This paper aims to develop a framework for generating synthetic data for the Swedish built environment to improve energy predictions and peak load management in heating systems. Heating and cooling are central aspects of reducing energy use in the residential sector. However, predicting the energy performance of buildings is currently a difficult task, partly due to energy use data from end-users being outdated and sometimes missing. In the end, we will contribute to future energy systems in a broader sense by developing a socio-technical and ethical methodological framework for working with synthetic energy data. We will map available data together with stakeholders' needs. Data will be collected and prepared as training data to develop and evaluate a model for synthetic data. The approach is interdisciplinary which will ensure the integration of socio-technical, ethical and gendered aspects of energy use and synthetic data.

Extreme weather events, such as heat waves, are occurring more frequently and intensively, imposing new climate-adaptation demands on municipal planning. We conducted a design study across the domains of urban planning and urban climate research, and identified challenges regarding a lack of heat-related information in current planning processes, and the high complexity of effective climate data representation. To address these challenges, and so enhance the information flow between these domains, we developed Urban Climate InteracTable, an immersive interface that supports exploratory analysis of spatio-temporal climate simulation data integrated with an urban environment representation. We describe several use cases in which this interface can be utilized to assist with planning-related decision processes and to communicate heat-related phenomena. We present the feedback obtained from our collaborating domain experts and relevant external experts, and reflect on our experiences throughout the design study. From this, we offer insights for future research.

This paper presents a case study conducted in the neighborhood Vallastaden in Linköping, Sweden, where developers built low energy cooperative housing intended to meet the passive house standards. Previous research highlights the need for integrated approaches to understand residents’ thermal comfort and energy use, as some low energy house concepts fail to meet energy performance requirements. The aim of the paper is to contribute to the understanding of cooperative housing and residents’ experiences of thermal comfort in newly built homes in Sweden. Through a detailed case study that included activity diaries, visualizations based on diary data and workshops with residents, we explored residents’ actions to achieve thermal comfort. With concepts from time-geography, we discuss insights regarding residents’ experiences and strategies. Results show that cooperative housing in Vallastaden, while being comfortably warm during winter, suffers from poor design and engineering to protect residents from overheating during hot summer days. Residents choreographed their space-time activities to adjust for the constraints of the designs by elaborate schedules for airing, remodeling their homes or sometimes staying away from the home. Exploring this everyday experience together with visualizations of thermal comfort brings forward new insight into the thermal comfort concerns of residents in cooperative housing.

As extreme weather events such as floods, storms, and heatwaves proliferate, local and regional authorities face challenges in predicting, monitoring, and assessing these events and their impacts. The introduction of impact-based warning services requires detailed, location-specific information on local vulnerability and impacts. This necessitates complementing conventional data with insights from local actors, and to explore novel methods for relevant public data monitoring through social media and news outlets. This paper presents a visual analytics pipeline that was co-developed with practitioners, aiming to detect impacts of extreme weather events, particularly floods, using Volunteered Geographic Information (VGI). The pipeline steps include: collecting VGI from social media, classifying and analysing the data, and visualizing it through an interactive interface. An empirical evaluation study was performed with meteorological and hydrological experts to assess the developed visual interface. The study collected and analysed feedback on the usability of the interface and identified interaction patterns from the experiment’s screen recordings.

We present a visual analytics approach for multi-level visual exploration of users' interaction strategies in an interactive digital environment. The use of interactive touchscreen exhibits in informal learning environments, such as museums and science centers, often incorporate frameworks that classify learning processes, such as Bloom's taxonomy, to achieve better user engagement and knowledge transfer. To analyze user behavior within these digital environments, interaction logs are recorded to capture diverse exploration strategies. However, analysis of such logs is challenging, especially in terms of coupling interactions and cognitive learning processes, and existing work within learning and educational contexts remains limited. To address these gaps, we develop a visual analytics approach for analyzing interaction logs that supports exploration at the individual user level and multi-user comparison. The approach utilizes algorithmic methods to identify similarities in users' interactions and reveal their exploration strategies. We motivate and illustrate our approach through an application scenario, using event sequences derived from interaction log data in an experimental study conducted with science center visitors from diverse backgrounds and demographics. The study involves 14 users completing tasks of increasing complexity, designed to stimulate different levels of cognitive learning processes. We implement our approach in an interactive visual analytics prototype system, named VISID, and together with domain experts, discover a set of task-solving exploration strategies, such as “cascading” and “nested-loop', which reflect different levels of learning processes from Bloom's taxonomy. Finally, we discuss the generalizability and scalability of the presented system and the need for further research with data acquired in the wild.

