What is Visual Data Analysis?

Visual data analysis blends highly advanced computational methods with sophisticated graphics engines to tap the extraordinary ability of humans to see patterns and structure in even the most complex visual presentations. Currently applied to massive, heterogeneous, and dynamic datasets, such as those generated in studies of astrophysical, fluidic, biological, and other complex processes, the techniques have become sophisticated enough to allow the interactive manipulation of variables in real time with compelling results. Ultra high-resolution displays allow teams of researchers to zoom into interesting aspects of the renderings, or to navigate along interesting visual pathways, following their intuitions and even hunches to see where they may lead. New research is now beginning to apply these sorts of tools to the social sciences as well, and the techniques offer considerable promise in helping us understand complex social processes like learning, political and organizational change, and the diffusion of knowledge.

Over the past century, data collection, storage, transmission, and display has changed dramatically, and scholars have undergone a profound transformation in the way they approach data-related tasks. Data collection and compilation is no longer the tedious, manual process it once was, and tools to analyse, interpret, and display data are increasingly sophisticated, and their use routine in many disciplines. The options for illustrating trends, relationships, and cause and effect have exploded, and it is now a relatively simple matter for anyone to do the sorts of analyses that were once only the province of statisticians and engineers.

In advanced research settings, scientists and others studying massively complex systems generate mountains of data, and have developed a wide variety of new tools and techniques to allow those data to be interpreted holistically, and to expose meaningful patterns and structure, trends and exceptions, and more. Researchers that work with data sets from experiments or simulations, such as computational fluid dynamics, astrophysics, climate study, or medicine draw on techniques from the study of visualization, data mining, and statistics to create useful ways to investigate and understand what they have found.

The blending of these disciplines has given rise to the new field of visual data analysis, which is not only characterized by its focus on making use of the pattern matching skills that seem to be hard-wired into the human brain, but also in the way in which it facilitates the work of teams collaborating to tease out meaning from complex sets of information. While the most sophisticated tools are still mostly found in research settings, a variety of tools are emerging that make it possible for almost anyone with an analytical bent to easily interpret all sorts of data.

Self-organizing maps are an approach that mimics the way our brains organize multi-faceted relationships; they create a grid of "neuronal units" such that neighbouring units recognize similar data, reinforcing important patterns so that they can be seen. Cluster analysis is a set of mathematical techniques for partitioning a series of data objects into a smaller amount of groups, or clusters, so that the data objects within one cluster are more similar to each other than to those in other clusters. Visual, interactive principal components analysis is a technique once only available to statisticians that is now commonly used to identify hidden trends and data correlations in multidimensional data sets. Gapminder (http://www.gapminder.org/), for example, uses this approach in its analysis of multivariate datasets over time.

These sorts of tools are now finding their way into common use in many other disciplines, where the analytical needs are not necessarily computational; visualization techniques have even begun to emerge for textual analysis and basic observation. Many are free or very inexpensive, bringing the ability to engage in rich visual interpretation to virtually anyone.

Online services such as Many Eyes, Wordle, Flowing Data, and Gapminder accept uploaded data and allow the user to configure the output to varying degrees. Many Eyes, for instance, allows people to learn how to create visualizations, to share and visualize their own data, and to create new visualizations from data contributed by others. Some, like Roambi, have mobile counterparts, making it easy to carry interactive, visual representations of data wherever one goes. Even quite public data, such as the posts made in Twitter, can be rendered visually to reveal creative insights. For instance, the New Political Interfaces project visualized political topics from 2009 as expressed on Twitter, charting which topics were — and were not — being discussed by politicians, news outlets, and other sources.

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(1) How might this technology be relevant to the educational sector you know best?

  • If we can continue to build our content around visuals (www.latoyaegwuekwe.com/geographyofarecession.html ), just imagine how we can shift the time spent on teaching/sharing the content and have that time replaced teaching critical/analytical thinking, synthesis and evaluation. (- michael.lambert michael.lambert Feb 20, 2011)
  • Since the educational enterprise, from cognition to educational systems, is complex with many things going on in an integrated fashion, visual representations that can simplify and make more efficient our understanding of what is going on is truly important. This means from an individual student metacognitively wrestling with concepts and their own progress to administrators trying to monitor and constantly improve their system.- chris.brown chris.brown Feb 21, 2011
  • This technology can provide new ways of working in several areas of the educational sector; learners, teachers or other facilitators of learning, and researchers and evaluators. It will require shifts at any place in this system, but some of the representations can be so compelling, the shift to the technology can be supported by the technology itself.- jeanne.century jeanne.century Feb 21, 2011
  • Working with such a wide variety of individuals and institutions from the educational sector, in-field practitioner community, policy making and research community, often times the structural element is lost. Using visual data analysis could enable partner organizations to easily grasp the complex and nuanced interconnectedness between all parties involved.
    - virginie.aimard virginie.aimard Feb 22, 2011
  • I am not sure this is framed completely right, but do believe that using data to inform classroom practice is probably the most important trend...and that is enabled by a variety of new technologies. - keith.krueger keith.krueger Feb 22, 2011 I am not seeing the section on data analytics on wiki, but that is what I guess I am arguing for.
  • This might provide for a useful collaborative environment where visualisation of diffictult scientific concepts can be co-constructed amongst fellow learners and students. - horncheah horncheah Feb 26, 2011

(2) What themes are missing from the above description that you think are important?

  • This is very much linked to another area on this wiki - the area of data analytics. That relationship should be addressed. - chris.brown chris.brown Feb 21, 2011
  • add your response here
  • New trends in linking traditional Student Information Systems with Content Management Systems...linking data to instructional strategies/interventions. - keith.krueger keith.krueger Feb 22, 2011

(3) What do you see as the potential impact of this technology on teaching, learning, or creative expression?

  • If there was easy access to visuals and they were categorized in some format for educators, this would reduce my ‘search/google’ time and allow me to focus on how to help students use the information to think critically and synthesize. (- michael.lambert michael.lambert Feb 20, 2011)
  • I think this can be very impactful for students monitoring their own progress, students and teachers investigatiing issues through project based learning that utilizes large data sets and for administrators looking to continously improve their systems. I also think this will be a key tool for an educational epidemiological discipline that I think will emerge in 5-10 years. For more on this, please see my comments on Data Analytics.- chris.brown chris.brown Feb 21, 2011
  • I agree with Chris above. Also, this technology can play an important role in engaging learners who don't necessarily consider themselves STEM oriented. Data visualizations provide ways of processing information that appeal to learners with different kinds of sensibilities and can provide ways to bridge for them interactions with data. - jeanne.century jeanne.century Feb 21, 2011
  • Apart from the obvious, students will have the ability to see structural relationships that prior they could not visualize on their own, they themselves, once familiarized with visual data analysis tools, will be able to move ideas from their personal visualizations into a computerized form for their fellow students to see. This kind of technology is not just limited to STEM applications or social sciences, but could also be applicable to the arts. Granted where the STEMs are concerned, it would be nice to have ‘virtual dissections’ take the place of animal dissections in high school anatomy courses.
    - virginie.aimard virginie.aimard Feb 22, 2011
  • Enormous potential. We don't lack data in education. but, we lack what to do with the data to inform instruction. The more it can be easily visualized for teachers/principals...and especially STUDENTS, the more high performing educational system we will have. - keith.krueger keith.krueger Feb 22, 2011

(4) Do you have or know of a project working in this area?

Please share information about related projects in our Horizon K-12 Project form.