Download - Data Visualization Summary iHub
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Data Visualization Nikhil Srivastava, 2015
Nikhil Srivastava
iHub Summer Data Jam
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Data Visualization Nikhil Srivastava, 2015
About this Lecture
• Shortened version of longer course
• Course website
– Slides, demos, extra material
– Code samples and libraries
– Final projects
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Data Visualization Nikhil Srivastava, 2015
Effective Data Visualization
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Data Visualization Nikhil Srivastava, 2015
Nikhil Srivastava
0713 987 262
I build products & businesses in the fields of finance & technology.
I organize & visualize information for teaching & understanding.
nikhilsrivastava.com
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Data Visualization Nikhil Srivastava, 2015
• What is Data Visualization?
• Thinking and Seeing
• From Data to Graphics
• Principles and Guidelines
• Building Visualizations
• Advanced Topics
introduction
foundation & theory
building blocks
design & critique
construction
advanced
Outline
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Data Visualization Nikhil Srivastava, 2015
• What is Data Visualization?
• Thinking and Seeing
• From Data to Graphics
• Principles and Guidelines
• Building Visualizations
• Advanced Topics
introduction
foundation & theory
building blocks
design & critique
construction
advanced
![Page 7: Data Visualization Summary iHub](https://reader035.vdocument.in/reader035/viewer/2022062313/55d0b6e5bb61eb93558b4595/html5/thumbnails/7.jpg)
Data Visualization Nikhil Srivastava, 2015
Data Visualization
Information Visualization
Scientific Visualization Infographics
Statistical GraphicsInformative Art
ArtScience
Statistics
JournalismDesign
Visual Analytics
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Data Visualization Nikhil Srivastava, 2015
City/Town County Population Ahero Kisumu 76,828 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 Ruiru Kiambu 238,858 Thika Kiambu 139,853
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Data Visualization Nikhil Srivastava, 2015
City/Town County Population Ahero Kisumu 76,828 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 Ruiru Kiambu 238,858 Thika Kiambu 139,853
• Which is the most populous
city in the list?
• Which county in the list has
the most cities?
• Which county in the list has
the largest average city?
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Data Visualization Nikhil Srivastava, 2015
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Data Visualization Nikhil Srivastava, 2015
• Which is the most populous
city in the list?
• Which county in the list has
the most cities?
• Which county in the list has
the largest average city?
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Data Visualization Nikhil Srivastava, 2015
• Which is the most populous
city in the list?
• Which county in the list has
the most cities?
• Which county in the list has
the largest average city?
• What is the population of
Limuru?
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Data Visualization Nikhil Srivastava, 2015
• Which is the most populous
city in the list?
• Which county in the list has
the most cities?
• Which county in the list has
the largest average city?
Data Visualization is:
• Useful
– Answers user questions
– Reduces user workload
(by design, not by default)
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Data Visualization Nikhil Srivastava, 2015
Anscombe’s quartet (1973)
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Data Visualization Nikhil Srivastava, 2015
Anscombe’s quartet (1973)
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Data Visualization Nikhil Srivastava, 2015
Data Visualization is:
• Important
– Understand structure and patterns
– Resolve ambiguity
– Locate outliers
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Data Visualization Nikhil Srivastava, 2015
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Data Visualization Nikhil Srivastava, 2015
Data Visualization is:
• Important
– Design decisions affect interpretation
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Data Visualization Nikhil Srivastava, 2015
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Data Visualization Nikhil Srivastava, 2015
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Data Visualization Nikhil Srivastava, 2015
Data Visualization is:
• Powerful
– Communicate, teach, inspire
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Data Visualization Nikhil Srivastava, 2015
Data Visualization is:
• Relevant
– In one second …
– Open data, open technologies
– Growing use in business,
education, media, advertising …
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Data Visualization Nikhil Srivastava, 2015
Definitions
• “the process that transforms (abstract) data into
interactive graphical representations” 1
• “finding the artificial memory that best supports
our natural means of perception” 2
• “visual representations of data to amplify
cognition” 3
• “giving information a visual representation” 4
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Data Visualization Nikhil Srivastava, 2015
Focus Extra
purpose communicate explore, analyze
data numerical,categorical
text, maps, graphs, networks
feature representation animation,Interactivity
Course Scope
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Data Visualization Nikhil Srivastava, 2015
• What is Data Visualization?
• Thinking and Seeing
• From Data to Graphics
• Principles and Guidelines
• Building Visualizations
• Advanced Topics
introduction
foundation & theory
building blocks
design & critique
construction
advanced
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Data Visualization Nikhil Srivastava, 2015
Bandwidth of Our Senses
Why Vision?
