The recent availability of affordable and lightweight tracking sensors allows researchers to collect large and complex movement datasets. These datasets require applications that are capable of handling them whilst providing an environment that enables the analyst(s) to focus on the task of analysing the movement in the context of the geographic environment it occurred in. We present a framework for collaborative analysis of geospatial-temporal movement data with a use-case in collective behavior analysis. It supports the concurrent usage of several program instances, allowing to have different perspectives on the same data in collocated or remote setups. The implementation can be deployed in a variety of immersive environments, e.g. on a tiled display wall or mobile VR devices.
%0 Conference Paper
%1 conf/vinci/0001ASEZS19
%A Klein, Karsten
%A Aichem, Michael
%A Sommer, Björn
%A Erk, Stefan
%A Zhang, Ying
%A Schreiber, Falk
%B Proceedings of the ACM Symposium on Visual Information Communication and Interaction (VINCI)
%D 2019
%I ACM
%K from:leonkokkoliadis 2019 sfbtrr161 D04
%P 9:1-9:5
%R 10.1145/3356422.3356450
%T TEAMwISE: Synchronised Immersive Environments for Exploration and Analysis of Movement Data
%U https://doi.org/10.1145/3356422.3356450
%X The recent availability of affordable and lightweight tracking sensors allows researchers to collect large and complex movement datasets. These datasets require applications that are capable of handling them whilst providing an environment that enables the analyst(s) to focus on the task of analysing the movement in the context of the geographic environment it occurred in. We present a framework for collaborative analysis of geospatial-temporal movement data with a use-case in collective behavior analysis. It supports the concurrent usage of several program instances, allowing to have different perspectives on the same data in collocated or remote setups. The implementation can be deployed in a variety of immersive environments, e.g. on a tiled display wall or mobile VR devices.
%@ 978-1-4503-7626-6
@inproceedings{conf/vinci/0001ASEZS19,
abstract = {The recent availability of affordable and lightweight tracking sensors allows researchers to collect large and complex movement datasets. These datasets require applications that are capable of handling them whilst providing an environment that enables the analyst(s) to focus on the task of analysing the movement in the context of the geographic environment it occurred in. We present a framework for collaborative analysis of geospatial-temporal movement data with a use-case in collective behavior analysis. It supports the concurrent usage of several program instances, allowing to have different perspectives on the same data in collocated or remote setups. The implementation can be deployed in a variety of immersive environments, e.g. on a tiled display wall or mobile VR devices.},
added-at = {2020-05-13T16:20:10.000+0200},
author = {Klein, Karsten and Aichem, Michael and Sommer, Björn and Erk, Stefan and Zhang, Ying and Schreiber, Falk},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/20bad21e4e3b89818ce8c3d4f60efa4d3/sfbtrr161},
booktitle = {Proceedings of the ACM Symposium on Visual Information Communication and Interaction (VINCI)},
doi = {10.1145/3356422.3356450},
ee = {https://doi.org/10.1145/3356422.3356450},
interhash = {4cd47a3cd18a6fdcfb94021f0805359d},
intrahash = {0bad21e4e3b89818ce8c3d4f60efa4d3},
isbn = {978-1-4503-7626-6},
keywords = {from:leonkokkoliadis 2019 sfbtrr161 D04},
pages = {9:1-9:5},
publisher = {ACM},
timestamp = {2020-05-13T14:20:10.000+0200},
title = {TEAMwISE: Synchronised Immersive Environments for Exploration and Analysis of Movement Data},
url = {https://doi.org/10.1145/3356422.3356450},
year = 2019
}
