Interaction and Information Design

There are no prerequisites.

Part 1: Introduction to Information Visualization and Interaction Design
• Definitions: DataVis, InfoVis, SciVis, Interaction Design
• Course roadmap, tools (D3, Vega-Lite, Unity, WebGL)
• Key concepts: interaction paradigms, storytelling, insight generation
Part 2: Perception, Cognition, and Data Structures in Visualization
• Visual perception, Gestalt laws, attention and memory
• Visual encoding basics: position, shape, size, color, motion
• Data types and structures: tabular, hierarchical, relational, spatial, temporal
Part 3: Visual Encoding and Design Principles
• Expressiveness and effectiveness
• Chart taxonomy: bar, line, area, pie, scatter, network, matrix
• Choosing visual encodings
• Task-based design: lookup, comparison, overview, filter, explore
Part 4: Multivariate and High-Dimensional Visualization
• Visualizing multivariate data: glyphs, scatterplot matrices, parallel coordinates
• Dimensionality reduction: PCA, t-SNE, UMAP
• Encoding multiple variables effectively
• Visual abstraction and reduction techniques
Part 5: Interaction Techniques in Visualization
• Interaction models: direct manipulation, brushing, linking, zooming, filtering
• State management and user feedback
• Dashboard composition and exploratory interfaces
Part 6: Uncertainty Visualization
• Types of uncertainty: data-level, model-level, perceptual
• Visual encoding of uncertainty: error bars, blur, animation
• Cognitive biases and visual trust
• Applications in AI and simulation
Part 7: Geospatial Visualization
• Coordinate systems, map projections, spatial joins
• Choropleths, dot maps, heatmaps, symbol maps
• Spatial-temporal data and dynamic rendering
Part 8: Temporal and Spatiotemporal Visualization
• Time series: line charts, small multiples, horizon graphs
• Calendars, event sequences, animations
• Combining spatial and temporal layers
Part 9: AR/VR for Data Visualization
• Principles of immersive visualization
• Head-mounted display (HMD) environments vs. handheld AR
• Spatial interaction and multi-modal input
• Case studies in scientific and urban-scale data
Part 10: Machine Learning and Explainable Visualization
• Model visualization (trees, layers, embeddings)
• XAI tools: SHAP, LIME, saliency maps
• Visual analytics for black-box models
• Ethics, bias, and decision support
Part 11: Real-Time Visualization and Visual analytics
• Progressive rendering, streaming data, sketch-based rendering
• Performance optimization in web contexts
• Principles of visual analytics: combining automated analysis with interactive visualization
• Sensemaking and decision-making based on visualization
Part 12: Storytelling with Data
• Narrative techniques in visualization
• Annotated charts, scrollytelling
• Case studies (NYT, Gapminder, Datawrapper)
Part 13: Collaborative Visualization and Multi-User Systems
• Synchronous and asynchronous collaboration
• Shared state, provenance, annotation
• Case studies: collaborative dashboards, citizen science, education
Part 14: Project Studio and Critique
• Design critiques: project iteration and peer feedback
• Evaluation frameworks for InfoVis: insight-based metrics, usability
• Wrap-up discussion: the future of interaction and visualization
Part 15: Final Project Presentations
• Final project demos and walkthroughs
• Peer + instructor feedback
• Submission of report and code/artifacts

At most of the lectures, last hour will be dedicated to presentation of state-of-the-art works from the corresponding topic.

• Knaflic, Cole Nussbaumer. Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons, 2015.
• Munzner, Tamara. Visualization analysis and design. CRC press, 2014.
• Ware, Colin. Information visualization: perception for design. Morgan Kaufmann, 2019.
• Kirk, Andy. "Data visualisation: A handbook for data driven design." (2019): 1-328.
• Tominski, Christian. Interaction for visualization. Morgan & Claypool Publishers, 2015.
• Knaflic, Cole Nussbaumer. Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons, 2015.
• Rosenberg, Daniel, and Anthony Grafton. Cartographies of time: A history of the timeline. Princeton Architectural Press, 2013.
• McCandless, David. Information is beautiful. London: Collins, 2012.
• Steele, Julie, and Noah Iliinsky. Beautiful visualization: Looking at data through the eyes of experts. " O'Reilly Media, Inc.", 2010.
Fry, Ben. Visualizing data: Exploring and explaining data with the processing environment. " O'Reilly Media, Inc.", 2007.

Objectives:
• Design and implement interactive data visualizations using appropriate visual encodings, layouts, and user interaction techniques.
• Analyze and evaluate data visualization systems with respect to usability, cognitive effectiveness, and visual perception.
• Integrate geospatial, multivariate, temporal, and uncertain data into coherent and effective visual representations.
• Develop immersive visualizations using AR/VR frameworks for spatial data exploration and interactive storytelling.

Competences:
• Understand the theoretical foundations of information visualization, including human perception, visual encoding, and interaction models.
• Learn to select appropriate visualization techniques based on data types, analysis goals, and user needs.
• Explore the role of interaction in supporting exploratory data analysis and storytelling across various domains.
• Critically assess the ethical, cognitive, and communicative dimensions of visual representations in real-world applications.

