designing with the user in mind

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

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


Line Charts

Overlap reduces legibility of individual time series

Small Multiples?


Stacked Time Series

http://www.babynamewizard.com

Stacked Time SeriesNot informative aggregation for many data types or negative values


Stacked Time SeriesNot informative aggregation for many data types or negative values


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

Both use Layered Position encoding of values

Horizon Graphs


Comparison across Band requires mental unstacking

Horizon Graphs


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


Which is bigger?


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


No significant difference between 2 and 3 bands


No significant difference between 2 and 3 bands

So Significant difference between Offset and Mirror charts

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


Disadvantage of line chart compared to both mirrored charts



Accuracy decreased at smaller chart heights



Accuracy decreased at smaller chart heights

2 band remained stable at lower heights


2-Band has lower baseline error rate, but higher virtual resolution at a the same resolution


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


2-Band higher Estimation time than 1-band or line chard


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

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


Object of study cannot usually be studied directly, tipically analyzed through a set of discrete samples


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


Scientific Visualization



Information Visualization



Information Visualization

Math Visualization

Continuous Models

Discrete Models

Classification of visualization tasks


Above/BelowRight/left

Inside/outside


What is connected to X? What is the child of Y?


Clusters Outliers


Study details of items and filter items


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

designing with the user in mind

Documents

estimation time

horizon chart

values horizon graphs

offset horizon graph

time series graphsdetermine

data types

horizon graph variant

estimation taskswhich