The Simple Ways of Information Visualization
There is no doubt that the human brain is an amazing and complex bodily organ, perhaps even the most amazing of them all. Personally, I do not think I will ever cease to be amazing by it. But, despite its impressive capabilities, the brain has its limitations too. In fact, Norman goes as far as to say that the brain, without the aid of external tools, is overrated (Norman in Card et al., 1999). He suggests that the brilliance of the human brain is to make use of external tools or cognitive artifacts in order to gain insight and/or knowledge about the world (Card et al.1999). Information visualization is one of the many cognitive artifacts that surrounds us and stands to enhance our cognition. Graphic aids for cognition like those found in the realm of information visualization have a long and ancient history. The more recent addition to this field is the accessibility of large amounts of data, and the availability of computers and online programs to make sense of the data and to visualize it in a comprehensive way.
The interest in information visualization has exploded in the last decades as computers have developed, and the possibilities of the field have expanded. The definition of information visualization may vary according to who you ask. Stephen Few highlights the difference between information visualization and scientific visualization stating that the latter usually deals with data which is physical in nature, while information visualization tends to deal with abstract data (Few 2009). Manuel Lima underscores the importance of distinguishing between information visualization and information art. Although they can coexist, the context, audience and goals of each project is different. In his view, information art focuses more on the aesthetics of the visualization, while information visualization should focus on the function of the visualization as a whole, with function meaning explanation and unveiling, which in turn leads to discovery and insight (Lima 2009). The general conclusion is still that the goal of information visualization is to make sense of data in order to increase our understanding of certain phenomena.
In order to make large quantities of data comprehensible, visualizations often include simplified information. Lev Manovich sees this as one of two core principles of information visualization. In order to be able to reveal patterns and structure in the data one relies on extreme reduction of information about objects (Manovich 2009). Efficient data analyses usually compare or correlate multiple sets of variables in order to highlight a connection between the different data. This approach is most useful in cases where there are point to point connections between objects. An example of this is John Snow’s map from 1854 showing cholera outbreaks in London. Through a simple spot map he illustrates how cases of cholera are centered on certain water pumps, concluding that cholera is transmitted by water (Friendly 2008).
As mentioned above, the human brain is unique in its capability to use external aids to help cognition. There is however another aspect that is unique to the human brain. That is the ability to understand concepts that can not be translated to text, numbers or images, such as political ideologies, historical developments or interhuman relationships.
The computer facilitates more complex visualizations, and the internet allows these to be available to a large audience. This audience can come from different backgrounds, and they might view the visualization with different eyes, so to speak. In the article the Information Visualization Manifesto Manuel Lima suggests that an effective information visualization “should be able to convey a message and easily encapsulate a compelling narrative” (Lima 2009). Is it possible to tell a story based only on facts? As the audience might have different prior knowledge of a subject before viewing a visualization the creator can never be sure of the conclusion the viewer draws when looking at it.
As information visualizations have moved from print to the computer they have also become more elaborate. It is possible to analyze larger sets of data, almost endless sets of variables and the visualization can also include interactivity. These new possibilities have inspired many ambitious projects. As the visualizations become more complex it can be difficult for the viewer to discover what lies behind the information. The visualization Health Care That Works shows how the six hospitals that closed between 1995 and 2005 were located in or near communities of color. The viewer can certainly see a pattern, but the underlying question of why this is the case is not being answered. A complex issue such as health care includes many variables or values that can not be easily expressed in text, numbers or images. One of the principles of information visualization is to gain insight in order to solve a problem. The question that remains is how information visualization can incorporate abstract concepts such as political processes in order to communicate a story to the viewer that will lead to increased understanding of a situation or phenomenon.
The advent of the Internet has allowed scholars to collect data in real time. Previously, this task had been difficult, if not impossible. Now, information can be made available for everyone as an event unfolds. Additionally, the Internet also facilitates the possibility of letting different sources create one data set. The web platform ushahidi.com is a tool that can be used to easily crowd source information using multiple channels, such as twitter, sms and e- mail. This information can then be used to create an interactive map of issues. An example is the information visualization about the conflict in the Eastern Democratic Republic of Congo. To use this kind of data to make sense of a chaotic event as it happens could be very useful in order to know where to direct attention, for example for journalists or international aid organizations. However, the information communicated through this kind of visualization comes with uncertainty. It might not be true. Or there could be events happening that are not being reported. To what extent should the creator of the visualization interfere with the data in order to make a more reliable source of information?
Information visualizations have developed over many years, but the move from traditional media to the online media represents a true turning point. This has enabled the visualizations to become more complex than the practitioners in the field could even dream of 20 years ago. Every stage of the creation of a visualization has become more advanced, from gathering the data, the size of the data set, to the final interface. I believe that this should lead the field to question some of the methods of information visualization. What information should be included and what should be excluded? Is it still valid to reduce an object in a data set to a single value? Can we illustrate a point to point connection when the issues are of great complexity? How can we make sure that the visualizations still leads to increased understanding, rather than a simplified picture of the world?
Card, Stuart K., Jock D. Mackinlay, and Ben Shneiderman. Readings in Information Visualization: Using Vision to Think. San Francisco, CA: Morgan Kaufmann, 1999. Print.
Few, Stephen. Now You See It: Simple Visualization Techniques for Quantitative Analysis. Oakland, CA: Analytics, 2009. Print.
Friendly, Michael. “A Brief History of Data Visualization.” Handbook of Data Visualization. Ed. Wolfgang Hardle and Antony Unwin. Berlin: Springer, 2008. Print.
Manovich, Lev. “What Is Visualization?” Mar. 2010. Web.
Lima, Manuel. “Information Visualization Manifesto.” Visual Complexity. 10 Aug. 2009. Web.