Alcohol, Prostitution, and Predictive Modeling

July 12, 2012 VMware Tanzu

Uber is one of the latest mobile apps to catch on like wildfire. Uber provides private drivers on-demand from anywhere, at any time. Here’s a real-life example: it’s 1:48 am, the Riptide bar on 47th and Taraval located in the Outer Sunset of San Francisco is about to close down, and there are rarely cabs driving in this neighborhood.

Using your GPS location, Uber will text you the approximate arrival time, followed by another text message when you will be picked up. An added benefit, there is no cash exchange or tipping involved, since they have your credit card on file and charge you based on distance.

It’s a pretty simple and smart solution for cities like San Francisco, which don’t have the supply of taxis needed to meet the growing demand. More importantly, their blog is full of creative analyses of Uber data, looking at the relationship between crime, cabs, and consumer demand in cities across America.

In a recent blog post, Bradley Voytek, a Neuroscientist studying human cognition, neuroplasticity, and brain computer interfacing, uses Stamen’s Crimespotting data with uber data to improve predictive modeling of where Uber drivers should be in higher demand. Read more at the Uber blog.

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