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Data & Analytics Club - Data Visualization Workshop | PPTX
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Data & Analytics Club - Data Visualization Workshop

This document provides an overview of data visualization techniques. It discusses how visualization can leverage human visual perception to more efficiently process and understand data. Various data types and encoding channels are described, along with common visualization types like scatter plots, line charts, bar charts and their applications and limitations. Design principles of integrity, effectiveness and aesthetics are also covered.

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Data Visualization Techniques•
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Presented by Nikhil Srivastava from Wharton Data & Analytics Club, offering a glimpse of the lecture content.

An overview of topics covered: definition, principles, building visualizations, and hands-on components.

Details on various types of visualizations including scientific, artistic, and statistical graphics. A dataset listing populations of various cities, prompting analysis of populous cities and states.

Explores the utility, importance, and power of data visualization in resolving ambiguities and telling a story.

Discussion of visual perception, emphasizing slow vs. fast processing and techniques for efficient data representation.

Tasks illustrating sequential vs. parallel processing in data visualization, highlighting the challenges with distractions.

Key lessons for effective visualizations, advocating for the use of pre-attentive attributes and caution against over-complexity.

Classification of data types (categorical, ordinal, numerical) and their visual representation strategies.

Effective channels for data encoding and the significance of spatial position in visualization.

Examples of different chart types (scatter, line, bar, pie) including their applications and pitfalls.

Core design principles focusing on integrity, effectiveness, and aesthetics in data visualization.

Guidelines for using color effectively in visualizations to enhance interpretation without overwhelming the viewer.

Discussion on various visualization tools available including Excel, Tableau, and Python, along with learning considerations.

Explorations of advanced visualizations such as treemaps, heat maps, and other specialized charts.

References and further reading materials for deepening understanding and exploration of data visualization.

