Tag: us-refugee-data

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.

 

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 

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:

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