Author: jwilliams

The story I chose to read was “The Ferryman” by Azam Ahmed, which you can read here. This is a first person narrative told from the perspective of Malik, a man who transports dead bodies in Afghanistan. In this particular instance he is asked to make a deal with a Tabilan commander, Raheem Gul, to bring back some American bodies. Doing so will put his friend, Commander Farhad in favor with the American soldiers.

Here is my network graph.

In making the network graph, I chose to define an edge as characters having direct conversation with each other. This story only has a few characters that speak, so it is not a very complex graph. However, think that it does illuminate something about the interactions between the characters. For one thing, it reveals that Malik is the main character of the story because he has direct conversation with everyone else. Additionally, we can see 3 real groups or communities that have formed. There is {Malik, Farad, and the American soldier}, {Malik and Bilal}, and {Malik and Raheem Gul}. These make sense because most of these characters have connections to Malik, but not to each other. The network graph illustrates this well.

One limitation of the network graph is that it fails to show the strength of the connection. For example, Malik and Farhad are friends, or at least know each other well, while Malik and the American soldier have never spoken before this conversation, yet both the connections appear to hold the same value on the graph. This could be somewhat remedied using weights on the edges, however, it would be difficult to operationalize relationships.

http://nyphilcollection.com/jwilliams/index.html

For this data visualization, I used my group’s data about the history of the NY Philharmonic. There is a lot going on in this dataset, so I wanted to create something that would help me grasp some trends in the data. I chose to look at the all composers whose pieces were part of a NY Philharmonic program over the 69 seasons that are contained within the dataset. I thought it would be interesting to see if certain composers were really popular at different times, or only at certain times, or whether there were a variety of composers that were used throughout the history.

I created a line graph to help illustrate change over time. Each line in the graph corresponds to a composer that appeared in at least one program over the 69 seasons. I think it does a good job of showing which composers were more popular over others during a specific season and it also shows which composers were consistently popular. However, it is not particularly good at comparing popularity between seasons as not all seasons had the same number of programs or the same number of pieces in each program. This being said, the graph makes certain composers look extremely popular because there were many pieces by the composer performed in that season, however in these cases there were also a large number of pieces performed during that season in general. For example, comparing the 1894-985 season with the 1900-01 season, it appears that Wagner had an insane increase in popularity in 1900-01 compared with 1894-95, however, one must take into account the total number of pieces performed (of which there were significantly more in 1900-01). Between specific seasons, it would be more accurate to compare each composer’s number of pieces to the total number performed.

This data visualization is definitely helpful in showing popularity, which would be difficult to see in the data when it is in a table form.

The dataset I chose to look at is the All City Departments by Payroll dataset for Los Angeles in 2015. It contains the data types of string, integer, and double (float). A record in this dataset consists of Department Title, Year, Job Class Title, Projected Annual Salary, Q1 Payments, Q2 Payments, Q3 Payments, Q4 Payments, Payments Over Base Pay, % Over Base, and Total Payments.

Based on the Wallack and Srinivasan reading, ontologies are “systems of categories and their interrelations by which groups order and manage information about the people, places, things, and events around them”; essentially, they are ways of defining realities surrounding an individual or group. In the case of this dataset, it seems the ontology is concerned with categorizing the data, namely salaries, based on city departments.

This data would be of most useful to someone whose main concern is finance, as that is what the dataset really highlights. For example: how much the city is spending on paying workers in total? Which department is spending the most money? How do these figures compare to other cities of a similar size? How do these figures compare to figures in past years? Specifically, this dataset focuses on the differences in total salaries for the workers in each department, so someone interested in the distribution of the city’s money may also find this data illuminating.

I think one phenomenon that this dataset shows is that the LAPD salaries take up almost ¼ of the total amount paid to city workers. This leads to a lot of potential questions, such as: how does this percentage compare to other cities? Why is it such a high percentage of the total amount of money spent on salaries? Overall, I think this dataset really highlights the stark differences in the total salaries of all the city departments. However, what is left out of this dataset, which I think is really critical, is the size of the department, i.e. how many workers does each department have? Without knowing this information, it is impossible to tell what exactly is causing the discrepancies in total salary between departments. Does the LAPD have many more workers than the other departments? Or are policemen being paid more than the average city worker? Without these numbers it is impossible to say.

If I was starting over with data collection with a different ontology, I would again look at finance and look at the salaries of city workers, but I would organize it not based on department but based on position within the department. So, instead of comparing departments against each other, the dataset would focus on comparing the salaries of those in a higher position in the department to those in lower positions and look at the distribution of the total salaries between these groups.

