Taking a New Look at Subway Map Design
For years Dr. Ruth Rosenholtz, a principal research scientist at the MIT Department of Brain and Cognitive Sciences and CSAIL, has studied how the human visual system, in particular peripheral vision, collects information. Rosenholtz and her research group have developed a computational model of human vision in an effort to understand peripheral visual processing. Beyond providing a greater understanding of how peripheral vision works, Rosenholtz’s computational model can be applied to the design of everything from navigational systems, websites and educational tools to advertisements to predict how usable a particular display is.
When Rosenholtz’s students heard about the MBTA’s map redesign competition - which encouraged the public to submit redesigned versions of the current subway map followed by a period of public voting to select a winner from six finalists, one of which may be selected as the official new subway map design - they decided to run the redesigned maps through their computational model to see which map looked best peripherally.
"Most people don't realize how important peripheral vision is for everyday life. Imagine how difficult it would be to read a subway map through a small aperture that prevented you from using your peripheral vision," said Rosenholtz. “Subway maps are visually complex, and you can’t expect to see everything at a glance. But a viewer should be able to retain some useful information.”
The team initially developed their model of peripheral vision based on their understanding of the perception of simple displays, and then advanced to testing it on the perception of geographical maps.
For any given image, the model developed by Rosenholtz and her team provides a visual representation of the information available in the periphery. For example, it can provide information on whether or not a viewer looking at the center of a street map can easily locate the city’s different public parks, major thoroughfares, and landmarks. In previous work, Rosenholtz’s group performed behavioral experiments in which human subjects were asked to perform tasks like finding a route on a complicated map using only peripheral vision. The team found that the model's predictions were consistent with the information human subjects were able to draw from the map.
“If you’re looking at an MBTA map, the model makes predictions about how clearly you can see different subways line and stops,” said Dr. Lavanya Sharan, a postdoctoral associate in Rosenholtz’s group. “We saw an opportunity with the MBTA contest to run our model on the different entries and predict which map might be the most useful.”
The group applied the model based on a user stationed at the Kendall/MIT stop looking at a large MBTA system map, the size of those currently posted in subway stations. By evaluating the different entries, the team found many interesting characteristics that may make a map more useful at a glance. One of the entries used raised bumps to depict subway stops, which made the stops more noticeable, as opposed to more commonly used white circles. In all entries, the Silver Line was hard to locate due to its light color, and the Green Lines were easier to comprehend when represented abstractly as parallel lines. The airport, a key location for many travelers in Boston, was difficult to distinguish in all but one entry that prominently highlighted its position. Additionally the "T" logo, while mostly irrelevant to using the map, was a prominent feature in most entries.
“Our model provides you with a representation of the information that you lose in your periphery when looking at these subway maps. It can show the uncertainty you have about features of the map that you aren't looking at directly,” said Shaiyan Keshvari, a graduate student in the MIT Department of Brain and Cognitive Sciences.
In the future, the team hopes to improve the model so that it can provide more quantitative information on what the human visual system gathers from images. The group recently received funding to work on understanding the limitations and strengths of Google Glass, and how the human visual system can simultaneously process images projected onto Google Glass and the visual input from the real world.
Former members of Rosenholtz’s research group Dr. Benjamin Balas, Dr. Alvin Raj and Dr. Krista Ehinger also contributed to this research. For more information on Rosenholtz’s work, please visit: http://persci.mit.edu/people/rosenholtz.
-Story by Abby Abazorius, CSAIL
-Photo by Jason Dorfman, CSAIL