As part of Uber Visualization's all-team hackathon, we built SpeedsUp, a project using machine learning to process average speeds across a city, cluster the results, and overlay them on a street map.
With the release of deck.gl version 7.3, Uber’s open source visualization tool now supports rendering massive geospatial data sets formatted according to the OGC 3D Tiles community standard.
Uber announces the release of the Autonomous Visualization System (AVS) as an open source project. AVS is a standard for creating a visual environment based on sensor data from autonomous vehicles, with playback available in multiple formats, including the web and video.
Uber open source projects leads give updates on seven of our projects, all of which will be showcased at the upcoming Uber Open Summit 2018.
Uber developed Maze, our funnel visualization platform, to identify possible UX bottlenecks and provide insight into the various ways riders and drivers interact with our platform.
deck.gl v5 incorporates simplified APIs, scripting support, and framework agnosticism, making the popular open source data visualization software more accessible than ever before.
Nicolas Garcia Belmonte, head of visualization, talks about his experience getting started in open source and the role it plays in his work at Uber.
To ring in the New Year, the Uber Engineering Blog shares some of our editor's picks for 2017.
Uber Engineering’s Data Visualization team uses their deck.gl and Voyager visualization platforms to map rider behavior during the August 21, 2017 solar eclipse.
Uber Engineering debuts deck.gl 4.0, the latest version of our open source data visualization framework featuring enhanced geospatial exploration, a re-architected codebase, and more comprehensive documentation.
When do people most frequently request rides? Using Uber Data in 2014, we see how cities around the world have different rhythms of movement. Here's the pulse of New York City, London, Los Angeles, San Francisco, Chicago and Miami.