Scaling Environmental Insights with Fused and H3
Farmers and analysts face a familiar challenge: weather and crop data is fragmented, slow to process, and hard to act on.
We worked with Emma Quirk (Senior Data Analyst) and Majid Alivand (Senior Data & Analytics Manager) to showcase how Fused can help bring all these datasets together. In this webinar they give an overview of the industry challenges interfacing backend data analytics with frontend data consumption. Emma walks through the notebook she used to model climate and irrigation patterns for vineyards.
Addressing the Challenge
Building useful & actionable weather models for environmental insights requires bringing datasets from:
- Different sources
- Different resolutions
- Different formats
To address these challenges, Emma & Majid used Fused + H3 to bring together datasets like GridMET climate, CDL crops, LANID irrigation, and gSSURGO soils by converting them from raster to H3 to build unified parquet files.
By building UDFs to manipulate each dataset, the team can iterate on their analysis in seconds while H3 allows them to more easily combine all the datasets together.
Why H3?
- Harmonized format across datasets and regions
- Scalable queries at multiple resolutions
- Compact storage parquets with improved spatial performance
- No reprojection needed for global analyses
- Equal neighbors for clean spatial logic
Try it out for yourself
Try out the notebook from the presentation for yourself:
- Colab Notebook (if you already have a Fused account & environment)
🚧 Coming Soon, under development 🚧:
- Colab Notebook (if you don't have a Fused account yet)
Join our waitlist to get access to Fused.