Rubén Rodríguez Casañ, Elisabet Carbó Catalan and Diana Roig-Sanz
Global Literary Studies Research Lab
Summary
Rubén Rodríguez CASAÑ, Elisabet Carbó CATALAN and Diana ROIG-SANZ create a visualization using data from the International Institute of Intellectual Cooperation (IIIC) archive to analyze global intellectual cooperation. The authors use different techniques of language processing and data science to reconstruct a co-occurrence network of cities and personalities. The visualization shows which areas of the world were more active in the network of intellectual cooperation, confirming the alleged Eurocentrism of the IIIC. However, other cities, such as Caracas and Bogotá, stand out due to their role as a bridge between Europe and South America. The authors discuss challenges they faced in preprocessing the data and in choosing between types of visualizations.
Keywords
translation studies, data science, language processing
Feedback
Tiziana ALOCCI critiques design decisions made in the mock-up visualizations; she claims that bar charts should show small differences among compared groups, not heavy outliers.
She further argues that for visualizations that build on geographies, it is fundamental to implement map projection ratios that are unbiased and objective - irrespective of the underlying infrastructure. ALOCCI also warns of the dangers of choosing wrong color sets for visuals, e.g., light colors on light backgrounds.
Keith ANDREWS attempts to answer the presenters’ questions by showing how comparisons of contemporary and historical geographical data can be implemented with the help of JavaScript libraries. He goes on to propose a more interactive environment for visuals that lets users zoom or expand on specific values, and which is not Google’s DataStudio.
Linda FREYBERG expresses uncertainty concerning the type of visuals chosen, mentioning that maps are not suitable for answering every research question. Regardless of the presenters’ choice of aesthetics, she refers to typologically different solutions as implemented in the TudorNetworks.
Slides
Resources
- Gorg, Carsten, Zhicheng Liu, Neel Parekh, Kanupriya Singhal, and John Stasko. ‘Visual Analytics with Jigsaw’. In 2007 IEEE Symposium on Visual Analytics Science and Technology, 201–2, 2007. https://doi.org/10.1109/VAST.2007.4389017.
- Merging Historical Maps in D3.Js v.5. https://datawanderings.com/2019/07/08/merging-historical-maps/.
- TopoJSON. JavaScript. 2012. Reprint, TopoJSON. https://github.com/topojson/topojson.
- Tudor Networks. Data Visualization. History. http://tudornetworks.net/.