strategies for effective data visualization...trends more effectively . make emphasis more effective...

Strategies for Effective Data Visualization Anneli Joplin November 8, 2017 anneli@rice.edu

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