Biking To A Healthier MIT: Impact

By Eric Lau, Iris Fung, Judy Chang, and Julia Appel

MIT puts student health and wellness at the forefront of much of its community programming. Community Wellness Classes range in topic from exercise and fitness, healthy eating, smoking cessation, stress management, and sexual health; the getfit@mit program encourages exercise via a 12-week team-based exercise challenge. Further, MIT offers a subsidy on Hubway membership for students to the tune of 70% of the total cost. We wanted to think of a way for MIT to publicize both their Hubway subsidy program and the benefits of using Hubway to increase physical activity and save time commuting around the area.  

Our goal with this project is to inform MIT students who are non-bikers and/or non-Hubway members about the benefits of the Hubway bike share program and biking as a form of physical activity, raise awareness about the MIT Hubway subsidy program, and encourage those in the MIT community to join for use as an alternative form of transportation to the T. Using a Hubway bike instead of the T will save the students time commuting, increase physical activity, and promote exercise, health, and wellness. As such, our intended audience is MIT students, or others in the MIT community, and our call to action is for currently unengaged students to join the Hubway bike share program.

Our hope was to evaluate impact of the game by tracking how many students actually used the coupon for a free Hubway (that would be easiest enough to track with access to Hubway data on single day passes). However, due to time constraints we were not able to connect with people who work at Hubway, and so decided to rely on a pre/post survey that we disseminated using Google sheets that asks indicator questions to represent the major goals of our piece. We asked game participants to take a survey before playing the game, and then again after the game happened on the following topics:

  1. General level of physical activity/enjoyment of physical activity
  2. Self-efficacy related to biking, and bike-commuting
  3. Awareness of Hubway bike share program/MIT subsidy program
  4. Likelihood of joining the program within a 10-day period.  

We asked audience members to take a survey before and after watching the video, to gauge the effectiveness of the video in increasing audience knowledge of and excitement for using Hubway in the future.


We received lots of positive and valuable feedback on our project from members of the MIT community who we recruited to game. (See more in our slideshow!) From key informant interviews with those who played the game and watched it being played, we found out that people really enjoyed the physical activity component of the game, learning about the MIT Hubway subsidy program, and some of the public health facts. They also enjoyed cheering for the biker, seeing the biker get the power up boost, and watching the biker complete the race. 80% of people who took the post-game survey said that they would probably take a free Hubway ride if they were given a coupon for one. 60% of players were thinking about or considering joining Hubway, and one rider changed her pre and post survey response, from “I might consider joining Hubway in the next 10 days” to “I will probably join Hubway in the next 10 days”. Some audience members were more interested in the MIT subsidized Hubway membership, which they said they learned about via the facts that pop up on the video.

We also received some unanticipated feedback on the game. First, many players and audience members were actually turned off by the GoPro footage: it was a bit too “real”, as it depicted biking up Mass Ave at rush hour, in the rain. (There were too many near brushes with cars, other cyclists, etc.) One game player said  that the “biking was fun but the traffic is terrifying, which is the main reason I don’t bike now.” To our dismay, biking self-efficacy did not increase as we’d expected, but rather decreased! The second piece of valuable feedback we received was to edit out those parts of the video where the person was stopped at a light, so the bike motion was continuous the entire time. We also received feedback on the physical bike stand setup: namely, that it incorporate some form of resistance in pedaling, so that the simulation is a bit more “real life.” Finally, we were told that having a leader board might encourage even more friendly competition among players.

With more time and resources, we would love to refilm the GoPro video, build a more realistic bike stand that incorporates resistance, and also build in the leaderboard component. Overall, though the audience and game players seemed to enjoy the game very much, especially the audience interaction and support pieces. They also took away valuable information about the MIT Hubway subsidy program, and the health benefits of biking, which were two of our main objectives with this piece. We are confident that, with a few tweaks and possibly a Hubway partnership, our game would be a big success.

Will you join us in biking to a healthier MIT?

Biking To A Healthier MIT: Methodology

By Eric Lau, Iris Fung, Judy Chang, and Julia Appel

Biking to a Healthier MIT draws on ideas generated during the participatory data games and maps/creative maps sketch projects. We began using data from Hubway, which lists the starting point, terminus, and length of every Hubway ride between April 2011 and November 2013, and analyzed routes that are frequently traveled by people from the Hubway station at the corner of Mass. Ave and Amherst St., right in front of 77 Mass Ave. We chose that station to maximize relevancy to our intended audience: the MIT community.  We analyzed length of frequently traveled routes from that station, and chose one of the most frequently traveled routes in each direction: northwestern to Harvard Square, and southeastern to Boston Commons. As mentioned, we chose two routes that were similar in distance (each about 2 miles from the starting point), and had similar projected average travel times on public transportation (Google maps estimates 13 minutes on the number 1 bus to Harvard Square, and 16 minutes to Boston Commons using the number 1 bus and Green Line D extension from Hynes Convention Center). Out of 33,685 rides taken from the Hubway terminal in front of Mass Ave, 2,419 were taken to the 5 Hubway terminals in and around Harvard Square, and 969 to the four in and around Boston Commons.

