CSc4730/6730 Scientific Visualization Lecture 26 Visualization Engines – Toward Automatic Visualization Design Ying Zhu Georgia State University

Motivation Make the construction of data visualization transparent to end users Given a data set, computer programs automatically generate data visualizations with minimal or no user input Users focus on problem solving rather than the technical details of creating data visualizations

Challenges Given a data set, there are many possible visualization configurations How to narrow down the possible chart types and visual variables? How to select the most fitting charts and visual variables for visualization?

General approach Specify a list of data types Specify a list of chart types and visual variables For each chart type, specify its visual elements (e.g., axes, visual variables, etc.) Create rules for matching data types to chart types Create a ranking system for different chart types with regards to different data types

General approach Specify rules for encoding data with visual variables Certain data types can only be mapped to certain visual variables Create a ranking system to select the most suitable visual variable for each data type Create rules for compositing multiple visual variables

General approach The process Collect data and tasks Select a chart type based on data, tasks, the rules and chart rankings For each data parameter, choose a visual variable based on data type, rules, and visual variable ranking Composite the visualization

Rules and rankings The most important part of a visualization engine is the rules and rankings Rules are created to put restrictions on the mapping between data types and visual elements Rankings are created to help select the most suitable visual element for a particular data type or task

Four examples Mackinlay, J. (1986). "Automating the Design of Graphical Presentations of Relational Information." ACM Transactions on Graphics 5(2): 110-141. Casner, S. M. (1991). "A task-analytic approach to the automated design of graphic presentation." ACM Transactions on Graphics 10(2): 111-151. Salisbury, L. D. P. (2001). Automatic Visual Display Design and Creation. PhD Thesis, Department of Department of Computer Science and Engineering, University of Washington. Mackinlay, J. D., P. Hanrahan, et al. (2007). "Show Me: Automatic Presentation for Visual Analysis." IEEE Transactions on Visualization and Computer Graphics 13(6): 1137-1144.

ADP by MacKinlay Mackinlay, J. (1986). "Automating the Design of Graphical Presentations of Relational Information." ACM Transactions on Graphics 5(2): 110-141.

Basic idea The fundamental assumption of the approach is that graphical presentations are sentences of graphical languages, which are similar to other formal languages in that they have precise syntactic and semantic definitions.

How to evaluate a visualization design? Expressiveness Encodes all the data in the set Encodes only the data in the set Effectiveness Based on Cleveland and McGill’s ranking system People interpret graphical presentation of quantitative information with different degrees of accuracy

Data types Quantitative Nominal Ordinal

Chart types, visual variables, etc.

Visual variables Position Length Angle Slope Area Color density Color hue, etc.

Cleveland and McGill’s ranking of visual variables

MacKinlay’s ranking of visual variables by data types

Rules for data encoding

Composition Principle of Composition: Compose two designs by merging parts that encode the same information.

Example

Example

Automated Design of Graphic Presentations Casner, S. M. (1991). "A task-analytic approach to the automated design of graphic presentation." ACM Transactions on Graphics 10(2): 111-151.

Basic idea Describe the task with Logical Task Description Language Much like a programming language Use logical operators to describe user’s tasks Convert the Logical Representation into Visual Representation Replace some logical operators with equivalent perceptual operators

Basic idea Use both the perceptual operator and data types as input to select chart types and visual variables The visual mapping part is largely based on MacKinlay’s method

Basic idea Casner’s main contribution is to introduce perceptual operators (tasks) as an input along with data types The perceptual tasks are extracted from an logical description of the user problem solving process

Logical Task Description

Logical Problem Description

Approach Replace logical operators with perceptual operators Consider each of the logical search and computation operators in the logical procedure Try to locate perceptual search and computation operators that give the user the same result

Visual variables

Perceptual operators (task)

Perceptual operators If more than one perceptual operator qualifies as a legal replacement for a given logical operator, then choose one based on a ranking system.

Ranking of perceptual operators

Rules for visual encoding

Rules for composition

Logical Problem Description

The perceptual description

Automatically generated visualization

Automated visualization design for urban planning Salisbury, L. D. P. (2001). Automatic Visual Display Design and Creation. PhD Thesis, Department of Computer Science and Engineering, University of Washington.

