Designing with the User in mind
Jamie Starke
Sizing the Horizon: The Effects of Chart Size and Layering on the Graphical Perception of Time Series Visualizations◦ J. Heer, N. Kong, M. Agrawala (2009). CI 2009
Rethinking Visualization: A High-Level Taxonomy◦ Melanie Tory and Torsten Moller. InfoVis 2004
Sizing the Horizon: The Effects of Chart Size and Layering on the Graphical Perception of Time Series Visualizations◦ J. Heer, N. Kong, M. Agrawala (2009). CI 2009
Rethinking Visualization: A High-Level Taxonomy◦ Melanie Tory and Torsten Moller. InfoVis 2004
Analysts often need to compare a large number of time series◦ Finance
Stocks, Exchange rates◦ Science
Temperatures, Polution levels◦ Public Policy
Crime Rates
Why?
Effective Presentation of multiple time series◦ Increase the amount of data with which human
analysts can effectively work◦ Maximize data density (Tufte)
Goal
Effective Presentation of multiple time series◦ Increase the amount of data with which human
analysts can effectively work◦ Maximize data density (Tufte)
Increased Data Density DOES NOT IMPLY
Increased Perception
Goal
Color hue ranks highly for nominal (category) data but poorly for quantitative data◦ Bertin
Graphical Perception
Line Charts
http://coralreefwatch.noaa.gov
Line Charts
Overlap reduces legibility of individual time series
http://coralreefwatch.noaa.gov
Line Charts
Overlap reduces legibility of individual time series
Small Multiples?
http://coralreefwatch.noaa.gov
Stacked Time Series
http://www.babynamewizard.com
Stacked Time SeriesNot informative aggregation for many data types or negative values
http://www.babynamewizard.com
Stacked Time SeriesNot informative aggregation for many data types or negative values
http://www.babynamewizard.com
Comparisons involve length rather than more accurate position judgements
Animation
http://graphs.gapminder.org
Animation
http://graphs.gapminder.org
Animation results in significantly lower accuracy in analytic tasks compared to small multiples of static charts
Horizon Graphs
Horizon Graphs
Horizon Graphs
Horizon Graphs
Horizon Graphs
Both use Layered Position encoding of values
Horizon Graphs
Both use Layered Position encoding of values
Comparison across Band requires mental unstacking
Horizon Graphs
Both use Layered Position encoding of values
Comparison across Band requires mental unstacking
Both mirror and offset show promise for increasing data density
How much does chart sizing and layering have on speed and accuracy of graphical perception◦ 2 experiments
Tasks: Discrimination and estimation tasks for points on time series graphs Determine the impact of band number and horizon graph
variant (mirrored or offset) on value comparisons between horizon graphs
Compare line charts to horizon graphs and investigate the effect of chart height on both
Used 80% trimmed means to analyze estimation time and accuracy
Evaluation
Discrimination and Estimation tasks
Discrimination and Estimation tasks
Which is bigger?
Discrimination and Estimation tasks
Which is bigger?
What is the Absolute Difference?
How does the choice of mirrored or offset horizon graph affect estimation time or accuracy?
How does the number of bands in a horizon chart affect estimation time or accuracy?
Experiment 1: Questions
Offset graphs would result in faster, more accurate comparisons than mirror graphs, as offset graphs do not require mentally flipping negative values
Increasing the number of bands would increase estimation time and decrease accuracy across graph variants
Experiment 1: Hypotheses
Experiment 1: Bands
Experiment 1: Estimation Error
Experiment 1: Estimation Error
No significant difference between 2 and 3 bands
Experiment 1: Estimation Error
No significant difference between 2 and 3 bands
So Significant difference between Offset and Mirror charts
Experiment 1: Estimation Time
Experiment 1: Estimation Time
Estimation time increases as the bands increase
As band count rose, participants experienced difficulty identifying and remembering which band contained a value and that performing mental math became fatiguing
Working with ranges of 33 values in the 3-band condition was more difficult than working with the ranges in the 2 and 4 band that were multiples of 5
Experiment 1: Observations
How do mirroring and layering affect estimation time and accuracy compared to line charts?
How does chart size affect estimation time and accuracy?
Experiment 2: Questions
At larger chart heights line charts would be faster and more accurate than mirror charts both with and without banding, and mirror charts without banding would be faster and more accurate than those with banding
As chart heights decreased, error would increase monotonically, but would do so unevenly across chart types due to their differing data densities.
