It's a Wrap! Visualisations that Wrap Around Cylindrical, Toroidal, or Spherical Topologies

TL;DR

This paper introduces a novel class of interactive wrapped visualisations for non-flat data on cylindrical, toroidal, or spherical surfaces, improving understanding of complex, cyclical, and geospatial data structures.

Contribution

It systematically explores the design space of wrapped visualisations, presents new layout algorithms, and demonstrates their effectiveness for various data types.

Findings

Wrapped visualisations better preserve spatial relations in geospatial data.

They reveal underlying structures more clearly in networks and cyclical time series.

The paper provides layout algorithms and toolkits for creating such visualisations.

Abstract

Traditional visualisations are designed to be shown on a flat surface (screen or page) but most data is not "flat". For example, the surface of the earth exists on a sphere, however, when that surface is presented on a flat map, key information is hidden, such as geographic paths on the spherical surface being wrapped across the boundaries of the flat map. Similarly, cyclical time-series data has no beginning or end. When such cyclical data is presented on a traditional linear chart, the viewer needs to perceive continuity of the visualisation across the chart's boundaries. Mentally reconnecting the chart across such a boundary may induce additional cognitive load. More complex data such as a network diagram with hundreds or thousands of links between data points leads to a densely connected structure that is even less "flat" and needs to wrap around in multiple dimensions. To improve the usability of these visualisations, this thesis explores a novel class of interactive wrapped data visualisations, i.e., visualisations that wrap around continuously when interactively panned on a two-dimensional projection of surfaces of 3D shapes, specifically, cylinder, torus, or sphere. We start with a systematic exploration of the design space of interactive wrapped visualisations, characterising the visualisations that help people understand the relationship within the data. Subsequently, we investigate a series of wrappable visualisations for cyclical time series, network, and geographic data. We show that these interactive visualisations better preserve the spatial relations in the case of geospatial data, and better reveal the data's underlying structure in the case of abstract data such as networks and cyclical time series. Furthermore, to assist future research and development, we contribute layout algorithms and toolkits to help create pannable wrapped visualisations.