Visual Analytics

September 6, 2011 § Leave a comment

Visual Analytics is the science of analytical reasoning facilitated by visual interactive interfaces. Being especially concerned with sense making and reasoning, it integrates new computational and theory-based tools with innovative interactive techniques and visual representations to enable human-information discourse. Visual analytics is a broad multidisciplinary field of which information visualisation is only a small subset.

Information Visualisation amplifies human cognitive capabilities by:

  1. Increasing Cognitive Resources – e.g. visuals expanding memory.
  2. Reducing Search Space – e.g. large amount of data in small space.
  3. Enhance Pattern Recognition – e.g. info organised spatially by time.
  4. Support Perceptual Inference of Relationships – often difficult.
  5. Perceptual Monitoring – e.g. large number of, and unexpected, events.
  6. Providing a Manipulation Medium – e.g. exploration, collaboration.

Information visualisation, combined with data analysis, can be applied to analytic reasoning to support the sense-making process.


The multidisciplinary field of Visual Analytics consists of:

  1. Analytical Reasoning Techniques
    method by which users obtain deep insights that directly support situation assessment, planning, and decision making.
  2. Data Representations and Transformations
    converting all types of conflicting and dynamic data to support visualisation and analysis.
  3. Techniques for Production, Presentation, & Dissemination
    of analysis results, communicating information to various audiences in the correct context.
  4. Visual Representation & Interaction Techniques
    allowing users to quickly explore and understand large amounts of data by utilising the human eye’s broad bandwidth.


Visual Analytics must facilitate high-quality human judgement with a limited time. They must enable diverse analytical tasks:

  1. Understanding Past & Present
    quickly, recognising trends and events that have produced current conditions.
  2. Identifying Potential Futures
    (and their Warning signs).
  3. Monitoring Current Events for Warning Signs
    and unexpected events.
  4. Determining Indicators
    of the Intent of an Action / Individual
  5. Supporting Decision Maker in Times of Crisis

These tasks are conducted through individual and collaborative analysis, often under extreme time pressure. Visual analytics must enable Hypothesis-Based and Scenario-Based Analytical Techniques, providing support for the analyst to reason based on available evidence.


Visualisation and Analysis of Large & Complex Networks

Established Graph Drawing algorithms often attempt to solve 1. Scalability, 2. Visual Complexity, or 3. Domain Complexity.

Such algorithms have included;


[ GEOMI (Geometry for Maximum Insight) is a visual analysis tool for large and complex networks. ]

Navigation can often be aided by animations that preserve the mental map.

Some visualisations like the actor-movie (two mode network), suitable use (p,q)-core for filtering. Like k-core, but using two variables per class clustering.



  • Illuminating the Path: The Research and Development Agenda for Visual Analytics (nvac)

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