Designing a Classification for User-authored Annotations in Data Visualization

Pierre Vanhulst, Florian Évéquoz, Raphaël Tuor, Denis Lalanne

Abstract

This article introduces a classification system for user-authored annotations in the domain of data visualization. The classification system was created with a bottom-up approach, starting from actual user-authored annotations. To devise relevant dimensions for this classification, we designed a data analysis web platform displaying four visualizations of a common dataset. Using this tool, 16 analysts recorded over 300 annotations that were used to design a classification system. That classification system was then iteratively evaluated and refined until a high inter-coder agreement was found. Use cases for such a classification includes assessing the expressiveness of visualizations on a common ground, based on the types of annotations that are produced with each visualization.

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Paper Citation


in Harvard Style

Vanhulst P., Évéquoz F., Tuor R. and Lalanne D. (2018). Designing a Classification for User-authored Annotations in Data Visualization.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, ISBN 978-989-758-289-9, pages 85-96. DOI: 10.5220/0006613700850096


in Bibtex Style

@conference{ivapp18,
author={Pierre Vanhulst and Florian Évéquoz and Raphaël Tuor and Denis Lalanne},
title={Designing a Classification for User-authored Annotations in Data Visualization},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP,},
year={2018},
pages={85-96},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006613700850096},
isbn={978-989-758-289-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP,
TI - Designing a Classification for User-authored Annotations in Data Visualization
SN - 978-989-758-289-9
AU - Vanhulst P.
AU - Évéquoz F.
AU - Tuor R.
AU - Lalanne D.
PY - 2018
SP - 85
EP - 96
DO - 10.5220/0006613700850096