The digital age offers historians easy access to large quantities of data and also the possibility of displaying and analyzing this data in an efficient way. Computational methods today allow historians to discover patterns of social interaction, market behavior, and even proximity among literary texts which had hitherto been buried in historical sources and literary works. Much of this discovery takes place through visual representation of our data as much as through more conventional styles of reading and analysis.
This panel bridges the two meanings of “to see” – to perceive with the eyes and to understand with the mind – by showing how visualization techniques transform historical practice in a digital age. Coming out of a two-week NEH Summer Institute on network analysis for humanities research in 2010 and scholarly endeavors which have ensued, papers in this panel demonstrate the way in which visualization techniques, combined with other digital tools of analysis, may advance our knowledge in different fields of historical inquiry.
This panel is particularly interested in showcasing the wide range of projects in which network graphs have propelled historical analysis. In all papers of this panel, network analysis and the visual display of relational data are a central component. Yet, the networks explored in these papers are of different nature and the visualization serves different research agendas. Two of the papers investigate the networks of people as a way of understanding social structure and social change; one reconstructs the corporate network in the art market; and the fourth paper employs network diagrams to visualize the proximity among a series of German novels. By putting together papers which cover an impressive spectrum of temporal, regional, and thematic foci, this panel seeks to show how network visualization may appeal to historians who work with different kinds of humanistic data and ask different research questions.
This panel will also illustrate the way in which the techniques of visualizing network data may be combined with other approaches of analysis and representation, such as topic modeling and visualization of spatial data in GIS. In the meanwhile, the panelists will also reflect upon the challenges and pitfalls of visualization each of them has encountered in his/her project.