DevLan/vispubs huggingface dataset:
VisuaLint: Sketchy In Situ Annotations of Chart Construction Errors
Authors: Aspen K. Hopkins, Michael Correll, Arvind Satyanarayan
Published at EuroVis, 2020

Chart construction errors, such as truncated axes or inexpressive visual encodings, can hinder reading a visualization, or worse, imply misleading facts about the underlying data. These errors can be caught by critical readings of visualizations, but readers must have a high level of data and design literacy and must be paying close attention. To address this issue, we introduce VisuaLint: a technique for surfacing chart construction errors in situ. Inspired by the ubiquitous red wavy underline that indicates spelling mistakes, visualization elements that contain errors (e.g., axes and legends) are sketchily rendered and accompanied by a concise annotation. VisuaLint is unobtrusive — it does not interfere with reading a visualization — and its direct display establishes a close mapping between erroneous elements and the expression of error. We demonstrate five examples of VisualLint and present the results of a crowdsourced evaluation (N = 62) of its efficacy. These results contribute an empirical baseline proficiency for recognizing chart construction errors, and indicate near‐universal difficulty in error identification. We find that people more reliably identify chart construction errors after being shown examples of VisuaLint, and prefer more verbose explanations for unfamiliar or less obvious flaws.
Warehouse Vis: A Visual Analytics Approach to Facilitating Warehouse Location Selection for Business Districts
Authors: Quan Li, Qiangqiang Liu, Chunfeng Tang, Z. W. Li, S. C. Wei, X. R. Peng, M. H. Zheng, Tianjian Chen, Q. Yang
Published at EuroVis, 2020

Selecting a proper warehouse location serving to satisfy the demands of the goods from a certain business area is important to a successful retail business. However, the large solution space, uncertain traffic conditions, and varying business preferences impose great challenges on warehouse location selection. Conventional approaches mainly summarize relevant evaluation criteria and compile them into an analysis report to facilitate rapid data absorption but fail to support a comprehensive and joint decision‐making process in warehouse location selection. In this paper, we propose a visual analytics approach to facilitating warehouse location selection. We first visually centralize relevant information of warehouses and adapts a widely‐used methodology to efficiently rank warehouse candidates. We then design a delivering estimation model based on massive logistics trajectories to resolve the uncertainty issue of traffic conditions of warehouses. Based on these techniques, an interactive framework is proposed to generate and explore the candidate warehouses. We conduct a case study and a within‐subject study with baseline systems to assess the efficacy of our system. Experts ‘feedback also suggests that our approach indeed helps them better tackle the problem of finding an ideal warehouse in the field of retail logistics management.
A Comparison of Geographical Propagation Visualizations
Authors: Vanessa Peña Araya, Anastasia Bezerianos, Emmanuel Pietriga
Published at CHI, 2020

Geographical propagation phenomena occur in multiple domains, such as in epidemiology and social media. Propagation dynamics are often complex, and visualizations play a key role in helping subject-matter experts understand and analyze them. However, there is little empirical data about the effectiveness of the various strategies used to visualize geographical propagation. To fill this gap, we conduct an experiment to evaluate the effectiveness of three strategies: an animated map, small-multiple maps, and a single map with glyphs. We compare them under five tasks that vary in one of the following dimensions: propagation scope, direction, speed, peaks, and spatial jumps. Our results show that small-multiple maps perform best overall, but that the effectiveness of each visualization varies depending on the task considered.
A Participatory Simulation of the Accountable Capitalism Act
Authors: Bill Tomlinson, M. Six Silberman, Andrew W. Torrance, Kurt Squire, Paramdeep S. Atwal, Ameya N. Mandalik, Sahil Railkar, Rebecca W. Black
Published at CHI, 2020

