Uber Movement allows users to select an origin and a destination zone to see the average, lower bound and upper bound travel times taken by drivers on from A to B on a given day or time interval across multiple cities worldwide.
In this article, I will dig into what that data looks like and some into of its characteristics, discuss a few of its issues and start a discussion on how to look at it from a time series forecasting perspective.
Unfortunately, if you’d like to download the data yourself, Uber only provides cross-sectional data across a span of a maximum of 3 months. …
I knew I wanted to do two things in the process of writing my bachelor’s thesis: improve my programming skills and work with time-series data prediction.
What I didn’t know, however, is what I wanted to study. However, it had to be something I truly liked and not necessarily connected to my major.
Since I like maps and… things that move, I decided to somehow use data from a website I had recently come across and fallen in love with (probably after playing many hours of SimCity as a teenager) — the Uber Movement website.
It allows you to visualize anonymized data for average travel times from a certain point (or zone) to any other point in that same city. …
I can’t remember how many times I’ve had to bend over backwards to answer “what type of music do you listen to?”, while trying not to sound pretentious or to describe it with meaningless adjectives.
When the music you listen to takes on many different palettes by distorting and stretching natural and artificial sounds, that question proves difficult to answer because, well, what type of music is it?
Many say Flume and producers alike belong to (mainly) future bass or vapor twitch. But if you are a careful listener, you’d be quick to notice they sound like neither— or like none of the 1500+ genres layed out at Every Noise at Once, for that matter. …