Interactive Visualizations: A Literature Review
Presentation Type
Poster
Student
Yes
Track
Tools
Abstract
Visualization has proven to be an effective method at reducing the cognitive processing of data. The primary goal of visualization is to amply cognition where the visual representations facilitate analytical reasoning, support decision-making and allow users to gain insight into complex problems (Card, Mackinlay, & Shneiderman, 1999; Thomas & Cook, 2006; Yi, Kang, Stasko, & Jacko, 2008). Different types of visualizations correspond to different kinds of information. Properly designed graphs reduce bias while simultaneously supporting the goal of visualization. Visualization experts, such as Stephen Few, have produced graph selection frameworks (Börner, 2015). These frameworks assist visualization designers in selecting the correct graph type for a given task, in order to properly design effective visualizations. As the volume of data grows and the complexity of data increase, it is unclear if these frameworks still apply. It is unknown if the selection matrices are as effective with dynamic visualizations as they are with static visualization. The effectiveness of a visualization hinges on two things: its ability to clearly and accurately represent information and the ability to interact with the information to figure out what it means (Few, 2009). The current frameworks for graph selection do not account for interaction techniques. This research discusses extant and emerging literature of interacting with visualizations.
Start Date
2-5-2019 12:00 PM
End Date
2-5-2019 1:00 PM
Interactive Visualizations: A Literature Review
Volstorff A
Visualization has proven to be an effective method at reducing the cognitive processing of data. The primary goal of visualization is to amply cognition where the visual representations facilitate analytical reasoning, support decision-making and allow users to gain insight into complex problems (Card, Mackinlay, & Shneiderman, 1999; Thomas & Cook, 2006; Yi, Kang, Stasko, & Jacko, 2008). Different types of visualizations correspond to different kinds of information. Properly designed graphs reduce bias while simultaneously supporting the goal of visualization. Visualization experts, such as Stephen Few, have produced graph selection frameworks (Börner, 2015). These frameworks assist visualization designers in selecting the correct graph type for a given task, in order to properly design effective visualizations. As the volume of data grows and the complexity of data increase, it is unclear if these frameworks still apply. It is unknown if the selection matrices are as effective with dynamic visualizations as they are with static visualization. The effectiveness of a visualization hinges on two things: its ability to clearly and accurately represent information and the ability to interact with the information to figure out what it means (Few, 2009). The current frameworks for graph selection do not account for interaction techniques. This research discusses extant and emerging literature of interacting with visualizations.