The kepler.gl mapping tools highlight time-space relationships and trends in a dataset. Starting with a CSV file of 130 text entries for the Slave Narratives project as its data source, kepler.gl produced a point map that visualized the locations of interviews with former slaves living in Alabama in 1937. Just the basic point map alone provided insight into where most interviews took place and where former slaves settled within Alabama after their emancipation. The heat and cluster map features articulated this information even more effectively; these visualizations distilled 130 individual location data points into concentrated “masses” that conveyed trends about the volume of interviews in different regions of the state. Unsurprisingly, most centered around the major cities of Mobile, Montgomery and Birmingham. The category feature in kepler.gl took the analysis one step further – not only could I learn about the number and locations of interviews but also about the interviewees themselves. This did require me to switch back to a point map; however, at this point, I was interested in more detailed information and found myself zooming in on specific cities to learn more about the nature of the work that these former slaves performed (the type categories being house, field, both or unknown).

At the same time, there are some limitations on these tools to consider. First, they are only as good as the underlying data. In the case of the Slave Interview project, the dataset included exact latitude and longitude coordinates which made for a very crisp and accurate visualization. Alternatively, the MGG project team encountered some issues in this space which they managed to address somewhat effectively. A considerable number of locations in the Damron guides did not list clear addresses that the project team could link to coordinates. As a result, some of their data entries use generalized city/town locations that produce skewed or misleading results. The project team dealt with this by creating an option to filter out these particular results, leaving the decision to the individual user based on their preference.

Other limitations worth mentioning are the embedded assumptions contained within maps, which Presner and Shepard foreground in their discussion of “spatial humanities.” While I believe that each of the projects and activities lent themselves best to a “traditional” cartography, in other circumstance, particularly when approaching topics in ancient history, a different approach may be appropriate. Presner and Shepard correctly identify these concerns, but do not help the reader to understand alternatives to overcome the modern, Western biases inherent in mapping. Perhaps in practice this would look like a mapping tool that focuses more on one object’s location in reference to another object, landmark or natural feature rather than on GPS coordinates, as ancient peoples would have understood their world more in these terms without a fully developed sense of latitude and longitude.

Using the Voyant text mining tools for the first time, I found the word frequency data and the corresponding visualizations (such as the Cirrus word cloud and the Trends graph) to be incredible valuable for gathering insights about the WPA Slave Narrative interviews. When done correctly, to include flagging common “stopwords” to filter out irrelevant data, an analysis of word frequency can prove fruitful for understanding the topics of emphasis for the authors of the corpus. And yet more than telling us the about the corpus as a whole, the distinctive words tool also makes an intra-text analysis possible – one in which the user can compare different sections of the corpus against each other. In the case of the WPA Slave Narrative documents, this tool revealed differences in dialects between interviewees of different states.

But as Sinclair and Rockwell remind us, these tools do not create inherent meaning from their results; determining the significance of the results still relies upon the researcher. For example, the Link tool revealed a relationship between the word “big” and the word “house.” Taking the results at face value would suggest that interviewees were simply referring to the physical size of the structures on the plantations they worked. However, as the student using the tool, it was up to me to connect this with the historical concept of the “big house,” which was embedded with cultural connotations connected to slave hierarchies and treatment on the plantation. Furthermore, as the Signs@40 activity demonstrates, our analysis is only as good as our corpus. In the process of selecting a corpus, whether from an archive or a journal in the case of Signs@40, we depend on the conscious or unconscious decisions of archivists and publishers make about what types of sources belong. Guldi refers to this as the “silence” of archives (523), and the Signs@40 project’s focus on the history of only one journal to serve as a stand-in for feminist discourse over several decades was one of its shortfalls.