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Recent grad (and current developer at Lever) Jon Sadka built an interactive data visualization of Uber prices in New York, San Francisco and Los Angeles. The project, which he started at Hack Reactor, and polished in his free time after he’d completed the program, allows users to see patterns and isolated events in the popular ride-sharing service’s prices.
“It’s essentially transportation data,” says Sadka. “The idea was to see if you could find trends within different cities. You can see when people start working or stop working. When you see surges in pricing that didn’t occur in the normal daily routine, it’s often that something happened in that area."
The essential data that Sadka’s project tracks is Uber’s surge multiplier. This is a measure of how much prices are inflated due to increased demand. When the multiplier is at 1, Uber’s prices are at their baseline level. Otherwise, the multiplier indicates the degree of increase in demand. Sadka points out a few trends that he noticed in a blog post related to the project:
“New York truly doesn’t sleep; the surge multiplier in New York is almost always above 1.”
Los Angeles social patterns were visible in the data:
“The most common time for people to take an Uber on weekends is between 5:00 pm to 7:00 pm and 2:00 am. 2:00 am is the closing time for most drinking establishments in California.”
San Francisco’s most recent citywide celebration caused a surge in Uber pricing:
“The Giants won the World Series on Thursday, October 30th and a celebration parade ensued on Friday October 31st at noon. The parade ended around 2:00 pm, causing the surge multiplier to rise for approximately two hours.”
The project proved popular, rising to the top of the popular tech news website Hacker News. While the initial challenge was to simply organize the data into meaningful charts and graphs, Sadka wanted to make the data visually enticing and fun to work with:
“I wanted people to enjoy playing around with it,” he notes.
The project displays data to and from three locations in each city Sadka worked with. Data shows the maximum price for a given route.
“It’s more beneficial to know what the worst case is than the best case,” he explains.
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