Data & Analytics Club - Data Visualization Workshop

Data Visualization Nikhil Srivastava, 2015

Nikhil Srivastava

Wharton Data & Analytics Club

hoster@wharton.upenn.edu

About this Lecture

• Shortened version of longer course

– Slides, demos, extra material

– Code samples and libraries

– Sample projects

• Questions

About You

• What is Data Visualization?

• Thinking and Seeing

• From Data to Graphics

• Principles and Guidelines

• Building Visualizations

• Advanced

introduction

foundation & theory

building blocks

design & critique

construction

Outline

• Advanced

introduction

foundation & theory

building blocks

design & critique

construction

Data Visualization

Information VisualizationScientific Visualization

Infographics

Statistical GraphicsInformative Art

ArtScience

Statistics

JournalismDesign

Visual Analytics

Business

City State Population Baton Rouge Louisiana 191,741 Birmingham Alabama 220,927 Broken Arrow Oklahoma 58,018 Eugene Oregon 115,890 Glendale Arizona 245,868 Huntsville Alabama 55,741 Lafayette Louisiana 87,737 Mobile Alabama 98,147 Montgomery Alabama 126,250 New Orleans Louisiana 322,172 Norman Oklahoma 101,590 Peoria Arizona 167,868 Portland Oregon 514,108 Salem Oregon 147,631 Scottsdale Arizona 134,335 Shreveport Louisiana 68,756 Surprise Arizona 90,548 Tempe Arizona 143,369 Tulsa Oklahoma 392,138

• Which is the most populous

city in the list?

• Which state in the list has

the most cities?

the largest average city?

City State Population Baton Rouge Louisiana 191,741 Birmingham Alabama 220,927 Broken Arrow Oklahoma 58,018 Eugene Oregon 115,890 Glendale Arizona 245,868 Huntsville Alabama 55,741 Lafayette Louisiana 87,737 Mobile Alabama 98,147 Montgomery Alabama 126,250 New Orleans Louisiana 322,172 Norman Oklahoma 101,590 Peoria Arizona 167,868 Portland Oregon 514,108 Salem Oregon 147,631 Scottsdale Arizona 134,335 Shreveport Louisiana 68,756 Surprise Arizona 90,548 Tempe Arizona 143,369 Tulsa Oklahoma 392,138

city in the list?

the most cities?

city in the list?

the most cities?

• What is the population of

Montgomery, Alabama?

Data Visualization is:• Useful

– Answers user questions

– Reduces user workload

(by design, not by default)

Anscombe’s quartet (1973)

Data Visualization is:• Useful

– Understand structure and patterns

– Resolve ambiguity

– Locate outliers

Data Visualization is:• Important

– Design decisions affect interpretation

Crimean War Deaths

Florence Nightingale, 1858 (re-colorized)

Gapminder Foundation

Data Visualization is:• Powerful

– Communicate, teach, inspire

purpose communicate explore, analyze

data type numerical,categorical

text, maps, graphs, networks

method staticrepresentation

animation,interactivity

Our Focus

• Advanced

introduction

foundation & theory

building blocks

design & critique

construction

The Hardware

The Software• High-level concepts: objects, symbols

• Involves working memory

• Slower, serial, conscious

• Sensory input

• Low-level features: orientation,

shape, color, movement

• Rapid, parallel, automatic

Visual Perception

“Bottom-up”

The Software• High-level concepts: objects, symbols

• Involves working memory

• Slow, sequential, conscious

• Sensory input

• Low-level features: orientation,

shape, color, movement

• Rapid, parallel, automatic

“Bottom-up”

“Top-down”

Visual Perception

Task: Counting

How many 3’s?

1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686

Task: Counting

How many 3’s?

1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686

Task: Counting

Slow, sequential, conscious

Rapid, parallel, automatic

1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686

Task: (Distracted) Search

Which side has the red circle?

Task: (Distracted) SearchSlow, sequential, conscious

Lessons for Visualization

• Use “pre-attentive” attributes when possible

– Color, shape, orientation (depth, motion)

– Faster, higher bandwidth

• Caveats

– Beware limits of working memory (<7)

– Be careful mixing attributes

Example: Inefficient Attributes

Example: Too Many Attributes

• Advanced

introduction

foundation & theory

building blocks

design & critique

construction

What kind of data do we

How can we represent the data

visually?

How can we organize this into a

visualization?

Visual Encoding

Data TypesCATEGORICAL ORDINAL NUMERICAL

Interval Ratio

Male / Female

Asia / Africa / Europe

True / False

Small / Med / Large

Low / High

Yes / Maybe / No

Latitude/Longitude

Compass direction

Time (event)

Length

Time (duration)

= = = =< > < > < >

- + -* /

Data TypesCATEGORICAL ORDINAL NUMERICAL

Interval Ratio

Male / Female

Asia / Africa / Europe

True / False

Small / Med / Large

Low / High

Yes / Maybe / No

Latitude/Longitude

Compass direction

Time (event)

Length

Time (duration)

Bin/Categorize

Difference/Normalize

Data Types (Advanced)

• Networks/Graphs

– Hierarchies/Trees

• Text

• Maps: points, regions, routes

What kind of data do we

How can we represent the data

visually?

How can we organize this into a

visualization?

Visual Encoding

Visual Encodings

volume

Channels

position

angle/tilt

Channel Effectiveness

“Spatial position is such a good visual

coding of data that the first decision of

visualization design is which variables get

spatial encoding at the expense of others”

What kind of data do we have?

How can we represent the data visually?

