International audience

Storyline visualizations are a powerful way to compactly visualize how the relationships between people evolve over time. Real-world relationships often also involve space, for example the cities that two political rivals visited together or alone over the years. By default, Storyline visualizations only show implicitly geospatial co-occurrence between people (drawn as lines), by bringing their lines together. Even the few designs that do explicitly show geographic locations only do so in abstract ways (e.g., annotations) and do not communicate geospatial information, such as the direction or extent of their political campains. We introduce Geo-Storylines, a collection of visualisation designs that integrate geospatial context into Storyline visualizations, using different strategies for compositing time and space. Our contribution is twofold. First, we present the results of a sketching workshop with 11 participants, that we used to derive a design space for integrating maps into Storylines. Second, by analyzing the strengths and weaknesses of the potential designs of the design space in terms of legibility and ability to scale to multiple relationships, we extract the three most promising: Time Glyphs, Coordinated Views, and Map Glyphs. We compare these three techniques first in a controlled study with 18 participants, under five different geospatial tasks and two maps of different complexity. We additionally collected informal feedback about their usefulness from domain experts in data journalism. Our results indicate that, as expected, detailed performance depends on the task. Nevertheless, Coordinated Views remain a highly effective and preferred technique across the board.

Geographical propagation phenomena occur in multiple domains, such as in epidemiology and social media. Propagation dynamics are often complex, and visualizations play a key role in helping subject-matter experts understand and analyze them. However, there is little empirical data about the effectiveness of the various strategies used to visualize geographical propagation. To fill this gap, we conduct an experiment to evaluate the effectiveness of three strategies: an animated map, small-multiple maps, and a single map with glyphs. We compare them under five tasks that vary in one of the following dimensions: propagation scope, direction, speed, peaks, and spatial jumps. Our results show that small-multiple maps perform best overall, but that the effectiveness of each visualization varies depending on the task considered.

Observing the relationship between two or more variables over space and time is essential in many domains. For instance, looking, for different countries, at the evolution of both the life expectancy at birth and the fertility rate will give an overview of their demographics. The choice of visual representation for such multivariate data is key to enabling analysts to extract patterns and trends. Prior work has compared geo-temporal visualization techniques for a single thematic variable that evolves over space and time, or for two variables at a specific point in time. But how effective visualization techniques are at communicating correlation between two variables that evolve over space and time remains to be investigated. We report on a study comparing three techniques that are representative of different strategies to visualize geo-temporal multivariate data: either juxtaposing all locations for a given time step, or juxtaposing all time steps for a given location; and encoding thematic attributes either using symbols overlaid on top of map features, or using visual channels of the map features themselves. Participants performed a series of tasks that required them to identify if two variables were correlated over time and if there was a pattern in their evolution. Tasks varied in granularity for both dimensions: time (all time steps, a subrange of steps, one step only) and space (all locations, locations in a subregion, one location only). Our results show that a visualization's effectiveness depends strongly on the task to be carried out. Based on these findings we present a set of design guidelines about geo-temporal visualization techniques for communicating correlation.

Qualitative coding offers the potential to obtain deep insights into social media, but the technique can be inconsistent and hard to scale. Researchers using qualitative coding impose structure on unstructured data through “codes” that represent categories for analysis. Our visual analytics interface, Aeonium, supports human insight in collaborative coding through visual overviews of codes assigned by multiple researchers and distributions of important keywords and codes. The underlying machine learning model highlights ambiguity and inconsistency. Our goal was not to reduce qualitative coding to a machine-solvable problem, but rather to bolster human understanding gained from coding and reinterpreting the data collaboratively. We conducted an experimental study with 39 participants who coded tweets using our interface. In addition to increased understanding of the topic, participants reported that Aeonium's collaborative coding functionality helped them reflect on their own interpretations. Feedback from participants demonstrates that visual analytics can help facilitate rich qualitative analysis and suggests design implications for future exploration.

Online Social Networks (OSN) have changed the way information is produced and consumed. Organizing and retrieving unstructured data extracted from these platforms is not an easy task. Galean is a visual and interactive tool that aims to help journalists and historians, among others, analyze news events discussed on Twitter. In this tool, news events are visually represented by the very countries from where the news originated, the date when they happened and their impact in the OSN. Galean considers countries as entities, as opposed to mere geographical locations as most of the tools in the state of the art. As a consequence, it allows users to explore and retrieve news not only by their geographical and temporal context, but also by the relationship among countries. With this tool users can search for behavioral patterns of news events and observe how countries are associated in specific events. We expect our work to become a public tool that helps conduct historical analyses of social media news coverage over time.

Nowadays, social media services are being used extensively as news sources and for spreading information on real-world events. Several studies have focused on detecting those events and locating them geographically. However, in order to study real-world events, for example, finding relationships between locations or detecting high impact events based on their coverage, we need more suitable models to represent events. In this work we propose a simple model to represent real-world news events using two sources of information: the locations that are mentioned in the event (where the event occurs), and the locations of users that discuss or comment on it. We then characterize a country based on the amount of events in which that country is mentioned and also participates on the event. We show some applications of the model: we find clusters of news events based on the level of participation of countries, identifying global and impactful events in certain areas. Also, we show groups of similar countries, finding promising insights about their relationships. This model can be useful at finding unsuspected relations among countries based on the news coverage and country participation, identifying different levels of news coverage in the world, and finding bias in international news sources.

Test coverage is about assessing the relevance of unit tests against the tested application. It is widely acknowledged that a software with a "good" test coverage is more robust against unanticipated execution, thus lowering the maintenance cost. However, insuring a coverage of a good quality is challenging, especially since most of the available test coverage tools do not discriminate software components that require a "strong" coverage from the components that require less attention from the unit tests.