June 2017
Intermediate to advanced
280 pages
7h 37m
English
Content preview from Advanced Analytics with Spark, 2nd Edition
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Chapter 8. Geospatial and Temporal Data Analysis on New York City Taxi Trip Data
Nothing puzzles me more than time and space; and yet nothing troubles me less, as I never think about them.
Charles Lamb
New York City is widely known for its yellow taxis, and hailing one is just as much a part of the experience of visiting the city as eating a hot dog from a street vendor or riding the elevator to the top of the Empire State Building.
Residents of New York City have all kinds of tips based on their anecdotal experiences about the best times and places to catch a cab, especially during rush hour and when it’s raining. But there is one time of day when everyone will recommend that you simply take the subway instead: during the shift change that happens between 4 and 5PM every day. During this time, yellow taxis have to return to their dispatch centers (often in Queens) so that one driver can quit for the day and the next one can start, and drivers who are late to return have to pay fines.
In March of 2014, the New York City Taxi and Limousine Commission shared an infographic on its Twitter account, @nyctaxi, that showed the number of taxis on the road and the fraction of those taxis that was occupied at any given time. Sure enough, there was a noticeable dip of taxis on the road from 4 to 6PM, and two-thirds of the taxis that were driving were occupied.
This tweet caught the eye of self-described urbanist, mapmaker, and data junkie Chris Whong, who sent a tweet to the ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.