Spatio-Temporal Constraints on Social Networks - The Visualization - - PowerPoint PPT Presentation

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Spatio-Temporal Constraints on Social Networks - The Visualization - - PowerPoint PPT Presentation

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Spatio-Temporal Constraints on Social Networks - The Visualization Perspective Shih-Lung Shaw, Ph.D. Professor Department of Geography University of Tennessee Knoxville, TN 37996-0925 U.S.A. E-mail: sshaw@utk.edu Specialist Meeting of

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Shih-Lung Shaw, Department of Geography

Spatio-Temporal Constraints on Social Networks

  • The Visualization Perspective

Shih-Lung Shaw, Ph.D.

Professor Department of Geography University of Tennessee Knoxville, TN 37996-0925 U.S.A. E-mail: sshaw@utk.edu

Specialist Meeting of Spatio-Temporal Constraints on Social Networks Upham Hotel, Santa Barbara, CA, December 13-14, 2010

(This research is partially sponsored by NSF Grant BCS-0616724.)

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Shih-Lung Shaw, Department of Geography

VisualComplexity.com

Visualization of Social Networks

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Shih-Lung Shaw, Department of Geography

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VisualComplexity.com

Many of them are based on Node-Link Network concepts focusing on topology and connectivity

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Shih-Lung Shaw, Department of Geography

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Shih-Lung Shaw, Department of Geography

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VisualComplexity.com

Visualization of network growth/changes

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Shih-Lung Shaw, Department of Geography

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VisualComplexity.com

Visualization with Time/Locations

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Shih-Lung Shaw, Department of Geography

A Space-Time GIS approach

Based on Hägerstrand’s Time Geography and a Temporal Dynamic Segmentation

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Shih-Lung Shaw, Department of Geography

Hägerstrand’s time geography examines human activities under various constraints in a space–time context.

Space and time are connected through the concept

  • f space–time path, which depicts the sequence of

an individual’s activities at various locations over a time period.

The possible locations that a person can visit within a given time window form a continuous space known as a space–time prism.

Hägerstrand’s Time Geography:

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Shih-Lung Shaw, Department of Geography

time space

1 2 3

Space-Time Path

time space

t2 t1

Space-Time Prism Potential Path Area

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Shih-Lung Shaw, Department of Geography

Temporal Dynamic Segmentation:

Physical activities:

  • 1. Drive to work
  • 3. Walk to lunch
  • 4. Have lunch
  • 5. Walk back from lunch
  • 6. Drive back home
  • 8. Grocery shopping
  • 9. Return home

Virtual activities:

  • 2. Instant messaging with colleagues
  • 7. Receive a cell phone call from

spouse to do grocery shopping

space-time path space time 1 2 4 6 8 9 7 3 5

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Shih-Lung Shaw, Department of Geography

Three types of constraints:

– Capability Constraints: Constraints that limit the

activities of an individual because of his/her biological construction and/or the tools he/she can command.

– Authority Constraints: Constraints imposed by a

domain within which things and events are under the control of a given individual or a given group.

– Coupling Constraints: Constraints that deal with the

interactions of an individual with other people or entities in a space–time context.

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Shih-Lung Shaw, Department of Geography Find locations of ST paths at time T

time space

T

L1 L2

Locate time for point A on the ST path

T interpolate A

time space

T6 T5 T4 T3 T2 T1

Find ST paths that visited location L

time space

L

time space

P

Find ST paths interacting with person P

Exploratory spatio-temporal analysis functions:

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Shih-Lung Shaw, Department of Geography Potential AP interactions Potential SP interactions space time t1 t2 l1 l2 space time l1 l2 t11 t12 t21 t22 Potential ST interactions space time t1 t2 l11 l12 l21 l22 Potential AT interactions space time t l11 l12 l21 l22

For additional information, please see Yu & Shaw (2008) in IJGIS.

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What does our Space-Time GIS look like?

Implemented as an ArcGIS extension with temporal dynamic segmentation

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Chen, Shaw and others (forthcoming) in Journal of Transport Geography.

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For additional information, please see Shaw & Yu (2009) in Journal of Transport Geography.

