Topics include

Information visualization techniques for

• graphs, trees, and other relational data
• high-dimensional data and dimensionality reduction
• multivariate data and heterogeneous data
• personal or social data (health, energy, finance, email, etc.)
• text and documents
• non-numeric data (categorical data, nominal data, etc.)
• non-expert audiences
• causality and uncertainty data
• time-series data
• any other non-spatial data
• spatial data that is visualized with a new spatial mapping
• streaming or time-varying data
• very large data sets (scalability)

Techniques for interacting with visualizations or supporting the data analysis process, including

• recordkeeping, sensemaking, and storytelling
• collaboration support, either co-located or distributed
• integration of visualization with other software tools
• post-WIMP interactions (pen, touch, speech, gestures, etc.)
• focus + context and overview + detail methods
• zooming, navigation, and distortion techniques
• brushing and linking
• coordinated multiple views
• data labeling, editing, and annotation

Integration of visualizations into the context of use, including

• visual design and aesthetics
• minimal attention contexts, e.g. ambient displays
• mobile and ubiquitous
• public environments

Information visualization fundamentals and methodologies

• visualization systems
• novel algorithms and mathematics
• taxonomies and models
• research methodology, discussions, and frameworks
• cognition and perception issues

Evaluation

• task and requirements analysis
• metrics and benchmarks
• qualitative and quantitative evaluation
• laboratory and field studies
• novel evaluation methods
• usability studies and focus groups
• case studies

Applied information visualization

• reports of information visualization in domains where it has impact
• using information visualization for education and teaching
• design studies