Northern Route to Harvard Square. (Pins represent Hubway terminals.)

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Southern Route to Boston Commons. (Pins represent Hubway terminals.)

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The game is designed to promote physical activity, increase excitement for and awareness of MIT’s Hubway subsidy program, and encourage audience participation among people watching. A stationary Hubway bike – an actual Hubway bike on a stand we built –  is set up in the second floor lobby of 77 Mass Ave. A poster hanging on the wall behind the bike read: “Can You Beat the T? Hop On!”, and prompts the rider to begin pedaling to start the game. The user enters their email address, chooses either the Northern or Southern route, and beings pedaling. Then, a GoPro video begins playing with footage of a bike trip along the chosen route, and a map pops up on the side of the page showing the precise location of the biker. Faster pedaling corresponds to speeding up in the game: the video plays more quickly, and the icon on the map moves more quickly. If you complete the route, you win the game! The user has the actual experience of biking – they are pedaling the bike and the GoPro video shows an actual bike ride along the route they chose – and gets the physical benefits of spending 4-5 minutes doing vigorous physical activity. The rider then receives an email with information about their ride: its duration, distance, average speed, and calories burned.  

The game is also built to encourage audience interaction and participation: as the person is riding the stationary bike, facts about the benefits of Hubway pop up on the screen (i.e. it increases physical activity, and saves time and money).  (For a complete list of facts that show up during the game, click here.)  Also, a sign to cheer for the person on the bike appears on the screen; if the audience cheers loudly enough, then the rider receives a “turbo boost”, goes more quickly, and is more likely to finish and win the game.

We felt it was important to simulate reality as much as possible and create a truly immersive user experience. To that end, we use an actual Hubway bike and several data streams – GoPro footage from the selected ride, computer vision speed inputs, and audience noise levels. There are several feedback loops built into the game, from the speed of pedaling to the noise level of the audience affecting the speed of the video and what is shown on the screen. The game incorporates several components and frameworks, including d3.js, Popcorn.js, OpenCV, and PyAudio. The multiple levels of user-controlled feedback create a dynamic experience where everybody – from the rider to the audience members – join in a collaborative, interactive journey and living story of biking to a healthier MIT.

Here is a link to a slideshow (with lots of pictures!) that details our methodology, and includes video footage of the game being played.

Opening Up Stop and Frisk

By: Maddie Kim, Julia Appel, Felipe Lozano-Landinez, and Iris Fung

The data show Stop and Frisk incidents and crimes reported in Boston in 2012 from the ACLU, reported crimes from the City of Boston Open Data Portal from 2012, and demographic data from the American Community Survey from 2007-2011. We created a scrollable op-ed piece piece similar to one you might see in the New York Times Upshot section, or on the Boston Globe’s website. (The title of our newspaper is “The Boston Times.”) 

To that end, our intended audience is informed and politically engaged readers of a major Boston newspaper. Our goal is to communicate our findings about the incidence of Stop and Frisk incidents and crime reporting, and to convince readers that timely release of data on Stop and Frisk is imperative for maintaining accountability among the Boston Police Department for fair and just policing practices. With a policing practice as controversial as this one, and as open to potential racially and demographically motivated abuse as this one has proven itself to be in other cities, clear and transparent accountability via open data release is an absolute necessity. Our call to action is to for readers to demand that Stop and Frisk data be open to the public. It is a bit more subtle than in past projects, because we felt like the medium in which we were working (a large, trusted, objective news source) would not run an op-ed piece with a very explicit call to action (like, for example, signing an ACLU petition). Rather, we tried to let the data speak for themselves via the maps and captions, and then guide the reader towards our call to action with the accompanying text. We think this is an appropriate way to tell this story because it combines the visual impact of a map with the context provided by our accompanying analysis and opinion text. This feels especially important with this dataset, because of the sensitivity demanded by policing activities, and the nuance involved with parsing, cleaning, and combining the three datasets. We felt a cohesive news story would be the best way to give the topic the context, ethical integrity, and thoughtfulness it deserves. Further, we felt that formatting our project as an opinion piece allowed us to communicate our goals and call to action to an audience that would likely be receptive to it.  

