Visualization, especially interactive visualization, is often the initial step in extracting meaningful insights from data. But it's too hard to quickly and interactively visualize large geospatial vector data in Python.

Ipyleaflet and Folium are great for small datasets, but their performance quickly suffers as data sizes grow into the tens of thousands. Pydeck supports slightly larger datasets, but it, too, struggles with data sizes above 100,000 coordinates.

This presentation introduces Lonboard, a cutting-edge open-source Python library designed to address this challenge by enabling fast, interactive geospatial vector data visualization within Jupyter notebooks.

Lonboard's performance stems from its innovative architecture, built on four key technologies: deck.gl for GPU-accelerated rendering, GeoArrow for efficient in-memory representation, GeoParquet for optimized data transfer to the browser, and anywidget for easy Jupyter integration.

On a dataset with 3 million points, Ipyleaflet crashed after 3.5 minutes, Pydeck crashed after 2.5 minutes, but Lonboard successfully rendered in 2.5 seconds.

This talk will give a brief overview of the internal innovations that make Lonboard so fast, then detail how to make the most of Lonboard's high-level APIs for visualizing large data.