Susanne Bleisch

Large differences in scale between the smallest items of interest and the full extent of data at focus pose challenges in the visualization of spatial data. Multivariate attributes further complicate the display of such information with the added challenge of representing multiple attributes simultaneously in an easily interpretable way. We introduce quantitative line glyphs to visualize multivariate attributes in visualizations of multi-scale geospatial data (Fig. 1). Their design is motivated by its application to the visualization of pavement roughness characteristics in the context of barrier-free routing and accessible urban planning. In barrier-free routing, structures of interest vary in size or amplitude from meters down to millimetres, while the length of the route may be 100 m or more. The need to visualize spatial structural information that spans several levels of scale is shared with other fields, e.g. biology (Cakmak et al. 2021). In the context of geospatial data, principles of cartographic generalization and abstraction are commonly applied to deal with a restricted display space and multi-scale information (Viola and Isenberg 2017; Cakmak et al. 2021). For our line glyph design, we draw upon such concepts to create dense, data-related glyphs which generalize to different view scales and work for 1D-(route representation) as well as 2D-visualizations (map representation). We combine the mapping of numeric attributes mostly to the visual variable length with the use of visual metaphors for surface profiles (Fig. 2). We map more than one attribute to each glyph element to create a condensed and generalized image of the underlying data. Visual metaphors and generalization are combined in other (linear) glyph designs to varying degrees (

Complexity, uniqueness, and dynamism are intrinsic characteristics of the physical environment that must be carefully addressed in the development of location-based, context-aware mobile learning applications. Designing such applications to deliver meaningful spatial experiences and support learning in extracurricular, field-based education requires identifying and overcoming challenges stemming from these properties, as these challenges influence not only the application’s design and content but also the overall quality of the user experience. We identify and discuss three distinct challenges: (1) the complexity of accurately modeling the form and relationships of spatial phenomena, (2) the constraints imposed by rigid formats and top-down approaches that fail to capture the distinct and inherently unique attributes of contextual entities, and (3) the need to manage the dynamism and ongoing changes in the environment throughout the application’s lifecycle.To address these challenges, we propose solutions grounded in template-based modularity, customizable experiences, and an adaptive, automated architecture. These approaches aim to support educators in authoring, publishing, and maintaining their applications efficiently. Through the demonstration of a prototype framework, we illustrate how these strategies can be implemented in practice while highlighting existing limitations and areas for further development.

The analysis of historical urban transformations and the development of the built environment as a whole is an important branch of research within architectural history. One of the challenges in this type of research is the appropriate visual 3D representation of these historical changes, both as a means of analysing them within the research and communicating the research findings. Conventional sources such as 2D plans and archival documents often fail to capture the spatial relationships and transformations within the built environment, necessitating the development of an appropriate visualisation concept. The concept uses 3D models with different levels of generalisation to visualise urban change in focus and context areas, suitably balancing abstraction and detail. A visualisation process was developed allowing highlighting building volumes and their contexts. The levels of generalisation include detailed representations and reconstructions, generalised building forms and block edge models. The levels are delimited by urban elements such as streets or rivers. The process utilises various sources of information, such as archive plans and historic photographs, to approximate historic structures. Key urban elements – landmarks and infrastructure such as streets and rivers – are integrated, and geometric abstractions and colour are used to direct the viewer's attention to selected focus areas. The approach was tested in selected urban environments with different urban morphologies. The results show the transferability of the approach to different urban environments and provide a tool for analysing the architectural historical development of urban building structures and for communicating research findings to a wider audience.

