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Design Principles of Visual Analytics

Monica Wachowicz


Outline

• Definitions• Visual Analytics

– When should I use visual analytics ?– How can I apply visual analytics?

• Guiding principles for effective visual analytics• Conclusions


Visualization is

• A way of communication• A cognitive process involving memory, thought,

and reasoning• To use vision to think (Card, Mackinlay and Schneiderman)

• An external aid in problem solving• The use of computer generated, interactive,

visual representations of data to amplify cognition


Visual Representations


Geovisualization• … a loosely bounded domain that addresses the

visual exploration, analysis, synthesis and presentation of geospatial data by integrating approaches from cartography with those from other information representation and analysis disciplines, including scientific visualization, image analysis, information visualization, exploratory data analysis and GI Science“

Dykes, MacEachren, Kraak, 2005


Scientific Visualization• Use of the human visual processing system

assisted by computer graphics, as a means for the direct analysis and interpretation of information. (Clarke 2001)

• Scientific visualization is a branch of computer graphics which is concerned with the presentation of interactive or animated digital images to scientists who interpret potentially huge quantities of laboratory or simulation data or the results from sensors out in the field. (Wikipedia 2006)


Information Visualization• A method of presenting data or information in

non-traditional, interactive graphical forms. By using 2-D or 3-D color graphics and animation, these visualizations can show the structure of information, allow one to navigate through it, and modify it with graphical interactions. (UIUC - DLI, 1998)

• As a subject in computer science, information visualization is the use of interactive, sensory representations, typically visual, of abstract data to reinforce cognition. (Wikipedia 2006)


Visual Data Mining

• Present the data in some visual form, allowing the human to get insight into the data, draw conclusions, and directly interact with the data. (Keim 2002)

• Is particularly useful when little is known about the data and exploration goals are vague. (Keim 2002)


MineSet

Visual Data Mining


Visual Analytics

• … is the science of analytical reasoning facilitated by interactive visual interfaces (National Visualization and Analytics Center, 2004)

• … detection of the expected and discovery of the unexpected within massive, dynamically changing information spaces (Wong and Thomas, 2004)


• Synthesize information and derive insight from massive dynamic, ambiguous, and often conflicting data

• Detect the expected and discover the unexpected• Provide timely, and understandable assessments• Communicate assessment effectively for action/decision

(NVAC 2006)


Do we need a distinction??


Visual Analytics


When should I use visual analytics?

• Volume of data, orders of magnitude larger and different levels of abstraction

• Complexity of information spaces into very high dimensions, 200 the norm

• Information often out of context, incomplete, fuzzy

• Information in all media types: text, imagery, video, voice, web, sensor data

• Spatial, yet non-spatial abstract data• Multiple ontologies, languages, cultures


How can I apply visual analytics?

• Define the problem/question• Determine the data:

– Characteristics of the relevant data– Types of data (nominal, ordinal, interval, ration– Quality of data– Size, dimensionality and number of data items per

sample• Determine the visual representations

(visualisation technique + interaction technique)


(Keim 2002)


Guiding principles for effective visual analytics (Norman, Tversky)

Appropriateness Principle

– Visual analytics should provide neither more nor less information than that needed for solving the problem.


More is not necessarily better !!!


Naturalness Principle

– Experimental cognition most effective when representation most closely matches the information being represented.

– New visual metaphors must match users cognitive model of information.


Benediktine Space, Cone Trees, Perspective Walls, Magic Lenses, Information Cube, Landscapes, etc...


Matching Principle

– Visual analytics must match the task to be performed.


Task Model• Identify• Locate• Distinguish• Categorize• Cluster• Associate• Correlate• etc…

Wehrend’s work on visual operators


Apprehension Principle

– The content of the representation should be accurately and easily perceived


Faces are generated using :• Head Eccentricity • Eye Eccentricity • Pupil Size • Eyebrow Slope • Nose Size • Mouth Vertical Offset • Eye Spacing • Eye Size • Mouth Width • Mouth Openness

Chernoff Faces


Must address

• Accuracy: avoid miscommunication of information

• Reliability: dependable for decision making?

• Reproducibility: consistent from data set to data set?

• Interactivity: allow visual exploration• Usability: fitness-to-use


Tree mapping – Visual Hierarchy

300 data values, 3-6 dimensions


This clip shows the same data, but instead we sonificate a 5th parameter, which has 4 categorical values (using samples saying thenumbers from 1 to 4).

Again we take a tour to get anoverview of the data distribution which looks like this, when we map it to color.


Sound supports color. Color only represents a land cover type (7 categorical values).

These are mapped directly to samples of a voicesaying the numbers from 1 to 7. The clip shows a tour through the data visualization to create an overview of the datadistribution.


The Top 10 Visual Analytics Research Challenges

Application Challenges

1. Engineering Analytics 2. Software Analytics 3. Environmental Monitoring (Climate & Weather) 4. Personal Information Management (Vis@Home) 5. Physics / Astronomy 6. Biology & Medicine / Health 7. Mobile Graphics / Traffic 8. Business 9. Security (Homeland, Network, ...) 10. Disaster / Emergency Management

Workshop on Visual Analytics, June 2005, Darmstadt Germany


The Top 10 Visual Analytics Research Challenges

Technical Challenges

1. Problem Solving / Decision Science / Human Information Discourse 2. Semantics (incl. Modeling Semantics) 3. Scalability in Problem Size 4. Data Streams: Data Compression & Feature Extraction 5. Evaluation 6. Synthesis of Problems in Applications 7. Data Quality / Uncertainity8. Data Provenance 9. User Acceptability 10. Integration with Automated Analysis, Databases, Statistics,Perception. ...

Workshop on Visual Analytics, June 2005, Darmstadt Germany


Conclusions

• Effective visual representations are vital to enable visual analysis and improve discovery

• Cognitive science, statistical machine learning, perception, design, and visualization principles and techniques must be incorporated to the next generation of tools

design principles of visual analytics - geo-informatie.nl · presentation of geospatial data by...

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