Dataset. Commercial ships periodically send out messages containing their position details (ship identifier, latitude, longitude, speed, ...) . These AIS messages are collected and visualized in systems such as MarineTraffic. We have 2.6 TB of AIS messages (TXT/AIVDM format) in the years 2016 and 2017.

S2: Running for Oil. Identify oil tankers (using e.g. IMO number), and group by company and country, and identify their trips and trip speed or even specific loitering. Try to correlate oil transportation and travel speed with oil price. Is there more or less traffic when oil prices are high or low? Can we predict future oil prices from movement on the ocean? Are ships delaying discharge (loitering) while prices are rising, resp. accelerating when prices are dropping?

Summary. Very well-executed project that performed significant data cleaning on our large, but very incomplete dataset of AIS messages, and identified trip duration of oil tankers between terminals as the quantity to analyze (to do so, trips also need to be identified). Because ship speeds vary also depending on what waters they are navigating and in which direction; so detecting meaningful signals in ship speeds in relation to oil prices need to adjust for that.

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