1. ENV 20069.1 Envisioning Information Lecture 9 – Time: Taxonomy & Techniques Ken Brodlie kwb@comp.leeds.ac.uk

2. ENV 20069.2 Time Many applications involve visualization of data over a period of time… … including the first visualization … and one of the most famous

3. ENV 20069.3 Time We are familiar with time series in many walks of life… Todays lecture looks at visualization and time http://quake.utah.edu/helicorder/heli/yellowstone/index.html Seismogram

4. ENV 20069.4 Taxonomy (Frank/Mueller/Schumann) Data: D = {(t 1,d 1 ), (t 2,d 2 ),.. (t n,d n )} where d i = f(t i ) d can be multivariate Representations can be: –Static –Dynamic Types of time… Discrete or interval time –Sequence of snapshots; or measured over interval such as days Linear or cyclic time –Start to end; or repeating like the seasons Ordered or branching time –Data values in strict time sequence; or branches with parallel time tracks Visualization Methods for Time-dependent Data – An Overview : Mueller and Schumann See also: http://infovis.uni-konstanz.de/events/VisAnalyticsWs05/pdf/07MuellerSchumann.pdf

5. ENV 20069.5 Discrete vs Interval Time DiscreteInterval

6. ENV 20069.6 Linear vs Cyclic Linear –Previous examples were linear Cyclic –Circle graphs (discrete) –Sector graphs (interval) discrete interval

7. ENV 20069.7 Ordered vs Branching Time Rather than a simple ordered sequence…. Scientists often experiment with simulations of processes Here a simulation is started and results obtained at a sequence of time steps… … but to investigate some feature in more detail, the scientist rolls back the simulation and restarts with a different parameter setting

8. ENV 20069.8 Visual Metaphors Often we can use existing visualization techniques… and consider time as just any other variable.. New visual metaphors have also been suggested however…

9. ENV 20069.9 Parallel Coordinates for Time Series Data! Map different time steps to different axes Garnett, 1903 Statistical atlas, 12 th census of US Axes are years (right to left) Position on axis Is ranking

10. ENV 20069.10 Visual Metaphors : Long time periods Special techniques have been proposed for visualization over very long time periods Themeriver technique has been used to depict evolutionary behaviour…..Bit like an interval time version of parallel coordinates?? Evolution of baby names.... Try it at: http://babynamewizard.com/namevoyager/lnv0105.html Laura and Martin Wattenberg

11. ENV 20069.11 Themeriver Themeriver for climate change… …

12. ENV 20069.12 River Metaphor Taglines –Visualizing tags attached to Flickr online image sharing –Evolution over time –Show tags that are specific to a time period Definition of interesting is the following calculation: –u = tag –t = specified time period –N(u,t) = no of occurrences of tag in period –N(u) = total no of occurences of tag –C = constant http://research.yahoo.com/taglines/ I(u,t) = N(u,t) / (C + N(u))

13. ENV 20069.13 Cluster and Calendar based Visualization of Time Series Data Jarke van Wijk has shown how visualization can be used in analysis of time series data Opposite is power demand within ECN (Netherlands Energy Research Centre)… … hard to pick out patterns of usage

14. ENV 20069.14 Cluster Approach Each day taken as an observation and cluster analysis performed Take two closest days and merge into an average day… … and keep repeating dendogram Full cluster tree for energy data

15. ENV 20069.15 Visualizing the Main Clusters Then we are able to visualize the key patterns of use… … but better still, in next slide we link to a calendar

16. ENV 20069.16 Calendar View of Power Demand

17. ENV 20069.17 Calendar View of Number of Employees at Work http://www.win.tue.nl/~vanwijk/clv.pdf What can you observe? (NB Dec 5 th )

18. ENV 20069.18 Timestore Timestore is a nice idea for organising mailboxes… Yiu, Baecker, Silver, Long U Toronto

19. ENV 20069.19 Spiral Graphs Spiral graphs are a space- efficient way of visualizing long time series… From Alexa et al

20. ENV 20069.20 Time Wheel The Time Wheel allows several time series to be viewed simultaneously… … how successful is this? … rotation can help, why? … again cf parallel coordinates? Tominski, Abello, Schumann - Rostock

21. ENV 20069.21 MultiComb Here is another idea from Rostock group – MultiComb Two variations: Time axes as spokesTime axes as perimeter

22. ENV 20069.22 TimeWheel in 3D The 3D TimeWheel has time in central axis, variable axes on opposite end of slices… …wheel can open out

23. ENV 20069.23 MultiComb in 3D MultiComb in 3D.... here there are 7 time series plots with a common time axis

24. ENV 20069.24 Kiviat Tubes Kiviat charts were used in parallel program performance visualization… … but are essentially star glyphs Here is a Kiviat Tube –Star glyphs laid out along time axis and surface created