tangram is a modular platform for real-time geospatial and air traffic management research. Built on a plugin-first architecture, it enables researchers to visualize and analyze moving entities, from aircraft to ships to weather patterns, in a unified web interface.
The system combines a high-performance backend (Python & Rust) with a modern web frontend (Vue & Deck.gl) to handle massive datasets with low latency. While the official plugins focus on air traffic management, the core framework is generic and adaptable to any domain.
- Plugin-first: Everything from data decoding to UI widgets is a plugin. Customize your stack by installing only what you need.
- Real-time: Built on Redis and WebSockets for instant streaming of state vectors and events.
- Performance: Critical data paths are written in Rust. Historical data is managed efficiently using Apache Arrow and Delta Lake.
Full documentation, including quickstart guides and API references, is available at https://mode-s.org/tangram/.
The system is designed to be modular, so each component is tested independently. Integration testing is currently limited to the construction of the container image, via the container build process (just c-build).
If you find this work useful and use it in your academic research, you may use the following BibTeX entry.
@article{tangram_2025,
author = {Olive, Xavier and Sun, Junzi and Huang, Xiaogang and Khalaf, Michel},
doi = {10.21105/joss.08662},
journal = {Journal of Open Source Software},
month = nov,
number = {115},
pages = {8662},
title = {{tangram, an open platform for modular, real-time air traffic management research}},
url = {https://joss.theoj.org/papers/10.21105/joss.08662},
volume = {10},
year = {2025}
}This project is currently funded by the Dutch Research Council (NWO)'s Open Science Fund, OSF23.1.051: https://www.nwo.nl/en/projects/osf231051.
In 2020, @junzis and @xoolive published a paper Detecting and Measuring Turbulence from Mode S Surveillance Downlink Data on how real-time Mode S data can be used to detect turbulence.
Based on this method, @MichelKhalaf started developing this tool as part of his training with @xoolive in 2021, which was completed in Summer 2022. After that, the project was then lightly maintained by @xoolive and @junzis, while we have been applying for funding to continue this tool.
Then in 2023, we received funding from NWO to continue the development of this tool. With this funding, @emctoo from Shinetech was hired to work alongside us on this open-source project and helped to improve the codebase and documentation, making it more accessible, improving the design with a component-based architecture. (version 0.1)
After reviewing the existing project for the JOSS submission, @abc8747 kindly contributed and helped to improve the software engineering practices so that all components can be packaged as simple-to-install Python packages. (version 0.2)