Interactions in time; Evaluation and redesign of three abstract temporal data visualisations
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Interactions in time; Evaluation and redesign of three abstract temporal data visualisations

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Interactions in time; Evaluation and redesign of three abstract temporal data visualisations Interactions in time; Evaluation and redesign of three abstract temporal data visualisations Presentation Transcript

  • Interactions in time Evaluation and redesign ofthree abstract temporal data visualisations Author: Lisa Koeman Supervisor: Christopher Power
  • 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]
  • 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]
  • past present future temporal dataYYYY-MM-DD, Event 1YYYY-MM-DD, Event 2YYYY-MM-DD, Event 3 etc.
  • past present future temporal dataYYYY-MM-DD, Event 1YYYY-MM-DD, Event 2YYYY-MM-DD, Event 3 etc.past present future time-series dataYYYY-MM-DD, Event 1, ValueYYYY-MM-DD, Event 2, ValueYYYY-MM-DD, Event 3, Value etc.
  • visualisation of temporal dataYYYY-MM-DD, Event 1, ValueYYYY-MM-DD, Event 2, Value difficult to interpret &YYYY-MM-DD, Event 3, Value etc. time-consuming raw data
  • visualisation of temporal dataYYYY-MM-DD, Event 1, ValueYYYY-MM-DD, Event 2, Value difficult to interpret &YYYY-MM-DD, Event 3, Value etc. time-consuming raw data data visualisation visualised data
  • visualisation of temporal dataYYYY-MM-DD, Event 1, ValueYYYY-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]
  • but are they any good?
  • method within-participants design 1 2 3three visualisations, three datasets & set of identical task kinds
  • 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?”
  • 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 xperceived ease of use preference
  • 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 departmentmean age of 26.2 years (ranging from 20 to 36)
  • results: completion time 75seconds 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
  • results: accuracy 100percent 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
  • results: ease of usefrequency 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
  • 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
  • commentscontent analysis on positive aspects, negativeaspects and suggestions for improvement: task presentation neitherkappa coefficient of 0.91using the qualitative feedback, redesigns of allvisualisations were produced
  • explanations: calendar [M. Bostock, On-line]
  • redesign: calendar1902 January February March April May June July August September October November DecemberSundayMondayTuesdayWednesdayThursdayFridaySaturday1903 January February March April May June July August September October November DecemberSundayMonday =TuesdayWednesdayThursdayFridaySaturday1904 January February March April May June July August September October November December =SundayMondayTuesdayWednesdayThursdayFridaySaturday1905 January February March April May June July August September October November DecemberSundayMondayTuesdayWednesdayThursdayFridaySaturday Show dates 0 - 20% 41 - 60% 81 - 100% 21 - 40% 61 - 80% Edit ranges...
  • visualisation 2: timeline [Shutterstock, On-line]
  • redesign 2: timeline Date: 02-12-1909 Value: 160350300250200150100 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
  • visualisation 3: radial [Tominski and Hadlak, On-line]
  • redesign 3: radialRange: 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:
  • 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.