CS 7450 - Information Visualization

Schedule

Overview

Week	Dates	Research Topic	Coding Topic	HW Due
1	Jan 10, 12	Introduction	Vega-Lite 1
2	Jan 17, 19	Covid Visualizations	Vega-Lite 2
3	Jan 24, 26	Post-WIMP Visualization	D3: Intro and resources
4	Jan 31, Feb 2	Cognitive Bias	D3: HTML, CSS, SVG	HW1 - Vega-Lite
5	Feb 7, 9	Natural Language and Visualization	D3: JavaScript and Data Loading
6	Feb 14, 16	Trust in Visualization	D3: Selection and Data Binding
7	Feb 21, 23	Storytelling Tools and Techniques	D3: Chart Types and Scales	HW2 - Web Technologies
8	Feb 28, Mar 2	Visual Perception and Misinterpretation	D3: Interaction 1
9	Mar 7, 9	Creating Visualizations without Programming	D3: Interaction 2	HW3 - D3 Chart Types
10	Mar 14, 16	Visualization Deception and Misinformation	D3: Interaction 3
11	Mar 21, 23	No Class - Spring break	No Class - Spring break
12	Mar 28, 30	Automating Insights and Infographics	D3: Layouts
13	Apr 4, 6	Visualization & Accessibility	D3: Scrollytelling
14	Apr 11, 13	Immersive visualization	D3: Network diagrams
15	Apr 18, 20	Visualization for Helping to Understand ML	D3:
16	Apr 25	Recap & Review

Detail

Below are the lists of papers for each class' topic. Tuesdays will be a focus on recent infovis research topics. Thursdays will be a focus on learning to develop visualization applications through programming.

Jan. 17 -- Covid Visualizations

Reading

Y. Zhang, Y. Sun, L. Padilla, S. Barua, E. Bertini, and A. G. Parker, "Mapping the Landscape of COVID-19 Crisis Visualizations", CHI '21. (link)

Y. Zhang, Y. Sun, J. D. Gaggiano, N. Kumar, C. Andris, and A. G. Parker, "Visualization Design Practices in a Crisis: Behind the Scenes with COVID-19 Dashboard Creators," TVCG '23. (link)

J.D. Ericson, W.S. Albert, and J.-N. Duane, "Political affiliation moderates subjective interpretations of COVID-19 graphs", Big Data & Society, '22. (link)

L. Padilla R. Fygenson S.C. Castro, and E. Bertini, "Multiple Forecast Visualizations (MFVs): Trade-offs in Trust and Performance in Multiple COVID-19 Forecast Visualizations", TVCG '23.(link)

Jan. 24 -- Post-WIMP Visualization

Reading

R. Sadana and J. Stasko, "Designing Multiple Coordinated Visualizations for Tablets", CGF '16. (link)

T. Horak, S. Badam, N. Elmqvist, and R. Dachselt, "When David Meets Goliath: Combining Smartwatches With a Large Vertical Display for Visual Data Exploration", CHI '18. (link)

A. Srinivasan, B. Lee, and J. Stasko, "Interweaving Multimodal Interaction with Flexible Unit Visualizations for Data Exploration," TVCG 21.
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R. Langner, M. Satkowski, W. Büschel, Wand R. Dachselt, "MARVIS: Combining Mobile Devices and Augmented Reality for Visual Data Analysis", CHI '21. (link)

Jan. 31 -- Cognitive Bias

Reading

E. Dimara, S. Franconeri, C. Plaisant, A. Bezerianos, and P. Dragicevic, "A Task-based Taxonomy of Cognitive Biases for Information Visualization", TVCG '20. (link)

E. Dimara, A. Bezerianos, P. Dragicevic, "The Attraction Effect in Information Visualization", TVCG '17. (link)

E. Dimara, G. Bailly, A. Bezerianos, S. Franconeri, "Mitigating the Attraction Effect with Visualizations", TVCG '19. (link)

E. Wall, L.M. Blaha, L. Franklin, and A. Endert, "Warning, Bias May Occur: A Proposed Approach to Detecting Cognitive Bias in Interactive Visual Analytics", VAST '17. (link)

Feb. 7 -- Natural Language & Visualization

Reading

T. Gao, M. Dontcheva, E. Adar, Z. Liu, and K. G. Karahalios. "DataTone: Managing Ambiguity in Natural Language Interfaces for Data Visualization". UIST 15. (link)

