February, 2014 Archives

You may visit Aspen, Colorado to go skiing, but how do you get around when visiting in the summer? We-Cycle has introduced bikesharing to this mountain town, giving residents and tourists a new option for getting around. They recently shared some trip history data with me, letting me create this short animation of how cyclists travel between the 13 stations.

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Bikesharing at a Ski Resort

Why do so many APIs offer geographic searches based on a single point and a radius, but not based on a bounding box using two points? Does your computer or mobile device have a round screen? If so, a radius search is perfect for you. But if your screen is rectangular, your search should be too. API designers need to wake up and realize that nobody has a round screen, and thus radius-based geo searches are vastly inferior to rectangular searches!

Here’s an example from Times Square. For a search at 42nd and Broadway, if you set the radius to be half the width of the screen, your search area would look like this green circle. On a square screen, those four corners outside the circle add up to 21% of the display (given a square display of width w, (w² – π×w²/4)/w² = 21%). For rectangular screens, it becomes higher. For example, putting a circle in a rectangle that’s twice as long as it is high means you are missing 61% of the display. This means you might not be including results that the user would expect to see. » Continue Reading…

The San Francisco region has joined the bikesharing movement, with the introduction of Bay Area Bikeshare in August 2013. I wanted to see if I could adapt any of my CaBi tools for the “BABS” system, but their open data is too limited to be of much use. They have a System Metrics page which offers only ridership and membership data, which is not very interesting. To analyze the system we need trip history data, like Capital Bikeshare shares every quarter.

Luckily, I discovered Eric Fischer, who has been tracking station statuses since late August. Every minute, he records the number of available bikes and docks at each station. While not as valuable as trip history data, this data does let us discover when stations are either full or empty.

The data he records is a copy of the current station data, available at bayareabikeshare.com/stations/json. I had to reduce the size of the file by writing a Java program to remove redundancy and unnecessary fields. Still, storing data for a single day takes a megabyte of space for even the condensed JSON file. » Continue Reading…

It used to be Valentine’s meant heart-shaped boxes filled with chocolates, but nowadays it means people running through the streets in red and pink underwear. Cupid’s Undie Run is a brilliant fundraising event that supports the Children’s Tumor Foundation while also making Capitol Hill a much more interesting place.

Here are the best shots from the 2014 Cupid’s Undie Run in Washington, DC:

2014 Cupid's Undie Run
Arriving via Capital Bikeshare
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Fundraising Their Pants Off

If you’re a total transit nerd, this will be exciting. To prepare for a bus-themed event for the Transportation Techies meetup group, we’re making public APC data sets. That’s automated passenger counter; electronic devices that measure people boarding and alighting. We’re sharing it in hopes that local programmers will use it to create visualizations of how people use the bus.

2013-09 Raw Stop Data.xlsx is from Arlington Transit. It has 12 columns and 20,460 rows (1.2MB). The data is for weekdays in September 2013. I’ve created a CSV version, 2013-09 Raw Stop Data.csv. Here’s what 3 sample rows looks like: » Continue Reading…

Force Diagram of WMATA Metro StationsWhat is the minimum information you need when planning a trip on the Metro system? If all you want to see is which stations are connected, the Force Diagram of WMATA Metro Stations is the Metro map for you.

This visualization was designed using the JavaScript library D3, which includes the Force Layout design. I was inspired to do a version for Washington, DC after seeing Muyueh Lee‘s visualization of the Taipei MRT system. You can click-and-drag stations to try to reposition them. The layout pays no attention to the geographic locations of the stations. The distribution starts off as a random mess, and then coalesces into positions based on simulating physical properties of the links between stations. This is an even-more-severe rendering than my isochronal Metro Distortion Map.

The code is relatively compact, and customizing it was a good way for me to learn D3. That’s the same tool I used to create the Voronoi Diagram of CaBi Stations and the interactive bar chart I used for Looking Back at 2013 CaBi Data.

A Bare-Minimum Metro Map