Flow maps allow users to perceive not only the location where interactions take place, but also the direction and volume of events. Previous studies have proposed numerous methods to produce flow maps. However, how to evaluate the usability of flow maps has not been well documented. In this study, we combined eye-tracking and questionnaire methods to evaluate the usability of flow maps through comparisons between (a) straight lines and curves and (b) line thicknesses and color gradients. The results show that curved flows are more effective than straight flows. Maps with curved flows have more correct answers, fixations, and percentages of fixations in areas of interest. Furthermore, we find that the curved flows require longer finish times but exhibit smaller times to first fixation than straight flows. In addition, we find that using color gradients to indicate the flow volume is significantly more effective than the application of different line thicknesses, which is mainly reflected by the presence of more correct answers in the color-gradient group. These empirical studies could help improve the usability of flow maps employed to visualize geo-data.

While a road pattern influences wayfinding and navigation, its influence on the gaze behaviours of navigating pedestrians is not well documented. In this study, we compared gaze behaviour differences between regular and irregular road patterns using eye-tracking technology. Twenty-one participants performed orientation (ORI) and shortest route selection (SRS) tasks with both road patterns. We used accuracy of answers and response time to estimate overall performance and time to first fixation duration, average fixation duration, fixation count and fixation duration to estimate gaze behaviour. The results showed that participants performed better with better accuracy of answers using irregular road patterns. For both tasks and both road patterns, the Label areas of interest (AOIs) (including shops and signs) received quicker or greater attention. The road patterns influenced gaze behaviour for both Road AOIs and Label AOIs but exhibited a greater influence on Road AOIs in both tasks. In summary, for orientation and route selection, users are more likely to rely on labels, and roads with irregular patterns are important. These findings may serve as the anchor point for determining how people’s gaze behaviours differ depending on road pattern and indicate that labels and unique road patterns should be highlighted for better wayfinding and navigation.

Mixed reality technology has been increasingly used for navigation. While most MR-based navigation systems are currently based on hand-held devices, for example, smartphones, head-mounted MR devices have become more and more popular in navigation. Much research has been conducted to investigate the navigation experience in MR. However, it is still unclear how ordinary users react to the first-person view and FOV (field of view)-limited navigation experience, especially in terms of spatial learning. In our study, we investigate how visualization in MR navigation affects spatial learning. More specifically, we test two related hypotheses: incorrect virtual information can lead users into incorrect spatial learning, and the visualization style of direction can influence users’ spatial learning and experience. We designed a user interface in Microsoft HoloLens 2 and conducted a user study with 40 participants. The user study consists of a walking session in which users wear Microsoft HoloLens 2 to navigate to an unknown destination, pre- and post-walking questionnaires, sketch map drawing, and a semi-structured interview about the user interface design. The results provide preliminary confirmation that users’ spatial learning can be misled by incorrect information, even in a small study area, but this misleading effect can be compensated by considerate visualization, for example, including lines instead of using only arrows as direction indicators. Arrows with or without lines as two visualization alternatives also influenced the user’s spatial learning and evaluation of the designed elements. Besides, the study shows that users’ preferences for navigation interfaces are diverse, and an adaptable interface should be provided. The results contribute to the design of head-mounted MR-based navigation interfaces and the application of MR in navigation in general.

Current studies on the artificial intelligence (AI) ethics focus either on very broad guidelines or on a very special domain. Therefore, the research outcome can hardly be converted into actionable measures or transferred to other domains. Potential correlations between various cases of AI ethics at different granularity levels are unexplored. To overcome these deficiencies, the authors designed a case-oriented ontological model (COOM) and a hyper-knowledge graph system (HKGS) for the research of collected AI ethics cases. COOM describes criteria for modelling cases by attributes from three perspectives: event attributes, relational attributes, and positional attributes on the value chain. Based on it, HKGS stores the correlation between cases as knowledge and allows advanced visual analysis. The correlations between cases and their dynamic changes on value chain can be observed and explored. In HKGS's implementation part, one of the collected ethics cases is used as an example to demonstrate how to generate a hyper-knowledge graph and to visually analyze it. The authors also anticipated how different practitioners of AI ethics, can achieve the desired outputs from HKGS in their diverse scenarios.