V. Setlur, S. E. Battersby, M. Tory, R. Gossweiler, and A. X. Chang, "Eviza: A natural language interface for visual analysis." UIST 16. (link)

A. Narechania, A. Srinivasan, and J. Stasko, "NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries", TVCG 21. (link)

A. Srinivasan, N. Nyapathy, B. Lee, S. M. Drucker, and J. Stasko, "Collecting and Characterizing Natural Language Utterances for Specifying Data Visualizations", Proceedings of ACM CHI '21 (link)

Y. Luo, N. Tang, G. Li, et al. "Synthesizing Natural Language to Visualization (NL2VIS) Benchmarks from NL2SQL Benchmarks," SIGMOD 21. (link)

Feb. 14 -- Trust in Visualization

Reading

E. Mayr, N. Hynek, S. Salisu, and F. Windhager, "Trust in Information Visualization", TrustVis '19. (link)

H. Elhamdadi, A. Gaba, Y.-S. Kim, and C. Xiong, "How Do We Measure Trust in Visual Data Communication?", BELIV '22. (link)

E. Peck, S. Ayuso, and O. El-Etr. "Data is Personal: Attitudes and Perceptions of Data Visualization in Rural Pennsylvania", CHI '19. (link)

C. Lee, T. Yang, G. Inchoco, G. M. Jones, A. Satyanarayan, "Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online", CHI '21. (link)

Feb. 21 -- Storytelling Tools and Techniques

Reading

N. Sultanum, F. Chevalier, Z. Bylinskii, Z. Liu, "Leveraging Text-Chart Links to Support Authoring of Data-Driven Articles with VizFlow", CHI '21. (link)

J. Zhao, S. Xu, S. Chandrasegaran, C. Bryan, F. Du, A. Mishra, X. Qian, Y. Li, and K.-L. Ma, "ChartStory: Automated Partitioning, Layout, and Captioning of Charts into Comic-Style Narratives", TVCG '23. (link)

Y. Shi, T. Gao. X. Jiao, N. Cao, "Breaking the Fourth Wall of Data Stories through Interaction", TVCG '23. (link)

M. Shin, J. Kim, Y. Han, L. Xie, M. Whitelaw, B.C. Kwon, S. Ko, and N. Elmqvist, "Roslingifier: Semi-Automated Storytelling for Animated Scatterplots", TVCG '23. (link)

Feb. 28 -- Visual Perception and Misinterpretation

Reading

S. Haroz, R. Kosara, and S. Franconeri, "ISOTYPE Visualization: Working Memory, Performance, and Engagement with Pictographs", CHI '15. (link)

K. B. Schloss, C. C. Gramazio, A. T. Silverman, M. L. Parker, A. S. Wang, "Mapping Color to Meaning in Colormap Data Visualizations", TVCG '19. (link)

E. Holder and C. Xiong, "Dispersion vs Disparity: Hiding Variability Can Encourage Stereotyping When Visualizing Social Outcomes", TVCG '23. (link)

C. Xiong, C. Stokes, Y.-S. Kim, S. Franconeri, "Seeing What You Believe or Believing What You See? Belief Biases Correlation Estimation", TVCG '23. (link)

Mar. 7 -- Creating Visualizations without Programming

Reading

Z. Liu, J. Thompson, A. Wilson, M. Dontcheva, J. Delorey, S. Grigg, B. Kerr, and J. Stasko. "Data Illustrator: Augmenting vector design tools with lazy data binding for expressive visualization authoring." CHI '18. (link)

D. Ren, B. Lee, and M. Brehmer. "Charticulator: Interactive construction of bespoke chart layouts.", TVCG '19. (link)

C. Wang, Y. Feng, R. Bodik, I. Dillig, A. Cheung, A.J. Ko, "Falx: Synthesis-Powered Visualization Authoring", CHI '21. (link)

W. Cui, J. Wang; H. Huang; Y. Wang, C.-Y. Lin, H. Zhang; D. Zhang, "A Mixed-Initiative Approach to Reusing Infographic Charts", TVCG '22. (link)

