Specific topics include, but are not limited to:

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