Basic approach Specify a list of cognitive components for user tasks Search, identify, compare, etc. Created a ranking system for chart types based on cognitive components of tasks Created a ranking system for visual variables Created rules for mapping data types to chart types and visual variables

Basic approach The process Collect data Users specify tasks The system selects a chart type based on data and user tasks The system maps data parameters to visual variables based on the rules and rankings

Basic approach Salisbury’s method is also based on MacKinlay’s Data types and visualization types are similar to MacKinlay’s The visual encoding rules are largely based on MacKinlay’s method The ranking of visual variables are largely based on MacKinlay’s

Basic approach Key differences: Introduced a more detailed task component classification Divide a task into cognitive components Created a chart type ranking based on two user studies Chart types are selected based on the cognitive components of a task

Chart types

Rules for chart configuration

Rules for encoding

Rules for matching data with visual variables

Create a chart ranking Create a chart ranking based on two user studies The first user study was designed to determine what types of visualizations urban planners are willing to use. The second user study was performed to find out the usefulness of the different visualization types and to verify the correctness and utility of our design approach and strategies.

Tasks

User study

User study

User study

User study

Tasks

Second user study

Basic process Break down the task into its constituent cognitive processing activities Choose the base visualization type Determine which encoding methods to use and how to map the data to the chosen encoding methods

Examples

Examples

“Show Me” in Tableau Mackinlay, J. D., P. Hanrahan, et al. (2007). "Show Me: Automatic Presentation for Visual Analysis." IEEE Transactions on Visualization and Computer Graphics 13(6): 1137-1144.

Show Me!

Data types Categorical (C) Quantitative Categorical date (Cdate) Quantitative dependent (measure) (Qd) Quantitative independent (dimension) (Qi)

Chart types Cross-table Bar chart Line chart Scatter plot Gantt chart

Visual variables Text Bar Line Shape Gantt Color

Rules for data encoding and chart selection

Ranking of charts Chart type Data elements Rank Text tables At least one field 1 (lowest) Aligned bars At least 1 Q 2 Stacked bars At least 2 C, at least 1 Q 3 Discrete lines At least 1 Cdate, at least 1 Q 4 Scatter plots Between 2 and 4 Q 5 Gantt charts At least 1 C, at least 1 Qi, 1 to 2 Q 6 (highest)

Small multiple views One of the uniqueness of Tableau’s “Show Me” feature is the ability to automatically generate multiple small views Previous automatic visualization generation tools only generate a single chart or a sequence of charts

Small multiple views

Rules for generating small multiple views Affinity The affinity heuristic supports the generation of effective small multiple displays by adding fields next to related fields.

Rules for generating small multiple views Rule for adding multiple categorical data The heuristic for adding categorical fields to views that have multiple fields is to consider the following shelves in order: Shape, Color, and Level of Detail. Rule for adding multiple quantitative data Combine measures together

Rules for generating small multiple views Rules for creating multiple views when user starts from empty view

Discussion MacKinlay’s work laid the foundation for most of the subsequent works on automatic visualization generation Focus on visualization expressiveness and perceptual efficiency Casner’s system considers tasks in addition to data types Still focused on perceptual efficiency

Discussion Salisbury’s program added a few new things Attempt to account for cognitive efficiency in addition to perceptual efficiency Conducted domain specific user studies to create a chart ranking system Use cognitive task efficiency to select chart types Visual mapping process is still largely based on MacKinlay’s framework

Discussion Tableau’s “Show Me” feature Introduced additional rules for creating small multiple views

Critique Lacks domain specific data classification Are the chart and visual variable rankings accurate? Not based on rigorous testing Still using Cleveland and McGill’s old experiments

Critique Assumption: if each visual variable is perceptually a good fit for the corresponding data variable, then the overall chart is effective. If the individual parts are good, the sum of them will be good too. Is this true?

Critique Charts and visual variables are selected on a individual basis Select the visual variable that best fits a particular data type Never evaluate the automatically generated chart as a whole

Is this a good visualization?

Is this a good visualization?

Is this a good visualization?

Is this a good visualization?

Is this a good visualization?

Critique No extensive user study to validate the effectiveness of these automatically generated visualizations

References Mackinlay, J. (1986). "Automating the Design of Graphical Presentations of Relational Information." ACM Transactions on Graphics 5(2): 110-141. Casner, S. M. (1991). "A task-analytic approach to the automated design of graphic presentation." ACM Transactions on Graphics 10(2): 111-151. Salisbury, L. D. P. (2001). Automatic Visual Display Design and Creation. PhD Thesis, Department of Computer Science and Engineering, University of Washington. Mackinlay, J. D., P. Hanrahan, et al. (2007). "Show Me: Automatic Presentation for Visual Analysis." IEEE Transactions on Visualization and Computer Graphics 13(6): 1137-1144.