A bit of STC history 1969/1970 1969/1970 1999/2000 M P K - - PowerPoint PPT Presentation

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A bit of STC history 1969/1970 1969/1970 1999/2000 M P K - - PowerPoint PPT Presentation

Apr 10, 2023 180 likes •539 views

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

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SLIDE 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

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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 http://geoanalytics.net

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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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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 3

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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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 4

STC everywhere

2012 2012 STC is visible to general public!

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 5

Spatio-temporal data

E t

  • Events
  • Time series
  • Flows between places
  • Trajectories of MPOs

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 6

STC for events

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 7

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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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 8

Spatial time series

N i tt ib t

  • Numeric attributes

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 9

Spatial time series

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 10

Spatial time series

N i l tt ib t

  • Nominal attributes

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 11

Flows between places

H l d i f

  • Hourly dynamics of

take-offs and flows between FR airports

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 12

Trajectories

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 13

Trajectories

O d t j t i d ti

  • One day trajectory in space and time

ti time space

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 14

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

Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 15

Space-time cube

O t j t

  • One year trajectory…

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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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 ) 

  • f 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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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 17

Clustering of trajectories

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 18

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

Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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Transformations with respect to temporal cycles: days

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 20

Transformations with respect to temporal cycles: weeks

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 21

Transformations with respect to individual lifelines

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 22

STC for trajectory attributes?

Si l l t

  • Single cluster
  • Transformations

with respect to with respect to temporal cycles: days

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 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)

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 24

Trajectory wall – focus on trajectory attributes

  • Time  ordering

Time  ordering

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 25

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

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 26

Trajectory wall

t t it

  • tortuosity

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 27

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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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 28

Trajectories + related events

E t

  • Encounters

{of different kinds}

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 29

Rotterdam data (S. van der Spek), cinema

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 30

Rotterdam data (S. van der Spek), Dudok

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 31

Trajectories + related events: a hint for semantic interpretation

t

  • stops

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 32

Trajectories + related events: cross-filtering

t

  • encounters

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 33

Trajectories + related events: cross-filtering

d ifti

  • drifting

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 34

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

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Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net

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SLIDE 35

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 trajectories

Deriving flows from trajectories

  • Computing time series of attributes
  • Specific interactivity
  • Open questions:

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p y

  • time transformations

p q

  • 3D geodata?
  • usability / guidelines

Space-Time Cube in Visual Analytics

MOVE STC worskhop, Enschede NL, June 2012 http://geoanalytics.net