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Strategies for Effective Data Visualization Anneli Joplin November 8, 2017 [email protected]
Visualization is inherently open-ended
Shade
Shape
Orientation
Size
Position
Linewidth
Hue
Arial Times
Font
Motion
Aspect ratio
Type
Scale
X
Y
Best practices depend on context
carelessapproach
Approach dictates likelihood of success
intentionalapproach
Probability of success…
Effective Ineffective
Effective Ineffective
Informative Confusing
An intentional approach to data visualization
What we know about perception
Data visualization design process
New ideas to consider
Implications of current perception research
Vision is a multistep process Light triggers a neural signal through the optic nerve
Colin Ware, Information Visualization, 2004
Vision is a multistep process Brain identifies basic features first, and then analyzes further
Colin Ware, Information Visualization, 2004
9
Preattention
“When something just catches our eye, it is tapping into our earliest stages of attention.” – Stephanie Evergreen, Presenting Data Effectively
Preattentive processing is instantaneous
4 6 7 2 8 1 2 7 6 2 7 3 8 9 4 9 8 2 5 4 2 1 8 3 2 5 6 5 4 5 1 9 9 2 4 6 7 5 5 6 7 8 2 1 3 4 9 9 8 4 6 2 0 0 2 5 4 8 6 9 7 5 2 1 5 8 6 5 2 3 2 1 2 2 1 3 4 5 8 9 0 0 1 1 0 0 1 4 5 7
4 6 7 2 8 1 2 7 6 2 7 3 8 9 4 9 8 2 5 4 2 1 8 3 2 5 6 5 4 5 1 9 9 2 4 6 7 5 5 6 7 8 2 1 3 4 9 9 8 4 6 2 0 0 2 5 4 8 6 9 7 5 2 1 5 8 6 5 2 3 2 1 2 2 1 3 4 5 8 9 0 0 1 1 0 0 1 4 5 7
Alberto Cairo, The Functional Art, 2013 / Colin Ware, Information Visualization, 2004
Preattentive processing < 10 msec per item
Typical processing ~ 40 msec per item
How many 3’s?
Pattern recognition Preattentive contrast
Gestalt principles of pattern recognition
The visual brain … • Evolved to detect patterns
• Groups similar objects
• Separates different objects
PROXIMITY Objects close together are grouped
Alberto Cairo, The Functional Art, 2013
4 6 7 2 8 1 2 7 6
5 6 1 2 7 1 2 7 9
9 4 8 2 7 8 2 3 9
1 1 3 5 7 6 1 3 3
4 6 7 2 8 1 2 7 6 5 6 1 2 7 1 2 7 9 9 4 8 2 7 8 2 3 9 1 1 3 5 7 6 1 3 3
PROXIMITY Objects close together are grouped
Alberto Cairo, The Functional Art, 2013
SIMILARITY Similar objects perceived as a group
Alberto Cairo, The Functional Art, 2013 / USA by Alexander Skowalsky for the Noun Project
Scatter bubble plot encode times in color Bubbles of various sizes are grouped via color (similarity)
Forsyth Alexander, When data imitates art, www.ibm.com/blogs/business-analytics
CONNECTEDNESS Linked objects form a natural group
Alberto Cairo, The Functional Art, 2013
CONNECTEDNESS Connecting lines visually identify pairs
Which dots do you group and why?
CONNECTEDNESS Connecting lines visually identify pairs
Connectedness relates the two circles from each category
ENCLOSURE Enclosed objects form a natural group
Alberto Cairo, The Functional Art, 2013
ENCLOSURE Enclosed objects are evaluated together
Bang Wong, Nature Methods, Vol. 7 No. 11, 2010
CLOSURE Tendency to perceive complete forms
Stephen Few, Show Me the Numbers, 2nd Edition, 2012
CLOSURE Tendency to perceive complete forms
No need to define area of graph completely
Stephen Few, Show Me the Numbers, 2nd Edition, 2012
Redundant enclosure introduces a distraction
SYMMETRY Symmetry suggests a visual whole
Colin Ware, Information Visualization, 2004
SYMMETRY Butterfly plots highlight differences
CONTINUITY Curved contours imply connection
Colin Ware, Information Visualization, 2004
Curved connections are easier to follow
Alberto Cairo, The Functional Art, 2013 / Colin Ware, Information Visualization, 2004ç
Why did we evolve to identify contours?
