Exploration Strategies for Discovery of Interactivity in Visualizations

We investigate how people discover the functionality of an interactive visualization that was designed for the general public. While interactive visualizations are increasingly available for public use, we still know little about how the general public discovers what they can do with these visualizations and what interactions are available. Developing a better understanding of this discovery process can help inform the design of visualizations for the general public, which in turn can help make data more accessible. To unpack this problem, we conducted a lab study in which participants were free to use their own methods to discover the functionality of a connected set of interactive visualizations of public energy data. We collected eye movement data and interaction logs as well as video and audio recordings. By analyzing this combined data, we extract exploration strategies that the participants employed to discover the functionality in these interactive visualizations. These exploration strategies illuminate possible design directions for improving the discoverability of a visualization's functionality

How to effectively use interactivity to improve visual analysis in groups of novices or experts

This study contributes with a methodology to evaluate the differences between different groups of individuals, using a prototype with different interactive visualizations, created with the Shiny package, using 6 quantitative and qualitative metrics for the validation. Using an ANOVA single factor test only 1 of the 6 variables showed statistically significant differences between both groups: the engagement. This means that this is the only metric where results can be improved in order to close the gap between the group of experts and novices. The heatmap and the bar chart were considered the best visualizations for both groups, and the worst were the choropleth map and the stacked bar chart. Regarding the interactive component, the select box was a better option for the group of novices and the radio box for the group of experts. Using this study, organizations will be able to create visualizations that are suitable for different audiences

How to tell stories using visualization: strategies towards narrative visualization

Os benefícios da utilização das narrativas são desde há muito conhecidos e o seu potencial para simplificar conceitos, transmitir valores culturais e experiências, criar ligações emocionais e capacidade para ajudar a reter a informação tem sido explorado em diferentes áreas. As narrativas não são só a principal forma como as pessoas obtêm o sentido do mundo, mas também a forma mais fácil que encontrámos para partilhar informações complexas. Devido ao seu potencial, as narrativas foram recentemente abordadas na área da Visualização de Informação e do Conhecimento, muitas vezes apelidada de Visualização Narrativa. Esta questão é particularmente importante para os media, uma das áreas que tem impulsionado a investigação em Visualização Narrativa. A necessidade de incorporar histórias nas visualizações surge da necessidade de partilhar dados complexos de um modo envolvente. Hoje em dia somos confrontados com a elevada quantidade de informação disponível, um desafio difícil de resolver. Os avanços da tecnologia permitiram ir além das formas tradicionais de narrativa e de representação de dados, dando-nos meios mais atraentes e sofisticados para contar histórias. Nesta tese, exploro os benefícios da introdução de narrativas nas visualizações. Adicionalmente também exploro formas de combinar histórias com a visualizações e métodos eficientes para representar e dar sentido aos dados de uma forma que permite que as pessoas se relacionem com a informação. Esta investigação está bastante próxima da área do jornalismo, no entanto estas técnicas podem ser aplicadas em diferente áreas (educação, visualização científica, etc.). Para explorar ainda mais este tema foi adotada um avaliação que utiliza diferentes metodologias como a tipologia, vários casos de estudo, um estudo com grupos de foco, e ainda estudos de design e análise de técnicas.The benefits of storytelling are long-known and its potential to simplify concepts, convey cultural values and experiences, create emotional connection, and capacity to help retain information has been explored in di erent areas, such as journalism, education, marketing, and others. Narratives not only have been the main way people make sense of the world, but also the easiest way humans found out to share complex information. Due to its potential narratives have also recently been approached in the area of Information and Knowledge Visualization, several times being referred to as Narrative Visualization. This matter is also particularly important for news media, one of the areas that has been pushing the research on Narrative Visualization. The necessity to incorporate storytelling in visualizations arises from the need to share complex data in a way that is engaging. Nowadays we also have the challenge of the high amount of information available, which can be hard to cope with. Advances in technology have enabled us to go beyond the traditional forms of storytelling and representing data, giving us more attractive and sophisticated means to tell stories. In this dissertation, I explore the benefits of infusing visualizations with narratives. In addition I also present ways of combining storytelling with visualization and e cient methods to represent and make sense of data in a way that allows people to relate with the information. This research is closely related to journalism, but these techniques can be applied to completely di erent areas (education, scientific visualization, etc.). To further explore this topic a mixedmethod evaluation that consists of a typology, several case studies and a focus group study was chosen, as well as design studies and techniques review. This dissertation is intended to contribute to the evolving understanding of the field of narrative visualization

