1. Interactions in time
Evaluation and redesign of
three abstract temporal data visualisations
Author: Lisa Koeman
Supervisor: Christopher Power

2. time
“a non spatial continuum that is measured in
terms of events which succeed one another
from past through present to future”
[Merriam-Webster Dictionary, 2012]

3. past present future
time
“a non spatial continuum that is measured in
terms of events which succeed one another
from past through present to future”
[Merriam-Webster Dictionary, 2012]

4. past present future
temporal data
YYYY-MM-DD, Event 1
YYYY-MM-DD, Event 2
YYYY-MM-DD, Event 3
etc.

5. past present future
temporal data
YYYY-MM-DD, Event 1
YYYY-MM-DD, Event 2
YYYY-MM-DD, Event 3
etc.
past present future
time-series data
YYYY-MM-DD, Event 1, Value
YYYY-MM-DD, Event 2, Value
YYYY-MM-DD, Event 3, Value
etc.

6. visualisation of temporal data
YYYY-MM-DD, Event 1, Value
YYYY-MM-DD, Event 2, Value
difficult to interpret &
YYYY-MM-DD, Event 3, Value
etc. time-consuming
raw data

7. visualisation of temporal data
YYYY-MM-DD, Event 1, Value
YYYY-MM-DD, Event 2, Value
difficult to interpret &
YYYY-MM-DD, Event 3, Value
etc. time-consuming
raw data
data visualisation
visualised data

8. visualisation of temporal data
YYYY-MM-DD, Event 1, Value
YYYY-MM-DD, Event 2, Value
difficult to interpret &
YYYY-MM-DD, Event 3, Value
etc. time-consuming
raw data
data visualisation
visualised data
“the use of computer-supported,
interactive, visual
representations of data to
digitally visualised data amplify cognition” [Card et al, 1999]

9. but are they any good?

10. method
within-participants design
1 2 3
three visualisations, three datasets
& set of identical task kinds

11. task kinds [MacEachren, 2004]
questio
n1 existence of a data element
question 2 example: “was a measurement made on 8 December 1977?”
q uestion
3 temporal location
question 4 example: “when was the lowest number of births?”
questio
n5 rate of change
question 6 example: “how much is the difference in number of births between
1 February 1977 and 1 February 1978?”
questio
n7 sequence
question 8 example: “did the number of births reach 331 before or after March
in 1982?”
question 9
temporal pattern
example: “when you look at the overall visualisation, do you see any
patterns in the data?”

12. visualisation 1: calendar
[M. Bostock, On-line]

13. visualisation 2: timeline
[Shutterstock, On-line]

14. visualisation 3: radial
[Tominski and Hadlak, On-line]

15. measurements
✓
completion time accuracy of answers
x
perceived ease of use preference

16. measurements
+ -
... and qualitative data on positive &
negative aspects of each visualisation -
and suggestions for improvement
+ observations

17. participants
18 participants (1 female, 17 male)
all part of Computer Science department
mean age of 26.2 years (ranging from 20 to 36)

18. results: completion time
75
seconds
calendar visualisation
timeline visualisation
spiral visualisation
50
25
0
existence of temporal rate of change sequence
data location
element task
significantly shorter completion
time in calendar visualisation

19. results: accuracy
100
percent
calendar visualisation
timeline visualisation
75 spiral visualisation
50
25
0
existence of temporal rate of change sequence
data location
element task
accuracy is significantly higher in
timeline visualisation, compared
to calendar visualisation

20. results: ease of use
frequency
9
calendar visualisation
timeline visualisation
7 spiral visualisation
5
2
0
very easy easy to use neither easy difficult to use very difficult
to use nor difficult to use
calendar visualisation was
perceived as significantly easier
to use than the spiral visualisation

21. results: preference
calendar 27,78%
timeline 55,56%
spiral 5,56%
no preference 11,11%
0% 15% 30% 45% 60%
percent of participants who preferred this option
preferences are significantly different from
an even distribution: timeline visualisation
is preferred by the majority of participants

22. comments
content analysis on positive aspects, negative
aspects and suggestions for improvement:
task
presentation
neither
kappa coefficient of 0.91
using the qualitative feedback, redesigns of all
visualisations were produced

23. explanations: calendar
[M. Bostock, On-line]

24. redesign: calendar
1902 January February March April May June July August September October November December
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
1903 January February March April May June July August September October November December
Sunday
Monday =
Tuesday
Wednesday
Thursday
Friday
Saturday
1904 January February March April May June July August September October November December
=
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
1905 January February March April May June July August September October November December
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Show dates 0 - 20% 41 - 60% 81 - 100%
21 - 40% 61 - 80% Edit ranges...

25. visualisation 2: timeline
[Shutterstock, On-line]

26. redesign 2: timeline
Date: 02-12-1909 Value: 160
350
300
250
200
150
100
50
1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912
Start date: 01/01/1902 End date: 03/11/1912
1980 1900 1910 1920

27. visualisation 3: radial
[Tominski and Hadlak, On-line]

28. redesign 3: radial
Range: 1994 - 1998
0 - 20% 41 - 60% 81 - 100%
Navigate to: dd/mm/yyyy
21 - 40% 61 - 80% Edit ranges...
Zoom: + -
1998 Jan
Dec
1997
v
Fe
No
b
1996
1995
Oc t
Mar
1994
Apr
Sep
M
g
ay
Au
Jul Jun
Preview of zoom:

29. conclusions
• significant differences found in task kinds
carried out in calendar, timeline and radial
visualisation: completion time, accuracy,
perceived ease of use and preference
• preference differs from actual measured
“performance” of participants, as does
familiarity
• informal evaluation of redesigns shows
improvements can be made
• results show that empirical evaluations give
insights that have implications for design

30. limitations of study
• debatable: evaluating data visualisations
using pre-defined tasks
• three specific implementations of types of
visualisations
• different levels of familiarity with
visualisations
• ideally, exact same tasks should be
compared, in exact same datasets
• participants not representative

31. future work
• more empirical evaluations of data
visualisations: better understanding of
components that influence performance
• ensures quicker, more accurate
performance, essential for many
professional domains
• working visualisations of redesigns should
be evaluated in similar fashion
• developing evaluation method that covers
real life interaction with visualisations
• what users want vs. what is best for them

32. references
• Merriam-Webster Dictionary, “Definition of ‘time’,” [On-line].
Available: http://www.merriam-webster.com/dictionary/time.
• S. Card, J. Mackinlay, and B. Shneiderman, Readings in information
visualization: using vision to think. Morgan Kaufmann, 1999.
• A. MacEachren, How maps work: representation, visualization, and
design. The Guilford Press, 2004.
• M. Bostock, “Calendar visualisation with D3.js,” [On-line]. Available:
http://d3js.org/.
• Shutterstock, “Rickshaw visualisation,” [On-line]. Available: http://
code.shutterstock.com/rickshaw/.
• C. Tominski and S. Hadlak, “Spiral visualisation,” [On-line]. Available:
www.informatik.uni-rostock.de/~ct/software/TTS/TTS.html,
University of Rostock.