Improving Visualization Interpretation Using Counterfactuals.

Complex, high-dimensional data is used in a wide range of domains to explore problems and make decisions. Analysis of high-dimensional data, however, is vulnerable to the hidden influence of confounding variables, especially as users apply ad hoc filtering operations to visualize only specific subsets of an entire dataset. Thus, visual data-driven analysis can mislead users and encourage mistaken assumptions about causality or the strength of relationships between features. This work introduces a novel visual approach designed to reveal the presence of confounding variables via counterfactual possibilities during visual data analysis. It is implemented in CoFact, an interactive visualization prototype that determines and visualizes counterfactual subsets to better support user exploration of feature relationships. Using publicly available datasets, we conducted a controlled user study to demonstrate the effectiveness of our approach; the results indicate that users exposed to counterfactual visualizations formed more careful judgments about feature-to-outcome relationships.

Survey on Visual Analysis of Event Sequence Data.

Event sequence data record series of discrete events in the time order of occurrence. They are commonly observed in a variety of applications ranging from electronic health records to network logs, with the characteristics of large-scale, high-dimensional and heterogeneous. This high complexity of event sequence data makes it difficult for analysts to manually explore and find patterns, resulting in ever-increasing needs for computational and perceptual aids from visual analytics techniques to extract and communicate insights from event sequence datasets. In this paper, we review the state-of-the-art visual analytics approaches, characterize them with our proposed design space, and categorize them based on analytical tasks and applications. From our review of relevant literature, we have also identified several remaining research challenges and future research opportunities.

Selection-Bias-Corrected Visualization via Dynamic Reweighting.

The collection and visual analysis of large-scale data from complex systems, such as electronic health records or clickstream data, has become increasingly common across a wide range of industries. This type of retrospective visual analysis, however, is prone to a variety of selection bias effects, especially for high-dimensional data where only a subset of dimensions is visualized at any given time. The risk of selection bias is even higher when analysts dynamically apply filters or perform grouping operations during ad hoc analyses. These bias effects threaten the validity and generalizability of insights discovered during visual analysis as the basis for decision making. Past work has focused on bias transparency, helping users understand when selection bias may have occurred. However, countering the effects of selection bias via bias mitigation is typically left for the user to accomplish as a separate process. Dynamic reweighting (DR) is a novel computational approach to selection bias mitigation that helps users craft bias-corrected visualizations. This paper describes the DR workflow, introduces key DR visualization designs, and presents statistical methods that support the DR process. Use cases from the medical domain, as well as findings from domain expert user interviews, are also reported.

A rapidly deployed, interactive, online visualization system to support fatality management during the coronavirus disease 2019 (COVID-19) pandemic.

OBJECTIVE: To create an online visualization to support fatality management in North Carolina. MATERIALS AND METHODS: A web application aggregates online datasets for coronavirus disease 2019 (COVID-19) infection rates and morgue utilization. The data are visualized through an interactive, online dashboard. RESULTS: The web application was shared with state and local public health officials across North Carolina. Users could adjust interactive maps and other statistical charts to view live reports of metrics at multiple aggregation levels (eg, county or region). The application also provides access to detailed tabular data for individual facilities. DISCUSSION: Stakeholders found this tool helpful for providing situational awareness of capacity, hotspots, and utilization fluctuations. Timely reporting of facility and county data were key, and future work can help streamline the data collection process. There is potential to generalize the technology to other use cases. CONCLUSIONS: This dashboard facilitates fatality management by visualizing county and regional aggregate statistics in North Carolina.