An intuitive way to externalize spatial memory is to sketch it. Compared to traditional paper-based sketches, virtual reality (VR) creates new opportunities to investigate the 3D aspect of spatial memory as it empowers users to express 3D information on a 3D interface directly. The goal of this study is to design a 3D sketch mapping tool for researchers and non-expert users without sketching expertise that enables externalizing memories of spatial information after some 3D-critical tasks. There exist 3D sketching tools using VR, but there are two issues with the current mid-air 3D sketching approach: (1) distortion of sketches due to depth perception errors and (2) increased cognitive and sensorimotor demands due to an increased degree of freedom and absence of physical support. To address these problems, we implemented VResin, a novel sketching interface that synergizes 3D mid-air sketching with 2D surface sketching to scaffold 3D sketching into a layer-by-layer process. An experimental study with 48 participants on multi-layer building scenarios showed that VResin supports users in creating less distorted sketches while maintaining the level of completeness and generalization compared to mid-air sketching in VR. We also demonstrate the potential applications that can benefit from 3D sketch maps and the suitability of VResin for a variety of building shapes.

Interdisciplinary integration of spatial cognition and spatial computation promises to create better spatial technology based on findings from cognitive psychology experiments. Using the example of psychological studies and computational modelling of landmarks, this paper argues that core evaluation criteria of both disciplines are not well aligned with the goal of evaluating landmark-enhanced navigation support systems that support users in everyday wayfinding. The paper raises two points. First, it reviews evaluation criteria used in the interdisciplinary field of landmark research. It is argued that when to consider the role of landmark-enhanced navigation support systems in everyday life of their users, different evaluation criteria are needed. If strictly-psychological or strictly-computational criteria continue being prioritised by the community, we risk undervaluing significant technological contributions. Second, it proposes one such potential criterion: testing whether the cognitive task has changed due to equipping users with the new technology. This goal might be achieved at the expense of criteria typical to strictly-psychological studies (such as spatial memory of landmarks along the travelled route) or strictly-computational studies (such as efficiency and accuracy of a landmark-selection algorithm). Thus, promoting and implementing alternative evaluation criteria comes with methodological risks. In order to mitigate them we propose a process based on pre-registration of "postdiction" studies and hope to stimulate a further debate on a consensus-based approach in the community.

Studying people's movement and social interaction in indoor spaces requires detecting where and when interactions happen. So far, sensor-based systems for detecting interactions focused on identifying physically occupied spaces, i.e., spaces determined by the presence of physically measurable objects. In contrast, our goal is to detect socially occupied spaces, i.e., spaces occupied not by people but by social interactions that they support. We focus on detecting F-Formations, a representation of the space structure between two or more potentially interacting building occupants. We propose a system that uses infrared depth cameras to anonymously derivate body orientation from skeleton joints. The orientation of the shoulders and spine, together with the face orientation and the temporal information on occupants' walking trajectories, is used to calculate the body orientation from which socially occupied space is identified. In a user study evaluating the system, we collected data in 32 patterns within two distinct cases: individuals and dyads and evaluated the system's accuracy. The evaluation included the intended orientation and the socially accepted orientation. Experimental results show accuracy well above 90% for assessing the socially-relevant body orientation. Our algorithm to detect body orientation contributes to the automated detection of socially occupied spaces. The system shows potential to detect more complex social structures. This would have an impact on research areas that study group interactions within complex indoor settings.

GPS-based navigation systems are widely used to get wayfinding assistance. Current navigation systems incorporate different map scales for presenting wayfinding instructions, however, the selection of scale is not supported by psychological findings. Different tasks of the users such as the identification of the next decision point or the orientation within the environment might be supported best at particular scales. We propose a new conceptual distinction of functional scales with respect to their role in supporting wayfinding and orientation. We suggest that these functional scales can have a benefit for supporting wayfinding and orientation if used for providing wayfinding instructions. This we aim to empirically evaluate in future work.

Sketching as a natural mode for human communication and creative processes presents opportunities for improving human–computer interaction in geospatial information systems. However, to use a sketch map as user input, it must be localized within the underlying spatial data set of the information system, the base metric map. This can be achieved by a matching process called qualitative map alignment in which qualitative spatial representations of the two input maps are used to establish correspondences between each sketched object and one or more objects in the metric map. The challenge is that, to the best of our knowledge, no method for matching qualitative spatial representations suggested so far is applicable in realistic scenarios due to excessively long runtimes, incorrect algorithm design or the inability to use more than one spatial aspect at a time. We address these challenges with a metaheuristic algorithm which uses novel data structures to match qualitative spatial representations of a pair of maps. We present the design, data structures and performance evaluation of the algorithm using real-world sketch and metric maps as well as on synthetic data. Our algorithm is novel in two main aspects. Firstly, it employs a novel system of matrices known as local compatibility matrices, which facilitate the computation of estimates for the future size of a partial alignment and allow several types of constraints to be used at the same time. Secondly, the heuristic it computes has a higher accuracy than the state-of-the-art heuristic for this task, yet requires less computation. Our algorithm is also a general method for matching labelled graphs, a special case of which is the one involving complete graphs whose edges are labelled with spatial relations. The results of our evaluation demonstrate practical runtime performance and high solution quality.