Class Blog

*note: the narrator’s gender is note ever mentioned or alluded to, which is why I will use “they” as the pronoun.

I chose to created my network based on the short story by Tao Lin, named Final Fantasy III. This was featured in Granta 127, on Japanese Fiction, which was released in April 2014. A stream of conscious short story, the narrator mentions people and conversations, but has very little direct conversations with characters outside of the narrator’s father and mother. Thus, I decided to base the network diagram on interactions of those mentioned in the story. I eliminated figures like Confucius and Ashton Kutcher, since A) Confucius is dead and B) Ashton Kutcher was just mentioned since the narrator and the mother were watching the Steve Jobs movie. Since the network diagram is directed, you can see the arrows that signify if a character had a one way connection to the other character, or if it was reciprocated.

For the purposes of this narrative and diagram, even though the narrator and his brother/brother’s wife definitely have a reciprocated relationship, since they never had any direct conversation or interaction, it’s considered “one-way”. This acts as a limitation, since it’s obvious the characters have a close connection — why else would the brother entrust his son’s care with the narrator if they had a bad relationship? But when creating the network diagram based on the rules previously decided upon, such relationships can be hidden from the network.

Within this network, since it is mostly about how the narrator has writer’s block and how they try to get around it by talking to various figures, while simultaneously describing what is going on around them. As a result, this network is small but very interconnected, since most of the characters mentioned have first degree connections to the narrator. Some mentioned, such as the homi-/suicidal singer who killed his bandmates, and the two girls talking at the cafe, were just observed by the narrator/their parents, so they exist separately from the main network cluster that the narrator and the other characters exist in. The main cluster only has one second degree connection, the “Her Boyfriend” character, who is the boyfriend of the “Girl I email” aka “girl the narrator sort of has a crush on but it’s ambiguous because she’s in England and taken”. Also, within the first degree connections, the network is even more closed off since it is primarily a network of the narrator’s family, sans the three characters: Yae Sushi’s manager, the “Girl I email” and the friend from the UK.

View my network graph here.

 

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 assignment, I selected the short story First Semester Rachel B. Glasser and John Maradik from Granta Magazine, edition 136 – Legacies of Love, published October 31, 2016.

The story revolves around the experiences of a group of young college students living away from home for the first time at an eastern university. As the primary protagonist Sarah begins the process of learning a new form of social navigation, her circle of friends and acquaintances morphs and expands to include a sexual and social rival knows as Opposite Worlds Girl.

To create a model of the social network, I created an edge list to include every character named, addressed and described by the authors.  This grew to include a total of 16 characters, who comprised the nodes of the list. I created links between any character that shared space, conversation or interaction with another.  I weighted the connections on a scale from 1 to 3 based upon the relative impact of one character’s role upon the other.

For example – Sarah and the Turtle – Sarah takes the Turtle in as a form of emotional recompense for her lack of ability to connect with David. In her care the Turtle dies.  The weight of this connection is a 3.

Sarah and Boy@Party1 – the boy is attracted to Sarah but nothing comes of it and he doesn’t reappear – the weight of the connection is a 1.

Georgie and Boy@Party2 – Georgie and the boy are attracted to each other and holding hands by the end of the party.  It is implied that they sleep together, but he fails to reappear the weight of the connection is a 2.

I used Prof. Posner’s tutorial to create a simple social network model, found here.

Carmen Maria Machado’s fictional short story, The Husband Stitch, is a first person narrative about a woman who wears a ribbon around her neck to keep her neck in place and connected to the rest of her body. I was interested in examining the relationships among the characters of the narrative and their actions. I created an edge list with three columns focusing on subjects and verb throughout the story. The first column, Source, includes the character name or pronoun. The second column, Target, includes the verb attributed to the given row in Source. The third column, Weight, counts the number of times the same combination of Target and Source appeared in the story.

The Husband Stitch Google Fusion Network Graph

The character Sources (I, you, we, she, he, mother, father, boy, girl, woman, man, women, boyfriend, girlfriend, son, baby, doctor, witches, killer, murderer, teacher, pig) are the blue nodes, and the verb Targets are the orange nodes.

The resulting Google Fusion network graph reveals that the most active characters are referred to by I, she, he, we, and you. The most commonly used verbs don’t reveal as much about the characters (“have,” “can,” “was”) . It’s interesting to see that more specific verbs such as “proposed” are limited to an individual (he), where as other more general verbs such as “can” are shared by more characters (I, you, he, woman). Certain verbs connect characters together, and reveal their similar behaviors. For example, “we” and “mother” are connected by “making.”

There are several limitations of this network graph. One flaw in the graph is that the gender pronouns are shared by various characters in the story, making the connecting verb nodes difficult to attribute to specific characters (i.e. she could be mother/girl/another female; he could be father/boy/another male). Another limitation is that the graph does not show the order or context in which the subject-verb relationships appeared throughout the narrative. The timeline of character appearances throughout the story could show which characters are more tangential (appear in a couple consecutive paragraphs) vs central characters (appear scattered across the narrative).

