Have you posted about this at length somewhere? If not, care to elaborate on what it took to design this system?
It isn't that difficult technically, if you have experts doing it, it just requires far more domain expertise to do correctly than I think people expect. You also need to be willing to write some of your own tooling to deal with the data efficiently and effectively.
It probably gets real interesting when they're trying to distinguish between various modes of transit, such as a city bus, an Uber/Lyft/taxi, and a private vehicle not participating in rideshares. Of those examples, the latter would suggest the highest degree of association.
Pure speculation, but I wouldn't doubt they take a peek at ridesharing data for co-traveler inference purposes. Knowing if a rideshare driver is on or off the clock would be incredibly valuable information in that context.
[0] https://www.washingtonpost.com/apps/g/page/world/how-the-nsa...
I was doing all of this five years ago, the capability has been around for a while.
The NSA only recruits in the USA. It's a fraction of the talent pool of the planet.
Increasing the precision by tenfold will likely increase the effort by a hundred fold or more. Just because it can be made harder and more expensive doesn't mean it has to be.
At the end of the day, a bit of precision doesn't change the nature of an effective planet scale mass surveillance system.
So it really isn't technically difficult. It's just lacking in data?
In your original post, you made it seem like it was extremely difficult. From my perspective, it seems like child's play from a technical standpoint.
> It isn't that difficult technically, if you have experts doing it
But your top comment implied it was extremely technically difficult.
There is a much smaller set of things that are technically difficult to execute even if you are highly experienced at doing it -- each time is a challenge. This is not one of those cases, it just has a severe learning curve.