TL;DR GLS uses Fused to create internal tooling to optimize routing for parcel delivery operations.

In the parcel delivery business, geospatial analyses are crucial to answer questions about daily operations. Do delivery drivers visit the same regions each day, letting them know their areas intimately? Or is there a high volatility of the regions? And of course, how do we optimize the routes of multiple drivers servicing the same region?

Those are the questions Antonius is working on at GLS Studio, an innovation lab by GLS (General Logistics Systems) which is an international parcel delivery service provider.

In this blog post, Antonius highlights how he uses Fused to create stable delivery areas for single-day and multi-day aggregates.

TL;DR Kristin shares a UDF to create training data for a corn yield prediction model using Zonal Statistics.

Hello friends, thanks for following my journey so far. To catch you up, I'm trying to solve the problem of farmers and traders relying on weak and untimely predictions of corn yield. Weak because they are at the national level and untimely because the predictions come once a month.

So here's the deal: farmers and traders have been relying on national-level corn yield predictions that are not only weak but also painfully slow, arriving just once a month. Imagine making critical decisions based on a single data point each month.

Not ideal, right? That's exactly the issue we're tackling in this blog post series.

TL;DR Jacob at VIDA uses Fused to streamline processing and rendering of CMIP6 climate risk models, improving data sharing and sanity checks.

VIDA uses dozens of the latest generation of climate models to have the most up-to-date climate information, collectively part of the Coupled Model Intercomparison Project 6 (CMIP6). These models provide a range of possible futures, under different emission scenarios, as well as differences in how each model does its forecast. Using this information, we can create ensembles from the models, and have higher confidence in the risks and hazards we derive from the models, and which we present to our customers.

In this blog post, I show how I created a UDF to pre-process and visually inspect the Zarrs we generate as the output from our climate risk models.

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 Naty shares a UDF to use Ibis with DuckDB's spatial extension to query and explore Overture Maps data.

Naty is a Senior Software Engineer and a contributor to Ibis, the portable Python dataframe library. One of her main contributions was enabling the DuckDB spatial extension for Ibis in 2023.

In this blog post, she shows us how to leverage the spatial extension in DuckDB with Ibis to query Overture data. Ibis works by compiling Python expressions into SQL, you write Python dataframe-like code, and Ibis takes care of the SQL. Thanks to Ibis integration with Pandas and GeoPandas, you only need to do to_pandas() to get your expression as a GeoDataFrame.

TL;DR Claudio used Fused to create an app to model road mobility networks in Lima, Peru, using GeoPandas, and OSMnx.

On December 2023, I visited the Institute for Metropolitan Planning (IMP) in Lima. The director had invited me to share some of my geospatial analysis projects from my master's studies and explore potential collaborations. Around that time, Lima's mayor had announced a bold infrastructure initiative: building 60 flyover bridges to ease traffic congestion in one of the most gridlocked cities in Latin America.

When I asked how the city was simulating the impact of new network designs on urban mobility, the answer was: "We are not simulating anything, our budget is constrained, and there is no political will to solve this problem." I couldn't think of anything else after this meeting. I started thinking about how I could create an easy-to-use tool to simulate urban mobility using open-source data, tools with no subscriptions or licenses, and without data privacy concerns.

My first attempt with FastAPI and React came to an unfortunate halt. Fused allowed me to revisit the idea and easily create an API endpoint and lightweight app I could easily share with anyone.

TL;DR Guillermo used Fused to build an interactive tool for exploring NEON hyperspectral data, making large-scale geospatial analysis more accessible and actionable for researchers.

As a research specialist focused on remote sensing applications in semi-arid rangelands, I'm constantly seeking tools that can enhance our ability to process and analyze large-scale geospatial data. The excitement of discovering new platforms that streamline complex workflows never gets old, especially when dealing with the massive datasets typical in remote sensing research.

My journey with Fused began unexpectedly through the "Minds Behind Maps" podcast, where host Maxime Lenormand interviewed Sina Kashuk, Co-Founder and CEO of Fused (see episode). The conversation sparked my curiosity, leading me to explore Fused's community examples and documentation. After joining their waitlist and receiving access, I knew exactly how I wanted to test it: an interactive tool for exploring NEON's Airborne Observation Platform (AOP) data.

TL;DR DigitalTwinSim uses Fused with Ibis and DuckDB to model high-resolution wireless networks.

Sameer, co-founder of DigitalTwinSim, leads the development of advanced geospatial analysis tools to support the telecom industry in strategic network planning. DigitalTwinSim specializes in using high-resolution data to optimize the placement of network towers ensuring reliable, high-speed connectivity.

In this blog post, Sameer shares how he leverages Ibis with a DuckDB backend, and Fused to model wireless networks at high resolution. This approach enables him to quickly generate network coverage models for his clients. He explains and shares a Fused UDF that processes data in an H3 grid to evaluate optimal locations for network towers.

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

Try it out for yourself!

You don't need to login or create an account to easily access the Foursquare POI points

• Join our waitlist to get full access to UDFs that you can edit in your browser
• Get familiar with Fused by checkout out our Quickstart docs
• Follow us on LinkedIn to keep up with updates
• Read this more in-depth look at the whole dataset from our community member Mark Litwintschik

TL;DR Stephen Kent shares his journey making maps with Fused using Python and SQL.

I am a self taught developer and data enthusiast. I first came across the spatial data community when I saw a Matt Forrest video on LinkedIn where he demonstrated how to visualize buildings from the Vida Combined Building Footprints dataset with DuckDB. Immediately I thought, what if you could see all the buildings in a country, say, Egypt? I set out to do just that and made this map with DuckDB and Datashader.