Impact of UglyFruit

View our infographic (use Google Chrome for best results)

Our main goal of this project was to convey how sizable of a problem hunger in this country is, and how food waste, which is also a huge problem which we all contribute to, is a possible solution to the problem. We want viewers to understand that while we, as individuals, waste a lot of food, grocery stores are also a big contributor to this problem. Lastly, we want viewers to understand that there are things that can be done about this, which are listed in our call to action section.

Our audience for this project is general American consumers, that are likely unfamiliar with food waste as an important issue and big problem. This is pretty broad but since almost everyone buy food, cooks food and eats food, this is relevant to everyone. Our intention is that this story would be put out by an organization such as Food for Free and their audience is the average, curious bystander who thinks the infographic looks cool so they make the decision to read through it.

The actions that we want viewers to take after viewing the infographic is: donating to Food for Free, signing the petition for the Food Waste Recovery Act, and pledging to stop overshopping.

While assessing the impact of our project, the first thing we asked our interviewees before they viewed the infographic, was the following: How many people do you think face food insecurity in the U.S.? How much food is wasted in the U.S.? While some interviewees had guesses much lower than the actual numbers, many were already familiar with this issue and guessed relatively accurate numbers, or numbers even higher than the actual amount.

For the people who were less familiar on the topic, the infographic did inform them of new information. When realizing that 40% of food is wasted, one person said “That’s a lot of food, it makes me feel bad” and another said “the growing image of the pile of food is memorable”. When asked what kind of changes they would make, one said, “I should try to plan more before going to the supermarket”, and another said “I’ll think about buying ugly fruit but then I think my kids probably wouldn’t eat it”. They also all said they would sign the petition.

For the people who were already familiar with the topic, the infographic did not have as much of an impact on them. They mostly were not surprised by the 45 million and 40% numbers, some even thought they seemed low. When asked what kind of actions they would take, they did not think signing the petition would be useful. One mentioned “legislation is the way to cause change, signing a petition won’t have any effect”, and another said “petitions don’t usually do anything, but if I was voting on something to donate food, I would”. Another interviewee added in context to ugly fruit, that she thinks “Americans are way too obsessed with aesthetics, Europeans are much better about it”.

Overall, it appears that our call to actions were not too successful. It seems like most viewers would not continue to do them. Our infographic, however, did engage the viewers. They read it to completion and seemed interested throughout. For the people already familiar with the issue, it does not seem like it will have much of any impact. However, for the people who were not, the infographic will at least be memorable to them, which will make them more aware of the problem in the future. Ideally, it could make sense to narrow down our audience to people less familiar with this issue, however there are difficulties with targeting that specific group.

Methodology of UglyFruit

View our infographic (use Google Chrome for best results)

We originally wanted to work with homelessness data again after the 5th mini-project due to the large quantity of data and relative cleanness, but could not find a concrete call-to-action to frame our story around. We brainstormed some alternative ideas; the strongest alternative was to tackle childhood obesity by drawing on CDC data, with a call to action of either encouraging kids to write letters to “Save Recess!”, or to help parents encourage their kids to play outside more. However, the CDC data was not available as a raw dataset, but only as already-prepared charts and graphs, and we also felt that the split audience of parents and kids would make it hard to find the appropriate tone for the project.

We pivoted to a project tackling hunger in America, drawing on data from Project Bread and the US Department of Agriculture, with a focus on reducing food waste using data from Food for Free. Since we were creating a webpage with a strong emphasis on narrative flow as opposed to hard statistics, we were able to concentrate on finding fewer, more salient numbers which most strongly supported our story (did you know that 40% of food in America goes uneaten or is otherwise wasted?), and back them up with graphics and animation. We also focused on looking for summary statistics that were pre-aggregated, as opposed to breakdowns along any dimension, to highlight that fact that this is a national problem; this meant very little data cleaning was necessary. The most difficult part of the data-gathering process was not finding appropriate statistics, but picking the most relevant portions. Because we had two main focuses (hunger and food waste), we first searched for multiple sources and datasets for each topic and collected them in a shared document. We then looked to focus our argument based on the data we had available; there is a wealth of information available on these large and complex problems, but with a fairly broad audience we needed to use numbers that brought the issues of hunger and waste to life without including too much. Another challenge was juxtaposing the hunger and waste data into a coherent narrative; we decided to open by introducing the immediate problem of hunger with some statistics as hooks, then pivot to food waste and a breakdown of the different ways it occurs.

