41-Issue 3

Permanent URI for this collection

EuroVis 2022 - 24th EG Conference on Visualization
Rome, Italy, June 13 - 17, 2022
Papers Awards Session
Of Course it's Political! A Critical Inquiry into Underemphasized Dimensions in Civic Text Visualization
Eric P. S. Baumer, Mahmood Jasim, Ali Sarvghad, and Narges Mahyar
Rich Screen Reader Experiences for Accessible Data Visualization
Jonathan Zong, Crystal Lee, Alan Lundgard, JiWoong Jang, Daniel Hajas, and Arvind Satyanarayan
Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response
Max Sondag, Cagatay Turkay, Kai Xu, Louise Matthews, Sibylle Mohr, and Daniel Archambault
Guidelines and Accessibility
Effective Use of Likert Scales in Visualization Evaluations: A Systematic Review
Laura South, David Saffo, Olga Vitek, Cody Dunne, and Michelle A. Borkin
How Accessible is my Visualization? Evaluating Visualization Accessibility with Chartability
Frank Elavsky, Cynthia Bennett, and Dominik Moritz
Seeing Through Sounds: Mapping Auditory Dimensions to Data and Charts for People with Visual Impairments
Ruobin Wang, Crescentia Jung, and Yea-Seul Kim
Visualization and Machine Learning
Interactively Assessing Disentanglement in GANs
Sangwon Jeong, Shusen Liu, and Matthew Berger
ModelWise: Interactive Model Comparison for Model Diagnosis, Improvement and Selection
Linhao Meng, Stef van den Elzen, and Anna Vilanova
SurfNet: Learning Surface Representations via Graph Convolutional Network
Jun Han and Chaoli Wang
Infographics Wizard: Flexible Infographics Authoring and Design Exploration
Anjul Tyagi, Jian Zhao, Pushkar Patel, Swasti Khurana, and Klaus Mueller
Workflows and Parameters
Reusing Interactive Analysis Workflows
Kiran Gadhave, Zach Cutler, and Alexander Lex
Leveraging Analysis History for Improved In Situ Visualization Recommendation
Will Epperson, Doris Jung-Lin Lee, Leijie Wang, Kunal Agarwal, Aditya G. Parameswaran, Dominik Moritz, and Adam Perer
Visual Parameter Selection for Spatial Blind Source Separation
Nikolaus Piccolotto, Markus Bögl, Christoph Muehlmann, Klaus Nordhausen, Peter Filzmoser, and Silvia Miksch
HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters
Gabriel Appleby, Mateus Espadoto, Rui Chen, Samuel Goree, Alexandru C. Telea, Erik W. Anderson, and Remco Chang
Life Sciences and Urbanism
Barrio: Customizable Spatial Neighborhood Analysis and Comparison for Nanoscale Brain Structures
Jakob Troidl, Corrado Cali, Eduard Gröller, Hanspeter Pfister, Markus Hadwiger, and Johanna Beyer
LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning
Jiayi Hong, Alain Trubuil, and Tobias Isenberg
Urban Rhapsody: Large-scale Exploration of Urban Soundscapes
João Rulff, Fabio Miranda, Maryam Hosseini, Marcos Lage, Mark Cartwright, Graham Dove, Juan Bello, and Claudio T. Silva
AirLens: Multi-Level Visual Exploration of Air Quality Evolution in Urban Agglomerations
Dezhan Qu, Cheng Lv, Yiming Lin, Huijie Zhang, and Rong Wang
High Dimensional Data
Where did my Lines go? Visualizing Missing Data in Parallel Coordinates
Alex Bäuerle, Christian van Onzenoodt, Simon der Kinderen, Jimmy Johansson Westberg, Daniel Jönsson, and Timo Ropinski
Optimizing Grid Layouts for Level-of-Detail Exploration of Large Data Collections
Steffen Frey
Six Methods for Transforming Layered Hypergraphs to Apply Layered Graph Layout Algorithms
Sara Di Bartolomeo, Alexis Pister, Paolo Buono, Catherine Plaisant, Cody Dunne, and Jean-Daniel Fekete
Exploring Multivariate Event Sequences with an Interactive Similarity Builder
Shaobin Xu, Minghui Sun, Zhengtai Zhang, and Hao Xue
Text and Music
CorpusVis: Visual Analysis of Digital Sheet Music Collections
Matthias Miller, Julius Rauscher, Daniel A. Keim, and Mennatallah El-Assady
LMFingerprints: Visual Explanations of Language Model Embedding Spaces through Layerwise Contextualization Scores
Rita Sevastjanova, Aikaterini-Lida Kalouli, Christin Beck, Hanna Hauptmann, and Mennatallah El-Assady
Engineering, Physics, and Math
Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data
Mengxi Wu, Yi-Jen Chiang, and Christopher Musco
An Interactive Approach for Identifying Structure Definitions
Natalia Mikula, Tom Dörffel, Daniel Baum, and Hans-Christian Hege
Level of Detail Exploration of Electronic Transition Ensembles using Hierarchical Clustering
Signe Sidwall Thygesen, Talha Bin Masood, Mathieu Linares, Vijay Natarajan, and Ingrid Hotz
A Flip-book of Knot Diagrams for Visualizing Surfaces in 4-Space
Huan Liu and Hui Zhang
Algorithms and Machine Learning
LOOPS: LOcally Optimized Polygon Simplification
Alireza Amiraghdam, Alexandra Diehl, and Renato Pajarola
Branch Decomposition-Independent Edit Distances for Merge Trees
Florian Wetzels, Heike Leitte, and Christoph Garth
SimilarityNet: A Deep Neural Network for Similarity Analysis Within Spatio-temporal Ensembles
Karim Huesmann and Lars Linsen
Neural Flow Map Reconstruction
Saroj Sahoo, Yuzhe Lu, and Matthew Berger
Social Sciences, Mobile, and VR/AR
Hybrid Touch/Tangible Spatial Selection in Augmented Reality
Mickael Sereno, Stéphane Gosset, Lonni Besançon, and Tobias Isenberg
Mobile and Multimodal? A Comparative Evaluation of Interactive Workplaces for Visual Data Exploration
Gabriela Molina León, Michael Lischka, Wei Luo, and Andreas Breiter
DanmuVis: Visualizing Danmu Content Dynamics and Associated Viewer Behaviors in Online Videos
Shuai Chen, Sihang Li, Yanda Li, Junlin Zhu, Juanjuan Long, Siming Chen, Jiawan Zhang, and Xiaoru Yuan
Empirical Studies
Exploring How Visualization Design and Situatedness Evoke Compassion in the Wild
Luiz Morais, Nazareno Andrade, and Dandara Sousa
Exploring Effects of Ecological Visual Analytics Interfaces on Experts' and Novices' Decision-Making Processes: A Case Study in Air Traffic Control
Elmira Zohrevandi, Carl A. L. Westin, Katerina Vrotsou, and Jonas Lundberg
Models and Frameworks
A Typology of Guidance Tasks in Mixed-Initiative Visual Analytics Environments
Ignacio Pérez-Messina, Davide Ceneda, Mennatallah El-Assady, Silvia Miksch, and Fabian Sperrle
VIBE: A Design Space for VIsual Belief Elicitation in Data Journalism
Shambhavi Mahajan, Bonnie Chen, Alireza Karduni, Yea-Seul Kim, and Emily Wall
A Grammar-Based Approach for Applying Visualization Taxonomies to Interaction Logs
Sneha Gathani, Shayan Monadjemi, Alvitta Ottley, and Leilani Battle
A Process Model for Dashboard Onboarding
Vaishali Dhanoa, Conny Walchshofer, Andreas Hinterreiter, Holger Stitz, Eduard Gröller, and Marc Streit
General Public
Misinformed by Visualization: What Do We Learn From Misinformative Visualizations?
Leo Yu-Ho Lo, Ayush Gupta, Kento Shigyo, Aoyu Wu, Enrico Bertini, and Huamin Qu
Investigating the Role and Interplay of Narrations and Animations in Data Videos
Hao Cheng, Junhong Wang, Yun Wang, Bongshin Lee, Haidong Zhang, and Dongmei Zhang
Nested Papercrafts for Anatomical and Biological Edutainment
Marwin Schindler, Thorsten Korpitsch, Renata Georgia Raidou, and Hsiang-Yun Wu

BibTeX (41-Issue 3)
                
@article{
10.1111:cgf.14518,
journal = {Computer Graphics Forum}, title = {{
Of Course it's Political! A Critical Inquiry into Underemphasized Dimensions in Civic Text Visualization}},
author = {
Baumer, Eric P. S.
and
Jasim, Mahmood
and
Sarvghad, Ali
and
Mahyar, Narges
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14518}
}
                
@article{
10.1111:cgf.14562,
journal = {Computer Graphics Forum}, title = {{
EuroVis 2022 CGF 41-3: Frontmatter}},
author = {
Borgo, Rita
and
Marai, G. Elisabeta
and
Schreck, Tobias
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14562}
}
                
@article{
10.1111:cgf.14519,
journal = {Computer Graphics Forum}, title = {{
Rich Screen Reader Experiences for Accessible Data Visualization}},
author = {
Zong, Jonathan
and
Lee, Crystal
and
Lundgard, Alan
and
Jang, JiWoong
and
Hajas, Daniel
and
Satyanarayan, Arvind
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14519}
}
                
