The role of Perception in Visualization

Data visualization is playing more and more important role in science and daily work. It ensures decision makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. The way how human brain process information is much eaiser when using charts and graphs instead of reports of a larg data. Data visualization is quick and easy way to convey consepts in an universal manner. Like a saying: “A picture is worth a thousand words.”

According to Cleveland and McGill (2), the first ranking of theory is “elementary perceptual tasks”, which are the most basic visual tasks we perform in our perception of graphs, and used to extract the quantiative information on a variety of vommon graph forms. Secondly, explained how accurately people perfom elementary percptual tasks.

From the research and experimental results, we understand that different visualization graphs of the same subjects can cause different results. And for different purposes and accuracy, we need to choose the right graphs. For basic shapes, Croxton (7) found over eight decades ago that bars were more effective in communicating comparative values than either circles, squares or cubes. Circles and squares were about as effective as the other. Cubes were undoubtedly the worst. More on 3d later. Because valume will include more information, and we need to pay more attention and spend more time to analyze the unrelated information, which is not efficient.

The perception in visualization will help readers to better understand the relationship between data, and can help readers easily find new patterns behind the graphs and make a better decisions.

From Kennedy Elliott, she summarized 39 theories of human perception on visualization. For example, from Eells (7) was among the first to publish a paper on this topic in 1926, For proportional judgement, pie charts and divided bar charts work the best, not just for their appeal but because of their scientific accuracy. However, as Hollands and Spence found, the elements in bar charts increase, the effectiveness of proportional judgement decreases. More example, More examples include: line charts are more suitable for trend, 2D charts are better than 3D charts to make impact on the accuracy of the charts. The findings about treemaps illustrate this notion: participants were more accurate with small multiples of bar charts than treemaps for leaf-to-leaf comparisons. That is helpful to consider, but it is quite possible that representing the entire ecosystem of data is more important. On the other hand, Kennedy further summarized deeper on human emotions. For example, people with negative emotions tended to make more mistakes compare to people with position emotions when judging the visualization.

Conclusion

In conclusion, in general, data is hight dimentions and multiple attributes, and it is more complex when analyzing it. by conveying data into graphs makes analyzing easier and simplier. Data visualization is a method designed to convey both quantitative and qualitative information more clearly, more simple and easier for perception. These information are encoded by a set of complex elementary graphical encodings. As graphical perception theory identified these encodings and ranked them based on their accuracy and effectiveness of conveying information, guidelines can be provided for selecting graph type for better data display.

Reference

  1. W. S. Cleveland, The Elements of Graphing Data (Wadsworth, Monterey, Calif., 1985).
  2. Cleveland W.S., McGill R. (1989). “Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods”. Journal of the American Statistical Association. 79(387): 521-534.
  3. Bar charts versus circle diagrams. Frederick Croxton and Roy Stryker, 1927.
  4. Judging Proportion in Graphs: The Summation Model. J.G. Hollands and Ian Spence, 1998.
  5. https://medium.com/@kennelliott/39-studies-about-human-perception-in-30-minutes-4728f9e31a73
  6. The relative merits of circles and bars for representing component parts. Walter Crosby Eells, 1926.
  7. Graphic comparisons by bars, squares, circles and cubes. Frederick Croxton, 1932.