This session is designed for participants interested in understanding how graphs can enhance geospatial analytics, offering a comprehensive look at integrating sophisticated tools and methodologies to handle various data types and operations at scale.
In this workshop, we will cover:
- Introduction to Apache Sedona and Spatial SQL: Dive into the capabilities of Apache Sedona and how to utilize Spatial SQL for managing and querying point, line, and polygon data at scale, providing a robust foundation for geospatial analytics.
- Integrating GeoPandas with Neo4j: Learn how to use GeoPandas, a powerful Python tool for geospatial data operations, in combination with Neo4j to construct and enhance geospatial knowledge graphs. This part of the session will focus on practical exercises that demonstrate the seamless flow of spatial data between Python and Neo4j, enabling you to enrich your graph-based projects with geospatial intelligence.
Trainer: Will Lyon, Wherobots
Links
- Neo4j Aura: https://development.neo4j.dev/cloud/platform/aura-graph-database/
- Wherobots Cloud: https://wherobots.com/wherobots-cloud/
- Slides: https://bit.ly/neo4j-sedona
- Analyzing The Physical World With Graphs & Neo4j: https://youtube.com/live/16d3rnNlNUU
- Spatial Periodic Table: https://gisgeography.com/spatial-analysis-periodic-table/
- Arrows https://arrows.app
- Cypher Spatial Cheat Sheet: https://lyonwj.com/blog/spatial-cypher-cheat-sheet
- Python Driver: https://development.neo4j.dev/docs/python-manual/current/
- GeoPandas: https://geopandas.org/en/stable/getting_started.html
- WorldClim: https://www.worldclim.org/data/index.html
#neo4j #graphdatabase #wherobots #geospatial #sedona #apache #spatial #sql