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Interactive Data Visualization

Introduction

http://romain.vuillemot.net/

Romain Vuillemot - romain.vuillemot@ec-lyon.fr

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MBTAViz

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WHY VISUALIZE?

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HOW TO COMMUNICATE THOSE TWO QUANTITIES?

🕑 5min

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INFOGRAPHICS

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SCIENTIFIC VISUALIZATION

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DATA VISUALIZATION

The use of computer-supported, interactive, visual representations of abstract data to amplify cognition.

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VISUAL ANALYTICS

The science of analytical reasoning facilitated by interactive visual interfaces.

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DESIGN CRITIQUE

Who is the audience?

What questions can be answered?

Is it efficient?

What are the limits?

How to improve it?

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DESIGN CRITIQUE

Who is the audience?

What questions can be answered?

Is it efficient?

What are the limits?

How to improve it?

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DESIGN CRITIQUE

Who is the audience?

What questions can be answered?

Is it efficient?

What are the limits?

How to improve it?

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DESIGN CRITIQUE

Who is the audience?

What questions can be answered?

Is it efficient?

What are the limits?

How to improve it?

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MACHINE LEARNING VISUALIZATION

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MACHINE LEARNING CONFIGURATION

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MACHINE LEARNING CONFIGURATION

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CHART JUNKS

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HUMAN PERCEPTION

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IS THERE A BLUE CIRCLE?

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IS THERE A RED SQUARE?

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FIND NUMBER 3

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FIND NUMBER 3

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PRE-ATTENTIVE PERCEPTION

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PRE-ATTENTIVE PERCEPTION ONLY WORK WHEN 1 DIFFERENCE

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IS THERE A BORDER?

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IS THERE A BORDER?

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OTHER PRE-ATTENTIVE FEATURES

-> pour encoder un signal (données importantes)

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VISUAL PROPERTIES RANKING

-> pour encoder une information (données)

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GESTALT PRINCIPLES

EMERGENCE

MULTI-STABILITY

INVARIANCE

GROUPING

CLOSURE

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GESTALT PRINCIPLES

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IMPLICATION FOR DATAVIZ

Use pre-attentive and gestalt properties to encode information

Also to facilitate visual seeking of a given element

Also make sure those properties do not encode information that does not exist (e.g. self-organizing graphs may visually group information that are not in the dataset)

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DATA TYPES AND MODELS

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DATA VISUALIZATION PIPELINE

Raw data

Processed data

Abstract visual form

Visual presentation

Visual mapping

Data transformations

View transformation

Physical presentation

Rendering

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DATA ITEMS AND ATTRIBUTES

ITEM

ROW

ATTRIBUTE

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DATA TYPES

"Visualization Analysis and Design. Tamara Munzner, with illustrations by Eamonn Maguire. A K Peters Visualization Series, CRC Press, 2014."

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DATA TYPES

"Visualization Analysis and Design. Tamara Munzner, with illustrations by Eamonn Maguire. A K Peters Visualization Series, CRC Press, 2014."

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DATA TYPES

"Visualization Analysis and Design. Tamara Munzner, with illustrations by Eamonn Maguire. A K Peters Visualization Series, CRC Press, 2014."

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DATA DISTRIBUTION

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VISUALIZATION MAPPING

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MARKS & VISUAL PROPERTIES

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DATA VISUALIZATION PIPELINE

Raw data

Processed data

Abstract visual form

Visual presentation

Visual mapping

Data transformations

View transformation

Physical presentation

Rendering

X

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Raw data

Processed data

Abstract visual form

Visual presentation

Visual mapping

Data transformations

View transformation

Physical presentation

Rendering

aggregation

selection

projection

filtrage

Filtrage temporel

Echantillonnage

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Raw data

Processed data

Abstract visual form

Visual presentation

Visual mapping

Data transformations

View transformation

Physical presentation

Rendering

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Raw data

Processed data

Abstract visual form

Visual presentation

Visual mapping

Data transformations

View transformation

Physical presentation

Rendering

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Raw data

Processed data

Abstract visual form

Visual presentation

Visual mapping

Data transformations

View transformation

Physical presentation

Rendering

A

A

B

B

A

A

B

B

A

A

B

C

C

(a)

(b)

(c)

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Raw data

Processed data

Abstract visual form

Visual presentation

Visual mapping

Data transformations

View transformation

Physical presentation

Rendering

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VISUAL MAPPING

Raw data

Processed data

Abstract visual form

Visual presentation

Visual mapping

Data transformations

View transformation

Physical presentation

Rendering

X

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MARKS & VISUAL PROPERTIES VARIATIONS

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DESIGN CRITIC

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“Discontinuity” introduced by rainbow color scaled

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DESIGN CRITIC

Who is the audience?

What questions can be answered?

Do you like it?

Limits?

How to improve it?

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STANDARD CHARTS

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LIST OF CHARTS TEMPLATES

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LIST OF CHARTS TEMPLATES

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LIST OF CHARTS TEMPLATES

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LIST OF CHARTS TEMPLATES

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BASIC DATAVIZ TEMPLATES

grid / isotype�[items]

stacked chart�(cf bar chart)

treemap�[hierarchy]

histogram�[bin(quantity) x qtity]

tree layout�[hierarchy]

bar chart�[category x quantity]

parallel coordinates�[category x cateogry x …]

line chart�[time x quantity]

slope graph�(cf line chart)

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Bar chart

Display multiple categories and associated quantities

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Histogram

Display a single quantity using a bar chart where categories are bins and quantities count over bins

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Scatterplot

Display 2 quantities (and even 2 more using size and color)

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Line chart

Display a temporal attribute over time

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Geo-map

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Pie chart

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Pie chart

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Parallel coordinates plot

Display multi-dimensional data, where each column is a (quantitative) dimension

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Treemap

Display proportions between categories; can also display hierarchical categories

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Treemap

Display proportions between categories; can also display hierarchical categories

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LIST OF CHARTS TEMPLATES

  • There is no universal list of charts
  • Only templates (abstract visual forms)
  • Then variations, combinations, etc
  • https://xeno.graphics/

bar chart, dot plot, node-link diagram, unit chart, star plot, pie chart, area chart, flow chart, bubble chart, calendar chart, waterfall chart, isotype, timeline, wordcloud, candelstik chart, violin chart, arc diagram, mosaic chart, time series, sparkline, dendogram, cartogram, grid map, stream graph, chropleth map, glyphs, treillis plot, small multiples, marginal plot, packed circle, radar chart, isochrone, map network, venn diagram, sankey diagram, gantt, chart cluster, map box, plot bullet plot pyramid chart, hive plot isopleth map, ..