Author: stefanhanish

For my blog post for Week 8, I decided to read the short story “Lessons” by Justin Torres. This story centers on the life of a hardworking multi-cultural family. The characters include the father, paps, and the mother, Ma, and their three sons, Joel, Manny, and the Narrator whose name is never explicitly stated. The story provides background on their life and how the boys ran around their home and provided two detailed scenes to show how the family lives. The first scene is the three little boys dancing with their father as he prepared dinner, and the other shows a family outing to a swimming hole and how Ma and the Narrator do not know how to swim. There are not many characters in the short story and the lack of an abundance of scenes provides limited interaction, but direct interaction between characters can be tracked to an extent.

The network data visualization that I created for this short story displays the direct interactions between members of the family. I chose to use “direct interactions” rather than characters speaking to each other because there is a lot of very important non-verbal communication between characters in the two main scenes.

This network can be used to identify the interactions between characters. Using this network, a viewer can make many assumptions as to what these interactions represent. This is because this network is very limited in the sense that it portrays a list of binary interactions by answering the question of whether two characters interacted. Nothing of deeper meaning can be extrapolated from this data without making large speculations. Some assumptions that could be made from this data is that Paps and Ma interact with the Narrator more because he is younger, because he needs to be disciplined more, because he needs to be taught, or simply because the story is told from his point-of-view.

Another pitfall of this data visualization is the fact that it is representing interactions between individuals when the short story wasn’t about characters interacting. The main point of this story was to provide a glimpse of the life of this family and how their history has influenced how they live on a day-to-day basis. While networks can be useful tools, they primarily just provide a more visually pleasing way to answer a binary question. I do not believe that the utilization of network graphs is the best way to represent data found in a short stories.

For this week’s blog post and critique of a digital humanities mapping project, I chose the 19^th Century Caribbean Cholera TimeMap created by ten individuals from the John Hope Franklin Humanities Institute. This mapping project combines the use of a geographical map, timeline, and several variables followed across both of the media as well as further description by employing the use of published articles and news stories concerning the occurrences. The timeline covers three separate time periods including: 1833-1834, 1850-1856, and 1865-1872. On the two displays, the variables shown are cholera outbreaks, hurricanes, tropical storms, and news articles. It is clear to see from observing the set of data without delving into the it too much that the main goal of this project is to find a correlation between natural disasters, hurricanes and tropical storms, and the outbreak, spread, and general prevalence of cholera in the Caribbean.

David Turnbull puts forth the argument that all mapping data visualizations are perspectival and subjective. This assertion falls in line with the definition of “narrative” that we have been exploring in class. Both of these statements mean to elaborate on how the researcher or data collector is the individual who deems what is important in the dataset and what will ultimately be used to argue for a stance of their choosing. This means that in some circumstances that the data is somewhat misrepresented to promote the researcher’s own agenda.

Using this knowledge garnered from Turnbull and what has been taught thus far concerning narratives, one can see how this mapping visualization can be skewed. The researchers are attributing cholera outbreaks solely to the occurrence of natural disasters. This is an over-generalization because cholera is due to the consumption unsanitary water or food. While it is true that tropical storms and hurricanes play a role in causing cholera outbreaks, they are not the only cause. Only using this dataset, one would come to the assumption that cholera outbreaks were directly caused by tropical storms and hurricanes. In reality, there are many causes to cholera outbreaks including poor infrastructure of water and sanitation systems, geographical location, and economic status and industrialization of the country in question.

This map reveals that there is a correlation between the occurrences of tropical storms and hurricanes with cholera outbreaks in the Caribbean; however, it obscures what the other possible causes may be and whether or not these tropical storms and hurricanes are simply correlated or have an actual causal effect.

I believe a better mapping visualization for this project would involve other variables that could provide causation or correlation for cholera outbreaks, a shaded gradient scale of the country’s GDP, and provide the year that the country industrialized. The inclusion of these variables would further elucidate the cause of cholera outbreaks.

For my blog post this week, I decided to examine the dataset that contained the presidential election voting results by party. This dataset includes the voting results from every post-World War II presidential election. The data is categorized by votes per party and votes per candidate. I thought that using this method for data visualization would show the voting trends by party lines over the course of seventy years better than any other form. This representation of the data allows the viewer to see how events in American history and reactions to certain presidencies changes overall voting.

From the data presented in this dataset, many correlations can be found between historical events and voting results and many trends can be viewed about the change in voting over time. There are many ways this dataset can be visualized due to the vast array of information presented. One can organize the data by republican candidate, democratic candidate, incumbent candidate, democratic total vote, republican total vote, or total vote. By having such a large list of categories to choose from, one can find many correlations of voting trends and habits based on different criteria.

One of the most evident trends that can be seen over time is the increase in voting as a nation. Many assumptions can be made about this increase in overall voting. One could postulate that this trend is solely due to the increasing population of America and the Baby Boom Era of the 1940s-1960s. If more data was presented along with this dataset such as economic details of the country or approval ratings of presidents, assumptions along the lines of an increasing national debt causing the general public to be more involved or a disdain for a certain president and want to change party could be posited.