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Data Visualization Nikhil Srivastava, 2015 Nikhil Srivastava Wharton Data & Analytics Club
Data Visualization Nikhil Srivastava, 2015 hoster@wharton.upenn.edu
Data Visualization Nikhil Srivastava, 2015 About this Lecture • Shortened version of longer course – Slides, demos, extra material – Code samples and libraries – Sample projects • Questions
Data Visualization Nikhil Srivastava, 2015 About You
Data Visualization Nikhil Srivastava, 2015 • 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
Data Visualization Nikhil Srivastava, 2015 • 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
Data Visualization Nikhil Srivastava, 2015 Data Visualization Information Visualization Scientific Visualization Infographics Statistical Graphics Informative Art Art Science Statistics JournalismDesign Visual Analytics Business
Data Visualization Nikhil Srivastava, 2015 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
Data Visualization Nikhil Srivastava, 2015 • Which is the most populous city in the list? • Which state in the list has the most cities? • Which state in the list has 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
Data Visualization Nikhil Srivastava, 2015
Data Visualization Nikhil Srivastava, 2015 • Which is the most populous city in the list? • Which state in the list has the most cities? • Which state in the list has the largest average city?
Data Visualization Nikhil Srivastava, 2015 • Which is the most populous city in the list? • Which state in the list has the most cities? • Which state in the list has the largest average city? • What is the population of Montgomery, Alabama?
Data Visualization Nikhil Srivastava, 2015 Data Visualization is: • Useful – Answers user questions – Reduces user workload (by design, not by default)
Data Visualization Nikhil Srivastava, 2015 Anscombe’s quartet (1973)
Data Visualization Nikhil Srivastava, 2015 Anscombe’s quartet (1973)
Data Visualization Nikhil Srivastava, 2015 Data Visualization is: • Useful – Understand structure and patterns – Resolve ambiguity – Locate outliers
Data Visualization Nikhil Srivastava, 2015
Data Visualization Nikhil Srivastava, 2015 Data Visualization is: • Important – Design decisions affect interpretation
Data Visualization Nikhil Srivastava, 2015 Crimean War Deaths Florence Nightingale, 1858 (re-colorized)
Data Visualization Nikhil Srivastava, 2015 Gapminder Foundation
Data Visualization Nikhil Srivastava, 2015 Data Visualization is: • Powerful – Communicate, teach, inspire
Data Visualization Nikhil Srivastava, 2015 purpose communicate explore, analyze data type numerical, categorical text, maps, graphs, networks method static representation animation, interactivity Our Focus
Data Visualization Nikhil Srivastava, 2015 • 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
Data Visualization Nikhil Srivastava, 2015 The Hardware
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”
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 “Bottom-up” “Top-down” Visual Perception
Data Visualization Nikhil Srivastava, 2015 Task: Counting How many 3’s? 1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686
Data Visualization Nikhil Srivastava, 2015 Task: Counting How many 3’s? 1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686 1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686
Data Visualization Nikhil Srivastava, 2015 Task: Counting Slow, sequential, conscious Rapid, parallel, automatic 1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686 1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686
Data Visualization Nikhil Srivastava, 2015 Task: (Distracted) Search Which side has the red circle?
Data Visualization Nikhil Srivastava, 2015 Task: (Distracted) Search Which side has the red circle?
Data Visualization Nikhil Srivastava, 2015 Task: (Distracted) Search Which side has the red circle?
Data Visualization Nikhil Srivastava, 2015 Task: (Distracted) Search Which side has the red circle?
Data Visualization Nikhil Srivastava, 2015 Task: (Distracted) Search Slow, sequential, conscious Rapid, parallel, automatic
Data Visualization Nikhil Srivastava, 2015 Task: (Distracted) Search
Data Visualization Nikhil Srivastava, 2015 Task: (Distracted) Search
Data Visualization Nikhil Srivastava, 2015 Task: (Distracted) Search
Data Visualization Nikhil Srivastava, 2015 Task: (Distracted) Search Slow, sequential, conscious Rapid, parallel, automatic (n=7) (n=5) (n=3)
Data Visualization Nikhil Srivastava, 2015 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
Data Visualization Nikhil Srivastava, 2015 Example: Inefficient Attributes
Data Visualization Nikhil Srivastava, 2015 Example: Too Many Attributes
Data Visualization Nikhil Srivastava, 2015 • 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
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? Visual Encoding
Data Visualization Nikhil Srivastava, 2015 Data Types CATEGORICAL 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) = = = = < > < > < > - + - * /
Data Visualization Nikhil Srivastava, 2015 Data Types CATEGORICAL 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) Bin/Categorize Difference/Normalize
Data Visualization Nikhil Srivastava, 2015 Data Types (Advanced) • Networks/Graphs – Hierarchies/Trees • Text • Maps: points, regions, routes
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? Visual Encoding
Data Visualization Nikhil Srivastava, 2015 Visual Encodings Marks point line area volume Channels position size shape color angle/tilt
Data Visualization Nikhil Srivastava, 2015 Channel Effectiveness
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”
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
Data Visualization Nikhil Srivastava, 2015 type mark channel data represented Scatter Plot point position 2 quantitative
Data Visualization Nikhil Srivastava, 2015 type mark channel data represented Scatter + Hue point position, color 2 quantitative, 1 categorical