One important technique that we wanted to incorporate was personal stories; we collected quotes from sources such as the Project Bread status reports in order to weave them into the narrative. However, we ultimately took the project in a slightly different direction of focusing on reducing food waste as a solution to hunger, and less on the problem of hunger itself, so we decided not to incorporate quotes about hunger. However, we did include a quote from the former President of Trader Joe’s about food waste in grocery stores, which helped add a more real-life connection to our website.


Creating More Beds for the Homeless

Team Members: Gary Burnett, Phillip Graham, Katie Marlowe

Finished Map

The data say that states that have a higher ratio of beds for the homeless to the amount of homeless people more frequently had a decrease in the number of homeless people from 2014-2015.

We want to tell this story because the homelessness epidemic is a big problem. There are 564,708 homeless people in the United States, and transitional housing is helping to lower this number.

This data would be presented at a convention about ending the homelessness epidemic, so our audience would be people attending the convention, who are most likely eager to help this issue. Our goal is to tell them that transitional housing can help be part of the solution, so that we can build support for transitional housing.

When looking at the data, we found that states with a decrease in homeless population tended to have more transitional housing. Specifically, they had a higher ratio of beds available to the number of homeless people.

There were, of course, some outliers. South Dakota had an increase in the homeless population by 17%, whereas no other state had an increase more than 10%, and they also have a high ratio of beds for the homeless. In general, the states in the North East also tended to not fit the trend. New York and New Hampshire both have a high ratio of beds, but had an increase in their homeless population.

We decided that a map would be a good representation for this data for a couple of reasons. First of all, this would be displayed at convention where a lot of people would walk by and look at it, so a map is an easy way for someone to locate their home state and see how they stack up to other states. It is also nice to see how different geographic regions compare. As stated earlier, the North East does not exactly fit the trend that most of the rest of the country follows. The South has, for the most part, seen a significant decrease in their homeless population, where the West Coast has seen a decent increase in their homeless population.

Do out of school suspensions correlate with school performance?

Team Members: Catherine Caruso, Jane Coffrin, Iris Fung, Katie Marlowe

The data say that a higher school performance correlates with a lower number of out of school suspensions. In addition, schools that only administer in school suspensions perform higher than schools that administer out of school suspensions. We want to tell this story because we’d like to advise the Louisiana State School Board against current methods of discipline that may not be good for the student or the school as a whole. We would like to recommend that all out of school suspensions become in school suspensions, or something of the sort. Our Audience is the Louisiana State School Board.

When a child acts up in school, there are many ways to discipline him/her- in school suspension, out of school suspension, even expulsion. However, some methods are better than others when it comes to the student’s academic trajectory and success throughout high school. Out of school suspension may seem like an attractive option for the school because then the child is off school grounds, and is no longer the school’s responsibility. However, out of school suspension is problematic for the child. Now, a child that is already having behavioral issues no longer has the structure, schedule and supervision that comes with being in a school. Removing a child from school may place them in an unsupervised home situation, or in an even worse situation on the street.Ultimately, out of school suspension may make the child less willing to follow rules and pay attention in class, causing his/her academic performance to decline. If there are enough out of school suspensions, the performance of the entire school may be negatively affected. (;

We targeted the Louisiana State School Board because school board members are in a position to actually make beneficial changes to the system in a way that parents or teachers cannot. It is also worth noting that Louisiana is notorious for strict disciplinary procedures – other groups have also worked to try to reduce or ban school suspensions (; but they have not been successful yet. In addition to showing that schools with few out of school suspensions perform much higher than schools with many out of school suspensions, we also included information about the difference in school performance for schools that administer in school vs. out of school suspensions. The schools that only suspend students in school have a much higher school performance, which makes the case that in school suspensions are a better option for the students and the school as a whole.