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Data Visualization Nikhil Srivastava, 2015
The Hardware
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Data Visualization Nikhil Srivastava, 2015
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”
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Data Visualization Nikhil Srivastava, 2015
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
Visual Perception
“Bottom-up”
“Top-down”
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Data Visualization Nikhil Srivastava, 2015
Task: Counting
How many 3’s?
1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686
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Data Visualization Nikhil Srivastava, 2015
Task: Counting
How many 3’s?
1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686
1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686
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Data Visualization Nikhil Srivastava, 2015
Task: Counting
Slow, sequential, conscious
Rapid, parallel, automatic
1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686
1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686
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Data Visualization Nikhil Srivastava, 2015
Task: (Distractor) Search
Which side has the red circle?
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Data Visualization Nikhil Srivastava, 2015
Task: (Distractor) Search
Which side has the red circle?
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Data Visualization Nikhil Srivastava, 2015
Task: Search
Which side has the red circle?
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Data Visualization Nikhil Srivastava, 2015
Task: Search
Which side has the red circle?
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Data Visualization Nikhil Srivastava, 2015
Task: Search
Slow, sequential, conscious
Rapid, parallel, automatic
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Data Visualization Nikhil Srivastava, 2015
Task: Unique SearchSlow, sequential, conscious
Rapid, parallel, automatic
(7)
(5)
(3)
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Data Visualization Nikhil Srivastava, 2015
Lessons for Visualization
• Use “pre-attentive” attributes when possible
– Color, shape, orientation (depth, motion)
– Faster, higher bandwidth
• Caveats
– Working memory: magical number 7 (+/- 2)
– Be careful mixing attributes
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Data Visualization Nikhil Srivastava, 2015
Example: Too Many Attributes
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Data Visualization Nikhil Srivastava, 2015
Example: Too Many Attributes
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Data Visualization Nikhil Srivastava, 2015
Eye != Camera
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Data Visualization Nikhil Srivastava, 2015
Eye != Camera
limited aperture
limited color
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Data Visualization Nikhil Srivastava, 2015
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Data Visualization Nikhil Srivastava, 2015
Eye != Camera
Saccades: limited time and location
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Data Visualization Nikhil Srivastava, 2015
Eye != Camera: Relative
A
B
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Data Visualization Nikhil Srivastava, 2015
Eye != Camera: Relative
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Data Visualization Nikhil Srivastava, 2015
Eye != Camera: Knowledge
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Data Visualization Nikhil Srivastava, 2015
Eye != Camera: Knowledge
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Data Visualization Nikhil Srivastava, 2015
Lessons for Visualization
• Human vision has limits and constraints:
aperture, color, time, location
• “What we see” depends on “what we
know”
• Attention and experience matters
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Data Visualization Nikhil Srivastava, 2015
• What is Data Visualization?
• Thinking and Seeing
• From Data to Graphics
• Principles and Guidelines
• Building Visualizations
• Advanced Topics
introduction
foundation & theory
building blocks
design & critique
construction
advanced
![Page 53: Data Visualization Summary iHub](https://reader035.vdocument.in/reader035/viewer/2022062313/55d0b6e5bb61eb93558b4595/html5/thumbnails/53.jpg)
Data Visualization Nikhil Srivastava, 2015
From Data to Graphics
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
Visual Encoding
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Data Visualization Nikhil Srivastava, 2015
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
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Data Visualization Nikhil Srivastava, 2015
Data as Input
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
DATA
Visualization Goals
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Data Visualization Nikhil Srivastava, 2015
Model and Attribute
item attribute A attribute B … attribute M
item 1 value1_A value1_B …
item 2 value2_A value2_B …
… … …
item N valueN_M
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Data Visualization Nikhil Srivastava, 2015
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
Count
Time (duration)
= = = =
< > < > < >
+ - + -
* /
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Data Visualization Nikhil Srivastava, 2015
Data Types: Example
• Which are categorical? (=)
• Which are ordinal? (= < >)
ID Gender Test Score Grade Size Temperature
1 Male 77 C Small 36.5
2 Female 85 B Large 37.2
3 Female 95 A Medium 36.7
4 Male 90 A Large 37.4
• Which are interval? (= < > + -)
• Which are ratio? (= < > + - * /)
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Data Visualization Nikhil Srivastava, 2015
