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I think probably the language and tone of the post more than either the data or the conclusions.
Interesting. In the more socially-aware fora I'm familiar with, what's called "tone policing" or "tone trolling" is seen as a major faux pas, an aggressive act designed to shut people out of the conversation.
I gotta say, I'm not really seeing the creepy / cringey / evil / whatever-else here... Anyone (especially the HN crowd) should know they have the data, and if you think they're not carefully analyzing it behind the scenes (like every other tech company who has your data), I've got things to sell you. I personally think a tiny peek like this into the data, much like the usage posts that OKCupid, YouPorn, and others gi…
(though it's not very well-written, some analysis a bit iffy, and the guesswork towards the peaks and dips in the graph rather low-effort)
Creepy/evil maybe no, because the data is clearly anonymised. However the cringe is all over this article. OKCupid's stuff could easily be just as cringey, but they know it's important to steer clear from that. Also they're a dating site, if they wrote an article about data-mining one-night stands, that would make sense. Not so much for a taxi company, especially not in light of Uber's general attitude.
The final sentence of the article definitely crossed from "cringe" into "creepy" for me, though. In particular from someone called "Uber".
The PDF in which this article was referenced did so to illustrate the availability of this data for frivolous purposes, and is right to call it "questionable" when considered in light of Hourdajian's statements about privacy and Uber's data-policies.
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Feels to me like someone saw the success of OKCupid and their content marketing strategy and tried to shoehorn in something similar with whatever data they had with less than stellar results.
What you've described is "poor judgment."
In another deleted post [0] the author talks about using a name-to-gender API to look at ride locations by gender, which implies that these analyses were not done using anonymized data. [0] https://web.archive.org/web/20140827195715/http://blog.uber....
You have to start with the original data, which is obviously de-anonymized. Full data -> [gender, time, origin neighborhood, destination neighborhood] leaves you with a pretty anonymous dataset, and is all that would be required for this analysis. Internal metrics teams nearly always have access to complete data. The issue is sharing non-anonymized data externally.
blog article was 2.5 yrs ago, they were pulling out all the stops to get traction. now they have critical mass they can transition into "full boring corp speak" HN don't throw stones, what boundaries are pushing to get traction right now?
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> But lets not kid ourselves. This isn't anonymized data. Uber's publishing in a format that is unspecific, but they have all of the detailed data and can poke through it and infer things at their leisure, and they have no compunction around how they're doing it or why. That's a fairly large accusation to make. This blog post was originally published in 2012 - two years ago. Since then has anything come out that woul…
Sure, i don't normally like linking to TC but this has a pretty good roundup of links: http://techcrunch.com/2014/11/20/following-pressure-from-u-s...
..even if it's from TC (I won't hold it against you).