Our choice to represent the data using a 3d diorama-like structure is a nod back to the grade school days of creating dioramas, a staple of school projects. The materials – pipe cleaners, construction paper – do the same, and the colors we chose are vibrant and eye-catching. The movement of children from middle school on the left  to high school in the middle to graduation on the right leads the viewer’s eye from left to right to read the graph. The pipe cleaner colors  – yellow for high performing schools and purple for low performing schools – contrast each other well, and yellow often represents high achievement in academic settings. The suspensions are represented in red, a color commonly used to mean warning or stop. We only represented seven of the highest performing schools and seven  of the lowest performing schools to simplify the information and to make the distinction between the two groups visually striking. Complete information about the schools we included, their suspension rates, and their school performance appears on the back of the sculpture for anyone seeking additional information. The inset about in school vs. out of school suspensions serves to offer a viable solution to the problem we have presented, in hopes of motivating the board members to not only absorb the information, but to also start thinking about what action they can take to remedy the situation.   
While a data sculpture is a rather unconventional method for presenting such serious information in a formal setting like a school board meeting, we thought our novel approach would surprise the board members, and pique their interest, giving us the opportunity to engage them on the topic and talk about the information and the issue at hand in more detail. It is also a tongue-in-cheek reference to projects their own students might be creating.

View of the front of our sculpture.
View of the front of our sculpture.
View of the back of our sculpture.
View of the back of our sculpture.

Hubway Rides by Neighborhood over Time

Aneesh Agrawal, Kenny Friedman, and Katie Marlowe

The data show routes that people commonly take by using Hubways. We want to tell this story because Hubway can be a great alternative transportation for routes that the MBTA does not cover.

Our data came from, which was a challenge in 2012 to visualize data from Hubway rides. Our data includes information on rides from 2011-2013. We picked a chord chart to visualize this data because this type of chart emphasizes the connections between various stations. The thickness of the chords corresponds to the relative frequency of rides between the neighborhoods. The chord chart points out specific routes that are taken frequently, which leads to the question: Why are people taking Hubways between these stations? Is it because the MBTA does not currently provide a good way to get between these destinations? Or is it just that there are a lot of people traveling between these areas? Specifically, we can look at the blue region (MIT) and the gray region (back bay). There is a thick chord between these areas. We know that it is pretty difficult to get between these areas via public transportation, there isn’t a T line that runs between them. This could be a good indication of a route that many people take without many options of how to get between, so many people decide to utilize Hubways.

If you look at the data over time, then you can see that some stations didn’t exist at the beginning, but were built in the middle of time this dataset is from. By the end of the timeframe, these stations become about half of overall monthly usage. This points to the conclusion that expanding the Hubway system is effective, and we recommend expanding it further. In late 2015, Hubway did announce some future plans for expansion.

View the visualization here.

Katie’s Data Log

Data created on February 7th:

  • Health app on iPhone tracks distance/steps walked and stairs climbed
  • Ski Tracks app tracks one of my skiing runs
  • sent text messages which are both stored on my phone and by AT&T
  • bought lunch with my credit card – restaurant and my credit card company have information
  • took photos with my phone
  • posted a photo to Facebook with a tagged location
  • listened to music on Spotify
  • entered information into the Calendar app on my phone
  • responded to emails
  • Nike Running app tracks the distance and speed of my run
  • swiped my ID card to get into Baker Dining
  • watched an episode of Late Night with Steven Colbert on
  • wrote down this list in the Notes app on my phone

Ragged Mountain Snowfall

As a member of the MIT Ski Team, I frequently look at the upcoming forecasts, hoping for more snow. Unfortunately, the unseasonably warm weather lately has made training very difficult. This is a chart of yearly snowfall at the team’s home mountain, Ragged Mountain, from It shows for each season from 2010-11 until now, what percentage of average snowfall the mountain has had. Ragged SnowfallThe chart uses circles of different heights to correspond to that’s years percentage, and it also includes different colors of the circles to go along with that. Seasons of low snowfall have orange circles, while seasons with more snowfall have bluer circles.

The audience of this chart is people like me, who ski at Ragged often. Since I’ve been skiing at Ragged for a few years now, I know how much snow they had last year and the year before. So when I look at the chart and see that this year has a much lower mark than the previous couple of years, I have a good sense of how much snow to expect. I believe that that’s the goal of the presentation. It shows skiers the trend in snowfall, in a way that’s easy to compare from year to year.

I think that this presentation is effective because I can easily see that this season I should expect less snow than in recent years, which lines up with what the weather has been like lately. However, one detail I wish this chart included is the actual number of inches, rather than only a percentage. Even just an indication of how many inches “Average” is would be a helpful specification.