Data Visualization Literature Review
EDUCAUSE Learning Initiative (2009, August). 7 Things You Should Know About Data Visualization II.
Data visualization is the graphical representation of information. Information technology combines the principles of visualization with powerful applications and large data sets to create sophisticated images and animations. Representing large amounts of disparate information in a visual form often allows you to see patterns that would otherwise be buried in vast, unconnected data sets. Data visualizations offer one way to harness infrastructure to find hidden trends and correlations that can lead to important discoveries. Visual literacy is an increasingly important skill, and data visualizations are another channel for students to develop their ability to process information visually.
Johnson, L., Levine, A., Smith, R., & Stone, S. (2010). The 2010 Horizon Report. Austin, Texas: The New Media Consortium.
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. Ultra high-resolution displays allow teams of researchers to zoom in to examine specific 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 and humanities 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.
MccGhee, G. Journalism in the age of data.
Journalists are coping with the rising information flood by borrowing data visualization techniques from computer scientists, researchers and artists. Some newsrooms are already beginning to retool their staffs and systems to prepare for a future in which data becomes a medium. But how do we communicate with data, how can traditional narratives be fused with sophisticated, interactive information displays?
Munzner, T. (2011). Keynote on Visualization Principles.
Tamara Munzner presents very lucid and useful guidelines for creating effective visualizations, including how to correctly rank visual channel types and how to use categorical color constraints. She explains advantages of 2D representation and drawbacks of 3D, immersive, or animated visualizations. She also describes how to create visualizations that reduce the viewer's cognitive load, and how to validate visualizations. This talk was presented at VIZBI 2011, an international conference series on visualizing biological data (vizbi.org) funded by NIH & EMBO.
Perception and understanding
Cleveland, W. S., & McGill, R. (1984). Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. Journal of the American Statistical Association, 531-554.
The subject of graphical methods for data analysis and for data presentation needs a scientific foundation. In this article, we take a few steps in the direction of establishing such a foundation. Our approach is based on graphical perception – the visual decoding of information encoded on graphs – and it includes both theory and experimentation to test the theory. The theory deals with a small but important piece of the whole process of graphical perception. The first part in an identification of a set of elementary perceptual tasks that are carried out when people extract quantitative information from graphs. The second part is an ordering of the tasks on the basis of how accurately people perform them. Elements of the theory are tested by experimentation in which subjects record their judgments of the quantitative information on graphics. The experiments validate these elements but also suggest that the set of elementary tasks should be expanded. The theory provides a guideline for graph construction: Graphs should emply elementary tasks as high in the ordering as possible. This principle is applied to a variety of graphs, including bar charts, divided bar charts, pie charts, and statistical maps with shading. The conclusion is that radical surgery on these popular graphs is needed, as as replacements we offer alternative graphic forms – dot charts, dot charts with grouping, and frame-rectangle charts.
Friel, S.N., Curcio, F. R & Bright, G.W. (2001). Making Sense of Graphs: Critical Factors Influencing Comprehension and Instructional Implications. Journal for Research in Mathematics Education, 32( 2), 124-158.
Our purpose is to bring together perspectives concerning the porcessing and use of statistical graphs to identify critical factors that appear to influence graph comprehension and to suggest instructional implications. After providing a synthesis of information about the nature and structure of graphs, we define graph construction. We consider four critical factors that appear to affect graph comprehension: the purpose of using graphs, task characteristics, discipline characteristics, and reader characteristics. A construct called graph sense is defined. A sequence for ordering the introduction of graphs is proposed. We conclude with a discussion of issues in making sense of quantitative information using graphs and ways instruction may be modified to promote such sense making.
Kosslyn, S.M. (1989). Understanding Charts and Graphs. Applied Cognitive Psychology, 3(3), 185-225.
Many charts and graphs do not convey information effectively. This article develops a way of analysing the information in charts and graphs that reveals the design flaws in the display. The analytic scheme requires isolating four types of constituents in a display, and specifying their structure and interrelations at a syntactic, semantic, and pragmatic level of analysis. As the description is constructed, one checks for violations of acceptability principles, which are derived from facts about human visual information processing and from an analysis of the nature of symbols. Violations of these principles reveal the source of potential difficulties in using a display.
Ware, C. (2008). Visual Thinking for Design. Waltham, MA. Morgan Kaufmann Publishers.
Increasingly, designers need to present information in ways that aid their audience's thinking process. Fortunately, results from the relatively new science of human visual perception provide valuable guidance. In Visual Thinking for Design, Colin Ware takes what we now know about perception, cognition, and attention and transforms it into concrete advice that designers can directly apply. He demonstrates how designs can be considered as tools for cognition - extensions of the viewer's brain in much the same way that a hammer is an extension of the user's hand.
Experienced professional designers and students alike will learn how to maximize the power of the information tools they design for the people who use them.
- Presents visual thinking as a complex process that can be supported in every stage using specific design techniques.
- Provides practical, task-oriented information for designers and software developers charged with design responsibilities.
- Includes hundreds of examples, many in the form of integrated text and full-color diagrams.
- Steeped in the principles of “active vision,” which views graphic designs as cognitive tools.
Designing and creating visualizations
Steele, J., & Iliinsky, N. (2011). Designing Data Visualizations: Representing Informational Relationships. Sebastopol, CA. O'Reilly Media.
Data visualization is an efficient and effective medium for communicating large amounts of information, but the design process can often seem like an unexplainable creative endeavor. This concise book aims to demystify the design process by showing you how to use a linear decision-making process to encode your information visually.
Delve into different kinds of visualization, including infographics and visual art, and explore the influences at work in each one. Then learn how to apply these concepts to your design process.
- Learn data visualization classifications, including explanatory, exploratory, and hybrid
- Discover how three fundamental influences—the designer, the reader, and the data—shape what you create
- Learn how to describe the specific goal of your visualization and identify the supporting data
- Decide the spatial position of your visual entities with axes
- Encode the various dimensions of your data with appropriate visual properties, such as shape and color
- See visualization best practices and suggestions for encoding various specific data types
Yau, N. (2011). Visualize This: The Flowing Data Guide to Design, Visualization, and Statistics. Indianapolis: Wiley.
Data doesn't decrease; it is ever-increasing and can be overwhelming to organize in a way that makes sense to its intended audience. Wouldn't it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks to the creative genius of Nathan Yau, we can. With this full-color book, data visualization guru and author Nathan Yau uses step-by-step tutorials to show you how to visualize and tell stories with data. He explains how to gather, parse, and format data and then design high quality graphics that help you explore and present patterns, outliers, and relationships.
- Presents a unique approach to visualizing and telling stories with data, from a data visualization expert and the creator of flowingdata.com, Nathan Yau
- Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design to find meaning in the numbers
- Contains numerous examples and descriptions of patterns and outliers and explains how to show them
Visualize This demonstrates how to explain data visually so that you can present your information in a way that is easy to understand and appealing.
Tufte, E. (2001). The visual display of quantitative information, 2nd edition. Cheshire, CT. Graphics Press.
(not available online)
The Visual Display of Quantitative Information contains 250 illustrations of the best (and a few of the worst) statistical charts, graphics, and tables, with a detailed analysis of how to display quantitative data for precise, quick, effective analysis. Highest quality book design and production throughout.