The design of intuitive three-dimensional user interfaces is vital for interaction in virtual reality, allowing to effectively close the loop between a human user and the virtual environment. The utilization of 3D gestural input allows for useful hand interaction with virtual content by directly grasping visible objects, or through invisible gestural commands that are associated with corresponding features in the immersive 3D space. The design of such interfaces remains complex and challenging. In this article, we present a design approach for a three-dimensional user interface using 3D gestural input with the aim to facilitate user interaction within the context of Immersive Analytics. Based on a scenario of exploring time-oriented data in immersive virtual reality using 3D Radar Charts, we implemented a rich set of features that is closely aligned with relevant 3D interaction techniques, data analysis tasks, and aspects of hand posture comfort. We conducted an empirical evaluation (n=12), featuring a series of representative tasks to evaluate the developed user interface design prototype. The results, based on questionnaires, observations, and interviews, indicate good usability and an engaging user experience. We are able to reflect on the implemented hand-based grasping and gestural command techniques, identifying aspects for improvement in regard to hand detection and precision as well as emphasizing a prototype’s ability to infer user intent for better prevention of unintentional gestures.

The size of knots is negatively correlated with bending strength in sawn timber and it is therefore used as a quality grading criterion in national roundwood grading standards. Some standards even use the size of the largest knot as the sole estimate for individual log knottiness. The size of knots is determined by crown horizontal extension, which in turn is dependent on the impact of competing trees. Thus, with knot size models that are competition-dependent, roundwood quality due to knottiness can be simulated for different management al-ternatives. However, these types of models, calibrated on uneven-sized Norway spruce in Fennoscandia, are currently not available. Therefore, the objective of this study is to develop a competition-dependent model framework for prediction of the largest knot size per stem height section, for application within uneven-sized Norway spruce stands. Data from terrestrial laser scanning of an uneven-sized stand in southern Sweden are used to calibrate a modular prediction framework, consisting of interlinked allometric statistical models. Alternative framework sub-models are presented and the preferred model combination can be selected according to context and available input data. The flexible modular format enables further development of separate sub-components for adaptation to growing conditions not covered by the current calibration range.

Collaboration is an essential part of data analysis, allowing multiple users to combine their expertise and to debate about the interpretation of data discoveries using their contextual knowledge. The design of collaborative interfaces within the context of Immersive Analytics remains challenging, particularly due to the various user-centered characteristics of immersive technologies. In this article, we present the use case of a system that enables multiple users to synchronously explore the same data in a collaborative scenario that combines immersive and non-immersive interfaces in an asymmetric role setup. Such a setup allows for bridging the gap when applying heterogeneous display and interaction technologies, enabling each analyst to have an independent and different view of the data, while maintaining important collaborative aspects during the joint data exploration. We developed an immersive VR environment (head- mounted display, 3D gestural input) and a non-immersive desktop terminal (monitor, keyboard and mouse) centered around spatio-temporal data exploration. Supported through a real-time communication interface, synchronous collaborative features are integrated in both interfaces, facilitating the users in their ability to establish a shared context and to make spatio-temporal references. We conducted an empirical evaluation with five participant pairs (within-subject design) to investigate aspects of usability, user engagement, and collaboration during a confirmative analysis task. Synthesis of questionnaire results in combination with additional log file analysis, audio activity analysis, and observations, revealed good usability scores, high user engagement, as well as overall close and balanced collaboration of enthusiastic pairs during the task completion independent of their interface type, validating our system approach in general. Further supported through the self-constructed Spatio-Temporal Collaboration Questionnaire, we are able to contribute with discussion and considerations of the presented scenario and the synchronous collaborative features for the design of similar applications.

In this paper, we present an approach to interact with time-oriented data in Virtual Reality within the context of Immersive Analytics. We implemented a Virtual Reality application that enables its user to explore data in an immersive environment (head-mounted display, 3D gestural input), utilizing potential advantages of immersive technologies, for instance, depth cues for better spatial understanding, natural interaction, and user engagement. The visualization design is inspired by the overall concept of a radar chart, and using the third dimension to represent time-series related data. We conducted a user study with 15 participants, encouraging them to examine a representative dataset within an explorative analysis scenario with no time constraints. Based on the results of usability and user engagement scores, task completion analysis, observations, and interviews, we were able to empirically validate the approach in general, and gain insights in the users’ interaction and data analysis strategies.

