DH and Visualization

Text and Images in DH

“DH is text heavy, visualization light, and simulation poor.” (Champion i25)

One takeaway from Champion’s writing is the definition and thus differentiation between model and simulation. One concern I constantly had for my mapping project is how my map represents “text” into visual. How would I translate the subtlety of written words–which enables different interpretations and mental visualizations (if possible)–and force it into a materialized visual forms? Building his argument on the symbiotic relation between image and text and furthering it with the need for “visualization literacy” (i27), Champion’s explanation for simulation made me realize that visualization will be approached in a similar fashion. It does not have to be a “model,” “a physical or digital representation of a product or process,” but a simulation, “the reconfigurative use of a model to reveal new and potential aspects of a model” (i27).  I thought of my mapping presenting or allowing only one selective aspect of a text even furthered by the fact the selection depends on the choices I make about which to visualize. But this differentiation between model and a simulation allows me to see that my maps will be a simulation that has the potential of another interpretation and that bears the thought of an embedded interpretation.

“[A] diagram or collection of data showing the spatial distribution of something or the relative positions of its components.” (Meirelles 115)

The definition of map alone supports Champion’s understanding of visualization of text material. Maps show the “relative” positions. Be it location, scale, movement, temporal relation, etc, mostly it is a selective visual encoding of data element that cannot be simply described as objective nor subjective. On page 127, Meirelles lists categorizes variables of the images–point, line, area, size, valuable, texture, color, symbols, etc. This also reminded me of Posner’s reading of digital humanities and how humanities scholars (should) challenge the grids of the fundamental. What would I achieve, and at what expense, if I chose to depict male monsters in pink and female monsters in black? Is my mapping project an experimental project that questions the “taken-for-granted” or public-accessed and tools for making the text be “seen” in a different medium?

! Another thing I found helpful was different hierarchical tree types of Cartesian systems. Perhaps use this for esri story map?




Meaning of Digital Humanities and DH Scholarship

Miriam Posner, “What’s Next: The Radical, Unrealized Potential of Digital Humanities.”

I have to admit, I have been a long doubter of digital humanities before I was involved in DH projects–and maybe long after that. I come from another culture and the general “feels” about digital humanities in East Asia, or the critical institution that I was apart of, is very negative. It is not even “not positive” stage. In a country hosting Samsung and LG, benefiting with world’s one of the most developed technological practices, people in subway all looking down at their cellphones, it is maybe ironic. And this may be the reason more so for the humanities professionals to be against anything that combines digital with the humanities.

And before I say every doubt you have is legitimate as well as beginning to be answered, here are some of the questions I had: What do humanities have to do with anything digital? Aren’t we supposed to be persons who endorse reading?  Will it need the professionals in humanities who were trained to read, analyze, think critically, etc. to work in realms fueled by corporate-driven funding and computer coding? As I learned to realize, most of the doubts I had was due to lack of information or understanding of what DH is. And now it seems even embarrassing to write down the questions I had.

By chance and will, I’m in an institution where the university as well as faculty members are open and even eager (wow) to the promotion of DH. In my first semester as a doctorate student, I did a mapping project using ArcGIS program, visualizing how “monsters” and ghosts of 18th and 19th Century differ in terms of gender of the creature and authors. I pinpointed the locale of creature’s existence, sightings, the amount they traveled, the course they took, the monstrosity of their appearance as well as actions, etc. I used excel to make a data collection, did tons of calculations that I already forgot how to, used technology to visualize the intensity of atrocity of monsters. It was a frustrating experience largely because I had to learn how to master a program that I have not even heard existed but mainly because I had “doubts.”

