Evaluating the Effects of Colour in LineSets

There are a number of graphical choices to be made when drawing LineSets; one of these choices is colour. This paper identifies how colour (hue, value, or monochrome) should be applied to LineSets drawn on networks

A Task-Based Evaluation of Combined Set and Network Visualization

This paper addresses the problem of how best to visualize network data grouped into overlapping sets. We address it by evaluating various existing techniques alongside a new technique. Such data arise in many areas, including social network analysis, gene expression data, and crime analysis. We begin by investigating the strengths and weakness of four existing techniques, namely Bubble Sets, EulerView, KelpFusion, and LineSets, using principles from psychology and known layout guides. Using insights gained, we propose a new technique, SetNet, that may overcome limitations of earlier methods. We conducted a comparative crowdsourced user study to evaluate all five techniques based on tasks that require information from both the network and the sets. We established that EulerView and SetNet, both of which draw the sets first, yield significantly faster user responses than Bubble Sets, KelpFusion and LineSets, all of which draw the network first

The State-of-the-Art of Set Visualization

Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net

Evaluating graphical manipulations in automatically laid out LineSets

This paper presents an empirical study to determine whether alterations to graphical features (colour and size) of automatically generated LineSets improve task performance. LineSets are used to visualise sets and networks. The increasingly common nature of such data suggests that having effective visualisations is important. Unlike many approaches to set and network visualisation, which often use concave or convex shapes to represent sets alongside graphs, LineSets use lines overlaid on a graph. LineSets have been shown to be advantageous over shape-based approaches. However, the graphical properties of LineSets have not been fully explored. Our results suggest that automatically drawn LineSets can be significantly improved for certain tasks through the considered use of colour alongside size variations applied to their graphical elements. In particular, we show that perceptually distinguishable colours, lines of varying width, and nodes of varying diameter lead to improved task performance in automatically laid-out LineSets

Evaluating the effects of size in linesets

LineSets represent information about sets by drawing one line for each set on an existing visualization of data items. This paper addresses the following question: does manipulating the size of visual elements affect the comprehension of LineSets? We empirically evaluated two types of size treatments applied to LineSets drawn on networks: varying set-line thickness, to reflect relative set cardinality, and varying node diameter, to reflect data items' relative degree of connectivity. The evaluation required participants to perform tasks that were thought to be aided by the size variations alongside tasks where no benefit was anticipated. Viewing comprehension through accuracy and time performance, we found that varying set-line thickness and node diameter significantly improves the effectiveness of LineSets. As a consequence, this research leads to the recommendation that LineSets vary sizes of lines and nodes

TimeSets: Timeline visualization with set relations

In this article, we introduce a novel timeline visualization technique, TimeSets, that helps make sense of complex temporal datasets by showing the set relationships among individual events. TimeSets visually groups events that share a topic, such as a place or a person, while preserving their temporal order. It dynamically adjusts the level of detail for each event to suit the amount of information and display estate. Various design options were explored to address issues such as one event belonging to multiple topics. A controlled experiment was conducted to evaluate its effectiveness by comparing it to the KelpFusion method. The results showed significant advantage in accuracy and user preference

TimeSets: timeline visualization with set relations

In this paper, we introduce a novel timeline visualization technique, TimeSets, that helps make sense of complex temporal datasets by showing the set relationships among individual events. TimeSets visually groups events that share a topic, such as a place or a person, while preserving their temporal order. It dynamically adjusts the level of detail for each event to suit the amount of information and display estate. Various design options were explored to address issues such as one event belonging to multiple topics. A controlled experiment was conducted to evaluate its effectiveness by comparing it to the KelpFusion method. The results showed significant advantage in accuracy and user preference

The impact of topological and graphical choices on the perception of Euler diagrams

This paper establishes the impact of topological and graphical properties on the comprehension of Euler diagrams. To-date, various studies have examined the impact of individual properties of Euler diagrams, such as curve shape and orientation. This has allowed us to establish guides for using these properties such as ‘draw Euler diagrams with circles’ and ‘draw Euler diagrams without regard to orientation’. However, until the work described here, questions still remain, for example ‘do these guides, when combined, make a significant difference to real-world Euler diagrams?’, and if so, ‘should they be used by those visualising set data with Euler diagrams?’ To answer these questions an empirical study was conducted to compare Euler diagrams that have been drawn by others for their real-world data, against versions that adhere to all of the guides in combination. The study establishes that both the accuracy and the speed with which information is derived from Euler diagrams is significantly improved when Euler diagrams adhere, where possible, to all the guides. The improvement is considerable when using the guided diagrams, with on average, the error rate being more than halved from 21.4% to 10.3%, and a 9 s improvement in the average time taken, from 34.2s to 24.9s. As Euler diagrams are regularly used to visualise information in a multitude of areas, ranging from crime control to social network analysis, our results indicate that applying the guides to these diagrams will improve the ability of users to accurately and quickly extract information