SimpleSets: Capturing Categorical Point Patterns with Simple Shapes

TL;DR

SimpleSets introduces a visualization method using simple shapes to enclose categorical point patterns on maps, reducing cognitive load and improving clarity over complex existing approaches.

Contribution

It proposes a novel set visualization technique with formal definitions, an efficient partitioning algorithm, and a rendering method for clear, aesthetically pleasing map visualizations.

Findings

SimpleSets effectively visualizes categorical point data with fewer shapes.

The method reduces user cognitive load compared to complex shape approaches.

Experimental results show improved clarity and interpretability.

Abstract

Points of interest on a map such as restaurants, hotels, or subway stations, give rise to categorical point data: data that have a fixed location and one or more categorical attributes. Consequently, recent years have seen various set visualization approaches that visually connect points of the same category to support users in understanding the spatial distribution of categories. Existing methods use complex and often highly irregular shapes to connect points of the same category, leading to high cognitive load for the user. In this paper we introduce SimpleSets, which uses simple shapes to enclose categorical point patterns, thereby providing a clean overview of the data distribution. SimpleSets is designed to visualize sets of points with a single categorical attribute; as a result, the point patterns enclosed by SimpleSets form a partition of the data. We give formal definitions of point patterns that correspond to simple shapes and describe an algorithm that partitions categorical points into few such patterns. Our second contribution is a rendering algorithm that transforms a given partition into a clean set of shapes resulting in an aesthetically pleasing set visualization. Our algorithm pays particular attention to resolving intersections between nearby shapes in a consistent manner. We compare SimpleSets to the state-of-the-art set visualizations using standard datasets from the literature.