The first part of our project development process was completion of background research on Stop and Frisk; we looked at other cities that have successfully utilized open data on Stop and Frisk to make positive changes to policing practices. Next, we coded all the Stop and Frisk incidents included in the dataset by neighborhood using the BPD District ID code from each report, and mapped each incident. Then we downloaded, cleaned, and coded crime report data from the BPD according to the same scheme as the Stop and Frisk data, and plotted the two data sets onto the same map. The results were surprising: there were some discrepancies in incidences of crime and Stop and Frisk incidents. Stop and Frisk is a policy that is meant to make policing more efficient, so we expected to see correlations between crime reports and Stop and Frisk incidents. From there, we chose two neighborhoods, one with high Stop and Frisk incidents (Mattapan), and one with high crime (West Roxbury). We compared the demographics of each neighborhood — racial composition, median income, unemployment — to see if those metrics had any correlation with the discrepancies we noticed. In our article, we bookend our maps and charts with text that expresses our article’s thesis: open data on Stop and Frisk will make the Boston Police Department more accountable to the City’s citizens, and help enforce policing practices that are not racially or demographically motivated, but rather are motivated by actual crime incidence.

Stop and Frisk is a controversial policing policy: proponents view it is a proactive way to patrol the streets and decrease crime, while objectors see it as racist, and a plain violation of human and civil rights; both a cause and an effect of a corrupt and unjust criminal justice system. Regardless of your opinion on the policing practice, one thing is certain: transparent compilation of data is an absolute necessity to ensuring that the public has accurate information and can hold the Boston Police Department accountable for their actions.

Here’s another link to our article.

Food for good: how to feed America with what we already have.

By Andrew Mikofalvy, Julia Appel, Kalki Seksaria and Kenneth Friedman

The New Food (Waste & Insecurity) Guide Pyramid

The Problem   The Solution

                    The problem                                      The solution

The data say that 40% of food produced in the US is wasted, while almost 50 million Americans are food insecure to some degree. We wanted to share  Maria’s Story, because it shows the impact that supermarkets, kitchens, restaurants, food banks, and non profit and community organizations can have when they work together to decrease both food insecurity and food wastage.

Our audience is the decision makers of grocery stores at the Massachusetts Food Association (a non-profit association of grocery stores) annual meeting.

Our call to action is that grocery stores donate excess food, and cash, to food banks to help address the insidious problem of food insecurity in America.

To appropriately convey the message to the grocery store representatives attending the meeting, we evaluated the important factors that employees might look at when considering implementing a food donation program at their own store, and then the costs and the benefits of participating in the food donation process.  Costs include monetary and legal barriers to donating food, while benefits include the positive effect on individuals lives and a more positive public perception of the store in question. In addition, many legal worries are alleviated by the Bill Emerson Good Samaritan Food Donation Act, while the benefits are augmented by tax deductions.

We designed a pyramid shaped business card that resembles the shape and structure of the easily-recognizable food guide graphic: food pyramid. The card is two sided, and is a mockup of a give away that we would distribute to people at the annual MA Food Association conference. The front side expresses problems associated with food insecurity and food waste, and the flipside presents solutions: references to programs that can help alleviate and explain the costs associated with entering the food donation arena. The shape allows us to present the data at several levels: national, state and individual. This image also serces to connect Maria’s story with state and national data on food insecurity, to provide an inspiration to act.

The flipside represents a solution, and provides informational resources for how grocery store owners can help address the problem. It starts with reasons and resources for companies to donate their food to help with food security. It concludes with how community programs and store donations have made Maria better off, thereby closing the loop and finishing the story.

We combined quantitative and qualitative data. We found Maria’s story in the Project Bread 2013 Annual Status Report, and our quantitative data from a variety of sources:

#HearForYou: A Sonification Experience

By Kendra Pierre-Louis, Reem Alfaiz, Maddie Kim, and Julia Appel

Sculpture Context: This is a project proposal for World Relief, an international relief and development organization that works in the United States and internationally providing help to those affected by war, poverty, and disaster.


World Relief US is seeking proposals for an interactive data-driven experience to be installed in the lobby of their Atlanta headquarters on World Refugee Day: June 20, 2016. The purpose of the installation is to raise awareness of the rampant anti-Muslim and anti-refugee sentiment that has reached a fever pitch in the presidential election cycle. The call to action is to donate money to World Relief to aid their ongoing efforts to help ease resettlement among Muslim refugees to the United States.   