Quality of collaborative group engagement (QCGE) is conceptualised as a complex, shared, and multidimensional construct critical to learning outcomes in collaborative learning. It is currently measured in a variety of ways, including rich observation-based methods. However, a standardised self-assessment method for the construct of QCGE has not yet been developed. This contribution presents a pragmatic self-assessment approach to QCGE for higher education contexts and a new self-assessment scale: the QCGE-SAS. Our findings from a study with N = 246 students support the four-factor structure of QCGE originally proposed by Sinha et al. (2015), namely behavioural, social, cognitive, and conceptual-to-consequential collaborative group engagement, and demonstrate that the QCGE-SAS can successfully capture the multidimensional nature of QCGE. Construct and criterion validity were supported by significant correlations with related scales and with self-reported and objective learning outcomes. However, while three subscales proved reliable, the social engagement dimension subscale showed unsatisfactory reliability, indicating further refinement of the scale in future research. Overall, our findings contribute to an accurate operationalisation of the QCGE construct and complement existing methods and comprehensive multi-method approaches to measuring QCGE, thereby informing CSCL research and practice.

Set visualization facilitates the exploration and analysis of set-type data. However, how sets should be visualized when the data is uncertain is still an open research challenge. To address the problem of depicting uncertainty in set visualization, we ask (i) which aspects of set type data can be affected by uncertainty and (ii) which characteristics of uncertainty influence the visualization design. We answer these research questions by first describing a conceptual framework that brings together (i) the information that is primarily relevant in sets (i.e., set membership, set attributes, and element attributes) and (ii) different plausible categories of (un)certainty (i.e., certainty, undefined uncertainty as a binary fact, and defined uncertainty as quantifiable measure). Following the structure of our framework, we systematically discuss basic visualization examples of integrating uncertainty in set visualizations. We draw on existing knowledge about general uncertainty visualization and previous evidence of its effectiveness.

Gaining an overview of large spatial data sets presents a challenge common to various domains. 'Overviewing' spatial data involves viewing different areas of focus and context at different scales and requires access to detail from zoomed-out views. Standard pan and zoom interfaces provide limited support with this. Motivated by the application scenario of flood risk monitoring, we extend pan and zoom affordances with a combination of focus+context techniques and multiple maps to support 'overviewing' spatial data with a graph-like information structure. A combination of transient overlays to preview context-on-demand as well as detail-on-demand with the option to decouple additional maps enables fast navigation through the graph-like information space. User-created and -positioned, resizable multiple maps allow for simultaneous exploration of distant regions at flexible scales. The seamless integration of these concepts and the versatility of its components allow for continuously adaptable, user-defined layouts that support various analysis situations. We present a prototype implementation of this interaction model and illustrate its working in application to a hydrometric network, but we believe the model could be transferred to graph-like data in other domains. Obtenir une vue d'ensemble sur de grands ensembles de données spatiales est un défi dans de nombreux domaines. La vue d'ensemble de données spatiales requiert la visualisation de différentes zones d'intérêt et leur contexte à différentes échelles et nécessite l'accès à des détails à partir de vues dézoomées. Les interfaces de déplacement et de zoom standards n'apportent qu'une aide partielle à ces besoins. Motivé par l'application de scénario de gestion des risques d'inondation, nous étendons les fonctionnalités de déplacement et de zoom grâce à la combinaison d'une technique de focalisation et contextualisation et de cartes multiples pour faciliter la vue d'ensemble de données spatiales avec une structure d'information de type graphe. Une combinaison de superpositions transitoires pour pré-visualiser le contexte à la demande et le détail à la demande, avec l'option de découpler des cartes additionnelles, permet une navigation rapide dans un espace d'information de type graphe. De multiples cartes, créées, centrées et redimensionnées par l'utilisateur permettent une exploration simultanée de régions distantes à des échelles flexibles. L'intégration transparente de ces concepts et la polyvalence de ces composants permettent des mises en page définies par l'utilisateur, continuellement adaptables, qui facilitent diverses situations d'analyse. Nous présentons un prototype de ce modèle d'interactions et nous illustrons son fonctionnement sur le cas d'un réseau hydrométrique mais nous pensons que ce modèle peut être appliqué à d'autres données de type graphe dans d'autres domaines.