Interactions in timeEvaluation and redesign of

three abstract temporal data visualisations

Lisa KoemanChristopher Power

Author:Supervisor:

“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]

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]

time

past present future

past present future

YYYY-MM-DD, Event 1YYYY-MM-DD, Event 2YYYY-MM-DD, Event 3

etc.

temporal data

YYYY-MM-DD, Event 1YYYY-MM-DD, Event 2YYYY-MM-DD, Event 3

etc.

temporal data

past present future

YYYY-MM-DD, Event 1, ValueYYYY-MM-DD, Event 2, ValueYYYY-MM-DD, Event 3, Value

etc.

time-series data

past present future

visualisation of temporal data

• evolved over many centures

• little knowledge on the visualisations; lack of empirical evaluations

• which types of visualisations are most appropriate for which kinds of tasks?

YYYY-MM-DD, Event 1, ValueYYYY-MM-DD, Event 2, ValueYYYY-MM-DD, Event 3, Value

etc.

raw data

difficult to interpret &

time-consuming

visualisation of temporal data

• evolved over many centures

• little knowledge on the visualisations; lack of empirical evaluations

• which types of visualisations are most appropriate for which kinds of tasks?

YYYY-MM-DD, Event 1, ValueYYYY-MM-DD, Event 2, ValueYYYY-MM-DD, Event 3, Value

etc.

difficult to interpret &

time-consuming

data visualisation

raw data

visualised data

visualisation of temporal data

• evolved over many centures

• little knowledge on the visualisations; lack of empirical evaluations

• which types of visualisations are most appropriate for which kinds of tasks?

YYYY-MM-DD, Event 1, ValueYYYY-MM-DD, Event 2, ValueYYYY-MM-DD, Event 3, Value

etc.

data visualisation

“the use of computer-supported, interactive, visual representations of data to amplify cognition” [Card et al, 1999]

raw data

visualised data

digitally visualised data

difficult to interpret &

time-consuming

• evolved over many centures

• little knowledge on the visualisations; lack of empirical evaluations

• which types of visualisations are most appropriate for which kinds of tasks?

but are they any good?

method

three visualisations, three datasets& set of identical task kinds

within-participants design

1 2 3

task kindsquestion 1

question 2

question 3

question 4

question 5

question 6

question 9

existence of a data elementexample: “was a measurement made on 8 December 1977?”

temporal locationexample: “when was the lowest number of births?”

rate of changeexample: “how much is the difference in number of births between1 February 1977 and 1 February 1978?”

sequenceexample: “did the number of births reach 331 before or after Marchin 1982?”

question 7

question 8

temporal patternexample: “when you look at the overall visualisation, do you see anypatterns in the data?”

[MacEachren, 2004]

visualisation 1: calendar

[M. Bostock, On-line]

visualisation 2: timeline

[Shutterstock, On-line]

visualisation 3: radial

[Tominski and Hadlak, On-line]

measurements

completion time accuracy of answers

perceived ease of use preference

✓x

measurements

... and qualitative data on positive & negative aspects of each visualisation -

and suggestions for improvement

+ observations

+ -

participants

18 participants (1 female, 17 male)

all part of Computer Science department

mean age of 26.2 years (ranging from 20 to 36)

results: completion time

0

25

50

75

rate of change sequence

calendar visualisationtimeline visualisationspiral visualisation

existence of data

element task

temporallocation

significantly shorter completion time in calendar visualisation

seco

nds

results: accuracy

existence of data

element task

temporallocation

accuracy is significantly higher in timeline visualisation, compared

to calendar visualisation

0

25

50

75

100

rate of change sequence

calendar visualisationtimeline visualisationspiral visualisation

perc

ent

results: ease of use

calendar visualisation was perceived as significantly easier

to use than the spiral visualisation

0

2

5

7

9

easy to use difficult to use

calendar visualisationtimeline visualisationspiral visualisationfr

eque

ncy

very easyto use

neither easynor difficult

very difficultto use

results: preference

preferences are significantly different froman even distribution: timeline visualisationis preferred by the majority of participants

calendar

timeline

spiral

no preference

0% 15% 30% 45% 60%

11,11%

5,56%

55,56%

27,78%

percent of participants who preferred this option

comments

content analysis on positive aspects, negative aspects and suggestions for improvement:

kappa coefficient of 0.91

using the qualitative feedback, redesigns of all visualisations were produced

taskpresentationneither

explanations: calendar

[M. Bostock, On-line]

redesign: calendarSundayMondayTuesdayWednesdayThursdayFridaySaturday

1902

SundayMondayTuesdayWednesdayThursdayFridaySaturday

1903

SundayMondayTuesdayWednesdayThursdayFridaySaturday

1904

SundayMondayTuesdayWednesdayThursdayFridaySaturday

1905

January February March April May June July August September October November December

January February March April May June July August September October November December

January February March April May June July August September October November December

January February March April May June July August September October November December

Show dates 0 - 20%

21 - 40%

41 - 60%

61 - 80%

81 - 100%

Edit ranges...

=

=

visualisation 2: timeline

[Shutterstock, On-line]

redesign 2: timeline

50

100

150

200

250

300

350

1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912

Date: 02-12-1909 Value: 160

1900 19101980 1920

Start date: 01/01/1902 End date: 03/11/1912

visualisation 3: radial

[Tominski and Hadlak, On-line]

redesign 3: radialRange: 1994 - 1998

Zoom: + -Navigate to: dd/mm/yyyy

0 - 20%

21 - 40%

41 - 60%

61 - 80%

81 - 100%

Edit ranges...

Jan

Feb

Mar

Apr

May

JunJul

Aug

Sep

Oct

Nov

Dec

1994

1995

1996

1997

1998

Preview of zoom:

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

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

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

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.