...less clear—there are also fewer observations in the dataset compared to “normal” days—but intuitively I have to believe that there’s a diminishing marginal effect of both, i.e. the difference between no rain and 0.1 inches of rain is more significant than the difference between 0.5 and 0.6 inches.
A Tale of Twenty-Two Million Citi Bikes: Analyzing the NYC Bike Share System - Todd W. Schneider
...hare operator could transport additional bikes to A to meet demand, but that costs time/money, so the operator probably wants to avoid it as much as possible. The data lets us measure how often bikes “magically” transport from one station to another, even though no one took a ride. I took each bike drop off, and calculated the percentage of rides where the bike’s next trip started at a different station from where the...
A Tale of Twenty-Two Million Citi Bikes: Analyzing the NYC Bike Share System - Todd W. Schneider
Disclaimer: I know nothing about the logistics of running a bike share system. I’d imagine, though, that one of the big issues is making sure that there are bikes available at stations where people want to pick them up. If sta...
A Tale of Twenty-Two Million Citi Bikes: Analyzing the NYC Bike Share System - Todd W. Schneider
On some level this shouldn’t be too surprising: a famous paper by Latanya Sweeney showed that 87% of the U.S. population is uniquely identified by birthdate, gender, and ZIP code. We probably have a bias toward underestimating how easy it is to identify people from what seems like limited data, and I hope that people think about that when they decide what data should be made ...
A Tale of Twenty-Two Million Citi Bikes: Analyzing the NYC Bike Share System - Todd W. Schneider
...an a linear regression in R to model the difference between actual and estimated travel time as a function of gender, age, and distance traveled. The point of the regression isn’t so much to make any accurate predictions—it’d be especially bad to extrapolate the regression for longer distance trips—but more to understand the relative magnitude of each variable’s impact:
A Tale of Twenty-Two Million Citi Bikes: Analyzing the NYC Bike Share System - Todd W. Schneider
The linear model is definitely not a great fit here, as the relationships aren’t particularly linear. Age is kind of linear above the age of 22, as older riders get consistently slower, but below 22 we see younger riders are slower
Distance traveled really doesn’t follow a linear relationship, especially for longer distances over ~3 miles. It would be very bad and inaccurate to extrapolate this model to long trips!
If we take all trips since Citi Bike’s expansion in August 2015, and again assume everyone followed Google Maps cycling directions, we can see which road segments throughout the city are most traveled by Citi Bikes. Here’s a map showing the most popular roads, where the thickness and brightness of the lines are based on the numb...
A Tale of Twenty-Two Million Citi Bikes: Analyzing the NYC Bike Share System - Todd W. Schneider
If we take all trips since Citi Bike’s expansion in August 2015, and again assume everyone followed Google Maps cycling directions, we can see which road segments throughout the city are most traveled by Citi Bikes. Here’s a map showing the most popular roads, wh...
A Tale of Twenty-Two Million Citi Bikes: Analyzing the NYC Bike Share System - Todd W. Schneider
I took Citi Bike trips from Wednesday, September 16, 2015, and created an animation using the Torque.js library from CartoDB, assuming that every trip followed the recommended cycling directions from Google Maps. There were a total of 51,179 trips that day,...
A Tale of Twenty-Two Million Citi Bikes: Analyzing the NYC Bike Share System - Todd W. Schneider
“We looked at the problem and I told Jeff I thought we could improve the load time to maybe two seconds. He wrote back and said, ‘It needs to be milliseconds,’” said Shailesh Prakash, who heads the Post’s technology team as chief information officer. “He has become our ultimate beta tester.”
Mr. Bezos helped solve the problem by suggesting loading low-resolution images onto the app first, allowing the page to load on readers’ screens more quickly. Bezos Takes Hands-On Role at Washington Post - WSJ
Mr. Bezos helped solve the problem by suggesting loading low-resolution images onto the app first, allowing the page to load on readers’ screens more quickly. Bezos Takes Hands-On Role at Washington Post - WSJ
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