Graphical perception as described by Cleveland and Mcgill is the visual decoding of information encoded on graphs (Cleveland & McGill, 1984). Putting it in very simple terms the ability to interpret and use statistical graphics hinges on the interface between the graph itself and the brain that perceives and interprets it. Schringer and Cooper explained in their 2001 paper that graphical display provide “powerful tools” for “sense-making” (data exploration) and communication (data presentation). A graph conveys information across in a better way compared to table with numerous rows and columns of data (Legge, Gu & Luebker, 1989). Understanding Graphical Perception was done easily by Cleveland and McGill’s ranking of Perceptual tasks.

Figure1

Figure1

Cleveland and McGill experiment found that judgment based on length are better that area judgment and area judgments are better than volume judgments. Figure 2 below shows us how a shaded graph on a map which was at the bottom of our perceptual hierarchy was converted into a map that can be easily understood through the framed rectangular bars.

Figure2

Figure2

Figure3

Figure3

When we look at a bar chart, the assessment of the height of each bar is the judgement of “position along a common scale”; when we look at pie chart, the information is extracted by judging the angle size, etc (1989). These elementary graphical encodings are empirically ranked as 1) position; 2) length, direction, and angle; 3) area; 4) volume and curvature; 5) shading and color saturation and the higher ranking in the hierarchy, the better perception of information conveyed (Cleveland and McGill, 1989). There were two more types added in which were related to the circular area in the 2010 paper by Heer, Bostock. (Heer, Bostock, 2010). The crowdsourcing experiments they conducted reached out to a wider audience yielding the same results as previous experiments that were conducted in a lab setting.

In conclusion, data visualization is actually a method designed to convey both quantitative and qualitative information for better perception. Data visualization is also very important to understand data analysis results. This is known as Data literacy. Zubiaga and Namee used a method similar to Heer and Bostock where they used a crowdsourced platform for their experiment. The results showed that histograms were better perceived and understood than density trees. All these studies prove how important graphical perception is to the field of data visualization and how we can create guidelines for selecting the right type of graph.

References:

  1. Cleveland W.S., McGill R. (1984). “Graphical Perception and Graphical Methods for Analyzing Scientific Data”. Science. 229(4716): 828-33. DOI: 10.1126/science.229.4716.828

  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. Cleveland, W. S. (1994). The Elements of Graphing Data. Murray Hill, NJ: AT&T Bell.

  4. Schriger D.L., Cooper R.J. (2001). “Achieving Graphical Excellence: Suggestions and Methods for Creating High-Quality Visual Displays of Experimental Data”. Annals of Emergency Medicine. 37(1):75-87. DOI: 10.1067/mem.2001.111570

  5. Heer J., Bostock M. (2010). “Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design”. ACM Human Factors in Computing Systems (CHI). 203-212

  6. Legge G.E., Gu Y.C., Luebker A. (1989). “Efficiency of Graphical Perception”. Percept Psychophys. 46(4): 365-74. DOI: 10.1016/j.injury.2008.01.050