Author: ktran427

For this network analysis assignment, I decided to create a network graph based off the characters from Misuyo Kakuta’s short story, Bucket of Eels. Bucket of Eels implicitly tells a narrative of Kakuta’s love life as she explores the city of Tokyo, Japan. As the story progress, we find the narrator empathize with the eel shop owner’s wife.

After reading the short story, I created an edge list consisting of two columns, eels stores and characters that the narrator interact with in corresponding store, representing my nodes. The connection between the store and character is depicted as “edges” to illustrate the interaction the narrator had with the said character at said store. I wanted to show the relationship between the narrator and the people she interacts with at the different stores, to illustrate any possible connection between each other. Ultimately, the readers can clearly see that the owner’s wife both appeared in both stores. As someone looking at this particular network visualization without any context of the story, one can be very puzzled by this because we see that the connection is the store owner’s wife of two different stores.

(note: I tried to embed the google fusion graph onto my blog post, but was very unsuccessful. So, here’s a snapshot!)

In fact, spoiler, the narrator sees herself in the store owner’s shoes, unhappy with her love life and struggles to be content with her partner. In the story, she eavesdrops the conversation between the store owner and his wife at the first eel store Isshin, noting the especially harsh tones and attacking language that’s not often displayed in public. As time progresses, she runs into the same woman at another store with what seems to be another eel chef. The narrator infers that the woman’s previous marriage was unsuccessful, which is later revealed that the previous husband eloped with another women. She empathizes with the store owner’s wife because she sees herself unhappy with the 3 people she dated in her life. The story sheds some light as the narrator realizes that she should not compare herself to others as it may have been all in her head when she confronts the store owner’s wife.

This network graph tells the audience quite a lot about the relationship between the narrator with these characters in the eel shops. However, upon closer inspection it only tells so much, that she ran into the same lady at the different stores, but it omits this powerful narrative disclosed in the paragraph above. Although this graph was very simple, I hope implement network graphs within my own digital humanities project to be able to make connections that may not have occurred to me before.

For this blog post, I decided to work with the Caribbean Cholera Map. This particular digital humanities project illustrates the outbreaks of the cholera disease around the Caribbean area. While simultaneously utilizing both a timeline and a set of points within a google map, it tells a very compelling argument of how these outbreaks are tied to naturals disasters, such as tropical storms and hurricanes. By depicting that these outbreaks occur subsequently after the storm on the timeline, the project logically proves that the origins of cholera were due to natural causes. The project managers were able to supply news journals and articles as evidence to further prove this. However, this project only reinforces David Turnbull’s argument of how maps have the ability to manipulate the narrative without meaning to.

The fact that it only provides information about the natural disasters implies that these storms are the only causes of the cholera outbreak. It assumes that there aren’t any other types of causes of cholera because it omits these possibilities on the timeline. According to my outside research, the first cholera outbreak originated in Russia through trade routes. Who is to say that human contact from the slave trade and poor conditions on the boat didn’t contribute to further development of cholera, yet it isn’t evident in the timeline.

Since this current map is very vague and generalizes the causes of the outbreak, an alternate, yet ideal map for this particular project would specify what other factors may have played into the causes of the outbreak. In the timeline, there are other storms that occur around the Caribbean area especially in 1937 to the early 1940s without an outbreak to cholera following it. This goes to show that there may have been another factor played into this, otherwise there would be multiple cholera outbreaks throughout that listed time period. Or could it possibly be because of the intensity of the storm? The map only provides us information of the duration and coordinates of the storm, but there could be very light storms that can go on for days as opposed to a heavy one. The map should provide some sort of measurement of the storms if possible. Also another question the map could answer is, what determines an outbreak of a disease? It’s very subjective in the sense that one definition of an outbreak can be very different to another. This goes to show how subjective maps are because it’s all about perspective.

http://www.nytenementproject.com/Kevin’swebsite/index.html

I chose to analyze the data set on men’s body fat measurements where the statistical data was provided by the Journal of Statistics of Education website. This specific data collection consists of different types of measurements of fat, ranging from their overall body fat to specifying measurement size of certain body parts, such as the abdomen, hip, knee, wrist, etc. From the metadata, I wanted to see if there was a relationship between body fat and certain sizes of a particular body part. If so, what kind of relationship?