How can we organize this into a visualization?

Athi River Machakos 139,380

Awasi Kisumu 93,369

Kangundo-Tala Machakos 218,557

Karuri Kiambu 129,934

Kiambu Kiambu 88,869

Kikuyu Kiambu 233,231

Kisumu Kisumu 409,928

Kitale Trans-Nzoia 106,187

Kitui Kitui 155,896

Limuru Kiambu 104,282

Machakos Machakos 150,041

Molo Nakuru 107,806

Mwingi Kitui 83,803

Naivasha Nakuru 181,966

Nakuru Nakuru 307,990

Nandi Hills Trans-Nzoia 73,626

type mark channel data represented

Scatter Plot point position 2 quantitative

Scatter + Hue point position,color

2 quantitative, 1 categorical

Scatter + Size (“Bubble”)

point position,size

3 quantitative

Scatter Plot – Applications

RELATIONSHIP GROUPING OUTLIERS

Scatter Plot – Dangers

OCCLUSION (DENSITY)

OCCLUSION (OVERLAP)

Line Chart line position(orientation)

2 quantitative

Area Chart area size (length) 2 quantitative

Line Chart – Applications

PATTERN OVER TIME COMPARISON

Line Chart – Dangers

Y SCALING

X SCALING

OVERLOAD

Bar Chart line size (length) 1 categorical,1 quantitative

Histogram line size (length) 1 ordinal/quantitative,1 quantitative (count)

Bar Chart – Applications

COMPARE CATEGORIES DISTRIBUTION

Bar Chart – Dangers

TOO MANY CATEGORIES

POORLY SORTED CATEGORIES

ZERO AXIS

Pie Chart area size (angle) 1 quantitative

Pie Chart – Dangers

AREA/ANGLE SCALE SIMILAR AREAS OVERLOAD

Multi-Series: Bar

“GROUPED” BAR CHART

“STACKED” BAR CHART

Multi-Series: Line

MULTIPLE LINE

STACKED AREA CHART

Normalization

NORMALIZED BAR NORMALIZED AREA

• Advanced

introduction

foundation & theory

building blocks

design & critique

construction

From Science to Art

• Design principles*

• Style guidelines*

*dependent on context and objective (and author)

Design Principles

• Integrity

– Tell the truth with data

• Effectiveness

– Achieve visualization objectives

• Aesthetics

– Be compelling, vivid, beautiful

Integrity

Lie Ratio = size of effect in graphic

size of effect in data

Integrity

“show data variation, not design variation”

Effectiveness*

Data/Ink Ratio = ink representing data

total ink

*Tufte

Effectiveness* *Tufte

avoid “chart junk”

Avoid Chart Junk

Effectiveness (Few)

Practical Guidelines

• Avoid 3-D charts

• Focus on substance over graphics

• Avoid separate legends and keys

• Use faint grids/guidelines

• Avoid unnecessary textures and colors

A Note on Color

• To label

• To emphasize

• To liven or decorate

Color as a ChannelCategorical Quantitative

Hue Good (6-8 max)

Value Poor Good

Saturation Poor Okay

Bad Color

Good Color

More Color Guidelines

• Use color only when necessary

• Saturated colors for small areas, labels

• Less saturated colors for large areas,

backgrounds

• Use tools like ColorBrewer

• Advanced

introduction

foundation & theory

building blocks

design & critique

construction

What Tools to Use? Athi River Machakos 139,380

Awasi Kisumu 93,369

Kangundo-Tala Machakos 218,557

Karuri Kiambu 129,934

Kiambu Kiambu 88,869

Kikuyu Kiambu 233,231

Kisumu Kisumu 409,928

Kitale Trans-Nzoia 106,187

Kitui Kitui 155,896

Limuru Kiambu 104,282

Machakos Machakos 150,041

Molo Nakuru 107,806

Mwingi Kitui 83,803

Naivasha Nakuru 181,966

Nakuru Nakuru 307,990

Nandi Hills Trans-Nzoia 73,626

CleanRestructure

ExploreAnalyze

Visualization Goals

Visualization Tools

TableauPlotly

Python

Matlab

Ubiq/Silk

How hard is it to learn?

How powerful & flexible is it?

I’ll have to write code

Visualization Tools

TableauPlotly

Python

Matlab

Ubiq/Silk

How hard is it to learn?

How powerful & flexible is it?

Google Charts

Highcharts

I’ll have to write code

Cheat Sheets

• For Hackathon participants

• Otherwise, email me

• Advanced

introduction

foundation & theory

building blocks

design & critique

construction

Small Multiples

Treemap(Hierarchical Data)

Strengths: nested relationships

Concerns: order, aspect ratio

Multi-Level Pie Chart(Hierarchical Data)

Strengths: nested relationships

Concerns: readability

Heat Map

(Table/Field Data) Strengths: pattern/outlier detectionConcerns: ordering, clustering, color

Choropleth(Region Data)

Strengths: geography

Concerns: region sizecolor

Cartogram

(Region Data) Strengths: geographic patternConcerns: base map knowledge

The Ebb and Flow of Movies

NY Times, 2008

Streamgraph

“Data Visualization” Wikipedia PageWordle

Word Cloud

Twitter NetworksPJ Lamberson, 2012

Blogs/Reference

• Infosthetics.com

• Visualizing.org

• FlowingData.com

Nikhil Srivastava

nsri@wharton.upenn.edu

Data & Analytics Club - Data Visualization Workshop

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Transcript of Data & Analytics Club - Data Visualization Workshop

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