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Extended Discussion Meeting: Three papers on ‘Data visualization’

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RSS discussion paper meetings

Wednesday 05 September 2018, 05:30pm - 07:30pm

Location Cardiff City Hall, Cardiff

Paper 1: ‘Visualizing spatiotemporal models with virtual reality: from fully immersive environments to applications in stereoscopic view’

Stefano Castruccio (University of Notre Dame, USA)
Marc G. Genton and Ying Sun (King Abdullah University of Science and Technology, Thuwal

Recent advances in computing hardware and software present an unprecedented opportunity for statisticians who work with data indexed in space and time to visualize, explore and assess the structure of the data and to improve resulting statistical models. We present results of a 3-year collaboration with a team of visualization experts on the use of stereoscopic view and virtual reality (VR) to visualize spatiotemporal data with animations on non-trivial manifolds. We first present our experience with fully immersive VR with motion tracking devices that enable users to explore global three-dimensional time–temperature fields on a spherical shell interactively. We then introduce a suite of applications with VR mode, freely available for smartphones, to port a visualization experience to any interested people. We also discuss recent work with head-mounted devices such as a VR headset with motion tracking sensors.

A preprint of the paper can be downloaded here.

Paper 2: ‘Visualization in Bayesian workflow’

Jonah Gabry (Columbia University, New York)
Daniel Simpson (University of Toronto)
Aki Vehtari (Aalto University, Espoo)
Michael Betancourt (Columbia University, New York, and Symplectomorphic, New York)
Andrew Gelman (Columbia University, New York

Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from the types of modern, high dimensional models that are used by applied researchers.

A preprint of the paper can be downloaded here.

Paper 3: ‘Graphics for uncertainty’

Adrian W. Bowman (University of Glasgow)

Graphical methods such as colour shading and animation, which are widely available, can be very effective in communicating uncertainty. In particular, the idea of a ‘density strip’ provides a conceptually simple representation of a distribution and this is explored in a variety of settings, including a comparison of means, regression and models for contingency tables. Animation is also a very useful device for exploring uncertainty and this is explored particularly in the context of flexible models, expressed in curves and surfaces whose structure is of particular interest. Animation can further provide a helpful mechanism for exploring data in several dimensions. This is explored in the simple but very important setting of spatiotemporal data.

A preprint of the paper can be downloaded here.

This meeting forms part of the RSS 2018 Conference and anyone registered for that day can automatically attend the meeting. If you are not able to attend the conference but wish to just attend the discussion meeting session please contact the This email address is being protected from spambots. You need JavaScript enabled to view it..

Organiser Name Judith Shorten

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Organising Group(s) RSS Discussion Meetings Committee