Ubiquitous, situated, and physical visualizations create entirely new possibilities for tasks contextualized in the real world, such as doctors inserting needles. During the development of situated visualizations, evaluating visualizations is a core requirement. However, performing such evaluations is intrinsically hard as the real scenarios are safety-critical or expensive to test. To overcome these issues, researchers and practitioners adapt classical approaches from ubiquitous computing and use surrogate empirical methods such as Augmented Reality (AR), Virtual Reality (VR) prototypes, or merely online demonstrations. This approach's primary assumption is that meaningful insights can also be gained from different, usually cheaper and less cumbersome empirical methods. Nevertheless, recent efforts in the Human-Computer Interaction (HCI) community have found evidence against this assumption, which would impede the use of surrogate empirical methods. Currently, these insights rely on a single investigation of four interactive objects. The goal of this work is to investigate if these prior findings also hold for situated visualizations. Therefore, we first created a scenario where situated visualizations support users in do-it-yourself (DIY) tasks such as crafting and assembly. We then set up five empirical study methods to evaluate the four tasks using an online survey, as well as VR, AR, laboratory, and in-situ studies. Using this study design, we conducted a new study with 60 participants. Our results show that the situated visualizations we investigated in this study are not prone to the same dependency on the empirical method, as found in previous work. Our study provides the first evidence that analyzing situated visualizations through different empirical (surrogate) methods might lead to comparable results.

While advances in mobile text entry enable smartphone users to type almost as fast as on hardware keyboards, text-heavy activities are still not widely adopted. One reason is the lack of shortcut mechanisms. In this article, we determine shortcuts for text-heavy activities, elicit shortcut gestures, implement them for a fully touch-sensitive smartphone, and conduct an evaluation with potential users. We found that experts perform around 800 keyboard shortcuts per day, which are not available on smartphones. Interviews revealed the lack of shortcuts as a major limitation that prevents mobile text editing. Therefore, we elicited gestures for the 22 most important shortcuts for smartphones that are touch-sensitive on the whole device surface. We implemented the gestures for a fully touch-sensitive smartphone using deep learning and evaluated them in realistic scenarios to gather feedback. We show that the developed prototype is perceived as intuitive and faster than recent commercial approaches.