Sculpture Intent: This sound sculpture is called #HearForYou. It was created using data from BuzzFeed on the inflow of refugees to the United States. We looked at the proportion of Muslim refugees to total refugees for the past 10 years, from 2005 through 2015. Using a Python code we translated those frequency data into midi files; we created two sound files, one corresponding to the total number of refugees over the 10 year period, and one to the total number of Muslim refugees over the 10 year period. We can follow the user through the experience using the schematic diagram shown in the Keynote presentation. The user walks into a long rectangular room with 10 speakers mounted on the wall on each side of the room, 10 labels on the floor, and a TV screen at the far and of the room. (Each speaker and label corresponds to one year.) The TV screen at the front of the room is playing a short video of clips of presidential candidates’ bombastic anti-refugee and anti-Muslim sentiment, with subtitles. As the user walks through the room, they hear music coming from either side of them: on the right, the music corresponds to the data set of total immigrants, on the left it corresponds to the data set of Muslim immigrants.

The cacophony of the music — tonal and varying in frequency, but not melodic — is meant to mirror the discordant sound of the anti-Muslim and anti-refugee political rhetoric that has become increasingly difficult to ignore. This is further emphasized by the video playing on loop in front of the user the entire time she is in the room, which shows political candidates bombasting their xenophobic policy positions. The final four shots of the video are as follows: a still image of refugees overflowing from a rickety boat, a still image that reminds the user of World Refugee Day, a still image of a mother and child taking refuge on the beach, and finally a still image of the World Relief logo with the call to action.

Call to Action: At the end of the video, overlain onto the World Relief logo, is a call to action that says

Spread The Sound. #HearForYou. Donate: 

Campaign Strategy 101: Winning Hearts and Minds

By Felipe Lozano-Landinez, Jane Coffrin, and Julia Appel

The Political TV Ad Archive contains information about the televised ads during the 2016 primary campaign season. Our goal with this project was to explore this data set and see what interesting campaign strategy insights we could derive by looking at which candidates sponsored ads on which TV shows. To do this, we cleaned/modified the data set to specifically focus on candidates via what ads they sponsored (not which ones they appeared in), the program on which each aired, and the ad’s emotive content (i.e. positive, negative, or mixed). We took a subset of the data (all TV programs with more than 500 ads aired as of the time that we downloaded the data), and also filtered out all the Presidential Candidates that haven’t been relevant in the race as of the last couple of weeks. Finally, we grouped TV shows into four “Show Type”: Talk Shows, Entertainment, Game Shows, and News.

We looked at the data in multiple layers through a series of increasingly granular questions: How did the ads gets segmented by “Show Type”? Did a particular political party dominate a specific “Show Type”? Were Republicans more likely to advertise on certain types of shows than democrats? Were there specific TV programs/shows that were targeted by specific candidates? Finally, were the ads sponsored by these candidates “pro” ads, meant to bolster their candidacy, or “con” ads, meant to bring down another candidate’s campaign?

We think this is an effective way to ask questions of the data, and ultimately derive an interesting story from them, because our top-down enabled us to look at the big picture, notice discrepancies, and then dig further to try and explain them.  We wanted to tell a few stories that surprised people; our approach helped us look at something that made sense on the surface (candidates advertise more on news shows), but maybe not at a deeper level (Donald Trump advertised significantly less than the two remaining Republican candidates in the race).

We believe that campaign strategists are strategic in their message targeting, but wanted to better understand how they target TV viewers, and whether or not they have different assumptions than we do about the political inclinations of TV viewers. We also wanted to see if the actions of a candidate’s campaign would differ from the conventional wisdom that normal Americans have about those candidates. On the surface level, our views/perspectives may align, but when we dig deeper we deconstruct our perspectives and demonstrate where things begin to differ, leading to greater understanding of the larger political atmosphere.


If you prefer to get late night comedy from Stephen Colbert than Jimmy Fallon and you’re a registered Republican planning on voting for Donald Trump, Marco Rubio’s campaign manager Terry Sullivan knows it. And he’s trying to change your mind.

While it may come as no surprise that campaign strategists profile TV viewers to target political ads and maximize impact, it may be surprising what shows they are actually targeting. What we found about the political ads you’ve seen this election cycle may give insight into the campaign strategies of the front running Republican and Democratic candidates. It also may give you a reason to change the channel.

First, we looked at which categories of TV show were most likely to air an ad from one of the nine major political candidates.

Distribution of Ads over 4 Show Types

If you’re watching a news show — the Today Show, for example — you’re almost twice as likely to see an ad for a political candidate than on any other type of TV show. Which political candidate? Bernie Sanders and Marco Rubio.


What surprised us most about the breakdown of ads on news shows wasn’t who was advertising most frequently; it was who wasn’t. 


