data sculptures – 2016 Data Storytelling Studio @ MIT

Getting to know the refugee populations of Massachusetts

Team: Michelle Thomas, Phillip Graham, Argyro Nicolaou

The data says that 16,214 out of more than 670,000 refugees resettled in the US since 2005 are hosted in Massachusetts. We want to tell the story of resettled refugees because the integration challenges that refugee populations face is something that should involve the entire host community.

For this project, our intended audience is native Massachusetts residents. The aim of the project is to make native MA residents get to know the refugee populations in their communities. We chose to focus on the state’s top three host cities for refugees, using data from the BuzzFeed US Refugee Relocation Dataset. We decided to use the total refugee population of cities, because upon looking at the data we were surprised to see that Boston wasn’t the top city, and felt that native MA residents would also share this reaction about the largest city in their state. We also used information from the US Department of State.

To contextualize our project, we provided information about the number of refugees relocated in the US and gave some background information on the relocation process. This information points to the length of the process, the effort required to actually be relocated to the US and the extent of the vetting process in order to give an idea of how hard it is to get to the USA as a refugee. While not part of our main message, this information was important for us to include because it humanizes the refugee population and helps combat some of the common misconceptions that refugees are a threat.

We chose the map structure because it is an image that every MA resident can relate to. We ‘physicalized’ the data in 3D bars that represent each of the top-3 refugee populations in each city. We chose the bar shape both because it is very legible and because it resembles a building, an image that situates our project within the urban and/or social context of each city. By adding the interactive element of having a sample card in the highest bar, we hope to engage our audience in a physical action intending to reveal more information about refugee population in that city. It was important for us to include a call to action as part of our project. For this reason, each card includes information about a community initiative that helps refugees, together with ways in which people can volunteer/contribute to that initiative’s efforts. The image on the backs of each card quite literally gives a glimpse into the lives of refugee populations in MA, featuring some cultural symbols from the top refugee populations in Springfield, Boston and Worcester. These images come together to reveal a larger image of the side of an apartment building. This intends to bring across our message for the need of of integration of refugee and native communities and to show how we are already all living side-by-side.

data sculpture

#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.

#HearForYou

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: www.worldreliefatlanta.org

Swaying from Truth: Candidates and Their Positions on Issues

Jyotishka Biswas, Eric Lau, Kalki Seksaria, Tiffany Wang

DataSculpture

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.

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. (http://pediatrics.aappublications.org/content/112/5/1206; http://www.teachsafeschools.org/alternatives-to-suspension.html)

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 (https://www.louisianabelieves.com/schools/public-schools/louisiana-safe-and-supportive-schools-initiative-(lsssi); http://www.nola.com/politics/index.ssf/2015/04/louisiana_student_suspensions.html) 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.

Fireworks: Fun & Dangerous

Judy Chang, Gary Burnett, Andrew Mikofalvy

We chose to use the National Electronic Injury Surveillance System (NEISS) as our dataset, accumulating the injury reports from 2009 to 2014. The data logs all injuries related to consumer products reported by a probability sample of hospitals across the country. We filtered the dataset to only look at injuries caused by fireworks. We want to tell this story because we want to raise awareness about the dangers of using fireworks. Our audience is consumers who may purchase fireworks to celebrate holidays, such as July 4th.

We only looked at fireworks-related injuries, and we counted the number of records by the body part injured via Tableau. Our goal was to see which parts of the body are most commonly injured by fireworks, and we found:

	Total
Hand	329
Eyeball	260
Finger	201
Face	177
Foot	62
Trunk, upper	61
Leg, upper	49
Ear	49
Leg, lower	45
Arm, lower	35
Trunk, lower	29
>50% body	29
Ankle	27
Head	27
Neck	27
Mouth	16
Wrist	15
Knee	15
Arm, upper	13
Shoulder	9
Pubic region	9
Toe	9
Elbow	8
Not recorded	3
Internal	1
25-50% of body	1

The most common injuries are in the face, fingers, eyeballs, and hands. We wanted to demonstrate the gravity of these injuries by highlighting these body parts on the human body. We noticed there are roughly 4 clusters for the number of injuries: 0-10, 10-30, 30-100, and more than 100. Our data sculpture is hence a mannequin, where we painted each body part with the shade of red that corresponds to the number of injuries. We used yellow strings on the mannequin to demonstrate the boundaries of the body parts recorded in the dataset.

