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.
翻译:传统的视觉化设计在平面( 屏幕或页面) 上显示传统视觉化, 但大多数数据不是“ 平面 ” 。 例如, 地球表面存在于一个球体上, 然而, 当一个球面在平面地图上显示时, 关键信息隐藏在隐蔽中, 比如球面表面的地理路径正在跨越平面地图的边界。 同样, 周期性的时间序列数据没有开始或结束。 当这种周期性数据显示在传统的线性图表上显示时, 查看者需要看到图层之间视觉化的连续性。 在这种边界上重新与图表重新连接时, 可能会带来额外的认知负荷。 更复杂的数据表层, 如在数据点之间有数百或数千个链接的网络结构, 关键信息隐藏到一个连结结构, 更少的“ 球面” 。 为了提高这些视觉化数据的可视化, 我们从一个系统化的图像序列中开始, 来了解这些视觉性的数据 。