Over the last few weeks, I have been exploring a variety of tools that are utilized in the Digital Humanities to read, interpret, map, and visualize data. Among these tools are “Voyant,” “kepler.gl,” and “Palladio.” This post will explore what each program does, my experiences with all three, and how I will approach utilizing these tools moving forward. As I am gaining a better understanding of digital tools and the Digital Humanities as a field, it has been really interesting to observe the capabilities of these programs!
Starting with Voyant: this is a tool that takes one’s submitted corpus and analyzes it through different, generated visualizations based on the preset parameters and filters that one can modify to their needs. Without modifying any of the windows, this includes five main features: the “cirrus” (essentially a word cloud for simplicity), a “reader” that displays the chosen text and words/phrases that one explores in other sections, “trends” or a relative frequency graph that analyzes the frequencies of most-used words by default (which can be modified to analyze specific texts and/or words), a “summary” tool that identifies distinctive qualities of the texts submitted and general trends throughout the corpus by document, and a “context” tool that will find instances of the word/phrase one has selected (preceding and proceeding words in the sentence in which the word/phrase is used in). Voyant has some of the capabilities of Palladio, which will be discussed briefly.
Kepler.gl is a mapping tool that takes a set of data (traditionally through .csv files generated through programs like Microsoft Excel), and maps it based on coordinates and other relevant metadata. There are a lot of different visualizations one can make with programs like kepler.gl, including heat maps, data clusters, point maps, timelines, and more! The capabilities of kepler.gl–at least in terms of what I explored–provides a lot of variety for visual storytelling. This is best reserved for regional analysis, as plotting points/trends on a global scale can get quite complex, especially when showing broader connections between points and the conclusions made from said points. This is not to say that it is not possible, but rather not the scale that mapping projects typically deal with.
Lastly, Palladio is a tool commonly utilized to make visualizations of data and connect two or more parameters to each other. Palladio does have mapping capabilities just as kepler.gl has, which is quite useful (although quite limited in comparison)! However, its main appeal lies in its variety of visualization techniques. I utilized “Network Maps” and “Network Graphs,” so I will speak on the functionality of both in-depth to represent the technology as a whole. After uploading files (in this case a few .csv files), I was able to create visualizations through two parameters that I set. I did this for several pairings involving interviews from formerly-enslaved people in Alabama. With the info contained in the .csv files, I was able to determine connections between the type of work that enslaved people were forced into relative to the topics that they discussed in their interviews, among other trends with the different data present in the files. Although visualizations can get a bit messy when analyzing many different points of data and themes that it pulls, there are still interesting conclusions one can draw–or at the very least explore–with visual aids.
I believe that all of these tools can all pair well together in some form. Voyant is great for text-based analysis and as a starting point for broader trends in large corpora. Voyant and Palladio for instance could have the potential to pair well together! Although situational, source material that initially is ran through Voyant could pre-emptively identify themes and make parsing through data when converted and transferred to Palladio quite a bit easier!
I believe that kepler.gl and Palladio have the most potential out of these three, however. While kepler.gl is best used for mapping, I believe that Palladio better identifies patterns within the data itself. If nothing else, kepler.gl can be used to map the .csv files, while Palladio provides basic mapping functionality; also creating visual connections that Voyant would not be able to do effectively with this type of format. Voyant could potentially analyze the source material itself however, and that is where I believe its strength lies when comparing these three tools.
Overall, I believe that all three of these tools have great potential and purposes, and should be used together! Although different projects/research will call for different needs, I believe that if one has the time and knowledge, these tools will be essential for a growing digital catalogue of source material and means of digitization. I would assume and hope that these methods will only improve in accessibility and availability over time, so it would not hurt to familiarize oneself with the world of digital tools!
My experiences with these tools make me wonder what else is out there. My professors at George Mason University have encouraged me to familiarize myself with the Digital Humanities, so I decided to take two courses related to the topic this semester. Through these courses, I am utilizing a variety of different tools, and in the process of completing projects using online exhibition tools and other mapping programs. Both traditional means of research and newer ideas (at least to me) revolving around digital representations for the public have been at the front of my mind all of this year. I have found new ways to uncover new angles, narrow my research parameters, and create accessible projects for topics that I care deeply about. Digital tools, while intimidating at first, have allowed me to see the work that goes into research that targets the public, which I am greatly interested in pursuing in some form. The present day is not just museums and websites, but so much more.