In Johanna Drucker’s piece titled, Graphical Approaches to the Digital Humanities , the author discusses the ways in which graphical analysis can be manipulated to suit a more ‘humanistic’ approach to visualizing data. She claims that data presented graphical is objective, and is simply a collection of columns, axis, data points that are presented the way that these pieces of information are collected. Drucker states that we are able to differentiate a humanistic approach to collecting data from this standard version of graphical analysis by giving the data a point of view, or a subject. This was extremely confusing to me because I do not see how providing the graphical information with a ‘point of view’ will represent the collected data in a different way.

Even though we live in such a data driven world with a plethora of visualizations thrown at us, challenges still exist with how we can use this data in a meaningful way. In the article, “Feminist Data Visualization,” several questions are proposed about the exploration of data and how we can best use data to be effective but also efficient. In the section titled, Design Process Questions, the authors propose the idea of many excellent considerations while conducting research. For example, they discuss questions like:

How is power distributed across the design team? Whose voice matters more and why? How can end users’ voices be more fully integrated into the design process? Can we build capacity in user communities, or enlarge our internal perspectives, by employing a more participatory design process?

Despite the focus of this article being about feminist data visualization, these data driven questions and solutions can be applied to almost anything in the growing business analytics field. This article did bring forth a lot of key thoughts to consider when using data and am excited to dig deeper into these questions during class. When reading other blog posts, I came across a post by BL about some of the same challenges with visualization. They talk about how “data can very much be subjectively created, and in doing, can be altered depending on how it is chosen to be recorded. It supports the importance of the role of data in telling  story, and questions some more traditional ways that we have been doing so.”

In the article “A Report Has Come Here” by Lauren Klein, Klein outlines how topic modeling works, as opposed to the reading we had by Meeks and Weingart. Klein explains that by using computing languages such as R, we can show the main ideas mentioned in data collected from papers, essays, interviews, etc. As one of my classmates wrote on his/her blog post (http://courses.shroutdocs.org/dcs104-fall2018/2018/10/30/a-report-has-come-here-the-bright-side-of-text-analysis-and-visualization-in-digital-humanities/), searching for main ideas without these programs would be like finding a needle in a haystack, but since we have these programs, it is much easier.

In the article “Feminist Data Visualization” D’Ignazio and Klein discuss how feminist theory can be intertwined with information visualization research. They address how they can bring feminist practice into areas such as STS and HCI. In order to expand thinking about the data visualizations field can be expanded they use how the research regarding feminism can expand the boundaries. I think it’s important to explore where women are currently sitting in the power hierarchy and continue to do research that allows the data field to be stretched to its greatest boundaries. I like the article’s idea of “working backwards” first looking at the individual or institution that has an idea and then proceeding to make a data visualization with that important information in mind.

Classmate BL references how this article is good about showing how  “incorporating a much more humanistic perspective” allows us to explore even more data than we have before. I think this is a good point about how people need to be more open about using different methods to explore already developed fields.

This article was one that I found very thought provoking. Our author was able to take an in-depth look into lynchings and create visualizations from data to different frequencies at which it occurred. It was very upsetting to learn that there was no official information collected on lynchings until people like Ida B. Wells and those of the Tuskegee institute worked on anti-lynching movements. This made me realize that though the visualization and information is useful, it is not complete. We cannot fully know how many lynches actually happened per week or how many lynching victims there actually were per county because there was never and initial data collection origin. I love how the author was able to connect this to today and the extrajudicial killings done by police. He is able to connect that there is no official collection of those deaths but if studied we would not see any new trends because there is not “a radical rise in the rate at which black men and women are killed by police, but only an increased prominence of their position in the public discourse.” Black bodies being punished for their race is not new, it is a trend that has always been carried out by the law.

