Data mapping is an essential tool for visualizing data that primarily revolves around geographic elements or relations. As Presner and Shepard explain in the reading, mapping can be defined as “a king of visualization that uses levels of abstraction, scale, coordinate systems, perspective, symbology, and other forms of representation to convey a set of relations;” how the mapping tool is used can be just as important as the data being mapped. The digital mapping software kepler.gl provides numerous kinds of maps to not just display data differently, but to emphasize different things that are best shown on a map. The basic point map is the most self-explanatory: it is useful in pinpointing the distribution of the data, but the primary use is in what their geographic relations reveal. More specific mapping tool answer more specific questions, such as the cluster and heat maps. These two are specialized in demonstrating density of sources for specific regions, usually through color categorization. In the example of the Photogrammar project, each county of the United States immediately conveys how many photographic sources it contains relatively to others by its color, which immediately conveys important information to the viewer than if they were simply a pile of points. In experimenting in kepler.gl with the data from the Alabama Slave Interviews, the cluster map clearly demonstrated concentrations in urban centers such as Mobile and Birmingham. By applying the categorization tool, the map showed that those urban clusters consisted of similar shares of both ‘field’ and ‘house’ experiences, with many of the rural outliers falling into the ‘unknown’ category. Hence, these map tools can answer questions pertaining to how these different types of experiences were spread across the state and where they were found in higher numbers, and from there more research can be done with that information. Digital mapping is not constrained to geographic relation, but can also just as effectively show change over time. The time map tool in kepler.gl showed when each interview took place as well as their location, and tracking that time frame shows that the majority occurred in the late summer months of 1937, and that most of those also came from urban centers. The example of the “Tracking the Gay Guides” project illustrates how much more can be learned through time maps; by comparing the different datapoints for each year’s Address Book on a map, users can approach questions pertaining to the development of the guide’s users, community, and related establishments – where they were more common, what the main local demographic would have been, where they dissipated and where they grew over the years, etc. Overall, digital mapping is much more than simply plotting data points on a map; there are many fundamental questions about the spatial relationship of data and there are many questions that can be asked and answered using mapping.

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