CS-GY 6313: Information Visualization - Fall 2025 Detailed Schedule

Detailed Schedule

Week 1 (Sept 5) - Introduction and Evaluation

Learning Objectives: Understand what visualization is, when to use it, and how to evaluate effectiveness

Lecture: Week 1 - Course Introduction & Syllabus

• Course overview and expectations
• What is information visualization?
• Visualization taxonomy and design space
• Evaluation frameworks and criteria
• Introduction to tools landscape (Vega-Lite, D3, Tableau, etc.)

Required Readings:

• Chapter 1: Information Visualization, in Readings in Information Visualization. Card, Mackinlay, and Shneiderman. 1999.
• Introduction to Vega-Lite (Observable notebook)

Optional Reading:

• Decision to Launch the Challenger, in Visual Explanations. Edward Tufte.

Lab: Week 1 Lab - Introduction to Observable and Vega-Lite

• Setup Observable accounts
• Create first basic charts in Vega-Lite
• Explore provided datasets
• Chart gallery exploration

Assignment: Exercise 1 - Visualization critique and basic Vega-Lite charts (due Sept 11)

Week 2 (Sept 12) - Analytical Questions and Data Transformation

Learning Objectives: Transform questions into visual queries; understand data transformation pipelines

Lecture: Week 2 - Analytical Questions and Data Transformation

• From questions to visual mappings
• Data types and structures
• Data transformation operations (filter, aggregate, derive)
• Query-based visualization systems
• Introduction to Observable notebooks

Required Readings:

• Data Types, Graphical Marks, and Visual Encoding Channels (Observable)
• Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases. Stolte, Tang, and Hanrahan. IEEE TVCG 2002.

Optional Reading:

• The Eyes Have It: A Task by Data Type Taxonomy, Shneiderman. 1996.

Lab: Week 2 Lab - Intro to Vega-Lite Data Transformations, Working with Real Datasets

• Vega-Lite data transformations
• Working with real datasets in Observable
• Data aggregation and filtering
• Creating derived fields

Assignment: Exercise 2 - Data questions and transformations using Vega-Lite (due Sept 18)

Week 3 (Sept 19) - Fundamental Graphs and Visual Encoding

Learning Objectives: Master basic chart types and understand when to use each; apply grammar of graphics

Lecture:

• Chart types and their purposes
• Marks and channels theory
• Grammar of graphics principles
• Comparison strategies
• When to use different chart types

Materials:

• Fundamental Graphs and Visual Encoding

Required Readings:

• Chapter 1: Graphical Excellence, in The Visual Display of Quantitative Information. Tufte.
• Chapter 2: Graphical Integrity, in The Visual Display of Quantitative Information. Tufte.
• Multi-View Composition (Observable)

Optional Reading:

• Vega-Lite: A Grammar of Interactive Graphics. Wongsuphasawat et al. OpenVis Conf 2017.

Lab: Lab: Fundamental Graphs and Visual Encoding

• Creating multiple chart types in Vega-Lite
• Exploring encoding alternatives for same data
• Small multiples and faceting
• Combining multiple views

Assignment: Exercise 3 - Chart design and encoding alternatives (due Sept 25)

Week 4 (Sept 26) - Visual Perception and D3 Foundations

Learning Objectives: Understand human visual perception principles; begin D3 programming

Lecture:

• Pre-attentive processing and visual attention
• Gestalt principles in visualization
• Color perception and accessibility
• Introduction to D3.js: concepts and architecture
• DOM manipulation basics

Required Readings:

• The Science of Visual Data Communication: What Works. Franconeri et al. Psychological Science in the Public Interest. 2021.
• 39 Studies About Human Perception in 30 Minutes. Kennedy Elliott.

Optional Reading:

• Graphical Perception: Theory, Experimentation and Application. Cleveland & McGill. 1984.

Lab: Lab: First D3 programming session, DOM manipulation, Data binding

• First D3 programming session
• DOM manipulation exercises
• Data binding concepts
• Create simple bar chart in D3

Assignment: Exercise 4 - Perception-based design decisions + D3 implementation (due Oct 2)

Week 5 (Oct 3) - Color and D3 Scales

Learning Objectives: Master color theory for visualization; implement D3 scales and color schemes

Lecture:

• Color theory fundamentals
• Perceptual color spaces (RGB, HSL, LAB)
• Colorblindness and accessibility
• Color palette design strategies
• D3 scales: linear, ordinal, time, color

Required Readings:

• Which color scale to use when visualizing data. Lisa Charlotte Rost.
• Modeling Color Difference for Visualization Design. Danielle Szafir. IEEE TVCG, 2017.

Optional Readings:

• Somewhere Over the Rainbow: An Empirical Assessment of Quantitative Colormaps. Liu & Heer. ACM CHI 2018.
• Color Use Guidelines for Mapping and Visualization. Cynthia Brewer. 1994.

