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Space-Time Cube in Visual Analytics Gennady Andrienko Natalia Andrienko Natalia Andrienko http://geoanalytics.net/and in cooperation with P.Gatalsky, G.Fuchs, K.Vrotsou, I.Peca, C.Tominski, H.Schumann inspired by T.Hagerstrand, M-J Kraak , M-P

  1. Space-Time Cube in Visual Analytics Gennady Andrienko Natalia Andrienko Natalia Andrienko http://geoanalytics.net/and in cooperation with P.Gatalsky, G.Fuchs, K.Vrotsou, I.Peca, C.Tominski, H.Schumann inspired by T.Hagerstrand, M-J Kraak , M-P Kwan and others inspired by T.Hagerstrand, M J Kraak , M P Kwan and others 1 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  2. A bit of STC history 1969/1970 1969/1970 1999/2000 M P K 1999/2000, M-P Kwan 2002/2003 MJ K 2002/2003, MJ Kraak+G,A*2 k G A*2 2 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  3. Interactive space-time cube T Traditional functionality: diti l f ti lit  - change of the viewpoint; - zooming in the spatial and temporal dimensions; zooming in the spatial and temporal dimensions; - moveable plane for additional temporal reference; - animation of the content of STC (aka waterfall); animation of the content of STC (aka waterfall); - selection of spatio-temporal objects to be displayed; - access to objects by pointing and dragging; - coordinated highlighting in multiple views; 3 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  4. STC everywhere 2012 2012 STC is visible to general public! 4 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  5. Spatio-temporal data E Events t  Time series  Flows between places  Trajectories of MPOs  5 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  6. STC for events 6 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  7. STC for events Clustering events, eliminating noise Cl t i t li i ti i  Replacing point events by convex hulls  Temporal zooming  7 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  8. Spatial time series N Numeric attributes i tt ib t  8 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  9. Spatial time series 9 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  10. Spatial time series N Nominal attributes i l tt ib t  10 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  11. Flows between places H Hourly dynamics of l d i f  take-offs and flows between FR airports 11 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  12. Trajectories 12 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  13. Trajectories One day trajectory in space and time O d t j t i d ti  ti time space 13 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  14. Trajectories O One day trajectory in space and time d t j t i d ti  stop t • morning part • evening part 14 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  15. Space-time cube O One year trajectory… t j t  15 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  16. Interactive space-time cube W We propose to add t dd  - Clustering of trajectories by similarity  of geometric properties (e.g. routes) f t i ti ( t )  … - dynamic time transformation  with respect to temporal cycles  with respect to the individual lifelines of the trajectories 16 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  17. Clustering of trajectories 17 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  18. Time transformation in space-time cube Transformations with respect to temporal cycles, which include T f ti ith t t t l l hi h i l d  - bringing the times of the trajectories to the same year or season, - the same month, the same month - week, - day, day, - hour Transformations with respect to the individual lifelines of the trajectories, p j ,  which include - bringing the trajectories to a common start moment, - a common end moment, - common start and end moments VAST 2010, ICC 2011 18 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  19. Transformations with respect to temporal cycles: days 19 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  20. Transformations with respect to temporal cycles: weeks 20 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  21. Transformations with respect to individual lifelines 21 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  22. STC for trajectory attributes? Si Single cluster l l t  Transformations  with respect to with respect to temporal cycles: days 22 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  23. Trajectory wall – focus on trajectory attributes Time  ordering (joint work with C Tominski & H Schumann InfoVis 2012) Time  ordering (joint work with C.Tominski & H.Schumann, InfoVis 2012)  23 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  24. Trajectory wall – focus on trajectory attributes Time  ordering Time  ordering  24 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  25. Trajectory wall: traffic jam patterns in 4,000+ trajectories, 7 days 25 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  26. Trajectory wall t tortuosity t it  26 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  27. STC showing frequent sequences of visited places I D I.Drecki & P.Forer, 2000 ki & P F 2000 D O D.Orellana et al, 2011 ll t l 2011 Andrienko*2, Bursch, Weiskopf, VAST 2012 27 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  28. Trajectories + related events E Encounters t  {of different kinds} 28 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  29. Rotterdam data (S. van der Spek), cinema 29 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  30. Rotterdam data (S. van der Spek), Dudok 30 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  31. Trajectories + related events: a hint for semantic interpretation stops t  31 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  32. Trajectories + related events: cross-filtering encounters t  32 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  33. Trajectories + related events: cross-filtering d ifti drifting  33 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  34. Open question: what’s about movement in 3D? 34 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

  35. Conclusion VA benefits from representing different types of spatio-temporal data in STC VA b fit f ti diff t t f ti t l d t i STC  Data selection  - Attribute-based, spatial, and temporal filtering - Clustering and subsequent interactive filtering - Search for frequent sequences, subsequent interactive filtering Search for freq ent seq ences s bseq ent interacti e filtering - Cross-filtering of multiple ST datasets Data transformation Data transformation  - Event extraction - Deriving flows from trajectories Deriving flows from trajectories - Computing time series of attributes Open questions: p q Specific interactivity p y   - 3D geodata? - time transformations - usability / guidelines 35 Space-Time Cube in Visual Analytics MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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