We present a hybrid Immersive Analytics system to support asymmetrical collaboration between a pair of users during synchronous data exploration. The system consists of an immersive Virtual Reality application, a non-immersive web application, and a real-time communication interface connecting both applications to provide features to facilitate the collaborators’ mutual understanding and their ability to make (spatial) references. We conducted a real world case study with pairs of language students, encouraging them to use the developed system to investigate a large multivariate Twitter dataset from a sociolinguistic perspective within an explorative analysis scenario. Based on the results of usability scores, log file analyses, observations, and interviews, we were able to validate the approach in general, and gain insights into the users’ collaboration with respect to awareness, deixis, and group dynamics.

In this article, we compare three different input technologies (gamepad, vision-based motion controls, room-scale) for an interactive virtual reality (VR) environment. The overall system is able to visualize (open) data from multiple online sources in a unified interface, enabling the user to browse and explore displayed information in an immersive VR setting. We conducted a user interaction study (n=24; n=8 per input technology, between-group design) to investigate experienced workload and perceived flow of interaction. Log files and observations allowed further insights and comparison of each condition. We have identified trends that indicate user preference of a visual (virtual) representation, but no clear trends regarding the application of physical controllers (over vision-based controls), in a scenario that encouraged exploration with no time limitations.

In recent years, large textual data sets, comprising many data points and rich metadata, have become a common object of investigation and analysis. Information Visualization and Visual Analytics provide practical tools for visual data analysis, most commonly as interactive two-dimensional (2D) visualizations that are displayed through normal computer monitors. At the same time, display technologies have evolved rapidly over the past decade. In particular, emerging technologies such as virtual reality (VR), augmented reality (AR), or mixed reality (MR) have become affordable and more user-friendly (LaValle 2016). Under the banner of “Immersive Analytics”, researchers started to explore the novel application of such immersive technologies for the purpose of data analysis (Marriott et al. 2018).

By using immersive technologies, researchers hope to increase motivation and user engagement for the overall data analysis activity as well as providing different perspectives on the data. This can be particularly helpful in the case of exploratory data analysis, when the researcher attempts to identify interesting points or anomalies in the data without prior knowledge of what exactly they are searching for. Furthermore, the data analysis process often involves the collaborative sharing of information and knowledge between multiple users for the goal of interpreting and making sense of the explored data together (Isenberg et al. 2011). However, immersive technologies such as VR are often rather single user-centric experiences, where one user is wearing a head-mounted display (HMD) device and is thus visually isolated from the real-world surroundings. Consequently, new tools and approaches for co-located, synchronous collaboration in such immersive data analysis scenarios are needed.

In this software demonstration, we present our developed VR system that enables two users to explore data at the same time, one inside an immersive VR environment, and one outside VR using a non-immersive companion application. The context of this demonstrated data analysis activity is centered around the exploration of the language variability in tweets from the perspectives of multilingualism and sociolinguistics (see, e.g. Coats 2017 and Grieve et al. 2017). Our primary data come from the the Nordic Tweet Stream (NTS) corpus (Laitinen et al. 2018, Tyrkkö 2018), and the immersive VR application visualizes in three dimensions (3D) the clustered Twitter traffic within the Nordic region as stacked cuboids according to their geospatial position, where each stack represents a color-coded language share (Alissandrakis et al. 2018). Through the utilization of 3D gestural input, the VR user can interact with the data using hand postures and gestures in order to move through the virtual 3D space, select clusters and display more detailed information, and to navigate through time (Reski and Alissandrakis 2019) ( https://vrxar.lnu.se/apps/odxvrxnts-360/ ). A non-immersive companion application, running in a normal web browser, presents an overview map of the Nordic region as well as other supplemental information about the data that are more suitable to be displayed using non-immersive technologies.

