Part 7: A Taxonomy for Visual Analytics

This is Part 7 of 7 of the highlights from the National Visualization Analysis Center. (NVAC). Unlike previous parts, this one focuses on a May 2009 article. Please see this post for an introduction to the study and access to the other 6 parts.

Jim Thomas, the co-author of “Illuminating the Path: A Research and Development Agenda for Visual Analytics,” and Director of the National Visualization Analysis Center (NVAC) recently called for the development of a taxonomy for visual analytics. Jim explains the importance of visual analytics as follows:

“Visual analytics are valuable because the tool helps to detect the expected, and discover the unexpected. Visual analytics combines the art of human intuition and the science of mathematical deduction to perceive patterns and derive knowledge and insight from them. With our success in developing and delivering new technologies, we are paving the way for fundamentally new tools to deal with the huge digital libraries of the future, whether for terrorist threat detection or new interactions with potentially life-saving drugs.”

In the latest edition of VAC Views, Jim expresses NVAC’s interest in helping to “define the study of visual analytics by providing an order and arrangement of topics—the taxa that are at the heart of studying visual analytics. The reason for such a “definition” is to more clearly describe the scope and intent of impact for the field of visual analytics.”

Jim and colleagues propose the following higher-order classifications:

  • Domain/Applications
  • Analytic Methods/Goals
  • Science and Technology
  • Data Types/Structures.

In his article in VAC Views, Jim requests feedback and suggestions for improving the more detailed taxonomy that he provides in the graphic below. The latter was not produced in very high resolution in VAC Views and does not reproduce well here, so I summarize below whilst giving feedback.


1. Domain/Applications

While Security (and Health) are included in the draft NVAC proposal as domains / applications, what is missing is Humanitarian Crises, Conflict Prevention and Disaster Management.

Perhaps “domain/applications” should not be combined since “applications” tends to be a subset of associated “domains” which poses some confusion. For example, law enforcement is a domain and crime mapping analysis could be considered as an application of visual analytics.

2. Analytic Methods/Goals

Predictive, Surveillance, Watch/Warn/Alert, Relationship Mapping, Rare Event Identification are included. There are a host of other methods not referred to here such as cluster detection, a core focus of spatial analysis. See the methods table in my previous blog post for examples of spatial cluster detection.

Again I find that combining both “analytic methods” and “goals” makes the classification somewhat confusing.

3. Scientific and Technology

This classification includes the following entries (each of which are elaborated on individually later):

  • Analytic reasoning and human processes
  • Interactive visualization
  • Data representations and theory of knowledge
  • Theory of communications
  • Systems and evaluations.

4. Data Types/Structures

This includes Text, Image, Video, Graph Structures, Models/Simulations, Geospatial Coordinates, time, etc.

Returning now to the sub-classifications under “Science and Technology”:

Analytic reasoning and human processes

This sub-classification, for example, includes the following items:

  • Modes of inference
  • Knowledge creation
  • Modeling
  • Hypothesis refinement
  • Human processes (e.g., perception, decision-making).

Interactive visualization

This is comprised of:

  • The Science of Visualization
  • The Science of Interaction.

The former includes icons, positioning, motion, abstraction, etc, while the latter includes language of discourse, design and art, user-tailored interaction and simulation interaction.

Data representations and theory of knowledge

This includes (but is not limited to):

  • Data Sourcing
  • Scale and Complexity
  • Aggregation
  • Ontology
  • Predictions Representations.

Theory of communications

This sub-classification includes for example the following:

  • Story Creation
  • Theme Flow/Dynamics
  • Reasoning representation.

Systems and evaluations

This last sub-classification comprises:

  • Application Programming Interface
  • Lightweight Standards
  • Privacy.

Patrick Philippe Meier

One response to “Part 7: A Taxonomy for Visual Analytics

  1. Pingback: Research Agenda for Visual Analytics « iRevolution

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