Mar. 14 -- Visualization Deception and Misinformation

Reading

A. V. Pandey, K. Rall, M. L. Satterthwaite, O. Nov, and E. Bertini, "How Deceptive are Deceptive Visualizations?", CHI '15. (link)

H.-K. Kong, Z. Liu, and K. Karahalios, "Trust and Recall of Information across Varying Degrees of Title-Visualization Misalignment", CHI '19. (link)

A. Fan, Y. Ma, M. Mancenido, & R. Maciejewski, "Annotating Line Charts for Addressing Deception", CHI '22. (link)

A. Karduni, I. Cho, R. Wesslen, S. Santhanam, S. Volkova, D.L. Arendt, & W. Dou, "Vulnerable to misinformation? Verifi!", IUI '19. (link)

Mar. 28 -- Automating Insights and Infographics

Reading

Z. Cui, S. K. Badam, M. A. Yal c ¸ in, and N. Elmqvist. DataSite: "Proactive visual data exploration with computation of insight-based recommendations" Information Visualization '19.(link)

A. Srinivasan, S. M. Drucker, A. Endert, and J. Stasko, "Augmenting Visualizations with Interactive Data Facts to Facilitate Interpretation and Communication", TVCG '19. (link)

Y. Wang, Z. Sun, H. Zhang, W. Cui, K. Xu, X. Ma, and D. Zhang. "DataShot: Automatic generation of fact sheets from tabular data", TVCG '19. (link)

C. Qian, S. Sun, W. Cui, J.-G. Lou, H. Zhang, and D. Zhang. "Retrieve-Then-Adapt: Example-based automatic generation for proportion-related infographics", TVCG '20. (link)

D. Shi, X. Xu, F. Sun, Y. Shi, and N. Cao. "Calliope: Automatic visual data story generation from a spreadsheet", TVCG '20. (link)

Apr. 4 -- Visualization & Accessibility

Reading

F. Elavsky, C. Bennett, and D. Moritz. "How accessible is my visualization? Evaluating visualization accessibility with Chartability", CGF '22. (link)

J. Choi, S. Jung, D.G. Park, J. Choo, N. Elmqvist, "Visualizing for the Non-Visual: Enabling the Visually Impaired to Use Visualization", CGF '19. (link)

P. Chundury, B. Patnaik, Y. Reyazuddin, C. W. Tang, J. Lazar, N. Elmqvist, "Towards Understanding Sensory Substitution for Accessible Visualization: An Interview Study," TVCG '22. (link)

J. Zong, C. Lee, A. Lundgard, J. Jang, D. Hajas, and A. Satyanarayan, "Rich Screen Reader Experiences for Accessible Data Visualization," CGF '22. (link)

Apr. 11 -- Immersive Visualization

Reading

K. Marriott, J. Chen, M. Hlawatsch, T. Itoh, M.A. Nacenta,G. Reina, & W. Stuerzlinger, "Immersive analytics: Time to reconsider the value of 3d for information visualisation." In Immersive Analytics (link)

Y. Yang, T. Dwyer, K. Marriott, B. Jenny, S. Goodwin, "Tilt map: Interactive transitions between Choropleth map, Prism map and Bar chart in immersive environments." TVCG '21. (link)

B. Xu, S. Guo, E. Koh, J. Hoffswell, R. Rossi, F. Du, "ARShopping: In-Store Shopping Decision Support Through Augmented Reality and Immersive Visualization", VIS '22. (link)

A. Batch, A. Cunningham, M. Cordeil, N. Elmqvist, T. Dwyer, B.H. Thomas, & K. Marriott, "There is no spoon: Evaluating performance, space use, and presence with expert domain users in immersive analytics." TVCG '19.(link)

Apr. 18 -- Visualization for Helping to Understand ML

Reading

A. Endert, W. Ribarsky, C. Turkay, BW Wong, I. Nabney, I.D. Blanco, F. Rossi, "The state of the art in integrating machine learning into visual analytics, " CGF '17. (link)

D. Ren, S. Amershi, B. Lee, J. Suh, J.D. Williams, "Squares: Supporting interactive performance analysis for multiclass classifiers," TVCG '16. (link)

F. Hohman, A. Head, R. Caruana, R. DeLine, S.M. Drucker, "Gamut: A design probe to understand how data scientists understand machine learning models, " CHI '19. (link)

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