For this assignment, I chose to look at the finding aid for the collection of material about Japanese American Internment, 1929-1956. There is a diverse range of material in this collection. From semi-annual reports about internment camps sponsored by the War Relocation Authority (WRA) to camp newsletters and high school yearbooks to newspaper articles about Japanese American resettlement, these documents come from both sides: arguing for and against Japanese American internment, from a variety of viewpoints over about a 30-year period. The sheer amount of information and opinions from different sources lends itself well to creating many narratives that these documents would support.

One interesting narrative would be creating a timeline about the internment camps, particularly Manzanar and Tule Lake, based on the information presented in the WRA’s annual and semi-annual reports about these internment camps. Particularly, it would be interesting to use these reports to count the amount of incidents or “disturbances” over time. The scope notes do not detail what is meant by “disturbance” but I assume it refers to protests and riots. This information in the form of a timeline would allow historians insight into ways that the people forced into these camps were trying to fight back and the emotional toll that the ordeal caused them. It could answer questions like, “Were there more incidents in the beginning when people first arrived at these camps?” “ Were there more towards the end as they had been in the camps for a long period of time?” “Or were incidents a common occurrence throughout the time people were interned?” If only the information in this specific collection were to be used in the narrative, it would really be missing personal accounts. All the reports came from the WRA, obviously a source that could contain a certain degree of bias (as the WRA was supported these camps), so it would balance out if the narrative could access personal accounts of these incidents as well. This could be remedied by finding more collections of documents or perhaps conducting interviews.

Another narrative to tell from these documents would be comparing perspectives on the internment camp conditions and day-to-day life. Again, there are the annual and semi-annual reports by the WRA, which would be one perspective, and there are newsletters and essays written by members of the interned community, the obvious other perspective. Comparing what was written about the camps would be interesting because historians would be able to see if there were any discrepancies between the reports, something that could lead into new research questions. The audience of the newsletters and essays was the community of the internment camp, however, it is not clear exactly who the audience of the WRA’s reports was. This would need to be further researched in order to give an accurate comparison between the content of the documents. If only documents from this collection were used for this narrative, it would be missing a real objective perspective. This could be remedied by finding another collection that contains photos of the barracks and other areas to look at the conditions of the camp; a more objective source.

The project I chose is Inventing Abstraction, a digital project with a goal of documenting the beginning of abstraction across artistic disciplines as well as illustrating the connections between artists that allowed the movement to thrive and spread quickly.

To begin with, the main source of information that this project uses is the MOMA exhibit Inventing Abstraction. This exhibit was shown from December 23, 2012 to April 15, 2013 and contains abstract art of various mediums, including paintings, photography, music, and film, from various artists from the years 1910 to 1915. Additional information about the individual artists and about each specific work of art is sourced from various curatorial assistants at MOMA.

In terms of processing, the first way the exhibit was processed was by taking pictures of all of the works that could be photographed and adding videos of other pieces (clips of dances, films, etc.). In addition to the images and videos documenting the physical pieces shown during the exhibition, the digital project delves farther, by also processing information about the artists themselves. The artists were processed by recording connections that artists had to one another. The project defines a connection as “individuals whose acquaintance with one another during these years could be documented”. This is a way of organizing the artists; it begins to support the argument that the movement of abstraction was an effort only possible through collaboration and these connections between like-minded individuals. Additional information about the individual artists and pieces was also organized alphabetically, by artist name.

In my opinion, the most interesting aspect of this digital project is how the information is presented to the audience. Obviously, the basic presentation of this project is over the web. More specifically, all of the information is presented in separate sections. To start with, the homepage has a basic description of the project and its purpose, and then a button “Explore Connections”. Clearly this was done because the creators felt that the “Connections” section was the most important, or at least what should be visited first by the audience. The “Connections” section largely contains a network diagram, easily the most impressive aspect of the project because it presents the connections in a way that is simple and straightforward to understand. Essentially, each artist is shown as a name next to a dot with red lines connecting various dots (artists). Artists with more than 24 connections within the network are highlighted in red. When the mouse is placed over an artist’s name, the lines are bolded and appear to pop out from the rest of the network. My only critique here is that the lines should change color as they pop out, as bolded red lines over red lines are still a little hard to differentiate. When an artist’s name is clicked, the user is taken to the artist’s page; this contains photos (or videos) of all the artist’s works as well as some other facts. These artist’s pages are also accessible under the “Artists” tab, which simply lists all of the artists who have work appearing in the exhibit. Lastly, there is the “Conversations” section; this contains videos giving more information about various works of art in the exhibition. My only critique here is that the videos are presented in a long list of thumbnails, making the user scroll through everything to find what they are looking for; this could be somewhat tedious.

I was not able to find the exact software or tools used to create the website or the network diagram; however, the project credits the design studio Second Story with the design and development of the network diagram.