Using Silk, a data visualization generator website, I was able to test my hypothesis using a line graph. Originally, I wanted to use a scatter plot graph, but it restricted me plotting two columns showing the only one set of data. The line graph can be misleading because the magnitude between each point shows that there’s some sort of relationship between all the each individual data point, but in fact that there’s no relationship between let’s say participant 156 and participant 87.

From the data visualization attached above, we can see that as the those who has more body fat tend to have a larger abdomen, as evident in the upward trend under abdomen. However, what I discovered was that body fat doesn’t necessarily have an affect every single part of your body. When I graphed all 252 men’s wrist size, I was able to see that there was a constant line, averaging around 18.2. One could easily assume that one’s wrist size could have a direct relationship to the how much body fat one has. With this visualization it was a lot easier to see the kind of relationship body fat has to the size of other body parts. Similar to what Nathan Yau said in “Data Points: Visualization That Means Something,” “With visualization, when you know how to interpret data and how graphical elements fit and work together, the results often come out better than software defaults.” If one were to view it from the excel sheet, it is definitely a lot hard to spot this trend.

From the L.A. Controller’s Office, I decided to explore the City Budget Expenditure dataset. This dataset demonstrates the how much the city of Los Angeles aims to spend as their budget compared to much how they actually spend under their expenditure. I found this dataset interesting because I’ve always wondered how much money is allocated to each department from the budget and how much is actually spent by the city.

 
The data set is categorized by many records types: Fiscal Year, Department Name, Fund Name, Account Name, Total Expenditure, Budget Transfer In/Out Amount, Total Budget, etc. Under each category, it allows readers to juxtapose how much money is being budgeted under a certain department as opposed to how much money is being expended from the account, and any other additional transactions to make this expenditure possible. We can begin to understand that certain departments required more funding, such as water and power.

 
This dataset is catered towards policy makers and city administrators to oversee the city budget to ensure that the money is being efficiently spent. And if the money is not being efficiently spent, they can continue to utilize this dataset to infer which policies need to be made in order to cater to departments that need more funding. However in order to further understand the dataset, we need to understand Wallack’s and Srinivasan’s article, “Local-Global: Reconciling Mismatched Ontologies in Development Information Systems.” They argue how “technologies that could improve living conditions and economic opportunities [are] rejected because they were inconvenient for community practice” (Wallack and Srinivasan 3). This creates a problem for policymakers because in order to be sustainable, they need to gain a better understanding on community ontologies. However, there isn’t enough information on state ontologies, creating information loss.

 
From this dataset, we can infer that the city officials overspend than budgeted. However what gets left out is how much of that actual money was effectively spent. If only there was a separate column specifying actual cost, we can see how much money is being spent to pay off the cost of each department’s projects and fees. And if we see that the expenditure amount exceeded the total cost, we can infer that the money was being spent on inadequate costs. This excess spending could have been reallocated to a department that needs the money more.

 
This dataset could also be seen from the point of view of citizens and voters, who have the power to either re-elect the current government officials or elect a new one if they are unsatisfied with the current one and their contribution to the LA community. This places many government officials in a tough spot because they want to do what is best for the community but at the same time they want to spend the money efficiently, which may not be beneficial for the entire community, so finding that fine balance is really hard.

The Collection of Material about Japanese American Internment, 1929-1956 bulk 1942-1946 consists of four boxes and one folder pertaining to the different narratives of those who were affected by the Japanese American internment. When the day of Pearl Harbor in 1941 happened, many Americans feared that the Japanese Americans would overthrow the government. Regardless of their citizenship, the government incarcerated many Japanese Americans during World War II. With that said, it justifies the main reason of the bulk collection around 1942-1946. Each box within the collection contains different types of documents ranging from speeches, transcribed broadcasts, to posters from those affected from the internment to portray the many different types of perspectives.

