Should the bikeshare industry adopt an open data standard? As bikesharing spreads to more cities, having a common method for accessing and analyzing data will become more important. We know that transit systems work best when agencies concentrate on their core mission. Transit agencies are not in the information technology business; all they should do is release their data to let third parties build apps that let passengers use the systems.
To use open data, programmers need to know: Where is the data? What are the files called? Which fields are available? What are the fields called?
Bikesharing systems should adopt the standard of having a “data” page which can be found by appending “data” immediately after the main URL. This is what many U.S. government web sites are doing (like justice.gov/data, dot.gov/data, state.gov/data, etc.) It would be awesome to have consistent URLs like capitalbikeshare.com/data and velib.paris.fr/data. » Continue Reading…
The Trip Visualizer has been updated with new data for Capital Bikeshare. Instead of just posting the new quarter, I made all of 2013 a single data set. That’s over two-and-a-half million trips (2,585,010 bikeouts) using 309 stations, summarized into a big origin-destination table.
Montgomery County joined the network in late September, introducing bikesharing to four regions in Maryland: Rockville, Bethesda/Chevy Chase, Silver Spring, and Takoma Park. 73% of bikeouts from Maryland went to other stations in Maryland, with 26% headed to DC. Just under 0.5% (21 trips out of 4,675) went from Maryland to Virginia. The fastest Maryland-to-Virginia ride took 33 minutes, from Friendship Heights Metro to Rosslyn Metro, a trip that takes 27 minutes on Metro. The longest MD/VA trip was 1 hour and 48 minutes, when someone biked from Crystal City Metro to Battery Lane in Silver Spring.
The Trip Visualizer lets you select a single station to see the most-significant trips to/from that station. You can use some hidden features to select clusters of stations, to examine networks. Hitting “M” will select all stations in Maryland. You can see how isolated Rockville is, with its closest station to Bethesda still over five miles away. » Continue Reading…
When I saw Dan Macy’s aerial photo of Washington, DC, I knew I had to turn it into the background for a map. Dan’s plane was flying into National Airport from New York City, and made an unusual entry over the Anacostia River, giving him a spectacular view of East Capitol Street, the Anacostia and Potomac Rivers, and the National Mall.
I had to figure out a not-too-complex way of mapping the latitude and longitude coordinates to the oblique photo. Unlike a map, the meridians (longitudes) aren’t vertical and the parallels (latitudes) aren’t horizontal. The bird’s-eye perspective also meant the meridians and parallels would be skewed. » Continue Reading…
Want to look back at 2013 using Capital Bikeshare data? I’ve put together an interactive tool to examine the 2013 daily ridership statistics for Capital Bikeshare. The data looks at daily “bikeout” totals, that is, how many bikes were checked out each day. You can summarize the data into weeks, months, quarters, and days of the week. The weekly view ignores December 31, in order to avoid having a 53rd week with only a single day in it.
You can compare the difference between bikeouts from subscribers (those with memberships for a month or a year) and casual riders (those with memberships for 1 or 3 days). You can also look at bikeout stats for any of the 306 individuals stations.
The program lets you find the correlation between any two data sets. You can use a second data set to color the bars of the first data set. The correlation is automatically calculated. It ranges from 1, a perfect positive correlation, to -1, a perfect negative correlation. 0 means there is no correlation. » Continue Reading…
New Capital Bikeshare data sets have been added to the Activity Mapper visualization tool. I started with showing bikein & bikeout totals for each station for a single day in September; the new data sets all look at the entire 3rd quarter of 2013, from July through September. Instead of summarizing subtotals in 5-minute chunks, the new data sets have a subtotal for every day. The 3rd quarter includes the 13 CaBi stations that debuted in Montgomery County at the end of September, but since their traffic is dwarfed by the rest of the system, there is not much to see.
New controls let you customize the display. Use the + and – keys to zoom in or out (clicking will zoom and center). Ctrl+ and Ctrl- increase and decrease the size of the circle. ← and → can control the time sequence by moving backwards or forwards in time, one segment at a time.
The program compares two sets of numbers for each station. Originally, I showed the number of bikeins and bikeouts per station. (CaBi also calls these actions rentals and returns, or un-dock and dock.) A new data set compares subscribers to casual riders. Subscribers buy memberships for a month or a year, while casual riders buy the 1- or 3-day memberships. Another new data set compares “on-time” rides to “late” rides. On-time means the trip took 30 minutes or less. The rides are counted against the station where the trip ended. » Continue Reading…
Here’s another way to analyze trip history data from Capital Bikeshare. Back in March, I had used Processing to create QuickTime animations that I uploaded to YouTube (see Neighborhood CaBi Animations). But I wanted a tool that let the user control the flow of time, as well as how to customize the display. A few weeks ago, I created a tool to do this for Metro (see A Day of Metro, Entries and Exits). Now you can use the same tool to show CaBi data: go to the Activity Display home page to select which data set to use, or add ?system=cabi to the URL to go straight to the CaBi display.
I show data in 5-minute increments for Saturday, September 14, 2013. That day was the busiest day in the 3rd quarter, with 84,8755 trips made in a single day, using the system’s 243 stations. (Today the system has 300 stations.)
This is now the third tool in my collection of data visualization programs. The Stat Mapper shows collections of single points; the Trip Visualizer is meant for displaying point-to-point data; and now the Activity Mapper is for chronological data. Over time I hope to add new data sets as well as new features.
The map plots each CaBi station. The boundaries are drawn at the half-way point between the closest two stations. When three or more cell boundaries meet, you are equidistant to them all. Inside the cell of a station, that station is the one that’s closest to you.
Justin showed me an interactive example of Voronoi Tesselation on GitHub. The interactive demo adds a new “seed” wherever your mouse is pointing. That point creates a Voronoi cell. » Continue Reading…
A few days ago I animated a day of Capital Bikeshare trips using a new Java program I had written in Processing (see Animating Data with Processing). I wanted the program to be flexible enough to allow people to customize for their own uses, so I put it to the test myself by making slight modifications to its display.
For the first customization, I wanted to zoom into Dupont Circle. It turns out that at this scale, drawing a frame every 60 seconds means bikes disappear from view without giving an impression of movement. So, I had to slow down the speed. The video below samples the data every 5 seconds (12 times slower than before), so you can follow individual bikes. The video displays 30 frames per second. The data is from October 5, 2012, from 8am to 8pm.
Want to create an animation from a set of data? If the data has spatial and chronological components, you can view it as a map-based movie. I’ve been making short animations from Capital Bikeshare data using Processing, a mini Java development tool. Here are steps you can follow to try making your own movies.
A new round of trip history data has been made public by Capital Bikeshare. I’ve created a new version of the CaBi Trip Visualizer for the 4th Quarter of 2012, covering October through December. Use the tool to analyze travel patterns for people using the bikeshare system.
475,736 trips were made in the 4th quarter. The bikesharing usage is highly seasonal, as ridership went down 25% from the 3rd quarter (the summer months: July, August and September). But compared to 2011’s 4th quarter, ridership is up 52%. Broken down by membership types, the number of rides by registered users went up 54%, and the number of rides by casual users went up 40%. Registered users are those who buy memberships for 1 month or 1 year; casual users buy memberships for 1 day or 5 days. » Continue Reading…