Details

Autor(en) / Beteiligte

• Wang, Yunhai
• Wang, Zeyu
• Zhu, Lifeng
• Zhang, Jian
• Fu, Chi-Wing
• Cheng, Zhanglin
• Tu, Changhe
• Chen, Baoquan

Titel

Is There a Robust Technique for Selecting Aspect Ratios in Line Charts?

Ist Teil von

• IEEE transactions on visualization and computer graphics, 2018-12, Vol.24 (12), p.3096-3110

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2018

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Explore

Beschreibungen/Notizen

• The aspect ratio of a line chart heavily influences the perception of the underlying data. Different methods explore different criteria in choosing aspect ratios, but so far, it was still unclear how to select aspect ratios appropriately for any given data. This paper provides a guideline for the user to choose aspect ratios for any input 1D curves by conducting an in-depth analysis of aspect ratio selection methods both theoretically and experimentally. By formulating several existing methods as line integrals, we explain their parameterization invariance. Moreover, we derive a new and improved aspect ratio selection method, namely the <inline-formula><tex-math notation="LaTeX">L_1 </tex-math> <inline-graphic xlink:href="wang-ieq1-2787113.gif"/> </inline-formula>-LOR (local orientation resolution), with a certain degree of parameterization invariance. Furthermore, we connect different methods, including AL (arc length based method), the banking to 45<inline-formula><tex-math notation="LaTeX"> ^\circ</tex-math> <inline-graphic xlink:href="wang-ieq2-2787113.gif"/> </inline-formula> principle, RV (resultant vector) and AS (average absolute slope), as well as <inline-formula> <tex-math notation="LaTeX">L_1</tex-math> <inline-graphic xlink:href="wang-ieq3-2787113.gif"/> </inline-formula>-LOR and AO (average absolute orientation). We verify these connections by a comparative evaluation involving various data sets, and show that the selections by RV and <inline-formula> <tex-math notation="LaTeX">L_1</tex-math> <inline-graphic xlink:href="wang-ieq4-2787113.gif"/> </inline-formula>-LOR are complementary to each other for most data. Accordingly, we propose the dual-scale banking technique that combines the strengths of RV and <inline-formula><tex-math notation="LaTeX">L_1 </tex-math> <inline-graphic xlink:href="wang-ieq5-2787113.gif"/> </inline-formula>-LOR, and demonstrate its practicability using multiple real-world data sets.