DatasetAdded By Ganglion
Uber allows anyone to request a ride with a private driver via a smart phone app or text message. The cars are all high-end sedans or SUVs and payment is handled seamlessly via a credit card tied to the customer’s Uber account. Tip is included in the fare, so there’s no need to carry cash or hand money to the driver! Every driver has an iPhone running an Uber app that sends GPS data to determine the car’s location and speed. Using this data, Uber is able to visualize and predict traffic patterns and things like average wait time before a car arrives.
The data provided here comes from GPS logs taken from iPhones in Uber cars driving around San Francisco. To protect the privacy of Uber clients, the data contains no names, trip starting locations or end locations, and no real dates – the actual dates have been scrambled, but weekdays and time of day are still intact. No personally identifiable information is presented in this dataset. It’s analogous to watching cars drive by from a window – you know what weekday and time it is, but you have no idea where the cars are coming from or where they will stop or who is in them; just an idea of how fast they are going as they pass by.
This dataset has been provided for the Data In Sight competition and contains geospatial localization data along with the (truncated) temporal evolution of individual rides.
A simple static visualization of these data would be to plot the location of all of the trips given, such as shown on our blog:
A most sophisticated static or dynamic visualization might break up the data by time of day or day of week.
Alternatively, the data can be animated to show the path of each ride taken across time.