A Comparison of Radial and Linear Charts for Visualizing Daily Patterns

GenoREC: A Recommendation System for Interactive Genomics Data Visualization

Interpretation of genomics data is critically reliant on the application of a wide range of visualization tools. A large number of visualization techniques for genomics data and different analysis tasks pose a significant challenge for analysts: which visualization technique is most likely to help them generate insights into their data? Since genomics analysts typically have limited training in data visualization, their choices are often based on trial and error or guided by technical details, such as data formats that a specific tool can load. This approach prevents them from making effective visualization choices for the many combinations of data types and analysis questions they encounter in their work. Visualization recommendation systems assist non-experts in creating data visualization by recommending appropriate visualizations based on the data and task characteristics. However, existing visualization recommendation systems are not designed to handle domain-specific problems. To address these challenges, we designed GenoREC, a novel visualization recommendation system for genomics. GenoREC enables genomics analysts to select effective visualizations based on a description of their data and analysis tasks. Here, we present the recommendation model that uses a knowledge-based method for choosing appropriate visualizations and a web application that enables analysts to input their requirements, explore recommended visualizations, and export them for their usage. Furthermore, we present the results of two user studies demonstrating that GenoREC recommends visualizations that are both accepted by domain experts and suited to address the given genomics analysis problem. All supplemental materials are available at https://osf.io/y73pt/.

Visualization in Motion: A Research Agenda and Two Evaluations

We contribute a research agenda for visualization in motion and two experiments to understand how well viewers can read data from moving visualizations. We define visualizations in motion as visual data representations that are used in contexts that exhibit relative motion between a viewer and an entire visualization. Sports analytics, video games, wearable devices, or data physicalizations are example contexts that involve different types of relative motion between a viewer and a visualization. To analyze the opportunities and challenges for designing visualization in motion, we show example scenarios and outline a first research agenda. Motivated primarily by the prevalence of and opportunities for visualizations in sports and video games we started to investigate a small aspect of our research agenda: the impact of two important characteristics of motion-speed and trajectory on a stationary viewer's ability to read data from moving donut and bar charts. We found that increasing speed and trajectory complexity did negatively affect the accuracy of reading values from the charts and that bar charts were more negatively impacted. In practice, however, this impact was small: both charts were still read fairly accurately.

Rotate or Wrap? Interactive Visualisations of Cyclical Data on Cylindrical or Toroidal Topologies

In this paper, we report on a study of visual representations for cyclical data and the effect of interactively wrapping a bar chart 'around its boundaries'. Compared to linear bar chart, polar (or radial) visualisations have the advantage that cyclical data can be presented continuously without mentally bridging the visual 'cut' across the left-and-right boundaries. To investigate this hypothesis and to assess the effect the cut has on analysis performance, this paper presents results from a crowdsourced, controlled experiment with 72 participants comparing new continuous panning technique to linear bar charts (interactive wrapping). Our results show that bar charts with interactive wrapping lead to less errors compared to standard bar charts or polar charts. Inspired by these results, we generalise the concept of interactive wrapping to other visualisations for cyclical or relational data. We describe a design space based on the concept of one-dimensional wrapping and two-dimensional wrapping, linked to two common 3D topologies; cylinder and torus that can be used to metaphorically explain one- and two-dimensional wrapping. This design space suggests that interactive wrapping is widely applicable to many different data types.