Mapping the Republic of Letters: Visualizing Early Modern Networks

Intellectual Geography / Wednesday 7 September, 2011

We know from the volumes of surviving letters that scholars in the early modern period had extensive correspondence networks; these networks formed an important part of what was called the Republic of Letters. What has been missing is the big picture of what these networks actually looked like. How geographically extensive were they in reality? Did these networks connect or overlap? Was there any evolution in the configuration of scholarly networks over time, from the beginnings of the Republic of Letters in the Renaissance to its flourishing in the Enlightenment?

Mapping the Republic of Letters is a collaborative research project based at the Stanford Humanities Center, bringing together humanities scholars with computer scientists and design researchers, to develop visual tools for humanistic modes of inquiry—not quantitative, but relational and contextual. In the various case-studies that constitute the Mapping the Republic of Letters project, we use visualization not as an end in itself, but to give researchers a means of exploring large corpora beyond the standard text search form. Our visualizations aim to uncover the spatial, temporal, and relational patterns in correspondence networks, along with those pertaining to the concomitant movement of people, ideas, books, or instruments that early modern correspondences supported or generated.

The data are, by and large, incomplete: for example, we do not have geographical or chronological metadata for the vast majority of the letters in these correspondences, and yet with location graphs we can identify immediately the major highways of communication. Similarly, a geographic visualization might direct the researcher’s eye to more peripheral letters, those sent to distant, exotic places (e.g., Voltaire’s letter to Kazakhstan), or to less commonly frequented countries (e.g., in Eastern or Northern Europe). Visualization, in this regard, can serve a heuristic purpose, leading the user toward less-known corners of the dataset, or even prompting new research questions (e.g., how did one send a letter to Kazakhstan in the eighteenth century?).