Digital mapping tools utilized in digital humanities projects allow researchers to reconceptualize space and time through the convergence of technological and intellectual exploration (Presner & Shepard, 2016). Digital mapping tools can connect images and geography to produce an understanding of scale on projects using archived images with metadata. These projects can also enable scholars to develop projects focused on historical memory and community-based mapping (Arnold et al., 2020; Brennan, 2014; Regan & Gonzaba, 2021).

Kepler.gl is a powerful open-source digital mapping tool that provides researchers with robust visualizations of location-based data (Robertson, 2025). Kepler.gl’s digital maps include point, cluster, heat, time, and category maps that visualize project data using georeferenced coordinates. Analyzing records in the WPA Slave Narrative Collection dataset provided an opportunity to navigate Kepler.gl mapping tools.

The Kepler.gl point map visualized the locations in Alabama where interviewers recorded slave narratives. It is clear there were clusters around the larger, densely populated cities of Columbus, Montgomery, and Birmingham. When examining the WPA Slave Narrative Collection data in Kepler.gl using heat maps, the visualization makes the data points more pronounced by grouping points that are close together. The heat map enhances the visualization by showing cluster density across a spectrum of colors.

While the point, cluster, and heat maps provide one type of visualization of geospatial data, the time map offers another for project data. When examining the WPA Slave Narrative Collection data using the Kepler.gl time map and the field indicating when the interviews took place, it is clear that most of the interviews in this dataset occurred during the warm months, between May and August 1937.

When examining the WPA Slave Narrative Collection data using the Kepler.gl category map, the power of this visualization tool is evident in its ability to take a data field, in the case of my analysis, Type of enslaved person, to match the response to the location of the interview, and to get a more thick map description of the data set, in this case the type of work the narrator did during enslavement. Most of the responses, according to the map, indicate that the narrators did not specify the type of work performed, or that the interviewer did not record it.

While the map allows users to visualize the large number of cases that fall into this category, it does not tell us what type of work the “unknown” cases performed. The large number of cases that fit this category is, however, still interesting, as it can correct assumptions made by most people that the majority of African Americans enslaved in Southern states were field slaves.