Dynamic Glyphs: Appropriating Causality Perception in Multivariate Visual Analysis

We investigate how to co-opt the perception of causality to aid the analysis of multivariate data. We propose Dynamic Glyphs (DyGs), an animated extension to traditional glyphs. DyGs encode data relations through seemingly physical interactions between glyph parts. We hypothesize that this representation gives rise to impressions of causality, enabling observers to reason intuitively about complex, multivariate dynamics. In a crowdsourced experiment, participants' accuracy with DyGs exceeded or was comparable to non-animated alternatives. Moreover, participants showed a propensity to infer higher-dimensional relations with DyGs. Our findings suggest that visual causality can be an effective 'channel' for communicating complex data relations that are otherwise difficult to think about. We discuss the implications and highlight future research opportunities

Uncertainty Visualization using Hypothetical Outcome Plots

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceData-driven decision-making is crucial for any business. With an increasing interest in Business Intelligence, Data Visualization is playing a major role in decision-making processes. For making a well-informed and accurate decision, it is important to understand uncertainty in the data visualizations. Uncertainty visualizations improve the way users understand the data, as well as the confidence in their conclusions. An important type of uncertainty visualizations is the Hypothetical Outcome Plots (HOPs), which allow the audience to gain an intuitive idea of uncertainty through animated sequences of random draws from a distribution, leading to a more accurate understanding and decision. This document intends to detail a proof-of-concept by carrying out a comparison of static visualization vs. HOPs in terms of efficiency and accuracy of results interpretation for Wayne Enterprises (fictional name) forecasting projects, in particularly the ones related with product launches and product loss of exclusivity. Wayne Enterprises is a world-leading supplier of advanced analytics, technological services and clinical investigation solutions for the life sciences industry. For that objective, it was built two prototypes using Python to support the proof-of-concept execution. A between-group experiment was carried out with 40 members of the German consulting team of Wayne Enterprises, where half answered a survey based on static visualizations and the other half based on HOPs. From this experiment, it is possible to conclude that HOPs can achieve similar results that static visualizations, with people taking the decision in less than half of the time when visualizing a HOP. Thus, it is possible to improve Wayne Enterprises decision-making process by accelerating it with Hypothetical Outcome Plots.A tomada de decisões baseada em dados é crucial para qualquer negócio. Com um interesse crescente em Business Intelligence, a Visualização de Dados está a desempenhar um papel importante nos processos de tomada de decisão. Para se tomar uma decisão bem informada e precisa, é importante compreender a incerteza nas visualizações de dados. As visualizações de incerteza melhoram a forma como os utilizadores compreendem os dados, bem como a confiança nas suas conclusões. Um tipo importante de visualizações de incerteza é o Hypothetical Outcome Plots (HOPs), que permite ao público obter uma ideia intuitiva da incerteza através de sequências animadas de desenhos aleatórios de uma distribuição, conduzindo a uma compreensão e decisão mais precisas. Este documento pretende detalhar uma prova de conceito através da realização de uma comparação entre visualizações estáticas e HOPs em termos de eficiência e exactidão de interpretação de resultados para projectos de forecast da Wayne Enterprises (nome fictício), em particular os relacionados com lançamentos de produtos e perda de exclusividade de produtos. A Wayne Enterprises é um líder mundial de análises avançadas, serviços tecnológicos e soluções de investigação clínica para a indústria das ciências da vida. Para esse objectivo, foram construídos dois protótipos utilizando Python para apoiar a execução da prova de conceito. Foi realizada uma experiência entre grupos com 40 membros da equipa de consultoria alemã da Wayne Enterprises, onde metade respondeu a um inquérito baseado em visualizações estáticas e a outra metade com base em HOPs. A partir desta experiência, é possível concluir que os HOPs podem alcançar resultados semelhantes aos das visualizações estáticas, com as pessoas a tomarem a decisão em menos de metade do tempo quando visualizam um HOP. Por conseguinte, é possível melhorar o processo de tomada de decisão da Wayne Enterprises, acelerando-o com Hypothetical Outcome Plots

