“I’m just super interested in how people get around,” said Gabriel Gaster, a Data Scientist with Chicago-based Datascope Analytics.
That interest then led Gaster to Divvy’s open source data set. In order to learn more about how the city moves, Gaster packaged all of the bike-sharing company’s 2013 data – 750,000+ trips total – and created a heatmap of aggregate bike trips from every station throughout the city.
Basically, the visualization shows how often a rider uses a station and what the most popular route to and from that particular station is. (I.e., where are people going)?
The Datascope Analytics team also mined several other insights from the data. For example, you can see that biking on city streets is different than biking on the lakefront path or the park; bikers that start on the path finish on the path and bikers that start off the path go all over. Also, streets without train lines that have bike paths, like Damen and Chicago, display high volume traffic.
Datascope, founded in 2009, is a team of data scientists, mathematicians, designers, and neuroscientists that design, execute, and analyze data-driven projects and initiatives.
The Divvy project, which Datascope “did for fun,” is really a “metaphor for what data scientists do,” said Gaster. “We use data to creatively answer problems. I wanted to know how this city moves and I used Divvy’s data to figure that out.” Check it out: