Network Analysis with Palladio

This post will explore Palladio; an online digital tool that creates malleable visualizations to interpret certain formats of data.

Initially, the process of using Palladio is similar to; where one submits data (in this case I used .csv files) into its repository, which then converts this into data that is readable through the program to plot one’s points to their specifications. Palladio is not a full-fledged mapping tool; but rather, it uses the data to create connections and visualizations of patterns, similar to Voyant, a text-mining tool. In my exploration of the program, I utilized data surrounding the interviews of formerly-enslaved people in Alabama, creating “network maps” and “network graphs,” mainly. Although there are other functions that one can explore through this service, I mainly utilized those two.

Network maps take points which can be plotted through simple points, or through “point-to-point” plotting, which displays connecting lines through the data; indicating a path or another sort of relation between two or more points. This is useful in identifying patterns within geographical data, but the full or specified metadata is not displayed, as that is not the primary function of Palladio. It is more about the overarching connections present in the data, which the next tool I utilized exemplifies further.

There are other customization features in the “map” section, being split up into “tiles” and “shapes.” Tiles mostly shows geographic, more 3-D means of representing a space; through terrain, streets, satellite imagery, infrastructure, and custom tiles that the user can insert! Shapes allows the user to insert shapes that presumably indicate signifiers determined by the user. This system is open-ended enough for the user to do anything they want with it; whether it is for emphasis or to serve as a key/legend.

Moving on to the “tables” section, Network Graphing takes the data one submitted, and identifies themes based on one’s parameters they documented. For instance, when exploring the interviews of formerly-enslaved people, one can find connections between the sexes and topics they discussed during their interviews, age groupings, region in which the interviews took place, and other combinations that one wishes to explore further (mostly between one set of data columns within the overall document). This essentially functions as an interconnected web, creating connections for the data entries so identifiable patterns can emerge and be interpreted by the user. It is all accessible, easy to adjust, and quite flexible in what it can do! Some screenshots will be attached at the bottom of this post to give you a sense of what I am describing. Due to the volume of data, these screenshots are representing the visualizations that Palladio can produce, rather than the content itself.

There are also other visualization methods, such as “galleries” and other formats for tables. I did not utilize these functions, so although I cannot describe their functionality in-depth, it is worth mentioning that there are a variety of ways to represent user data.

Overall, Palladio enabled me to generate context and seek patterns in the data that I may have otherwise not spotted! The user experience was the best for me during my analysis of type of enslavement corresponding to the topics that were brought up in the respective interviews through network graphing. Although overwhelming at first, organizing the data through dragging circles to desired locations within the space (another great feature) enabled me to identify patterns of lingo, priorities, and the hierarchy of each condition represented in the data. It was something I was not entirely expecting, mostly because I did not know what to expect out of this program. It was surprising, and something that would have been possible through manual review; just not on this scale. This kind of technology is interesting, and something that I want to utilize more often.

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