Data Visualization Nikhil Srivastava, 2015 type mark channel data represented Scatter + Size (“Bubble”) point position, size 3 quantitative
Data Visualization Nikhil Srivastava, 2015 Scatter Plot – Applications RELATIONSHIP GROUPING OUTLIERS
Data Visualization Nikhil Srivastava, 2015 Scatter Plot – Dangers OCCLUSION (DENSITY) OCCLUSION (OVERLAP) 3-D
Data Visualization Nikhil Srivastava, 2015 type mark channel data represented Line Chart line position (orientation) 2 quantitative
Data Visualization Nikhil Srivastava, 2015 type mark channel data represented Area Chart area size (length) 2 quantitative
Data Visualization Nikhil Srivastava, 2015 Line Chart – Applications PATTERN OVER TIME COMPARISON
Data Visualization Nikhil Srivastava, 2015 Line Chart – Dangers Y SCALING X SCALING OVERLOAD
Data Visualization Nikhil Srivastava, 2015 type mark channel data represented Bar Chart line size (length) 1 categorical, 1 quantitative
Data Visualization Nikhil Srivastava, 2015 type mark channel data represented Histogram line size (length) 1 ordinal/quantitative, 1 quantitative (count)
Data Visualization Nikhil Srivastava, 2015 Bar Chart – Applications COMPARE CATEGORIES DISTRIBUTION
Data Visualization Nikhil Srivastava, 2015 Bar Chart – Dangers TOO MANY CATEGORIES POORLY SORTED CATEGORIES ZERO AXIS
Data Visualization Nikhil Srivastava, 2015 type mark channel data represented Pie Chart area size (angle) 1 quantitative
Data Visualization Nikhil Srivastava, 2015 Pie Chart – Dangers AREA/ANGLE SCALE SIMILAR AREAS OVERLOAD
Data Visualization Nikhil Srivastava, 2015 Multi-Series: Bar “GROUPED” BAR CHART “STACKED” BAR CHART
Data Visualization Nikhil Srivastava, 2015 Multi-Series: Line MULTIPLE LINE STACKED AREA CHART
Data Visualization Nikhil Srivastava, 2015 Normalization NORMALIZED BAR NORMALIZED AREA
Data Visualization Nikhil Srivastava, 2015 • 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
Data Visualization Nikhil Srivastava, 2015 From Science to Art • Design principles* • Style guidelines* *dependent on context and objective (and author)
Data Visualization Nikhil Srivastava, 2015 Design Principles
Data Visualization Nikhil Srivastava, 2015 Design Principles • Integrity – Tell the truth with data • Effectiveness – Achieve visualization objectives • Aesthetics – Be compelling, vivid, beautiful
Data Visualization Nikhil Srivastava, 2015 Integrity Lie Ratio = size of effect in graphic size of effect in data
Data Visualization Nikhil Srivastava, 2015 Integrity
Data Visualization Nikhil Srivastava, 2015 Integrity “show data variation, not design variation”
Data Visualization Nikhil Srivastava, 2015 Effectiveness* Data/Ink Ratio = ink representing data total ink *Tufte
Data Visualization Nikhil Srivastava, 2015 Effectiveness* *Tufte avoid “chart junk”
Data Visualization Nikhil Srivastava, 2015 Avoid Chart Junk
Data Visualization Nikhil Srivastava, 2015 Avoid Chart Junk
Data Visualization Nikhil Srivastava, 2015 Avoid Chart Junk
Data Visualization Nikhil Srivastava, 2015 Avoid Chart Junk
Data Visualization Nikhil Srivastava, 2015 Avoid Chart Junk
Data Visualization Nikhil Srivastava, 2015 Avoid Chart Junk
Data Visualization Nikhil Srivastava, 2015 Effectiveness (Few)
Data Visualization Nikhil Srivastava, 2015 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
Data Visualization Nikhil Srivastava, 2015 A Note on Color • To label • To emphasize • To liven or decorate
Data Visualization Nikhil Srivastava, 2015 Color as a Channel Categorical Quantitative Hue Good (6-8 max) Poor Value Poor Good Saturation Poor Okay
Data Visualization Nikhil Srivastava, 2015 Bad Color
Data Visualization Nikhil Srivastava, 2015 Good Color
Data Visualization Nikhil Srivastava, 2015 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
Data Visualization Nikhil Srivastava, 2015 • 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
Data Visualization Nikhil Srivastava, 2015 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 Clean Restructure Explore Analyze DATA Visualization Goals
Data Visualization Nikhil Srivastava, 2015 Visualization Tools Excel Tableau Plotly Python R Matlab Ubiq/Silk How hard is it to learn? How powerful & flexible is it? I’ll have to write code
Data Visualization Nikhil Srivastava, 2015 Visualization Tools Excel Tableau Plotly Python R Matlab Ubiq/Silk How hard is it to learn? How powerful & flexible is it? Google Charts Highcharts d3 I’ll have to write code
Data Visualization Nikhil Srivastava, 2015 Cheat Sheets • For Hackathon participants • Otherwise, email me
Data Visualization Nikhil Srivastava, 2015 • 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
Data Visualization Nikhil Srivastava, 2015 Small Multiples
Data Visualization Nikhil Srivastava, 2015 Treemap (Hierarchical Data) Strengths: nested relationships Concerns: order, aspect ratio
Data Visualization Nikhil Srivastava, 2015 Multi-Level Pie Chart (Hierarchical Data) Strengths: nested relationships Concerns: readability
Data Visualization Nikhil Srivastava, 2015 Heat Map (Table/Field Data) Strengths: pattern/outlier detection Concerns: ordering, clustering, color
Data Visualization Nikhil Srivastava, 2015 Choropleth (Region Data) Strengths: geography Concerns: region size color
Data Visualization Nikhil Srivastava, 2015 Cartogram (Region Data) Strengths: geographic pattern Concerns: base map knowledge
Data Visualization Nikhil Srivastava, 2015 The Ebb and Flow of Movies NY Times, 2008 Streamgraph
Data Visualization Nikhil Srivastava, 2015 “Data Visualization” Wikipedia Page Wordle Word Cloud
Data Visualization Nikhil Srivastava, 2015
Data Visualization Nikhil Srivastava, 2015 Twitter Networks PJ Lamberson, 2012
Data Visualization Nikhil Srivastava, 2015 Blogs/Reference • Infosthetics.com • Visualizing.org • FlowingData.com
Data Visualization Nikhil Srivastava, 2015 Nikhil Srivastava nsri@wharton.upenn.edu