Data Type TransformationCATEGORICAL ORDINAL NUMERICAL
Interval Ratio
Male / Female
Asia / Africa / Europe
True / False
Small / Med / Large
Low / High
Yes / Maybe / No
Time
Latitude/Longitude
Compass direction
Time
Length
Count
Binning/Categorizing
Differencing/Normalization
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Data Visualization Nikhil Srivastava, 2015
Advanced Data Types
• Networks/Graphs
– Hierarchies/Trees
• Text
• Maps: points, regions, routes
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Data Visualization Nikhil Srivastava, 2015
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
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Data Visualization Nikhil Srivastava, 2015
Visual Encodings
Marks
point
line
area
volume
Channels
position
size
shape
color
angle/tilt
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Data Visualization Nikhil Srivastava, 2015
Channel Effectiveness
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Data Visualization Nikhil Srivastava, 2015
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”
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Data Visualization Nikhil Srivastava, 2015
Color as a Channel
Categorical Quantitative
Hue Good (6-8 max)
Poor
Value Poor Good
Saturation Poor Okay
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Data Visualization Nikhil Srivastava, 2015
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
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Data Visualization Nikhil Srivastava, 2015
type mark channel data represented
Scatter Plot point position 2 quantitative
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Data Visualization Nikhil Srivastava, 2015
type mark channel data represented
Scatter + Hue point position,color
2 quantitative, 1 categorical
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Data Visualization Nikhil Srivastava, 2015
type mark channel data represented
Scatter + Size (“Bubble”)
point position,size
3 quantitative
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Data Visualization Nikhil Srivastava, 2015
Scatter Plot – Applications
CORRELATION GROUPING OUTLIERS
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Data Visualization Nikhil Srivastava, 2015
Scatter Plot – Dangers
OCCLUSION (DENSITY)
OCCLUSION (OVERLAP)
3-D
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Data Visualization Nikhil Srivastava, 2015
type mark channel data represented
Line Chart line position(orientation)
2 quantitative
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Data Visualization Nikhil Srivastava, 2015
type mark channel data represented
Area Chart area size (length) 2 quantitative
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Data Visualization Nikhil Srivastava, 2015
Line Chart – Applications
PATTERN OVER TIME COMPARISON
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Data Visualization Nikhil Srivastava, 2015
Line Chart – Dangers
Y SCALING
X SCALING
OVERLOAD
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Data Visualization Nikhil Srivastava, 2015
type mark channel data represented
Bar Chart line size (length) 1 categorical,1 quantitative
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Data Visualization Nikhil Srivastava, 2015
type mark channel data represented
Histogram line size (length) 1 ordinal/quantitative,1 quantitative (count)
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Data Visualization Nikhil Srivastava, 2015
Bar Chart – Applications
COMPARE CATEGORIES DISTRIBUTION
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Data Visualization Nikhil Srivastava, 2015
Bar Chart – Dangers
TOO MANY CATEGORIES
POORLY SORTED
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Data Visualization Nikhil Srivastava, 2015
type mark channel data represented
Pie Chart area size (angle) 1 quantitative
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Data Visualization Nikhil Srivastava, 2015
Pie Chart – Dangers
AREA SCALE SIMILAR AREAS OVERLOAD
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Data Visualization Nikhil Srivastava, 2015
Multi-Series Bar Charts
GROUPED BAR CHART
STACKED BAR CHART
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Data Visualization Nikhil Srivastava, 2015
Multi-Series Line Charts
MULTIPLE LINE
STACKED AREA CHART
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Data Visualization Nikhil Srivastava, 2015
Normalization
NORMALIZED BAR NORMALIZED AREA
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Data Visualization Nikhil Srivastava, 2015
Small Multiples Chart
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Data Visualization Nikhil Srivastava, 2015
Advanced Charts
Treemap (Hierarchical Data)
Channels: ?
Strengths:
nested relationships
Concerns:
order vs aspect ratio
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Data Visualization Nikhil Srivastava, 2015
Advanced Charts
Multi-Level Pie(Hierarchical Data)
Channels: ?
Strengths:
nested relationships
Concerns:
readability
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Data Visualization Nikhil Srivastava, 2015
Advanced Charts
Heat Map(Table/Field Data)
Channels: ?
Strengths: pattern/outlier detection
Concerns: ordering/ clustering
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Data Visualization Nikhil Srivastava, 2015
Advanced Charts
Choropleth Map(Region Data)
Channels: ?
Strengths:
geography
Concerns:
region size
color spectrum
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Data Visualization Nikhil Srivastava, 2015
Advanced Charts
Cartogram(Region Data)
Channels: ?
Strengths: geographic pattern
Concerns: base map knowledge
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Data Visualization Nikhil Srivastava, 2015
• What is Data Visualization?