Interactive computing systems increasingly allow for experimental evaluations of fundamental issues in law, government, and society. In this paper, we describe a participatory simulation of the Accountable Capitalism Act, a bill proposed in 2018 by US Senator Elizabeth Warren. We present findings from an empirical study conducted using this system, relating to the impact of 1) interactive visualization and 2) the Accountable Capitalism Act legal framework on the behavior of participants acting as corporate directors. From this study, we draw lessons about research possibilities at the juncture of HCI and legal and policy studies. This study contributes an analysis and evaluation of a design probe used to investigate potential impacts of the Accountable Capitalism Act, experimental evidence from a study conducted using the design probe, and guidance for future participatory simulations that seek to inform the design of social institutions.
A Probabilistic Grammar of Graphics
Authors: Xiaoying Pu, Matthew Kay 0001
Published at CHI, 2020

Visualizations depicting probabilities and uncertainty are used everywhere from medical risk communication to machine learning, yet these probabilistic visualizations are difficult to specify, prone to error, and their designs are cumbersome to explore. We propose a Probabilistic Grammar of Graphics (PGoG), an extension to Wilkinson's original framework. Inspired by the success of probabilistic programming languages, PGoG makes probability expressions, such as P(A|B), a first-class citizen in the language. PGoG abstractions also reflect the distinction between probability and frequency framing, a concept from the uncertainty communication literature. It is expressive, encompassing product plots, density plots, icon arrays, and dotplots, among other visualizations. Its coherent syntax ensures correctness (that the proportions of visual elements and their spatial placement reflect the underlying probability distribution) and reduces edit distance between probabilistic visualization specifications, potentially supporting more design exploration. We provide a proof-of-concept implementation of PGoG in R.
Addressing Cognitive and Emotional Barriers in Parent-Clinician Communication through Behavioral Visualization Webtools
Authors: Ha Kyung Kong, Karrie Karahalios
Published at CHI, 2020

Effective communication between clinicians and parents of young children with developmental delays can decrease parents' anxiety, help them handle bad news, and improve their adherence to proposed interventions. However, parents have reported dissatisfaction regarding their current communication with clinicians, and they face cognitive and emotional challenges when discussing their child's developmental delays. In this paper, we present visualization as a facilitator of parent-clinician communication and how it could address existing communication challenges. Parents and clinicians anticipated visualization webtools would aid their communication by helping parents gain a better understanding of their child, acting as objective evidence, and highlighting the strength of the child as well as important medical concepts. In addition, visualization can act as a longitudinal record, helping parents track, explore, and share their child's developmental progress. Finally, we propose visualization as a tool to guide parents in their transition from feeling emotional and disempowered to advocating with confidence.
Assessing 2D and 3D Heatmaps for Comparative Analysis: An Empirical Study
Authors: Matthias Kraus 0002, Katrin Angerbauer, Juri Buchmüller, Daniel Schweitzer, Daniel A. Keim, Michael Sedlmair, Johannes Fuchs 0001
Published at CHI, 2020

Heatmaps are a popular visualization technique that encode 2D density distributions using color or brightness. Experimental studies have shown though that both of these visual variables are inaccurate when reading and comparing numeric data values. A potential remedy might be to use 3D heatmaps by introducing height as a third dimension to encode the data. Encoding abstract data in 3D, however, poses many problems, too. To better understand this tradeoff, we conducted an empirical study (N=48) to evaluate the user performance of 2D and 3D heatmaps for comparative analysis tasks. We test our conditions on a conventional 2D screen, but also in a virtual reality environment to allow for real stereoscopic vision. Our main results show that 3D heatmaps are superior in terms of error rate when reading and comparing single data items. However, for overview tasks, the well-established 2D heatmap performs better.
Augmenting Static Visualizations with PapARVis Designer
Authors: Zhutian Chen, Wai Tong, Qianwen Wang, Benjamin Bach, Huamin Qu
Published at CHI, 2020

