering managed spatial databases, serverless processing, and event-driven spatial architectures.
ArchitectureFrom Monolithic GIS to Cloud-Native Spatial Intelligence
How organisations are breaking free from expensive, vendor-locked GIS platforms and embracing cloud-native spatial architectures that deliver greater flexibility, lower costs, and superior scalability.
Tools & Technology
LiDAR and Point Clouds at Scale: PDAL, COPC, and Web Delivery
National LiDAR programmes are the largest open geospatial datasets in existence, and almost nobody uses them well. A practical guide to PDAL pipelines, ground classification, COPC, and streaming billions of points to a browser without a tile server.
Tools & TechnologyDitching the Basemap: Open Source Maps and Vector-Only Overlays
A clear-eyed look at basemap options — from OpenStreetMap and ESRI free tiers to self-hosted PMTiles — and the cases where you can skip the basemap entirely, delivering only your data layer for a faster, cheaper, and more legible map.
Tools & TechnologyQuerying NetCDF Data via API: XArray and FastAPI for Analytical Speed
How to load multi-dimensional climate and scientific datasets into memory with XArray and serve efficient spatial slices via FastAPI — eliminating legacy bulk file downloads and enabling web-scale analytical access to large raster datasets.
Tools & TechnologyVector Tiles and the Modern Web Mapping Architecture
From slow WMS/WFS requests to blazing-fast vector tiles — how PMTiles, MapLibre GL JS, and modern tiling strategies have transformed what is possible with spatial data on the web.
Tools & TechnologyThe Open Source Geospatial Stack: PostGIS, GDAL, and Beyond
A practical guide to the mature, battle-tested open source tools that have displaced proprietary GIS software — covering PostGIS, GDAL, GeoServer, QGIS, GeoPandas, and the modern web mapping stack.
Analytics & Intelligence
Routing and Isochrones on Open Data: pgRouting, OSRM, and Valhalla
Commercial routing APIs charge per element for something you can self-host on a $20 VPS. A decision framework across pgRouting, OSRM, Valhalla, and GraphHopper, with a 10,000 × 10,000 matrix benchmark and honest notes on where paid traffic data still wins.
Analytics & IntelligenceThe Modern Case for Rasters: Why Data Engineers Keep Missing a Trick
Modern data engineers default to row-based pipelines and PostGIS geometries — often missing the fact that a pre-indexed, spatially-registered numerical array can answer the same question 100x to 1000x faster. A practical argument for rasters in modern geospatial workflows.
Analytics & IntelligenceUsing H3 for Aggregated Spatial Analytics at Scale
Uber's H3 hexagonal grid system makes large-scale spatial aggregation fast, consistent, and zoom-aware. Learn how to pre-process data counts into H3 cells, switch resolution levels dynamically, and build a Leaflet map that renders the right level of detail at every zoom.
Analytics & IntelligenceThe Future of Geointelligence: AI, Foundation Models, and Spatial Analytics
How large language models, vision foundation models, and AI-native geospatial platforms are reshaping the field — from satellite image segmentation to natural language spatial querying and digital twin simulation.
Analytics & IntelligenceDeriving Intelligence from Location Data: From Coordinates to Insight
A deep dive into the analytical techniques that transform raw location data into actionable intelligence — covering spatial clustering, movement analysis, predictive analytics, and geospatial machine learning.
Fundamentals
Python Performance
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.
Python PerformanceGDAL VRT: Virtual Raster Mosaics Without Copying Data
A GDAL Virtual Raster (VRT) file stitches hundreds of GeoTIFFs into a single virtual mosaic in milliseconds — no data copying, no reprojection, no storage cost. Essential for national-scale raster workflows in Python.
Python PerformanceRasterio Windowed Reading: Processing Large Rasters Without Loading Them
Rasterio's windowed read API lets you process arbitrarily large rasters by reading and processing small tiles at a time. Learn the block-aligned reading pattern, CRS-to-pixel coordinate conversion, and a complete tiling pipeline for a 50GB DEM.
Python Performancestackstac: Lazy Satellite Time Series from STAC Catalogues
stackstac turns STAC API search results into lazy Dask-backed XArray DataArrays — loading only the pixels you need, when you need them. The practical guide to building cloud-native satellite time series pipelines without downloading data.
Python PerformanceXArray and Zarr: Chunked Raster Storage for Cloud Workflows
Zarr is the cloud-native array storage format that pairs with XArray to make petabyte raster datasets queryable without full downloads. Understand chunk strategy, compression, and how to build read-optimised Zarr stores for spatial workflows.
Python PerformanceXArray GroupBy for Temporal Raster Aggregation: Monthly Means at Scale
XArray's GroupBy and resample operations run on lazy Dask arrays, enabling temporal aggregation of multi-year satellite raster stacks without loading data into memory. Walk through computing monthly mean NDVI for a decade of Landsat tiles.
Python PerformanceDuckDB Spatial Analytics on GeoParquet: In-Process SQL for Geodata
DuckDB's spatial extension brings full PostGIS-style SQL to GeoParquet files without a database server. Run spatial joins, aggregations, and geometry operations directly on files at PostGIS-comparable speeds — in-process, zero infrastructure.
Python PerformanceFlatGeobuf: Streaming Spatial Data Without a Server
FlatGeobuf is a binary vector format with a built-in spatial index that supports HTTP range requests — enabling bbox-filtered streaming of large datasets directly from cloud storage without any server infrastructure. Here is how it works and when to use it.
Python PerformanceGeoParquet Spatial Partitioning: Optimising for Parallel Reads
Spatial partitioning of GeoParquet files turns a slow sequential scan into a parallelisable set of file reads. Learn how to partition by H3 cell, bounding grid, and administrative region — with DuckDB and Dask-GeoPandas integration patterns.
Python PerformanceGeoParquet: The Analytical Format for Vector Geodata
GeoParquet brings the columnar, compressed, analytics-optimised Parquet format to vector geometry. Understand the format internals, why it outperforms Shapefile and GeoJSON for analytical workloads, and how to use it effectively in Python.
Python PerformanceNumba-Accelerated Spatial Kernels: JIT-Compiling Custom Geometry Operations
When Shapely's built-in functions don't cover your algorithm, Numba's JIT compiler can accelerate custom spatial kernels by 100x. Learn the patterns for writing vectorised spatial code with Numba, including a Haversine distance matrix and weighted spatial smoothing example.
Python PerformancePyProj Transformer Array API: Reprojecting Millions of Coordinates
PyProj's Transformer.transform() accepts NumPy arrays directly, enabling reprojection of millions of coordinates in a single C-level call. Eliminate per-point CRS transforms that throttle your spatial pipelines.
Python PerformanceShapely STRtree: Mass Nearest-Neighbour and Intersection Queries
The STRtree spatial index in Shapely 2.0 enables nearest-neighbour and intersection queries on millions of geometries without nested loops. Learn query_nearest, query, and bulk pattern matching with benchmarks.
Python PerformanceGeoPandas at Scale: Escaping the .apply() Trap
Using .apply() for spatial operations is the single most common GeoPandas performance anti-pattern. Learn the vectorised replacements that deliver 10–100x improvements, with benchmarks from a real 2-million-ping GPS classification task.
Python PerformanceShapely 2.0: The Vectorisation Revolution in Python Geometry
Shapely 2.0 rewrote geometry operations from Python-level loops to C-level array operations, delivering 50–200x speedups for batch geometry tasks. Understand what changed, why it matters, and how to rewrite your code to take advantage.