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Data & Analytics Club - Data Visualization Workshop

  • 1.
    Data Visualization NikhilSrivastava, 2015 Nikhil Srivastava Wharton Data & Analytics Club
  • 2.
    Data Visualization NikhilSrivastava, 2015 hoster@wharton.upenn.edu
  • 3.
    Data Visualization NikhilSrivastava, 2015 About this Lecture • Shortened version of longer course – Slides, demos, extra material – Code samples and libraries – Sample projects • Questions
  • 4.
    Data Visualization NikhilSrivastava, 2015 About You
  • 5.
    Data Visualization NikhilSrivastava, 2015 • 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
  • 6.
    Data Visualization NikhilSrivastava, 2015 • 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
  • 7.
    Data Visualization NikhilSrivastava, 2015 Data Visualization Information Visualization Scientific Visualization Infographics Statistical Graphics Informative Art Art Science Statistics JournalismDesign Visual Analytics Business
  • 8.
    Data Visualization NikhilSrivastava, 2015 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
  • 9.
    Data Visualization NikhilSrivastava, 2015 • Which is the most populous city in the list? • Which state in the list has the most cities? • Which state in the list has 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
  • 10.
    Data Visualization NikhilSrivastava, 2015
  • 11.
    Data Visualization NikhilSrivastava, 2015 • Which is the most populous city in the list? • Which state in the list has the most cities? • Which state in the list has the largest average city?
  • 12.
    Data Visualization NikhilSrivastava, 2015 • Which is the most populous city in the list? • Which state in the list has the most cities? • Which state in the list has the largest average city? • What is the population of Montgomery, Alabama?
  • 13.
    Data Visualization NikhilSrivastava, 2015 Data Visualization is: • Useful – Answers user questions – Reduces user workload (by design, not by default)
  • 14.
    Data Visualization NikhilSrivastava, 2015 Anscombe’s quartet (1973)
  • 15.
    Data Visualization NikhilSrivastava, 2015 Anscombe’s quartet (1973)
  • 16.
    Data Visualization NikhilSrivastava, 2015 Data Visualization is: • Useful – Understand structure and patterns – Resolve ambiguity – Locate outliers
  • 17.
    Data Visualization NikhilSrivastava, 2015
  • 18.
    Data Visualization NikhilSrivastava, 2015 Data Visualization is: • Important – Design decisions affect interpretation
  • 19.
    Data Visualization NikhilSrivastava, 2015 Crimean War Deaths Florence Nightingale, 1858 (re-colorized)
  • 20.
    Data Visualization NikhilSrivastava, 2015 Gapminder Foundation
  • 21.
    Data Visualization NikhilSrivastava, 2015 Data Visualization is: • Powerful – Communicate, teach, inspire
  • 22.
    Data Visualization NikhilSrivastava, 2015 purpose communicate explore, analyze data type numerical, categorical text, maps, graphs, networks method static representation animation, interactivity Our Focus
  • 23.
    Data Visualization NikhilSrivastava, 2015 • 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
  • 24.
    Data Visualization NikhilSrivastava, 2015 The Hardware
  • 25.
    Data Visualization NikhilSrivastava, 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”
  • 26.
    Data Visualization NikhilSrivastava, 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 “Bottom-up” “Top-down” Visual Perception
  • 27.
    Data Visualization NikhilSrivastava, 2015 Task: Counting How many 3’s? 1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686
  • 28.
    Data Visualization NikhilSrivastava, 2015 Task: Counting How many 3’s? 1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686 1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686
  • 29.
    Data Visualization NikhilSrivastava, 2015 Task: Counting Slow, sequential, conscious Rapid, parallel, automatic 1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686 1281768756138976546984506985604982826762 9809858458224509856358945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686
  • 30.
    Data Visualization NikhilSrivastava, 2015 Task: (Distracted) Search Which side has the red circle?
  • 31.
    Data Visualization NikhilSrivastava, 2015 Task: (Distracted) Search Which side has the red circle?
  • 32.
    Data Visualization NikhilSrivastava, 2015 Task: (Distracted) Search Which side has the red circle?
  • 33.
    Data Visualization NikhilSrivastava, 2015 Task: (Distracted) Search Which side has the red circle?