We also detached the hands to further illustrate the by-far most injured part of the body. The hands of the mannequin are also holding a firework, to show the audience the “source” of the red paint, and a stop sign, that warns the audience that fireworks cause at least 1500 injuries every year and to use caution when they use fireworks.

Our dataset is only a subset of all fireworks related injuries; however, the number of injuries by body part is representative of the fireworks related injuries nationwide.

Can We Afford To Integrate Refugees Into the US?

By: Kenny Friedman, Mike Drachkovitch, and Felipe Lozano-Landinez

The data say that it costs about $65,000, on average, to integrate a refugee into the United States over a period of five years. We want to tell this story because in today’s political environment, which is exhibiting significant anti-immigrant and anti-refugee rhetoric, it is important to understand what it would actually take to grant asylum to global citizens in need in 2016.

Our audience for our data sculpture is the American citizens that reside in the State of New Hampshire. We further characterize this audience as those whose primary concern in the refugee debate is the economic impact of taking in refugees on their state resources, and would also venture to say that this audience is of a more conservative political inclination. Our goal is to help them understand the economic viability of taking in refugees in New Hampshire and encourage them to support refugee in-take for this year.

In order to tell this story, we used three data sets:

The first data set is from a Buzzfeed article about US Refugee Data by Jeremy Singer-Vine, and can be found in raw format in Github. We used this data set to estimate the number of refugees that New Hampshire could expect to take in in the Year 2016 (457), taking into account Obama’s increase in the refugee quota (from 70,000 to 85,000), the percentage of the quota that the US has fulfilled over the last 10 years (82%), and the percentage of US admitted refugees that New Hampshire took in annually between 2005-2015 (0.65%). This was a clean data set recommended to us by Rahul Bhargava.

The second data set is an analysis from the Center for Immigration Studies (CIS) regarding the cost of taking in a refugee over the first five years. We used this data set to estimate how much it would cost, on average, to integrate a refugee into the United States. We define “integrate” as having a refugee be resettled and established over a time span of five years in the US, to be consistent with the CIS analysis. For our “expected annual cost per single refugee integration” calculation, we used the aggregate five-year figure in the analysis and divided by 5 to get our number of $12,874.

Though we understand that CIS very much seems to have its biases against allowing immigration, we decided to use their data for two reasons: 1) Their analysis was the most thorough that we found online with regards to the economic cost of a refugee, and their methodology and data sources appear to of good objective merit, well thought out and fairly done. The bias seems to come from the way the calculations are used, not the calculations themselves. 2) We realized that CIS’s potential bias would be of benefit to our story, because if it manifested in their calculations it would be in their interest to have the economic cost be as high as possible. Our story is about showing that this economic cost is not nearly as high as people think in the big picture; we are essentially using a “worst case” cost, and if our story can be impactful with it then it can only be stronger with a purportedly less biased estimate.

Our last data set is the 2016-2017 State of New Hampshire Budget, which provided us with the 2016 allocated state budget ($5.7 billion) information that we needed to appropriately size our data sculpture. This was taken directly from the Governor’s 2016-2017 Budget Bill.

We think our data sculpture is an appropriate and effective way to tell the data story because it re-frames large, abstract, and scary concepts of cost (spreadsheet numbers that are in the millions and billions) to more familiar conceptualizations of relative weight and relative volume. As such, comparisons can be made much more intuitively between how much it costs to integrate a refugee annually vs. the amount of money that is already in circulation for government purposes. The data is also very personal in the sense that it presents information specific to New Hampshire to citizens from New Hampshire. The experience of placing only a few Jelly Beans (each one represents $3M) from a bucket full of them (the state budget) and tipping the “balance of fate” for hundreds of refugees towards hope is very powerful, both because the audience has agency in this interactive display and also because it takes so little effort to make a huge impact.

Photo:

Final Project Pic

Video Demonstration (to turn into a GIF, you can right-click and click on “Loop”):