We will present two complementary applications, each with a different objective within the collaborative data analysis framework. The design and implementation of certain connectivity and collaboration features within these applications facilitate the co-located, synchronous exploration and sensemaking. For instance, the VR user’s position and orientation are displayed and updated in real-time within the overview map of the non-immersive application. The other way around, the selected cluster of the non-immersive user is also highlighted for the user in VR. Initial tests with pairs of language students validated the proof-of-concept of the developed collaborative system and encourage the conduction of further future investigations in this direction.

We demonstrate an approach that utilizes immersive virtual reality (VR) to explore and interact with corpus linguistics data. Our case study focuses on the language identification parameter in the Nordic Tweet Stream corpus, a dynamic corpus of Twitter data where each tweet originated within the Nordic countries. We demonstrate how VR can provide previously unexplored perspectives into the use of English and other non-indigenous languages in the Nordic countries alongside the native languages of the region and showcase its geospatial variation. We utilize a head-mounted display (HMD) for a room-scale VR scenario that allows 3D interaction by using hand gestures. In addition to spatial movement through the Nordic areas, the interface enables exploration of the Twitter data based on time (days, weeks, months, or time of predefined special events), making it particularly useful for diachronic investigations.

In addition to demonstrating how the VR methods aid data visualization and exploration, we briefly discuss the pedagogical implications of using VR to showcase linguistic diversity. Our empirical results detail students’ reactions to working in this environment. The discussion part examines the benefits, prospects and limitations of using VR in visualizing corpus data.

In this paper we describe our work-in-progress to create an interface that enables users to browse and select data within an Augmented Reality environment, using a virtual cube object that can be interacted with through 3D gestural input. We present the prototype design (including the graphical elements), describe the interaction possibilities of touching the cube with the hand/finger, and put the prototype into the context of our Augmented Reality for Public Engagement (PEAR) framework. An interactive prototype was implemented and runs on a typical off-the-shelf smart-phone device.

In recent years, data visualization has become a major area in Digital Humanities research, and the same holds true also in linguistics. The rapidly increasing size of corpora, the emergence of dynamic real-time streams, and the availability of complex and enriched metadata have made it increasingly important to facilitate new and innovative approaches to presenting and exploring primary data. This demonstration showcases the uses of Virtual Reality (VR) in the visualization of geospatial linguistic data using data from the Nordic Tweet Stream (NTS) project (see Laitinen et al 2017). The NTS data for this demonstration comprises a full year of geotagged tweets (12,443,696 tweets from 273,648 user accounts) posted within the Nordic region (Denmark, Finland, Iceland, Norway, and Sweden). The dataset includes over 50 metadata parameters in addition to the tweets themselves.

We demonstrate the potential of using VR to efficiently find meaningful patterns in vast streams of data. The VR environment allows an easy overview of any of the features (textual or metadata) in a text corpus. Our focus will be on the language identification data, which provides a previously unexplored perspective into the use of English and other non-indigenous languages in the Nordic countries alongside the native languages of the region.

Our VR prototype utilizes the HTC Vive headset for a room-scale VR scenario, and it is being developed using the Unity3D game development engine. Each node in the VR space is displayed as a stacked cuboid, the equivalent of a bar chart in a three-dimensional space, summarizing all tweets at one geographic location for a given point in time (see: https://tinyurl.com/nts-vr). Each stacked cuboid represents information of the three most frequently used languages, appropriately color coded, enabling the user to get an overview of the language distribution at each location. The VR prototype further encourages users to move between different locations and inspect points of interest in more detail (overall location-related information, a detailed list of all languages detected, the most frequently used hashtags). An underlying map outlines country borders and facilitates orientation. In addition to spatial movement through the Nordic areas, the VR system provides an interface to explore the Twitter data based on time (days, weeks, months, or time of predefined special events), which enables users to explore data over time (see: https://tinyurl.com/nts-vr-time).

In addition to demonstrating how the VR methods aid data visualization and exploration, we will also briefly discuss the pedagogical implications of using VR to showcase linguistic diversity.