Experiment 2: Hypotheses
Experiment 2: Chart Type
Experiment 2: Estimation error
Experiment 2: Estimation error
Disadvantage of line chart compared to both mirrored charts
Experiment 2: Estimation error
Disadvantage of line chart compared to both mirrored charts
Accuracy decreased at smaller chart heights
Experiment 2: Estimation error
Disadvantage of line chart compared to both mirrored charts
Accuracy decreased at smaller chart heights
2 band remained stable at lower heights
Experiment 2: Estimation Error
Experiment 2: Estimation Error
2-Band has lower baseline error rate, but higher virtual resolution at a the same resolution
Experiment 2: Estimation Error
2-Band has lower baseline error rate, but higher virtual resolution at a the same resolution
Banded mirrored charts had nearly identical error levels at matching virtual resolution
Experiment 2: Estimation Time
Experiment 2: Estimation Time
2-Band higher Estimation time than 1-band or line chard
Experiment 2: Estimation Time
2-Band higher Estimation time than 1-band or line chard
No significant difference between Line Chart and 1-Band mirrored Chart
Mirroring does not hamper graphical perception
Layered bands are beneficial as chart size decreases
Optimal chart sizing◦ Line Chart or 1-Band Mirrored: 24 px Height◦ 2-band Mirrored: 12 and 6 px
Implications
Sizing the Horizon: The Effects of Chart Size and Layering on the Graphical Perception of Time Series Visualizations◦ J. Heer, N. Kong, M. Agrawala (2009). CI 2009
Rethinking Visualization: A High-Level Taxonomy◦ Melanie Tory and Torsten Moller. InfoVis 2004
Sizing the Horizon: The Effects of Chart Size and Layering on the Graphical Perception of Time Series Visualizations◦ J. Heer, N. Kong, M. Agrawala (2009). CI 2009
Rethinking Visualization: A High-Level Taxonomy◦ Melanie Tory and Torsten Moller. InfoVis 2004
Definition of visualization:“… the use of computer-supported, interactive,
visual representations of data to amplify cognition…”
Card et al.
Application area is scientific (scientific visualization) or non-scientific (information visualization)
Data is physically based (scientific visualization) or abstract (information visualization)
Spatialization is given (scientific visualization) or chosen (information visualization)
Scientific vs Information Visualization
Based on characteristics of models of the data rather then characteristics of data itself◦ Model-Based visualization taxonomy
Taxonomy
Definitions
DefinitionsIdea or physical object being investigated
DefinitionsIdea or physical object being investigated
Object of study cannot usually be studied directly, tipically analyzed through a set of discrete samples
DefinitionsIdea or physical object being investigated
Object of study cannot usually be studied directly, typically analyzed through a set of discrete samples
Set of assumptions of the designer about the data which are build into the algorithm
Users set of assumptions about the object of study and interpretations of data that affect their understanding
Object of study◦ Patient who has shown worrisome symptoms
The Data◦ MRI or CT images of the patient’s brain stored
digitally User Model
◦ How Physicians think about data. Determines the visualization they will choose
Design Model◦ Designer of visualizations assumptions about the
data that will be visualized
Example
Idea Being investigated Varies depending on users and their
interests
Primary care givers◦ Study a particular patient
Research physicians◦ Study an illness
Object of Study
Design Models◦ Explicitly encoded by designers into visualization
algorithms User Models
◦ In the mind of the user
User and Design Models
May include assumptions about the data and the display algorithm, developing hypotheses, searching for evidence to support or contradict hypotheses, and refining the model
Constructing User Models
Based on Design Model◦ User models are closely related to design models
because users choose visualizations that match their ideas and intentions
◦ Emphasizes human size of visualization
Proposed Taxonomy
Continuous◦ Data can be interpolated
Discrete◦ Data can not be interpolated
Discrete/Continuous Classification
Interval and ratio data can be visualized as continuous or discrete model techniques
Nominal and ordinal data can often only be visualized by discrete model techniques, as interpolating is not meaningful
Types of Data
Continuous to discrete is just a matter of leaving data points as discrete entities, sampling or aggregating data points into bins or categories
Discrete to continuous requires parameterizing the model or embedding it into a continuous space
Converting
Design Model Classification
Design Model Classification
Scientific Visualization
Design Model Classification
Scientific Visualization
Information Visualization
Design Model Classification
Scientific Visualization
Information Visualization
Math Visualization
Continuous Models
Discrete Models
Classification of visualization tasks
Classification of visualization tasks
Above/BelowRight/left
Inside/outside
Classification of visualization tasks
What is connected to X? What is the child of Y?
Classification of visualization tasks
Clusters Outliers
Classification of visualization tasks
Study details of items and filter items
Classification of visualization tasks
Study TrendsIncreasing Decreasing
Complaints (migraine headaches)◦ Points on a timeline
Long-term events (Pain, drug treatments)◦ Bars on a timeline
Ongoing measurements (blood pressure)◦ Line graphs, scatter plot, bar charts
Example: Medical Records
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