Semantic Resizing of Charts Through Generalization:A Case Study with Line Charts

TL;DR

This paper introduces a technique inspired by cartographic principles for automatically resizing line charts while preserving their key semantic information and reducing clutter, adaptable to different display sizes.

Contribution

It presents a novel generalization algorithm for line charts that automatically determines appropriate simplification operators based on spatial and semantic factors.

Findings

Generalized line charts preserved overall data shape

Reduced visual clutter in resized charts

Prototype evaluation showed effective semantic preservation

Abstract

Inspired by cartographic generalization principles, we present a generalization technique for rendering line charts at different sizes, preserving the important semantics of the data at that display size. The algorithm automatically determines the generalization operators to be applied at that size based on spatial density, distance, and the semantic importance of the various visualization elements in the line chart. A qualitative evaluation of the prototype that implemented the algorithm indicates that the generalized line charts pre-served the general data shape, while minimizing visual clutter. We identify future opportunities where generalization can be extended and applied to other chart types and visual analysis authoring tools.