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Info Vis: Multi-Dimensional Data

This overview explores key concepts in multi-dimensional data visualization, particularly in the context of Human-Computer Interaction (HCI). It delves into attributes like dimensions, fields, data types (quantitative, ordinal, categorical), and the mapping of data items to visual marks (points, lines, areas). The discussion includes design guidelines for effective visualization, emphasizing the importance of mapping data attributes to visual attributes for accurate representation. Examples from tools like Spotfire illustrate various visualization methods, showcasing how users can interact with complex datasets.

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Info Vis: Multi-Dimensional Data

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  1. Info Vis:Multi-Dimensional Data Chris North cs3724: HCI

  2. Multi-dimensional Data Table Attributes (aka: dimensions, fields, variables, columns, …) • Data Values • Data Types: • Quantitative • Ordinal • Categorical/Nominal Items (aka: data points, records,tuples, rows, …)

  3. Basic Visualization Model Data Visual Mapping Visualization Interaction

  4. Visual Mapping • Map: data items  visual marks • Visual marks: • Points • Lines • Areas • Volumes

  5. Visual Mapping • Map: data items  visual marks • Map: data item attributes  visual mark attributes • Visual mark attributes: • Position, x, y • Size, length, area, volume • Orientation, angle, slope • Color, gray scale, texture • Shape

  6. Example • Hard drives for sale: • price ($), capacity (MB), quality rating (1-5) p c

  7. Example: Spotfire • Film database • Year  X • Length  Y • Popularity  size • Subject  color • Award?  shape

  8. Ranking Visual Attributes • Position • Length • Angle, Slope • Area, Volume • Color Design guideline: • Map more important data attrs to more accurate visual attrs(based on user task) Increased accuracy for quantitative data (Cleveland and McGill) • Categorical data: • Position • Color, Shape • Length • Angle, slope • Area, volume • (Mackinlay hypoth.)

  9. Pie vs. Bar • Clevelands rules: bar better • Bar scales better

  10. Stacked Bar AK AL AR CA CO …

  11. Visualization Design Process Primary factors: • Data: • Information type • Scale • Semantics • Users • Tasks • Expertise • Characteristics • Bag of tricks: • Mappings • Interaction strategies Design Visualization

  12. Data Scale • # of attributes (dimensionality) • # of items • # of possible values (e.g. bits/value)

  13. User Tasks Forms can do this • Easy stuff: • Reduce to only 1 data item or value • Stats: Min, max, average, % • Search: known item • Hard stuff: • Require seeing the whole • Patterns: distributions, trends, frequencies, structures • Outliers: exceptions • Relationships: correlations, multi-way interactions • Tradeoffs: combined min/max • Comparisons: choices (1:1), context (1:M), sets (M:M) • Clusters: groups, similarities • Validity: data errors • Paths: distances, ancestors, decompositions, … Visualization can do this!

  14. Spotfire • Mapping data to graphics (x, y, size, color, shape…) • Multiple views: brushing and linking • Dynamic Queries • Details window Cars data

  15. TableLens (Eureka by Inxight) • Visual encoding of cell values • Details expand within context (fisheye) • Sorting Cars data

  16. Parallel Coordinates • Bag cartesian orthogonal layout • Parallel axes • Data point = connected line segment • (0, 1, -1, 2) = x y z w 0 0 0 0

  17. Parallel Coordinates (XmdvTool) • Re-order axes • Highlight lines • Query regions Cars data

  18. Glyphs Cars data

  19. Scatter Plot Matrix • All possible pairings Cars data

  20. Comparison • Spotfire: • <5 attributes in plot, infinite with DQ • <10K items • Familiar, low learning time • Plot good at 2D Correlation tasks • Some tradeoff between attrs and items • TableLens: • <20 attribs • <1000 items, aggregation enables more items • Overview of all attribs, 1:M attrib correlations • Familiar layout • Parallel coords: • <10 attrs • <500 items • Overview, Correlate adjacent axes • High learn time, unfamiliar

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