2026-09-02 –, Himawari
Open-source GIS tools such as QGIS and PostGIS excel at spatial analysis, but in earthquake-prone regions like Japan, layered maps alone are often insufficient. This talk introduces a practical extension that adds relational modeling to existing workflows, improving transparency and decision support in complex disaster scenarios.
Open-source geospatial tools such as QGIS, PostGIS, GeoServer, and OpenStreetMap have made spatial analysis accessible to everyone. We can visualize hazards, overlay infrastructure, and perform powerful spatial queries. For many tasks, this is enough.
But in disaster-prone regions like Japan, real-world situations are more complex than overlapping polygons.
When earthquakes strike, bridges affect evacuation routes. Power stations affect hospitals. Administrative responsibility affects response speed. These are not just spatial intersections—they are chains of dependency.
In several disaster modeling projects, I encountered a recurring problem: spatial layers showed where things were, but not clearly how they depended on each other. Critical relationships were scattered across reports, spreadsheets, and institutional documents.
In this talk, I introduce a practical extension to standard open-source GIS workflows that adds relational modeling alongside geometry. The approach keeps PostGIS as the geometry engine and integrates a lightweight graph layer to represent infrastructure dependencies, responsibility links, and cascading effects.
This is not a replacement for GIS. It is an enhancement built entirely on open-source components.
You will see:
- How to link spatial features with dependency structures
- How hybrid queries combine spatial predicates and relational traversal
- How this improves explainability in disaster-response scenarios
- How the approach remains reproducible and open
All examples are based on open datasets from a seismically active region in Japan. Code, schema definitions, and sample datasets will be openly released.
This session is aimed at practitioners, developers, and researchers who work with open GIS tools and face complex decision contexts. Whether you are building disaster dashboards, infrastructure models, or urban analytics systems, this approach offers a practical way to make spatial reasoning more transparent and structured.
Koji Annoura is a Japan-based knowledge infrastructure and graph researcher with over four decades of experience in software engineering and long-standing leadership in open-source communities. He co-founded the Neo4j Users Group Tokyo and later established the Apache Hop User Group Japan, organizing more than 50 technical events. He speaks internationally on graph technologies and knowledge design, focusing on durable knowledge infrastructures. He is the author of A Technical Guidebook to Cloud Native Databases and The Practical Guide to macOS X Server, and served as technical reviewer for Graph Data Processing with Cypher.