Download Information Visualization - McMaster Computing and Software

Information Visualization
CS4HC3 / SE4HC3/ SE6DO3
Fall 2011
Instructor: Kevin Browne
[email protected]
Slide content is based heavily on Chapter 14 of the textbook:
Designing the User Interface: Strategies for Effective Human-Computer
Interaction / 5th edition, by Ben Schneiderman & Catherine Plaisant
Information Visualization
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Information visualization: “the use of interactive
visual representations of abstract data to
amplify cognition” (Ware, 2008)
Typical concerns: discovery of patterns, trends,
clusters, outliers and gaps in data
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Stock prices, social relationships, patient records
Human visual ability is “high bandwidth”, “high
performance”
Goal: be more than look slick, show
measurable usability benefits across different
platforms and users
Information Visualization
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Data type by task taxonomy
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Taxonomy for describing and characterizing
problems, visualizations
Seven data types and seven tasks
Data types: 1D linear, 2D map, 3D world,
multidimensional, temporal, tree, network
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First four are dimensional, last three are structural
Tasks: Overview, zoom, filter, details-on-demand,
relate, history, extract
Seven Data Types
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1D linear data
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Items which can be organized sequentially
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e.g. text document, list of names
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Design issues:
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Colours, sizes, layout
Scrolling, selection methods
Example user tasks: check which items have some
required attribute
Seven Data Types
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2D map data
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Items make up some part of the 2D area
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Not necessarily rectangular, e.g. Lake on Google Map
e.g. Geographic map, floor plans
Example user tasks: finding items, finding paths
between items
Seven Data Types
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3D world data
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Items with complex relationships with other items
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e.g. Volume, temperature, density
e.g. Medical imaging, architectural drawing,
scientific simulations
Design issues: position, orientation and navigation
for viewing 3D application
Example user tasks: temperature, density
Seven Data Types
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Multidimensional data
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Items with n attributes in n-dimensional space
Relational database contents can be treated this
way
Interface may allow user to view 2 dimensions at a
time
Seven Data Types
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Temporal data
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Very close idea to 1D sequential data, but warrant a
distinct data type in the taxonomy as temporal data
is so common
e.g. Stock market data, weather
Items have a beginning and end time, may overlap
in time
Example user tasks: finding events during a time
period, searching for periodical behaviour
Seven Data Types
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Tree data
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Non-root items have a link to a parent item
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Items, links can have multiple attributes
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e.g. Windows file explorer
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Example user tasks: how many items are children
of a node, how deep or shallow is the graph
Seven Data Types
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Network Data
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Items linked to arbitrary number of other items
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Example user task: shortest path, least costly path
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How to visualize, layout the network?
Seven Basic Tasks
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Overview task
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Give user overview of entire set of items
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e.g. Zoom out
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e.g. Field-of-view box
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e.g. Fish eye strategy
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If complete overview is impossible, is there an
effective overview strategy?
Seven Basic Tasks
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Zoom task
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Allow users to focus in on or enlarge items of
interest
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May allow users to control the “zoom factor”
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Extra importance if small display is a possibility
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e.g. Google maps on smartphone
Seven Basic Tasks
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Filter task
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Allows users to remove items
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e.g. Dynamic queries applied to data
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Highlighting desired items
Seven Basic Tasks
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Details-on-demand task
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Selecting item(s) and allowing users to get more
details
e.g. Click on an item, new window
e.g. Searching for a book on Amazon, can click on
different editions and get more details
Seven Basic Tasks
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Relate task
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Relating items within a set
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How to show relationships?
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Proximity
Containment
Connected lines
Colour-coding
Seven Basic Tasks
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History task
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History of tasks which can be undone, replayed,
refined
Much work is a “process”, allowing for refinement,
steps, important
e.g. Retrieval of past searches
Seven Basic Tasks
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Extract task
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Extraction of items
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Based on query parameters
Allow user to “save”, publish, examine extracted
items
Information Visualization Challenges
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Importing and cleaning data
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Combining visual representations with textual
labels
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Open Hamilton – data cleaning
How to put on text labels (e.g. on a map) without
covering what you wish to display?
Finding related information
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Proper judgement often requires looking at data
derived from multiple sources
Information Visualization Challenges
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Viewing large volumes of data
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Thousands of items of data? What about millions?
