Fundamentals for Python
Storage, indexing, query planning, and security primitives for spatial graphs.
Python for Spatial Graph Databases & Network Routing
Production-ready patterns for backend, data and logistics engineers building, querying, routing and scaling spatial graph networks with Python and Neo4j / GraphDB.
This site is a focused engineering reference for shipping spatial routing systems that survive real production load. It pairs Cypher with async Python drivers, spatial indexing, and topology-aware ingestion so that distance filters, KNN searches, and shortest-path queries stay sub-second as your graph scales to millions of nodes.
You'll find concrete patterns for OSM ingestion, POI enrichment, attribute synchronization, query-planner tuning, and multi-tenant spatial security — each grounded in working Python and Cypher snippets. The goal is to treat spatial predicates as first-class operators, not post-processing filters.
Pick a section below to dive in. Each page links to deeper subtopics, and every code block can be copied with one click.
Fundamentals for Python
Storage, indexing, query planning, and security primitives for spatial graphs.
Explore
Cypher Spatial Queries & Pathfinding
Index-backed distance filters, KNN, joins, performance tuning.
Explore
Graph Construction & OSM Ingestion
Pipelines, POI enrichment, attribute sync, async batching.
Explore
Browse the content
Three top-level sections, each with subtopics and deep-dive pages.
Cypher Spatial Queries & Pathfinding
Index-backed distance filters, KNN, joins, performance tuning.
Graph Construction & OSM Ingestion
Pipelines, POI enrichment, attribute sync, async batching.