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Width-Scale Bar Charts for Data with Large Value Range

Höhn, Markus and Wunderlich, Marcel and Ballweg, Kathrin and Landesberger, Tatiana von (2020):
Width-Scale Bar Charts for Data with Large Value Range.
pp. 103-107, EuroVis 2020: 11th International EuroVis Workshop on Visual Analytics, virtual Conference, 25.-29.05.2020, DOI: 10.2312/evs.20201056,
[Conference or Workshop Item]

Abstract

Data sets with large value range are difficult to visualize with traditional linear bar charts. Usually, a logarithmic scale isused in these cases. However, the logarithmic scale suffers from non-linearity. Recently, scale-stack bar charts and magnitudemarkers, improve the readability of values. However, they have other disadvantages such as various scales or several objectsfor visualizing one value. We propose the width-scale bar chart that uses width, height and color to cover a large value rangewithin one linear scale. A quantitative user study shows advantages of our design – especially for reading values.

Item Type: Conference or Workshop Item
Erschienen: 2020
Creators: Höhn, Markus and Wunderlich, Marcel and Ballweg, Kathrin and Landesberger, Tatiana von
Title: Width-Scale Bar Charts for Data with Large Value Range
Language: English
Abstract:

Data sets with large value range are difficult to visualize with traditional linear bar charts. Usually, a logarithmic scale isused in these cases. However, the logarithmic scale suffers from non-linearity. Recently, scale-stack bar charts and magnitudemarkers, improve the readability of values. However, they have other disadvantages such as various scales or several objectsfor visualizing one value. We propose the width-scale bar chart that uses width, height and color to cover a large value rangewithin one linear scale. A quantitative user study shows advantages of our design – especially for reading values.

Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: EuroVis 2020: 11th International EuroVis Workshop on Visual Analytics
Event Location: virtual Conference
Event Dates: 25.-29.05.2020
Date Deposited: 29 May 2020 06:26
DOI: 10.2312/evs.20201056
Official URL: https://conferences.eg.org/egev20/program/
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