Author: msloureiro

 

For this week’s blog post, I decided to read a short story called “The Tenant.” This story is about a woman in her mid-40s named Marie who becomes a tenant in a small house in Tucson. Her landlords, the McGregors, live in a shabby house only a hundred yards away. The story centers around the unlikely bond between Marie and Harland, the McGregor’s fifteen-year-old son, as Marie struggles with the loneliness and sadness brought about by her alcoholic tendencies. Harland, always bruised, also sheds light on the fact that the McGregors are not without problems of their own.

In creating the network graph, I decided that the connections between characters would be based on which characters speak to each other. Thus, the network graph is able to highlight the strained relationships present in the short story. For example, Marie’s mother and father only appear in the story as speaking to her briefly, but do not actually interact with each other at any point. In addition, Linda (Mrs. McGregor), has no interaction with her husband, Mr. McGregor, who in turn is the lone wolf in the story as he has no connections with anyone else. It can also be seen that Harland only truly has a connection with Marie and his sister, Lacy.

Though the network graph shows us which characters speak to each other throughout the story, it fails to highlight the more complex aspects of relationships between characters. For example, there is no way of us knowing what those interactions between characters were like (were they happy, argumentative, etc). The network graph also has its limitations in obscuring the histories of each character and the reasons for certain limited contact between specific characters. As for the special bond between Harland and Marie, the network graph only shows that as a single line, with no indication of its value.

Although the network graph is a decent way for us to visualize something specific such as how many times the characters speak to each other, it fails to show us the quality of those conversations and the more complex aspects of the relationships between characters.

For this week’s blog post, I decided to look at the Caribbean Cholera Map. Opening the site, I was first struck by the very simple layout, consisting of a map constructed with Google, a timeline above ranging from 1833 to 1872, and a “map key” on the right side detailing what each colored landmark on the map signified (either cholera outbreaks, specific hurricanes, tropical storms, or news articles).

However, this simplistic map actually turned out to be quite troublesome. Although the purpose of this map was to portray the cholera outbreaks throughout the caribbean during the nineteenth-century, it seems to be told in the perspective of someone simply trying to tally the number of cholera outbreaks instead of expanding on the implications that it had on both the Caribbean as an economic society and a cultural one. By hovering over the “News Articles” button in specific locations, the main focus is put on showcasing articles that deal with slaves, creating the assumption that the Caribbean was a mostly slave society with nothing else to offer. In addition, many of the news articles focus on the effects that the cholera outbreaks had on the large plantations that held many of the slaves. Thus, many personal stories of hardships within the slave community as a result of the cholera outbreak are obscured, instead revealing the hardships of the plantations.

In addition, hovering over the cholera outbreaks, one is presented with only numbers regarding the numbers of deaths in the population. This undoubtedly takes away from the “human experience” aspect of disease, equating a human passing as just another death in the multitude of lives that cholera took.

The map also reveals a weather aspect such as hurricanes and tropical storms, but it is not made entirely clear the reason that these measures are included. The map nonetheless obscures the impact many of these catastrophic natural events had on the places they struck, only giving us the date and location of where the natural disaster occurred.

As Turnbull points out, all maps are perspectival and this becomes clear when exploring this map. Whilst exploring, I felt that I was looking through the eyes of the white colonizers at the time and their views of natives as “savages.” It is disappointing that the Caribbean painted by the creators of this map is one that is equated with slavery and nothing else. I would have loved to have been presented with particular stories that highlighted the suffering that is disease, especially during a time with such limited medical knowledge.

http://modernizingamericandance.com/marianawebsite/index.html

 

For my blog post this week, I decided to take a look at the Titanic dataset provided. It provided information on the different classes that were aboard (distinguished as crew, first class, second class, and third class), the sex of the passengers, their age (distinguished as “adult” or “child”), and whether or not they survived the tragic sinking of the ship.

I specifically focused on exploring the relationship between the passengers’ class and whether or not they survived. For anyone that has seen the movie, it was obvious that the first class was privileged in more ways than one, most importantly when it came to escaping first as soon as tragedy struck. I wanted to see how this story compared to the “real” data (i.e. if there really was a correlation between class and survival). Looking at the dataset, this was not immediately evident as there were several data points.

Using Tableau, I was able to create a pie chart to better visualize the information I was given. Each of the colors correspond to a particular class and the size of each “slice” denotes the sum of how many in that particular class survived. As can be seen from this data visualization, first class and crew did indeed have a certain privilege when it came to survival, while the second class was not so lucky. The most peculiar thing, however, is that the visualization shows that the third class somehow had a large number of survivors. I did some research online and found that although third class passengers were mainly immigrants forced to stay below deck, no one knows exactly how such a large number survived. Overall, this data visualization was able to show the relationship between class and survival, and even a mysterious component which was not obvious from simply looking at the overwhelming dataset.

For this week’s blog post, I chose to explore the Neighborhood Council’s Expenditure for the fiscal year of 2014 through the L.A. Controller’s Office. I thought this would be an extremely interesting section to explore as I would get to know the “behind the scenes” aspect of what types of things Neighborhood Council’s invest in.

