A collection of hands-on, developer-friendly tutorials for working with geospatial data. From enterprise ETL pipelines for critical infrastructure to spatial clustering algorithms—these guides cover everything from extracting OpenStreetMap data to building production-ready geospatial systems using PostGIS, GDAL, Docker, and modern data engineering practices.
Energy Infrastructure Intelligence
Mapping the Invisible | How OpenStreetMap Reveals Our Power Grid
When millions go dark, the invisible grid becomes visible—here’s how crowd-sourced data illuminates the backbone of civilisation
Discover how to transform OpenStreetMap’s vast geographic database into actionable electrical infrastructure intelligence. This comprehensive case study demonstrates enterprise-grade ETL capabilities through a production pipeline that processes millions of power-related features—from transmission lines to renewable generators. Perfect for energy professionals, data engineers, and infrastructure analysts seeking to understand grid topology, assess renewable capacity, and support critical infrastructure planning.
Building a Modern ETL Pipeline for Critical Infrastructure Data
From chaos to clarity—how enterprise-grade data engineering transforms scattered geographic fragments into strategic intelligence
A technical deep-dive into building scalable, high-performance ETL pipelines for geospatial infrastructure data. Learn the architectural decisions, performance optimisations, and domain expertise that enable processing country-scale datasets with sub-second query times. Features containerised microservices, intelligent unit conversion, and automated data quality validation—showcasing modern data engineering practices applied to energy sector challenges.
Clustering Spatial Data with DBSCAN in PostGIS
Cluster Building Polygons Directly in SQL—No Python Required
Learn how to run spatial clustering on non-point geometries (like building footprints) using ST_ClusterDBSCAN in PostGIS. This tutorial walks you through DBSCAN clustering entirely within SQL, compares it with scikit-learn’s implementation, and shows how to choose the right distance strategy for your spatial use case.
Unlocking the Power of Spatial Clustering with DBSCAN
Understand How DBSCAN Works and When to Use It
Before diving into code, understand the theory and use cases behind DBSCAN. This article explains how DBSCAN handles clusters of irregular shape, detects noise, and why it’s superior to K-means for spatial analysis. It also compares different implementations and which ones support non-point geometries.
Managing and Converting Geospatial Data with PostGIS and ogr2ogr
Convert, Import, and Export Geospatial Formats with GDAL’s ogr2ogr Tool
Need to move geospatial data between formats like Shapefile, GeoJSON, KML, or GeoPackage? Learn how to use ogr2ogr—a command-line tool from the GDAL suite—to seamlessly convert, import, and export geospatial data into a PostGIS database. Ideal for GIS analysts, devs, and data engineers.
Building a Geospatial Data Science Stack with Docker Compose
PostGIS, GDAL, and JupyterLab—Containerized and Ready to Go
Tired of installing geospatial libraries manually? This guide shows how to spin up a full geospatial data science environment using Docker Compose. It includes containers for PostGIS, GDAL, and JupyterLab, with custom Dockerfiles for reproducibility. Great for ETL pipelines, analysis, and collaborative notebooks.
- Quickstart: geospatial stack a quick tutorial
- In depth walkthrough geospatial stack in depth
A Practical Guide to OpenStreetMap’s Overpass API Query Language
Extract Exactly the Data You Need from OpenStreetMap
Skip the full planet file—learn how to write efficient, custom Overpass QL queries to fetch just the OpenStreetMap data you care about. This guide walks through examples like finding cafés in Paris, bus stops in Berlin, or buildings missing addresses in NYC. Ideal for spatial devs, civic tech, and OSM analysts.
Start Exploring
Whether you’re building enterprise-grade ETL pipelines for critical infrastructure, clustering spatial data, or setting up geospatial analytics environments, these articles provide practical, production-ready solutions you can apply immediately.
Recommended Starting Points:
- For Infrastructure & Energy Projects: Start with Mapping the Invisible to see enterprise ETL in action
- For Spatial Analytics: Try DBSCAN in PostGIS for immediate clustering results
- For Development Setup: Begin with geospatial stack a quick tutorial to get your environment running
- For Data Extraction: Explore OpenStreetMap Data to master the Overpass API