Interaction and Information Design

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

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

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