 

Digital Harlem is a project conducted by the University of Sydney in Australia, which seeks to reveal the daily realities of black New Yorkers from the periods of 1915 to 1930.

This mapping represents a picture of black American life whose perspective dominantly reflects the legal records of the court, local law enforcement, and newspaper stories of the time. This clearly bred a bias which reasonably could afflict all documents in the period between 1915 and 1930, which were prone to racial discrimination. While the general coverage of individual stories does not necessarily mean outright discrimination, the breadth of the types of stories available mostly cover crime and violence amidst the black community, which is a limited view that ignores the vibrancy and cultural legacy present in this citizen body pre-1950. This notion is underscored in Turnbull’s essay which mentions the subjective perspective present in archival processes, which explains that bias is an inevitable part of the collection of historical material (the choices made of what to record color the nature of the history we remember).

In light of these observations, this map explicitly obscures the history of “everyday life” for black Americans in New York circa 1915-1930 through a lens which mainly focuses on illegal gambling, number and location of arrests, church locations, and the highlighting of an individual sexual crime investigation. While the map does acknowledge that Harlem churches were a center of cultural richness and expression, these observations are dwarfed by the disproportional amount of news stories and court records which misrepresents the black community (and their “everyday life”) in a criminal light. The news stories in particular almost exclusively focus on police raids and arrests for illegal activity.

What it does reveal is the interest of the NYPD in cracking down on illegal gambling circa 1915-1930, and the prominence of churches in unifying the local black community.  That in mind, if I were to rework this map, I would not necessarily change the visualization of the mapping (which works well in my opinion), but would instead diversify the types of data I am working with. Given that the title of the map specifically seeks to represent everyday life, I find that finding newspaper articles which highlight the accomplishments and cultural institutions of the black community to provide a much more comprehensive and accurate depiction of their daily reality and contributions to the city of Harlem. The act of only recording the community’s arrests and criminal charges misrepresents it as a whole.

This week I decided to analyze the Digital Harlem mapping project. This project aims to depict everyday life in New York City’s Harlem area within the years of 1915-1930. It  has been drawn from legal records, newspapers and other archival and published sources gathered between those times. It aims to focus not on black artists and the black middle class, but on the lives of ordinary African New Yorkers.

The map allows options to view the borders of black settlements during 1920, 1925, and 1930. You are able to filter the view of the map by search categories which encompass events, places, and specific people. Within each of these categories you can narrow down your search by limiting the criteria to options like keyword, dates, charge/conviction, race, gender, occupation, name, and street address.

As the Digital Harlem site is examined, it is important to keep in mind what assumptions the underlying data is built on. If one were to initially look at crime data during this time period, the map would almost be completely filled up with data focused on African Americans. However, this kind of scenario might not at all be an accurate representation of what the daily life actually was like for residents of Harlem during this time. Legal records, newspapers, and published sources might all be inherently biased to a perspective that is promoted by police officers, the government, and even the local media of that time. The site loses out on information and data that could have come from the perspective of a local citizen of Harlem at the time for the categories that Digital Harlem site is based off of. Legal records and newspapers show you information based on altercations/infractions with the dominant class of those times while ignoring simple events which wouldn’t be noteworthy to have been recorded during those times.

If I were to create an alternate map I would first try to see if I could gather data from a more “ground”-focused approach. I would try to get information from people who had relatives who had lived in the area during that time period to get stories and data for how everyday life typically was for them.

This week I chose to explore the project, Locating London’s Past. The project allows users to search through a variety of records from six different databases in order to map different data types on five contrasting base maps: (1) a GIS compliant version of John Rocque’s 1746 map of London, (2) the 1869-80 Ordinary Survey map, (3) a modern day Google map, (4) a satellite view map, (5) and a blank map. Using different base maps to map the same data allows users to compare an eighteenth-century representation London to the first OS map and to current day Google Maps. Below are pictures of Google Maps, the OS map, and Rocque’s map.

This project is unique because it provides users with empty maps, asking the users to map the data that they find relevant and important. To create my map, I searched through data from the Old Bailey Proceedings data set, which contain accounts of trials that took place at the Old Bailey courthouse. Users can find incredibly detailed and specific records from this data set, as the data types include the defendant’s home/crime location and gender, the victim’s gender, the offense category and subcategory, the verdict category and subcategory, the punishment category and subcategory, and the years that the case was on trial. I chose to look at records on those imprisoned for murder, and then I mapped the locations of these murders. Clicking on one hit (seen below in red) creates a pop-up that provides users with more information about the case and suggests links for further investigation. From here, users can actually add more data on top of the existing map, so I chose to add population densities (seen in green) to view the amount of murders relative to how many people were living in each area.

