For my second final project, I have expanded on my earlier blog post on text mapping in La fila india by Antonio Ortuño. I won’t go into a description of the use of TopoText, as I already did that in my previous blog, but instead will discuss Carto and the advantages of it over GoogleMaps interface, and then will provide some brief analysis of the text using the maps I’ve created, and also describe some other ways the text could be analyzed using Topotext.
The first map that I made for La fila india, or actually Indian File, the translation I did of the novel for my Capstone project, was done on GoogleMaps. The map did a decent job and demonstrating the overall of representing the distribution of places. But, it was excessively cluttered, and the placement of the color-coded legend which described what type of feature each marker’s color correlated to was pushed into an “about” section rather than as an actual legend, making it difficult to use. Recreating the map in Carto allowed for a more aesthetically pleasing and easier to use map (with hover-pop up capability which I find easier to use than GoogleMaps’ click-based one). For this first map featured below, I had to add a “type” column to my dataset which numerically separated the different types of points. Carto could then turn this into different colored dots, and while I think toggle-able polygons would have been ideal, it was really outside the scope of my abilities, but the below map I think really emphasizes both distribution and difference in type.
Now the only issue is that I have been unable to get the legend to show up when I embed the map as I have above, but you can see it here, or in the screenshot below.
Another advantage of Carto is the ability to use the widget feature to create the histogram you see with the map. It is slightly confusing because the point types become numbers (1=regions, 2=sites smaller than cities, 3=cities, 4=countries, 5=states, and 6=geographic features), but the impact can be the same. What is noticeable instantly is that most of the references are to cities, countries and states. This is not surprising in itself, but what is interesting is that cities is much larger than all the others, and is in fact even bigger than states and countries combined. I read this as a de-nationalization of the geography of the novel, that is to say the novel is far more concerned with thinking about the border as porous, and so geographic entities that are more closely tied to showing the distinction between nations (countries or states along the border) are less referenced than ones that function as floating entities, markers that exist outside of their existence in countries. This creates a fluid understanding of Central and North America, one where countries bleed into each other and the defining features are cities. This makes sense for a novel about migration and human trafficking: the geographic rendering acknowledges the flow of human beings whether authorized by governments or not. It also justifies my translation methodology where I consider Spanish as a domestic language of the US and attempt to reconsider national literatures to an extent. It is important to note though that borders still matter of course, and it is interesting that the states in Mexico referenced are the ones on the north and south borders, while those mentioned in the US, besides the outlier Vermont which I’ll discuss with the next map, are logically those on the border with Mexico.
The other map that I made, which would have been impossible (to the best of my knowledge) on GoogleMaps, shows the weight, or number of references, of each geographic location named in the novel. It can be seen below, with a screenshot farther down that has the legend, but can be best experienced (with the pop-ups) here.
Certain locations immediately jump out as having many more references, namely Santa Rita (the fictional town in southern Mexico where the work is set), Disneyland (where some of the characters have planned a trip), Mexico, and Tamaulipas (where a massacre and some of the plot occurs). The histogram I was able to create actually tells us more, in my opinion. The vast majority of places are only referenced between one and three times, a handful are referenced between five and ten, and only Santa Rita (with 77) is referenced more. This shows the centrality of the city to the novel, but the outliers (or actually the majority of places) are interesting as well. Those locations mentioned once or twice are usually referenced but are not the scene of actualized narrative action, or even potential narrative action. They are used as reference points or in poetic ramblings by the father character, who goes on frequent rants. I find that this points to the way the characters see and expand their world via references to countries or cities or states they’ve never visited. This is especially true of the middle class characters.
If I was going to do more work on this particular text mapping project, I would utilize the TopoText function that allows the user to see the text that surrounds a geographic place name. I would examine more thoroughly which places are referenced in a narrative or poetic way and which are directly tied to plot action. I would also try and correlate this to number of mentions and which characters (middle or lower class) make certain references and what the opinion of those places, or surrounding mood in the text, is. In all, this exercise initially helped me better understand the novel I was translating for my Capstone thesis, and returning to it deepened that understanding and gave me things to consider as I revise further to potentially send the work to the author or to presses and publications.
My dataset is available here: lafilaindialocations-3