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:

Lightning fast algorithms can lighten the load of 3D hologram generation
Tokyo, Japan - Researchers from Tokyo Metropolitan University have developed a new way of calculating simple holograms for heads-up displays (HUDs) and near-eye displays (NEDs).
Synergy emergence in deep reinforcement motor learning
Human motor control has always been efficient at executing complex movements naturally, efficiently, and without much thought involved.
Machine learning could improve the diagnosis of mastitis infections in cows
Artificial intelligence could help vets to more accurately diagnose the origin of mastitis on dairy herds, according to a new study from experts at the University of Nottingham.
How a new quantum approach can develop faster algorithms to deduce complex networks
Complex networks are ubiquitous in the real world, from artificial to purely natural ones, and they exhibit very similar geometric properties.
Algorithms 'consistently' more accurate than people in predicting recidivism, study says
In a study with potentially far-reaching implications for criminal justice in the United States, a team of California researchers has found that algorithms are significantly more accurate than humans in predicting which defendants will later be arrested for a new crime.
AI for #MeToo: Training algorithms to spot online trolls
Machine learning could be a powerful tool for allowing social media platforms to spot online trolls.
Developing a new AI breast cancer diagnostic tool
Scientists are developing a new way to identify the unique chemical 'fingerprints' for different types of breast cancers.
Artificial intelligence-based algorithm for intensive care of traumatic brain injury
A recent Finnish study, published in Scientific Reports, presents the first artificial intelligence (AI) based algorithm that may be utilized in the intensive care unit for treating patients with severe traumatic brain injury.
New algorithms train AI to avoid specific bad behaviors
Robots, self-driving cars and other intelligent machines could become better-behaved if machine-learning designers adopt a new framework for building AI with safeguards against specific undesirable outcomes.
New machine learning algorithms offer safety and fairness guarantees
Writing in Science, Thomas and his colleagues Yuriy Brun, Andrew Barto and graduate student Stephen Giguere at UMass Amherst, Bruno Castro da Silva at the Federal University of Rio Grande del Sol, Brazil, and Emma Brunskill at Stanford University this week introduce a new framework for designing machine learning algorithms that make it easier for users of the algorithm to specify safety and fairness constraints.
More Algorithms News and Algorithms Current Events

Trending Science News

Current Coronavirus (COVID-19) News

Top Science Podcasts

We have hand picked the top science podcasts of 2020.
Now Playing: TED Radio Hour

Making Amends
What makes a true apology? What does it mean to make amends for past mistakes? This hour, TED speakers explore how repairing the wrongs of the past is the first step toward healing for the future. Guests include historian and preservationist Brent Leggs, law professor Martha Minow, librarian Dawn Wacek, and playwright V (formerly Eve Ensler).
Now Playing: Science for the People

#566 Is Your Gut Leaking?
This week we're busting the human gut wide open with Dr. Alessio Fasano from the Center for Celiac Research and Treatment at Massachusetts General Hospital. Join host Anika Hazra for our discussion separating fact from fiction on the controversial topic of leaky gut syndrome. We cover everything from what causes a leaky gut to interpreting the results of a gut microbiome test! Related links: Center for Celiac Research and Treatment website and their YouTube channel
Now Playing: Radiolab

The Flag and the Fury
How do you actually make change in the world? For 126 years, Mississippi has had the Confederate battle flag on their state flag, and they were the last state in the nation where that emblem remained "officially" flying.  A few days ago, that flag came down. A few days before that, it coming down would have seemed impossible. We dive into the story behind this de-flagging: a journey involving a clash of histories, designs, families, and even cheerleading. This show is a collaboration with OSM Audio. Kiese Laymon's memoir Heavy is here. And the Hospitality Flag webpage is here.