Star Glyph Insets for Overview Preservation of Multivariate Data

Abstract

Exploring vast spatial datasets often requires to drill down in order to inspect details, thus leading to a loss of contextual overview. An additional challenge rises if the visualized data is of multivariate nature, which we encounter in various domains such as healthcare, nutrition, crime reports, or social networks. Existing overview-plus-detail approaches do provide context but only limited support for multivariate data and often suffer from distortion. In this paper, we dynamically integrate star glyphs as insets into the spatial representation of multivariate data thus providing overview while inspecting details. Star glyphs pose an efficient and space saving method to visualize multivariate data, which qualifies them as integrated data representative. Furthermore, we demonstrate the usefulness of our approach in two use cases: The spatial exploration of multivariate crime data collected in San Francisco and the exploration of multivariate whisky data. Introduction Multivariate data accompanies us in our day-to-day life. Prominent examples represent data from healthcare, nutrition, crime reports, or social networks, among others. We typically use spatial representations in order to determine patterns and correlations among dimensions. An example represents the exploration of a huge set of malt whiskies: Each whisky is assigned to the geo-location of its distillery and has several diverse taste categories. The task can be either to seek correlations between particular taste categories and geo-locations, or to find patterns of whiskies for certain taste categories. The latter case can be achieved by applying dimension reduction methods which project the data to a lower dimensional space. When exploring such vast amounts of spatial data, at some point we use zooming and panning interactions to focus on certain regions of interest to obtain a detailed view. However, due to the limited size of the display screen, zooming and panning interactions lead to an inevitable loss of the contextual overview. Overview can be regained by zooming out resulting in a continuous trade-off between overview and detail. Jerding and Stasko argue that the limited size of the display makes it difficult to create efficient global views [25]. Existing Overview-and-Detail and Focus-plus-Context approaches provide comprehensive methods that typically operate in image space. Overview-and-Detail techniques attach a second viewport to the visualization. Although overview is provided, the user is forced to split his attention, which can result in increased cognitive load [19]. In contrast, Focus-plus-Context techniques integrate overview and detail, but use image-based distortion which restricts the interface by means of zooming levels [36]. In this paper, we propose a novel data-driven Off-Screen visualization technique for spatial multivariate data. More specifically, we contribute a dynamic integration of star glyphs as efficient visual insets for the representation of multivariate off-screen data objects. To do so, we augment the viewport with a dedicated border region including star glyph insets. A result of our approach is depicted in Figure 1. The remainder of this paper is organized as follows: First, we discuss related work. Then, we introduce the design of our approach and show the usefulness in two use cases, before we conclude and outline future work. Related Work In order to preserve the overview of multivariate data during exploration, we need to consider the potentials of both multivariate data visualization and overview preserving visualization. Following, we discuss related work of these areas. Multivariate Data Visualization Visual analysis of multivariate data has the objective of allowing the user to identify correlations and patterns among dimensions. Dimensions in multivariate data are not supposed to be considered independently but simultaneously, because they typically provide combined information that contributes to the overall understanding of the data [33]. Various techniques have been presented to visualize multivariate data. Prominent examples of geometric projections are parallel coordinates [22], Andrew curves[1], or star coordinates [27]. Pixel-oriented techniques include recursive patterns [28] and pixel barcharts [29]. However, aforementioned techniques are not optimal to be integrated as space efficient inset giving a coarse overview of dimensions; glyph-based techniques such as Chernoff faces [7] or star glyphs [5] meet these requirements. Integration of Overview and Detail In order to allow efficient navigation and provide support for data analysis, the integrated preservation of the contextual overview is crucial. In this paper, the term context refers to the overview of the multivariate nature of the data including information about location and in some cases topology of the data. Following, we give a brief overview of integrated techniques, namely Focus-plus-Context and Off-screen Visualization techniques. Distortion-oriented Techniques The pioneering approach of Apperley et al. [2] provides a maximum focus region while all surrounding areas are distorted. Variations of this approach apply the technique for example to one-dimensional visualizations [32, 39]. Furnas [14] further introduced the degree-of-interest (DOI) function as basis for the wellknown Focus-plus-Context systems [40, 4]. Additional Focus-