DATA

  VISUALISATION

    SANDRAVIZ.COM | 2023

 

0 | DATAVIZ SKILLS

 

 

0 | DATAVIZ SKILLS

 

 

0 | DATAVIZ SKILLS

 

 

0 | DATAVIZ SKILLS

 

INTRO

B. SCHNEIDERMANN

H. ROSLING

H. THOREAU

 

01

 

A. CAIRO

E. TUFTE

WHAT IS DATA VIZ?

 

01 | THE OBJECTIVE

 

Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.

E. TUFTE

 

E.VELSON, 2011, PARA,. 6

 

01

 

 

01 BIG DATA ALLOWS FOR PATTERN DETECTION

 

 

01

 

 

01 | VISUAL ENCODINGS

 

 

01 | EXAMPLE

 

 

01 | OBSERVATION VS. PREDICTION PLOT

 

 

01 | OBSERVATION VS. PREDICTION PLOT

 

 

01 | VISUAL ENCODINGS

 

 

01 | VISUAL ANIMATION TO PRESENT SIMULATION

 

 

01 | SCROLL-Y-TELLING TO EXPLAIN MACHINE LEARNING

 

 

01 | ANIMATED VISUALIZATION AS THE FLOW OF THE STORY

 

 

01 | FINAL CONCLUSION

M. BOSTOCK

 

01

 

 

01 | COMPLEX CONCEPTS VISUALLY EXPLAINED

 

01 | STATISTICAL CONCEPTS EXPLAINED THROUGH INTERACTIIVE VIZ

 

 

 

 

 

01 | DATA ART

WIKIPEDIA

 

01 | CONSTELLATION MAPS

 

 

01 | VISUAL EONCODINGS

PRACTICE

DEFINE

OBJECTIVES

DATA

EXPLORE

 

02 | THE COMMON FLOW

 

M. STEFANER

DEFINE

OBJECTIVES

DATA

IDEA

RESULT

TOOL

EXPLORE

DESIGN

CREATE

REVIEW

 

02 | THE COMMON FLOW

 

 

02 | APPLIES TO A DATAVIZ PROJECT TOO

 

DATAVIZ TYPE

 

02 | DATAVIZ CATALOG

 

 

02 | 25 VISUALIZATIONS 1 DATASET

 

 

02 | EXAMPLES WITH CODE

 

THE SPAGHETTI PLOT

 

02 | STANDARD LINE CHARTS FOM THE 90s

 

 

02 | CHART EVALUATION

 

02 | LINE CHART WITH HIGHLIGHT OPTION

 

 

02 | CHART EVALUATION

 

 

02 | LINE CHART WITH FILTER OPTION

 

 

02 | CHART EVALUATION

 

02 | SMALL MULTIPLE AREA CHART

 

 

02 | SMALL MULTIPLE AREA CHART

 

 

02 | SMALL MULTIPLE PLUS FILTER & LINKED HIGHLIGHT OPTION

 

 

02 | SMALL MULTIPLE PLUS FILTER & LINKED HIGHLIGHT OPTION

 

 

02 | SMALL MULTIPLE PLUS FILTER & HIGHLIGHT OPTION

 

 

02 | SMALL MULTIPLE PLUS FILTER & HIGHLIGHT OPTION

 

DATAVIZ TOOLS

 

02 | ALL RESOURCES - TOOLS

 

 

02 | WEB BASED TOOLS FOR QUICK EXPLORATION

 

 

 

 

 

02 | VISUALIZATION FOR DATA SCIENTISTS

 

 

02 | INDUSTRY STANDARDS

 

 

02 | EXPLORATIVE ANALYSIS

 

 

02 | DYNAMIC VISUALIZATION FOR THE WEB

 

 

02 | LOCATION BASED DATA MAPPING

 

 

02 | WebGL

GOOD & BAD VIZ

 

02

 

 

02 | WHAT MAKES A GODO VIZ

 

 

02

 

 

02 | OVERPLOTTING PROBLEM

 

 

02 | SHOW THE DATA

 

 

02 | AWARD WINNERS

 

 

02 | TIPS & TRICKS

 

 

02 | GOOD PRACTICE

 

VIZ

 

02 | LEARNING FROM THE BAD

 

VISUAL ENCODING

 

03 | SEEING = UNDERSTANDING

 

 

 

 

03 | DIFFERENT TYPES OF VISUAL ENCODING

 

 

03 | DEFINITION

 

When a graph is constructed, quantitative and categorical information is encoded, chiefly through position, shape, size, symbols, and color.