Curves help the viewer visually follow connections through crowded data
Social Networks, behance.net/gallery
Gestalt summary
• Proximity • Similarity • Connectedness • Enclosure • Closure • Symmetry • Continuity
Take advantage of pattern recognition tendencies
Pattern recognition Preattentive contrast
Range of evolved preattentive attributes
Stephen Few, Show Me the Numbers, 2nd Edition, 2012
Type AttributeForm Length
Width Orientation Shape Size Enclosure
Color Hue Intensity
Spatial position 2D position
Rolandi, M. et al. Adv. Mater. 2011
Alter a preattentive attribute to make something stand out
Limits to distinct perception
Preattentive processing limited to 1 attribute at a time
Stephen Few, Show Me the Numbers, 2nd Edition, 2012
Color intensity only Intensity and shape
Overwhelming repetition results in loss of meaning
Martin Krzywinski, Nature Methods, Vol. 10 No. 5 2013
Too many bright colors means nothing stands out
Natural scenes exhibit muted colors
Brightmetrics, Using Color in Data Visualization, 2010
Reserve bright colors for emphasis
Above all else, show the data
Data ink ratio = data inktotal ink used in the graphic
Edward Tufte, The Visual Display of Quantitative Information, 1983
TUFTE
The Visual Display of Quantitative Information
Clutter distracts from preattentive cues
Edward R. Tufte, The Visual Display of Quantitative Information, 1983
• Distracting patterns
• Gridlines
• Elements only for “artistic appeal”
Remove all chartjunk, for example:
3D effects are almost always chartjunk
Nils Gehlenborg and Bang Wong, Nature Methods, Vol. 9 No. 9 2012
Visually separate data from other elements
Martin Krzywinski, Nature Methods, Vol. 10 No. 3 2013
Similarity between the ellipses and lines reduces visual contrast
“Sometimes clarity demands more space” – Stephen Few
BEFORE AFTER
Separating traces into trellis display highlights trends more effectively
Make emphasis more effective by eliminating excess decoration
Size Matters, https://www.onepager.com/community/blog/size-matters/
What would you remove from this chart?
Preattentive contrast summary
• Rely on muted colors
• Soften gridlines, axes, labels, etc.
• Remove chartjunk
Limit preattentive attributes to emphasis
Visual information requires decoding
Colin Ware, Information Visualization, 2004
Visual information requires decoding
1. Working memory limits number of items remembered
2. Perception accuracy is distance dependent
3. Accuracy of perception influenced by visuals
Implications for data visualization –
Keep the number of items displayed in one visualization to ~ 4 if possible
Reduce distance between comparable data to increase accuracy
Marc Streit and Nils Gehlenborg , Nature Methods, Vol. 11 No. 2 2014
Select attribute based on purpose
Company Participant Molecule Order (1, 2, 3) Address
Divide information
Time Count Intensity Profit
Measure things
Categorical
Data types
Quantitative
Few attributes can encode quant. data
Stephen Few, Show Me the Numbers, 2nd Edition, 2012
Type Attribute Quantitative? Form Length Yes
Width Yes, but limited Orientation No Size Yes, but limited Shape No Enclosure No
Color Hue No Intensity Yes, but limited
Spatial position 2D position Yes
Shifts in color are not visually equivalent to changes in value
Bang Wong, Nature Methods, Vol. 8 No. 3 20111
Commonly utilized color scales are not perceived accurately
Perception of color depends on surroundings
Bang Wong, Nature Methods, Vol. 7 No. 8 2010
Use color for labeling, emphasis or when value doesn’t matter
Length is perceived quantitatively Number and visual length are tied together
This works to our advantage in a bar chart, for example
Bar charts must start at zero
Scale = 0 – 100
Length has an inherent numerical value
Scale = 60 – 100
Data encoded with length is highly distorted with a shortened scale
An alternative – the dot plot2D position does not elicit a numerical value
Scale = 0 – 100 Scale = 60 – 100
2D position does not require a 0 value for quantitative comparison
