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Assignment 1

Assignment #1

Mapping 31 days in Iraq
Programming Languages Influence Network

I chose these two visualizations various reasons. The top visualization shows a map of Iraq showing deaths from January 2006. The bottom one is a visualization of different programming languages and their influences. The first visualization is a static visualization, where you can look through the image but cannot interact with it. Although it is static, it does present the data in a new perspective. It shows the deaths over a month, where most of the people who died are civilians. This visualization attempts to show the humans who died rather than just giving a number. As D’Ignazio and Klein stated, “the perspective of only one group of bodies becomes invisibly embedded in a larger system”; in this case the perspective of the American media is the only perspective we see. This perspective usually fails to show the innocent lives lost in Iraq due to war. Much like how Du Bois had “the politics of visuality, and the very question of black visibility” in the back of his mind, the visualization attempts to make visible the invisible innocent lives lost during the war (15). It shows the human cost of war.

The second visualization is dynamic and interactive. The visualization shows a hierarchical system where languages are connected to their influencers and languages they influenced. This visualization can be seen as a genealogy tree, which “incorporates the tree to illustrate growth and subdivision over time” (Lima 25). The interactive aspect of the second visualization allows user to click on any specific language to focus and center it. The shape represents the numbers of languages influenced by it. The color represents the type of language. As Meirelles states, “we process spatial properties separately from object properties” (19). Here it is apparent that one type of language can influence another type. These visualizations allow users to explore, understand and discover new ways of understanding the data.

The two visualizations from the Digital Humanities Sample Book I chose are the Native Land and the Spread of U.S. Slavery. I chose these two maps because they help visualize information in different ways. The Native Land is an Indigenous-led project created by Victor Temprano. The map is dynamic meaning users can click through and see what native tribe lived in specific area. Users can look by territory, language and treaties. It provides users multiple ways to look through the data.

The Spread of U.S. Slavery is a map created by Lincoln Mullen. The dynamic map can be used to look at the number of slaves from 1790-1860. It also shows the number of free African Americans in particular place in the US. The timeline on the map can be used to see how the spread of slavery moves down to the south while the number of free African Americans starts increasing in the north. Both of these visualizations allow users to look at the data through different perspective as well as learn and discover information.

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Practice

Blog Post #1

Being able to read and work with data is becoming increasingly important in today’s society. Data literacy is essential as more and more data are collected, used and misused. As more data is collected and used, it can result in more bias and could be misused. As a computer science major, I understand how biased many datasets are and how these biases can influence the world. A biased data leads to biased algorithms and biased people, causing more bias in the world. As expressed in their article, Catherine D’Ignazio and Lauren Klein, most data does not equally represent everyone thus it is inherently biased. This is where data literacy is important as that skill can be used to account for the biases in the data.

When used correctly, data visualization can provide a quick and simple method of reading a complex dataset. However, when misused, data visualization can misinform or even harm society. An example of a harmful data visualization is when politicians misrepresent their influence. The image below misrepresents data by visualizing an increase in petrol price as a decrease.

Tweet from India’s Prime Minister showing the “decrease” in petrol price.

The image below shows another example of a bad or misleading data visualization. The data is represented by a pie chart, where the whole does not add up to 100%. The pie chart leaves out the remaining 35% of the people.

Pie chart attempting to show how much of Skimm’rs have received treatment.

The biases in data and the misrepresentation of data in a visualization are two of the many reasons why data literacy is an important skill in the modern society.