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Data Visualization

A Short Course in. Data Visualization. THE GOAL... Give the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.” - attributed to Tufte.

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Data Visualization

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  1. A Short Course in Data Visualization

  2. THE GOAL...Give the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.”- attributed to Tufte

  3. THE GOAL...Give the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.”- attributed to Tufte

  4. Challenger January 28, 1986 73 seconds after launch

  5. Challenger Faxed Charts (1 of 13)

  6. ChallengerPresidential Commission Investigation

  7. ChallengerAll Previous Launches

  8. Challenger Sorted Dataset

  9. ChallengerDamage vs. Temperature Shuttle Destroyed Shuttle OK Challenger Launch

  10. Information: overall patterns & detailed behavior Overall – 11 year sunspot cycle visible Detail – rise is faster than fall (seen when banked to 45)

  11. Flaws:Display 0.0 on X-axis should be 0.6 X & Y should be on the same scale, this makes the equal emission line (Y = X) easier to understand Deviations from line are to be measured vertically, not orthogonally

  12. Flaws:Reasoning Flights without O-ring damage omitted Severity of O-ring damage omitted

  13. Flaws:Display, reasoning & message Apparent message – body mass & brain mass are correlated Claimed message – “the beast with the largest brain mass for body weight is called Homo Sapiens” Desired message – humans have a larger brain mass for body surface than other organisms

  14. Data Visualization • Pattern Perception • Focuses on the relationships between values • Decoding (detection & assembly) • Estimation (discrimination, ranking, ratioing) • Table look-up • Focuses on the individual values themselves • Scanning • Interpolation • Matching

  15. Decoding:Color Brain does not naturally order hues Perceptual merging occurs at 7 to 15 hues

  16. Decoding:Color Two distinct hues can clearly demark boundaries Use distinct hues (cyan, magenta, green, orange, blue) for categorical variables

  17. Decoding:Color Use differing lightness (or saturation) for quantitative variables

  18. Decoding:texture Texture symbols are used when color is not available Some symbols pairs are easier to distinguish than others

  19. Decoding:texture Choice of symbol set greatly affects perception

  20. Decoding:overlap If few overlap problems exist… try , , , ,  If moderate overlap problems exist… try o, +, <, S, W If extreme overlaps exist… try o, /, , ,  (or jittering)

  21. Decoding:reference grids Weber’s Law: the greater the percentage increase in line length, the greater the probability of a difference being detected

  22. Decoding:reference grids The grid allows for the perception of the differences in the dips in the left column of each set of graphs The grid is not as dark as the actual line as it is for reference only

  23. Decoding:reference grids Without grids, distances between curves are detected orthogonal to the lines However, distances between the lines are correctly measured vertically If distances between the lines is the information of interest, graph the subtraction instead

  24. Decoding:reference grids Lack of inherent reference grids in pie charts hinders pattern assembly In the pie chart at left two sizes are perceived… odd wedges are small and even wedges are large In the dot plot at left, variation is seen within the group of odd wedges and within the group of even wedges

  25. Decoding:reference grids Lack of inherent reference grids in divided bar charts hinders pattern assembly… the Hart age effect is lost in the graph on the left

  26. Decoding:reference grids Lack of inherent reference grids in area charts hinders pattern assembly… the bend is lost in the graph on the left

  27. Decoding:slopes Patterns in slopes are perceived best when the graph is banked to 45 The horizontal and vertical scales are adjusted so the average of the absolute angles of the individual segments is 45 degrees

  28. Decoding:ordering Ordering of categories impacts which pattern is perceived from the data... On the left, it is easy to see that sheep do not follow the general livestock pattern. On the right, it is easy to see that Greece differs from the general country pattern.

  29. Estimation • Discrimination • Same or different • Ranking • Larger or smaller • Ratioing • Magnitude change

  30. Table look-up “Identify the largest yield” • Scanning • Move eyes to locate symbol furthest to right • Interpolation • Estimate value encoded by the symbol relative to scale • Matching • Compare the symbol to the keys “It’s Waseca No. 462 in 1931 with 65 bushels/acre”

  31. Table look-up • Certain graph techniques force pattern perception to be a table look-up operation… • Pie charts • Divided bar charts • Area charts • Alphabetically ordered categories

  32. Perception failures (and possible corrections) Excessive clutter

  33. Perception failures (and possible corrections) Data on the scale

  34. Perception failures (and possible corrections) Unnecessary information inside the data rectangle

  35. Perception failures (and possible corrections) Symbols lost in the overlap

  36. Perception failures (and possible corrections) Pattern lost in the overlap

  37. Perception failures (and possible corrections) Pattern changed by scale breaks

  38. Encoding:Your choices affect pattern perception

  39. Poor visualization ignores implied scales

  40. Use area or volume to represent univariate data (LF = 2.8)(LF =1317)

  41. Poor visualization uses “building plots”

  42. Poor visualizationuses “ribbon plots”

  43. Poor visualization changes the scales in mid-axis

  44. Poor visualization includes annoying optical illusions

  45. Poor visualization uses the wrong format

  46. Poor visualization distorts the truth

  47. Poor visualizationis not always accidental... ...this is the easiest way to to “lie” with statistics.

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