An method for systematically surveying data visualizations in infectious disease genomic epidemiology

Data visualization is an important tool for exploring and communicating findings from genomic and health datasets. Yet, without a systematic way of understanding the design space of data visualizations, researchers do not have a clear sense of what kind of visualizations are possible, or how to distinguish between good and bad options. We have devised an approach using both literature mining and human-in-the-loop analysis to construct a visualization design space from a corpus of scientific research papers. We ascertain why and what visualizations were created, and how they are constructed. We applied our approach to derive a Genomic Epidemiology Visualization Typology (GEViT), and operationalized our results to produce an explorable gallery of the visualization design space containing hundreds of categorized visualizations. We are the first to take such a systematic approach to visualization analysis, which can be applied by future visualization tool developers to areas that extend beyond genomic epidemiology.