  • 34.
    Data Visualization NikhilSrivastava, 2015 Task: (Distracted) Search Slow, sequential, conscious Rapid, parallel, automatic
  • 35.
    Data Visualization NikhilSrivastava, 2015 Task: (Distracted) Search
  • 36.
    Data Visualization NikhilSrivastava, 2015 Task: (Distracted) Search
  • 37.
    Data Visualization NikhilSrivastava, 2015 Task: (Distracted) Search
  • 38.
    Data Visualization NikhilSrivastava, 2015 Task: (Distracted) Search Slow, sequential, conscious Rapid, parallel, automatic (n=7) (n=5) (n=3)
  • 39.
    Data Visualization NikhilSrivastava, 2015 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
  • 40.
    Data Visualization NikhilSrivastava, 2015 Example: Inefficient Attributes
  • 41.
    Data Visualization NikhilSrivastava, 2015 Example: Too Many Attributes
  • 42.
    Data Visualization NikhilSrivastava, 2015 • 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
  • 43.
    Data Visualization NikhilSrivastava, 2015 What kind of data do we have? How can we represent the data visually? How can we organize this into a visualization? Visual Encoding
  • 44.
    Data Visualization NikhilSrivastava, 2015 Data Types CATEGORICAL 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) = = = = < > < > < > - + - * /
  • 45.
    Data Visualization NikhilSrivastava, 2015 Data Types CATEGORICAL 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) Bin/Categorize Difference/Normalize
  • 46.
    Data Visualization NikhilSrivastava, 2015 Data Types (Advanced) • Networks/Graphs – Hierarchies/Trees • Text • Maps: points, regions, routes
  • 47.
    Data Visualization NikhilSrivastava, 2015 What kind of data do we have? How can we represent the data visually? How can we organize this into a visualization? Visual Encoding
  • 48.
    Data Visualization NikhilSrivastava, 2015 Visual Encodings Marks point line area volume Channels position size shape color angle/tilt
  • 49.
    Data Visualization NikhilSrivastava, 2015 Channel Effectiveness
  • 50.
    Data Visualization NikhilSrivastava, 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”
  • 51.
    Data Visualization NikhilSrivastava, 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
  • 52.
    Data Visualization NikhilSrivastava, 2015 type mark channel data represented Scatter Plot point position 2 quantitative
  • 53.
    Data Visualization NikhilSrivastava, 2015 type mark channel data represented Scatter + Hue point position, color 2 quantitative, 1 categorical
  • 54.
    Data Visualization NikhilSrivastava, 2015 type mark channel data represented Scatter + Size (“Bubble”) point position, size 3 quantitative
  • 55.
    Data Visualization NikhilSrivastava, 2015 Scatter Plot – Applications RELATIONSHIP GROUPING OUTLIERS
  • 56.
    Data Visualization NikhilSrivastava, 2015 Scatter Plot – Dangers OCCLUSION (DENSITY) OCCLUSION (OVERLAP) 3-D
  • 57.
    Data Visualization NikhilSrivastava, 2015 type mark channel data represented Line Chart line position (orientation) 2 quantitative
  • 58.
    Data Visualization NikhilSrivastava, 2015 type mark channel data represented Area Chart area size (length) 2 quantitative
  • 59.
    Data Visualization NikhilSrivastava, 2015 Line Chart – Applications PATTERN OVER TIME COMPARISON
  • 60.
    Data Visualization NikhilSrivastava, 2015 Line Chart – Dangers Y SCALING X SCALING OVERLOAD
  • 61.
    Data Visualization NikhilSrivastava, 2015 type mark channel data represented Bar Chart line size (length) 1 categorical, 1 quantitative
  • 62.
    Data Visualization NikhilSrivastava, 2015 type mark channel data represented Histogram line size (length) 1 ordinal/quantitative, 1 quantitative (count)
  • 63.
    Data Visualization NikhilSrivastava, 2015 Bar Chart – Applications COMPARE CATEGORIES DISTRIBUTION
  • 64.
    Data Visualization NikhilSrivastava, 2015 Bar Chart – Dangers TOO MANY CATEGORIES POORLY SORTED CATEGORIES ZERO AXIS
  • 65.
    Data Visualization NikhilSrivastava, 2015 type mark channel data represented Pie Chart area size (angle) 1 quantitative
  • 66.
    Data Visualization NikhilSrivastava, 2015 Pie Chart – Dangers AREA/ANGLE SCALE SIMILAR AREAS OVERLOAD
  • 67.
    Data Visualization NikhilSrivastava, 2015 Multi-Series: Bar “GROUPED” BAR CHART “STACKED” BAR CHART
  • 68.
    Data Visualization NikhilSrivastava, 2015 Multi-Series: Line MULTIPLE LINE STACKED AREA CHART
  • 69.
    Data Visualization NikhilSrivastava, 2015 Normalization NORMALIZED BAR NORMALIZED AREA
