SimpleSets: Capturing Categorical Point Patterns with Simple Shapes

Research output: Contribution to journalArticleAcademicpeer-review

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.
Original languageEnglish
Article number10679736
Pages (from-to)262-271
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume31
Issue number1
Early online date12 Sept 2024
DOIs
Publication statusPublished - 1 Jan 2025
EventIEEE VIS 2024 - Virtual/Online
Duration: 13 Oct 202418 Oct 2024

Keywords

  • algorithms
  • geographic visualization
  • Set visualization

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