Lab: Color scale exercises, Choropleth maps, Accessibility testing

• D3 scales implementation
• Color scheme creation and testing
• Accessibility testing tools
• Apply color theory to previous D3 examples

Assignment: Exercise 5 - Color design with D3 scales (due Oct 9)

Week 6 (Oct 10) - Group Projects and Design Ethics

Learning Objectives: Understand group project requirements and milestones; identify misleading visualizations; recognize ethical design principles

Lecture: Week 6 - Group Projects

• Group project overview and timeline
• Five milestones: Proposal, Data & Sketches, First Draft, Second Draft, Final
• Team formation and collaboration strategies
• Choosing topics and datasets (focus on NYC urban data)
• Example projects and evaluation criteria
• Plus: Deceptive visualization and design ethics discussion

Required Readings:

• Chapter 2: Graphical Integrity, in The Visual Display of Quantitative Information. Edward Tufte. 2001.
• Misinformed by Visualization: What Do We Learn From Misinformative Visualizations?. Lo, Gupta & Shigyo. EuroVis 2022.

Optional Readings:

• Truncating the Y-Axis: Threat or Menace?. Correll, Bertini & Franconeri. 2020.
• Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online. Lee et al. ACM CHI 2021.

Lab: Lab: Intro to Interactions and Deceptive Visualizations

• Team formation activities
• NYC Open Data exploration
• Project brainstorming and proposal planning
• Start forming teams on Discord #project-teams

Assignment:

• Form teams by Oct 17
• Browse NYC Open Data for project ideas
• Exercise 6 - Design misleading vs. honest versions of same data (due Oct 16)

Fall Break (Oct 11-13) - NO CLASS

Week 7 (Oct 17) - Interaction in Visualization

Learning Objectives: Understand why interaction is essential for data exploration; master the 12 interactive dynamics; design effective interactive visualizations

Lecture: Week 7 - Interactivity in Information Visualization

• Why interaction matters: From presentation to exploration
• Bridging the gulfs (HCI concepts)
• Shneiderman’s Visual Information Seeking Mantra
• The 12 interactive dynamics (Heer & Shneiderman taxonomy):
  • Data & View Specification: Visualize, Filter, Sort, Derive
  • View Manipulation: Select, Navigate, Coordinate, Organize
  • Process & Provenance: Record, Annotate, Share, Guide
• Modern interaction frameworks (Libra)
• Case studies: FilmFinder, VisTrails, TaxiVis

Required Readings:

• The Eyes Have It: A Task by Data Type Taxonomy. Shneiderman. IEEE VIS 1996.
• Interactive Dynamics for Visual Analysis. Heer & Shneiderman. ACM Queue 2012.

Recommended Readings:

• Toward a Deeper Understanding of the Role of Interaction in Information Visualization. Yi et al. IEEE TVCG 2007.
• The Science of Interaction. Pike et al. Information Visualization 2009.
• Libra: Composable Interactions. Zhao et al. CHI 2025.

Lab: Lab: Building Interactive Visualizations

• D3 event handling (hover, click, brush)
• Implementing filtering and dynamic queries
• Tooltip and details-on-demand
• Brushing and linking across multiple views
• Creating coordinated visualizations

Assignment: Exercise 7 - Interactive visualization design and implementation (due Oct 23)

Week 8 (Oct 24) - Geographic and Urban Visualization I

Learning Objectives: Understand map projections and geographic data; create effective choropleth and point maps

Lecture:

• Map projections and their trade-offs
• Geographic data formats (GeoJSON, TopoJSON, Shapefiles)
• Choropleth map design principles
• Point mapping and density visualization
• Multi-scale geographic visualization

Required Readings:

• GeoLinter: A Linting Framework for Choropleth Maps. Lei, Fan, MacEachren & Maciejewski. IEEE TVCG 2023.
• Research Challenges in Geovisualization. MacEachren & Kraak. Cartography and Geographic Information Science 2001.

Optional Readings:

• When Maps Shouldn’t Be Maps. Matthew Ericson. 2011.
• Surprise! Bayesian Weighting for De-Biasing Thematic Maps. Correll & Heer. IEEE InfoVis 2017.

Lab:

• D3 geo projection setup
• Loading and displaying maps
• Creating choropleth maps with real data
• Point mapping exercises

Assignment: Mini-project 1 begins - Geographic visualization (due Nov 6)

Week 9 (Oct 31) - Temporal Data and Urban Dynamics

Learning Objectives: Design effective time series visualizations; understand temporal patterns in urban data

Lecture:

• Time series design principles
• Temporal data types and structures
• Animation vs. static temporal representation
• Seasonal and cyclical patterns
• Urban temporal dynamics

Required Readings:

• Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips. Ferreira, Poco, Vo, Freire & Silva. IEEE TVCG 2013.
• Urbane: A 3D Framework to Support Data Driven Decision Making in Urban Development. Ferreira et al. IEEE VIS 2015.

