Preference-Optimal Multi-Metric Weighting for Parallel Coordinate Plots

TL;DR

This paper introduces a method for visualizing and calculating optimal metric weights in parallel coordinate plots, enabling users to better interpret multi-metric data through preference-based visualization techniques.

Contribution

It presents a novel formulation for optimal metric weighting based on user preferences and visualizes trade-offs using radar charts and UMAP, improving interpretability in multi-metric analysis.

Findings

Effectively visualizes metric trade-offs with radar charts.

Identifies unique control parameter importance patterns for different user preferences.

Demonstrates usefulness in pedestrian flow guidance planning.

Abstract

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.