BlockSets: A Structured Visualization for Sets with Large Elements

TL;DR

BlockSets is a new visualization method for set systems with large elements, using grid placement and rectilinear shapes to improve clarity and accommodate complex element representations.

Contribution

It introduces a novel visualization technique with ILP-based layouts and shape stacking algorithms for large-element set systems.

Findings

Successfully visualizes large elements like images and charts.

Provides high-quality, readable layouts for complex set systems.

All tested real-world datasets could be stacked with the proposed algorithm.

Abstract

Visualizations of set systems frequently use enclosing geometries for the sets in combination with reduced representations of the elements, such as short text labels, small glyphs, or points. Hence they are generally unable to adequately represent sets whose elements are larger text fragments, images, or charts. In this paper we introduce BlockSets, a novel set visualization technique specifically designed for sets with large elements. BlockSets places the elements on a grid and uses rectilinear shapes as enclosing geometries. We describe integer linear programs that find high-quality layouts of the elements on the grid. Since not all set systems allow a compact contiguous representation in this form, we also present an algorithm that splits the visualization into parts when needed; our visual encoding highlights the parts for the user in the final visualization. BlockSets utilizes orthoconvex shapes which offer a good trade-off between compactness and readability. Finally, BlockSets renders the enclosing geometries as stacked opaque shapes. We describe an algorithm that finds a stacking order such that all shapes can be inferred. Such a stacking does not have to exist, but our algorithm did find a stacking for all real-world data sets that we tested.