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

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

Basic Visualization Model Data Visualization Visual Mapping Interaction

Visual Mapping 1.Map: data items  visual marks Visual marks: Points Lines Areas Volumes

Visual Mapping 1.Map: data items  visual marks 2.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

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

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

Ranking Visual Attributes 1.Position 2.Length 3.Angle, Slope 4.Area, Volume 5.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: 1.Position 2.Color, Shape 3.Length 4.Angle, slope 5.Area, volume (Mackinlay hypoth.)

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

Stacked Bar AK AL AR CA CO …

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

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

User Tasks 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, … Forms can do this Visualization can do this!

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

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

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

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

Glyphs Cars data

Scatter Plot Matrix All possible pairings Cars data

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