This paper presents an authoring environment for augmenting static visualizations with virtual content in augmented reality.Augmenting static visualizations can leverage the best of both physical and digital worlds, but its creation currently involves different tools and devices, without any means to explicitly design and debug both static and virtual content simultaneously. To address these issues, we design an environment that seamlessly integrates all steps of a design and deployment workflow through its main features: i) an extension to Vega, ii) a preview, and iii) debug hints that facilitate valid combinations of static and augmented content. We inform our design through a design space with four ways to augment static visualizations. We demonstrate the expressiveness of our tool through examples, including books, posters, projections, wall-sized visualizations. A user study shows high user satisfaction of our environment and confirms that participants can create augmented visualizations in an average of 4.63 minutes.
Automatic Annotation Synchronizing with Textual Description for Visualization
Authors: Chufan Lai, Zhixian Lin, Ruike Jiang, Yun Han, Can Liu 0004, Xiaoru Yuan
Published at CHI, 2020

In this paper, we propose a technique for automatically annotating visualizations according to the textual description. In our approach, visual elements in the target visualization, along with their visual properties, are identified and extracted with a Mask R-CNN model. Meanwhile, the description is parsed to generate visual search requests. Based on the identification results and search requests, each descriptive sentence is displayed beside the described focal areas as annotations. Different sentences are presented in various scenes of the generated animation to promote a vivid step-by-step presentation. With a user-customized style, the animation can guide the audience's attention via proper highlighting such as emphasizing specific features or isolating part of the data. We demonstrate the utility and usability of our method through a user study with use cases.
Awareness, Understanding, and Action: A Conceptual Framework of User Experiences and Expectations about Indoor Air Quality Visualizations
Authors: Sunyoung Kim, Muyang Li
Published at CHI, 2020

With the advent of new sensors and technologies, smart devices that monitor the level of indoor air quality (IAQ) are increasingly available to create a healthy home environment. However, little has been studied regarding design principles for effective IAQ visualizations to help better understand and improve IAQ. We analyzed Amazon reviews of IAQ monitors and their design components for IAQ visualizations. Based on our findings, we created a conceptual framework to explain the process of facilitating an effective IAQ visualization with a proposed set of design considerations in each stage. The process includes helping users easily understand what is happing to IAQ (awareness), what it means to them (understanding), and what to do with the information (action), which results in two outcomes, knowledge gain and emotional relief. We hope our framework can help practitioners and researchers in designing eco-feedback system and beyond to advance both research and practice.
Cheat Sheets for Data Visualization Techniques
Authors: Zezhong Wang 0001, Lovisa Sundin, Dave Murray-Rust, Benjamin Bach
Published at CHI, 2020

This paper introduces the concept of 'cheat sheets' for data visualization techniques, a set of concise graphical explanations and textual annotations inspired by infographics, data comics, and cheat sheets in other domains. Cheat sheets aim to address the increasing need for accessible material that supports a wide audience in understanding data visualization techniques, their use, their fallacies and so forth. We have carried out an iterative design process with practitioners, teachers and students of data science and visualization, resulting six types of cheat sheet (anatomy, construction, visual patterns, pitfalls, false-friends and well-known relatives) for six types of visualization, and formats for presentation. We assess these with a qualitative user study using 11 participants that demonstrates the readability and usefulness of our cheat sheets.
COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations
Authors: Ashraf M. Abdul, Christian von der Weth, Mohan S. Kankanhalli, Brian Y. Lim
Published at CHI, 2020

Interpretable machine learning models trade -off accuracy for simplicity to make explanations more readable and easier to comprehend. Drawing from cognitive psychology theories in graph comprehension, we formalize readability as visual cognitive chunks to measure and moderate the cognitive load in explanation visualizations. We present Cognitive-GAM (COGAM) to generate explanations with desired cognitive load and accuracy by combining the expressive nonlinear generalized additive models (GAM) with simpler sparse linear models. We calibrated visual cognitive chunks with reading time in a user study, characterized the trade-off between cognitive load and accuracy for four datasets in simulation studies, and evaluated COGAM against baselines with users. We found that COGAM can decrease cognitive load without decreasing accuracy and/or increase accuracy without increasing cognitive load. Our framework and empirical measurement instruments for cognitive load will enable more rigorous assessment of the human interpretability of explainable AI.
DataQuilt: Extracting Visual Elements from Images to Craft Pictorial Visualizations
Authors: Jiayi Eris Zhang, Nicole Sultanum, Anastasia Bezerianos, Fanny Chevalier
Published at CHI, 2020

