Optimizing Symbol Visibility through Displacement

TL;DR

This paper studies how to optimize the placement of symbols in visualizations to improve visibility by minimizing overlap, using algorithms for specific container sizes and configurations.

Contribution

It introduces algorithms for symbol placement that maximize visibility within constrained containers, including staircase layouts and approximation methods.

Findings

Staircase layout is near-optimal for containers up to size 2.

A 2-approximation algorithm effectively improves symbol visibility.

Minimum visible perimeter of 2 is always achievable with a generic construction.

Abstract

In information visualization, the position of symbols often encodes associated data values. When visualizing data elements with both a numerical and a categorical dimension, positioning in the categorical axis admits some flexibility. This flexibility can be exploited to reduce symbol overlap, and thereby increase legibility. In this paper, we initialize the algorithmic study of optimizing symbol legibility via a limited displacement of the symbols. Specifically, we consider closed unit square symbols that need to be placed at specified $y$-coordinates. We optimize the drawing order of the symbols as well as their $x$-displacement, constrained within a rectangular container, to maximize the minimum visible perimeter over all squares. If the container has width and height at most $2$, there is a point that stabs all squares. In this case, we prove that a staircase layout is arbitrarily close to optimality and can be computed in $O(n\log n)$ time. If the width is at most $2$, there is a vertical line that stabs all squares, and in this case, we design a 2-approximation algorithm (assuming fixed container height) that runs in $O(n\log n)$ time. As it turns out that a minimum visible perimeter of 2 is always achievable with a generic construction, we measure this approximation with respect to the visible perimeter exceeding 2. We show that, despite its simplicity, the algorithm gives asymptotically optimal results for certain instances.