1. Jamie Starke

2.  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

3.  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

5.  Analysts often need to compare a large number of time series ◦ Finance  Stocks, Exchange rates ◦ Science  Temperatures, Polution levels ◦ Public Policy  Crime Rates

6.  Effective Presentation of multiple time series ◦ Increase the amount of data with which human analysts can effectively work ◦ Maximize data density (Tufte)

7.  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

11.  Color hue ranks highly for nominal (category) data but poorly for quantitative data ◦ Bertin

12. http://coralreefwatch.noaa.gov

13. Overlap reduces legibility of individual time series http://coralreefwatch.noaa.gov

14. Overlap reduces legibility of individual time series Small Multiples? http://coralreefwatch.noaa.gov

15. http://www.babynamewizard.com

16. Not informative aggregation for many data types or negative values http://www.babynamewizard.com

17. Not informative aggregation for many data types or negative values http://www.babynamewizard.com Comparisons involve length rather than more accurate position judgements

18. http://graphs.gapminder.org

19. Animation results in significantly lower accuracy in analytic tasks compared to small multiples of static charts

24. Both use Layered Position encoding of values

25. Comparison across Band requires mental unstacking

26. Both use Layered Position encoding of values Comparison across Band requires mental unstacking Both mirror and offset show promise for increasing data density

27.  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

29. Which is bigger?

30. What is the Absolute Difference?

31.  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?

32.  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

35. No significant difference between 2 and 3 bands

36. So Significant difference between Offset and Mirror charts

38. Estimation time increases as the bands increase

39.  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

40.  How do mirroring and layering affect estimation time and accuracy compared to line charts?  How does chart size affect estimation time and accuracy?

41.  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.

44. Disadvantage of line chart compared to both mirrored charts

45. Accuracy decreased at smaller chart heights

46. Disadvantage of line chart compared to both mirrored charts Accuracy decreased at smaller chart heights 2 band remained stable at lower heights

48. 2-Band has lower baseline error rate, but higher virtual resolution at a the same resolution

49. Banded mirrored charts had nearly identical error levels at matching virtual resolution

51. 2-Band higher Estimation time than 1- band or line chard

52. No significant difference between Line Chart and 1-Band mirrored Chart

53.  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

54.  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

55.  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

56.  Definition of visualization: “… the use of computer-supported, interactive, visual representations of data to amplify cognition…” Card et al.

57.  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)

58.  Based on characteristics of models of the data rather then characteristics of data itself ◦ Model-Based visualization taxonomy

60. Idea or physical object being investigated

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

62. Idea or physical object being investigated 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

63.  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

64.  Idea Being investigated  Varies depending on users and their interests  Primary care givers ◦ Study a particular patient  Research physicians ◦ Study an illness

65.  Design Models ◦ Explicitly encoded by designers into visualization algorithms  User Models ◦ In the mind of the user

66.  May include assumptions about the data and the display algorithm, developing hypotheses, searching for evidence to support or contradict hypotheses, and refining the model

67.  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

68.  Continuous ◦ Data can be interpolated  Discrete ◦ Data can not be interpolated

69.  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

70.  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

72. Scientific Visualization

73. Information Visualization

74. Scientific Visualization Information Visualization Math Visualization

78. Above/Below Right/left Inside/outside Above/Below Right/left Inside/outside

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

80. Clusters Outliers

81. Study details of items and filter items

82. Study Trends Increasing Decreasing Study Trends Increasing Decreasing

83.  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