Visualizations are often used to communicate about data, for example in the media where interactive graphics are designed to supplement text articles, and in communicating analysis in research and industry settings. Presentation order, annotation, and consistency in the design of multiple visualizations are just a few considerations that impact communication-oriented visualization.
Most visualization tools, however, focus on supporting analysis. I have worked in the area of narrative visualization, or the use of statistical graphics to tell stories around data. One aim of my work in this area is to develop better tools for automatically and semi-automatically constructing such visualizations. For example, how can we develop semi-automated algorithms that help suggest good presentation orders and designs to a narrative visualization designer? Can we automatically construct annotated visualizations to make it easier for news and other organizations to generate visualizations to contextualize text articles?
- A Deeper Understanding of Sequence in Narrative Visualization
- Contextifier: Automatic Generation of Annotated Stock Visualizations
- NewsViews: An Automated Pipeline for Creating Custom Geovisualizations ...
- Visualization Rhetoric: Framing Effects in Narrative Visualization
- Content, Context, & Critique: Commenting on a Visualization-Oriented News Blog
- Evaluating Visualization Sets: Trade-offs Between Local Effectiveness and Global Consistency
Many people, including analysts, find uncertainty and probability difficult to reason about when working with data. The use of null hypothesis significance testing is increasingly criticized, however we lack good representations of uncertainty that can provide analysts and readers of scientific literature with “cognitive evidence” for understanding variation, reliability, and related statistical concepts. I’m interested in novel tools that can provide such evidence.
I've been developing a method for visualizing uncertainty more concretely as a set of possible outcomes. By watching possible outcomes “play out” in an animated or interactive format, the user gains a better sense of which data patterns are reliable and which are not. The approach generalizes to a number of data inputs and complex visualization types that lack uncertainty representations. I'm also interested in how visualization can be used to support Bayesian thinking, such as by incorporating the viewer's expectations of the data in interaction with the visualized data.
- Hypothetical Outcome Plots Outperform Error Bars and Violin Plots for ...
- A blog post on Hypothetical Outcome Plots.
- My OpenVis2016 talk on The Visual Uncertainty Experience
- When(ish) is my Bus? User Centered Visualizations of Uncertainty ...
- Evaluating Approaches to Crowdsourced Visual Analytics
- Why Evaluating Uncertainty Visualization is Error Prone
Thinking with Data
Many visualization systems focus on expert users, such as trained statisticians or researchers. The focus of research around such systems is often driven by technical or perceptual questions, rather than cognitive ones. My work develops tools and theory around thinking with data more broadly, including for using visualizations for learning, reasoning, and social intepretation.
- Generating Personalized Spatial Analogies for Distances and Areas
- Our Chrome plugin for generating personalized spatial analogies.
- How Prior Knowledge Affects the Processing of Visualized Data
- Benefitting InfoVis with Visual Difficulties
- The Impact of Social Information on Visual Judgments
Talking About Science
Scientific jargon makes scientific research inaccessible to lay people and scientists in other fields. Text simplification approaches can help, but we lack usable tools that can help authors (such as science journalists or bloggers) and readers access possible simplifications of jargon when they need them. We are developing interfaces that use word embeddings and other methods to learn simplifications (e.g., mappings between complex and simple terms) from large corpora and then suggest them on demand as a person reads or writes about science.