When a person looks at a graph, the information is visually decoded by the person’s visual system. A graphical method is successful only if the decoding is effective.

No matter how clever and how technologically impressive the encoding, it fails if the decoding process fails.

Informed decisions about how to encode data can be achieved only through an understanding of this visual decoding process, which we call graphical perception.

CLEVELAND & MCGILL

 

03 | EXPLANATION

How do we make sure the audience is able to decode the information?

Legends (e.g. size bubble legend to endorse comparison)

Labels (if there is enough space you can add labels directly)

Keys (e.g. color scale: provide a key for each one)

 

 

COLOR

 

03 | COLOUR WHEEL

 

 

03 | RULES

 

 

03 | DON'T USE COLOURS TO SHOW INTENSITY

 

 

03 | SUPPORT TOOLS | COLOUR BREWER

 

 

 

03 | ADVANCED TOOLS

 

 

03 | ADVANCED TOOLS

 

 

03 | COLOR CHECK

 

 

03

 

ENVIRONMENT | NATURE | PERMISSION

 

03

 

 

03

 

SUN | HAPPINESS | PLAYFUL

 

03

 

 

03

 

DANGER | PASSION | BLOOD | LOVE | AGGRESSION

 

03

 

 

03

 

WATER | COOL | QUIETNESS | HOPE

 

03

 

 

03

 

DEATH | LUXURY | SOPHISTICATION

 

03

 

 

03

 

WEDDING | PURE | INNOCENT

 

03

 

 

03 | NEON COLOUR WITH DARK BACKGROUND

 

 

03 | ENDORSEMENT OF DIRECT COMPARISON

 

 

 

03 | OVERLAPPING PATTERN STRUCTURES

 

 

 

03 | PATTERN STRUCTURES THROUGH COLORS

 

 

 

03 | INTUITIVE CATEGORIZATION THROUGH COLORS

 

 

POSITION

 

03 | POSITIONING IN THE COORDINATE SYSTEM

 

 

 

 

 

03 | LEFT RIGHT RELATIONSHIP | COMPARISON

 

 

 

03 | ORDERING FROM TOP LEFT TO BUTTOM RIGHT

 

 

03 | USING COLORS TO INDICATE GOOD VS. BAD

 

 

REDUNDANCY

 

03 | LABELING & LEGEND ARE SHOWING THE SAME INFORMATION

 

 

 

 

 

03 | COLOR SUPPORTS THE POSITION ENCODING

 

 

 

 

HUMAN PERCEPTION

USERS ARE

HUMANS

 

04 | SAME MEAN, VARIANCE AND CORRELATION - SAME DATA?

 

 

 

04 | SCATTER PLOT SHOWS THE DIFFERENCES

 

 

 

04 | HOW MANY MORE POINTS ARE IN THE QUADRANT BELOW?

 

 

04 | HOW MANY MORE POINTS ARE IN THE QUADRANT BELOW?

 

 

04 | THE ABSOLUTE DIFFERENCE IS THE SAME

 

 

04 | WHICH BAR IS LARGER?

 

 

 

04 | WHICH BAR IS LARGER?

 

 

 

04 | IS THE COLOR OF THE QUADRANT BEHIND THE LETTER A & B THE SAME?

 

 

 

04 | IS THE COLOR OF THE QUADRANT BEHIND THE LETTER A & B THE SAME?

 

 

USERS

U X

 

04 WHO ARE THE USERS?

 

 

04 | AVOID CHARTJUNK

 

 

 

 

04 | PRESENTING TRUTH

 

 

 

04 | PRESENTING TRUTH

 

 

 

04 | SHOWING REAL PROPORTIONS

 

 

04 | HANDELING OUTLIERS

 

 

 

04 | HANDELING OUTLIERS THROUGH RE-SCALING

 

 

 

04 | GENERAL WAYS OF VISUALIZING OUTLIERS

 

 

VISUAL COMPLEXITY

Even publications, such as NY times assume that people are intelligent enough to read complex prose, but too stupid to read complex graphics.

E. TUFTE

 

04

 

04 | NUMBER OF VISUAL ENCODINGS

 

 

04 | LEVEL OF INNOVATION

 

 

 

04 | LEVEL OF INNOVATION

 

 

 

04 | GUIDANCE

 

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