10 research outputs found
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
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
Constructing Frameworks for Task-Optimized Visualizations
Visualization is crucial in today’s data-driven world to augment and enhance human understanding and decision-making. Effective visualizations must support accuracy in visual task performance and expressive data communication. Effective visualization design depends on the visual channels used, chart types, or visual tasks. However, design choices and visual judgment are co-related, and effectiveness is not one-dimensional, leading to a significant need to understand the intersection of these factors to create optimized visualizations. Hence, constructing frameworks that consider both design decisions and the task being performed enables optimizing visualization design to maximize efficacy. This dissertation describes experiments, techniques, and user studies to model user perception for visualization design optimization and data transformation for low-level visual tasks. To begin with, I identify the limitations through a taxonomized state-of-the-art survey on perception-based visualization studies focusing on how visualization effectiveness is task-dependent.With a specific focus on the scatterplot, I developed perceptual models for cluster perception and design optimization. In addition to design guidelines from the first experiments, I employ the findings to show design choices based on the visual density of the scatterplot could influence the user’s judgment on visual tasks. Further, I address the challenge of assessing line chart smoothing effectiveness for a range of analytical tasks. Finally, I elaborate on utilizing the framework to provide less ambiguous data presentations, leading to better quality and higher confidence in decision-making
Constructing Frameworks for Task-Optimized Visualizations
Visualization is crucial in today’s data-driven world to augment and enhance human understanding and decision-making. Effective visualizations must support accuracy in visual task performance and expressive data communication. Effective visualization design depends on the visual channels used, chart types, or visual tasks. However, design choices and visual judgment are co-related, and effectiveness is not one-dimensional, leading to a significant need to understand the intersection of these factors to create optimized visualizations. Hence, constructing frameworks that consider both design decisions and the task being performed enables optimizing visualization design to maximize efficacy. This dissertation describes experiments, techniques, and user studies to model user perception for visualization design optimization and data transformation for low-level visual tasks. To begin with, I identify the limitations through a taxonomized state-of-the-art survey on perception-based visualization studies focusing on how visualization effectiveness is task-dependent.With a specific focus on the scatterplot, I developed perceptual models for cluster perception and design optimization. In addition to design guidelines from the first experiments, I employ the findings to show design choices based on the visual density of the scatterplot could influence the user’s judgment on visual tasks. Further, I address the challenge of assessing line chart smoothing effectiveness for a range of analytical tasks. Finally, I elaborate on utilizing the framework to provide less ambiguous data presentations, leading to better quality and higher confidence in decision-making
A Case-Study on Variations Observed in Accelerometers Across Devices
Every year we grow more dependent on wearable devices to gather personalized
data, such as our movements, heart rate, respiration, etc. To capture this
data, devices contain sensors, such as accelerometers and gyroscopes, that are
able to measure changes in their surroundings and pass along the information
for better informed decisions. Although these sensors should behave similarly
in different devices, that is not always the case. In this case study, we
analyze accelerometers from three different devices recording the same actions
with an aim to determine whether the discrepancies are due to variability
within or between devices. We found the most significant variation between
devices with different specifications, such as sensitivity and sampling
frequency. Nevertheless, variance in the data should be assumed, even if data
is gathered from the same person, activity, and type of sensor
Automatic Scatterplot Design Optimization for Clustering Identification
Scatterplots are among the most widely used visualization techniques.
Compelling scatterplot visualizations improve understanding of data by
leveraging visual perception to boost awareness when performing specific visual
analytic tasks. Design choices in scatterplots, such as graphical encodings or
data aspects, can directly impact decision-making quality for low-level tasks
like clustering. Hence, constructing frameworks that consider both the
perceptions of the visual encodings and the task being performed enables
optimizing visualizations to maximize efficacy. In this paper, we propose an
automatic tool to optimize the design factors of scatterplots to reveal the
most salient cluster structure. Our approach leverages the merge tree data
structure to identify the clusters and optimize the choice of subsampling
algorithm, sampling rate, marker size, and marker opacity used to generate a
scatterplot image. We validate our approach with user and case studies that
show it efficiently provides high-quality scatterplot designs from a large
parameter space