Recent years have seen an increasing interest in the authoring and crafting of personal visualizations. Mainstream data analysis and authoring tools lack the flexibility for customization and personalization, whereas tools from the research community either require creativity and drawing skills, or are limited to simple vector graphics. We present DataQuilt, a novel system that enables visualization authors to iteratively design pictorial visualizations as collages. Real images (e.g., paintings, photographs, sketches) act as both inspiration and as a resource of visual elements that can be mapped to data. The creative pipeline involves the semi-guided extraction of relevant elements of an image (arbitrary regions, regular shapes, color palettes, textures) aided by computer vision techniques; the binding of these graphical elements and their features to data in order to create meaningful visualizations; and the iterative refinement of both features and visualizations through direct manipulation. We demonstrate the usability of DataQuilt in a controlled study and its expressiveness through a collection of authored visualizations from a second open-ended study.
Dear Pictograph: Investigating the Role of Personalization and Immersion for Consuming and Enjoying Visualizations
Authors: Hugo Romat, Nathalie Henry Riche, Christophe Hurter, Steven Mark Drucker, Fereshteh Amini, Ken Hinckley
Published at CHI, 2020

Much of the visualization literature focuses on assessment of visual representations with regard to their effectiveness for understanding data. In the present work, we instead focus on making data visualization experiences more enjoyable, to foster deeper engagement with data. We investigate two strategies to make visualization experiences more enjoyable and engaging: personalization, and immersion. We selected pictographs (composed of multiple data glyphs) as this representation affords creative freedom, allowing people to craft symbolic or whimsical shapes of personal significance to represent data. We present the results of a qualitative study with 12 participants crafting pictographs using a large pen-enabled device and while immersed within a VR environment. Our results indicate that personalization and immersion both have positive impact on making visualizations more enjoyable experiences.
Debugging Database Queries: A Survey of Tools, Techniques, and Users
Authors: Sneha Gathani, Peter Lim, Leilani Battle
Published at CHI, 2020

Database management systems (or DBMSs) have been around for decades, and yet are still difficult to use, particularly when trying to identify and fix errors in user programs (or queries). We seek to understand what methods have been proposed to help people debug database queries, and whether these techniques have ultimately been adopted by DBMSs (and users). We conducted an interdisciplinary review of 112 papers and tools from the database, visualisation and HCI communities. To better understand whether academic and industry approaches are meeting the needs of users, we interviewed 20 database users (and some designers), and found surprising results. In particular, there seems to be a wide gulf between users' debugging strategies and the functionality implemented in existing DBMSs, as well as proposed in the literature. In response, we propose new design guidelines to help system designers to build features that more closely match users debugging strategies.
Decipher: An Interactive Visualization Tool for Interpreting Unstructured Design Feedback from Multiple Providers
Authors: Yu-Chun (Grace) Yen, Joy O. Kim, Brian P. Bailey
Published at CHI, 2020

Feedback from diverse audiences can vary in focus, differ in structure, and contradict each other, making it hard to interpret and act on. While prior work has explored generating quality feedback, our work helps a designer interpret that feedback. Through a formative study with professional designers (N=10), we discovered that the interpretation process includes categorizing feedback, identifying valuable feedback, and prioritizing which feedback to incorporate in a revision. We also found that designers leverage feedback topic and sentiment, and the status of the provider to aid interpretation. Based on the findings, we created a new tool (Decipher) that enables designers to visualize and navigate a collection of feedback using its topic and sentiment structure. In a preliminary evaluation (N=20), we found that Decipher helped users feel less overwhelmed during feedback interpretation tasks and better attend to critical issues and conflicting opinions compared to using a typical document-editing tool.
Design Study "Lite" Methodology: Expediting Design Studies and Enabling the Synergy of Visualization Pedagogy and Social Good
Authors: Uzma Haque Syeda, Prasanth Murali, Lisa Roe, Becca Berkey, Michelle A. Borkin
Published at CHI, 2020