@article{
10.1111:cgf.14522,
journal = {Computer Graphics Forum}, title = {{
How Accessible is my Visualization? Evaluating Visualization Accessibility with Chartability}},
author = {
Elavsky, Frank
and
Bennett, Cynthia
and
Moritz, Dominik
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14522}
}
                
@article{
10.1111:cgf.14521,
journal = {Computer Graphics Forum}, title = {{
Effective Use of Likert Scales in Visualization Evaluations: A Systematic Review}},
author = {
South, Laura
and
Saffo, David
and
Vitek, Olga
and
Dunne, Cody
and
Borkin, Michelle A.
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14521}
}
                
@article{
10.1111:cgf.14520,
journal = {Computer Graphics Forum}, title = {{
Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response}},
author = {
Sondag, Max
and
Turkay, Cagatay
and
Xu, Kai
and
Matthews, Louise
and
Mohr, Sibylle
and
Archambault, Daniel
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14520}
}
                
@article{
10.1111:cgf.14523,
journal = {Computer Graphics Forum}, title = {{
Seeing Through Sounds: Mapping Auditory Dimensions to Data and Charts for People with Visual Impairments}},
author = {
Wang, Ruobin
and
Jung, Crescentia
and
Kim, Yea-Seul
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14523}
}
                
@article{
10.1111:cgf.14524,
journal = {Computer Graphics Forum}, title = {{
Interactively Assessing Disentanglement in GANs}},
author = {
Jeong, Sangwon
and
Liu, Shusen
and
Berger, Matthew
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14524}
}
                
@article{
10.1111:cgf.14525,
journal = {Computer Graphics Forum}, title = {{
ModelWise: Interactive Model Comparison for Model Diagnosis, Improvement and Selection}},
author = {
Meng, Linhao
and
Elzen, Stef van den
and
Vilanova, Anna
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14525}
}
                
@article{
10.1111:cgf.14526,
journal = {Computer Graphics Forum}, title = {{
SurfNet: Learning Surface Representations via Graph Convolutional Network}},
author = {
Han, Jun
and
Wang, Chaoli
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14526}
}
                
@article{
10.1111:cgf.14527,
journal = {Computer Graphics Forum}, title = {{
Infographics Wizard: Flexible Infographics Authoring and Design Exploration}},
author = {
Tyagi, Anjul
and
Zhao, Jian
and
Patel, Pushkar
and
Khurana, Swasti
and
Mueller, Klaus
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14527}
}
                
@article{
10.1111:cgf.14529,
journal = {Computer Graphics Forum}, title = {{
Leveraging Analysis History for Improved In Situ Visualization Recommendation}},
author = {
Epperson, Will
and
Lee, Doris Jung-Lin
and
Wang, Leijie
and
Agarwal, Kunal
and
Parameswaran, Aditya G.
and
Moritz, Dominik
and
Perer, Adam
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14529}
}
                
@article{
10.1111:cgf.14528,
journal = {Computer Graphics Forum}, title = {{
Reusing Interactive Analysis Workflows}},
author = {
Gadhave, Kiran
and
Cutler, Zach
and
Lex, Alexander
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14528}
}
                
@article{
10.1111:cgf.14530,
journal = {Computer Graphics Forum}, title = {{
Visual Parameter Selection for Spatial Blind Source Separation}},
author = {
Piccolotto, Nikolaus
and
Bögl, Markus
and
Muehlmann, Christoph
and
Nordhausen, Klaus
and
Filzmoser, Peter
and
Miksch, Silvia
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14530}
}
                
@article{
10.1111:cgf.14531,
journal = {Computer Graphics Forum}, title = {{
HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters}},
author = {
Appleby, Gabriel
and
Espadoto, Mateus
and
Chen, Rui
and
Goree, Samuel
and
Telea, Alexandru C.
and
Anderson, Erik W.
and
Chang, Remco
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14531}
}
                
@article{
10.1111:cgf.14532,
journal = {Computer Graphics Forum}, title = {{
Barrio: Customizable Spatial Neighborhood Analysis and Comparison for Nanoscale Brain Structures}},
author = {
Troidl, Jakob
and
Cali, Corrado
and
Gröller, Eduard
and
Pfister, Hanspeter
and
Hadwiger, Markus
and
Beyer, Johanna
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14532}
}
                
@article{
10.1111:cgf.14533,
journal = {Computer Graphics Forum}, title = {{
LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning}},
author = {
Hong, Jiayi
and
Trubuil, Alain
and
Isenberg, Tobias
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14533}
}
                
@article{
10.1111:cgf.14535,
journal = {Computer Graphics Forum}, title = {{
AirLens: Multi-Level Visual Exploration of Air Quality Evolution in Urban Agglomerations}},
author = {
Qu, Dezhan
and
Lv, Cheng
and
Lin, Yiming
and
Zhang, Huijie
and
Wang, Rong
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14535}
}
                
@article{
10.1111:cgf.14534,
journal = {Computer Graphics Forum}, title = {{
Urban Rhapsody: Large-scale Exploration of Urban Soundscapes}},
author = {
Rulff, João
and
Miranda, Fabio
and
Hosseini, Maryam
and
Lage, Marcos
and
Cartwright, Mark
and
Dove, Graham
and
Bello, Juan
and
Silva, Claudio T.
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14534}
}
                
@article{
10.1111:cgf.14536,
journal = {Computer Graphics Forum}, title = {{
Where did my Lines go? Visualizing Missing Data in Parallel Coordinates}},
author = {
Bäuerle, Alex
and
Onzenoodt, Christian van
and
Kinderen, Simon der
and
Westberg, Jimmy Johansson
and
Jönsson, Daniel
and
Ropinski, Timo
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14536}
}
                
@article{
10.1111:cgf.14537,
journal = {Computer Graphics Forum}, title = {{
Optimizing Grid Layouts for Level-of-Detail Exploration of Large Data Collections}},
author = {
Frey, Steffen
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14537}
}
                
@article{
10.1111:cgf.14538,
journal = {Computer Graphics Forum}, title = {{
Six Methods for Transforming Layered Hypergraphs to Apply Layered Graph Layout Algorithms}},
author = {
Bartolomeo, Sara Di
and
Pister, Alexis
and
Buono, Paolo
and
Plaisant, Catherine
and
Dunne, Cody
and
Fekete, Jean-Daniel
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14538}
}
                
@article{
10.1111:cgf.14539,
journal = {Computer Graphics Forum}, title = {{
Exploring Multivariate Event Sequences with an Interactive Similarity Builder}},
author = {
Xu, Shaobin
and
Sun, Minghui
and
Zhang, Zhengtai
and
Xue, Hao
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14539}
}
                
@article{
10.1111:cgf.14540,
journal = {Computer Graphics Forum}, title = {{
CorpusVis: Visual Analysis of Digital Sheet Music Collections}},
author = {
Miller, Matthias
and
Rauscher, Julius
and
Keim, Daniel A.
and
El-Assady, Mennatallah
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14540}
}
                
@article{
10.1111:cgf.14541,
journal = {Computer Graphics Forum}, title = {{
LMFingerprints: Visual Explanations of Language Model Embedding Spaces through Layerwise Contextualization Scores}},
author = {
Sevastjanova, Rita
and
Kalouli, Aikaterini-Lida
and
Beck, Christin
and
Hauptmann, Hanna
and
El-Assady, Mennatallah
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14541}
}
                
@article{
10.1111:cgf.14542,
journal = {Computer Graphics Forum}, title = {{
Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data}},
author = {
Wu, Mengxi
and
Chiang, Yi-Jen
and
Musco, Christopher
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14542}
}
                
@article{
10.1111:cgf.14543,
journal = {Computer Graphics Forum}, title = {{
An Interactive Approach for Identifying Structure Definitions}},
author = {
Mikula, Natalia
and
Dörffel, Tom
and
Baum, Daniel
and
Hege, Hans-Christian
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14543}
}
                
@article{
10.1111:cgf.14544,
journal = {Computer Graphics Forum}, title = {{
Level of Detail Exploration of Electronic Transition Ensembles using Hierarchical Clustering}},
author = {
Sidwall Thygesen, Signe
and
Masood, Talha Bin
and
Linares, Mathieu
and
Natarajan, Vijay
and
Hotz, Ingrid
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14544}
}
                
@article{
10.1111:cgf.14545,
journal = {Computer Graphics Forum}, title = {{
A Flip-book of Knot Diagrams for Visualizing Surfaces in 4-Space}},
author = {
Liu, Huan
and
Zhang, Hui
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14545}
}
                
@article{
10.1111:cgf.14546,
journal = {Computer Graphics Forum}, title = {{
LOOPS: LOcally Optimized Polygon Simplification}},
author = {
Amiraghdam, Alireza
and
Diehl, Alexandra
and
Pajarola, Renato
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14546}
}
                
@article{
10.1111:cgf.14548,
journal = {Computer Graphics Forum}, title = {{
SimilarityNet: A Deep Neural Network for Similarity Analysis Within Spatio-temporal Ensembles}},
author = {
Huesmann, Karim
and
Linsen, Lars
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14548}
}
                
@article{
10.1111:cgf.14547,
journal = {Computer Graphics Forum}, title = {{
Branch Decomposition-Independent Edit Distances for Merge Trees}},
author = {
Wetzels, Florian
and
Leitte, Heike
and
Garth, Christoph
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14547}
}
                
@article{
10.1111:cgf.14549,
journal = {Computer Graphics Forum}, title = {{
Neural Flow Map Reconstruction}},
author = {
Sahoo, Saroj
and
Lu, Yuzhe
and
Berger, Matthew
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14549}
}
                
