20 research outputs found
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. This narrow focus inhibits our understanding of how generalizable these guidelines are, particularly to tasks like visual inference that require synthesis and judgement across multiple percepts. Furthermore, the emphasis on traditional ramp designs (e.g., sequential or diverging) may sideline other key metrics or design strategies. We study how a cognitive metric-color name variation-impacts people's ability to make model-based judgments. In two graphical inference experiments, participants saw a series of color-coded scalar fields sampled from different models and assessed the relationships between these models. Contrary to conventional guidelines, participants were more accurate when viewing colormaps that cross a variety of uniquely nameable colors. We modeled participants' performance using this metric and found that it provides a better fit to the experimental data than do existing design principles. Our findings indicate cognitive advantages for colorful maps like rainbow, which exhibit high color categorization, despite their traditionally undesirable perceptual properties. We also found no evidence that color categorization would lead observers to infer false data features. Our results provide empirically grounded metrics for predicting a colormap's performance and suggest alternative guidelines for designing new quantitative colormaps to support inference. The data and materials for this paper are available at: https://osf.io/tck2r/
Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning
Quality colormaps can help communicate important data patterns. However,
finding an aesthetically pleasing colormap that looks "just right" for a given
scenario requires significant design and technical expertise. We introduce
Cieran, a tool that allows any data analyst to rapidly find quality colormaps
while designing charts within Jupyter Notebooks. Our system employs an active
preference learning paradigm to rank expert-designed colormaps and create new
ones from pairwise comparisons, allowing analysts who are novices in color
design to tailor colormaps to their data context. We accomplish this by
treating colormap design as a path planning problem through the CIELAB
colorspace with a context-specific reward model. In an evaluation with twelve
scientists, we found that Cieran effectively modeled user preferences to rank
colormaps and leveraged this model to create new quality designs. Our work
shows the potential of active preference learning for supporting efficient
visualization design optimization.Comment: CHI 2024. 12 pages/9 figure
Measuring Categorical Perception in Color-Coded Scatterplots
Scatterplots commonly use color to encode categorical data. However, as
datasets increase in size and complexity, the efficacy of these channels may
vary. Designers lack insight into how robust different design choices are to
variations in category numbers. This paper presents a crowdsourced experiment
measuring how the number of categories and choice of color encodings used in
multiclass scatterplots influences the viewers' abilities to analyze data
across classes. Participants estimated relative means in a series of
scatterplots with 2 to 10 categories encoded using ten color palettes drawn
from popular design tools. Our results show that the number of categories and
color discriminability within a color palette notably impact people's
perception of categorical data in scatterplots and that the judgments become
harder as the number of categories grows. We examine existing palette design
heuristics in light of our results to help designers make robust color choices
informed by the parameters of their data.Comment: The paper has been accepted to the ACM CHI 2023. 14 pages, 7 figure
Effects of data distribution and granularity on color semantics for colormap data visualizations
To create effective data visualizations, it helps to represent data using
visual features in intuitive ways. When visualization designs match observer
expectations, visualizations are easier to interpret. Prior work suggests that
several factors influence such expectations. For example, the dark-is-more bias
leads observers to infer that darker colors map to larger quantities, and the
opaque-is-more bias leads them to infer that regions appearing more opaque
(given the background color) map to larger quantities. Previous work suggested
that the background color only plays a role if visualizations appear to vary in
opacity. The present study challenges this claim. We hypothesized that the
background color modulate inferred mappings for colormaps that should not
appear to vary in opacity (by previous measures) if the visualization appeared
to have a "hole" that revealed the background behind the map (hole hypothesis).