Some of the data types included in the expenditure includes the name of the neighborhood council, date of the purchase, description of purpose, vendor, spending category, task, and the amount spent (expenditure). There are records which go under each of these specific data types.

Srinivasan’s and Wallack’s article highlights the discrepancy that is too often found between ontologies of the state and local communities. Through their definition, this becomes obvious as the dataset truly reflects a state ontology. As they describe, “State data systems are the infrastructure of administration” (Srinivasan, 1). Thus, the data is found to be very structured, with only 7 categories used to fully explain neighborhood council expenditures. It is easy to see that this ontology makes the most sense to a state’s point of view as the information can be easily found through “spending categories.” From this dataset, I can only see a cut and dry version of what goes into the upkeep of a neighborhood. A community ontology is notably absent as no history is provided regarding specific neighborhoods or what conditions are unique to each of them that would draw a connection to the “why” of each expenditure. For example, an “ANC-Narconon Drug Prevention and Education” program is purchased and put under the simple “Task” of non-profit. There is no information about why this program was brought in or its influence on the particular neighborhood. In addition, there are several “operational expenses” that only offer a vague description of “MISC PERSONAL SERVICES” without giving any more detail of what these specific services are. Overall, this ontology reflects the state’s need to be concise and to the point, without offering any community perspective that could highlight the diversity of each neighborhood.

If I was starting over with data collection, I would create an ontology based on the community’s experience, and therefore their unique reality and what they are surrounded by. I would make sure to include the history of each neighborhood and more detailed reasons for the necessity of purchasing specific things. Most importantly, I would highlight both the immediate and long lasting impact each purchase has on the members of its neighborhood. Thus, the state would come closer to having what Srinivasan and Wallack describe as an “effective engagement with communities” that would prevent an overshadowing of citizens’ concerns.

I chose to explore the Finding Aid for the Walt Disney Productions Publicity Ephemera, 1938-198x. Right off the bat, I was able to tell from the title that I would be introduced to the era of publicity for the Walt Disney films (more specifically from 1938 to the 1980s). The abstract ensured me that I could expect to see things in the collection such as press books, press kits, and photographs, among other publicity related materials. In addition, I found out that the collection was housed at UCLA and that one could only access the collection with advance notice to the UCLA Library, Performing Arts Special Collections Reference Desk.

The Finding Aid also gives a brief biography of the Walt Disney company as well as the content of the collection, stating that the collection contains publicity for over 150 Disney titles. As noted, this collection is organized alphabetically by project title.

However, I was extremely disappointed to find that the “container list” which listed the title of a Disney production, what item of publicity it was, and which box and folder the particular item was in, did not give any additional description of the item or how the production was received by audiences. Therefore, the only true narrative that I was able to pull from this Finding Aid was derived from the biography that gave a brief history of the Walt Disney Productions. Based entirely on the records of this collection, my narrative of this collection is missing key history tied to each particular film/short animation and the effect that the press had on each of these (i.e. how it was received by audiences and its success in the theatres). Most importantly, because the biography states that the company reputation suffered after Walt Disney’s death in 1966, I would have liked to know how this affected the production company’s efforts to publicize its animations. Overall, I did not find the Finding Aid for the Walt Disney Productions Publicity Ephemera very informative. However, it did convince me to go see the collection for myself in the near future.

The Photogrammar project aims to showcase the mission taken on by the Farm Security Administration – Office of War of providing photographs during one of America’s toughest times: the Great Depression. Photogrammar takes us on an interactive journey of exploring the havoc the Great Depression left in its wake throughout America, allowing users to see the greater history that resides within the country’s map.

It is clear that the source for this project comes from the collection of photographs from the Farm Security Administration – Office of War during the years of 1935 to 1934. In order to receive the necessary support for government programs aimed at providing relief, the FSA-OWI took it upon itself to document this time. The Library of Congress is responsible for cataloging this remarkable collection.

Photogrammar utilizes processes which enhance the interactivity of the project. An interactive map of the United States links places with photographs that portray the particular situation there during the Great Depression. This way, the user is exposed to a sort of “Big Brother-Esque” window into a particular time in our history. A spreadsheet separates components of the map into categories, such as by place, photographer, and date. Within the map, there are two general categorizations which can separate the database by counties and “dots.” These dots basically represent the different photographers and their respective photographs around the map. In addition, there is an option to visualize the 1937 Vico Motor Oil route. Overall, the processes utilized by the programmers build up the complexity of the project.

The “Photogrammar Labs” portion of the site depicts the data tools used to ensure a sleek presentation. The “Treemap” is used to visualize the classification system that Vanderbilt used to categorize the photographs. A “Metadata Dashboard” builds relationships between categories such as date, photographer, county and even subject of the photos according to their respective state. Although it is still in development, this allows for the utmost organization of the site. In addition, a development that is coming soon called “ColorSpace” organizes the color photographs based on different elements of color such as hue and saturation.

Upon entering the site, the user is greeted with a big “Welcome” which directs him/her to a prominent blue “Start Exploring” button that stands in stark contrast to the overall gray, white, and black background. The simplicity of the layout makes navigating much easier. A search option on the top right corner of the site allows the user to easily search any photograph by various categories. The overall way they chose to present the photographs allows them to reach their goal of presenting an interactive visualization of such a hard time in our history.