In a world of ever-growing information, it is of paramount importance to promote visualization literacy. This research aims at creating analytical opportunities for audiences beyond the population of InfoVis experts. This imposes the need of designing better tools that, ultimately, shape the current InfoVis tools landscape. Thus, this research will explore some of the steps that are needed to make InfoVis accessible to wider audiences in more varied analytical scenarios. Finally, given the interaction design challenges identified so far, this research will also contribute with new techniques to interact with data in opportunistic settings.
Information Visualization (InfoVis) often supports the analysis of structured data that is organized in documents with specific formats such as databases, Excel tables, or comma-separated files. Informal analyses that take place without anticipation and away from the desktop, however, might involve the use of data contained in digital artifacts that lack this structure (e.g., photographs, bitmaps, web pages). Such artifacts cannot provide immediate input for most existing visualization systems, as the data they contain does not exist as a set of variables with associated values. This research seeks to explore new opportunities in the design and implementation spaces of InfoVis authoring tools to support visualization in opportunistic scenarios. It defines the Opportunistic Visualization (OpportuVis) domain as “data analysis anywhere, anytime, from anything” and contributes iVoLVER, a research prototype visualization tool that supports the construction of interactive visuals from non-traditional data sources. This research also investigates how people’s visualization processes are influenced by iVoLVER’s underlying construction approach in comparison with other traditional visualization tools.
Collaboration with External Partners
iVoLVER is an open source project currently available at https://github.com/ggmendez/iVoLVER. An API will be soon released to allow programmers building new, domain-specific visual syntaxes. This will enable the creation of more specialized visual languages that exploit iVoLVER’s current implementation.
In addition, the researchers involved are starting to discuss potential collaboration with research teams from the USA to use visual tools to bridge the analytical and visual components of data analysis in tools than can be used by visualization novices.
This research is undertaken in collaboration with Dr. Miguel A. Nacenta, from the University of St Andrews.
Gonzalo Gabriel Méndez is a PhD student at the Human Computer Interaction Group (SACHI) of the University of St Andrews. He holds a master degree in Computer Science from the Free University of Brussels and completed his bachelor in Computer Science at the Escuela Superior Politecnica del Litoral (ESPOL) in Guayaquil, Ecuador. His previous research includes education and learning technologies. Gonzalo’s is currently focused on the design, development and evaluation of novel visualization tools that enable data analysis and promote learning Information Visualization principles.