This is Part 5 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.
Data Visualization
The visualization of information “amplifies human cognitive capabilities in six basic ways” by:
- Increasing cognitive resources, such as by using a visual resource to expand human working memory;
- Reducing search, such as by representing a large amount of data in a small place;
- Enhancing the recognition of patterns, such as when information is organized in space by its time relationships;
- Supporting the easy perceptual inference of relationships that are otherwise more difficult to induce;
- Enabling perceptual monitoring of a large number of potential events;
- Providing a manipulable medium that, unlike static diagrams, enables the exploration of a space of parameter values.
The table below provides additional information on how visualization amplifies cognition:
Clearly, “these capabilities of information visualization, combined with computational data analysis, can be applied to analytic reasoning to support the sense-making process.” The National Visualization and Analysis Center (NVAC) thus recommends developing “visually based methods to support the entire analytic reasoning process, including the analysis of data as well as structured reasoning techniques such as the construction of arguments, convergent-divergent investigation, and evaluation of alternatives.”
Since “well-crafted visual representations can play a critical role in making information clear […], the visual representations and interactions we develop must readily support users of varying backgrounds and expertise.” To be sure, “visual representations and interactions must be developed with the full range of users in mind, from the experienced user to the novice working under intense pressure […].”
As NVACs notes, “visual representations are the equivalent of power tools for analytical reasoning.” But just like real power tools, they can cause harm if used carelessly. Indeed, it is important to note that “poorly designed visualizations may lead to an incorrect decision and great harm. A famous example is the poor visualization of the O-ring data produced before the disastrous launch of the Challenger space shuttle […].”
Effective Depictions
This is why we need some basic principles for developing effective depictions, such as the following:
- Appropriateness Principle: the visual representation should provide neither more or less information than that needed for the task at hand. Additional information may be distracting and makes the task more difficult.
- Naturalness Principle: experiential cognition is most effective when the properties of the visual representation most closely match the information being represented. This principle supports the idea that new visual metaphors are only useful for representing information when they match the user’s cognitive model of the information. Purely artificial visual metaphors can actually hinder understanding.
- Matching Principle: representations of information are mst effective when they match the task to be performed by the user. Effective visual representations should present affordances suggestive of the appropriate action.
- Congruence Principle: the structure and content of a visualization should correspond to the structure and content of the desired mental representation.
- Apprehension Principle: the structure and content of a visualization should be readily and accurately perceived and comprehended.
Further research is needed to understand “how best to combine time and space in visual representation. “For example, in the flow map, spatial information is primary” in that it defines the coordinate system, but “why is this the case, and are there visual representations where time is foregrounded that could also be used to support analytical tasks?”
In sum, we must deepen our understanding of temporal reasoning and “create task-appropriate methods for integrating spatial and temporal dimensions of data into visual representations.”
Interactive Interface Design
It is important in the visual analytics process that researchers focus on visual representations of data and interaction design in equal measure. “We need to develop a ‘science of interaction’ rooted in a deep understanding of the different forms of interaction and their respective benefits.”
For example, one promising approach for simplifying interactions is to use 3D graphical user interfaces. Another is to move beyond single modality (or human sense) interaction techniques.
Indeed, recent research suggests that “multi-modal interfaces can overcome problems that any one modality may have. For example, voice and deictic (e.g., pointing) gestures can complement each other and make it easier for the user to accomplish certain tasks.” In fact, studies suggest that “users prefer combined voice and gestural communication over either modality alone when attempting graphics manipulation.”
Patrick Philippe Meier
iRevolutions 