  • 70.
    Data Visualization NikhilSrivastava, 2015 • 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
  • 71.
    Data Visualization NikhilSrivastava, 2015 From Science to Art • Design principles* • Style guidelines* *dependent on context and objective (and author)
  • 72.
    Data Visualization NikhilSrivastava, 2015 Design Principles
  • 73.
    Data Visualization NikhilSrivastava, 2015 Design Principles • Integrity – Tell the truth with data • Effectiveness – Achieve visualization objectives • Aesthetics – Be compelling, vivid, beautiful
  • 74.
    Data Visualization NikhilSrivastava, 2015 Integrity Lie Ratio = size of effect in graphic size of effect in data
  • 75.
    Data Visualization NikhilSrivastava, 2015 Integrity
  • 76.
    Data Visualization NikhilSrivastava, 2015 Integrity “show data variation, not design variation”
  • 77.
    Data Visualization NikhilSrivastava, 2015 Effectiveness* Data/Ink Ratio = ink representing data total ink *Tufte
  • 78.
    Data Visualization NikhilSrivastava, 2015 Effectiveness* *Tufte avoid “chart junk”
  • 79.
    Data Visualization NikhilSrivastava, 2015 Avoid Chart Junk
  • 80.
    Data Visualization NikhilSrivastava, 2015 Avoid Chart Junk
  • 81.
    Data Visualization NikhilSrivastava, 2015 Avoid Chart Junk
  • 82.
    Data Visualization NikhilSrivastava, 2015 Avoid Chart Junk
  • 83.
    Data Visualization NikhilSrivastava, 2015 Avoid Chart Junk
  • 84.
    Data Visualization NikhilSrivastava, 2015 Avoid Chart Junk
  • 85.
    Data Visualization NikhilSrivastava, 2015 Effectiveness (Few)
  • 86.
    Data Visualization NikhilSrivastava, 2015 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
  • 87.
    Data Visualization NikhilSrivastava, 2015 A Note on Color • To label • To emphasize • To liven or decorate
  • 88.
    Data Visualization NikhilSrivastava, 2015 Color as a Channel Categorical Quantitative Hue Good (6-8 max) Poor Value Poor Good Saturation Poor Okay
  • 89.
    Data Visualization NikhilSrivastava, 2015 Bad Color
  • 90.
    Data Visualization NikhilSrivastava, 2015 Good Color
  • 91.
    Data Visualization NikhilSrivastava, 2015 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
  • 92.
    Data Visualization NikhilSrivastava, 2015 • 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
  • 93.
    Data Visualization NikhilSrivastava, 2015 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 Clean Restructure Explore Analyze DATA Visualization Goals
  • 94.
    Data Visualization NikhilSrivastava, 2015 Visualization Tools Excel Tableau Plotly Python R Matlab Ubiq/Silk How hard is it to learn? How powerful & flexible is it? I’ll have to write code
  • 95.
    Data Visualization NikhilSrivastava, 2015 Visualization Tools Excel Tableau Plotly Python R Matlab Ubiq/Silk How hard is it to learn? How powerful & flexible is it? Google Charts Highcharts d3 I’ll have to write code
  • 96.
    Data Visualization NikhilSrivastava, 2015 Cheat Sheets • For Hackathon participants • Otherwise, email me
  • 97.
    Data Visualization NikhilSrivastava, 2015 • 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
  • 98.
    Data Visualization NikhilSrivastava, 2015 Small Multiples
  • 99.
    Data Visualization NikhilSrivastava, 2015 Treemap (Hierarchical Data) Strengths: nested relationships Concerns: order, aspect ratio
  • 100.
    Data Visualization NikhilSrivastava, 2015 Multi-Level Pie Chart (Hierarchical Data) Strengths: nested relationships Concerns: readability
  • 101.
    Data Visualization NikhilSrivastava, 2015 Heat Map (Table/Field Data) Strengths: pattern/outlier detection Concerns: ordering, clustering, color
  • 102.
    Data Visualization NikhilSrivastava, 2015 Choropleth (Region Data) Strengths: geography Concerns: region size color
  • 103.
    Data Visualization NikhilSrivastava, 2015 Cartogram (Region Data) Strengths: geographic pattern Concerns: base map knowledge
  • 104.
    Data Visualization NikhilSrivastava, 2015 The Ebb and Flow of Movies NY Times, 2008 Streamgraph
  • 105.
    Data Visualization NikhilSrivastava, 2015 “Data Visualization” Wikipedia Page Wordle Word Cloud
  • 106.
    Data Visualization NikhilSrivastava, 2015
  • 107.
    Data Visualization NikhilSrivastava, 2015 Twitter Networks PJ Lamberson, 2012
  • 108.
    Data Visualization NikhilSrivastava, 2015 Blogs/Reference • Infosthetics.com • Visualizing.org • FlowingData.com
  • 109.
    Data Visualization NikhilSrivastava, 2015 Nikhil Srivastava nsri@wharton.upenn.edu

