TimeTables: embodied exploration of immersive spatio-temporal data

Yidan Zhang; Barrett Ens; Kadek Ananta Satriadi; Arnaud Prouzeau; Sarah Goodwin

doi:10.1109/VR51125.2022.00080

TimeTables: embodied exploration of immersive spatio-temporal data

Yidan Zhang, Barrett Ens, Kadek Ananta Satriadi, Arnaud Prouzeau, Sarah Goodwin

Department of Human Centred Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Abstract

We propose TimeTables, a novel prototype system that aims to support data exploration, using embodiment with space-time cubes in virtual reality. TimeTables uses multiple space-time cubes on virtual tabletops, which users can manipulate by extracting time layers or individual buildings to create new tabletop views. The surrounding environment includes a large space for multiple linked tabletops and a storage wall. TimeTables presents information at different time scales by stretching layers to drill down in time. Users can also jump into tabletops to inspect data from an egocentric perspective. We present a use case scenario of energy consumption displayed on a university campus to demonstrate how our system could support data exploration and analysis over space and time. From our experience and analysis we believe the system has a high potential in assisting spatio-temporal data exploration and analysis.

Original language	English
Title of host publication	Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2022
Editors	Christos Mousas, Mohammed Safayet Arefin
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	599-605
Number of pages	7
ISBN (Electronic)	9781665496179
ISBN (Print)	9781665496186
DOIs	https://doi.org/10.1109/VR51125.2022.00080
Publication status	Published - 2022
Event	IEEE Conference on Virtual Reality and 3D User Interfaces 2022 - Online, New Zealand Duration: 12 Mar 2022 → 16 Mar 2022 Conference number: 29th https://ieeexplore.ieee.org/xpl/conhome/9756663/proceeding (Proceedings) https://ieeevr.org/2022/ (Website)

Publication series

Name	Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2022
Publisher	IEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)	2642-5246
ISSN (Electronic)	2642-5254

Conference

Conference	IEEE Conference on Virtual Reality and 3D User Interfaces 2022
Abbreviated title	VR 2022
Country/Territory	New Zealand
Period	12/03/22 → 16/03/22
Internet address	https://ieeexplore.ieee.org/xpl/conhome/9756663/proceeding (Proceedings) https://ieeevr.org/2022/ (Website)

Keywords

Human computer interaction (HCI)
Human-centered computing
Information visualization
Interaction paradigms
Interaction techniques
Virtual reality
Visualization
Visualization application domains

Access to Document

10.1109/VR51125.2022.00080

Cite this

Zhang, Y., Ens, B., Satriadi, K. A., Prouzeau, A., & Goodwin, S. (2022). TimeTables: embodied exploration of immersive spatio-temporal data. In C. Mousas, & M. Safayet Arefin (Eds.), Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2022 (pp. 599-605). (Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2022). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/VR51125.2022.00080

Zhang, Yidan ; Ens, Barrett ; Satriadi, Kadek Ananta et al. / TimeTables : embodied exploration of immersive spatio-temporal data. Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2022. editor / Christos Mousas ; Mohammed Safayet Arefin. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2022. pp. 599-605 (Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2022).

@inproceedings{def81d1564ee4b15baadde41586f4dbe,

title = "TimeTables: embodied exploration of immersive spatio-temporal data",

abstract = "We propose TimeTables, a novel prototype system that aims to support data exploration, using embodiment with space-time cubes in virtual reality. TimeTables uses multiple space-time cubes on virtual tabletops, which users can manipulate by extracting time layers or individual buildings to create new tabletop views. The surrounding environment includes a large space for multiple linked tabletops and a storage wall. TimeTables presents information at different time scales by stretching layers to drill down in time. Users can also jump into tabletops to inspect data from an egocentric perspective. We present a use case scenario of energy consumption displayed on a university campus to demonstrate how our system could support data exploration and analysis over space and time. From our experience and analysis we believe the system has a high potential in assisting spatio-temporal data exploration and analysis. ",

