On the Truthfulness of Petal Graphs for Visualisation of Data

A petal graph is an aesthetically attractive and applauded tool for visualising parameter sets. For instance, petal graphs are often used by Norwegian policy makers and decision makers in higher education as the Ministry of Education and Research relies on petal graphs in their reports. This study argues that petal graphs are prone to misinterpretation. It is challenging to interpret a petal graph in general, it is hard to compare two or more petal graphs and this study demonstrates that the physical characteristics of petal graphs can be incorrect in terms of the parameters on display. This study concludes that the use of petal graphs should be abolished and that other visualisation techniques to be used instead. Several alternatives are suggested

Unboxing Cluster Heatmaps

Background: Cluster heatmaps are commonly used in biology and related fields to reveal hierarchical clusters in data matrices. This visualization technique has high data density and reveal clusters better than unordered heatmaps alone. However, cluster heatmaps have known issues making them both time consuming to use and prone to error. We hypothesize that visualization techniques without the rigid grid constraint of cluster heatmaps will perform better at clustering-related tasks. Results: We developed an approach to “unbox” the heatmap values and embed them directly in the hierarchical clustering results, allowing us to use standard hierarchical visualization techniques as alternatives to cluster heatmaps. We then tested our hypothesis by conducting a survey of 45 practitioners to determine how cluster heatmaps are used, prototyping alternatives to cluster heatmaps using pair analytics with a computational biologist, and evaluating those alternatives with hour-long interviews of 5 practitioners and an Amazon Mechanical Turk user study with approximately 200 participants. We found statistically significant performance differences for most clustering-related tasks, and in the number of perceived visual clusters. Visit git.io/vw0t3 for our results. Conclusions: The optimal technique varied by task. However, gapmaps were preferred by the interviewed practitioners and outperformed or performed as well as cluster heatmaps for clustering-related tasks. Gapmaps are similar to cluster heatmaps, but relax the heatmap grid constraints by introducing gaps between rows and/or columns that are not closely clustered. Based on these results, we recommend users adopt gapmaps as an alternative to cluster heatmaps

Testing Ontology Embedding Visualization

This dissertation presents an experiment conducted with human participants on human-information interaction with visualizations of ontologies. The research question is whether embedding visualizations or graph based visualizations lead to better task performance for human-information interaction. A literature review of word embeddings, information retrieval applications, cartesian and radial visualizations, and knowledge graph visualizations is conducted. This literature review is grounded in a facet analysis of the intersecting topics of the central research question. The context of embeddings as used for information retrieval in the 20th century, as opposed to more recent 21st century inventions such as Google's word2vec is explored. A training ontology, the African Wildlife Ontology (AWO) was selected. It was extended using public lexical resources taken from the internet to include classes of common African plants and animals. This ontology was then visualised both as vectorspace embeddings and as a classical graph visualization. Participants were presented with one of four different knowledge graph visualizations: WebVOWL, OntoGraf, SquareVis and CircleVis and had to perform a specific information retrieval task. This task was to record as many African animals as they could find on the chart. The results are analyzed in terms of precision, recall, spam and average time. Although ultimately the results do not reject the null hypothesis, there is an opportunity for further research in the visualization of embeddings of knowledge graphs, especially for information retrieval

Empowering Citizens in the Digital Age : a systematic evaluation of voting advice : applications and best practices for their design

Voting Advice Applications (VAAs) are online tools that match voters and parties based on common positions on a series of issues. Starting as tools for political education, VAAs have nowadays become relevant political actors. Besides providing information, they also raise electoral turnout, improve political knowledge and influence party choice. The degree in which they do so depends on their design. In this thesis, I view the design of VAAs as a process and focus on two steps: the questionnaire and the visualizations. For the questionnaire I focus on two aspects: the scales used to position users on the political map and the formulation of the questions. I assess the scales using various data-reduction techniques and rank them on unidimensionality, quality and reliability. I find that most scales score insufficient, though the score depends on the construction method used for the scale. The cause of these problematic scales is that VAA users often apply simplification methods. This means they do not always understand the question or use the response categories as intended. This results in problematic scales, resulting in a political map that is difficult to interpret. Also, I find that altering the questions in the main questionnaire to have either a positive or negative formulation, not only influences the responses of the users but the match between the user and the party as well. For the visualization, I run an online experiment in which I ask users to answer questions related to various kinds of visualization. I find that not only do they have difficulty to finish some basic tasks, they also have diverging interpretations of popular VAA visualizations. My main conclusion is therefore that the design of VAAs cannot be neutral. Also, the underlying information used to calculate the match and visualize the political map are often troublesome. Yet, I also show designers can use simple methods to improve their VAAs. This is important as VAAs are likely to become even more popular than they are already at this moment

Using communication technologies to deliver public health agendas in National Health Service food and drink automated vending

This research responded to a National Health Service (NHS) wide problem. The problem is how to create healthier automated food and drink vending services. The research’s’ interpretation of this central research problem is embedded in the Facilities Management (FM) perspective. Vending retail products do not support government healthy lifestyle policies and initiatives. FMs have to change this through catering contracts. However, there is little guidance on how to design, evidence and operationalise improvement. The research tested vending point of sale designs over a year, trying to reduce the sale of unhealthy products. Secondly, it developed a novel application of a nutritional profile to enable the service design process and evidence change. Thirdly, the research baselined service level information through survey n=1,292. Night shift staff were a key stakeholder as it was thought that vending was their only retail catering and the impact was unknown. Regression modelling and multivariate analysis was used in the survey and design tests. Linear regression was used to understand the impact of vending point of sale design on sales. Logistic regression was used to test service level perceptions in the survey. The statistical methods used were flexible. The survey design and analysis is widely applicable to evaluate many services. The research found that in combination, changing product ranges, adding nutritional labels, and moving water to eye level significantly reduces unhealthy sales. However real change requires healthier vending products. The nutritional profile adapted is highly suitable to standardise service and evaluate how healthy vending products really are. The survey was a novel and statistically robust addition to FM service evaluation. It proved staff perception of poor catering, inadequate breaks, innutritious food and need for staff food education. Vending was central. Finally, making meaningful service improvements and setting thresholds in the statistical models confidently required in depth first-hand knowledge

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions