Blog Post 7 – While the Nightjar sleeps

This week I read the short story While the Nightjar Sleeps from the online Granta magazine and chose to examine it through a network analysis.

The story narrated in first person through the eyes of the young boy. It starts off on a cold winter evening when the boy has finally grown old enough to go hunting with an uncle – Mr. Davidson. During this hunt, they come across a Nightjar that the boy is instructed to take back to the house.

As they near the house, the boy hears laughter and assumes that Mr. and Mrs. Davidson have invited their friends over. Ever since his father died, his mother and him rarely visit this house anymore unless Mr. and Mrs. Davidson are hosting something. The boy is congratulated on catching the bird, and the bird is placed in a bowl. In the meantime, one of the guests – referred to as the mole man, begins to tell the legend of the Nightjar as part of tradition. After the story, everyone rises and forms a circle around the bird and start laughing hysterically. The boy is confused and frightened, when the mother signals for him to follow her and tells him they are the chosen ones this year.

He follows his mother upstairs, into his father’s study where he sees a man, with his back faced towards them, sitting in his father’s chair. His mother breaks down and starts talking to her husband, when the boy knows that this really isn’t his father. It’s just an illusion created by the “red dust” the people have created.

week-8-post-viz

I chose to represent a network diagram of the relationship between the prominent characters. I decided that each relationship would signify that one character directed addressed the other and communicated with them. Due to the short length of the story, there are only few characters that had prominent speaking roles. Additionally, since it was in first person, most of the dialogues were spoken to the boy. The size of the bubbles shows which characters were interacted with the most; hence the boy with the biggest node.

Blog Post 6 – Digital Harlem

This week I chose the Digital Harlem  project to analyze the mapping techniques utilized to represent such data. This project chose to visualize everyday life in Harlem city of New York, by drawing from legal records and newspapers in the time period 1915-1930. The map is extremely interactive and consists of multiple layers that you can overlap to view to data according to your needs. The map also depicts details of that particular event when you click on it if the user wants to go more in depth.

bp

This map is definitely subjective since they used newspaper articles and records to depict “everyday life”. Since newspapers only cover significant events, the map mostly depicts events of crime or arrests that would commonly appear in the local news at that time. Additionally, this data is presented from the perspective of the police officers, who were predominantly white males which skews the data. This creates the bias of an unsafe or “bad” neighborhood that was mainly occupied by black people since most of the crimes are concentrated on one area on the map.

If I had to accurately depict everyday life, I would add more features like events happening around the area or community gatherings. I would also depict the popularity of these events or community places, by recording the number of people attending and so on. In this way the map could be used to understand how most people spent their lives during that time rather than cover events of crime.

 

Blog Post 4 – Visualization

For this week’s blog post, I decided to use our project data and create a visualization that would help understand our assigned data better. For our final project, we have been assigned the Marvel and DC database of superheroes. However, for this visualization I only used the DC database. I created an alluvial diagram on RAM to show the correlation between the genders of superheroes and their eye color.

screen-shot-2016-10-24-at-8-46-42-pm

We notice how the distribution of eye color between both genders is pretty similar. The affinity for blue eyes is high in both male and female characters. We also notice many varied colors that we would normally not think of eye colors, such as violet, amber and pink. This visualization is also interactive, that is, if you hover over any of the relations it tells you exactly how many heroes in that gender have that specific eye color. Additionally we notice the sheer difference in the number of male characters as compared to female or genderless or transgender characters.

Creating this simple visualization really helped me look at my data in a different way. It even helped me realize that there were discrepancies in my data, and gave me direction to clean and filter it. For example, I noticed there is a segment of auburn hair in the eye color column, which means some data must have been entered in the wrong column by error. Although, the purpose of this visualization was not to find discrepancies in your data, rather to provide further insight. I think it really helped me understand my data better as a person with no prior knowledge handling data or any visualizations, especially in such large quantities.

 

Blog Post 3 – Top Earners

For this week, I decided to analyze the Top Earners dataset from the LA City Controller’s Office. The data primarily looks at different occupations vs. the amount they earn. Their salary is further broken down into categories to help us better understand the data. The data types are the occupation, salary, and the types of pay. Some of the types of pay are base pay,  bonus pay, overtime pay, and others. A record can be described as the salary for each occupation. The recorded this information from payrolls from the year 2013, and are updated on a quarterly basis.

Wallack and Srinivasan define an ontology as “systems of categories and their interrelations by which groups order and manage information about the people, places, things, and events around them.” The ontology in this particular dataset is how a single record of salary is broken up into multiple parts to better understand how the salary is structured within different occupations. For example, we can see how a main chunk of the fire captain’s salary is made up due to overtime pay, whereas the port pilots is due to bonus pay. We can also compare the different base pays across occupations and whether the majority of their income is due to other varied factors.

screen-shot-2016-10-17-at-9-54-20-am

We can draw a conclusion that most of the highest earning individuals are port pilots, apart from that particular fire captain, in the top ten. This information is useful for researchers who would like to know how the salaries of these positions are divided and which occupations earn more on overtime vs. bonuses.

I found it interesting to see how the actually salaries were almost double the base pay, and it was very interesting to learn how they were structured. I think adding more details like how long the people held the position for, and how their salaries evolved with experience might have been useful to a researcher. It might also be helpful to see the gender of the person in this role and compare the differences in the salary of the same post between genders.

 

 

Blog Post 2 – George Meyer on Simpsons

This week I chose to explore the finding aid for George Meyer Simpsons Script Files . This aid consists of the script files, for seasons two through six, for the popular television show – The Simpsons.

According to Hayden and based on the discussions in class, we concluded that events become history only once you add narrative to these series of events. History is shaped by the author of the story and his/her personal bias plays a big role in the interpretation of the events. This aid helps us understand Meyer as a person before looking at the scripts he worked on. George Meyer was born in Pennsylvania and graduated from Harvard in 1978. He worked on multiple well known platforms such as the Harvard Lampoon, David Letterman Show, and Saturday Night Live before he moved onto the Simpsons. Through these prior platforms he built his network in the entertainment industry while gaining experience. Meyer began by writing for the show and turned into an executive producer later on.

The files include everything Meyer has worked on or assisted in from notes, outlines and drafts of episodes to designs of characters, objects and so on. The collection is arranged alphabetically by script title, which doesn’t really organize the files for a third party. I would have preferred for them to be ordered by episode numbers or air dates to create a timeline instead. Since the Simpsons is a satirical show, the timeline would help us draw comparisons from the episode topic to the current situations during that time in history, which would encourage narrative. The list doesn’t give us a lot of information about the files. Most of the files contain the episode name, the writers names, the date, and the type of document it is. I’m not completely aware about whether finding aids are supposed to provide more detail, but I would have loved to further explore each sub file to gain more insight.

If I had to write a narrative based on this aid, I would mostly be able to talk about Meyer’s biography and how his background led him to work on the Simpsons show. Additionally, I would be able to talk about the various types of pieces he worked on and the people he worked with during different times. This is not enough information for me to create an accurate narrative of the events without delving deeper into each file.

Blog Post 1 – Early African American Film

screen-shot-2016-10-03-at-8-31-59-am

I chose to reverse engineer the website Early African American Film, which aims to reconstruct the history of African American silent race films, before the 1930s, through a database. This database was put together by drawing from multiple primary and secondary sources and consists of the films, the actors involved, and the production companies. Race films are essentially movies created for African American audiences featuring an all African American cast.

The team created the database using multiple primary and secondary sources like the George P. Johnson Negro Film Collection in UCLA’s Special Collections, Mayme Agnew Clayton Collection of African-American History and Culture at the Mayme A. Clayton Library & Museum (MCLM), and so on. These collections consist of varied media such as production documents, newspaper and magazine clippings, and posters. The team personally went through these and noted which items fit their criteria of a race film.

After filtering through the sources and digitalizing the data that was required for this project, the team presented the data in a table format. The team also documented pictures of the items they deemed necessary. The database was created using Airtable, and consists of four main tabs: People, Films, Companies, and Sources. This table provides easy usability through features like ability to filter, group and modify the data to download for the user’s own purpose. There are also tutorials on the website to help the user create their own visualizations with the data.

The data is presented in multiple ways on this website and is categorized by films, people, and production companies. The films are illustrated by a histogram depicting the number of films premiered each year during the specific time period. The people are depicted in two interactive visualizations, one where you can view which people are connected through films and the second where you can see how they are connected. The production companies are represented by a time map that shows the regions where production companies were founded each year.

screen-shot-2016-10-03-at-9-37-08-am              screen-shot-2016-10-03-at-9-37-22-am        screen-shot-2016-10-03-at-9-37-34-am

Overall, this website is extremely user oriented and encourages the user to work with the data and expand on it. It is very efficiently organized and easy to use and understand. It also does a very good job of explaining the data and increase interest in the subject of African American silent race films.