Nav: Home

Algorithms can exploit human perception in graph design

April 05, 2017

Algorithms can now exploit models and measures of human perception to generate scatterplot designs.

Scatterplots are widely used in various disciplines and areas beyond sciences to visually communicate relationships between two data variables. Yet, very few users realize the effect the visual design of scatterplots can have on the human perception and understanding. Moreover, default designs of scatterplots often represent the data poorly, and manually fine tuning the design is difficult.

Researchers have recently found an algorithmic approach to automatically improve the design of scatterplots by exploiting models and measures of human perception.

"A scatterplot is designed successfully when humans can effectively decode the message that was originally encoded graphically into the scatterplot. On the contrary, poor designs could miscommunicate the intended message", tells postdoctoral researcher Luana Micallef.

Automatic and optimized scatterplot designs

The optimizer developed by the researchers can predict how users would respond to a given design. The human perception has a number of capabilities and limitations, which a visualization should respectively exploit and mitigate to effectively communicate a message to a reader.

"As the owner of a dataset, you do not necessarily know how others will perceive the scatterplot and large datasets are also difficult to visualize. With our new algorithmic method, we can optimize the design of the scatterplot for any data and analysis tasks the user requires", explains Professor Antti Oulasvirta.

Every design aspect of a scatterplot, be it the size, opacity and color of the markers or the aspect ratio of the plot, matters. These aspects have a great impact on the correlation, outliers and classes detected in the scatterplot by the human perception.

"Even when you are a visualization expert, an automated design helps saving time, especially for very large data sets. This time is better invested in interpreting the visualizations rather than fiddling around with tedious parameter settings", says the recently graduated postdoctoral researcher Gregorio Palmas.

"This is only the beginning. We are in the middle of a shift where we automate at least parts of our data analysis, necessitated due to the sheer size of the data alone. The interactive data analysis methods such as scatterplots will continue to serve us well, but even more so when augmented with some level of machine intelligence", explains Professor Tino Weinkauf.

The new algorithmic approach was most successful in terms of task completion time. According to the researchers, Luana Micallef, Gregorio Palmas, Antti Oulasvirta and Tino Weinkauf, even users that are non-expert in visualization design can use the optimizer to produce effective scatterplot designs. With such algorithmic methods, unintended miscommunication may be diminished in the future.
-end-
More information:

Antti Oulasvirta
Professor
Aalto University
Department of Communications and Networking
antti.oulasvirta@aalto.fi
tel. +358 50 3841561

Luana Micallef
Postdoctoral researcher
Aalto University
Helsinki Institute for Information Technology HIIT, Department of Computer Science
luana.micallef@aalto.fi
tel. +358 50 4210445

Tino Weinkauf
Professor
KTH Royal Institute of Technology
School of Computer Science and Communication
weinkauf@kth.se
tel. +46 8 790 60 75

Project page: http://userinterfaces.aalto.fi/scatterplot_optimization/

Article: Micallef, L., Palmas, G., Oulasvirta, A. & Weinkauf, T. (2017), Towards Perceptual Optimization of the Visual Design of Scatterplots, IEEE Transactions on Visualization and Computer Graphics 23(6) : 1-12. http://ieeexplore.ieee.org/abstract/document/7864468/

Aalto University

Related Algorithms Articles:

New algorithm can distinguish cyberbullies from normal Twitter users with 90% accuracy
A team of researchers, including faculty at Binghamton University, have developed machine learning algorithms which can successfully identify bullies and aggressors on Twitter with 90 percent accuracy.
AI learns complex gene-disease patterns
A deep learning model improves the ability to identify genes potentially involved in disease.
New brain map could improve AI algorithms for machine vision
An international team of researchers led by neuroscientists from CSHL and University of Sydney published an updated view on the primate brain's visual system organization, and they found that parts of the primate visual system may work differently than previously thought.
Two new algorithms can identify patients at risk of HIV
Two new studies developed algorithms that can identify patients who are at risk of acquiring HIV and may benefit from preventive care.
Scientists stack algorithms to improve predictions of yield-boosting crop traits
To help researchers better predict high-yielding crop traits, a team from the University of Illinois have stacked together six high-powered, machine learning algorithms that are used to interpret hyperspectral data -- and they demonstrated that this technique improved the predictive power of a recent study by up to 15 percent, compared to using just one algorithm.
More Algorithms News and Algorithms Current Events

Best Science Podcasts 2019

We have hand picked the best science podcasts for 2019. Sit back and enjoy new science podcasts updated daily from your favorite science news services and scientists.
Now Playing: TED Radio Hour

Rethinking Anger
Anger is universal and complex: it can be quiet, festering, justified, vengeful, and destructive. This hour, TED speakers explore the many sides of anger, why we need it, and who's allowed to feel it. Guests include psychologists Ryan Martin and Russell Kolts, writer Soraya Chemaly, former talk radio host Lisa Fritsch, and business professor Dan Moshavi.
Now Playing: Science for the People

#537 Science Journalism, Hold the Hype
Everyone's seen a piece of science getting over-exaggerated in the media. Most people would be quick to blame journalists and big media for getting in wrong. In many cases, you'd be right. But there's other sources of hype in science journalism. and one of them can be found in the humble, and little-known press release. We're talking with Chris Chambers about doing science about science journalism, and where the hype creeps in. Related links: The association between exaggeration in health related science news and academic press releases: retrospective observational study Claims of causality in health news: a randomised trial This...