@article{
10.1111:cgf.14550,
journal = {Computer Graphics Forum}, title = {{
Hybrid Touch/Tangible Spatial Selection in Augmented Reality}},
author = {
Sereno, Mickael
and
Gosset, Stéphane
and
Besançon, Lonni
and
Isenberg, Tobias
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14550}
}
                
@article{
10.1111:cgf.14552,
journal = {Computer Graphics Forum}, title = {{
DanmuVis: Visualizing Danmu Content Dynamics and Associated Viewer Behaviors in Online Videos}},
author = {
Chen, Shuai
and
Li, Sihang
and
Li, Yanda
and
Zhu, Junlin
and
Long, Juanjuan
and
Chen, Siming
and
Zhang, Jiawan
and
Yuan, Xiaoru
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14552}
}
                
@article{
10.1111:cgf.14553,
journal = {Computer Graphics Forum}, title = {{
Exploring How Visualization Design and Situatedness Evoke Compassion in the Wild}},
author = {
Morais, Luiz
and
Andrade, Nazareno
and
Sousa, Dandara
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14553}
}
                
@article{
10.1111:cgf.14551,
journal = {Computer Graphics Forum}, title = {{
Mobile and Multimodal? A Comparative Evaluation of Interactive Workplaces for Visual Data Exploration}},
author = {
León, Gabriela Molina
and
Lischka, Michael
and
Luo, Wei
and
Breiter, Andreas
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14551}
}
                
@article{
10.1111:cgf.14554,
journal = {Computer Graphics Forum}, title = {{
Exploring Effects of Ecological Visual Analytics Interfaces on Experts' and Novices' Decision-Making Processes: A Case Study in Air Traffic Control}},
author = {
Zohrevandi, Elmira
and
Westin, Carl A. L.
and
Vrotsou, Katerina
and
Lundberg, Jonas
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14554}
}
                
@article{
10.1111:cgf.14555,
journal = {Computer Graphics Forum}, title = {{
A Typology of Guidance Tasks in Mixed-Initiative Visual Analytics Environments}},
author = {
Pérez-Messina, Ignacio
and
Ceneda, Davide
and
El-Assady, Mennatallah
and
Miksch, Silvia
and
Sperrle, Fabian
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14555}
}
                
@article{
10.1111:cgf.14556,
journal = {Computer Graphics Forum}, title = {{
VIBE: A Design Space for VIsual Belief Elicitation in Data Journalism}},
author = {
Mahajan, Shambhavi
and
Chen, Bonnie
and
Karduni, Alireza
and
Kim, Yea-Seul
and
Wall, Emily
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14556}
}
                
@article{
10.1111:cgf.14558,
journal = {Computer Graphics Forum}, title = {{
A Process Model for Dashboard Onboarding}},
author = {
Dhanoa, Vaishali
and
Walchshofer, Conny
and
Hinterreiter, Andreas
and
Stitz, Holger
and
Gröller, Eduard
and
Streit, Marc
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14558}
}
                
@article{
10.1111:cgf.14557,
journal = {Computer Graphics Forum}, title = {{
A Grammar-Based Approach for Applying Visualization Taxonomies to Interaction Logs}},
author = {
Gathani, Sneha
and
Monadjemi, Shayan
and
Ottley, Alvitta
and
Battle, Leilani
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14557}
}
                
@article{
10.1111:cgf.14559,
journal = {Computer Graphics Forum}, title = {{
Misinformed by Visualization: What Do We Learn From Misinformative Visualizations?}},
author = {
Lo, Leo Yu-Ho
and
Gupta, Ayush
and
Shigyo, Kento
and
Wu, Aoyu
and
Bertini, Enrico
and
Qu, Huamin
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14559}
}
                
@article{
10.1111:cgf.14560,
journal = {Computer Graphics Forum}, title = {{
Investigating the Role and Interplay of Narrations and Animations in Data Videos}},
author = {
Cheng, Hao
and
Wang, Junhong
and
Wang, Yun
and
Lee, Bongshin
and
Zhang, Haidong
and
Zhang, Dongmei
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14560}
}
                
@article{
10.1111:cgf.14561,
journal = {Computer Graphics Forum}, title = {{
Nested Papercrafts for Anatomical and Biological Edutainment}},
author = {
Schindler, Marwin
and
Korpitsch, Thorsten
and
Raidou, Renata Georgia
and
Wu, Hsiang-Yun
}, year = {
2022},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14561}
}

