Introduction
Information Visualization

John Alexis Guerra Gómez | john.guerra[at]gmail.com | @duto_guerra
https://johnguerra.co/lectures/information_visualization_spring2022/01_Introduction/

Partially based on slides from Tamara Munzner
and previous adaptations with Andy Reagan

What we are going to learn

Infovis definition
Why should we visualize?
What are insights?
Problem abstraction
Visualization tools landscape

Definitions

Defining Information Visualization (vis)

Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.

Why?

Have the human in the loop

Visualization is suitable when there is a need to augment human capabilities rather than replace people with computational decision-making methods.

Three requirements

Users

Data

Tasks

A good visualization enables users to complete tasks effectively on the data.

When not to use vis

Don’t need vis when fully automatic solution exists and is trusted

But...

Many analysis problems are ill-specified.

Don’t know exactly what questions to ask in advance

What vis allows for

Long-term use for end users (e.g., exploratory analysis of scientific data)
Presentation of known results
Stepping stone to better understanding of requirements before developing models
Helps developers of automatic solution refine/debug, determine parameters
Helps end users of automatic solutions verify, build trust

Why use an external representation?

Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.

Why use an external representation?

External representation: replace cognition with perception

[Cerebral: Visualizing Multiple Experimental Conditions on a Graph with Biological Context. Barsky, Munzner, Gardy, and Kincaid. IEEE TVCG (Proc. InfoVis) 14(6):1253-1260, 2008.]

Why use a computer in the loop?

Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.

Why have a computer in the loop?

Beyond human patience
Scale to large datasets
Support interactivity

Why depend on vision?

Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.

Why depend on vision?

Human visual system is high-bandwidth channel to brain

Overview possible due to background processing
Subjective experience of seeing everything simultaneously
Significant processing occurs in parallel and pre-attentively

Sound: lower bandwidth and different semantics

Overview not supported
Subjective experience of sequential stream

Touch/haptics: impoverished record/replay capacity
- Only very low-bandwidth communication thus far
Taste, smell: no viable record/replay devices

Why show data in detail?

Summaries lose information.

Confirm expected and find unexpected patterns.
Assess validity of statistical model.

Visualization Design Space

Idioms

Distinct approach to creating or manipulating visual representations

Think about...

In how many ways can you visualize the numbers 13 and 23?

Idiom design space

The design space of possible vis idioms is huge and includes the considerations of both how to create and how to interact with visual representations.

Idioms

How to draw it: visual encoding idiom

Many possibilities for how to create

How to manipulate it: interaction idiom

Even more possibilities
Make single idiom dynamic
Link multiple idioms together through interaction

Why focus on tasks and effectiveness?

Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.

Why focus on tasks and effectiveness?

Tasks serve as constraint on design (as do data)

Idioms do not serve all tasks equally!
Challenge: recast tasks from domain-specific vocabulary to abstract forms

Most possibilities ineffective

Validation is necessary, but tricky
Increases chance of finding good solutions if you understand full space of possibilities

What counts as effective?

Novel: enable entirely new kinds of analysis
Faster: speed up existing workflows

Resource limitations

Vis designers must take into account three very different kinds of resource limitations: those of computers, of humans, and of displays.

Computational limits

Processing time
System memory

Human limits

Human attention
Memory
Retention

Display limits

Pixels are precious resource, the most constrained resource
Information density: ratio of space used to encode information vs. unused white space

Trade-off between clutter and wasting space, find sweet spot between dense and sparse

Why should we visualize?

The purpose of visualization is insight, not pictures

Ben Shneiderman

The purpose of data science is insight, not (just) models

John Alexis Guerra Gómez

Anscombe's quartet

I		II		III		IV
x	y	x	y	x	y	x	y
10.0	8.04	10.0	9.14	10.0	7.46	8.0	6.58
8.0	6.95	8.0	8.14	8.0	6.77	8.0	5.76
13.0	7.58	13.0	8.74	13.0	12.74	8.0	7.71
9.0	8.81	9.0	8.77	9.0	7.11	8.0	8.84
11.0	8.33	11.0	9.26	11.0	7.81	8.0	8.47
14.0	9.96	14.0	8.10	14.0	8.84	8.0	7.04
6.0	7.24	6.0	6.13	6.0	6.08	8.0	5.25
4.0	4.26	4.0	3.10	4.0	5.39	19.0	12.50
12.0	10.84	12.0	9.13	12.0	8.15	8.0	5.56
7.0	4.82	7.0	7.26	7.0	6.42	8.0	7.91
5.0	5.68	5.0	4.74	5.0	5.73	8.0	6.89

Property	Value
Mean of x	9
Variance of x	11
Mean of y	7.50
Variance of y	4.125
Correlation between x and y	0.816
Linear regression	y = 3.00 + 0.500x
Coefficient of determination of the linear regression	0.67