Conclusion was that yes, you can predict locations of the terminals, but it's quite hard to find and produce repetitive schedules from the data because it requires the ships not change their schedule for too long, and the reality is just too dynamic to properly predict this.

For seagoing vessels I expect that they have more predictable schedules, but the hard part there is knowing container volumes, because there is no way to predict from public data what the volume of containers loaded/discharged is at a specific terminal. The closest metric is measuring the turnaround time of the ship, but that is not a great predictor for number of containers moved because handling times per container will differ per port, as will the number of cranes assigned to the ship, as will the ratio between 20ft and 40ft boxes, as well as the capability per port to do twinning (picking up 2x20ft boxes in one handling) and dual-cycling (discharging and loading a container as part of the same crane cycle).

Basically: it's a tough problem, with too many variables you cannot reasonably deduce.

(Also, even if you have the container volumes, I personally think the highest potential for optimization is in the hinterland, but that's a different story altogether)

(note that if one has access to SWIFT data, one can not only watch the goods transported in one direction but also the payments flowing in the other.)

In other graphs, I believe California has (had?) superior petrol environmental standards to the rest of the US because its pipeline network is largely independent: https://www.api.org/~/media/Oil-and-Natural-Gas-images/Pipel...

In yet other graphs, Great War mobilisation was largely a function of the rail networks. To this day, it's possible to buy a "rail" ticket between finland and sweden that winds up "air gapped" via bus transfer.

Ships are classified into size-based categories. For example, a "capesize" vessel is a massive dry bulk ship of 100,000+ ton (and often much bigger) carrying capacity. Since they are the largest ships, there are many fewer of them in the world, they are size-restricted on the ports that they can visit, it is only economical to carry particular types of cargo in these volumes, and because of the volumes, the ports often sit close to the source.

Now, when you collect AIS data for capesize ships, you'll see trade lanes from Australia to China - these mostly carry Iron Ore from Australia's Pilbara region to steel/etc. factories in China (companies like Rio Tinto, BHP and FMG focus on this space). You'll see a similar trade lane from Brazil to China - this is also iron ore and led by a company called Vale. By analysing the trade lanes, cross referencing that with the raw materials that come out of each company, using Google Maps to investigate what the ships might be loading, etc., you can infer what the vessels carry and begin to extrapolate these routes. The same applies for many types of commodities, and is not limited to dry bulk.

It can be easier in some cases to figure out the trading routes for ocean transportation of containers because the carriers often publish their routes in advance, but this obviously doesn't apply to the rail and trucking side of things, containers are often transported a fair distance before reaching a port, and containers will often be trans-shipped (i.e., transferred from one vessel to another at a hub port like Singapore), so it will be difficult to find the root nodes here.

As you go down in vessel size, it gets harder. A small ship like a "handysize" could carry anything really - grain, potash, windmills, yachts (yachts often aren't insurable when crossing the open ocean so the owners typically have them shipped around the world instead). And there are tons of them. And their cargoes might be shipped long distances to the port. You can still find these nodes of course, but it will take a lot more work.

[1]: https://www.vortexa.com/