Information Visualization

1. Information Visualization Prof. Mario Doulis Merz Akademie Stuttgart Information Visualization

2. Information Visualization Objectives • Students know main goals and issues of information visualization • Students know main techniques and actual trends

3. Information Visualization Overview Information Visualization • Definition • Goals • Examples • Tasks • Problems and Challenges Visualization Techniques • Visualization Types • Main Principles • Design Aspects • Perceptional Phenomena

4. Source: Spence, R., (2006). “Information Visualization: Design for Interaction”, 2. Auflage Information Visualization Information Visualization • The Purpose

5. A famous example of information visualization was Dr. John Snow's map of deaths from a cholera outbreak in London, 1854, in relation to the locations of public water pumps.The map showed that cholera occurred almost entirely near the Broad Street water pump. Closing the pump ended the neighborhood epidemic which had killed more than 500 people. Information Visualization Information Visualization • Successful Examples: The 1854 London Cholera Epidemic

6. In 2003, the US Federal Energy Regulatory Commission posted about 1.5 million messages from Enron's e-mail servers on its Web site. After duplicates were weeded out, a half-million e-mails were left from about 150 accounts, including those of the company's top executives. Most were sent from 1999 to 2001, a period when Enron executives were manipulating financial data, making false public statements, engaging in insider trading, and the company was coming under scrutiny by regulators. This graph produced by The New York Times reveals a map of a week's e-mail patterns in May 2001, when a new name suddenly appeared. Scientists found that this week's pattern differed greatly from others, suggesting different conversations were taking place that might interest investigators. http://www.visualcomplexity.com/vc/project.cfm?id=271 Source: The New York Times. Dr. Carey E. Priebe and Youngser Park, Johns Hopkins University. Information Visualization Information Visualization • Successful Examples: Enron's E-Mail Pattern

7. Exchange rate in Switzerland 1982 – 2007 automatically generated visualization (Microsoft Excel) Information Visualization Information Visualization • The Challenge http://www2.sims.berkeley.edu/research/projects/how-much-info-2003

8. Information Visualization

9. Information Visualization

10. Theo van Doesburg: Kontra–Konstruktion der Maison Particulière, 1923 Information Visualization

11. Information Visualization Information Visualization • The Challenge • More heterogenous data (especially abstract data) • More heterogenous user groups • Broader range of application areas • Need for specific visual representations • Need for customizable and easy-to-use tools

12. Keim, Daniel A. (2001): Datenvisualisierung und Data Mining, BTW'01, Oldenburg Information Visualization Information Visualization • The Challenge • To support the abilities of both, humans and computers

13. Information Visualization Information Visualization • Definitions • Information visualizations attempt to efficiently map data variables onto visual dimensions in order to create graphic representations. Gee, A.G., Yu, M., and Grinstein, G.G. • … involves abstract, nonspatial data. [Tory and Möller, 2004] • The use of computer-supported, interactive, visual representations of abstract data to amplify cognition. [Card et al., 1999]

14. Information Visualization Information Visualization • Abstract data • Items, entities, things which do not have a direct physical correspondence • Examples: football statistics, currency fluctuations, co-citation between scientists • Amplify cognition • Increasing memory and processing resources available • Reducing the amount of time to search • Enhancing the detections of patterns and enabling perceptual inference operations • Aiding perceptual monitoring • Encoding information in a manipulable medium Card S., Mackinlay J., Shneiderman, B. (1999) Readings in Information Visualization-Using Vision to Think, p16

15. Abbildung nach Rehäuser, Jakob; Krcmar, Helmut (1996): Wissensmanagement im Unternehmen. In: Schreyögg, Georg; Conrad, Peter (Hrsg.): Wissensmanagement. Berlin, S. 1-40 Information Visualization Information Visualization • From Characters to Information

16. Source: Shedroff, Nathan (2001): An overview of understanding. In: Wurman, Richard Saul: Information Anxiety 2. Indianapolis, S. 27-29 Information Visualization Information Visualization • From Data to Wisdom

17. Visual Form Data Raw Data Data Tables Visual Structures Views User DataTransformations VisualMappings ViewTransformations Human Interaction Information Visualization Information Visualization • Reference Model [Card et al., 1999] Data Transformations - Mapping raw data into an organization fit for visualization Visual Mappings - Encoding abstract data into a visual representation View Transformations - Changing the view or perspective onto the visual representation