Tillgång till vädervarningar med information om förväntade konsekvenser av vädret är nödvändigt för god krisberedskap hos myndigheter, kommuner, näringsliv och privatpersoner. Vidareutveckling av varningssystem som fokuserar på förväntade störningar (konsekvensbaserade varningssystem) är därför en viktig komponent i samhällets hantering av klimatförändringar. Forskningsprojektet AI för klimatanpassning (AI4CA) har analyserat möjligheter och hinder med att inkludera AI-baserad text- och bildanalys som stöd till SMHI:s konsekvensbaserade vädervarningssystem och på sikt även stödja långsiktig klimatanpassning. 

An essential task of an air traffic controller is to manage the traffic flow by predicting future trajectories. Complex traffic patterns are difficult to predict and manage and impose cognitive load on the air traffic controllers. In this work we present an interactive visual analytics interface which facilitates detection and resolution of complex traffic patterns for air traffic controllers. The interface supports air traffic controllers in detecting complex clusters of aircraft and further enables them to visualize and simultaneously compare how different re-routing strategies for each individual aircraft yield reduction of complexity in the entire sector for the next hour. The development of the concepts was supported by the domain-specific feedback we received from six fully licensed and operational air traffic controllers in an iterative design process over a period of 14 months.

The continuous growth in availability and access to data presents a major challenge to the human analyst. As the manual analysis of large and complex datasets is nowadays practically impossible, the need for assisting tools that can automate the analysis process while keeping the human analyst in the loop is imperative. A large and growing body of literature recognizes the crucial role of automation in Visual Analytics and suggests that automation is among the most important constituents for effective Visual Analytics systems. Today, however, there is no appropriate taxonomy nor terminology for assessing the extent of automation in a Visual Analytics system. In this article, we aim to address this gap by introducing a model of levels of automation tailored for the Visual Analytics domain. The consistent terminology of the proposed taxonomy could provide a ground for users/readers/reviewers to describe and compare automation in Visual Analytics systems. Our taxonomy is grounded on a combination of several existing and well-established taxonomies of levels of automation in the human-machine interaction domain and relevant models within the visual analytics field. To exemplify the proposed taxonomy, we selected a set of existing systems from the event-sequence analytics domain and mapped the automation of their visual analytics process stages against the automation levels in our taxonomy.

Automation in Air Traffic Control (ATC) is gaining an increasing interest. Possible relevant applications are in automated decision support tools leveraging the performance of the Air Traffic Controller (ATCO) when performing tasks such as Conflict Detection and Resolution (CD & R). Another important area of application is in ATCOs training by aiding instructors to assess the trainees strategies. From this perspective, models that capture the cognitive processes and reveal ATCOs work strategies need to be built. In this work, we investigated a novel approach based on topic modelling to learn controllers work patterns from temporal event sequences obtained by merging eye movement data with data from simulation logs. A comparison of the work phases exhibited by the topic models and the Conflict Life Cycle (CLC) reference model, derived from post-simulation interviews with the ATCOs, indicated that there was a correspondence between the phases captured by the proposed method and the CLC framework. Another contribution of this work is a method to assess similarities between ATCOs work strategies. A first proof-of-concept application targeting the CD & R task is also presented.

Extreme weather events, such as flooding, are expected to increase in frequency and intensity. Therefore, the prediction of extreme weather events, assessment of their local impacts in urban environments, and implementation of adaptation measures are becoming high-priority challenges for local, regional, and national agencies and authorities. To manage these challenges, access to accurate weather warnings and information about the occurrence, extent, and impacts of extreme weather events are crucial. As a result, in addition to official sources of information for prediction and monitoring, citizen volunteered geographic information (VGI) has emerged as a complementary source of valuable information. In this work, we propose the formulation of an approach to complement the impact-based weather warning system that has been introduced in Sweden in 2021 by making use of such alternative sources of data. We present and discuss design considerations and opportunities towards the creation of a visual analytics (VA) pipeline for the identification and exploration of extreme weather events and their impacts from VGI texts and images retrieved from social media. The envisioned VA pipeline incorporates three main steps: (1) data collection, (2) image/text classification and analysis, and (3) visualization and exploration through an interactive visual interface. We envision that our work has the potential to support three processes that involve multiple stakeholders of the weather warning system: (1) the validation of previously issued warnings, (2) local and regional assessment-support documentation, and (3) the monitoring of ongoing events. The results of this work could thus generate information that is relevant to climate adaptation decision making and provide potential support for the future development of national weather warning systems.