Dataset. Commercial airplanes periodically send out radio messages containing their position details (plane identifier, flight number, latitude, longitude, height, speed, ...) . These ADS-B messages are picked up by enthusiasts and collected in systems such as the OpenSky network or Flightradar24. We have obtained 700 GB of compressed ADS-B messages from September 2016 and 2017 in a compressed format.

P3: Flight Visualization. Generate an interactive flight path animation (GIF?) of all flights based on their accurate location data. Speed up time. Reduce amount of flights if necessary through stratified sampling of diverse flight routes.

Summary: This is an excellently conducted project, especially considering it was done alone. The methodology of taking samples, creating visualizations of these samples and testing and tuning algorithms before going to the cluster is very well executed. Also, the back-of-the-envelope data size and complexity considerations, working towards a goal of interactive presentation on low-power computers, is well to our liking. The paper identifies relevant previous work (Douglas-Peucker) and integrates it handily in the big data processing environment of spark. The visualization is very cool.

Data Curiosity: ****
Paper Writing: ***
Technical difficulties mastered: ****
Visualization coolness: ****