Dot plots display multiple data sets more clearly than bar charts
Lollipop charts also compare values without emphasizing length
Bar chart with less emphasis on length
Cleveland and McGill identified 10 elementary perceptual tasks
William Cleveland, Graphical Perception, 1984
Graphical perception attributes in order of accuracy
William Cleveland, The Elements of Graphing Data, 1994 / Alberto Cairo, The Functional Art
Allows more accurate
judgments
Allows more generic
judgments
*Accuracy is not always better, just make intentional choices based on purpose
position along a common scale
position along nonaligned scales
length
angle
area
volume
curvature
shading, color saturation
Bar charts are easier to evaluate accurately than pie charts
Position along common scale >>> area or angle
Simple bar charts more accurate than stacked bars
Position along common scale >>> length
Use small multiples instead of stacked bars when numbers matter
Retains common axis, but also enables comparison
Curve comparisons are difficult, plot difference instead
Curves A and B Difference (A – B)
Select an aspect ratio that places key lines close to 45° Angles around 45° are perceived accurately
Small angles are more difficult to assess
Naomi B. Robbins, Creating More Effective Graphs, 2005
Aspect ratio affects perception of dataHow to select the aspect ratio that allows for accurate judgment?
Naomi B. Robbins, Creating More Effective Graphs, 2005
Rescale line graph segments in multiple panels to improve angle perception
Gregor McInerny, Martin Krzywinski, Nature Methods, Vol. 12 No. 7 2015
Graphical perception summary
• Perception accuracy is distance dependent
• Position on a common axis perceived most accurately
• Bar graphs outperform pie charts
• Small multiples outperform stacked bars
• Curve perception is not accurate
• Angles close to 45° are evaluated most easily
Select encoding attributes based on purpose
Exercise 1 – Accounting for perception
How would you apply visual perception principles to improve this chart?
Example curated by Melissa Clarkson, melissaclarkson.com
One solution – Bar chart trellis display allows comparison across samples
Redesign created by Melissa Clarkson, melissaclarkson.com
How would you apply visual perception principles to improve this chart?
Example curated by Melissa Clarkson, melissaclarkson.com
One solution – Dot plot allows easy comparison across conditions
Redesign created by Melissa Clarkson, melissaclarkson.com
Strategies to facilitate effective data visualization
Field of data visualization HOLMES
Designer’s Guide to Creating Charts and Diagrams
TUFTE
The Visual Display of Quantitative Information
Tufte prioritized function, Holmes form
VS
Nigel Holmes, Designer’s Guide to Creating Charts and Graphs, 1984
TUFTE HOLMES
Approach > style guidelines
intentionalapproach
Probability of success…
Effective Ineffective
1. Explore data visually 2. Identify visualization message 3. Select a chart type and create 4. Evaluate and revise 5. Take advantage of templates
Recommended design process
1. Explore data visually 2. Identify visualization message 3. Select a chart type and create 4. Evaluate and revise 5. Take advantage of templates
Recommended design process
Scatter plot matrix
Stephen Turner, Scatterplot Matrices in R, 2011, r-bloggers.com
Streamline with a visualization dashboard
Alberto Cairo, thefunctionalart.com, 2017
Preset charts provide an instant view of new data
Add interactive components to quickly filter and display data
Alberto Cairo, thefunctionalart.com, 2017
Exploring high dimensional data
Online data display 1. Embedding projector
2. Hypertools
Rice Visualization Lab (closed for relocation)
Projection
TensorFlow, Embeddings, tensorflow.org, 2017 / Andrew C. Heuser, Hypertools, 2017
1. Explore data visually 2. Identify visualization message 3. Select a chart type and create
4. Evaluate and revise 5. Take advantage of templates
Recommended design process
Evaluate visualizations on both informative and emotive aspects
Usefulness
Completeness
Perceptibility
Truthfulness