Other interesting trends can be seen when showing the total party vote for a certain candidate in a certain election. These trends can be used to show what attributes in a candidate are more effective when running for president and which ones are more deleterious. It can also show the margins by which a presidency was won and to what extent overall voter turnout had an effect on the outcome of the election.

Overall, the vast amount of data that is presented by this data set allows the viewer to manipulate it in many different ways and achieve various outputs. This data allows the viewer to make many connections and even more assumptions about presidential voting in America. However, this dataset could be improved by the addition of a qualitative element to show historical events or controversial bills that presidents passed along with more quantitative elements such as approval ratings and economic descriptions of the eras. These would allow more concrete postulations to be made about the data

I chose to view the City Budget Expenditures for the city of Los Angeles. This dataset goes categorizes all information by budget year, fund name, account name, adopted budget, total expenditures, budget change amount, budget transfer in amount, budget transfer out amount, total budget transfer, encumbrance amount, pre-encumbrance amount, account group name, fund name, account, and department. This data set is a complete record of all expenditures by the city of Los Angeles from 2012 through prospective expenditures in 2017.

A record in this data set is constituted by the combination of several inputs in order to categorize an expenditure made by the city of Los Angeles through their specific city budget. This combination of data is used to organize all expenditures into an orderly format that is easily digestible and trackable by the viewer.

Wallace and Srinivasan define ontology as “the distinct systems of categories and their interrelations by which groups order and manage information about the people, places, and events around them.” This dataset’s ontology is how the city budget of Los Angeles is utilized by various city and local level governmental agencies. From viewing this dataset, the priority levels for the use of the city’s money can be distinguished as well as which groups are allocated what sum of money. This data will be found interesting from officials in the treasury department at local, city, state, and federal levels of government. Additionally, any individual wanting more information on the use of taxpayer money can be enlightened by information from this dataset. For example, a viewer of this dataset can find information on how much money Los Angeles spends on salaries for the Recreation and Parks department or how much money is used on the upkeep of the Granada Hills Pool and Aquatic Center by the aforementioned department.

While this dataset does a great job at showing what expenditures were made by the city and when they were done, it fails to go into enough depth to actually allow the viewer to fully comprehend what the expenditure actually accomplished. For example, when looking at the General Services Department category, one can see that just under $5.6 million was allocated toward a fund and account named “General Fund: Maintenance Materials.” This fails to shed any light on what was gained by this expenditure. I believe this dataset could be improved by including very brief one sentence descriptions of what was accomplished by the use of this money for each data point. While this may seem tedious, taxpayers should know what their money is going towards in their community. Furthermore, creating completely separate folders by year would make this dataset more readily understandable because viewing expenditures anywhere from 2012 to 2016 in the same area can cause the data to become murky.

I chose to examine the Finding Aid for the George Meyer The Simpsons script files. The physical collection is held at the Charles E. Young Research Library at UCLA.­ The collection is an all-inclusive seventy-eight box file on all of George Meyers work on The Simpsons. The boxes contain script files for the television program from its second season through its sixth. They also include story notes, outlines, and various drafts of scripts written by Meyer.

There is a short biography included on the site before it delves into the manuscripts themselves. This biography of Meyer helps to put the rest of the site into context and allows the viewer to understand what inspired his creative process and the subject matter of his writings for The Simpsons. The biography tells of how Meyer graduated from Harvard in 1978 and was accepted into medical school but never enrolled. During his time at Harvard he began professionally writing for Lampoon and, shortly thereafter, took a job writing for the David Letterman Show. It goes on to detail how Meyer created some of Letterman’s signature bits which, with a knowledge of his work, can be deemed to have inspire aspects of plot lines in The Simpsons. He then took a few more writing jobs including one at Saturday Night Live and writing a magazine before he ultimately settled down and began to write the life of Homer Simpson. Using the biographical information that is provided in combination with a knowledge of The Simpsons and the rest of the collection, one can put together a narrative that can of Meyer’s artistic process and the key points in his life combined with the information gleaned from the collection.

Viewing the collection itself, one is presented with a vast array of scripts, story notes, and outlines from the show. The arrangement of the files appear haphazard because they are organized alphabetically rather than chronologically. A chronological list of the items would help clean up the appearance of the information and allow the reader to peruse it more readily. Additionally, it would shed light on Meyer’s development as a writer throughout his tenure with the show. Furthermore, beyond titles of documents and limited descriptions, the finding aid does not provide the viewer with extensive information of the works. The fact that so much is left wanting from the viewing of this collection takes away the ability of the viewer to construct an accurate narrative of Meyer’s work in context with his life.

Ultimately, this finding aid does an average job in presenting the viewer with information from George Meyer’s tenure as a writer for The Simpsons. While a lot of from his life can be learned from reading the biographical section, it remains difficult to accurately follow and put his work into context with his life.