Knowledge graphs help contextualize and enrich data by modeling and querying relationships between entities using a graph database and have been successfully used alongside geospatial data and map tooling for use cases such as logistics and supply chain analysis, fraud detection, investigations, suitability analysis, real estate, and data journalism. In this presentation, we examine how the open-source Neo4j graph database can be used with QGIS and Python for making sense of geospatial data using graph algorithms and graph data visualization while combining data from OpenStreetMap, cadastral data, and public data portals to find insights that address the use cases mentioned above.
Speakers: William Lyon
Format: Full Session 30-45 min
Level: Advanced
Topics: #database , #visualization , #knowledgegraph , #analytics , #general , #advanced
Region: AMERICAS
Slides: https://dist.neo4j.com/nodes-20202-slides/022%20Making%20Sense%20of%20Geospatial%20Data%20With%20Knowledge%20Graphs%20-%20NODES2022%20-%20AMERICAS%20Advanced%206%20-%20William%20Lyon.pdf
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