Optional Readings:

• Graphical Perception of Multiple Time Series. Heer, Kong & Agrawala. IEEE InfoVis 2009.
• The Connected Scatterplot for Presenting Paired Time Series. Haroz, Kosara & Franconeri. IEEE TVCG 2016.

Lab:

• D3 time scales and axes
• Line charts and area charts for time series
• Brushing and zooming for temporal data
• Small multiples for temporal comparison

Assignment: Mini-project 2 begins - Temporal visualization (due Nov 20)

Week 10 (Nov 7) - Uncertainty and Data Quality

Learning Objectives: Represent uncertainty visually; assess and communicate data quality issues

Lecture:

• Types of uncertainty in data
• Visual encoding of uncertainty
• Error bars and confidence intervals
• Alternative uncertainty representations
• Data quality assessment and communication

Required Readings:

• The Visual Uncertainty Experience. Jessica Hullman. OpenVis Conf 2016.
• Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error. Correll & Gleicher. IEEE InfoVis 2014.

Optional Reading:

• When(ish) is My Bus? User-centered Visualizations of Uncertainty. Kay et al. ACM CHI 2016.

Lab:

• Implementing uncertainty visualizations in D3
• Confidence intervals and error representations
• Alternative uncertainty encodings
• User testing uncertainty representations

Assignment: Continue Mini-project 2 + Exercise 8 - Uncertainty visualization (due Nov 13)

Week 11 (Nov 14) - Network Data and Urban Systems

Learning Objectives: Understand network visualization techniques; apply to urban infrastructure and social systems

Lecture:

• Network data structures and properties
• Node-link diagrams and layout algorithms
• Matrix representations of networks
• Hierarchical and multilevel networks
• Urban networks (transportation, social, infrastructure)

Required Readings:

• Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data. Danny Holten. InfoVis 2006.
• Squarified Treemaps. Bruls, Huizing & van Wijk. 2000.

Optional Reading:

• ManyNets: An Interface for Multiple Network Analysis and Visualization. Freire et al. ACM CHI 2010.

Lab:

• D3 force simulation setup
• Creating node-link diagrams
• Network layout algorithms
• Interactive network exploration

Assignment: Mini-project 3 begins - Network visualization (due Dec 4)

Week 12 (Nov 21) - Scalability and Performance

Learning Objectives: Handle large datasets; optimize visualization performance; understand progressive loading

Lecture:

• Challenges of large dataset visualization
• Data aggregation and sampling strategies
• Progressive loading and level-of-detail
• Performance optimization techniques
• Introduction to WebGL for visualization

Required Readings:

• A Structured Review of Data Management Technology for Interactive Visualization. Battle & Scheidegger. IEEE TVCG. 2020.
• Falcon: Balancing Interactive Latency and Resolution Sensitivity. Moritz, Howe & Heer. ACM CHI 2019.

Optional Readings:

• imMens: Real-time Visual Querying of Big Data. Liu, Jiang & Heer. EuroVis 2013.
• Trust, but Verify: Optimistic Visualizations of Approximate Queries. Moritz et al. ACM CHI 2017.

Lab:

• Data aggregation techniques
• Performance profiling and optimization
• Canvas vs SVG performance comparison
• Introduction to visualization with large datasets

Assignment: Continue Mini-project 3 + optimization exercises (due Nov 25)

Make-up Class (Nov 26 - Wednesday) - Geographic and Urban Visualization II

Learning Objectives: Advanced geographic techniques; intensive project development time

Extended Workshop (2.5 hours with breaks):

• Advanced D3 geo techniques
• Multi-scale mapping strategies
• Spatial analysis and visualization
• Working with urban datasets
• Intensive hands-on work on Mini-project 1
• Individual guidance and troubleshooting

Assignment: Complete any outstanding work on Mini-project 1

Week 13 (Nov 28) - NO CLASS (Thanksgiving)

Assignment: Complete Mini-project 3 and prepare group project presentations

Week 14 (Dec 5) - Advanced Topics and Project Presentations I

Learning Objectives: Explore emerging trends; present and critique visualization projects

Lecture (45 min):

• Emerging trends in visualization
• AI and machine learning visualization
• Virtual and augmented reality applications
• Future directions and career paths

Presentations:

• Group project presentations (first half of teams)
• Peer feedback and discussion
• Q&A and critique sessions

Assignment: Prepare final presentations and project documentation

Week 15 (Dec 12) - Project Presentations II and Course Wrap-up

Learning Objectives: Complete project presentations; reflect on learning; plan continued development

Presentations:

• Final group project presentations (second half)
• Peer feedback and evaluation
• Class discussion of projects and techniques

Wrap-up:

• Course reflection and key takeaways
• Resources for continued learning
• Career advice and next steps
• Course evaluations