EVM: Incorporating Model Checking into Exploratory Visual Analysis

Visual analytics (VA) tools support data exploration by helping analysts quickly and iteratively generate views of data which reveal interesting patterns. However, these tools seldom enable explicit checks of the resulting interpretations of data -- e.g., whether patterns can be accounted for by a model that implies a particular structure in the relationships between variables. We present EVM, a data exploration tool that enables users to express and check provisional interpretations of data in the form of statistical models. EVM integrates support for visualization-based model checks by rendering distributions of model predictions alongside user-generated views of data. In a user study with data scientists practicing in the private and public sector, we evaluate how model checks influence analysts' thinking during data exploration. Our analysis characterizes how participants use model checks to scrutinize expectations about data generating process and surfaces further opportunities to scaffold model exploration in VA tools

Capture, analyse, visualise:An exemplar of performance analysis in practice in field hockey

The goal of performance analysis is to capture the multitude of factors that affect sports strategy, and present them in an informative, interpretable, and accessible format. The aim of this study was to outline a performance analysis process in field hockey that captures, analyses and visualises strategy in layers of detail culminating in the creation of an RStudio Shiny application. Computerised notational analysis systems were developed to capture in-game events and ball tracking data of 74 matches from the Women’s Pro League 2019. Game styles were developed using k-means cluster analysis to reduce detailed in-game events into practical profiles to identify the attack types, game actions and tempo of a team’s strategy. Ball movement profiles were developed to identify the predictability (entropy) and direction (progression rates) of ball movements, and consequent distribution of possession in different attacking zones. The Shiny application, an interactive web-platform, links the information from simple game profiles with detailed game variables to understand each teams’ holistic game plan, how they are different, and how to exploit these differences. The process outlined can be applied to any team invasion sport to understand, develop and communicate successful strategies under different match situations

Simple methods for improving the communication of uncertainty in species’ temporal trends

Temporal trends in species occupancy or abundance are a fundamental source of information for ecology and conservation. Model-based uncertainty in these trends is often communicated as frequentist confidence or Bayesian credible intervals, however, these are often misinterpreted in various ways, even by scientists. Research from the science of information visualisation indicates that line ensemble approaches that depict multiple outcomes compatible with a fitted model or data may be superior for the clear communication of model-based uncertainty. The discretisation of continuous probability information into frequency bins has also been shown to be useful for communicating with non-specialists. We present a simple and widely applicable approach that combines these two ideas, and which can be used to clearly communicate model-based uncertainty in species trends (or composite indicators) to stakeholders. We also show how broader ontological uncertainty can be communicated via trend plots using risk-of-bias visualisation approaches developed in other disciplines. The techniques are demonstrated using the example of long-term plant distributional change in Britain, but are applicable to any temporal data consisting of averages and associated uncertainty measures. Our approach supports calls for full transparency in the scientific process by clearly displaying the multiple sources of uncertainty that can be estimated by researchers

Communicating qualitative uncertainty in data visualization

Qualitative uncertainty refers to the implicit and underlying issues that are imbued in data, such as the circumstances of its collection, its storage or even biases and assumptions made by its authors. Although such uncertainty can jeopardize the validity of the data analysis, it is often overlooked in visualizations, due to it being indirect and non-quantifiable. In this paper we present two case studies within the digital humanities in which we examined how to integrate uncertainty in our visualization designs. Using these cases as a starting point we propose four considerations for data visualization research in relation to indirect, qualitative uncertainty: (1) we suggest that uncertainty in visualization should be examined within its socio-technological context, (2) we propose the use of interaction design patterns to design for it, (3) we argue for more attention to be paid to the data generation process in the humanities, and (4) we call for the further development of participatory activities specifically catered for understanding qualitative uncertainties. While our findings are grounded in the humanities, we believe that these considerations can be beneficial for other settings where indirect uncertainty plays an equally prevalent role