18. Goals of Information Visualization • Map data onto visual dimensions for representation • Present overview of information • Present information from various viewpoints • Present information at several levels of detail • Tell stories about the data Information Visualization

19. Information Visualization Examples and Resources • http://flare.prefuse.org/apps/job_voyager Examples of the prefuse visualization toolkit • http://www.fusioncharts.com/LiveDemos.asp demo applications of the fusionchart software • http://www.cs.uncc.edu/~jfan/video.html cross-media video indexing and visualization • http://www.wallstats.com/zoom/ infographics using a zoom interface • http://manyeyes.alphaworks.ibm.com/manyeyes/ visualization platform provided by IBM with very good descriptions about visualization types • http://www.visual-literacy.org/periodic_table/periodic_table.html overview about different visualization types in form of a periodic table • http://www.visualcomplexity.com/vc/search.cfm?input=enron weblog about the visualization of complex networks • http://infosthetics.com/ weblog about information aesthetics with a plenty of examples and links • http://infovis-wiki.net/index.php?title=Main_Pagerecommendable infoviz wiki with a good glossar

20. History of Information Visualization • Milestones in the History of Data Visualization pdf document giving a brief overview about data visualization • http://www.math.yorku.ca/SCS/Gallery/ very good website about the history of data visualization with a lot of examples and figures Information Visualization

21. Tasks of Information Visualization • Search • Finding a specific piece of information in a data set • Browsing • Look over or inspect something in a more casual manner, seek interesting information • Analysis • Comparison-difference, find outliers and extremes, spot patterns • Categorize, associate • Locate, rank • Identify, reveal • Monitor, maintain awareness See also John Staskos course notes on "Information Visualization" Information Visualization

22. Schneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations, Proc. 1996 IEEE, Visual Languages, Boulder, CO, Sept.3-6,1996, pp. 336-343 Information Visualization Tasks of Information Visualization • Task by Data Type Taxonomy • Data Types • 1-D linear • 2-D map • 3-D world • Temporal • Multi-dimensional • Tree • Network • InfoVis Tasks • Overview • Zoom • Filter • Details-on-demand • Relate • History • Extract

23. Information Visualization • Classification of Information Visualization Techniques Keim, Daniel A. (2002): Datenvisualisierung und Data Mining, Datenbank-Spektrum, 2, pp. 30-39 Information Visualization

24. Unsolved Problems of Information Visualization [Chen, C.] • Usability • Understanding elementary perceptual-cognitive tasks • Prior knowledge • Education and training • Intrinsic quality measurement • Scalability • Aesthetics • Paradigm shift from structures to dynamics • Causality, visual inference, and predictions • Knowledge domain visualization Chen, C. (2005). Top 10 Unsolved Information Visualization Problems, IEEE Computer Graphics and Applications, 25 (4), pp. 12-16 Information Visualization

25. Challenges of Information Visualization [Plaisant, C.] • High data dimensionality • Scale • Advanced filtering mechanisms – which variables produce a potentially interesting visualization? • Usability and evaluation of usability Plaisant, C., 2004: The Challenges of Information Visualization Evaluation, Proceedings of IEEE AVI 2004 Information Visualization

26. Tabelle 2: Erwerbstätige nach Geschlecht, Nationalität und Beschäftigungsgrad in der Schweiz, 1970-2000 Quelle: Bundesamt für Statistik Zähljahr 1970 1980 1990 2000 Total 2847570 2878572 3390896 3606713 Vollzeit Erwerbstätige 2482821 2436079 2717999 2624778 Teilzeit Erwerbstätige 364749 442493 672897 981935 Männer Total 1874851 1842276 2066704 2028479 Vollzeit Erwerbstätige 1796894 1759595 1961572 1849670 Teilzeit Erwerbstätige 77957 82681 105132 178809 Frauen Total 972719 1036296 1324192 1578234 Vollzeit Erwerbstätige 685927 676484 756427 775108 Teilzeit Erwerbstätige 286792 359812 567765 803126 Information Visualization Information Visualization 20.12.2019 Visualization Types • Tables 26

27. A scatter plot uses cartesian coordinates to display values for two variables of a data set. Adding visual attributes to the dots (e.g., colour, shape, etc.) allows to increase the number of variables. Information Visualization Information Visualization 20.12.2019 Visualization Types • Scatter Plots 27

28. Using text labeling allows to easily integrate nominal attributes. Information Visualization Information Visualization 20.12.2019 Visualization Types • Scatter Plot with text labeling 28

