Visual Analytics: Responding to the Challenge

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Visual Analytics: Responding to the Challenge

Research and development (R&D) in visual analytics helps address these
challenges.

Visual analytics is the science of analytical reasoning facilitated by interactive
visual interfaces. People use visual analytics tools and techniques to
synthesize information and derive insight from massive, dynamic, ambiguous,
and often conflicting data; detect the expected and discover the unexpected; provide
timely, defensible, and understandable assessments; and communicate
assessment effectively for action.

Visual analytics is a multidisciplinary field that includes the following focus areas:

• Analytical reasoning techniques that enable users to obtain deep insights
that directly support assessment, planning, and decision making
• Visual representations and interaction techniques that take advantage of
the human eye’s broad bandwidth pathway into the mind to allow users to
see, explore, and understand large amounts of information at once
• Data representations and transformations that convert all types of conflicting
and dynamic data in ways that support visualization and analysis
• Techniques to support production, presentation, and dissemination of the
results of an analysis to communicate information in the appropriate context
to a variety of audiences.

Recommendation:
Build upon theoretical foundations of reasoning, sense-making, cognition,
and perception to create visually enabled tools to support collaborative
analytic reasoning about complex and dynamic problems.

Recommendation:
Conduct research to address the challenges and seize the opportunities
posed by the scale of the analytic problem. The issues of scale are manifested
in many ways, including the complexity and urgency of the analytical
task, the massive volume of diverse and dynamic data involved in the analysis,
and challenges of collaborating among groups of people involved in
analysis, prevention, and response efforts.

Recommendation:
Create a science of visual representations based on cognitive and perceptual
principles that can be deployed through engineered, reusable components.
Visual representation principles must address all types of data, address scale
and information complexity, enable knowledge discovery through information
synthesis, and facilitate analytical reasoning.

Recommendation:
Develop a new suite of visual paradigms that support the analytical reasoning
process.
These visualizations must:
• Facilitate understanding of massive and continually growing collections of
data of multiple types
• Provide frameworks for analysis of spatial and temporal data
• Support understanding of uncertain, incomplete, and often misleading
information
• Provide user- and task-adaptable, guided representations that enable full
situation awareness while supporting development of detailed actions
• Support multiple levels of data and information abstraction
• Facilitate knowledge discovery through information synthesis, which is
the integration of data based on their meaning rather than the original
data type.

Recommendation:
Develop a new science of interactions that supports the analytical reasoning
process. This interaction science must provide a taxonomy of interaction
techniques ranging from the low-level interactions to more complex interaction
techniques and must address the challenge to scale across different
types of display environments and tasks.

Recommendation:
Develop both theory and practice for transforming data into new scalable
representations that faithfully represent the content of the underlying data.

Recommendation:
Create methods to synthesize information of different types and from different
sources into a unified data representation so that analysts, first
responders, and border personnel may focus on the meaning of the data.

Recommendation:
Develop methods and principles for representing data quality, reliability, and
certainty measures throughout the data transformation and analysis process.

Recommendation:
Develop methodology and tools that enable the capture of the analytic
assessment, decision recommendations, and first responder actions into
information packages. These packages must be tailored for each intended
receiver and situation and permit expansion to show supporting evidence
as needed.

Recommendation:
Develop technologies that enable analysts to communicate what they know
through the use of appropriate visual metaphor and accepted principles of
reasoning and graphic representation. Create techniques that enable effective
use of limited, mobile forms of technologies to support situation
assessment by first responders. Support the need for effective public alerts
with the production of a basic handbook for common methods for communicating
risks.

Recommendation:
Create visual analytics data structures, intermediate representations, and
outputs that support seamless integration of tools so that data requests and
acquisition, visual analysis, note-taking, presentation composition, and dissemination
all take place within a cohesive environment that supports
around-the-clock operation and provides robust privacy and security control.

Recommendation:
Develop an infrastructure to facilitate evaluation of new visual analytics
technologies.

Recommendation:
Create and use a common security and privacy infrastructure, with support
for incorporating privacy-supporting technologies, such as data minimization
and data anonymization.