Browse

Recent Submissions

Now showing 1 - 45 of 45
  • Item
    Of Course it's Political! A Critical Inquiry into Underemphasized Dimensions in Civic Text Visualization
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Baumer, Eric P. S.; Jasim, Mahmood; Sarvghad, Ali; Mahyar, Narges; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Recent developments in critical information visualization have brought the field's attention to political, feminist, ethical, and rhetorical aspects of data visualization. However, less work has explored the interplay between design decisions and political ramifications-structures of authority, means of representation, etc. In this paper, we build upon these critical perspectives and highlight the political aspect of civic text visualization especially in the context of democratic decision-making. Based on a critical analysis of survey papers about text visualization in general, followed by a review on the status quo of text visualization in civics, we argue that civic text visualization inherits an exclusively analytic framing. This framing leads to a series of issues and challenges in the fundamentally political context of civics, such as misinterpretation of data, missing minority voices, and excluding the public from decision making processes. To span this gap between political context and analytic framing, we provide a series of two-pole conceptual dimensions, such as from singular user to multiple relationships, and from complexity to inclusivity of visualization design. For each dimension, we discuss how the tensions between these poles can help surface the political ramifications of design decisions in civic text visualization. These dimensions can thus help visualization researchers, designers, and practitioners attend more intentionally to these political aspects and inspire their design choices. We conclude by suggesting that these dimensions may be useful for visualization design across a variety of application domains, beyond civic text visualization.
  • Item
    EuroVis 2022 CGF 41-3: Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
  • Item
    Rich Screen Reader Experiences for Accessible Data Visualization
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Zong, Jonathan; Lee, Crystal; Lundgard, Alan; Jang, JiWoong; Hajas, Daniel; Satyanarayan, Arvind; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Current web accessibility guidelines ask visualization designers to support screen readers via basic non-visual alternatives like textual descriptions and access to raw data tables. But charts do more than summarize data or reproduce tables; they afford interactive data exploration at varying levels of granularity-from fine-grained datum-by-datum reading to skimming and surfacing high-level trends. In response to the lack of comparable non-visual affordances, we present a set of rich screen reader experiences for accessible data visualization and exploration. Through an iterative co-design process, we identify three key design dimensions for expressive screen reader accessibility: structure, or how chart entities should be organized for a screen reader to traverse; navigation, or the structural, spatial, and targeted operations a user might perform to step through the structure; and, description, or the semantic content, composition, and verbosity of the screen reader's narration. We operationalize these dimensions to prototype screen-reader-accessible visualizations that cover a diverse range of chart types and combinations of our design dimensions. We evaluate a subset of these prototypes in a mixed-methods study with 13 blind and visually impaired readers. Our findings demonstrate that these designs help users conceptualize data spatially, selectively attend to data of interest at different levels of granularity, and experience control and agency over their data analysis process.
  • Item
    How Accessible is my Visualization? Evaluating Visualization Accessibility with Chartability
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Elavsky, Frank; Bennett, Cynthia; Moritz, Dominik; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Novices and experts have struggled to evaluate the accessibility of data visualizations because there are no common shared guidelines across environments, platforms, and contexts in which data visualizations are authored. Between non-specific standards bodies like WCAG, emerging research, and guidelines from specific communities of practice, it is hard to organize knowledge on how to evaluate accessible data visualizations. We present Chartability, a set of heuristics synthesized from these various sources which enables designers, developers, researchers, and auditors to evaluate data-driven visualizations and interfaces for visual, motor, vestibular, neurological, and cognitive accessibility. In this paper, we outline our process of making a set of heuristics and accessibility principles for Chartability and highlight key features in the auditing process. Working with participants on real projects, we found that data practitioners with a novice level of accessibility skills were more confident and found auditing to be easier after using Chartability. Expert accessibility practitioners were eager to integrate Chartability into their own work. Reflecting on Chartability's development and the preliminary user evaluation, we discuss tradeoffs of open projects, working with high-risk evaluations like auditing projects in the wild, and challenge future research projects at the intersection of visualization and accessibility to consider the broad intersections of disabilities.
  • Item
    Effective Use of Likert Scales in Visualization Evaluations: A Systematic Review
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) South, Laura; Saffo, David; Vitek, Olga; Dunne, Cody; Borkin, Michelle A.; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Likert scales are often used in visualization evaluations to produce quantitative estimates of subjective attributes, such as ease of use or aesthetic appeal. However, the methods used to collect, analyze, and visualize data collected with Likert scales are inconsistent among evaluations in visualization papers. In this paper, we examine the use of Likert scales as a tool for measuring subjective response in a systematic review of 134 visualization evaluations published between 2009 and 2019. We find that papers with both objective and subjective measures do not hold the same reporting and analysis standards for both aspects of their evaluation, producing less rigorous work for the subjective qualities measured by Likert scales. Additionally, we demonstrate that many papers are inconsistent in their interpretations of Likert data as discrete or continuous and may even sacrifice statistical power by applying nonparametric tests unnecessarily. Finally, we identify instances where key details about Likert item construction with the potential to bias participant responses are omitted from evaluation methodology reporting, inhibiting the feasibility and reliability of future replication studies. We summarize recommendations from other fields for best practices with Likert data in visualization evaluations, based on the results of our survey.
  • Item
    Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Sondag, Max; Turkay, Cagatay; Xu, Kai; Matthews, Louise; Mohr, Sibylle; Archambault, Daniel; ; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Epidemiologists use individual-based models to (a) simulate disease spread over dynamic contact networks and (b) to investigate strategies to control the outbreak. These model simulations generate complex 'infection maps' of time-varying transmission trees and patterns of spread. Conventional statistical analysis of outputs offers only limited interpretation. This paper presents a novel visual analytics approach for the inspection of infection maps along with their associated metadata, developed collaboratively over 16 months in an evolving emergency response situation. We introduce the concept of representative trees that summarize the many components of a time-varying infection map while preserving the epidemiological characteristics of each individual transmission tree. We also present interactive visualization techniques for the quick assessment of different control policies. Through a series of case studies and a qualitative evaluation by epidemiologists, we demonstrate how our visualizations can help improve the development of epidemiological models and help interpret complex transmission patterns.
  • Item
    Seeing Through Sounds: Mapping Auditory Dimensions to Data and Charts for People with Visual Impairments
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Wang, Ruobin; Jung, Crescentia; Kim, Yea-Seul; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Sonification can be an effective medium for people with visual impairments to understand data in visualizations. However, there are no universal design principles that apply to various charts that encode different data types. Towards generalizable principles, we conducted an exploratory experiment to assess how different auditory channels (e.g., pitch, volume) impact the data and visualization perception among people with visual impairments. In our experiment, participants evaluated the intuitiveness and accuracy of the mapping of auditory channels on different data and chart types. We found that participants rated pitch to be the most intuitive, while the number of tappings and the length of sounds yielded the most accurate perception in decoding data. We study how audio channels can intuitively represent different charts and demonstrate that data-level perception might not directly transfer to chart-level perception as participants reflect on visual aspects of the charts while listening to audio. We conclude by how future experiments can be designed to establish a robust ranking for creating audio charts.
  • Item
    Interactively Assessing Disentanglement in GANs
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Jeong, Sangwon; Liu, Shusen; Berger, Matthew; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Generative adversarial networks (GAN) have witnessed tremendous growth in recent years, demonstrating wide applicability in many domains. However, GANs remain notoriously difficult for people to interpret, particularly for modern GANs capable of generating photo-realistic imagery. In this work we contribute a visual analytics approach for GAN interpretability, where we focus on the analysis and visualization of GAN disentanglement. Disentanglement is concerned with the ability to control content produced by a GAN along a small number of distinct, yet semantic, factors of variation. The goal of our approach is to shed insight on GAN disentanglement, above and beyond coarse summaries, instead permitting a deeper analysis of the data distribution modeled by a GAN. Our visualization allows one to assess a single factor of variation in terms of groupings and trends in the data distribution, where our analysis seeks to relate the learned representation space of GANs with attribute-based semantic scoring of images produced by GANs. Through use-cases, we show that our visualization is effective in assessing disentanglement, allowing one to quickly recognize a factor of variation and its overall quality. In addition, we show how our approach can highlight potential dataset biases learned by GANs.
  • Item
    ModelWise: Interactive Model Comparison for Model Diagnosis, Improvement and Selection
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Meng, Linhao; Elzen, Stef van den; Vilanova, Anna; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Model comparison is an important process to facilitate model diagnosis, improvement, and selection when multiple models are developed for a classification task. It involves careful comparison concerning model performance and interpretation. Current visual analytics solutions often ignore the feature selection process. They either do not support detailed analysis of multiple multi-class classifiers or rely on feature analysis alone to interpret model results. Understanding how different models make classification decisions, especially classification disagreements of the same instances, requires a deeper model understanding. We present ModelWise, a visual analytics method to compare multiple multi-class classifiers in terms of model performance, feature space, and model explanation. ModelWise adapts visualizations with rich interactions to support multiple workflows to achieve model diagnosis, improvement, and selection. It considers feature subspaces generated for use in different models and improves model understanding by model explanation. We demonstrate the usability of ModelWise with two case studies, one with a small exemplar dataset and another developed with a machine learning expert with real-world perioperative data.
  • Item
    SurfNet: Learning Surface Representations via Graph Convolutional Network
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Han, Jun; Wang, Chaoli; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    For scientific visualization applications, understanding the structure of a single surface (e.g., stream surface, isosurface) and selecting representative surfaces play a crucial role. In response, we propose SurfNet, a graph-based deep learning approach for representing a surface locally at the node level and globally at the surface level. By treating surfaces as graphs, we leverage a graph convolutional network to learn node embedding on a surface. To make the learned embedding effective, we consider various pieces of information (e.g., position, normal, velocity) for network input and investigate multiple losses. Furthermore, we apply dimensionality reduction to transform the learned embeddings into 2D space for understanding and exploration. To demonstrate the effectiveness of SurfNet, we evaluate the embeddings in node clustering (node-level) and surface selection (surface-level) tasks. We compare SurfNet against state-of-the-art node embedding approaches and surface selection methods. We also demonstrate the superiority of SurfNet by comparing it against a spectral-based mesh segmentation approach. The results show that SurfNet can learn better representations at the node and surface levels with less training time and fewer training samples while generating comparable or better clustering and selection results.