This article really provides an interesting take on data visualization, incorporating a much more humanistic perspective. It furthers the idea for me that data can very much be subjectively created, and in doing, can be altered depending on how it is chosen to be recorded. It supports the importance of the role of data in telling  story, and questions some more traditional ways that we have been doing so.   One aspect of the article that caught my eye was rethinking the representation of gender in “binaristic categories”, a notion that is closely related with computers and data. How this could be adapted to change how we think about data is something that I’d very curious to learn more about . It makes me think about other ways that we as humans think in a binaristic manner, and ways that we could potentially change this way of thinking.

In the article, “Using Metadata to Find Paul Revere,” I thought the author presented a very interesting way of analyzing and presenting data.  They started out by using a certain amount of organizations and then looking at what each person in their list was a part of, which limited the amount of organizations that they were looking at. If they did it the other way around, a lot more organizations could have been brought up, making the data set even bigger and more difficult to deal with. From there, they were able to invert the table and find different associations between different people. I had never thought of this approach before, but it seems like it is very effective. One of my classmates noted that they liked the tone in which the author wrote the article because it seemed like a “class lecture or tutorial.” However, I felt as though that type of tone wasn’t necessary and drew away from the interesting and informational work he was presenting. When the author said that they couldn’t “show [readers] the whole Person by Person matrix, because [the author] would have to kill [the reader],” it made me pause and distracted me from the reading I was supposed to be paying attention to.

The article, Using Metadata to Find Paul Revere, was a very interesting article and somewhat an echo of the Laura Klein article. Discussing social networks and their ability to identify individuals linked to acts of terrorism with only the use of metadata, and without knowing any context.

This therefore led me to think about the 9/11 terrorist attacks and if any social networks had been done after the attacks, or during to identify those involved. I found that Valdis Krebs, a data scientist and developer of a network analysis software, created a social network of the attack while information of the attack was being made public. When the identity of the 19 hijackers had been made public, he began his social network analysis and visually mapped out their ties. In his research article he discusses the way in which network analysis is being used today is mostly in prosecution and not necessarily in prevention. He explains that uncovering criminal networks is often very difficult because of the nature of their network, in that they are covert network in which associations between those involved are made less apparent by the fact that there are not being activated or are activated through other means that won’t appear as strongly on the map.

So whilst I was persuaded from the Paul Revere reading that social networks analysis would be the easiest way to uncover terrorists plots. From what I gathered from Krebs research, social network analysis is not as simple as it sounds when dealing with illegal organisations.

Link to Krebs article: http://insna.org/PDF/Connections/v24/2001_I-3-7.pdf 

I liked this article because the author wrote it like a class lecture or tutorial. He continued along a plot line trying to understand Paul Revere’s involvement in the many organizations within Boston. Using data analysis he picked his way through the multiple people and parties to find how Revere connects to many different organizations around Boston and signifies his ability have a lot of knowledge about the city. Highlighted by his famous “midnight ride,” this shows how he had the possible connections to understand such an event. A classmate noted “that the correlations and conclusions drawn from network analysis are only helpful in relation to prior knowledge or a data point that acts as a point of contention.” I would agree with this statement because there is a relative amount of bias within network analysis and without a data point of contention, there needs to be an amount of prior knowledge in order to lack bias in the network analysis.

I read “Using Metadata to Find Paul Revere” for class today and found it extremely interesting. At first, I took note of how they used 0 and 1 to represent whether or not the people where in an organization. This is often used in collecting data and let me to wonder why. However, as I kept reading and seeing how many other cool things that could be done with that simple layout of data, I realized how effective using 0 and 1 were and how effective numbers in general can be. This was seen in the matrix that showed which organizations are linked through the people that belong to them both. For example, North Cactus and St. Andrew’s Lodge had three people who belonged to both of them, so there was a three where those two connected in the matrix. This could not be shown as easily and clearly without using numbers, if they had used “yes” and “no” for example when showing if people belonged in the organizations. This was so interesting to me and gave me a better handle on the use of certain techniques in data, especially techniques that allow you to rearrange the data in so many ways, like what was done in this article.