We found that spatial aspects of the map contributed to inferred mappings,
though the effects were inconsistent with the hole hypothesis. Our work raises
new questions about how spatial distributions of data influence color semantics
in colormap data visualizations
Data, Data, Everywhere: Uncovering Everyday Data Experiences for People with Intellectual and Developmental Disabilities
Data is everywhere but may not be accessible to everyone. Conventional data
visualization tools and guidelines often do not actively consider the specific
needs and abilities of people with Intellectual and Developmental Disabilities
(IDD), leaving them excluded from data-driven activities and vulnerable to
ethical issues. To understand the needs and challenges people with IDD have
with data, we conducted 15 semi-structured interviews with individuals with IDD
and their caregivers. Our algorithmic interview approach situated data in the
lived experiences of people with IDD to uncover otherwise hidden data
encounters in their everyday life. Drawing on findings and observations, we
characterize how they conceptualize data, when and where they use data, and
what barriers exist when they interact with data. We use our results as a lens
to reimagine the role of visualization in data accessibility and establish a
critical near-term research agenda for cognitively accessible visualization
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. Data visualizations allow observers to leverage their ability to perform visual ensemble statistics on distributions of spatial or featural visual information to estimate actual statistics on data. We survey the types of visual statistical tasks that occur within data visualizations across everyday examples, such as scatterplots, and more specialized images, such as weather maps or depictions of patterns in text. We divide these tasks into four categories: identification of sets of values, summarization across those values, segmentation of collections, and estimation of structure. We point to unanswered questions for each category and give examples of such cross-pollination in the current literature. Increased collaboration between the data visualization and perceptual psychology research communities can inspire new solutions to challenges in visualization while simultaneously exposing unsolved problems in perception research
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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. Data visualizations allow observers to leverage their ability to perform visual ensemble statistics on distributions of spatial or featural visual information to estimate actual statistics on data. We survey the types of visual statistical tasks that occur within data visualizations across everyday examples, such as scatterplots, and more specialized images, such as weather maps or depictions of patterns in text. We divide these tasks into four categories: identification of sets of values, summarization across those values, segmentation of collections, and estimation of structure. We point to unanswered questions for each category and give examples of such cross-pollination in the current literature. Increased collaboration between the data visualization and perceptual psychology research communities can inspire new solutions to challenges in visualization while simultaneously exposing unsolved problems in perception research
A Qualitative Analysis of Common Practices in Annotations: A Taxonomy and Design Space
Annotations are a vital component of data externalization and collaborative
analysis, directing readers' attention to important visual elements. Therefore,
it is crucial to understand their design space for effectively annotating
visualizations. However, despite their widespread use in visualization, we have
identified a lack of a design space for common practices for annotations. In
this paper, we present two studies that explore how people annotate
visualizations to support effective communication. In the first study, we
evaluate how visualization students annotate bar charts when answering
high-level questions about the data. Qualitative coding of the resulting
annotations generates a taxonomy comprising enclosure, connector, text, mark,
and color, revealing how people leverage different visual elements to
communicate critical information. We then extend our taxonomy by performing
thematic coding on a diverse range of real-world annotated charts, adding trend
and geometric annotations to the taxonomy. We then combine the results of these
studies into a design space of annotations that focuses on the key elements
driving the design choices available when annotating a chart, providing a
reference guide for using annotations to communicate insights from
visualizations
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. To
mitigate these challenges, we introduce a computational design pipeline to 3D
print network physicalizations with integrated sensing capabilities. Networks
are ubiquitous, yet their complex geometry also requires significant
engineering considerations to provide intuitive, effective interactions for
exploration. Using our pipeline, designers can readily produce network
physicalizations supporting selection-the most critical atomic operation for
interaction-by touch through capacitive sensing and computational inference.
Our computational design pipeline introduces a new design paradigm by
concurrently considering the form and interactivity of a physicalization into
one cohesive fabrication workflow. We evaluate our approach using (i)
computational evaluations, (ii) three usage scenarios focusing on general
visualization tasks, and (iii) expert interviews. The design paradigm
introduced by our pipeline can lower barriers to physicalization research,
creation, and adoption.Comment: 11 pages, 8 figures; to be published in Proceedings of IEEE VIS 202