MovementFinder

Visual analytics of origin-destination patterns from geo-tagged social media

Siming Chen, Cong Guo, Xiaoru Yuan, Jiawan Zhang, Xiaolong Luke Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Geo-tagged social media data can be viewed as sampling of people's trajectories in daily life. It consists of people's movements and embeds the semantics of movements. However, it is challenging to reveal patterns from the sparse and irregular sampling data. We proposed an interactive multi-filter visualization approach to analyze the spatial-temporal movement pattern in people's daily life. People's trajectories are visualized on the map with multiple functional layers. With our visual analytics tools, users are able to drill down to details, with the awareness of the origin-destination flow patterns of spatial, temporal, and semantic meaning.

Original languageEnglish (US)
Title of host publication2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings
EditorsChris North, Min Chen, David Ebert
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages239-240
Number of pages2
ISBN (Electronic)9781479962273
DOIs
StatePublished - Feb 13 2015
Event2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Paris, France
Duration: Oct 9 2014Oct 14 2014

Publication series

Name2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings

Other

Other2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014
CountryFrance
CityParis
Period10/9/1410/14/14

Fingerprint

Semantics
Trajectories
Sampling
Flow patterns
Visualization

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Cite this

Chen, S., Guo, C., Yuan, X., Zhang, J., & Zhang, X. L. (2015). MovementFinder: Visual analytics of origin-destination patterns from geo-tagged social media. In C. North, M. Chen, & D. Ebert (Eds.), 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings (pp. 239-240). [7042509] (2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VAST.2014.7042509
Chen, Siming ; Guo, Cong ; Yuan, Xiaoru ; Zhang, Jiawan ; Zhang, Xiaolong Luke. / MovementFinder : Visual analytics of origin-destination patterns from geo-tagged social media. 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings. editor / Chris North ; Min Chen ; David Ebert. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 239-240 (2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings).
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abstract = "Geo-tagged social media data can be viewed as sampling of people's trajectories in daily life. It consists of people's movements and embeds the semantics of movements. However, it is challenging to reveal patterns from the sparse and irregular sampling data. We proposed an interactive multi-filter visualization approach to analyze the spatial-temporal movement pattern in people's daily life. People's trajectories are visualized on the map with multiple functional layers. With our visual analytics tools, users are able to drill down to details, with the awareness of the origin-destination flow patterns of spatial, temporal, and semantic meaning.",
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Chen, S, Guo, C, Yuan, X, Zhang, J & Zhang, XL 2015, MovementFinder: Visual analytics of origin-destination patterns from geo-tagged social media. in C North, M Chen & D Ebert (eds), 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings., 7042509, 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 239-240, 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014, Paris, France, 10/9/14. https://doi.org/10.1109/VAST.2014.7042509

MovementFinder : Visual analytics of origin-destination patterns from geo-tagged social media. / Chen, Siming; Guo, Cong; Yuan, Xiaoru; Zhang, Jiawan; Zhang, Xiaolong Luke.

2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings. ed. / Chris North; Min Chen; David Ebert. Institute of Electrical and Electronics Engineers Inc., 2015. p. 239-240 7042509 (2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Geo-tagged social media data can be viewed as sampling of people's trajectories in daily life. It consists of people's movements and embeds the semantics of movements. However, it is challenging to reveal patterns from the sparse and irregular sampling data. We proposed an interactive multi-filter visualization approach to analyze the spatial-temporal movement pattern in people's daily life. People's trajectories are visualized on the map with multiple functional layers. With our visual analytics tools, users are able to drill down to details, with the awareness of the origin-destination flow patterns of spatial, temporal, and semantic meaning.

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Chen S, Guo C, Yuan X, Zhang J, Zhang XL. MovementFinder: Visual analytics of origin-destination patterns from geo-tagged social media. In North C, Chen M, Ebert D, editors, 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 239-240. 7042509. (2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings). https://doi.org/10.1109/VAST.2014.7042509