Designing with Pictographs

Designing with Pictographs

A set of 8 infographics in two rows. Each row of charts appear identical in layout, text, and color, except the top row uses small symbols in place of the more traditional blocky shapes of the lower charts.
How do infographics with pictograph arrays influence understanding when compared to those that use geometric areas? The figure above displays 4 of the 6 pairs of charts evaluated in this study. Each pair consists of a chart using a pictograph array to encode a part-to-whole relationship (upper row) and a chart using a geometric area to encode the same information (lower row).

Led by: Alyx Burns

Related Papers:
PDF Designing with Pictographs: Envision Topics without Sacrificing Understanding

Past studies have shown that when a visualization uses pictographs to encode data, they have a positive effect on memory, engagement, and assessment of risk. However, little is known about how pictographs affect one’s ability to understand a visualization, beyond memory for values and trends. We conducted two crowdsourced experiments to compare the effectiveness of using pictographs when showing part-to-whole relationships. In Experiment 1, we compared pictograph arrays to more traditional bar and pie charts. We tested participants’ ability to generate high-level insights following Bloom’s taxonomy of educational objectives via 6 free-response questions. We found that accuracy for extracting information and generating insights did not differ overall between the two versions. To explore the motivating differences between the designs, we conducted a second experiment where participants compared charts containing pictograph arrays to more traditional charts on 5 metrics and explained their reasoning. We found that some participants preferred the way that pictographs allowed them to envision the topic more easily, while others preferred traditional bar and pie charts because they seem less cluttered and faster to read. These results suggest that, at least in simple visualizations depicting part-to-whole relationships, the choice of using pictographs has little influence on sensemaking and insight extraction. When deciding whether to use pictograph arrays, designers should consider visual appeal, perceived comprehension time, ease of envisioning the topic, and clutteredness.

If you are interested in working on or learning more about this project, please contact Alyx Burns at alyxanderbur@cs.umass.edu

Mahmood Jasim was awarded a Computing for Common Good Fellowship – Congratulations, Mahmood!

Mahmood Jasim was awarded a Computing for Common Good Fellowship by UMass CICS to develop a tool for inclusive public input collection and analysis.

Mahmood Jasim and Professor Narges Mahyar will work with the town of Amherst officials to design and develop CommunityClick-virtual — an online collaborative tool that combines machine learning, data visualization, and citizensourcing technology for inclusive public input collection and analysis. The project will enable people from all social backgrounds to equitably share their opinions on civic issues and allow the town officials to make sense of large-scale public input to inform themselves prior to making critical policy decisions.

Welcome to our 8 new lab members who joined this Fall!

The HCI-VIS lab is delighted to welcome 8 new members this fall, including 2 post-docs: Mennatullah Hendawy and Zhiqiu Jiang and 6 PhD students: Hamza Elhamdadi, Aimen Gaba, Prateek Mantri, Mashrur Rashik, Mahsa Sahebdel Alamdari, and Zack While.

We will additionally be joined by Swapna Joshi, who will begin her Post-Doc in the Spring.

“Designing with Pictographs” to appear in TVCG

A new paper by lab members Alyx Burns, Dr. Cindy Xiong, and Dr. Narges Mahyar was accepted for publication in TVCG. The paper, titled “Designing with Pictographs: Envision Topics without Sacrificing Understanding,” examines the effects of replacing abstract shapes in traditional charts and graphs with pictograph arrays. They found that the pictographs had no impact on participant understanding, but impacted participants’ experience.

You can find a PDF of the paper on our website.

CommunityPulse received an Honorable Mention Award at DIS 2021 – Congrats Mahmood, Dr. Sarvghad, and Dr. Mahyar!

This past June, Mahmood Jasim, Enamul Hoque, Ali Sarvghad and Narges Mahyar’s paper titled “CommunityPulse: Facilitating Community Input Analysis by Surfacing Hidden Insights, Reflections, and Priorities” received an Honorable Mention Award at DIS 2021. Congratulations to all of the authors on this achievement!

You can find a PDF of the paper here.

Prof. Mahyar co-organized the Chart Question Answering Workshop at CVPR 2021

Alongside Daniel Haehn, Steven Franconeri, Jessica Hullman, Nikolaus Kriegeskorte, and Hanspeter Pfister, Professor Narges Mahyar co-organized the Chart Question Answering Workshop as a part of the 2021 Computer Vision and Pattern Recognition Conference that took place in June 2021. You can find more information on the workshop and a video of its proceedings at the workshop’s website: https://cqaw.github.io/