Shape It Up: An Empirically Grounded Approach for Designing Shape Palettes.

Shape is commonly used to distinguish between categories in multi-class scatterplots. However, existing guidelines for choosing effective shape palettes rely largely on intuition and do not consider how these needs may change as the number of categories increases. Unlike color, shapes can not be represented by a numerical space, making it difficult to propose general guidelines or design heuristics for using shape effectively.

A Qualitative Analysis of Common Practices in Annotations: A Taxonomy and Design Space.

Annotations play a vital role in highlighting critical aspects of visualizations, aiding in data externalization and exploration, collaborative sensemaking, and visual storytelling. However, despite their widespread use, we identified a lack of a design space for common practices for annotations. In this paper, we evaluated over 1,800 static annotated charts to understand how people annotate visualizations in practice.

Beyond Vision Impairments: Redefining the Scope of Accessible Data Representations.

The increasing ubiquity of data in everyday life has elevated the importance of data literacy and accessible data representations, particularly for individuals with disabilities. While prior research predominantly focuses on the needs of the visually impaired, our survey aims to broaden this scope by investigating accessible data representations across a more inclusive spectrum of disabilities. After conducting a systematic review of 152 accessible data representation papers from ACM and IEEE databases, we found that roughly 78% of existing articles center on vision impairments.

CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering.

Visual clustering is a common perceptual task in scatterplots that supports diverse analytics tasks (e.g., cluster identification).

A Computational Design Pipeline to Fabricate Sensing Network Physicalizations.

Interaction is critical for data analysis and sensemaking. However, designing interactive physicalizations is challenging as it requires cross-disciplinary knowledge in visualization, fabrication, and electronics. Interactive physicalizations are typically produced in an unstructured manner, resulting in unique solutions for a specific dataset, problem, or interaction that cannot be easily extended or adapted to new scenarios or future physicalizations.

Rainbow Colormaps Are Not All Bad.

Some 15 years ago, Visualization Viewpoints published an influential article titled Rainbow Color Map (Still) Considered Harmful (Borland and Taylor, 2007). The paper argued that the "rainbow colormap's characteristics of confusing the viewer, obscuring the data and actively misleading interpretation make it a poor choice for visualization." Subsequent articles often repeat and extend these arguments, so much so that avoiding rainbow colormaps, along with their derivatives, has become dogma in the visualization community.

Cultivating Visualization Literacy for Children Through Curiosity and Play.

Fostering data visualization literacy (DVL) as part of childhood education could lead to a more data literate society. However, most work in DVL for children relies on a more formal educational context (i.e.

Visualizing temperature trends: Higher sensitivity to trend direction with single-hue palettes.

Design plays a key role in the interpretability of complex visualizations. Many applied domains utilize large quantities of data to make predictions, ranging from maps showing the spread of infectious disease to line graphs displaying global temperature changes. These visualizations tap into the visual system's ability to extract information from groups of similar objects, a process known as ensemble processing, and the cognitive system's ability to relate visual features such as color to meaningful concepts such as disease or temperature.

Professional Differences: A Comparative Study of Visualization Task Performance and Spatial Ability Across Disciplines.

Problem-driven visualization work is rooted in deeply understanding the data, actors, processes, and workflows of a target domain. However, an individual's personality traits and cognitive abilities may also influence visualization use. Diverse user needs and abilities raise natural questions for specificity in visualization design: Could individuals from different domains exhibit performance differences when using visualizations? Are any systematic variations related to their cognitive abilities? This study bridges domain-specific perspectives on visualization design with those provided by cognition and perception.

The Weighted Average Illusion: Biases in Perceived Mean Position in Scatterplots.

Scatterplots can encode a third dimension by using additional channels like size or color (e.g. bubble charts).

Enabling Crosscutting Visualization for Geoscience.

Our world is a complex ecosystem of interdependent processes. Geoscientists collect individual datasets addressing hyperspecific questions, which seek to probe these deeply intertwined processes. Scientists are beginning to explore how investigating relationships between disciplines can foster richer and more holistic research, but visualization tools are conventionally designed to address hyperspecific, rather than holistic, analysis.

A Design Space of Vision Science Methods for Visualization Research.

A growing number of efforts aim to understand what people see when using a visualization. These efforts provide scientific grounding to complement design intuitions, leading to more effective visualization practice. However, published visualization research currently reflects a limited set of available methods for understanding how people process visualized data.

Rainbows Revisited: Modeling Effective Colormap Design for Graphical Inference.

Color mapping is a foundational technique for visualizing scalar data. Prior literature offers guidelines for effective colormap design, such as emphasizing luminance variation while limiting changes in hue. However, empirical studies of color are largely focused on perceptual tasks.

Color Crafting: Automating the Construction of Designer Quality Color Ramps.

Visualizations often encode numeric data using sequential and diverging color ramps. Effective ramps use colors that are sufficiently discriminable, align well with the data, and are aesthetically pleasing. Designers rely on years of experience to create high-quality color ramps.

Designing for Mobile and Immersive Visual Analytics in the Field.

Data collection and analysis in the field is critical for operations in domains such as environmental science and public safety. However, field workers currently face data- and platform-oriented issues in efficient data collection and analysis in the field, such as limited connectivity, screen space, and attentional resources. In this paper, we explore how visual analytics tools might transform field practices by more deeply integrating data into these operations.

Broadening Intellectual Diversity in Visualization Research Papers.

Promoting a wider range of contribution types can facilitate healthy growth of the visualization community, while increasing the intellectual diversity of visualization research papers. In this paper, we discuss the importance of contribution types and summarize contribution types that can be meaningful in visualization research. We also propose several concrete next steps we can and should take to ensure a successful launch of the contribution types.

Zika discourse in the Americas: A multilingual topic analysis of Twitter.

This work examines Twitter discussion surrounding the 2015 outbreak of Zika, a virus that is most often mild but has been associated with serious birth defects and neurological syndromes. We introduce and analyze a collection of 3.9 million tweets mentioning Zika geolocated to North and South America, where the virus is most prevalent.

Where's My Data? Evaluating Visualizations with Missing Data.

Many real-world datasets are incomplete due to factors such as data collection failures or misalignments between fused datasets. Visualizations of incomplete datasets should allow analysts to draw conclusions from their data while effectively reasoning about the quality of the data and resulting conclusions. We conducted a pair of crowdsourced studies to measure how the methods used to impute and visualize missing data may influence analysts' perceptions of data quality and their confidence in their conclusions.

Modeling Color Difference for Visualization Design.

Color is frequently used to encode values in visualizations. For color encodings to be effective, the mapping between colors and values must preserve important differences in the data. However, most guidelines for effective color choice in visualization are based on either color perceptions measured using large, uniform fields in optimal viewing environments or on qualitative intuitions.

Four types of ensemble coding in data visualizations.

Ensemble coding supports rapid extraction of visual statistics about distributed visual information. Researchers typically study this ability with the goal of drawing conclusions about how such coding extracts information from natural scenes. Here we argue that a second domain can serve as another strong inspiration for understanding ensemble coding: graphs, maps, and other visual presentations of data.

Lightness Constancy in Surface Visualization.

Color is a common channel for displaying data in surface visualization, but is affected by the shadows and shading used to convey surface depth and shape. Understanding encoded data in the context of surface structure is critical for effective analysis in a variety of domains, such as in molecular biology. In the physical world, lightness constancy allows people to accurately perceive shadowed colors; however, its effectiveness in complex synthetic environments such as surface visualizations is not well understood.