Data visualization is widely used to convey information, to prove certain facts and to show trends. But often the visualization are deceptive, they are modified in such a way that they prove a certain claim. Following are some techniques to identify data visualization deception:
- Truncated Axis: The Y-axis can be altered with to exaggerate the values being represented. Instead of having the origin as 0, it can be started with any different value to give an illusion of higher values. This is one the most common techniques for deception.
- Area as Quantity: Using area coverage to denote quantity is also a widely used data deception technique. The values can be denoted as circles or any other shape denoting area. Some area shapes can appear to be greater in size but may not have the correct interpretation of the information. One-to-one mapping between data and graphical is a better way of using area as quantity.
- Changes in Aspect Ratio: This type of deception is applied to line charts more often. The aspect ratio change may give an illusion of increase or decrease of one quantity against the other. Changes in it can alter the viewers perception about a graph.
- Inverted Axis: Inversion of axis leads to the change in the direction of the trend. This gives the user a notion of the reversal of the correct information. This technique doesn’t exaggerate or underestimate but completely change the notion of a visualization.
Source: https://medium.com/@Infogram/study-asks-how-deceptive-are-deceptive-visualizations-8ff52fd81239#.58vad76t0
This is really helpful, especially while working on creating deceptive visualizations intentionally.
Thanks Saumya!
Agreed. These tricks can be cherry on top of the cake with the approach which professor taught us for creating deceptive visualizations.
Oh yes Roopa! These techniques will be really useful when combined with class concepts
This is really helpful.
Thanks Jinal!