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Flickr Data (I)

Space-Time GIS visualization Flickr data provided by Sumang (Sam) Liu,

  • Dept. of Geography, Univ. of Tennessee
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Shih-Lung Shaw, Department of Geography

(Flickr data provided by Sumang Liu, Department of Geography, University of Tennessee, Knoxville)

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Video of Visualization in Space-Time GIS

C:\Shaw\New-Conferences\2010 Space-Time Constraints on Scoial Networks\Videos\Flickr_Sam\Flickr_Sam_Video1\Flickr_Sam_Video1_fs.htm

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Shih-Lung Shaw, Department of Geography

Flickr Data (II)

Space-Time GIS visualization Flickr data provided by Dongpo Deng, ITC/Univ. of Twente, The Netherlands deng@itc.nl

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Shih-Lung Shaw, Department of Geography

(Flickr data provided by Dongpo Deng, Dept. of Geo-Processing, ITC/Univ. of Twente, The Netherlands)

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Shih-Lung Shaw, Department of Geography

(Flickr data provided by Dongpo Deng, Dept. of Geo-Processing, ITC/Univ. of Twente, The Netherlands)

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Shih-Lung Shaw, Department of Geography

(Flickr data provided by Dongpo Deng, Dept. of Geo-Processing, ITC/Univ. of Twente, The Netherlands)

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Shih-Lung Shaw, Department of Geography

(Flickr data provided by Dongpo Deng, Dept. of Geo-Processing, ITC/Univ. of Twente, The Netherlands)

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Shih-Lung Shaw, Department of Geography

(Flickr data provided by Dongpo Deng, Dept. of Geo-Processing, ITC/Univ. of Twente, The Netherlands)

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Shih-Lung Shaw, Department of Geography

(Flickr data provided by Dongpo Deng, Dept. of Geo-Processing, ITC/Univ. of Twente, The Netherlands)

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Facebook Data

Space-Time GIS visualization Facebook data provided by Ling Yin,

  • Dept. of Geography, Univ. of Tennessee
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Database

…… … … ……

1 test user 50 friends 9251 friends’ friends 51 users’ communication data 3837 users with current location Time span: January 2006 - Nov 25, 2010

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville.)

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Shih-Lung Shaw, Department of Geography Person A’s social space-time path geographical space time

Social Space-Time Path: movement in a physical-virtual hybrid space

person A person B person C Post or comment on his/her own wall Post or comment on friends’ walls

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville.)

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Shih-Lung Shaw, Department of Geography

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville)

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Shih-Lung Shaw, Department of Geography

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville)

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Shih-Lung Shaw, Department of Geography

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville)

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Shih-Lung Shaw, Department of Geography

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville)

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Shih-Lung Shaw, Department of Geography

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville)

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Shih-Lung Shaw, Department of Geography

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville)

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Shih-Lung Shaw, Department of Geography

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville)

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Shih-Lung Shaw, Department of Geography

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville)

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Shih-Lung Shaw, Department of Geography

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville)

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Shih-Lung Shaw, Department of Geography

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville)

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Facebook Data

Space-Time Patterns Clustering Analysis

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Space-Time Patterns

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville.)

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Group 1: Never posted or commented on friends’ walls

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville.)

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Group 2: Occasionally posted or commented on friends’ walls

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville.)

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Group 3: Only visited local friends

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville.)

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Group 4: Regularly visited domestic friends

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville.)

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Group 5: Regularly visited international friends

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville.)

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Group 6: Regularly visited domestic & international friends

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville.)

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Group 7: Other patterns

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville.)

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Facebook Data

Incoming vs. Outgoing Traffic

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Social space-time path and virtual communications in Space-Time GIS

A person, who never visited his/her friends’ walls, is visited by his/her friends.

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville.)

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Social space-time path and virtual communications in Space-Time GIS

A person, who is active

  • n friends’ walls, is also

visited by his/her friends.

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville.)

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A user’s communications with his/her friends

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville.)

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Communications between two friends

Unbalanced visits between two friends.

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville.)

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Communications between two friends

More balanced visits between two fiends.

(Facebook data provided by Ling Yin, Department of Geography, University of Tennessee, Knoxville.)

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Shih-Lung Shaw, Department of Geography

Thank you!

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