Preference-Optimal Multi-Metric Weighting for Parallel Coordinate Plots
This work addresses a visualization problem for users of parallel coordinate plots in multi-metric scenarios, representing an incremental improvement.
The paper tackles the challenge of visualizing multi-metric data in parallel coordinate plots by proposing a method to calculate optimal weights based on user preferences, enabling identification of unique control parameter patterns for different preferences in pedestrian flow guidance planning.
Parallel coordinate plots (PCPs) are a prevalent method to interpret the relationship between the control parameters and metrics. PCPs deliver such an interpretation by color gradation based on a single metric. However, it is challenging to provide such a gradation when multiple metrics are present. Although a naive approach involves calculating a single metric by linearly weighting each metric, such weighting is unclear for users. To address this problem, we first propose a principled formulation for calculating the optimal weight based on a specific preferred metric combination. Although users can simply select their preference from a two-dimensional (2D) plane for bi-metric problems, multi-metric problems require intuitive visualization to allow them to select their preference. We achieved this using various radar charts to visualize the metric trade-offs on the 2D plane reduced by UMAP. In the analysis using pedestrian flow guidance planning, our method identified unique patterns of control parameter importance for each user preference, highlighting the effectiveness of our method.