Space-Time Cube in Visual Analytics

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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 Kwan and others

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inspired by T.Hagerstrand,

M J Kraak

, M P Kwan and others

Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012

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A bit of STC history

1969/1970 1999/2000 M P K 2002/2003 MJ K k G A*2

1969/1970 1999/2000, M-P Kwan 2002/2003, MJ Kraak+G,A*2

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Interactive space-time cube

T diti l f ti lit

 Traditional functionality:

- 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;

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STC everywhere

2012

STC is visible to general public!

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Spatio-temporal data

E t

 Events

 Time series

 Flows between places

 Trajectories of MPOs

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STC for events

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STC for events

Cl t i t li i ti i

 Clustering events, eliminating noise

 Replacing point events by convex hulls

 Temporal zooming

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Spatial time series

N i tt ib t

 Numeric attributes

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Spatial time series

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Spatial time series

N i l tt ib t

 Nominal attributes

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Flows between places

H l d i f

 Hourly dynamics of

take-offs and flows between FR airports

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Trajectories

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Trajectories

O d t j t i d ti

 One day trajectory in space and time

ti time

space

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Trajectories

O d t j t i d ti

 One day trajectory in space and time

t stop

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• morning part • evening part

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Space-time cube

O t j t

 One year trajectory…

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Interactive space-time cube

W t dd

 We propose to add

- Clustering of trajectories by similarity

f t i ti ( t )

 of geometric properties (e.g. routes)

 …

- dynamic time transformation

 with respect to temporal cycles

 with respect to the individual lifelines of the trajectories

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Clustering of trajectories

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Time transformation in space-time cube

T f ti ith t t t l l hi h i l d

 Transformations with respect to temporal cycles, which include

- 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

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VAST 2010, ICC 2011

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Transformations with respect to temporal cycles: days

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Transformations with respect to temporal cycles: weeks

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Transformations with respect to individual lifelines

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STC for trajectory attributes?

Si l l t  Single cluster  Transformations with respect to with respect to temporal cycles: days 22

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Trajectory wall – focus on trajectory attributes

 TimeTime  orderingordering (joint work with C Tominski & H Schumann InfoVis 2012)(joint work with C.Tominski & H.Schumann, InfoVis 2012)

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Trajectory wall – focus on trajectory attributes

 TimeTime  orderingordering

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

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Trajectory wall

t t it

 tortuosity

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STC showing frequent sequences of visited places

I D ki & P F 2000 D O ll t l 2011

I.Drecki & P.Forer, 2000 D.Orellana et al, 2011

Andrienko*2, Bursch, Weiskopf, VAST 2012

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Trajectories + related events

E t

 Encounters

{of different kinds}

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Rotterdam data (S. van der Spek), cinema

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Rotterdam data (S. van der Spek), Dudok

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Trajectories + related events: a hint for semantic interpretation

t

 stops

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Trajectories + related events: cross-filtering

t

 encounters

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Trajectories + related events: cross-filtering

d ifti

 drifting

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

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Conclusion

VA b fit f ti diff t t f ti t l d t i STC

 VA benefits from representing different types of spatio-temporal data in STC

 Data selection

- Attribute-based, spatial, and temporal filtering - Clustering and subsequent interactive filtering

Search for freq ent seq ences s bseq ent interacti e filtering - Search for frequent sequences, subsequent interactive filtering - Cross-filtering of multiple ST datasets

Data transformation

 Data transformation

- Event extraction

- Deriving flows from trajectoriesDeriving flows from trajectories - Computing time series of attributes

 Specific interactivity  Open questions:

35 p y - time transformations p q - 3D geodata? - usability / guidelines