Notice Donald Trump, Republican nominee frontrunner and winner of seven states on Super Tuesday. He ran nearly 2/3 fewer ads than Marco Rubio, half as many as Jeb Bush (who didn’t even make it to Super Tuesday), and 300 less than Ted Cruz.

Emotive Content of Republican Candidate Sponsored News Ads Also, Donald Trump didn’t waste time attacking his opponents: he ran no attack ads (those marked with “con” emotive content) on news shows. Jeb Bush, Ted Cruz, and Marco on the other hand, were slinging mud all over the place.

Why are we seeing fewer ads, and no negative ads from Donald Trump? Maybe he doesn’t need to run attack ads, since much of his media presence revolves around negative commentary of his opponents? Maybe he doesn’t need to spend as much money on traditional media because of his polarizing candidacy? Whatever the reason, it looks like we won’t be seeing any traditionally slanderous campaign ads from the Donald any time soon.

After looking at news shows, we looked at the category that aired the second most political ads: talk shows. This is where Marco Rubio’s campaign strategy got interesting… We wanted to see the breakdown of advertisements from Republican candidates on talk shows. 
Marco Rubio out-advertised his rivals by a margin of 2 to 1. Jeb Bush and Donald Trump were, again, distant second and third place runners-up to the TV advertising machine, Marco Rubio.

Republican Ads on The Late Show and The Tonight Show


Rubio’s advertisements weren’t all positive, either. Note the lack of attack ads from Donald Trump on both shows, as compared with Rubio, Bush, and Cruz.

Again we wondered: why is Marco Rubio trying to win the hearts and minds of these TV viewers? Is he trying to attract young voters, and perhaps draw them from the front-running Democratic candidate? Is he trying to appeal to young voters as a moderate candidate? Is he a moderate candidate?

Only time will tell whether these ad strategies — or lack thereof — will really influence voters; until then, we will continue to wonder how the political strategists are targeting our favorite TV shows.

Julia’s Data Journal for 2/8/16

Below are the activities I did that generated some form of data collection on Monday, February 8th, 2016. It was a relatively normal day, and when I was forced to think about it I was surprised at the number of activities for which I generated over the course of an average Monday.

  • Used iPhone (texted, checked email, browsed web) | Apple and Gmail app collected information on geographic location, metadata on email communication, metadata on web clicks
  • Used iPhone (Google Maps) | Google Maps app collected data about my geographic location
  • Bought coffee, Charlie Card, sandwich (used credit card) | Charles Schwab collected financial information about where/when I made purchases
  • Rode MBTA (swiped Charlie Card) | MBTA collected data about my location/transit riding
  • Entered library at Tufts (swiped Tufts ID) | TUPD collected data about when I entered buildings on campus and my geographic location
  • Logged onto Tufts wireless network | Tufts IT department collected data about my computer (i.e. IP address, network usage)
  • Browsed the internet | Companies collected information about my preferences based on what I clicked on
  • Listened to Pandora | Website collected information about my music preferences to tailor my station
  • Called an Uber | Uber app collected information about where I was and where I was travelling, and also credit card information
  • Watched TV | TV companies collected information about my TV viewing habits

Skittles for days

This “Nursing Your Sweet Tooth” graphic makes a visual argument that we (Americans) are eating too much sugar by hyperbolically representing the amount of sugar the average American consumes over time with absurd physical objects (i.e. in a lifetime, this much).

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The presentation also gives information about the main sources of sugar in the American diet (focusing specifically on soda), and negative health outcomes associated with high intake. The graphic is on, so it seems likely that the intended audience is white, middle to upper class businessmen, who, interestingly, consume comparatively less sugar than lower-income black or Hispanic consumers.

The goal of the data presentation seems to be fear mongering: awaken people to the ill effects of their high dietary sugar consumption, and they will be so disgusted with their habits that they will never again touch a can of soda or a candy bar. The colors of the graphic – red, black, white and gray – and some of the typology evoke Coca-Cola, which helps drive home the point about soda’s contribution of sugar to the American diet. I think the graphic is effective in conveying a sense that the American diet is too high in added sugar, which is certainly true: 13% of calories in the American diet come from added sugars, which is significantly higher than the amount from every authoritative organization (USDA, WHO, etc). However, in terms of behavior change impact on the intended audience, the graphic seems relatively ineffective. First, the intended audience probably does not consume high levels of sugar, so it doesn’t make sense to target them as a population that needs to cut down on sugar intake. Second, some of the illustrations meant to shock the reader are over-exaggerated and nonsensical (i.e. in one lifetime we eat the amount of sugar in 1,767,900 Skittles), and end up conveying very little substantive information.

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