Uber analyzed a year’s worth of trip data from 12 US cities to compile a list of top destinations, including brunch, local favorites and date nights.
By analyzing the pick-up and drop-off points in 12 cities across the United States, Uber’s new restaurant guide depends solely on volume of traffic to each destination. The new guide lists six different categories for each of the cities. Based on the time of each trip and the type of transport used, the restaurant guide’s data analysis divides destinations into top brunch spots, date night spots, weekend picks, up-and-coming, local favorites and of course, the overall most popular.
As well as expected entries like Los Angeles, Chicago and New York City, the guide includes listings for Denver, Nashville, Pittsburgh, Phoenix and more. The company plans to continue updating entries and possibly expand to include other cities. Finding ways to make data work harder is essential to increasing general efficiency. Much of the work in urban areas has focused on opening up sets of information for public analysis and use.
This platform helps make government data more understandable, and this transport platform aggregates publicly available information for use in app development. Where else could large sets of data be used in unexpected ways?