  • Item
    Infographics Wizard: Flexible Infographics Authoring and Design Exploration
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Tyagi, Anjul; Zhao, Jian; Patel, Pushkar; Khurana, Swasti; Mueller, Klaus; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Infographics are an aesthetic visual representation of information following specific design principles of human perception. Designing infographics can be a tedious process for non-experts and time-consuming, even for professional designers. With the help of designers, we propose a semi-automated infographic framework for general structured and flow-based infographic design generation. For novice designers, our framework automatically creates and ranks infographic designs for a user-provided text with no requirement for design input. However, expert designers can still provide custom design inputs to customize the infographics. We will also contribute an individual visual group (VG) designs dataset (in SVG), along with a 1k complete infographic image dataset with segmented VGs in this work. Evaluation results confirm that by using our framework, designers from all expertise levels can generate generic infographic designs faster than existing methods while maintaining the same quality as hand-designed infographics templates.
  • Item
    Leveraging Analysis History for Improved In Situ Visualization Recommendation
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Epperson, Will; Lee, Doris Jung-Lin; Wang, Leijie; Agarwal, Kunal; Parameswaran, Aditya G.; Moritz, Dominik; Perer, Adam; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Existing visualization recommendation systems commonly rely on a single snapshot of a dataset to suggest visualizations to users. However, exploratory data analysis involves a series of related interactions with a dataset over time rather than one-off analytical steps. We present Solas, a tool that tracks the history of a user's data analysis, models their interest in each column, and uses this information to provide visualization recommendations, all within the user's native analytical environment. Recommending with analysis history improves visualizations in three primary ways: task-specific visualizations use the provenance of data to provide sensible encodings for common analysis functions, aggregated history is used to rank visualizations by our model of a user's interest in each column, and column data types are inferred based on applied operations. We present a usage scenario and a user evaluation demonstrating how leveraging analysis history improves in situ visualization recommendations on real-world analysis tasks.
  • Item
    Reusing Interactive Analysis Workflows
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Gadhave, Kiran; Cutler, Zach; Lex, Alexander; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Interactive visual analysis has many advantages, but an important disadvantage is that analysis processes and workflows cannot be easily stored and reused. This is in contrast to code-based analysis workflows, which can simply be run on updated datasets, and adapted when necessary. In this paper, we introduce methods to capture workflows in interactive visualization systems for different interactions such as selections, filters, categorizing/grouping, labeling, and aggregation. These workflows can then be applied to updated datasets, making interactive visualization sessions reusable. We demonstrate this specification using an interactive visualization system that tracks interaction provenance, and allows generating workflows from the recorded actions. The system can then be used to compare different versions of datasets and apply workflows to them. Finally, we introduce a Python library that can load workflows and apply it to updated datasets directly in a computational notebook, providing a seamless bridge between computational workflows and interactive visualization tools.
  • Item
    Visual Parameter Selection for Spatial Blind Source Separation
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Piccolotto, Nikolaus; Bögl, Markus; Muehlmann, Christoph; Nordhausen, Klaus; Filzmoser, Peter; Miksch, Silvia; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Analysis of spatial multivariate data, i.e., measurements at irregularly-spaced locations, is a challenging topic in visualization and statistics alike. Such data are integral to many domains, e.g., indicators of valuable minerals are measured for mine prospecting. Popular analysis methods, like PCA, often by design do not account for the spatial nature of the data. Thus they, together with their spatial variants, must be employed very carefully. Clearly, it is preferable to use methods that were specifically designed for such data, like spatial blind source separation (SBSS). However, SBSS requires two tuning parameters, which are themselves complex spatial objects. Setting these parameters involves navigating two large and interdependent parameter spaces, while also taking into account prior knowledge of the physical reality represented by the data. To support analysts in this process, we developed a visual analytics prototype. We evaluated it with experts in visualization, SBSS, and geochemistry. Our evaluations show that our interactive prototype allows to define complex and realistic parameter settings efficiently, which was so far impractical. Settings identified by a non-expert led to remarkable and surprising insights for a domain expert. Therefore, this paper presents important first steps to enable the use of a promising analysis method for spatial multivariate data.
  • Item
    HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Appleby, Gabriel; Espadoto, Mateus; Chen, Rui; Goree, Samuel; Telea, Alexandru C.; Anderson, Erik W.; Chang, Remco; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Projection algorithms such as t-SNE or UMAP are useful for the visualization of high dimensional data, but depend on hyperparameters which must be tuned carefully. Unfortunately, iteratively recomputing projections to find the optimal hyperparameter values is computationally intensive and unintuitive due to the stochastic nature of such methods. In this paper we propose HyperNP, a scalable method that allows for real-time interactive hyperparameter exploration of projection methods by training neural network approximations. A HyperNP model can be trained on a fraction of the total data instances and hyperparameter configurations that one would like to investigate and can compute projections for new data and hyperparameters at interactive speeds. HyperNP models are compact in size and fast to compute, thus allowing them to be embedded in lightweight visualization systems. We evaluate the performance of HyperNP across three datasets in terms of performance and speed. The results suggest that HyperNP models are accurate, scalable, interactive, and appropriate for use in real-world settings.
  • Item
    Barrio: Customizable Spatial Neighborhood Analysis and Comparison for Nanoscale Brain Structures
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Troidl, Jakob; Cali, Corrado; Gröller, Eduard; Pfister, Hanspeter; Hadwiger, Markus; Beyer, Johanna; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    High-resolution electron microscopy imaging allows neuroscientists to reconstruct not just entire cells but individual cell substructures (i.e., cell organelles) as well. Based on these data, scientists hope to get a better understanding of brain function and development through detailed analysis of local organelle neighborhoods. In-depth analyses require efficient and scalable comparison of a varying number of cell organelles, ranging from two to hundreds of local spatial neighborhoods. Scientists need to be able to analyze the 3D morphologies of organelles, their spatial distributions and distances, and their spatial correlations. We have designed Barrio as a configurable framework that scientists can adjust to their preferred workflow, visualizations, and supported user interactions for their specific tasks and domain questions. Furthermore, Barrio provides a scalable comparative visualization approach for spatial neighborhoods that automatically adjusts visualizations based on the number of structures to be compared. Barrio supports small multiples of spatial 3D views as well as abstract quantitative views, and arranges them in linked and juxtaposed views. To adapt to new domain-specific analysis scenarios, we allow the definition of individualized visualizations and their parameters for each analysis session. We present an in-depth case study for mitochondria analysis in neuronal tissue and demonstrate the usefulness of Barrio in a qualitative user study with neuroscientists.
  • Item
    LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Hong, Jiayi; Trubuil, Alain; Isenberg, Tobias; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    We describe LineageD-a hybrid web-based system to predict, visualize, and interactively adjust plant embryo cell lineages. Currently, plant biologists explore the development of an embryo and its hierarchical cell lineage manually, based on a 3D dataset that represents the embryo status at one point in time. This human decision-making process, however, is time-consuming, tedious, and error-prone due to the lack of integrated graphical support for specifying the cell lineage. To fill this gap, we developed a new system to support the biologists in their tasks using an interactive combination of 3D visualization, abstract data visualization, and correctable machine learning to modify the proposed cell lineage. We use existing manually established cell lineages to obtain a neural network model. We then allow biologists to use this model to repeatedly predict assignments of a single cell division stage. After each hierarchy level prediction, we allow them to interactively adjust the machine learning based assignment, which we then integrate into the pool of verified assignments for further predictions. In addition to building the hierarchy this way in a bottom-up fashion, we also offer users to divide the whole embryo and create the hierarchy tree in a top-down fashion for a few steps, improving the ML-based assignments by reducing the potential for wrong predictions. We visualize the continuously updated embryo and its hierarchical development using both 3D spatial and abstract tree representations, together with information about the model's confidence and spatial properties. We conducted case study validations with five expert biologists to explore the utility of our approach and to assess the potential for LineageD to be used in their daily workflow. We found that the visualizations of both 3D representations and abstract representations help with decision making and the hierarchy tree top-down building approach can reduce assignments errors in real practice.
  • Item
    AirLens: Multi-Level Visual Exploration of Air Quality Evolution in Urban Agglomerations
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Qu, Dezhan; Lv, Cheng; Lin, Yiming; Zhang, Huijie; Wang, Rong; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    The precise prevention and control of air pollution is a great challenge faced by environmental experts in recent years. Understanding the air quality evolution in the urban agglomeration is important for coordinated control of air pollution. However, the complex pollutant interactions between different cities lead to the collaborative evolution of air quality. The existing statistical and machine learning methods cannot well support the comprehensive analysis of the dynamic air quality evolution. In this study, we propose AirLens, an interactive visual analytics system that can help domain experts explore and understand the air quality evolution in the urban agglomeration from multiple levels and multiple aspects. To facilitate the cognition of the complex multivariate spatiotemporal data, we first propose a multi-run clustering strategy with a novel glyph design for summarizing and understanding the typical pollutant patterns effectively. On this basis, the system supports the multi-level exploration of air quality evolution, namely, the overall level, stage level and detail level. Frequent pattern mining, city community extraction and useful filters are integrated into the system for discovering significant information comprehensively. The case study and positive feedback from domain experts demonstrate the effectiveness and usability of AirLens.
  • Item
    Urban Rhapsody: Large-scale Exploration of Urban Soundscapes
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Rulff, João; Miranda, Fabio; Hosseini, Maryam; Lage, Marcos; Cartwright, Mark; Dove, Graham; Bello, Juan; Silva, Claudio T.; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Noise is one of the primary quality-of-life issues in urban environments. In addition to annoyance, noise negatively impacts public health and educational performance. While low-cost sensors can be deployed to monitor ambient noise levels at high temporal resolutions, the amount of data they produce and the complexity of these data pose significant analytical challenges. One way to address these challenges is through machine listening techniques, which are used to extract features in attempts to classify the source of noise and understand temporal patterns of a city's noise situation. However, the overwhelming number of noise sources in the urban environment and the scarcity of labeled data makes it nearly impossible to create classification models with large enough vocabularies that capture the true dynamism of urban soundscapes. In this paper, we first identify a set of requirements in the yet unexplored domain of urban soundscape exploration. To satisfy the requirements and tackle the identified challenges, we propose Urban Rhapsody, a framework that combines state-of-the-art audio representation, machine learning and visual analytics to allow users to interactively create classification models, understand noise patterns of a city, and quickly retrieve and label audio excerpts in order to create a large high-precision annotated database of urban sound recordings. We demonstrate the tool's utility through case studies performed by domain experts using data generated over the five-year deployment of a one-of-a-kind sensor network in New York City.
