Denna ”post-sanningens-tid” innebär en osannolik överbelastning avinformation. Det utmanar skolan att stödja elevernas kunskapsuppbyggnad,förmåga att hitta relevant information och samtidigt kritiskt hantera uppgifterfrån olika perspektiv.Detta design-baserade projekt har därför undersökt hur Visual Analytics (VA)och verktyg för kunskapsvisualisering (KV) kan bidra till eleversinformationsprocesser som inte bara syftar till att upptäcka fakta, se sambandoch dra slutsatser utan även utvecklar kunskaper i kombination med attityder,perspektiv och åsikter.Med stöd av aktörnätverks-teori har relationer analyserats mellan visualiseringarpå bildskärmar, interaktivitetens erbjudanden, betydelsebärande semiotiskabudskap och skolans kunskapskrav. Ambitionen har varit att förstå hur VAtillsammans med KV kan stödja lärandet i samhällskunskap på grundskolanshögstadium.Resultaten visar hur VA i undervisning påverkar läraktiviteterna med bådemervärden och utmaningar. Elever kan använda VA, dra slutsatser och göradatavisualiseringar men de måste erbjudas alternativa sätt att visa sina kunskaper(KV), jämfört med vad skolan oftast erbjuder. Här kan en kombination avmuntlig och bildlig kommunikation fungera mer flexibelt. 

Parallel coordinates are commonly used for non-temporal multivariate data, but there is little support for their usability for displaying temporal multivariate data. In this paper, we introduce a study evaluating the usability of 2D and 3D parallel coordinates for pattern identification in temporal multivariate data. The results indicate that 3D parallel coordinates have higher usability, as measured with higher accuracy and faster response time as well as subjective ratings, compared to 2D.

The indoor climate is closely related to human health, well-being, and comfort. Thus, an understanding of the indoor climate is vital. One way to improve the indoor climates is to place an aesthetically pleasing active plant wall in the environment. By collecting data using sensors placed in and around the plant wall both the indoor climate and the status of the plant wall can be monitored and analyzed. This manuscript presents a user study with domain experts in this field with a focus on the representation of such data. The experts explored this data with a Line graph, a Horizon graph, and a Stacked area graph to better understand the status of the active plant wall and the indoor climate. Qualitative measures were collected with Think-aloud protocol and semi-structured interviews. The study resulted in four categories of analysis tasks: Overview, Detail, Perception, and Complexity. The Line graph was found to be preferred for use in providing an overview, and the Horizon graph for detailed analysis, revealing patterns and showing discernible trends, while the Stacked area graph was generally not preferred. Based on these findings, directions for future research are discussed and formulated. The results and future directions of this research can facilitate the analysis of multivariate temporal data, both for domain users and visualization researchers.

Eye tracking is used to analyze and compare user behaviour within numerous domains, but long duration eye tracking experiments across multiple users generate millions of eye gaze samples, making the data analysis process complex. Usually the samples are labelled into Areas of Interest (AoI) or Objects of Interest (OoI), where the AoI approach aims to understand how a user monitors different regions of a scene while OoI identification uncovers distinct objects in the scene that attract user attention. Using scalable clustering and cluster merging techniques that require minimal user input, we label AoIs across multiple users in long duration eye tracking experiments. Using the common AoI labels then allows direct comparison of the users as well as the use of such methods as Hidden Markov Models and Sequence mining to uncover common and distinct behaviour between the users which, until now, has been prohibitively difficult to achieve.

Transforming cities into low-carbon, resilient, and sustainable places will require action encompassing most segments of society. However, local governments struggle to overview and assess all ongoing climate activities in a city, constraining well-informed decision-making and transformative capacity. This paper proposes and tests an assessment framework developed to visualize the implementation of urban climate transition (UCT). Integrating key transition activities and process progression, the framework was applied to three Swedish cities. Climate coordinators and municipal councillors evaluated the visual UCT representations. Results indicate that their understanding of UCT actions and implementation bottlenecks became clearer, making transition more governable. To facilitate UCT, involving external actors and shifting priorities between areas were found to be key. The visual UCT representations improved system awareness and memory, building local transformative capacity. The study recommends systematic assessment and visualization of process progression as a promising method to facilitate UCT governance, but potentially also broader sustainability transitions.

Intense drone traffic, exceeding human capabilities of manual control, is expected to occur during the last stage of Unified Traffic Management (UTM) and Unmanned Airspace System (UAS) service deployment in cities. In this paper, we discuss how humans and automation could collaborate to manage this airspace. We review theory on options for UTM airspace structure (volumes, points, networks, layers), machine learning, optimization, and human-automation collaboration. Based on simulation and visualization of two cities, we discuss four abilities: to discern traffic patterns, to recognize situations, to predict situational developments, and to function in varying conditions of rule-following habits of airspace users. We then discuss the challenge of collaborating though the use of advanced visual dashboards, for human-in-the loop AI but also for society-in-the-loop. Finally, we discuss how the challenge of humanautomation collaboration can be expected to shift, as the capabilities of the machine increases.

Eye-tracking has become an invaluable tool for the analysis of working practices in many technological fields of activity. Typically studies focus on short tasks and use static expected areas of interest (AoI) in the display to explore subjects’ behaviour, making the analyst’s task quite straightforward. In long-duration studies, where the observations may last several hours over a complete work session, the AoIs may change over time in response to altering workload, emergencies or other variables making the analysis more difficult. This work puts forward a novel method to automatically identify spatial AoIs changing over time through a combination of clustering and cluster merging in the temporal domain. A visual analysis system based on the proposed methods is also presented. Finally, we illustrate our approach within the domain of air traffic control, a complex task sensitive to prevailing conditions over long durations, though it is applicable to other domains such as monitoring of complex systems. 

The parallel coordinates technique is widely used for the analysis of multivariate data. During recent decades significant research efforts have been devoted to exploring the applicability of the technique and to expand upon it. resulting in a variety of extensions. Of these many research activities, a surprisingly small number concerns user-centred evaluations investigating actual use and usability issues for different tasks, data and domains. The result is a clear lack of convincing evidence to support and guide uptake by users as well as future research directions. To address these issues this paper contributes a thorough literature survey of what has been done in the area of user-centred evaluation of parallel coordinates. These evaluations are divided into four categories based on characterization of use, derived from the survey. Based on the data from the survey and the categorization combined with the authors experience of working with parallel coordinates, a set of guidelines for future research directions is proposed.

This article discusses the state of the art in geovisualization supporting climate change adaptation. We reviewed twenty selected map-based Web tools, classified by their content and functionality, and assessed them by visual representations, interactive functions, information type, target audience, and how vulnerability and adaptation to climate change are addressed. Our study concludes that the tools (1) can be classified as data viewers with basic functionality and data explorers offering more sophisticated interactive functions; (2) mostly feature moderate or high richness of data content; and (3) predominantly target expert users.