New data science method makes charts easier to read


  • Author: Statistics Views
  • Date: 06 December 2018
  • Copyright: Gettyimages (full story available on Science Daily)

Doctors reading EEGs in emergency rooms, first responders looking at multiple screens showing live data feeds from sensors in a disaster zone, brokers buying and selling financial instruments all need to make informed decisions very quickly. Visualization complexity can complicate decision-making when one is looking at data on a chart. When timing is critical, it is essential that a chart be easy to read and interpret.

thumbnail image: New data science method makes charts easier to read

To help decision-makers in scenarios like these, computer scientists at Columbia Engineering and Tufts University have developed a new method -- "Pixel Approximate Entropy" -- that measures the complexity of a data visualization and can be used to develop easier to read visualizations. Eugene Wu, assistant professor of computer science, and Gabriel Ryan, who was then a Masters student and now PhD student at Columbia, will present their paper at the IEEE VIS 2018 conference on Thursday, October 25, in Berlin, Germany.

"This is a brand new approach to working with line charts with many different potential applications," says Ryan, first author on the paper. "Our method gives visualisation systems a way to measure how difficult line charts are to read, so now we can design these systems to automatically simplify or summarise charts that would be hard to read on their own."

Wu's group provided a "visual complexity score" that can automatically identify difficult charts. They modified a low dimensional entropy measure to operate on line charts, and then conducted a series of user studies that demonstrated the measure could predict how well users perceived charts.

"In fast-paced settings, it is important to know if the visualisation is going to be so complex that the signals may be obscured," says Wu, who is also co-chair of the Data, Media, & Society Centre in the Data Science Institute. "The ability to quantify complexity is the first step towards automatically doing something about this."

The team expects their system, which is an open source, will be especially useful to data scientists and engineers who are developing AI-driven data science systems. By providing a method that allows the system to better understand the visualisations it is displaying, Pixel Approximate Entropy will help to drive the development of more intelligent data science systems.

Wu's group plans to extend data visualisation to use these models to automatically alert users and designers when visualisations may be too complex and suggest smoothing techniques, and to develop other quantitative perceptual models that can inform the design of data processing and visualisation systems.

Journal Reference:

Gabriel Ryan and Eugene Wu, Abigail Mosca and Remco Chang. At a Glance: Pixel Approximate Entropy as a Measure of Line Chart Complexity. IEEE Trans Vis Comput Graph, 2018 DOI: 10.1109/TVCG.2018.2865264

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