info vis: multi-dimensional data chris north cs3724: hci

Info Vis:Multi-Dimensional Data

Chris North

cs3724: HCI

Presentations

• jerome holman• john gibson

• Vote: UI Hall of Fame/Shame?

Quiz

• Why visualization?•

• Class motto:•

VisualizationDesign Principles

Increase Data Density• Calculate data/pixel

“A pixel is a terrible thing to waste.”

Eliminate “Chart Junk”

• How much “ink” is used for non-data?

• Reclaim empty space (% screen empty)

• Attempt simplicity(e.g. am I using 3djust for coolness?)

Information Visualization Mantra• Overview first, zoom and filter, then details on demand• Overview first, zoom and filter, then details on demand• Overview first, zoom and filter, then details on demand• Overview first, zoom and filter, then details on demand• Overview first, zoom and filter, then details on demand• Overview first, zoom and filter, then details on demand

InfoVis Design Principles

• Increase data density

• Eliminate “chart junk”• Mantra: Overview first, zoom&filter, details on demand

• Insight factor• Does the design reveal the data?

• Does the design help me explore, learn, understand?

• Show me the data!

Visualizing Multi-dimensional data

Multi-dimensional Data TableAttributes (aka: dimensions, fields, variables, columns, …)

Items

(aka: data points, records,tuples, rows, …)

Data Values

Data Types:•Quantitative•Ordinal•Categorical/Nominal

Basic Visualization Model

Data VisualizationVisual 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. Size

5. Color

Increased accuracy for quantitative data

-W.S. Cleveland

Color better for categorical data

-J. Mackinlay

Basic Charts…

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North

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1st Qtr 2nd Qtr 3rd Qtr 4th QtrEast

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Factors in Visualization Design

• User tasks

• Data

• Data scale:• # recs

• # attrs

• # possible data values

Data Scale

• # of attributes (dimensionality)

• # of items

• # of possible values (e.g. bits/value)

Spotfire

• Multiple views: brushing and linking

• Dynamic Queries

• Details window

TableLens (Eureka by Inxight)

• Visual encoding of cell values, sorting

• Details expand within context

Parallel Coordinates

• Bag cartesian orthogonal layout

• Parallel axes

• Data point = connected line segment

• (0, 1, -1, 2) =

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Parallel Coordinates (XmdvTool)

Parallel Coordinates

Info. Vis. Topics

• Information types:• Multi-dimensional: databases,…

• 1D, 2D, 3D

• Trees, Graphs

• Text, document collections

• Interaction strategies:• Overview+Detail

• Focus+Context

• Zooming

• How (not) to lie with visualization

Homework #2: Info. Vis. Tools

• Get some data:• Tabular, >=5 attributes (columns), >=500 items (rows)

• Use 2 visualization tools + Excel:• Spotfire, TableLens, Parallel Coordinates

• Mcbryde 104c

• 2 page report:• Discoveries in data

• Comparison of tools

• Due:• Feb 19: A-K

• Feb 21: L-Z

Project 2: Java

• 3 students per team

• Ambitious project

• 0: form team (feb 14)

• 1: design (feb 28)

• 2: initial implementation (mid march)

• 3: final implementation (end march)

Next

Presentations: proj1 design, UI critique

• Thurs: john randal, tom shultz

• Next Tues: mohamed hassoun, aaron dalton

• Next Thurs: nadine edwards, steve terhar