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

Visualizing Sets with Linear Diagrams.

This paper presents the first design principles that optimize the visualization of sets using linear diagrams.These principles are justified through empirical studies that evaluate the impact of graphical features on taskperformance. Linear diagrams represent sets using straight line segments, with line overlaps correspondingto set intersections. This study builds on recent empirical research, which establishes that linear diagramscan be superior to prominent set visualization techniques, namely Euler and Venn diagrams. We addressthe problem of how to best visualize overlapping sets using linear diagrams. To solve the problem, weinvestigate which graphical features of linear diagrams significantly impact user task performance. Tothis end, we conducted seven crowdsourced empirical studies involving a total of 1,760 participants. Thesestudies allowed us to identify the following design principles, which significantly aid task performance: usea minimal number of line segments, use guidelines where overlaps start and end, and draw lines that arethin as opposed to thick bars. We also evaluated the following graphical properties that did not significantlyimpact task performance: color, orientation, and set order. The results are brought to life through a freelyavailable software implementation that automatically draws linear diagrams with user-controlled graphicalchoices. An important consequence of our research is that users are now able to create effective visualizationsof sets automatically, thus improving human–computer interaction

Impact of COVID-19 lockdown on the incidence and mortality of acute exacerbations of chronic obstructive pulmonary disease: national interrupted time series analyses for Scotland and Wales

The COVID-19 pandemic and ensuing national lockdowns have dramatically changed the healthcare landscape. The pandemic’s impact on people with chronic obstructive pulmonary disease (COPD) remains poorly understood. We hypothesised that the UK-wide lockdown restrictions were associated with reductions in severe COPD exacerbations. We provide the first national level analyses of the impact of the COVID-19 pandemic and first lockdown on severe COPD exacerbations resulting in emergency hospital admissions and/or leading to death as well as those recorded in primary care or emergency departments

Survey of Surveys (SoS) ‐ Mapping The Landscape of Survey Papers in Information Visualization

Information visualization as a field is growing rapidly in popularity since the first information visualization conference in 1995.However, as a consequence of its growth, it is increasingly difficult to follow the growing body of literature within the field.Survey papers and literature reviews are valuable tools for managing the great volume of previously published research papers,and the quantity of survey papers in visualization has reached a critical mass. To this end, this survey paper takes a quantumstep forward by surveying and classifying literature survey papers in order to help researchers understand the current landscapeof Information Visualization. It is, to our knowledge, the first survey of survey papers (SoS) in Information Visualization. Thispaper classifies survey papers into natural topic clusters which enables readers to find relevant literature and develops thefirst classification of classifications. The paper also enables researchers to identify both mature and less developed researchdirections as well as identify future directions. It is a valuable resource for both newcomers and experienced researchers in andoutside the field of Information Visualization and Visual Analytic

The Painter's Problem: Covering a Grid with Colored Connected Polygons.

Motivated by a new way of visualizing hypergraphs, we study the following problem. Consider a rectangular grid and a set of colors χ. Each cell s in the grid is assigned a subset of colors χs⊆χ and should be partitioned such that for each color c∈χs at least one piece in the cell is identified with c. Cells assigned the empty color set remain white. We focus on the case where χ={red,blue}. Is it possible to partition each cell in the grid such that the unions of the resulting red and blue pieces form two connected polygons? We analyze the combinatorial properties and derive a necessary and sufficient condition for such a painting. We show that if a painting exists, there exists a painting with bounded complexity per cell. This painting has at most five colored pieces per cell if the grid contains white cells, and at most two colored pieces per cell if it does not

Twins in Subdivision Drawings of Hypergraphs

Visualizing hypergraphs, systems of subsets of some universe, has continuously attracted research interest in the last decades. We study a natural kind of hypergraph visualization called subdivision drawings. Dinkla et al. [Comput. Graph. Forum ’12] claimed that only few hypergraphs have a subdivision drawing. However, this statement seems to be based on the assumption (also used in previous work) that the input hypergraph does not contain twins, pairs of vertices which are in precisely the same hyperedges (subsets of the universe). We show that such vertices may be necessary for a hypergraph to admit a subdivision drawing. As a counterpart, we show that the number of such “necessary twins” is upper-bounded by a function of the number m of hyperedges and a further parameter r of the desired drawing related to its number of layers. This leads to a linear-time algorithm for determining such subdivision drawings if m and r are constant; in other words, the problem is linear-time fixed-parameter tractable with respect to the parameters m and r

ML-ModelExplorer: An Explorative Model-Agnostic Approach to Evaluate and Compare Multi-class Classifiers

International audienceA major challenge during the development of Machine Learning systems is the large number of models resulting from testing different model types, parameters, or feature subsets. The common approach of selecting the best model using one overall metric does not necessarily find the most suitable model for a given application, since it ignores the different effects of class confusions. Expert knowledge is key to evaluate, understand and compare model candidates and hence to control the training process. This paper addresses the research question of how we can support experts in the evaluation and selection of Machine Learning models, alongside the reasoning about them. ML-ModelExplorer is proposed – an explorative, interactive, and model-agnostic approach utilising confusion matrices. It enables Machine Learning and domain experts to conduct a thorough and efficient evaluation of multiple models by taking overall metrics, per-class errors, and individual class confusions into account. The approach is evaluated in a user-study and a real-world case study from football (soccer) data analytics is presented.ML-ModelExplorer and a tutorial video are available online for use with own data sets: www.ml-and-vis.org/me