- This event has passed.
Note: Timings
for
all events
are listed in the local timezone detected
from your browser -
Road to NODES 2023: Analyzing The Physical World With Graphs & Neo4j
Road to NODES is back in 2023 and we are excited to host a couple of hands-on workshops ahead of NODES 2023. Covering various topics we hope to give you some guidance as well as tips&tricks for your journey with Neo4j.
In this hands-on workshop we will explore how graphs and Neo4j can add value when working with geospatial data and specifcally how Neo4j fits into geospatial data science workflows. We will cover how to import and model geospatial data in Neo4j, the spatial functionality available in the Cypher query language, how to leverage Neo4j’s Graph Data Science functionality when working with geospatial data, how to visualize the results of our analysis, and how to use Neo4j with common geospatial tooling in the Python ecosystem such as GeoPandas, Matplotlib, Shapely, and Fiona.
No initial setup is required. We will use the free Neo4j Sandbox cloud hosted Neo4j instances combined with Python notebooks that can be run either in GitHub’s free Codespaces environment or Google’s Colab cloud environment (or run locally in a local Jupyter environment).
In this hands-on workshop we will explore how graphs and Neo4j can add value when working with geospatial data and specifcally how Neo4j fits into geospatial data science workflows. We will cover how to import and model geospatial data in Neo4j, the spatial functionality available in the Cypher query language, how to leverage Neo4j’s Graph Data Science functionality when working with geospatial data, how to visualize the results of our analysis, and how to use Neo4j with common geospatial tooling in the Python ecosystem such as GeoPandas, Matplotlib, Shapely, and Fiona.
No initial setup is required. We will use the free Neo4j Sandbox cloud hosted Neo4j instances combined with Python notebooks that can be run either in GitHub’s free Codespaces environment or Google’s Colab cloud environment (or run locally in a local Jupyter environment).