In Maps are Territories, David Turnbull says “a map is always selective […] the mapmaker determines what is, and equally importantly, what is not included in the representation.” This idea that maps are inherently subjective is especially true for this particular project, because a user’s final map reflects not only what data he or she found important, but also which records and which data sets the workers on this project found important AND what data types recorders in seventeenth and eighteenth century London found important to document in the first place. My final map, therefore, is a culmination of deliberate decisions from 3 parties, all completely separated by time and space, of what should and should not have been included in a representation of London’s past. All possible maps consequently reflect the values and ontologies of governing bodies and archaeologists, of the scholars who built the website, and of the users themselves. In my map pictured above, for example, the darker green areas indicate larger population sizes. The statistics on these population numbers come from the Bills of Mortality, or burial records. Clearly, the workers on this project found old burial records to be a reasonable, or at least the best known measure of population numbers. Maybe this is because, from their perspective, every burial = death, so every death = burial. Or maybe its because, in 17th and 18th century London, every single dead person was buried. But for cultures today that participate in, say, cremation, a death would not automatically = burial, and their differing ontologies would create a disconnect for the project’s mapping abilities.

 

This week I chose to analyze the Digital Harlem map, which congregates legal records from newspapers, police records, and other sources to demonstrate the events that occurred from 1915 to 1930.

The “About” screen states that the project was created to not only provide an exposé of African American artists during the time period, but also to give a representation of everyday life of African American individuals in New York City. The website displays a map, of which the user can toggle between the years 1920, 1925, and 1930 to see Harlem’s growing size over the span of ten years. Additionally, users are able to toggle the layers that they wish to see on the map. Some examples of the layers include law violations such as arrests, illegal gambling, assault, pickpocketing, etc… as well as provides layers for everyday life events such as the locations for pick-up basketball games, parties, weddings, and charitable events. When creating a layer, the user may input a name for the layer, then the map displays location-based points for where the event occurred, and the date it occurred. There are also layers that have already been created by the allows the user to select through a set of four different predetermined layers: January 1925, Number Arrests, Fuller Long, and Churches. I chose to explore the Number Arrests layer. When the user chooses the Number Arrests, the map immediately fills with blue dots to indicate various arrests throughout the specified years. If one chooses to click on an point on the map, the map will display the individual’s name that was arrested and what he or she was arrested for. If one chooses to click the “More Detail” link, the dialog box will display a news clipping of the news story from when the arrest was initially reported, the date of the arrest, the parties involved, the addresses involved, and the legal consequences.

While the map does provide the user with a great wealth of information, I would argue that the map does not necessarily display the everyday life individuals living in Harlem from 1915-1930. In fact, the map paints a more grueling image for those living there, and focuses more heavily on crime more than anything else. Doing so conjures a more grim picture, and might not necessarily be realistic of the greater majority of the individuals living in Harlem during the time. Additionally, while the map tells the user textual details about the events that occurred, it does not include images. Had images been provided, the user would be able to construct a greater archetypal image for “Daily Life” in Harlem from 1915 to 1930.

This week, I examined the 19th Century Caribbean Cholera Timemap  created by Duke University Haiti Laboratory. The interactive program offers both a timeline and a map, while locating all documented cases of Cholera outbreak, hurricanes, tropical storms, and news coverage in the Caribbean. As demonstrated by the timeline, the Timemap features 3 major periods of cholera outbreaks (1833-1834, 1850-1856, 1865-1872). It includes the location relative to other outbreaks and the corresponding primary media sources. Clicking on each event will make the map display a short description of the outbreak, storm, or news article.

I believe that this map reflects a scholarly (statistician or epidemiologist) , point of view of cholera outbreaks. As the news articles reported by mainstream media at the time, the map only illustrates the mathematical  data of people affected by cholera, instead of mentioning the life and suffering of the individuals. Such a focus distinguishes the map from a traditional research in the humanities: it excels at factual representation of deaths resulted from the Cholera outbreaks and the climate conditions; however, it fails to measure the data in a historical and humanities context.

The creators of this map assume that all Cholera outbreaks are recorded and mostly published. Yet, lack of documentation does not equal to nonexistence. For casual viewers, this map seemed to be a complete collection of every hurricane, outbreak, and storm that took place. However, the chronological and geographical attribute of this map make obtaining and maintaining a comprehensive record nearly impossible. The display of the map also hides valuable details about the Cholera outbreaks, such as their cause, carrier, and severity. Furthermore, the mapping team attempts to relate Cholera outbreaks across the Caribbean to a single variable: climate. By labeling the amount of outbreaks and stormy weathers as the only pair of value, a correlation in between becomes obvious. But in this way, potential correlations between Cholera and other variables are easily overseen.

To make an alternate map, I would first narrow down the scale of the map to focus on the Caribbean region, since the original design of a global map was a waste of resources and functionality. Instead, I would leave more space to show the details of each location and outbreak. Secondly, the model of display can be improved by designing independent icons to represent each of the 4 data types, so that events such as storms and Cholera outbreaks can be distinguished from each other. Also, offering an option to display a heat map can lead to a better visualization than the original scatter plot. Moreover, if records like immigration patterns or economical status across the region are available, I would introduce them into the map as well to show other potential factors of Cholera outbreaks.  Last but not least,  there seems to be a minor bug where viewers can select timelines even prior to year 1833, although there is no information available back then.