keywords = "Human computer interaction (HCI), Human-centered computing, Information visualization, Interaction paradigms, Interaction techniques, Virtual reality, Visualization, Visualization application domains",

author = "Yidan Zhang and Barrett Ens and Satriadi, {Kadek Ananta} and Arnaud Prouzeau and Sarah Goodwin",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; IEEE Conference on Virtual Reality and 3D User Interfaces 2022, VR 2022 ; Conference date: 12-03-2022 Through 16-03-2022",

year = "2022",

doi = "10.1109/VR51125.2022.00080",

language = "English",

isbn = "9781665496186",

series = "Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2022",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

pages = "599--605",

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booktitle = "Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2022",

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Zhang, Y, Ens, B , Satriadi, KA, Prouzeau, A & Goodwin, S 2022, TimeTables: embodied exploration of immersive spatio-temporal data. in C Mousas & M Safayet Arefin (eds), Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2022. Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2022, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 599-605, IEEE Conference on Virtual Reality and 3D User Interfaces 2022, New Zealand, 12/03/22. https://doi.org/10.1109/VR51125.2022.00080

TimeTables : embodied exploration of immersive spatio-temporal data. / Zhang, Yidan; Ens, Barrett ; Satriadi, Kadek Ananta et al.

Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2022. ed. / Christos Mousas; Mohammed Safayet Arefin. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2022. p. 599-605 (Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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T1 - TimeTables

T2 - IEEE Conference on Virtual Reality and 3D User Interfaces 2022

AU - Zhang, Yidan

AU - Ens, Barrett

AU - Satriadi, Kadek Ananta

AU - Prouzeau, Arnaud

AU - Goodwin, Sarah

N1 - Conference code: 29th

PY - 2022

Y1 - 2022

N2 - We propose TimeTables, a novel prototype system that aims to support data exploration, using embodiment with space-time cubes in virtual reality. TimeTables uses multiple space-time cubes on virtual tabletops, which users can manipulate by extracting time layers or individual buildings to create new tabletop views. The surrounding environment includes a large space for multiple linked tabletops and a storage wall. TimeTables presents information at different time scales by stretching layers to drill down in time. Users can also jump into tabletops to inspect data from an egocentric perspective. We present a use case scenario of energy consumption displayed on a university campus to demonstrate how our system could support data exploration and analysis over space and time. From our experience and analysis we believe the system has a high potential in assisting spatio-temporal data exploration and analysis.

AB - We propose TimeTables, a novel prototype system that aims to support data exploration, using embodiment with space-time cubes in virtual reality. TimeTables uses multiple space-time cubes on virtual tabletops, which users can manipulate by extracting time layers or individual buildings to create new tabletop views. The surrounding environment includes a large space for multiple linked tabletops and a storage wall. TimeTables presents information at different time scales by stretching layers to drill down in time. Users can also jump into tabletops to inspect data from an egocentric perspective. We present a use case scenario of energy consumption displayed on a university campus to demonstrate how our system could support data exploration and analysis over space and time. From our experience and analysis we believe the system has a high potential in assisting spatio-temporal data exploration and analysis.

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KW - Human-centered computing

KW - Information visualization

KW - Interaction paradigms

KW - Interaction techniques

KW - Virtual reality

KW - Visualization

KW - Visualization application domains

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DO - 10.1109/VR51125.2022.00080

M3 - Conference Paper

AN - SCOPUS:85129439130

SN - 9781665496186

T3 - Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2022

SP - 599

EP - 605

BT - Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2022

A2 - Mousas, Christos

A2 - Safayet Arefin, Mohammed

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

Y2 - 12 March 2022 through 16 March 2022

ER -

Zhang Y, Ens B , Satriadi KA, Prouzeau A, Goodwin S. TimeTables: embodied exploration of immersive spatio-temporal data. In Mousas C, Safayet Arefin M, editors, Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2022. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2022. p. 599-605. (Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2022). https://doi.org/10.1109/VR51125.2022.00080

TimeTables: embodied exploration of immersive spatio-temporal data

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Keywords

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