Interactions in Visualizations to Support Knowledge Activation

Humans have several exceptional abilities, one of which is the perceptual tasks of their visual sense. Humans have the unique ability to perceive data and identify patterns, trends, and outliers. This research investigates the design of interactive visualizations to identify the benefits of interacting with information. The research question leading the investigation is how does interacting with visualizations support analytical reasoning of emergent information to activate knowledge? The study uses the theory of distributed cognition and human-information interaction to apply the design science research framework. The motivation behind the research is to identify guidelines for interactive visualizations to enhance a user’s ability to make decisions in dynamic situations and apply knowledge gleaned from the visualization. An experiment is used to analyze the use of an interactive dashboard in a dynamic decision-making situation. The results of this experiment specifically look at the combination of interactions as they support the distribution of cognition over three spaces of a human-visualization cognitive system. The results provide insight into the benefits that interactions have for enhancing analytical reasoning, expanding the use of visualizations beyond communicating or disseminating information. Providing a broad range of interactions that work with multiple views of information increases the opportunities that users have to complete tasks. This research contributes to the information visualization discipline by expanding the focus from representing data to representing and interacting with information. Secondly, my results provide an example of a qualitative assessment based on the value of visualization, in comparison to traditional usability assessment

Enabling Interactive Analytics of Secure Data using Cloud Kotta

Research, especially in the social sciences and humanities, is increasingly reliant on the application of data science methods to analyze large amounts of (often private) data. Secure data enclaves provide a solution for managing and analyzing private data. However, such enclaves do not readily support discovery science---a form of exploratory or interactive analysis by which researchers execute a range of (sometimes large) analyses in an iterative and collaborative manner. The batch computing model offered by many data enclaves is well suited to executing large compute tasks; however it is far from ideal for day-to-day discovery science. As researchers must submit jobs to queues and wait for results, the high latencies inherent in queue-based, batch computing systems hinder interactive analysis. In this paper we describe how we have augmented the Cloud Kotta secure data enclave to support collaborative and interactive analysis of sensitive data. Our model uses Jupyter notebooks as a flexible analysis environment and Python language constructs to support the execution of arbitrary functions on private data within this secure framework.Comment: To appear in Proceedings of Workshop on Scientific Cloud Computing, Washington, DC USA, June 2017 (ScienceCloud 2017), 7 page

A Review and Characterization of Progressive Visual Analytics

Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions

Narrative visualization with augmented reality

The following study addresses, from a design perspective, narrative visualization using augmented reality (AR) in real physical spaces, and specifically in spaces with no semantic relation with the represented data. We intend to identify the aspects augmented reality adds, as narrative possibilities, to data visualization. Particularly, we seek to identify the aspects augmented reality introduces regarding the three dimensions of narrative visualization—view, focus and sequence. For this purpose, we adopted a comparative analysis of a set of fifty case studies, specifically, narrative visualizations using augmented reality from a journalistic scope, where narrative is a key feature. Despite the strong explanatory character that characterizes the set of analyzed cases, which sometimes limits the user’s agency, there is a strong interactive factor. It was found that augmented reality can expand the narrative possibilities in the three dimensions mentioned—view, focus and sequence—but especially regarding visual strategies where simulation plays an essential role. As a visual strategy, simulation can provide the context for communication or be the object of communication itself, as a replica.publishe

Thinking interactively with visualization

Interaction is becoming an integral part of using visualization for analysis. When interaction is tightly and appropriately coupled with visualization, it can transform the visualization from display- ing static imagery to assisting comprehensive analysis of data at all scales. In this relationship, a deeper understanding of the role of interaction, its effects, and how visualization relates to interaction is necessary for designing systems in which the two components complement each other. This thesis approaches interaction in visualization from three different perspectives. First, it considers the cost of maintaining interaction in manipulating visualization of large datasets. Namely, large datasets often require a simplification process for the visualization to maintain interactivity, and this thesis examines how simplification affects the resulting visualization. Secondly, example interactive visual analytical systems are presented to demonstrate how interactivity could be applied in visualization. Specifically, four fully developed systems for four distinct problem domains are discussed to determine the common role of interactivity in these visualizations that make the systems successful. Lastly, this thesis presents evidence that interactions are important for analytical tasks using visualizations. Interaction logs of financial analysts using a visualization were collected, coded, and examined to determine the amount of analysis strategies contained within the interaction logs. The finding supports the benefits of high interactivity in analytical tasks when using a visualization. The example visualizations used to support these three perspectives are diverse in their goals and features. However, they all share similar design guidelines and visualization principles. Based on their characteristics, this thesis groups these visualizations into urban visualization, visual analytical systems, and interaction capturing and discusses them separately in terms of lessons learned and future directions