  • Item
    Where did my Lines go? Visualizing Missing Data in Parallel Coordinates
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Bäuerle, Alex; Onzenoodt, Christian van; Kinderen, Simon der; Westberg, Jimmy Johansson; Jönsson, Daniel; Ropinski, Timo; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    We evaluate visualization concepts to represent missing values in parallel coordinates. We focus on the trade-off between the ability to perceive missing values and the concept's impact on common tasks. For this purpose, we identified three missing value representation concepts: removing line segments where values are missing, adding a separate, horizontal axis onto which missing values are projected, and using imputed values as a replacement for missing values. For the missing values axis and imputed values concepts, we additionally add downplay and highlight variations. We performed a crowd-sourced, quantitative user study with 732 participants comparing the concepts and their variations using five real-world datasets. Based on our findings, we provide suggestions regarding which visual encoding to employ depending on the task at focus.
  • Item
    Optimizing Grid Layouts for Level-of-Detail Exploration of Large Data Collections
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Frey, Steffen; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    This paper introduces an optimization approach for generating grid layouts from large data collections such that they are amenable to level-of-detail presentation and exploration. Classic (flat) grid layouts visually do not scale to large collections, yielding overwhelming numbers of tiny member representations. The proposed local search-based progressive optimization scheme generates hierarchical grids: leaves correspond to one grid cell and represent one member, while inner nodes cover a quadratic range of cells and convey an aggregate of contained members. The scheme is solely based on pairwise distances and jointly optimizes for homogeneity within inner nodes and across grid neighbors. The generated grids allow to present and flexibly explore the whole data collection with arbitrary local granularity. Diverse use cases featuring large data collections exemplify the application: stock market predictions from a Black-Scholes model, channel structures in soil from Markov chain Monte Carlo, and image collections with feature vectors from neural network classification models. The paper presents feedback by a domain scientist, compares against previous approaches, and demonstrates visual and computational scalability to a million members, surpassing classic grid layout techniques by orders of magnitude.
  • Item
    Six Methods for Transforming Layered Hypergraphs to Apply Layered Graph Layout Algorithms
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Bartolomeo, Sara Di; Pister, Alexis; Buono, Paolo; Plaisant, Catherine; Dunne, Cody; Fekete, Jean-Daniel; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Hypergraphs are a generalization of graphs in which edges (hyperedges) can connect more than two vertices-as opposed to ordinary graphs where edges involve only two vertices. Hypergraphs are a fairly common data structure but there is little consensus on how to visualize them. To optimize a hypergraph drawing for readability, we need a layout algorithm. Common graph layout algorithms only consider ordinary graphs and do not take hyperedges into account. We focus on layered hypergraphs, a particular class of hypergraphs that, like layered graphs, assigns every vertex to a layer, and the vertices in a layer are drawn aligned on a linear axis with the axes arranged in parallel. In this paper, we propose a general method to apply layered graph layout algorithms to layered hypergraphs. We introduce six different transformations for layered hypergraphs. The choice of transformation affects the subsequent graph layout algorithm in terms of computational performance and readability of the results. Thus, we perform a comparative evaluation of these transformations in terms of number of crossings, edge length, and impact on performance. We also provide two case studies showing how our transformations can be applied to real-life use cases.
  • Item
    Exploring Multivariate Event Sequences with an Interactive Similarity Builder
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Xu, Shaobin; Sun, Minghui; Zhang, Zhengtai; Xue, Hao; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Similarity-based exploration is an effective method in knowledge discovery. Faced with multivariate event sequence data (MVES), developing a satisfactory similarity measurement for a specific question is challenging because of the heterogeneity introduced by numerous attributes with different data formats, coupled with their associations. Additionally, the absence of effective validation feedback makes judging the goodness of a measurement scheme a time-consuming and error-prone procedure. To free analysts from tedious programming to concentrate on the exploration of MVES data, this paper introduces an interactive similarity builder, where analysts can use visual building blocks for assembling similarity measurements in a drag-and-drop and incremental fashion. Based on the builder, we further propose a visual analytics framework that provides multi-granularity visual validations for measurement schemes and supports a recursive workflow for refining the focus set. We illustrate the power of our prototype through a case study and a user study with real-world datasets. Results suggest that the system improves the efficiency of developing similarity measurements and the usefulness of exploring MVES data.
  • Item
    CorpusVis: Visual Analysis of Digital Sheet Music Collections
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Miller, Matthias; Rauscher, Julius; Keim, Daniel A.; El-Assady, Mennatallah; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Manually investigating sheet music collections is challenging for music analysts due to the magnitude and complexity of underlying features, structures, and contextual information. However, applying sophisticated algorithmic methods would require advanced technical expertise that analysts do not necessarily have. Bridging this gap, we contribute CorpusVis, an interactive visual workspace, enabling scalable and multi-faceted analysis. Our proposed visual analytics dashboard provides access to computational methods, generating varying perspectives on the same data. The proposed application uses metadata including composers, type, epoch, and low-level features, such as pitch, melody, and rhythm. To evaluate our approach, we conducted a pair-analytics study with nine participants. The qualitative results show that CorpusVis supports users in performing exploratory and confirmatory analysis, leading them to new insights and findings. In addition, based on three exemplary workflows, we demonstrate how to apply our approach to different tasks, such as exploring musical features or comparing composers.
  • Item
    LMFingerprints: Visual Explanations of Language Model Embedding Spaces through Layerwise Contextualization Scores
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Sevastjanova, Rita; Kalouli, Aikaterini-Lida; Beck, Christin; Hauptmann, Hanna; El-Assady, Mennatallah; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Language models, such as BERT, construct multiple, contextualized embeddings for each word occurrence in a corpus. Understanding how the contextualization propagates through the model's layers is crucial for deciding which layers to use for a specific analysis task. Currently, most embedding spaces are explained by probing classifiers; however, some findings remain inconclusive. In this paper, we present LMFingerprints, a novel scoring-based technique for the explanation of contextualized word embeddings. We introduce two categories of scoring functions, which measure (1) the degree of contextualization, i.e., the layerwise changes in the embedding vectors, and (2) the type of contextualization, i.e., the captured context information. We integrate these scores into an interactive explanation workspace. By combining visual and verbal elements, we provide an overview of contextualization in six popular transformer-based language models. We evaluate hypotheses from the domain of computational linguistics, and our results not only confirm findings from related work but also reveal new aspects about the information captured in the embedding spaces. For instance, we show that while numbers are poorly contextualized, stopwords have an unexpected high contextualization in the models' upper layers, where their neighborhoods shift from similar functionality tokens to tokens that contribute to the meaning of the surrounding sentences.
  • Item
    Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Wu, Mengxi; Chiang, Yi-Jen; Musco, Christopher; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Key time steps selection, i.e., selecting a subset of most representative time steps, is essential for effective and efficient scientific visualization of large time-varying volume data. In particular, as computer simulations continue to grow in size and complexity, they often generate output that exceeds both the available storage capacity and bandwidth for transferring results to storage, making it indispensable to save only a subset of time steps. At the same time, this subset must be chosen so that it is highly representative, to facilitate post-processing and reconstruction with high fidelity. The key time steps selection problem is especially challenging in the in situ setting, where we can only process data in one pass in an online streaming fashion, using a small amount of main memory and fast computation. In this paper, we formulate the problem as that of optimal piece-wise linear interpolation. We first apply a method from numerical linear algebra to compute linear interpolation solutions and their errors in an online streaming fashion. Using that method as a building block, we can obtain a global optimal solution for the piece-wise linear interpolation problem via a standard dynamic programming (DP) algorithm. However, this approach needs to process the time steps in multiple passes and is too slow for the in situ setting. To address this issue, we introduce a novel approximation algorithm, which processes time steps in one pass in an online streaming fashion, with very efficient computing time and main memory space both in theory and in practice. The algorithm is suitable for the in situ setting. Moreover, we prove that our algorithm, which is based on a greedy update rule, has strong theoretical guarantees on the approximation quality and the number of time steps stored. To the best of our knowledge, this is the first algorithm suitable for in situ key time steps selection with such theoretical guarantees, and is the main contribution of this paper. Experiments demonstrate the efficacy of our new techniques.
  • Item
    An Interactive Approach for Identifying Structure Definitions
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Mikula, Natalia; Dörffel, Tom; Baum, Daniel; Hege, Hans-Christian; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Our ability to grasp and understand complex phenomena is essentially based on recognizing structures and relating these to each other. For example, any meteorological description of a weather condition and explanation of its evolution recurs to meteorological structures, such as convection and circulation structures, cloud fields and rain fronts. All of these are spatiotemporal structures, defined by time-dependent patterns in the underlying fields. Typically, such a structure is defined by a verbal description that corresponds to the more or less uniform, often somewhat vague mental images of the experts. However, a precise, formal definition of the structures or, more generally, of the concepts is often desirable, e.g., to enable automated data analysis or the development of phenomenological models. Here, we present a systematic approach and an interactive tool to obtain formal definitions of spatiotemporal structures. The tool enables experts to evaluate and compare different structure definitions on the basis of data sets with time-dependent fields that contain the respective structure. Since structure definitions are typically parameterized, an essential part is to identify parameter ranges that lead to desired structures in all time steps. In addition, it is important to allow a quantitative assessment of the resulting structures simultaneously. We demonstrate the use of the tool by applying it to two meteorological examples: finding structure definitions for vortex cores and center lines of temporarily evolving tropical cyclones. Ideally, structure definitions should be objective and applicable to as many data sets as possible. However, finding such definitions, e.g., for the common atmospheric structures in meteorology, can only be a long-term goal. The proposed procedure, together with the presented tool, is just a first systematic approach aiming at facilitating this long and arduous way.
  • Item
    Level of Detail Exploration of Electronic Transition Ensembles using Hierarchical Clustering
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Sidwall Thygesen, Signe; Masood, Talha Bin; Linares, Mathieu; Natarajan, Vijay; Hotz, Ingrid; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    We present a pipeline for the interactive visual analysis and exploration of molecular electronic transition ensembles. Each ensemble member is specified by a molecular configuration, the charge transfer between two molecular states, and a set of physical properties. The pipeline is targeted towards theoretical chemists, supporting them in comparing and characterizing electronic transitions by combining automatic and interactive visual analysis. A quantitative feature vector characterizing the electron charge transfer serves as the basis for hierarchical clustering as well as for the visual representations. The interface for the visual exploration consists of four components. A dendrogram provides an overview of the ensemble. It is augmented with a level of detail glyph for each cluster. A scatterplot using dimensionality reduction provides a second visualization, highlighting ensemble outliers. Parallel coordinates show the correlation with physical parameters. A spatial representation of selected ensemble members supports an in-depth inspection of transitions in a form that is familiar to chemists. All views are linked and can be used to filter and select ensemble members. The usefulness of the pipeline is shown in three different case studies.
