Visual Integration of Meteorological and Sensor Data for Identifying Suspicious Company Behavior

Seebacher, Daniel; Schneider, Bruno; Behrisch, Michael

Visual Integration of Meteorological and Sensor Data for Identifying Suspicious Company Behavior

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Date

2017

Publication type

Contribution to a conference collection

Publication status

Published

Published in

2017 IEEE Conference on Visual Analytics Science and Technology (VAST), Proceedings / Fisher, Brian; Liu, Shixia; Schreck, Tobias (ed.). - Piscataway, NJ : IEEE, 2017. - pp. 225-226. - ISBN 978-1-5386-3163-8

Abstract

We present an approach developed in course of the VAST 2017 Mini-Challenge 2. To help the ornithologist Mitch to investigate the noxious gases emitted by the four companies south of the nature preserve, we employ a combination of interactive visualizations that allow for an exploration of the data. In this paper, we present our visual-interactive approach for analyzing suspicious patterns in the data. By taking the wind data into consideration, as well, our approach allows the retrieval of patterns in the chemical releases and identify key polluters.

Subject (DDC)

004 Computer Science

Conference

2017 IEEE Conference on Visual Analytics Science and Technology (VAST), Oct 3, 2017 - Oct 6, 2017, Phoenix, AZ

Cite This

ISO 690

SEEBACHER, Daniel, Bruno SCHNEIDER, Michael BEHRISCH, 2017. Visual Integration of Meteorological and Sensor Data for Identifying Suspicious Company Behavior. 2017 IEEE Conference on Visual Analytics Science and Technology (VAST). Phoenix, AZ, Oct 3, 2017 - Oct 6, 2017. In: FISHER, Brian, ed., Shixia LIU, ed., Tobias SCHRECK, ed.. 2017 IEEE Conference on Visual Analytics Science and Technology (VAST), Proceedings. Piscataway, NJ:IEEE, pp. 225-226. ISBN 978-1-5386-3163-8. Available under: doi: 10.1109/VAST.2017.8585436

BibTex

@inproceedings{Seebacher2017Visua-44808,
  year={2017},
  doi={10.1109/VAST.2017.8585436},
  title={Visual Integration of Meteorological and Sensor Data for Identifying Suspicious Company Behavior},
  isbn={978-1-5386-3163-8},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={2017 IEEE Conference on Visual Analytics Science and Technology (VAST), Proceedings},
  pages={225--226},
  editor={Fisher, Brian and Liu, Shixia and Schreck, Tobias},
  author={Seebacher, Daniel and Schneider, Bruno and Behrisch, Michael}
}

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Bibliography of Konstanz

Yes