As a person who were used to “close-reading” analysis, and who believed that was the “legitimate” way to “do” literature, counting word frequency, making a monstrosity level spectrum (i.e. murder being the highest, haunting being the lowest, for example) as well as empathy level spectrum, and above all, presenting literature as a spectacle one could understand at first glance was disturbing for me. (And perhaps the fact that I would show this to non-humanities-professionals and they say “it’s cool!” was discouraging.) Still, my data proved (and “showed”) that female monsters have less travel-distance (i.e. home-bound) and were more empathy-evoking. I concluded the project linking female monsters/ghosts demonstrating their fears and angers as well as limitations about domestic spheres they were confined to. Though my findings were wonderful and received great reviews, I still was hesitant. How can I judge the Creature from Frankenstein’s murder of a little boy is less monstrous than Count Dracula’s possession which will later lead to another killing? How can I assess a female ghost’s vindictive haunting to her failed lover is less monstrous than a male monster’s random killing?

Miriam Posner says, this is exactly what humanities in DH should be doing. Digital humanities is not just about using software, transferring data to an online collection, archiving, producing visual data that layperson can look and convinces themselves of granting you the funding. Our job is to ask questions, to tease out the assumptions, especially ideologically-driven ones, and to ask for change.

In the chapter above mentioned, she focuses her attention to gender and race. As a person who does Age Studies, I wonder the same argument could be made for age-category. Just like the distinction or the opting of black/white, male/female, we take the distinction of age granted. Does “senior years” start with 65? Why? Mostly because it is the time when pension begins not because of some inherent quality associated with the number. Oftentimes when I’m doing DH projects, I have the need to categorize humans, cultures, events, not only identifying them but also making inclusive-exhaustive definitions. Sometimes I am frustrated with this process because it is necessary to get the “result.” It is not so satisfying (not for me though) to have many unclassified/double/triple/or more overlaying elements in your data. And sometimes you have to make sacrifices. When I was using z-axis map, it incorrectly assumed words that carry negative connotation to be used to describe negative event/emotion. But without making some sacrifices, it seems meta-data is not possible. But is it? Posner suggests that we will get results and data, only that it might not conform to already existing social norms. If we add–or figure out a way to represent/categorize gender, we will not have how many women vs men have jobs but something radically different. It could be women vs men vs many other categorizes but also could be of different ramification. It might even further the idea of the futility of gender categorization–or the opposite.

I am not familiar with technology. I use my phone until it breaks, rarely install anything that doesn’t come with the laptop, doesn’t have a twitter account, just started trusting Starbucks phone app. And the talk of digital presence scares me still. But I don’t believe DH is about one’s preference. It’s about the responsibility as well as the ability of people in humanities to ask meaningful questions about a cultural phenomena and contributing to it with valuable doubts.

Mapping Monsters: Spatial Representations of “Monsters”

Result of data analysis for monster mapping
Example of Male monster minimal bounding

Mapping Monsters

Spatial Representations of “Monsters”: Gender Difference and Its Meaning

: This project was done as a part of a graduate course at Michigan State University. To open the map, ArcGIS is required. Web-based map can be viewed but this map does not allow any group-layer features. Screenshots of the map shows some snapshots of the map.

  • Description: This project concerns how gender affects spatial representation of so-called “monster” characters and how these representations are related to effects they produce such as levels of monstrosity and sympathy. Monsters that easily come to mind such as Dracula or the Creature from Frankenstein scare us readers not only with their evil doings and strange appearances. Part of their monstrosity lies also in their ability to escape human grasp. They move from one country to the other with the least effort and contaminate entire humanity unavailing our effort to pin them down to one place. However, there are other monsters who stay local and threaten relatively small number of people: female vampires who do not leave the castle, witches who demonize county people, ghosts bounded to houses, etc. The fear that we have for this type of monsters are quite different from wide-roving creatures. This project starts from a question whether this distinction stems from gender of the monster and looks to find a connection between distance traveled/areas of coverage and type of “monstrosity” monsters embody.

This map has largely three layers that allow above comparison analysis, each one including available points, lines, minimal bounding, and kriging: 1. Female monsters vs. male monsters 2. Female author vs. male author 3. MM vs. MF vs. FM vs. FF (gender of the monster/gender of the author (F: female, M: male), for example: MM means male monster created by male author).

  • Central Question: Does gender of the monster affect the distance traveled? How is the distance related to a level of monstrosity and level of sympathy?

Do female monsters show smaller trajectory and consequently lower level of monstrosity than male monsters? Do they have different habitation patterns than male monsters? How does spatial representation affect level of sympathy? Will the gender of the author complicate any pattern?

