|
|
|
|
|
|
by Hominem
4373 days ago
|
|
|
What I meant was if you already know the time and location, say you know what time the barista left work and the lat/Lon of the coffee shop. It would just be a matching it up with the data in the table to find the drop off. What I don't think is practical is identifying everyone who may or may not have left a particular bar. |
|
|
This is weaker than your original claim which was that deducing passenger identities is "practically impossible". You've now conceded that the barista scenario is plausible and left open several others I mentioned.
But let's examine this one, just bars. The basis of your criticism is that some bars are located in residential buildings. First off this still leaves quite a number of bars that aren't. But even for those that are, the time of day and direction of travel is a pretty fair indicator of people who are bar patrons vs. residents. I.e. trips departing the building after 1am and arriving at a residential location are probably a lot more likely to be bar patrons than residents.
And don't forget that this public data set is also potentially privacy-violating when combined with other data about the destination, such as information that other residents of that location may know. So even if the general public couldn't determine much from a trip from a gay bar to a home residence one night, a live-in parent could.