29. A line graph is a classic method for visualizing continuous change. Several lines allow the comparison of multiple data sets. Information Visualization Information Visualization 20.12.2019 Visualization Types • Line Graphs 29

30. A bar graph is a classic method for numerical comparisons. Typical arrangements are vertical, horizontal, grouped and stacked bar graphs Information Visualization Information Visualization 20.12.2019 Visualization Types • Bar Graphs 30

31. A pie chart is proportions of values. It is divided into sectors, illustrating relative magnitudes or frequencies. Information Visualization Information Visualization 20.12.2019 Visualization Types • Pie Chart 31

32. A box-and-whisker plot graphically depicts groups of numerical data through their five-number summaries upper and lower extreme, upper and lower hinge, and median. Information Visualization Information Visualization 20.12.2019 Visualization Types • Box-and-Whisker Plots 32

33. A radar chart displays multivariate data as a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point. Information Visualization Information Visualization 20.12.2019 Visualization Types • Radar Chart (Spider Chart) 33

34. Visualization Types • Parallel Coordinates Chart A parallel coordinate chart allows to show the relationship of attributes of multivariate data sets. Information Visualization

35. Visualization Types • Mosaic Plot A mosaic plot is a multidimensional barchart deviding the data-set into multiple categories. Information Visualization

36. A choropleth map is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map. Information Visualization Information Visualization 20.12.2019 Visualization Types • Thematic Maps - Choropleth Maps 36

37. A cartogram is a map in which some thematic mapping variable is substituted for land area. The geometry or space of the map is distorted in order to convey the information of this alternate variable. http://www.worldmapper.org Information Visualization Information Visualization 20.12.2019 Visualization Types • Thematic Maps - Cartogram 37

38. Visualization Types • Information Graphics Information graphics are visual representations of information, data or knowledge. These graphics are used where complex information and processes need to be explained quickly and clearly. Information Visualization Information Visualization 20.12.2019 38

39. Two images are used for presentation. One shows a rough overview of the complete information space and neglects details. The other one shows a small portion of the information space and visualizes details. Both images are either shown sequentially or in parallel. http://infovis-wiki.net/index.php?title=Overview-plus-Detail Information Visualization Information Visualization 20.12.2019 Main Principles • Overview & Detail 39

40. Information Visualization Information Visualization 20.12.2019 Main Principles • Overview & Detail by Zoom and Pan 40

41. Information Visualization Information Visualization 20.12.2019 Main Principles • Overview & Detail by Zoom and Pan 41

42. Information Visualization Information Visualization 20.12.2019 Main Principles • Focus & Context "Focus+Context start from three premises: First, the user needs both overview (context) and detail information (focus) simultaneously. Second, information needed in the overview may be different from that needed in detail. Third, these two types of information can be combined within a single (dynamic) display, much as in human vision." Stuart Card 42

43. Information Visualization Information Visualization 20.12.2019 Main Principles • Focus & Context by Distortion 43

44. Main Principles • Focus & Context by Distortion Source: Spence, Information Visualization. 2007. => Mac OS X dock Information Visualization Information Visualization 20.12.2019 44

45. Main Principles • Focus & Context by Zoom and Pan http://www.wallstats.com/zoom/ Information Visualization Information Visualization 20.12.2019 45

46. Main Principles • Linking and Brushing "The idea of linking and brushing is to combine different visualization methods to overcome the shortcomings of single techniques. Interactive changes made in one visualization are automatically reflected in the other visualizations. Note that connecting multiple visualizations through interactive linking and brushing provides more information than considering the component visualizations independently." Daniel A. Keim Information Visualization Information Visualization 20.12.2019 46

47. Main Principles • Linking and Brushing http://www.infovis-wiki.net/index.php?title=Linking_and_Brushing Information Visualization Information Visualization 20.12.2019 47

48. Source: Visualisation 1 - Graphical Communication. Computer Graphics Unit, Manchester Computing Centre, University of Manchester Information Visualization Information Visualization 20.12.2019 Design Aspects • Elements of Graphs 48

49. size of effect shown in graphic lie factor = size of effect in data Design Aspects • Lie Factor Tufte, E. R., 2001: “The Visual Display of Quantitative Information”, 2. Auflage Information Visualization

50. data–ink data–ink ratio = total ink used to draw the graphic Design Aspects • Data Ink Tufte, E. R., 2001: “The Visual Display of Quantitative Information”, 2. Auflage Information Visualization