Data is lending insights into how some of the 90,000 bicyclists traverse the city.
This story was originally posted by Data-Smart City Solutions.
In New York City, 90,000 commuters bike to work every day. If you’re one of those commuters who use New York’s Citi Bike bikeshare system, two scenarios are all too common. In the first, you go to grab a bike at the station closest to your apartment on your way to work and find there are no bikes left, forcing you to wander around until you find the next closest station, making you late for work. In the other, you find a bike near your apartment and ride to work, but once you’re there, you can’t find a free dock at the station nearest your office so you have to weave through New York's rush hour traffic to locate another station (and again you’re late for work).
Ensuring that both bikes and free docks are available when needed was the impetus for the NYC Citi Bike Visualization, a geographic information systems (GIS) project from the NYC Data Science Academy. Analyzing publicly available data on the start and end point of every Citi Bike trip during March of 2017, the visualization uses a Leaflet platform to map the number of trips started and ended at each station in the city. Red points indicate stations that more riders use as origins than destinations (meaning these stations lose bikes throughout the day), whereas green points indicate stations that more riders use as destinations (meaning they gain bikes throughout the day). The visualization also shows the most popular bike routes, displayed via orange lines.
The map also allows users to filter by time, date, and the gender of riders—all data Citi Bike provides publicly. For example, users can see that between 8am and 9am on weekdays, there are more green stations in the centers of Manhattan and Brooklyn and more red in peripheries of these boroughs, meaning most riders travel from the edges of the city into the center for work. On the other hand, as riders travel home between 5pm and 6pm, the city centers are dominated by red points and the peripheries by green ones.
At left: between 8am and 9am on weekdays, Citi Bike riders converge on the city center. At right: between 5pm and 6pm, riders travel to the peripheries.
Using this information, the city may want to ensure that in the morning, there are more bikes available in the outlying areas and more free docks in the city centers, and vice versa in the evenings. The more difficult question is how the city can ensure this proper distribution, as the most obvious solution—manually transporting bikes from station to station—is costly and time-consuming.
In response, Citi Bike has implemented the Bike Angels program, which rewards riders for taking bikes from stations needing free docks to those needing bikes. The program assigns point values to Citi Bike stations—for example, creating two-point pick up and one-point drop off stations—and rewards riders with Citi Bike passes and even money once they’ve earned enough points.
The visualization also shows that the most popular routes run on the west side of Manhattan through Central Park and Chelsea Pier, as well as through Grand Central and Penn Station. Riders bike more along streets running east to west than along avenues running north to south, presumably because there are more uptown and downtown subways than crosstown trains.
Most Citi Bike routes bisect the city east to west.
This information can help the city decide where to direct funding for dedicated bike paths, new Citi Bike stations, and other bike-friendly interventions. Moreover, by looking at the most popular routes during different times of the day and week, the city can more effectively deploy traffic officers to ensure the safety of cyclists.
The NYC Citi Bike Visualization provides a wealth of insights that can make Citi Bike safer and more convenient. The ability to display temporal and geographic trends simultaneously makes GIS data visualizations particularly well suited to improving commutes for residents, whether they travel on bike or via some other mode of transportation.