Immersive Analytics (IA) is defined as “the use of engaging, embodied analysis tools to support data understanding and decision making” [5].
How can VR/AR help?
Immersive environments (IE) are proposed to: offer increased spatial opportunities research is still needed to understand navigation, spatial knowledge and orientation, and how structural understanding is enabled
My interest in this area
Navigation
Orientation
Structural understanding
Information seeking behaviours
Overview of studies we’re examining
Two articles provide a view of navigation, orientation and behavioural patterns that provide some insight into how IA heightens understanding of complex multivariate datasets.
Data was collected on voluntary demographics, telemetry from their HMD, video observation, screen recording, audio recording, and exit interviews consisting of survey and open ended questions
Expert users at a US government agency which included data analysts, expert analysts and economists (n=6)
The authors recorded users working with the IA tool defined in Cordeil et al.
Summative study
Contextual inquiry, in office with 12 participants
directed tutorial, exploration phase (30 minutes), presentation phase (30 minutes) and a post-test semi-structured interview
Summative data was analyzed to understand user motivations of actions and behaviours as well as user sentiment
Findings
Expert users had few effects of fatigue
Legibility was not a concern
Participants with little VR experience were able to learn and create advanced visualizations
During the exploration stage, users would place ad-hoc visualizations close to them
Collected visualizations would then be curated as an understanding of data developed
Participants did not use the full space available, just a capped spherical arrangement close to them
Critical assessment
The use of space was dictated by object interaction limitations.
Visual cues of depth: monocular and binocular depth cues or just monocular?
Other cues such including ergonomics, display fidelity, proprioception, FOV, but also auditory, vestibular, somatosensory may influence near-body placement.
Towards an understanding of augmented reality extensions for existing 3D data analysis tools. https://doi.org/10.1145/3313831.3376657
Motivation
Particle physics researchers must synthesize data from multiple sources to understand complex particle physics events. IA has not been used to explore this domain yet.
Domain experts depend on existing analysis tools and are reluctant to adopt new tech.
Research objective
To understand behaviours of exploration and structural comprehension using 3D models of a particle physics events from a dataset
Applying AR using an optical passthrough HWD (Hololens) to meet contextual needs of domain experts who need to synthesize data from multiple sources, including 2D data from desktop displays.
Methodology
7 domain experts
Data was collected with video, audio, and observer notation.
The three part experiment consisted of:
explanation and tutorials
free exploration and thinking-aloud protocol
post-test questionnaires and semi-structured interviews
Findings
7 emergent themes
interface, perception and data understanding, synchronazation, input, walking around, application and collaboration, and prediction of realistic usage.
Support for hybrid applications where AR supplements 2D desktop work
The expanding AR ‘screen space’ is highly advantageous
“walking through” the data was more beneficial than view manipulation.
Critical assessment
Ergonomics - while the authors identified the walking through data was beneficial, questions of ergonomics were not discussed, possibly due to the short nature of the test. Would we continue to see effort expended to walk around if there were other means of manipulation?
If the virtual object was not anchored in the real world with registration markers?
How might the benefits of walk-through change with fatigue?
Conclusions
IA can be easy to learn and work with advanced visualizations
Users will optimize the space nearest to them
Hybrid AR can be advantageous for analyzing complex data
Walking through and expanding the view considered beneficial
Future research
Monoscopic depth cues were sufficient - why use AR/VR?
Scatterplots in all conditions - what about other visual idioms? Static or animated?
Information was perceived to be easier to find - is novelty influencing this?
Required less navigation and effort - for the task.
Increase to task completion times - does this affect adoption?
Accomodation is 2.0m away with the hololens - does this contribute to task fatigue?
Discussion
Do experiences with other magical environments (such as games) give some users an advantage to spatial utilization/navigation/orientation? (Should we include games as part of conditioning?)
Fatigue and ergonomics - While this is novel the first time, do you always play like this?