Design studies are frequently used to conduct problem-driven visualization research by working with real-world domain experts. In visualization pedagogy, design studies are often introduced but rarely practiced due to their large time requirements. This limits students to a classroom curriculum, often involving projects that may not have implications beyond the classroom. Thus we present the Design Study "Lite" Methodology, a novel framework for implementing design studies with novice students in 14 weeks. We utilized the Design Study "Lite" Methodology in conjunction with Service-Learning to teach five Data Visualization courses and demonstrate that it benefits not only the students but also the community through service to non-profit partners. In this paper, we provide a detailed breakdown of the methodology and how Service-Learning can be incorporated with it. We also include an extensive reflection on the methodology and provide recommendations for future applications of the framework for teaching visualization courses and research.
DFSeer: A Visual Analytics Approach to Facilitate Model Selection for Demand Forecasting
Authors: Dong Sun 0001, Zezheng Feng, Yuanzhe Chen, Yong Wang 0021, Jia Zeng, Mingxuan Yuan, Ting-Chuen Pong, Huamin Qu
Published at CHI, 2020

Selecting an appropriate model to forecast product demand is critical to the manufacturing industry. However, due to the data complexity, market uncertainty and users' demanding requirements for the model, it is challenging for demand analysts to select a proper model. Although existing model selection methods can reduce the manual burden to some extent, they often fail to present model performance details on individual products and reveal the potential risk of the selected model. This paper presents DFSeer, an interactive visualization system to conduct reliable model selection for demand forecasting based on the products with similar historical demand. It supports model comparison and selection with different levels of details. Besides, it shows the difference in model performance on similar products to reveal the risk of model selection and increase users' confidence in choosing a forecasting model. Two case studies and interviews with domain experts demonstrate the effectiveness and usability of DFSeer.
DoughNets: Visualising Networks Using Torus Wrapping
Authors: Kun-Ting Chen, Tim Dwyer, Kim Marriott, Benjamin Bach
Published at CHI, 2020

We investigate visualisations of networks on a 2-dimensional torus topology, like an opened-up and flattened doughnut. That is, the network is drawn on a rectangular area while "wrapping" specific links around the border. Previous work on torus drawings of networks has been mostly theoretical, limited to certain classes of networks, and not evaluated by human readability studies. We offer a simple interactive layout approach applicable to general graphs. We use this to find layouts affording better aesthetics in terms of conventional measures like more equal edge length and fewer crossings. In two controlled user studies we find that torus layout with either additional context or interactive panning provided significant performance improvement (in terms of error and time) over torus layout without either of these improvements, to the point that it is comparable to standard non-torus layout.
Du Bois Wrapped Bar Chart: Visualizing Categorical Data with Disproportionate Values
Authors: Alireza Karduni, Ryan Wesslen, Isaac Cho, Wenwen Dou
Published at CHI, 2020

We propose a visualization technique, Du Bois wrapped bar chart, inspired by work of W.E.B Du Bois. Du Bois wrapped bar charts enable better large-to-small bar comparison by wrapping large bars over a certain threshold. We first present two crowdsourcing experiments comparing wrapped and standard bar charts to evaluate (1) the benefit of wrapped bars in helping participants identify and compare values; (2) the characteristics of data most suitable for wrapped bars. In the first study (n=98) using real-world datasets, we find that wrapped bar charts lead to higher accuracy in identifying and estimating ratios between bars. In a follow-up study (n=190) with 13 simulated datasets, we find participants were consistently more accurate with wrapped bar charts when certain category values are disproportionate as measured by entropy and H-spread. Finally, in an in-lab study, we investigate participants' experience and strategies, leading to guidelines for when and how to use wrapped bar charts.