7 research outputs found
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
Evaluating Visualizations of Sets and Networks that Use Euler Diagrams and Graphs
This paper presents an empirical evaluation of state-of-the-art visualization techniques that combine Euler diagrams and graphs to visualize sets and networks. Focusing on SetNet, Bubble Sets and WebCola – techniques for which there is freely available software – our evaluation reveals that they can inaccurately and ineffectively visualize the data. Inaccuracies include placing vertices in incorrect zones, thus incorrectly conveying the sets in which the represented data items lie. Ineffective properties, which are known to hinder cognition, include drawing Euler diagrams with extra zones or graphs with large numbers of edge crossings. The results demonstrate the need for improved techniques that are more accurate and more effective for end users.The Leverhulme Trus
Evaluating the Effect of Timeline Shape on Visualization Task Performance
Timelines are commonly represented on a horizontal line, which is not
necessarily the most effective way to visualize temporal event sequences.
However, few experiments have evaluated how timeline shape influences task
performance. We present the design and results of a controlled experiment run
on Amazon Mechanical Turk (n=192) in which we evaluate how timeline shape
affects task completion time, correctness, and user preference. We tested 12
combinations of 4 shapes -- horizontal line, vertical line, circle, and spiral
-- and 3 data types -- recurrent, non-recurrent, and mixed event sequences. We
found good evidence that timeline shape meaningfully affects user task
completion time but not correctness and that users have a strong shape
preference. Building on our results, we present design guidelines for creating
effective timeline visualizations based on user task and data types. A free
copy of this paper, the evaluation stimuli and data, and code are available at
https://osf.io/qr5yu/Comment: 12 pages, 5 figure
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