In the first series of the collection, it contains many text-heavy articles provided by the War Relocation Authority like publications and speeches. These documents support the decision to forced Japanese Americans out of their homes and into deserted, unsanitary, and prison-like institutions. From their point of view, many Japanese Americans were seen as a threat because of what the Japanese were capable of at Pearl Harbor. Assuming that they would be loyal to their own home country, the American government immediately took action and did what was right for the country, regardless of the status of the individual’s citizenship. The second series of the collection tells the narrative from the internees’ perspective through publications and newsletters. Those who had to endure the abuse and mistreatment of the camp. Given that majority of the victims were locked away essentially due to their ethnicity, these Japanese Americans were oppressed because they racialized to cause harm to society. The living conditions in the camp was beyond intolerable. It has such a negative impact on so many individuals that there are many people suffering with post traumatic stress disorder from it til this day. The last series labeled miscellaneous is compiled of different types of media, such as articles, posters, radio broadcast which serves as evidence of the repercussions of Pearl Harbor. Although these primary sources do not take a side on the internment issue like the previous two boxes, it provides a more neutral narrative to give more context of the time period.

I feel like one huge thing that this collection is missing especially is sources from those who were affected from the internment indirectly. For example, neighbors or employers who had any personal connection with Japanese Americans should talk about how the internment had any positive or negative effect on them. These sources can clarify whether or not some people felt safer that their Japanese American friends were taken away to a prison camp or if they critiqued the idea of internment. One can find articles or sources of how the overall population felt from the internment and how they reacted to such context. Their input helps expands the narratives of those who were indirectly affected and will raise more awareness about the collection and the issue.

The FSA-OWI, Farm Security Administration – Office of War Information, was a program created by Roosevelt during time of the First New Deal as a reaction to the Great Depression to assist poor, displaced farmers for resettlement. The FSA compiled an archive of photos demonstrating daily life in order to challenge rural poverty. The main source of pictures were gradually compiled and stored in the Office of War Information in Washington DC. The Liberty of Congress helped contributed through reorganizing, cataloging, and maintaining the photos for the collection. The interface Photogrammar makes it user friendly to view these primary sources and categorizes each individual photo based on time, location, and photographer.

 

Under Maps, Photogrammar does a wonderful job tracing these photos based on location, over the United States, and time, from 1935-1946. Using the leaflet program, the creators of Photogrammar’s interactive map tell a story about the various photographers’ journey as their work illustrate the lifestyle of the many displaced farmers to depict the repercussions of the post great depression. The map organizes the pictures in ways where you can filter it based on artist and time. It also has two subcategories, the “countries” and “dots,” which demonstrated where a certain photographer’s work was heavily concentrated at, or if a photographer traveled to a certain destination as evident in Jack Delano’s. Once you click on a specific spot on the map after filtering the year and photographer, you can view their collection of photos specifically on that time and place. As the photos accumulate over time and among other photographers, it forms a very grand compilation of photos for people to view.

If the user is trying to find something specific, one can easily utilize the search feature in Photogrammar. This feature is catered to people like historians to trace back something that they are looking for in particular, such as images from a specific city during a specific year. Rather than clicking and waiting for the page to load after every distinguished filter on the map, the search feature has many categories so that one can filter the archives of images to help you find what you’re looking for within a click, given that all the desired information is inputted.

What I found really interesting about the website was the very last tab labeled “Labs.” It thinks outside the box and gives these pictures a different type of interpretation. For example, for the Treemap feature, it starts of with very general list of keywords to describe an aspect of human interaction on a daily basis, such as transportation, religion, etc. Once you click on a specific aspect, it creates a new set of subcategories that falls under it. As you continue to narrow down your search, you are able to find a collection of photos that fall under that specific topic. It allows many individuals to expand their search if they are focusing on a certain topic, rather than limiting themselves to a time, place, and photographer. The Photogrammar website make is a lot easier for historians, researchers, and students navigate and learn about the social issue of rural rehabilitation.