User-controlled aggregation mechanisms?
Integrating data mining
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Data mining also about finding patterns... but
through algorithms & machine learning
Data mining is objective, but outliers, discontinuities
may be far less obvious
Visualizing such that data miners can gone in on
interesting parts of data
Information Visualization Challenges
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Integrating with analytical reasoning techniques
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Collaborating with others
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Use data to support or disclaim hypotheses
e.g. IBM - Many Eyes
Achieving universal usability
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Text, tactile or sonic representations?
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e.g. National Cancer Institute's cancer atlas
Evaluation
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How to evaluate information visualizations?
Information Visualization
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Pre-attentive processing
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Unconscious accumulation of information from the
environment
Information that “stands out” is selected for attentive
(conscious) processing
Why does some information “stand out”?
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Not exactly sure!
But it has something to do with the stimulus itself, and the
person's current intentions or goals
Information Visualization
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Pure-capture model
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Focuses on stimulus salience
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“bottom-up”
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Do certain properties of the stimulus “stand out”
from the rest?
Contingent-capture model
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Focuses on person's current intentions and goals
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“top-down”
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e.g. Brain directs thought towards red square if
shown red square before viewing field of shapes
Gestalt Laws
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Gestalt psychology
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Berlin School, theory of mind
Brain is holistic, parallel, analog, self-organizing
tendencies
“the whole is greater than the sum of the parts”
Fundamental Gestalt Law (law of prägnanz):
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We tend to order our experience in a manner that is
regular, orderly, symmetric and simple
Gestalt Laws
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Law of Closure
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Law of Similarity
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The mind may experience elements it does not
perceive through sensation, in order to complete a
regular figure (that is, to increase regularity).
The mind groups similar elements into collective
entities or totalities. This similarity might depend on
relationships of form, colour, size, or brightness.
Law of Proximity
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Spatial or temporal proximity of elements may
induce the mind to perceive a collective or totality.
Gestalt Laws
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Law of Symmetry (Figure ground relationships)
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Law of Continuity
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Symmetrical images are perceived collectively,
even in spite of distance.
The mind continues visual, auditory, and kinetic
patterns.
Law of Common Fate
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Elements with the same moving direction are
perceived as a collective or unit.
Mackinlay's rankings of encodings
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Graphical perception
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“ability of viewers to interpret visual (graphical)
encodings of informationn andtherebyy decode
information in graphs” - Jeffrey Heer,Stanford
University
Which visual variables best encode
quantitative, ordinal or nominal data?
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Position
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Length
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Angle
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etc.
Weber's law
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Weber's law
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“just noticeable difference”
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I – original intensity of the stimulus
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Change in I is the minimum difference required for it
to be perceived (jnd)
K constant
Weber's law
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Practically, what this means...
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To be heard in a crowded room you may have to
shout, in a library you may have to whisper
When lifting a 2 lb weight a 0.2 lb increase may be
felt, but when lifting a 5 lb weight a 0.5 lb difference
may be required
For HCI:
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When are negative changes apparent?
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Don't want users to know...
When are positive changes apparent?
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We want users to realize they exist...
References
Designing the User Interface: Strategies for Effective Human-Computer Interaction / 5th edition, by Ben Schneiderman & Catherine
Plaisant (2010)
Folk, C.L., & Remington, R. Top-down modulation of preattentive processing: Testing the recovery account of contingent capture.
Visual Cognition, 14, 445-465.
Heer, Jeffery. Visualization Re-Design. CS4488 (http://hci.stanford.edu/courses/cs448b/f11/lectures/CS448B-20111004-Redesign.pdf )
Oct. 4 2011.
Keiman, Gabriel. Visual Object Recognition. Neurobiology 230 – Harvard / GSAS 78454
( http://klab.tch.harvard.edu/academia/classes/hms_neuro300_vision/slides/hms230_2011_Lecture6.pdf )
Montag, Ethan D., Weber's Law, ( http://www.cis.rit.edu/people/faculty/montag/vandplite/pages/chap_3/ch3p1.html ).
Ware, Clin, Visual Thinking for Design, Morgan Kaufmann, San Francisco, CA (2008).