Chana Kraus-Friedberg
Background:
This project explores different ways to visualize and understand the ongoing global refugee crisis. I became interested in this topic due to the Syrian refugee crisis, which was prominent in the news slightly before I started working on this project. The more I dug into the data and read, the more I realized that our public discussions about refugees are almost completely divorced from what actually happens in the US and what our legal responsibilities are towards those who flee danger in their countries of origin. I wasn’t able to include all the information I found in this project, but my goal was to make the public discussion about refugees in the US at least a bit clearer.
CUTDDV
Context:
Environments of News Consumption: desktops, laptops, public transportation. Unfortunately, Tableau isn’t ideal for tablets or phones, since a lot of its interactive capacities are really designed for laptops or desktops. I therefore tried to make images that are informative on their faces (even if you can’t interact very much with them).
Users:
News readers: relatively educated/literate. I tried to be sensitive to issues of color blindness-Tableau is unfortunately not amazing for users with limited eyesight or mobility.
Task:
To clarify the discussion surrounding refugees in the US: I wanted to give users some insight into what the key definitions are, how the US compares with the rest of the world, and how the US sets its priorities in terms of admitting refugees.
Data: spatial, temporal, continuous
Data Visualization:
Story Point 1:tree map, allowed me to compare a large number of variables (refugee resettlement numbers for every receiving country in the UN database) in a way that wasn’t unwieldy-bar and line charts would have been difficult to format, since every country would need its own line or bar.
Story Point 2: I wanted to include the text on this page because I couldn’t see how those definitions could be visualized, and they are essential to understanding the difference between the data in the first story point and the later data. I used an image to split up the text a bit, and made the text relatively large and dark to make it easier to read.
Story Point 3: packed bubble chart, because it allowed me to compare a large number of values, similar to the tree map, while also allowing me to use color to code the countries by region. I also like the visual impact of having different sized bubbles at different places in the charts.
Story Point 4: scatter plot, displays the relationship between two variables. I created this one because I was wondering how representative the percent-of-applicants-admitted really is—in some cases it might be possible (especially given the fact that the US does set yearly limits on refugee admissions) that a low percentage admitted might occur because there were so many applicants from a given country. By the same token, a low percentage admitted might actually represent a high number of refugees if the applicant pool was large enough. I labelled what I thought were important outliers on the chart, but users can also filter the chart by continent/region.
Story Point 5: this image is partly a way to compensate for the random choice of the year 2014 for examination. I also like the visual impact of watching the colors of the countries change as you click through the years.
I have neither given nor received aid while working on this assignment. I have completed the graded portion BEFORE looking at anyone else's work on this assignment. Chana Kraus-Friedberg