  • Explanation and Compromises
  • Corpus: Included in the database are eighteen texts with thirty characters in total, fairly balanced between gender of the monster and author’s sex. Texts include Gothic literature, Ghost literature, and other horror literature that features in-between humans, animals, witches, etc. mostly from nineteenth-century literature.
  • Features: Points are marked when a character is mentioned/indicated to be at such location, and lines demonstrate their travel trajectory. This line also allows the measurement of distance traveled. Minimal bounding, which is a smallest polygon that includes all the points of a character, is used to show and calculate square measure of each character’s coverage of area. Kriging analysis shows the different numeric number each character scored in that specific location and allows one to see at one glance at which point the highest numeric value exists. I used different symbols and lines to represent different category each element belongs to.
  • Uncertainties/Assumptions: Almost in every text, there are pointed locations that are based on assumptions and a range of accuracy vary from point to point. For example, in Lois the Witch, I used two historical maps of Salem to indicated sites of prison, execution place, courts, etc., but locations of character’s houses were selected randomly out of several points of residents. The difference of scale should be noticed since there are large differences in detailedness of geographic information. Whereas texts like The Succubus gives out even street names, The Vampyre or “Tomb of Sarah” sets its characters in large settings such as “Rome,” or “Greece.” Fictional spaces were also mapped within a larger context of the text. Even when ‘Dwoldling,’ a set place for “Phantom Coach” turned out to be fictional, it was mapped according to the information that it was 12 miles from ‘Wyke,’ a moorland far north of England.
  • Numeric Scale: To quantify a level of monstrosity and sympathy, I developed a monstrosity scale and sympathy scale.

Monstrosity scale measures how monstrous a character is displayed by each text. I collected every monstrous actions and appearances that is mentioned in a text and used that list to make a scale and ordered/ revised the numeric system considering the context of each. By making a corpus of monstrous action/appearance, I could overcome the difficulty of translating a subtlety of representation of literature to a numeric value. For instance, it could be said that ‘mind controlling’ is less ‘monstrous’ than ‘murder attempt’ at first glance and thus should score a high level of monstrosity. However, when it is taken within the context of the text, say, it leads to a cruel victimization of an innocent person or is described in such a detail that it leads to horrify its reader, these contexts cannot be overlooked by ‘objective’ measures. This is not to say this scale is objective. When considering the ‘context,’ cartographer’s own interpretation of the situation and emotions each display of monstrosity is eliciting are embodied. However, it can be said that it is more consistent and coherent as a whole.

Sympathy scale measures how a character is viewed by other characters and by the narrator. Having a numeric value for sympathy proved to be very challenging. Sometimes, characters differ in their view of the monster characters and the narrator describes the monster in a subtly sympathetic way even when s/he is received as not sympathy-worthy. There are also texts that evoke sympathy in readers not through other character or through narration but with plot itself. It is less uncomfortable for readers to feel sympathy toward a ‘monster’ who haunts her murderer or who hurts one who killed her baby. I included these different levels of sympathy each text arouses in readers to determine how a text depicts monstrosity.

  • Limitations

Even when there’s a difference, features that did not have enough numeric value—male monsters’ monstrosity, sympathy scale for both male and female monsters—could not be demonstrated using kriging tool.

  • Answers from the map:
    1. Distance-Monstrosity

Male monsters travel ten times more than female monsters on average even though female monsters have higher level of monstrosity. This shows that the nature of their monstrosity as well as horror monsters elicit differ by gender.


2. MM, MF, FM, FF

Although there is a stark difference between male monster and female monster, adding author’s sex in the comparison enables much further analysis. Although female monsters created by female author show longer distance traveled, minimal bounding proves that they move about the same place, thus has higher numeric value for distance but has limited roving.


Tag: #monster #gender #horror #ghost #witch #literature #sex




Male monster minimal bounding
female monster points and lines
female monster minimal bounding
MF points and lines
FM points and lines
FF points and lines
female monster krigging
female monster krigging zoomed