Swaying from Truth: Candidates and Their Positions on Issues

Jyotishka Biswas, Eric Lau, Kalki Seksaria, Tiffany Wang


The data say that Donald Trump and Hillary Clinton differ wildly both in political ideology and in general truthfulness, with Clinton trumping Trump in the latter. We want to tell this story because with the upcoming election, it’s important that people know both where the candidates stand and what they will say to gain political support.

In celebration (trepidation?) of the upcoming election, we looked at two data sets: the candidate files at PolitiFact and this New York Times interactive article on where the presidential candidates stand on various political issues.

We focused on Hillary Clinton and Donald Trump, who are the leading candidates (as of 3/14/2016) of their respective parties, and three issues — immigration, economy, and healthcare. From the New York Times article, we calculated how liberal each candidate is on a particular issue. From PolitiFact, we calculated the average truthfulness of statements within each issue category. In the resulting “pendulum chart”, we hoped to show a clear difference between the truthfulness and political stances of Clinton and Trump.

For our intended audience of moderately informed likely voters, we wanted to provide a lighthearted but informative view of these candidates. The sculpture was designed to be interactive — the pendulum heads have example statements from the candidates on one side and pictures of their faces on the other which vary corresponding to the average truthfulness, and are designed to swing slightly when picked up to support the “swaying from the truth” metaphor. To help with this, we created a smaller two-sided card containing the legend and information about the display, meant to be picked up and read by the interested viewer.

Yet we wanted the presentation to be just as useful when viewed from a distance, which informed our use of bright colors, bold text, and the easy-to-understand physical variables of position and length. The result, we hope, is a presentation that provides information at finer levels of granularity as the viewer approaches it, but for which the general message is clear throughout. As for the message, our aim was to avoid showing obvious bias through visual design differences between the candidates — the goal is for the data, through the presentation, to speak for itself.

A Day in the Life of a Hubway


By Jyotishka Biswas, Phillip Graham, and Maddie Kim

The data say that Hubway has had a positive impact on health and the environment in the Greater Boston Area. We want to tell this story to show that choosing to bike can make a difference.

The Hubway Bike Share system launched in 2011, and completed over 1 million rides over the next two years. We decided to look at the benefits of biking on health and the environment, and to quantify the impact that Hubway has had along these dimensions.

We chose to focus on the positive message for this assignment, as if we were part of Hubway’s marketing team, which guided many of the decisions we made. The first was to de-emphasize the charts — we used only two charts, to show the age and gender breakdown of Hubway users, statistics which were fun facts rather than central to our message. When it came to the core of the infographic, we presented medians rather than distributions to avoid unnecessary complexity. Primarily, we focused on keeping the tone light and fun, to make the reader more receptive to the message.

The result is a scrollable infographic, in which the story is told in a loose sequential frame format. The numbers are communicated in the context of a day in the life of a Hubway bike, and small comments in speech bubbles are used to signpost the flow of the story and provide some humor. We used bright colors to frame our content, and large, bold text to emphasize important numbers. We made the conscious decision to have a clear opinion and message, rather than to lay out our analysis and ask readers to assess it for themselves. We believe that this resulted in a more accessible presentation, and hopefully one which is as informative as it is enjoyable.

You can find the infographic here. (It’s made up of large images, so don’t click if you’re worried about data usage.)




Sunday (2/7) was pretty light for me in terms of data creation. I was traveling for most of the day, so most of the data I create regularly by browsing the web was eliminated. Here’s what’s left:

  1. A constantly running health app on my phone tracked and stored my location and some other sensor data.
  2. I was in a car for several hours, so I probably showed up on at least one traffic camera.
  3. I sent multiple texts throughout the day.
  4. Other passive data usage on my phone was also tracked.
  5. I participated in a singing competition which was recorded and will probably be posted online.
  6. I was in a group photo which will also likely be posted online.
  7. I listened to music on Spotify, which keeps track of what I play.
  8. I watched the Super Bowl, whose viewership, like other TV programs, is tracked.
  9. I watched a show on Netflix, which also keeps track of these things.
  10. I checked my email, informing the mail server that I’ve looked at the messages.
  11. I followed a link to a website that very likely tracks visits.
  12. I looked at some course websites to plan my week.
  13. I used electricity, which shows up on my bill each month through an electronic system.

I probably missed some things, but if a day in which I spent the majority of my time doing very little produces this, it would be a daunting task to enumerate every point of data I produce on an average day.

Visualizing Binary Data

Original visualization and Author’s comments

binvis.io isn’t so much a statement as an exploration of what files on computers “look like”. In essence, it takes a binary file containing arbitrary data and generates an interactive visualization which allows users to inspect regions of the file for values such as byteclass (useful for distinguishing text from other data, for example), local entropy, and byte-level details. Admittedly, the target audience of this presentation is likely technically-minded, but the key ideas apply to a lot of areas. To keep it short, I’ll go over just two of them.


The first is the representation of parametrized data in 2D. Many visualizations try to present multidimensional data in 2D, often using 3D computer graphics and animation. This visualization tackles the opposite problem: presenting a 1D sequence of points in 2D. The author exploits spatial locality by clustering contiguous regions of similar type, but also allows simple sequential display of the data on the grid. He also explains the techniques he used right in the help menu.

The second feature is that information isn’t withheld. For credibility, a presentation should be verifiable by giving sources or making the raw data available. This visualization takes this idea a step further by showing the raw data directly alongside the visualization, albeit in readable chunks. An even cooler feature is that the data which the user is focusing on is highlighted in each of the components (see the screenshot above). This creates a visual mapping between the raw data and the presentation, a powerful technique for showing connections across levels of abstraction and inviting users to ask their own questions.