38 research outputs found
OSCAR: A Semantic-based Data Binning Approach
Binning is applied to categorize data values or to see distributions of data.
Existing binning algorithms often rely on statistical properties of data.
However, there are semantic considerations for selecting appropriate binning
schemes. Surveys, for instance, gather respondent data for demographic-related
questions such as age, salary, number of employees, etc., that are bucketed
into defined semantic categories. In this paper, we leverage common semantic
categories from survey data and Tableau Public visualizations to identify a set
of semantic binning categories. We employ these semantic binning categories in
OSCAR: a method for automatically selecting bins based on the inferred semantic
type of the field. We conducted a crowdsourced study with 120 participants to
better understand user preferences for bins generated by OSCAR vs. binning
provided in Tableau. We find that maps and histograms using binned values
generated by OSCAR are preferred by users as compared to binning schemes based
purely on the statistical properties of the data.Comment: 5 pages (4 pages text + 1 page references), 3 figure
Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts
While visualizations are an effective way to represent insights about
information, they rarely stand alone. When designing a visualization, text is
often added to provide additional context and guidance for the reader. However,
there is little experimental evidence to guide designers as to what is the
right amount of text to show within a chart, what its qualitative properties
should be, and where it should be placed. Prior work also shows variation in
personal preferences for charts versus textual representations. In this paper,
we explore several research questions about the relative value of textual
components of visualizations. 302 participants ranked univariate line charts
containing varying amounts of text, ranging from no text (except for the axes)
to a written paragraph with no visuals. Participants also described what
information they could take away from line charts containing text with varying
semantic content. We find that heavily annotated charts were not penalized. In
fact, participants preferred the charts with the largest number of textual
annotations over charts with fewer annotations or text alone. We also find
effects of semantic content. For instance, the text that describes statistical
or relational components of a chart leads to more takeaways referring to
statistics or relational comparisons than text describing elemental or encoded
components. Finally, we find different effects for the semantic levels based on
the placement of the text on the chart; some kinds of information are best
placed in the title, while others should be placed closer to the data. We
compile these results into four chart design guidelines and discuss future
implications for the combination of text and charts.Comment: 11 pages, 4 tables, 6 figures, accepted to IEEE Transaction on
Visualization and Graphic
EmphasisChecker: A Tool for Guiding Chart and Caption Emphasis
Recent work has shown that when both the chart and caption emphasize the same
aspects of the data, readers tend to remember the doubly-emphasized features as
takeaways; when there is a mismatch, readers rely on the chart to form
takeaways and can miss information in the caption text. Through a survey of 280
chart-caption pairs in real-world sources (e.g., news media, poll reports,
government reports, academic articles, and Tableau Public), we find that
captions often do not emphasize the same information in practice, which could
limit how effectively readers take away the authors' intended messages.
Motivated by the survey findings, we present EmphasisChecker, an interactive
tool that highlights visually prominent chart features as well as the features
emphasized by the caption text along with any mismatches in the emphasis. The
tool implements a time-series prominent feature detector based on the
Ramer-Douglas-Peucker algorithm and a text reference extractor that identifies
time references and data descriptions in the caption and matches them with
chart data. This information enables authors to compare features emphasized by
these two modalities, quickly see mismatches, and make necessary revisions. A
user study confirms that our tool is both useful and easy to use when authoring
charts and captions.Comment: IEEE VIS 202
Toward a Scalable Census of Dashboard Designs in the Wild: A Case Study with Tableau Public
Dashboards remain ubiquitous artifacts for presenting or reasoning with data
across different domains. Yet, there has been little work that provides a
quantifiable, systematic, and descriptive overview of dashboard designs at
scale. We propose a schematic representation of dashboard designs as node-link
graphs to better understand their spatial and interactive structures. We apply
our approach to a dataset of 25,620 dashboards curated from Tableau Public to
provide a descriptive overview of the core building blocks of dashboards in the
wild and derive common dashboard design patterns. To guide future research, we
make our dashboard corpus publicly available and discuss its application toward
the development of dashboard design tools.Comment: *J. Purich and A. Srinivasan contributed equally to the wor
More: A Mobile Open Rich Media Environment
‘Rich media ’ is a term that implies the integration of all of the ad-vances we have made in the mobile space delivering music, speech, text, graphics and video. This is true, but it is more than the sum of its parts. Rich media is the ability to deliver these modalities, to interact with these modalities, and to do it in a way that allows for the construction, delivery and use of compelling mobile services in an effective and economic manner. In this paper, we introduce a sys-tem called Mobile Open Rich-media Environment (‘MORE’) that helps realize such mobile rich media services, combining various technologies of W3C, OMA, 3GPP and IETF standards. The differ-ent components of the system include formatting, packaging, trans-porting, rendering and interacting with rich media files and streams. 1
Is that a smile?: gaze dependent facial expressions
Figure 1: a) A low spatial frequency filter reveals a more prominent smile. b) A more neutral expression is seen under high spatial frequency