Part 1: Visual Analytics

This is Part 1 of 7 of the highlights from “Illuminating the Path: The Research and Development Agenda for Visual Analytics.” Please see this post for an introduction to the study and access to the other 6 parts.

NVAC defines Visual Analytics (VA) as “the science of analytical reasoning facilitated by interactive visual interfaces. People use VA tools and techniques to synthesize information and derive insights 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.”

The field of VA is necessarily multidisciplinary and combines “techniques from information visualization with techniques from computational transformation and analysis of data.” VA 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 to into the mind to allow users to see, explore, and understand large amounts of information at once”;
  • Data representation and transformations, “that convert all types of conflicting and dynamic data in ways that support visualization and analysis”;
  • Production, presentation and dissemination techniques, “to communicate information in the appropriate context to a variety of audiences.”

As is well known, “the human mind can understand complex information received through visual channels.” The goal of VA is thus to facilitate the analytical reasoning process “through the creation of software that maximizes human capacity to perceive, understand, and reason about complex and dynamic situations.”

In sum, “the goal is to facilitate high-quality human judgment with a limited investment of the analysts’ time.” This means in part to “expose all relevant data in a way that facilitates the reasoning process to enable action.” To be sure, solving a problem often means representing it so that the solution is more obvious (adapted from Herbert Simon). Sometimes, the simple act of placing information on a timeline or a map can generate clarity and profound insight.” Indeed, both “temporal relationships and spatial patterns can be revealed through timelines and maps.”

VA also reduces the costs associated with sense-making in two primary ways, by:

  1. Transforming information into forms that allow humans to offload cognition onto easier perceptual processes;
  2. Allowing software agents to do some of the filtering, representation translation, interpretation, and even reasoning.

That said, we should keep in mind that “human-designed visualizations are still much better than those created by our information visualization systems.” That is, there are more “highly evolved and widely used metaphors created by human information designers” than there are “successful new computer-mediated visual representations.”

Patrick Philippe Meier

One response to “Part 1: Visual Analytics

  1. Pingback: Research Agenda for Visual Analytics « iRevolution

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