Xun Li, a Software Engineer at Foursquare who works on the Foursquare Spatial Agent and the Foursquare H3 Hub, presented three free tools Foursquare has built on H3. The key point: when every dataset already shares the same H3 cells, an AI agent can answer a spatial question from a single sentence, because it only has to choose and weight layers rather than solve geometry. The talk was pre-recorded and played at the H3 Conference 2026 in New York City.
TL;DR Dr. Qiusheng walks through how you can call Fused UDFs to load data into leafmap maps using Jupyter Notebooks.
Dr. Qiusheng Wu is an Associate Professor of Geography and Sustainability at the University of Tennessee and a Founding Editorial Board Member at the Cloud-Native Geospatial Forum (CNG). As part of his commitment to making open-source geospatial analysis and visualization more accessible, he has developed several widely used open-source packages, including geemap, leafmap, and segment-geospatial.
In this Notebook Qiusheng shows a few examples of how Cloud Native Geospatial datasets help you easily load data into a Jupyter Notebook environment using leafmap. His practical examples showcase how you can call the Overture Maps UDF and Foursquare Places UDF to load data into a custom area of interest and render it in a leaflet map.
TL;DR The Fused Team made Foursquare's open dataset of 100M global places accessible via GeoParquet files which you can access via a UDF.
Foursquare just released an open dataset of over 100M global places of interest.
We at Fused have partitioned these points into easily accessible GeoParquet files, and hosted them on Source Cooperative
On top of that, we've build a publicly available User Defined Function (UDF) that anyone can use to easily look at & download to GeoJSON, all from the browser