  • Item
    A Flip-book of Knot Diagrams for Visualizing Surfaces in 4-Space
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Liu, Huan; Zhang, Hui; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Just as 2D shadows of 3D curves lose structure where lines cross, 3D graphics projections of smooth 4D topological surfaces are interrupted where one surface intersects itself. They twist, turn, and fold back on themselves, leaving important but hidden features behind the surface sheets. In this paper, we propose a smart slicing tool that can read the 4D surface in its entropy map and suggest the optimal way to generate cross-sectional images - or ''slices'' - of the surface to visualize its underlying 4D structure. Our visualization thinks of a 4D-embedded surface as a collection of 3D curves stacked in time, very much like a flip-book animation, where successive terms in the sequence differ at most by a critical change. This novel method can generate topologically meaningful visualization to depict complex and unfamiliar 4D surfaces, with the minimum number of cross-sectional diagrams. Our approach has been successfully used to create flip-books of diagrams to visualize a range of known 4D surfaces. In this preliminary study, our results show that the new visualization and slicing tool can help the viewers to understand and describe the complex spatial relationships and overall structures of 4D surfaces.
  • Item
    LOOPS: LOcally Optimized Polygon Simplification
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Amiraghdam, Alireza; Diehl, Alexandra; Pajarola, Renato; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Displaying polygonal vector data is essential in various application scenarios such as geometry visualization, vector graphics rendering, CAD drawing and in particular geographic, or cartographic visualization. Dealing with static polygonal datasets that has a large scale and are highly detailed poses several challenges to the efficient and adaptive display of polygons in interactive geographic visualization applications. For linear vector data, only recently a GPU-based level-of-detail (LOD) polyline simplification and rendering approach has been presented which can perform locally-adaptive LOD visualization of large-scale line datasets interactively. However, locally optimized LOD simplification and interactive display of large-scale polygon data, consisting of filled vector line loops, remains still a challenge, specifically in 3D geographic visualizations where varying LOD over a scene is necessary. Our solution to this challenge is a novel technique for locally-optimized simplification and visualization of 2D polygons over a 3D terrain which features a parallelized point-inside-polygon testing mechanism. Our approach is capable of employing any simplification algorithm that sequentially removes vertices such as Douglas-Peucker and Wang-Müller. Moreover, we generalized our technique to also visualizing polylines in order to have a unified method for displaying both data types. The results and performance analysis show that our new algorithm can handle large datasets containing polygons composed of millions of segments in real time, and has a lower memory demand and higher performance in comparison to prior methods of line simplification and visualization.
  • Item
    SimilarityNet: A Deep Neural Network for Similarity Analysis Within Spatio-temporal Ensembles
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Huesmann, Karim; Linsen, Lars; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Latent feature spaces of deep neural networks are frequently used to effectively capture semantic characteristics of a given dataset. In the context of spatio-temporal ensemble data, the latent space represents a similarity space without the need of an explicit definition of a field similarity measure. Commonly, these networks are trained for specific data within a targeted application. We instead propose a general training strategy in conjunction with a deep neural network architecture, which is readily applicable to any spatio-temporal ensemble data without re-training. The latent-space visualization allows for a comprehensive visual analysis of patterns and temporal evolution within the ensemble. With the use of SimilarityNet, we are able to perform similarity analyses on large-scale spatio-temporal ensembles in less than a second on commodity consumer hardware. We qualitatively compare our results to visualizations with established field similarity measures to document the interpretability of our latent space visualizations and show that they are feasible for an in-depth basic understanding of the underlying temporal evolution of a given ensemble.
  • Item
    Branch Decomposition-Independent Edit Distances for Merge Trees
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Wetzels, Florian; Leitte, Heike; Garth, Christoph; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Edit distances between merge trees of scalar fields have many applications in scientific visualization, such as ensemble analysis, feature tracking or symmetry detection. In this paper, we propose branch mappings, a novel approach to the construction of edit mappings for merge trees. Classic edit mappings match nodes or edges of two trees onto each other, and therefore have to either rely on branch decompositions of both trees or have to use auxiliary node properties to determine a matching. In contrast, branch mappings employ branch properties instead of node similarity information, and are independent of predetermined branch decompositions. Especially for topological features, which are typically based on branch properties, this allows a more intuitive distance measure which is also less susceptible to instabilities from small-scale perturbations. For trees with O(n) nodes, we describe an O(n4) algorithm for computing optimal branch mappings, which is faster than the only other branch decomposition-independent method in the literature by more than a linear factor. Furthermore, we compare the results of our method on synthetic and real-world examples to demonstrate its practicality and utility.
  • Item
    Neural Flow Map Reconstruction
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Sahoo, Saroj; Lu, Yuzhe; Berger, Matthew; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    In this paper we present a reconstruction technique for the reduction of unsteady flow data based on neural representations of time-varying vector fields. Our approach is motivated by the large amount of data typically generated in numerical simulations, and in turn the types of data that domain scientists can generate in situ that are compact, yet useful, for post hoc analysis. One type of data commonly acquired during simulation are samples of the flow map, where a single sample is the result of integrating the underlying vector field for a specified time duration. In our work, we treat a collection of flow map samples for a single dataset as a meaningful, compact, and yet incomplete, representation of unsteady flow, and our central objective is to find a representation that enables us to best recover arbitrary flow map samples. To this end, we introduce a technique for learning implicit neural representations of time-varying vector fields that are specifically optimized to reproduce flow map samples sparsely covering the spatiotemporal domain of the data. We show that, despite aggressive data reduction, our optimization problem - learning a function-space neural network to reproduce flow map samples under a fixed integration scheme - leads to representations that demonstrate strong generalization, both in the field itself, and using the field to approximate the flow map. Through quantitative and qualitative analysis across different datasets we show that our approach is an improvement across a variety of data reduction methods, and across a variety of measures ranging from improved vector fields, flow maps, and features derived from the flow map.
  • Item
    Hybrid Touch/Tangible Spatial Selection in Augmented Reality
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Sereno, Mickael; Gosset, Stéphane; Besançon, Lonni; Isenberg, Tobias; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    We study tangible touch tablets combined with Augmented Reality Head-Mounted Displays (AR-HMDs) to perform spatial 3D selections. We are primarily interested in the exploration of 3D unstructured datasets such as cloud points or volumetric datasets. AR-HMDs immerse users by showing datasets stereoscopically, and tablets provide a set of 2D exploration tools. Because AR-HMDs merge the visualization, interaction, and the users' physical spaces, users can also use the tablets as tangible objects in their 3D space. Nonetheless, the tablets' touch displays provide their own visualization and interaction spaces, separated from those of the AR-HMD. This raises several research questions compared to traditional setups. In this paper, we theorize, discuss, and study different available mappings for manual spatial selections using a tangible tablet within an AR-HMD space. We then study the use of this tablet within a 3D AR environment, compared to its use with a 2D external screen.
  • Item
    DanmuVis: Visualizing Danmu Content Dynamics and Associated Viewer Behaviors in Online Videos
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Chen, Shuai; Li, Sihang; Li, Yanda; Zhu, Junlin; Long, Juanjuan; Chen, Siming; Zhang, Jiawan; Yuan, Xiaoru; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Danmu (Danmaku) is a unique social media service in online videos, especially popular in Japan and China, for viewers to write comments while watching videos. The danmu comments are overlaid on the video screen and synchronized to the associated video time, indicating viewers' thoughts of the video clip. This paper introduces an interactive visualization system to analyze danmu comments and associated viewer behaviors in a collection of videos and enable detailed exploration of one video on demand. The watching behaviors of viewers are identified by comparing video time and post time of viewers' danmu. The system supports analyzing danmu content and viewers' behaviors against both video time and post time to gain insights into viewers' online participation and perceived experience. Our evaluations, including usage scenarios and user interviews, demonstrate the effectiveness and usability of our system.
  • Item
    Exploring How Visualization Design and Situatedness Evoke Compassion in the Wild
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Morais, Luiz; Andrade, Nazareno; Sousa, Dandara; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    This work explores how the design and situatedness of data representations affect people's compassion with a case study concerning harassment episodes in a public place. Results contribute to advancing the understanding of how visualizations can evoke emotions and their impact on prosocial behaviors, such as helping people in need. Recent literature examined the effect of different on-screen data representations on emotion or prosociality, but little has been done concerning visualizations shown in a public place - especially a space contextually relevant to the data - or presented through unconventional media formats such as physical marks. We conducted two in-the-wild studies to investigate how different factors affect people's selfreported compassion and intention to donate. We compared three ways of presenting data about the harassment cases: (1) communicating data only verbally; (2) using a printed poster with aggregated information; and (3) using a physicalization with detailed information about each story. We found that the physicalization influenced people to donate more than only hearing about the data, but it is unclear if the same applied to the poster visualization. Also, passers-by reported a likely small increase in compassion when they saw the physicalization instead of the poster. We also examined the role of situatedness by showing the physicalization in a site that is not contextually relevant to the data. Our results suggest that people had a similar intention to donate and levels of compassion in both places. Those findings may indicate that using specific visualization designs to support campaigns about sensitive causes (e.g., sexual harassment) can increase the emotional response of passers-by and may motivate them to help, independently of where the data representation is shown. Finally, this work also informs on the strengths and weaknesses of using research in the wild to evaluate data visualizations in public spaces.
  • Item
    Mobile and Multimodal? A Comparative Evaluation of Interactive Workplaces for Visual Data Exploration
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) León, Gabriela Molina; Lischka, Michael; Luo, Wei; Breiter, Andreas; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Mobile devices are increasingly being used in the workplace. The combination of touch, pen, and speech interaction with mobile devices is considered particularly promising for a more natural experience. However, we do not yet know how everyday work with multimodal data visualizations on a mobile device differs from working in the standard WIMP workplace setup. To address this gap, we created a visualization system for social scientists, with a WIMP interface for desktop PCs, and a multimodal interface for tablets. The system provides visualizations to explore spatio-temporal data with consistent WIMP and multimodal interaction techniques. To investigate how the different combinations of devices and interaction modalities affect the performance and experience of domain experts in a work setting, we conducted an experiment with 16 social scientists where they carried out a series of tasks with both interfaces. Participants were significantly faster and slightly more accurate on the WIMP interface. They solved the tasks with different strategies according to the interaction modalities available. The pen was the most used and appreciated input modality. Most participants preferred the multimodal setup and could imagine using it at work. We present our findings, together with their implications for the interaction design of data visualizations.