Intuitiveness
Very useful
All relevant data
Clear and easy
Accurate
Familiar, easy to read
Useless
No relevant data
Unclear and difficult
Inaccurate
Unfamiliar
Aesthetics
Engagement Beautiful
Draws one in
Ugly
Distracts from data
Pleasing to the eye
Neutral
Stephen Few, Perceptual Edge, Visual Business Intelligence Newsletter, 2017
Exercise 2 – Evaluating visualizations
Example 1 – Scatterplot
Jonathan A. Schwabish, An Economist’s Guide to Visualizing Data, 2014
Coloring and labeling key data facilitates interpretation
Jonathan A. Schwabish, An Economist’s Guide to Visualizing Data, 2014
Example 2 – Bar / dot plot
Jonathan A. Schwabish, An Economist’s Guide to Visualizing Data, 2014
Horizontal dot plot visually compares categories at two points in time
Jonathan A. Schwabish, An Economist’s Guide to Visualizing Data, 2014
Example 3 – Stacked bar chart
Castro-Nallar, E. et al Peer J, 2015, accessed at peerj.com/articles/1140/
1. Explore data visually 2. Identify visualization message 3. Select a chart type and create 4. Evaluate and revise 5. Take advantage of design templates
Recommended design process
Create templates to save time
Templates eliminate mundane design decisions
Spreadsheets (Excel, Origin)
Secondary components (Illustrator, InDesign, PowerPoint)
Scripts (Matlab, Python, Origin)
Dashboards (Excel, Tableau)
Resources on campus
Rice Visualization Lab Digital Media Commons
GIS Data Center
CWOVC online resources
Center for Research Computing
Exploring the frontiers of data visualization
Less common chart types provide new means of data exploration
Severino Ribecca, The Data Visualization Catalogue, dataviscatalogue.com/methods/
Raw graphs – a free way to experiment with less common visualizations
rawgraphs.io
Supports conventional and unconventional chart types
Sunburst diagramCapable of displaying hierarchies over multiple levels
Severino Ribecca, The Data Visualization Catalogue, dataviscatalogue.com/methods/
Use to show subdivisions of a multi-level structure
Sunburst diagram applied to visualize memory usage
Butterfly plot
Deviation bar chart
Slopegraph utilizes angle to compare change between groups
Displays change across categories using slope
Chart from the New York Times / http://www.nytimes.com/imagepages/2009/04/06/health/infant_stats.html
Axes labels also serve as ranked lists
Parallel coordinates showcase trends across dimensionsExtension of slopegraph to high dimensional data
Protovis, A Graphical Toolkit for Visualization, http://mbostock.github.io/protovis/ex/cars.html
Order matters – place the dimensions you aim to compare close together
Brushing highlights relevant data rangesInteractivity allows exploration of trends in the data
Robert Kosara, Parallel Coordinates, eagereyes.org
Select a category range
Heat map
Severino Ribecca, The Data Visualization Catalogue, dataviscatalogue.com/methods/
Use to highlight overall data trends
Expanded heat map presents large scale patterns in a compact way
Statistical Computing and Graphics Newsletter, Volume 20, December 2009
Radar chartsRepresent the value of multiple variables as a polygon
Severino Ribecca, The Data Visualization Catalogue, dataviscatalogue.com/methods/
Sunburst style chart shows cyclic relationship
*good for high dimensional data*
Moritz Stefaner, The Rhythm of Food, truth-and-beauty.net
Horizon chart – high dimensional area chart
Stephen Few, Time on the Horizon, Perceptual Edge, 2008
Value encoded in color and intensity
Horizon chart – high dimensional area chart
Stephen Few, Time on the Horizon, Perceptual Edge, 2008
Tiers grouped to improve perception of differences
Horizon chart – high dimensional area chart
Stephen Few, Time on the Horizon, Perceptual Edge, 2008
Horizon chart – high dimensional area chart
Stephen Few, Time on the Horizon, Perceptual Edge, 2008
Collapsed stacks present compact information
Horizon chart – high dimensional area chart
Stephen Few, Time on the Horizon, Perceptual Edge, 2008
High intensity pockets stand out
Learn more at cwovc.rice.edu