Editor's Notes

  • #7 Alright, let’s get started – what is data visualization?
  • #8 It’s difficult to define precisely: as a field, DV has many related and overlapping goals and descriptions. It is often used interchangeably with different terms, and it falls under many different disciplines.
  • #9 Better than a definition is an example. Let’s take a look at this table of Kenyan cities showing city name, county name, and city population. Take a moment to understand the structure of this data, because I’m about to ask you a few questions on it.
  • #10 Better than a definition is an example. Let’s take a look at this table of Kenyan cities showing city name, county name, and city population. Take a moment to understand the structure of this data, because I’m about to ask you a few questions on it.
  • #11 Now, let’s answer the same questions by using the visualization. What are the cognitive steps required? How easy or difficult is the process?
  • #12 Now, let’s answer the same questions by using the visualization. What are the cognitive steps required? How easy or difficult is the process?
  • #13 Now let’s ask an additional question we didn’t ask before.
  • #14 We’ve learned that data visualization can be useful in telling us things about a set of data, making it easier to find information and answer questions. We’ve also learned that this usefulness depends both on the design of the visualization and the specific information we are looking for.
  • #15 Let’s take a look at another example. This is a data set called Anscombe’s Quartet, named after the statistician who devised it. It consists of four separate sets of data, each of which is a list of ten pairs of numbers. So there are ten different X and Y values that are paired. To make this a bit more concrete, you can imagine that each data set describes ten people, X represents their height and Y represents their weight. The interesting thing is that all four of these data sets have exactly the same relationship between the X and Y numbers. All X values have the same average and standard deviation, and so do all Y values. Furthermore, the correlation between X and Y is the same for all sets. And except for the last one (which has a bunch of 8s), there’s not much we can do to distinguish them or describe them meaningfully by just looking at the numbers in the table. Now let’s see what happens when we plot them.
  • #16 Here we’ve visualized the data in what’s known as a scatter plot. Each dot represents one of the ten pairs, located on the horizontal axis by X value and on the vertical axis by Y value. By visualizing the data, we see patterns, outliers, and relationships that were impossible to detect in the chart.
  • #17 So we’ve learned that DV is important. It can help us resolve ambiguous data, locate outliers, and generally understand the structure and pattern of a data set.
  • #20 Infographic of twitter activity in Africa in late 2013 produced by Portland Communications.
  • #21 Interactive tool from the Gapminder Foundation animating the health and wealth of world countries over time. This screenshot shows the historical path of Kenya from 1800 to 2013. Note the number of data types (life expectancy, GDP, population per country and year) and variety of visual encodings (x- and y- position, size, color, time).
  • #24 Alright, let’s get started – what is data visualization?
  • #51 Readings in Information Visualization: Using Vision to Think (Ben Schneiderman et al, 1999)