I just wonder what would happen if you were able to take data from travel sites like expedia.com and others as the input for your model.

To get "return home" flights, maybe you could write some code that would review previous posts of twitterers who return a result where the "Just landed…" destination = twitter hometown. There is a good chance that they reference places they were visiting. For example…someone from NYC returns to NYC. If they were in London….maybe 8 days prior they said "Just landed in…London" or they posted "@Big Ben in London" during their trip. Might be an interesting way to try to harvest the data and infer where the trip originated.

To readers:
As the modeler wrote, this is a very cool visualization, but the orange arcs are not true flight paths. Travelers from SF to Australia generally do not cross all of Africa and Asia (they cross the Pacific).

The data collection excludes flights that represent people returning home. The data modeler assumed the start location was the twitterer's listed home town. For example If someone said they landed in New York City (perhaps from London) AND have New York City listed as their hometown, we will not see their London –> NYC trip because his model will only read "NYC (twitter hometown) –> NYC ("landed in…" destination).

Landing in a new place is always more exciting than coming back to your own country, so I would expect people to give the former more mentions. In any case, one can see that longer distances (across the globe) are mentioned more than the shorter ones (within the country)

What if the map was portrayed as a rotating globe and not a plane? Would it be as interesting?

It would be great if you can monitor the movements a few days before important global events and create specific videos for the same. Like the Olympics, World Cup, the Copenhagen Summit, Commonwealth games, G8 summit, etc.

Keep up the wonderful work. Looking forward to more.

Regards,

Arushi