Tracing Tuples Across Dimensions: A Comparison of Scatterplots and Parallel Coordinate Plots

Tracing Tuples Across Dimensions: A Comparison of Scatterplots and Parallel Coordinate Plots

Authors

Xiaole Kuang, Haimo Zhang, Shengdong Zhao, Michael J. McGuffin

Paper

Presentation

Abstract

One of the fundamental tasks for analytic activity is retrieving (i.e., reading) the value of a particular quantity in an information visualization. However, few previous studies have compared user performance in such value retrieval tasks for different visualizations. We present an experimental comparison of user performance (time and error distance) across four multivariate data visualizations. Three variants of scatterplot (SCP) visualizations, namely SCPs with common vertical axes (SCP-common), SCPs with a staircase layout (SCP-staircase), and SCPs with rotated axes between neighboring cells (SCP-rotated), and a baseline parallel coordinate plots (PCP) were compared. Results show that the baseline PCP is better than SCP-rotated and SCP-staircase under all conditions, while the difference between SCP-common and PCP depends on the dimensionality and density of the dataset. PCP shows advantages over SCP-common when the dimensionality and density of the dataset are low, but SCPcommon eventually outperforms PCP as data dimensionality and density increase. The results suggest guidelines for the use of SCPs and PCPs that can benefit future researchers and practitioners.

Shen

Shen is an HCI professor at the National University of Singapore working on realizing his vision of HeadsUp Computing, a new Interaction paradigm that can transform the way we live and interact with computers. In his free time, Shen loves to read, run, spend time with family and friends, and explore nature.

Leave a Reply