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Info Vis: Multi-Dimensional Data

Info Vis: Multi-Dimensional Data. Chris North cs3724: HCI. 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, …).

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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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