Johanna Schmidt

We introduce GraphHeatY, a Visual Analytics approach for exploring and comparing heat transfer behavior in buildings. A building can be conceptualized as a connected graph, where nodes represent rooms or external areas, and edges represent the building elements that connect them. Heat flows along these connections. We employ node‐link diagrams and glyph overlays to emphasize the connectivity within buildings. Donut glyphs and bar charts support analysis at both global (i.e., building) and local (i.e., room) levels. GraphHeatY integrates spatial and non‐spatial visualizations to support detailed inspection of heat flows while preserving an overview. Further, GraphHeatY allows users to interactively explore differences in insulation, material composition, and system settings, and assess their impact on the building's thermal state. User evaluations confirmed that GraphHeatY enhances comparative reasoning in early‐stage building design.

Hydropower turbines are large-scale equipment essential to sustainable energy supply chains, and engineers have few opportunities to examine their internal structure. Our Immersive Analytics (IA) application is part of a research project that combines and compares simulated water turbine flows and sensor-measured data, looking for data-driven predictions of the lifetime of the mechanical parts of hydroelectric power plants. Our prototype combines spatial and abstract data in an immersive environment in which the user can navigate through a full-scale model of a water turbine, view simulated water flows of three different energy supply conditions, and visualize and interact with sensor-collected data situated at the reference position of the sensors in the actual turbine. In this paper, we detail our design process, which resulted from consultations with domain experts and a literature review, give an overview of our prototype, and present its evaluation, resulting from semi-structured interviews with experts and qualitative thematic analysis. Our findings confirm the current literature that IA applications add value to the presentation and analysis of situated data, as they show that we advance in the design directions for IA applications for domain experts that combine abstract and spatial data, with conclusions on how to avoid skepticism from such professionals.

Building Information Modeling (BIM) describes a central data pool covering the entire life cycle of a construction project. Similarly, Building Energy Modeling (BEM) describes the process of using a 3D representation of a building as a basis for thermal simulations to assess the building's energy performance. This paper explores the intersection of BIM and BEM, focusing on the challenges and methodologies in converting BIM data into BEM representations for energy performance analysis. BEMTrace integrates 3D data wrangling techniques with visualization methodologies to enhance the accuracy and traceability of the BIM-to-BEM conversion process. Through parsing, error detection, and algorithmic correction of BIM data, our methods generate valid BEM models suitable for energy simulation. Visualization techniques provide transparent insights into the conversion process, aiding error identification, validation, and user comprehension. We introduce context-adaptive selections to facilitate user interaction and to show that the BEMTrace workflow helps users understand complex 3D data wrangling processes.

This paper outlines the design process and rationale when designing data visualization and data analytics technology for different target groups. The objective is to document how design considerations, tailored to diverse sets of requirements and needs, influence the development of such technologies. We focus on two distinct use cases: one involving the design process tailored to the needs and preferences of practitioners, such as data analysts, and the other directed towards meeting the requirements of non-professional, volunteer-based participants from the general public engaged in participatory citizen science. In general, we observed diverse requirements and preferences concerning data visualization choices, additional functionalities, and analytical measures. These observations closely align with the specific goals of the tool and the expertise levels of its users.

Interactive visualization empowers users to actively engage with data. This article introduces interactive visualization’s key features and its applications in data analysis, business, science, and journalism. It also highlights challenges, including scalability, complexity, and user engagement, and discusses how two applications address these issues. Despite these challenges, interactive visualization remains a potent tool for data exploration, fostering accessibility and understanding. This introduction sets the stage for exploring its potential in solving real-world problems.

Visualization approaches have been successfully applied to a variety of application fields. Continuously published novel visualization approaches reflect the need for and success of visualization approaches. However, when looking at visualization techniques that designers apply in real-world applications targeted at a larger user group (e.g., medical experts, engineers, journalists, and public authorities), novel, published approaches hardly end up being used in real-world scenarios. The lack of readily available visualization software solutions and guidelines on applying these is unfortunate, especially since data science and data analytics are emerging fields, with many users needing data visualization. As shown in the different use-case scenarios outlined in this issue, implementing novel solutions within existing domain workflows is still a significant challenge for visualization researchers and domain experts. Challenges and guidelines for integrating interactive visualization applications in existing workflows are outlined in different real-world applications presented in this issue. The papers in this issue cover various domains and data types and comprise success stories of visualization approaches in different application domains.

Modern science and industry rely on computational models for simulation, prediction, and data analysis. Spatial blind source separation (SBSS) is a model used to analyze spatial data. Designed explicitly for spatial data analysis, it is superior to popular non-spatial methods, like PCA. However, a challenge to its practical use is setting two complex tuning parameters, which requires parameter space analysis. In this paper, we focus on sensitivity analysis (SA). SBSS parameters and outputs are spatial data, which makes SA difficult as few SA approaches in the literature assume such complex data on both sides of the model. Based on the requirements in our design study with statistics experts, we developed a visual analytics prototype for data type agnostic visual sensitivity analysis that fits SBSS and other contexts. The main advantage of our approach is that it requires only dissimilarity measures for parameter settings and outputs (Fig. 1). We evaluated the prototype heuristically with visualization experts and through interviews with two SBSS experts. In addition, we show the transferability of our approach by applying it to microclimate simulations. Study participants could confirm suspected and known parameter-output relations, find surprising associations, and identify parameter subspaces to examine in the future. During our design study and evaluation, we identified challenging future research opportunities.

Draco has been developed as an automated visualization recommendation system formalizing design knowledge as logical constraints in ASP (Answer-Set Programming). With an increasing set of constraints and incorporated design knowledge, even visualization experts lose overview in Draco and struggle to retrace the automated recommendation decisions made by the system. Our paper proposes an Visual Analytics (VA) approach to visualize and analyze Draco's constraints. Our VA approach is supposed to enable visualization experts to accomplish identified tasks regarding the knowledge base and support them in better understanding Draco. We extend the existing data extraction strategy of Draco with a data processing architecture capable of extracting features of interest from the knowledge base. A revised version of the ASP grammar provides the basis for this data processing strategy. The resulting incorporated and shared features of the constraints are then visualized using a hypergraph structure inside the radial-arranged constraints of the elaborated visualization. The hierarchical categories of the constraints are indicated by arcs surrounding the constraints. Our approach is supposed to enable visualization experts to interactively explore the design rules' violations based on highlighting respective constraints or recommendations. A qualitative and quantitative evaluation of the prototype confirms the prototype's effectiveness and value in acquiring insights into Draco's recommendation process and design constraints.

Modern science and industry rely on computational models for simulation, prediction, and data analysis. Spatial blind source separation (SBSS) is a model used to analyze spatial data. Designed explicitly for spatial data analysis, it is superior to popular non-spatial methods, like PCA. However, a challenge to its practical use is setting two complex tuning parameters, which requires parameter space analysis. In this paper, we focus on sensitivity analysis (SA). SBSS parameters and outputs are spatial data, which makes SA difficult as few SA approaches in the literature assume such complex data on both sides of the model. Based on the requirements in our design study with statistics experts, we developed a visual analytics prototype for data type agnostic visual sensitivity analysis that fits SBSS and other contexts. The main advantage of our approach is that it requires only dissimilarity measures for parameter settings and outputs. We evaluated the prototype heuristically with visualization experts and through interviews with two SBSS experts. In addition, we show the transferability of our approach by applying it to microclimate simulations. Study participants could confirm suspected and known parameter-output relations, find surprising associations, and identify parameter subspaces to examine in the future. During our design study and evaluation, we identified challenging future research opportunities.