• Thinking and Seeing
• From Data to Graphics
• Principles and Guidelines
• Building Visualizations
• Advanced Topics
introduction
foundation & theory
building blocks
design & critique
construction
advanced
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Data Visualization Nikhil Srivastava, 2015
From Science to Art
• Design principles*
• Style guidelines*
*dependent on visualization context
and objective (and author)
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Data Visualization Nikhil Srivastava, 2015
Design Principles
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Data Visualization Nikhil Srivastava, 2015
Design Principles
• Integrity
– Tell the truth with data
• Effectiveness
– Achieve visualization objectives
• Aesthetics
– Be compelling, vivid, beautiful
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Data Visualization Nikhil Srivastava, 2015
Integrity
Lie Ratio = size of effect in graphic
size of effect in data
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Data Visualization Nikhil Srivastava, 2015
Integrity
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Data Visualization Nikhil Srivastava, 2015
Integrity
“show data variation, not design variation”
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Data Visualization Nikhil Srivastava, 2015
Effectiveness*
Data/Ink Ratio = ink representing data
total ink
*according to Tufte
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Data Visualization Nikhil Srivastava, 2015
Effectiveness* *according to Tufte
avoid “chart junk”
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Data Visualization Nikhil Srivastava, 2015
Avoid Chart Junk
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Data Visualization Nikhil Srivastava, 2015
Avoid Chart Junk
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Data Visualization Nikhil Srivastava, 2015
Avoid Chart Junk
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Data Visualization Nikhil Srivastava, 2015
Avoid Chart Junk
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Data Visualization Nikhil Srivastava, 2015
Avoid Chart Junk
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Data Visualization Nikhil Srivastava, 2015
Avoid Chart Junk
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Data Visualization Nikhil Srivastava, 2015
Effectiveness (Few)
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Data Visualization Nikhil Srivastava, 2015
Practical Guidelines
• Avoid 3-D charts
• Focus on substance over graphics
• Avoid separate legends and keys
• Faint grids/guidelines
• Avoid unnecessary textures and colors
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Data Visualization Nikhil Srivastava, 2015
Color Guidelines
• To label
• To emphasize
• To liven or decorate
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Data Visualization Nikhil Srivastava, 2015
Bad Color
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Data Visualization Nikhil Srivastava, 2015
Good Color
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Data Visualization Nikhil Srivastava, 2015
More Color Guidelines
• Use color only when necessary
• Use saturated colors for data labels, thin
lines, small areas
• Use less saturated colors for large areas,
backgrounds
• Use tools like ColorBrewer
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Data Visualization Nikhil Srivastava, 2015
• What is Data Visualization?
• Thinking and Seeing
• From Data to Graphics
• Principles and Guidelines
• Building Visualizations
• Advanced Topics
introduction
foundation & theory
building blocks
design & critique
construction
advanced
![Page 113: Data Visualization Summary iHub](https://reader035.vdocument.in/reader035/viewer/2022062313/55d0b6e5bb61eb93558b4595/html5/thumbnails/113.jpg)
Data Visualization Nikhil Srivastava, 2015
What Software 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
DATA
Visualization Goals
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Data Visualization Nikhil Srivastava, 2015
Visualization Software
• Web friendly
– Highcharts
– InfoVis
– Processing
– D3
• Statistics
– Python (Matplotlib)
– R (ggplot2)
• Maps
– Google Charts
– Leaflet
– CartoDB
• Dashboards
• Graphs
– GraphViz
– Gephi
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Data Visualization Nikhil Srivastava, 2015
Highcharts - Reference
• Examples
– Hello, Chart
– Basic Charts
• Documentation, API
• Highcharts Cloud
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Data Visualization Nikhil Srivastava, 2015
• What is Data Visualization?
• Thinking and Seeing
• From Data to Graphics
• Principles and Guidelines
• Building Visualizations
• Advanced Topics
introduction
foundation & theory
building blocks
design & critique
construction
advanced
![Page 117: Data Visualization Summary iHub](https://reader035.vdocument.in/reader035/viewer/2022062313/55d0b6e5bb61eb93558b4595/html5/thumbnails/117.jpg)
Data Visualization Nikhil Srivastava, 2015
The Ebb and Flow of Movies
NY Times, 2008
Advanced Visualizations
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Data Visualization Nikhil Srivastava, 2015
Word Cloud - “Data Visualization” Wikipedia PageWordle
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Data Visualization Nikhil Srivastava, 2015
ZIPScribbleRobert Kosara, 2006
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Data Visualization Nikhil Srivastava, 2015
Twitter NetworksPJ Lamberson, 2012
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Data Visualization Nikhil Srivastava, 2015
Blogs
• Infosthetics.com
• Visualizing.org
• FlowingData.com