5 research outputs found
Searching the Visual Style and Structure of D3 Visualizations
We present a search engine for D3 visualizations that allows queries based on
their visual style and underlying structure. To build the engine we crawl a
collection of 7860 D3 visualizations from the Web and deconstruct each one to
recover its data, its data-encoding marks and the encodings describing how the
data is mapped to visual attributes of the marks. We also extract axes and
other non-data-encoding attributes of marks (e.g., typeface, background color).
Our search engine indexes this style and structure information as well as
metadata about the webpage containing the chart. We show how visualization
developers can search the collection to find visualizations that exhibit
specific design characteristics and thereby explore the space of possible
designs. We also demonstrate how researchers can use the search engine to
identify commonly used visual design patterns and we perform such a demographic
design analysis across our collection of D3 charts. A user study reveals that
visualization developers found our style and structure based search engine to
be significantly more useful and satisfying for finding different designs of D3
charts, than a baseline search engine that only allows keyword search over the
webpage containing a chart
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
SeeChart: Enabling Accessible Visualizations Through Interactive Natural Language Interface For People with Visual Impairments
Web-based data visualizations have become very popular for exploring data and
communicating insights. Newspapers, journals, and reports regularly publish
visualizations to tell compelling stories with data. Unfortunately, most
visualizations are inaccessible to readers with visual impairments. For many
charts on the web, there are no accompanying alternative (alt) texts, and even
if such texts exist they do not adequately describe important insights from
charts. To address the problem, we first interviewed 15 blind users to
understand their challenges and requirements for reading data visualizations.
Based on the insights from these interviews, we developed SeeChart, an
interactive tool that automatically deconstructs charts from web pages and then
converts them to accessible visualizations for blind people by enabling them to
hear the chart summary as well as to interact through data points using the
keyboard. Our evaluation with 14 blind participants suggests the efficacy of
SeeChart in understanding key insights from charts and fulfilling their
information needs while reducing their required time and cognitive burden.Comment: 28 pages, 13 figure