Space-Time Cube in Visual Analytics

Transcription

1 Gennady Andrienko Natalia Andrienko /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 1

2 A bit of STC history 1969/ /2000, M-P Kwan 2002/2003, MJ Kraak+G,A*2 2

3 Interactive space-time cube Traditional functionality: - change of the viewpoint; - zooming in the spatial and temporal dimensions; - moveable plane for additional temporal reference; - 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

4 STC everywhere 2012 STC is visible to general public! 4

5 Spatio-temporal data Events Time series Flows between places Trajectories of MPOs 5

6 STC for events 6

7 STC for events Cl t i events, Clustering t eliminating li i ti noise i Replacing point events by convex hulls Temporal zooming 7

8 Spatial time series Numeric attributes t 8

9 Spatial time series 9

10 Spatial time series Nominal attributes t 10

11 Flows between places Hourly dynamics of take-offs and flows between FR airports 11

12 Trajectories 12

13 Trajectories One day trajectory t in space and time time space 13

14 Trajectories One day trajectory t in space and time stop morning part evening part 14

15 Space-time cube One year trajectory t 15

16 Interactive space-time cube We propose to add - Clustering of trajectories by similarity of geometric properties (e.g. routes) - dynamic time transformation with respect to temporal cycles with respect to the individual lifelines of the trajectories 16

17 Clustering of trajectories 17

18 Time transformation in space-time cube Transformations with respect to temporal cycles, which h include - bringing the times of the trajectories to the same year or season, - the same month, - week, - day, - hour Transformations with respect to the individual lifelines of the trajectories, which include - bringing the trajectories to a common start moment, - a common end moment, - common start and end moments VAST 2010, ICC

19 Transformations with respect to temporal cycles: days 19

20 Transformations with respect to temporal cycles: weeks 20

21 Transformations with respect to individual lifelines 21

22 STC for trajectory attributes? Single cluster Transformations with respect to temporal cycles: days 22

23 Trajectory wall focus on trajectory attributes Time ordering (joint work with C.Tominski & H.Schumann, InfoVis 2012) 23

24 Trajectory wall focus on trajectory attributes Time ordering 24

25 Trajectory wall: traffic jam patterns in 4,000+ trajectories, 7 days 25

26 Trajectory wall tortuosityt 26

27 STC showing frequent sequences of visited places ID I.Drecki ki& PF P.Forer, 2000 DO D.Orellana et al, 2011 Andrienko*2, Bursch, Weiskopf, VAST

28 Trajectories + related events Encounters {of different kinds} 28

29 Rotterdam data (S. van der Spek), cinema 29

30 Rotterdam data (S. van der Spek), Dudok 30

31 Trajectories + related events: a hint for semantic interpretation stops 31

32 Trajectories + related events: cross-filtering encounters 32

33 Trajectories + related events: cross-filtering drifting 33

34 Open question: what s about movement in 3D? 34

35 Conclusion VA benefits from representing different types of spatio-temporal t data in STC Data selection - Attribute-based, spatial, and temporal filtering - Clustering and subsequent interactive filtering - Search for frequent ent sequences, ences subsequent ent interactive e filtering - Cross-filtering of multiple ST datasets Data transformation - Event extraction - Deriving flows from trajectories - Computing time series of attributes Specific interactivity Open questions: - 3D geodata? - time transformations - usability / guidelines 35