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Home , Visual analytics

Principles of Visual Analytics

Monica Wachowicz

160606 WUR-CGI/MW GRS-30806 Outline

• Definitions • Visual Analytics – When should I use visual analytics ? – How can I apply visual analytics? • Guiding principles for effective visual analytics • Conclusions

160606 WUR-CGI/MW GRS-30806 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

160606 WUR-CGI/MW GRS-30806 Visual Representations

160606 WUR-CGI/MW GRS-30806 Visual Representations

160606 WUR-CGI/MW GRS-30806

• … a loosely bounded domain that addresses the visual exploration, analysis, synthesis and presentation of geospatial data by integrating approaches from with those from other information representation and analysis disciplines, including , image analysis, information visualization, exploratory data analysis and GI Science“

Dykes, MacEachren, Kraak, 2005 160606 WUR-CGI/MW GRS-30806 Scientific Visualization

• Use of the human visual processing system assisted by , 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)

160606 WUR-CGI/MW GRS-30806 160606 WUR-CGI/MW GRS-30806 160606 WUR-CGI/MW GRS-30806 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)

160606 WUR-CGI/MW GRS-30806 160606 WUR-CGI/MW GRS-30806 160606 WUR-CGI/MW GRS-30806 Visual

• 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)

160606 WUR-CGI/MW GRS-30806 Visual Data Mining

MineSet

160606 WUR-CGI/MW GRS-30806 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)

160606 WUR-CGI/MW GRS-30806 • 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)

160606 WUR-CGI/MW GRS-30806 Do we need a distinction??

160606 WUR-CGI/MW GRS-30806 160606 WUR-CGI/MW GRS-30806 Visual Analytics

160606 WUR-CGI/MW GRS-30806 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 160606 WUR-CGI/MW GRS-30806 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 + )

160606 WUR-CGI/MW GRS-30806 (Keim 2002) 160606 WUR-CGI/MW GRS-30806 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.

160606 WUR-CGI/MW GRS-30806 More is not necessarily better !!!

160606 WUR-CGI/MW GRS-30806 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.

160606 WUR-CGI/MW GRS-30806 Benediktine Space, Cone Trees, Walls, Magic Lenses, Information Cube, Landscapes, etc...

160606 WUR-CGI/MW GRS-30806 Matching Principle

– Visual analytics must match the task to be performed.

160606 WUR-CGI/MW GRS-30806 Task Model • Identify • Locate • Distinguish • Categorize •Cluster • Associate • Correlate •etc…

Wehrend’s work on visual operators

160606 WUR-CGI/MW GRS-30806 Apprehension Principle

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

160606 WUR-CGI/MW GRS-30806 Faces are generated using : • Head Eccentricity • Eye Eccentricity Chernoff Faces • Pupil Size • Eyebrow Slope • Nose Size • Mouth Vertical Offset • Eye Spacing • Eye Size • Mouth Width • Mouth Openness

160606 WUR-CGI/MW GRS-30806 160606 WUR-CGI/MW GRS-30806 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

160606 WUR-CGI/MW GRS-30806 Tree mapping – Visual Hierarchy

300 data values, 3-6 dimensions

160606 WUR-CGI/MW GRS-30806 This clip shows the same data, but instead we sonificate a 5th parameter, which has 4 categorical values (using samples saying the numbers from 1 to 4).

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

160606 WUR-CGI/MW GRS-30806 Sound supports color. Color only represents a land cover type (7 categorical values).

These are mapped directly to samples of a voice saying the numbers from 1 to 7. The clip shows a tour through the to create an overview of the data distribution.

160606 WUR-CGI/MW GRS-30806 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 160606 WUR-CGI/MW GRS-30806 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 / Uncertainity 8. Data Provenance 9. User Acceptability 10. Integration with Automated Analysis, Databases, Statistics,Perception. ...

Workshop on Visual Analytics, June 2005, Darmstadt Germany

160606 WUR-CGI/MW GRS-30806 Conclusions

• Effective visual representations are vital to enable visual analysis and improve discovery • , statistical machine learning, perception, design, and visualization principles and techniques must be incorporated to the next generation of tools

160606 WUR-CGI/MW GRS-30806