A collection of hands-on, developer-friendly tutorials for working with geospatial data. Whether you’re clustering buildings, building a spatial analytics environment, or extracting data from OpenStreetMap — these guides will help you do it efficiently with open tools like PostGIS, GDAL, Docker, and the Overpass API.
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 new to geospatial development or looking to scale your workflows with Docker and PostGIS, these articles are designed to be practical, code-driven, and ready to apply to your own projects.
→ Tip: Start with DBSCAN in PostGIS or Overpass API for quick wins with real-world data.