  • Item
    Exploring Effects of Ecological Visual Analytics Interfaces on Experts' and Novices' Decision-Making Processes: A Case Study in Air Traffic Control
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Zohrevandi, Elmira; Westin, Carl A. L.; Vrotsou, Katerina; Lundberg, Jonas; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Operational demands in safety-critical systems impose a risk of failure to the operators especially during urgent situations. Operators of safety-critical systems learn to make decisions effectively throughout extensive training programs and many years of experience. In the domain of air traffic control, expensive training with high dropout rates calls for research to enhance novices' ability to detect and resolve conflicts in the airspace. While previous researchers have mostly focused on redesigning training instructions and programs, the current paper explores possible benefits of novel visual representations to improve novices' understanding of the situations as well as their decision-making process. We conduct an experimental evaluation study testing two ecological visual analytics interfaces, developed in a previous study, as support systems to facilitate novice decisionmaking. The main contribution of this paper is threefold. First, we describe the application of an ecological interface design approach to the development of two visual analytics interfaces. Second, we perform a human-in-the-loop experiment with fortyfive novices within a simplified air traffic control simulation environment. Third, by performing an expert-novice comparison we investigate the extent to which effects of the proposed interfaces can be attributed to the subjects' expertise. The results show that the proposed ecological visual analytics interfaces improved novices' understanding of the information about conflicts as well as their problem-solving performance. Further, the results show that the beneficial effects of the proposed interfaces were more attributable to the visual representations than the users' expertise.
  • Item
    A Typology of Guidance Tasks in Mixed-Initiative Visual Analytics Environments
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Pérez-Messina, Ignacio; Ceneda, Davide; El-Assady, Mennatallah; Miksch, Silvia; Sperrle, Fabian; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Guidance has been proposed as a conceptual framework to understand how mixed-initiative visual analytics approaches can actively support users as they solve analytical tasks. While user tasks received a fair share of attention, it is still not completely clear how they could be supported with guidance and how such support could influence the progress of the task itself. Our observation is that there is a research gap in understanding the effect of guidance on the analytical discourse, in particular, for the knowledge generation in mixed-initiative approaches. As a consequence, guidance in a visual analytics environment is usually indistinguishable from common visualization features, making user responses challenging to predict and measure. To address these issues, we take a system perspective to propose the notion of guidance tasks and we present it as a typology closely aligned to established user task typologies. We derived the proposed typology directly from a model of guidance in the knowledge generation process and illustrate its implications for guidance design. By discussing three case studies, we show how our typology can be applied to analyze existing guidance systems. We argue that without a clear consideration of the system perspective, the analysis of tasks in mixed-initiative approaches is incomplete. Finally, by analyzing matchings of user and guidance tasks, we describe how guidance tasks could either help the user conclude the analysis or change its course.
  • Item
    VIBE: A Design Space for VIsual Belief Elicitation in Data Journalism
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Mahajan, Shambhavi; Chen, Bonnie; Karduni, Alireza; Kim, Yea-Seul; Wall, Emily; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    The process of forming, expressing, and updating beliefs from data plays a critical role in data-driven decision making. Effectively eliciting those beliefs has potential for high impact across a broad set of applications, including increased engagement with data and visualizations, personalizing visualizations, and understanding users' visual reasoning processes, which can inform improved data analysis and decision making strategies (e.g., via bias mitigation). Recently, belief-driven visualizations have been used to elicit and visualize readers' beliefs in a visualization alongside data in narrative media and data journalism platforms such as the New York Times and FiveThirtyEight. However, there is little research on different aspects that constitute designing an effective belief-driven visualization. In this paper, we synthesize a design space for belief-driven visualizations based on formative and summative interviews with designers and visualization experts. The design space includes 7 main design considerations, beginning with an assumed data set, then structured according to: from who, why, when, what, and how the belief is elicited, and the possible feedback about the belief that may be provided to the visualization viewer. The design space covers considerations such as the type of data parameter with optional uncertainty being elicited, interaction techniques, and visual feedback, among others. Finally, we describe how more than 24 existing belief-driven visualizations from popular news media outlets span the design space and discuss trends and opportunities within this space.
  • Item
    A Process Model for Dashboard Onboarding
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Dhanoa, Vaishali; Walchshofer, Conny; Hinterreiter, Andreas; Stitz, Holger; Gröller, Eduard; Streit, Marc; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Dashboards are used ubiquitously to gain and present insights into data by means of interactive visualizations. To bridge the gap between non-expert dashboard users and potentially complex datasets and/or visualizations, a variety of onboarding strategies are employed, including videos, narration, and interactive tutorials. We propose a process model for dashboard onboarding that formalizes and unifies such diverse onboarding strategies. Our model introduces the onboarding loop alongside the dashboard usage loop. Unpacking the onboarding loop reveals how each onboarding strategy combines selected building blocks of the dashboard with an onboarding narrative. Specific means are applied to this narration sequence for onboarding, which results in onboarding artifacts that are presented to the user via an interface. We concretize these concepts by showing how our process model can be used to describe a selection of real-world onboarding examples. Finally, we discuss how our model can serve as an actionable blueprint for developing new onboarding systems.
  • Item
    A Grammar-Based Approach for Applying Visualization Taxonomies to Interaction Logs
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Gathani, Sneha; Monadjemi, Shayan; Ottley, Alvitta; Battle, Leilani; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Researchers collect large amounts of user interaction data with the goal of mapping user's workflows and behaviors to their high-level motivations, intuitions, and goals. Although the visual analytics community has proposed numerous taxonomies to facilitate this mapping process, no formal methods exist for systematically applying these existing theories to user interaction logs. This paper seeks to bridge the gap between visualization task taxonomies and interaction log data by making the taxonomies more actionable for interaction log analysis. To achieve this, we leverage structural parallels between how people express themselves through interactions and language by reformulating existing theories as regular grammars.We represent interactions as terminals within a regular grammar, similar to the role of individual words in a language, and patterns of interactions or non-terminals as regular expressions over these terminals to capture common language patterns. To demonstrate our approach, we generate regular grammars for seven existing visualization taxonomies and develop code to apply them to three public interaction log datasets. In analyzing these regular grammars, we find that the taxonomies at the low-level (i.e., terminals) show mixed results in expressing multiple interaction log datasets, and taxonomies at the high-level (i.e., regular expressions) have limited expressiveness, due to primarily two challenges: inconsistencies in interaction log dataset granularity and structure, and under-expressiveness of certain terminals. Based on our findings, we suggest new research directions for the visualization community to augment existing taxonomies, develop new ones, and build better interaction log recording processes to facilitate the data-driven development of user behavior taxonomies.
  • Item
    Misinformed by Visualization: What Do We Learn From Misinformative Visualizations?
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Lo, Leo Yu-Ho; Gupta, Ayush; Shigyo, Kento; Wu, Aoyu; Bertini, Enrico; Qu, Huamin; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Data visualization is powerful in persuading an audience. However, when it is done poorly or maliciously, a visualization may become misleading or even deceiving. Visualizations give further strength to the dissemination of misinformation on the Internet. The visualization research community has long been aware of visualizations that misinform the audience, mostly associated with the terms ''lie'' and ''deceptive.'' Still, these discussions have focused only on a handful of cases. To better understand the landscape of misleading visualizations, we open-coded over one thousand real-world visualizations that have been reported as misleading. From these examples, we discovered 74 types of issues and formed a taxonomy of misleading elements in visualizations. We found four directions that the research community can follow to widen the discussion on misleading visualizations: (1) informal fallacies in visualizations, (2) exploiting conventions and data literacy, (3) deceptive tricks in uncommon charts, and (4) understanding the designers' dilemma. This work lays the groundwork for these research directions, especially in understanding, detecting, and preventing them.
  • Item
    Investigating the Role and Interplay of Narrations and Animations in Data Videos
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Cheng, Hao; Wang, Junhong; Wang, Yun; Lee, Bongshin; Zhang, Haidong; Zhang, Dongmei; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Combining data visualizations, animations, and audio narrations, data videos can increase viewer engagement and effectively communicate data stories. Due to their increasing popularity, data videos have gained growing attention from the visualization research community. However, recent research on data videos has focused on animations, lacking an understanding of narrations. In this work, we study how data videos use narrations and animations to convey information effectively. We conduct a qualitative analysis on 426 clips with visualizations extracted from 60 data videos collected from a variety of media outlets, covering a diverse array of topics. We manually label 816 sentences with 1226 semantic labels and record the composition of 2553 animations through an open coding process. We also analyze how narrations and animations coordinate with each other by assigning links between semantic labels and animations. With 937 (76.4%) semantic labels and 2503 (98.0%) animations linked, we identify four types of narration-animation relationships in the collected clips. Drawing from the findings, we discuss study implications and future research opportunities of data videos.
  • Item
    Nested Papercrafts for Anatomical and Biological Edutainment
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Schindler, Marwin; Korpitsch, Thorsten; Raidou, Renata Georgia; Wu, Hsiang-Yun; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    In this paper, we present a new workflow for the computer-aided generation of physicalizations, addressing nested configurations in anatomical and biological structures. Physicalizations are an important component of anatomical and biological education and edutainment. However, existing approaches have mainly revolved around creating data sculptures through digital fabrication. Only a few recent works proposed computer-aided pipelines for generating sculptures, such as papercrafts, with affordable and readily available materials. Papercraft generation remains a challenging topic by itself. Yet, anatomical and biological applications pose additional challenges, such as reconstruction complexity and insufficiency to account for multiple, nested structures-often present in anatomical and biological structures. Our workflow comprises the following steps: (i) define the nested configuration of the model and detect its levels, (ii) calculate the viewpoint that provides optimal, unobstructed views on inner levels, (iii) perform cuts on the outer levels to reveal the inner ones based on the viewpoint selection, (iv) estimate the stability of the cut papercraft to ensure a reliable outcome, (v) generate textures at each level, as a smart visibility mechanism that provides additional information on the inner structures, and (vi) unfold each textured mesh guaranteeing reconstruction. Our novel approach exploits the interactivity of nested papercraft models for edutainment purposes.