Production engineering patterns for building, querying, routing, and scaling spatial graph networks with Python and Neo4j.

🌐 Read it at spatialgraphdatabases.org →

spatialgraphdatabases.org is a focused, deeply technical reference for engineers who put spatial data on a graph and route across it. Every guide is grounded in working, runnable Python and Cypher — the official async neo4j driver, native point geometry, spatial indexes, and the Graph Data Science library — with the goal of treating spatial predicates and shortest‑path search as first‑class database operators rather than post‑processing filters.

It is written for backend and data engineers, logistics and mobility developers, and spatial analytics teams building routing systems that have to stay sub‑second as the graph grows to tens of millions of nodes.

The site is organised into four in‑depth tracks, each with hands‑on guides and complete code:

• Spatial Graph Database Fundamentals for Python — storage and topology, schema design, async driver integration, spatial indexing, query planning, and multi‑tenant security boundaries.
• Cypher Spatial Queries & Pathfinding Patterns — index‑backed distance filters, k‑nearest‑neighbour search, spatial joins, isochrone and service‑area analysis, and query‑plan tuning.
• Spatial Graph Construction & OSM Ingestion — building routing graphs from OpenStreetMap, POI enrichment, attribute synchronization, and async batch ingestion with backpressure.
• Network Routing Algorithms in Python — Dijkstra, A* with a Haversine heuristic, contraction hierarchies, turn‑restriction and time‑dependent routing, and choosing between Neo4j GDS and hand‑written Cypher.

• Runnable, not hand‑wavy. Every page carries a complete, self‑contained async Python + Cypher example you can copy and run — no pseudo‑code.
• Production‑first. Guides lead with what breaks under real load — index push‑down, connection‑pool exhaustion, write amplification, topology corruption — and how to harden against it.
• Grounded in real cost models. Haversine distance, A* admissibility bounds, bounding‑box math, and plan‑cache behaviour are explained with the formulas and PROFILE output that back them.
• Hand‑drawn diagrams. Each concept is illustrated with an original, theme‑aware SVG built specifically for the page.

This repository contains the full source of the site, an Eleventy (11ty) static build deployed to Cloudflare:

npm install
npm run build     # generate the static site into _site/
npm run serve     # local dev server at http://localhost:8080

Content lives as Markdown under src/, with Nunjucks layouts in src/_includes/ and site data in src/_data/.

→ www.spatialgraphdatabases.org

Start with the Fundamentals, then follow the inline links — every guide cross‑references the deeper and adjacent topics it builds on.