Students will be able to:
• Demonstrate a critical understanding of the theoretical foundations of information visualization, including perceptual and cognitive principles, visual encoding, and interaction models.
• Design and implement interactive visualization systems that effectively communicate patterns in complex data, including geospatial, temporal, multivariate, and uncertain datasets.
• Develop immersive and spatial visualizations using AR/VR technologies and 3D interaction techniques for real-time or context-aware data exploration.
• Select and justify appropriate visualization techniques and tools for specific data types, analytical goals, and user contexts within real-world applications.
• Communicate design decisions and technical solutions effectively through visual storytelling, documentation, and presentation of interactive prototypes.

Lectures using audio visual equipment. Laboratory work with special hardware and software tools. Individual and team assignments.Practical work and evaluation of products.

Continuing (homework, midterm exams, project work)
Final (written and oral exam)
grading: 5 (fail), 6-10 (pass) (according to the Statute of UL)

Izvirni zanstveni članki najtesneje povezani z vsebino predmeta:

[1] KHAN, Dawar, BOHAK, Ciril, VIOLA, Ivan. Dr. KID : direct remeshing and K-set isometric decomposition for scalable physicalization of organic shapes. IEEE transactions on visualization and computer graphics. Jan. 2024, vol. 30, iss. 1, str. 705-715, ilustr. ISSN 1077-2626. https://ieeexplore.ieee.org/document/10290929, Repozitorij Univerze v Ljubljani – RUL, DOI: 10.1109/TVCG.2023.3326595. [COBISS-SI-ID 173697795]
[2] REY RAMIREZ, Julio, RAUTEK, Peter, BOHAK, Ciril, STRNAD, Ondřej, ZHANG, Zheyuan, LI, Sai, VIOLA, Ivan, HEIDRICH, Wolfgang. GPU accelerated 3D tomographic reconstruction and visualization from noisy electron microscopy tilt-series. IEEE transactions on visualization and computer graphics. Jul. 2024, vol. 30, no. 7, str. 3331-3345, ilustr. ISSN 1077-2626. https://ieeexplore.ieee.org/document/9992117, DOI: 10.1109/TVCG.2022.3230445. [COBISS-SI-ID 135069955]
[3] LESAR, Žiga, ALHARBI, Ruwayda, BOHAK, Ciril, STRNAD, Ondřej, HEINZL, Christoph, MAROLT, Matija, VIOLA, Ivan. Volume conductor : interactive visibility management for crowded volumes. The visual computer. Feb. 2024, vol. 40, iss. 2, str. 1005-1020, ilustr. ISSN 0178-2789. https://link.springer.com/article/10.1007/s00371-023-02828-8, Repozitorij Univerze v Ljubljani – RUL, DOI: 10.1007/s00371-023-02828-8. [COBISS-SI-ID 147129603]
[4] ŠMAJDEK, Uroš, LESAR, Žiga, MAROLT, Matija, BOHAK, Ciril. Combined volume and surface rendering with global illumination caching. The visual computer. 2024, vol. 40, iss. 4, str. 2491-2503, ilustr. ISSN 0178-2789. https://link.springer.com/article/10.1007/s00371-023-02932-9, Repozitorij Univerze v Ljubljani – RUL, DOI: 10.1007/s00371-023-02932-9. [COBISS-SI-ID 157669379]
[5] ALHARBI, Ruwayda, STRNAD, Ondřej, LUIDOLT, Laura R., WALDNER, Manuela, KOUŘIL, David, BOHAK, Ciril, KLEIN, Tobias, GRÖLLER, Eduard, VIOLA, Ivan. Nanotilus : generator of immersive guided-tours in crowded 3D environments. IEEE transactions on visualization and computer graphics. Mar. 2023, vol. 29, iss. 3, str. 1860-1875, ilustr. ISSN 1077-2626. https://ieeexplore.ieee.org/document/9645360, DOI: 10.1109/TVCG.2021.3133592. [COBISS-SI-ID 92052739]
[6] NGUYEN, Ngan, BOHAK, Ciril, ENGEL, Dominik, MINDEK, Peter, STRNAD, Ondřej, WONKA, Peter, LI, Sai, ROPINSKI, Timo, VIOLA, Ivan. Finding Nano-Ötzi : cryo-electron tomography visualization guided by learned segmentation. IEEE transactions on visualization and computer graphics. Oct. 2023, vol. 29, no. 10, str. 4198-4214, ilustr. ISSN 1077-2626. https://ieeexplore.ieee.org/document/9806341, DOI: 10.1109/TVCG.2022.3186146. [COBISS-SI-ID 112947459]
[7] KORDEŽ, Jaka, MAROLT, Matija, BOHAK, Ciril. Real-time interpolated rendering of terrain point cloud data. Sensors. Jan. 2023, vol. 23, iss. 1, str. 1-17, ilustr. ISSN 1424-8220. https://www.mdpi.com/1424-8220/23/1/72, DOI: 10.3390/s23010072. [COBISS-SI-ID 135043331]
[8] BOHAK, Ciril, SLEMENIK, Matej, KORDEŽ, Jaka, MAROLT, Matija. Aerial LiDAR data augmentation for direct point-cloud visualisation. Sensors. Apr. 2020, vol. 20, no. 7, str. 1-17, ilustr. ISSN 1424-8220. https://www.mdpi.com/1424-8220/20/7/2089/html, Repozitorij Univerze v Ljubljani – RUL, DOI: 10.3390/s20072089. [COBISS-SI-ID 1538566595]

Celotna bibliografija je dostopna na SICRISu:
https://bib.cobiss.net/biblioweb/biblio/si/slv/cris/30062