Frame of Reference Interaction.

We present a unified set of 3D interaction techniques that demonstrates an alternative way of thinking about the navigation of large virtual spaces in non-immersive environments. Our alternative conceptual framework views navigation from a cognitive perspective—as a way of facilitating changes in user attention from one reference frame to another—rather than from the mechanical perspective of moving a camera between different points of interest. All of our techniques link multiple frames of reference in some meaningful way. Some techniques link multiple windows within a zooming environment while others allow seamless changes of user attention between static objects, moving objects, and groups of moving objects. We present our techniques as they are implemented in GeoZui3D, a geographic visualization system for ocean dat

Integrating Multiple 3D Views through Frame-of-reference Interaction

Frame-of-reference interaction consists of a unified set of 3D interaction techniques for exploratory navigation of large virtual spaces in nonimmersive environments. It is based on a conceptual framework that considers navigation from a cognitive perspective, as a way of facilitating changes in user attention from one reference frame to another, rather than from the mechanical perspective of moving a camera between different points of interest. All of our techniques link multiple frames of reference in some meaningful way. Some techniques link multiple windows within a zooming environment while others allow seamless changes of user focus between static objects, moving objects, and groups of moving objects. We present our techniques as they are implemented in GeoZui3D, a geographic visualization system for ocean data

Linking focus and context in three-dimensional multiscale environments

The central question behind this dissertation is this: In what ways can 3D multiscale spatial information be presented in an interactive computer graphics environment, such that a human observer can better comprehend it? Toward answering this question, a two-pronged approach is employed that consists of practice within computer user-interface design, and theory grounded in perceptual psychology, bound together by an approach to the question in terms of focus and context as they apply to human attention. The major practical contribution of this dissertation is the development of a novel set of techniques for linking 3D windows to various kinds of reference frames in a virtual scene and to each other---linking one or more focal views with a view that provides context. Central to these techniques is the explicit recognition of the frames of reference inherent in objects, in computer-graphics viewpoint specifications, and in the human perception and cognitive understanding of space. Many of these techniques are incorporated into the GeoZui3D system as major extensions. An empirical evaluation of these techniques confirms the utility of 3D window proxy representations and orientation coupling. The major theoretical contribution is a cognitive systems model that predicts when linked focus and context views should be used over other techniques such as zooming. The predictive power of the model comes from explicit recognition of locations where a user will focus attention, as well as applied interpretations of the limitations of visual working memory. The model\u27s ability to predict performance is empirically validated, while its ability to model user error is empirically founded. Both the model and the results of the related experiments suggest that multiple linked windows can be an effective way of presenting multiscale spatial information, especially in situations involving the comparison of three or more objects. The contributions of the dissertation are discussed in the context of the applications that have motivated them

Hardware-accelerated interactive data visualization for neuroscience in Python.

Large datasets are becoming more and more common in science, particularly in neuroscience where experimental techniques are rapidly evolving. Obtaining interpretable results from raw data can sometimes be done automatically; however, there are numerous situations where there is a need, at all processing stages, to visualize the data in an interactive way. This enables the scientist to gain intuition, discover unexpected patterns, and find guidance about subsequent analysis steps. Existing visualization tools mostly focus on static publication-quality figures and do not support interactive visualization of large datasets. While working on Python software for visualization of neurophysiological data, we developed techniques to leverage the computational power of modern graphics cards for high-performance interactive data visualization. We were able to achieve very high performance despite the interpreted and dynamic nature of Python, by using state-of-the-art, fast libraries such as NumPy, PyOpenGL, and PyTables. We present applications of these methods to visualization of neurophysiological data. We believe our tools will be useful in a broad range of domains, in neuroscience and beyond, where there is an increasing need for scalable and fast interactive visualization

Vaex: Big Data exploration in the era of Gaia

We present a new Python library called vaex, to handle extremely large tabular datasets, such as astronomical catalogues like the Gaia catalogue, N-body simulations or any other regular datasets which can be structured in rows and columns. Fast computations of statistics on regular N-dimensional grids allows analysis and visualization in the order of a billion rows per second. We use streaming algorithms, memory mapped files and a zero memory copy policy to allow exploration of datasets larger than memory, e.g. out-of-core algorithms. Vaex allows arbitrary (mathematical) transformations using normal Python expressions and (a subset of) numpy functions which are lazily evaluated and computed when needed in small chunks, which avoids wasting of RAM. Boolean expressions (which are also lazily evaluated) can be used to explore subsets of the data, which we call selections. Vaex uses a similar DataFrame API as Pandas, a very popular library, which helps migration from Pandas. Visualization is one of the key points of vaex, and is done using binned statistics in 1d (e.g. histogram), in 2d (e.g. 2d histograms with colormapping) and 3d (using volume rendering). Vaex is split in in several packages: vaex-core for the computational part, vaex-viz for visualization mostly based on matplotlib, vaex-jupyter for visualization in the Jupyter notebook/lab based in IPyWidgets, vaex-server for the (optional) client-server communication, vaex-ui for the Qt based interface, vaex-hdf5 for hdf5 based memory mapped storage, vaex-astro for astronomy related selections, transformations and memory mapped (column based) fits storage. Vaex is open source and available under MIT license on github, documentation and other information can be found on the main website: https://vaex.io, https://docs.vaex.io or https://github.com/maartenbreddels/vaexComment: 14 pages, 8 figures, Submitted to A&A, interactive version of Fig 4: https://vaex.io/paper/fig

VisIVO - Integrated Tools and Services for Large-Scale Astrophysical Visualization

VisIVO is an integrated suite of tools and services specifically designed for the Virtual Observatory. This suite constitutes a software framework for effective visual discovery in currently available (and next-generation) very large-scale astrophysical datasets. VisIVO consists of VisiVO Desktop - a stand alone application for interactive visualization on standard PCs, VisIVO Server - a grid-enabled platform for high performance visualization and VisIVO Web - a custom designed web portal supporting services based on the VisIVO Server functionality. The main characteristic of VisIVO is support for high-performance, multidimensional visualization of very large-scale astrophysical datasets. Users can obtain meaningful visualizations rapidly while preserving full and intuitive control of the relevant visualization parameters. This paper focuses on newly developed integrated tools in VisIVO Server allowing intuitive visual discovery with 3D views being created from data tables. VisIVO Server can be installed easily on any web server with a database repository. We discuss briefly aspects of our implementation of VisiVO Server on a computational grid and also outline the functionality of the services offered by VisIVO Web. Finally we conclude with a summary of our work and pointers to future developments

Collocating Interface Objects: Zooming into Maps

May, Dean and Barnard [10] used a theoretically based model to argue that objects in a wide range of interfaces should be collocated following screen changes such as a zoom-in to detail. Many existing online maps do not follow this principle, but move a clicked point to the centre of the subsequent display, leaving the user looking at an unrelated location. This paper presents three experiments showing that collocating the point clicked on a map so that the detailed location appears in the place previously occupied by the overview location makes the map easier to use, reducing eye movements and interaction duration. We discuss the benefit of basing design principles on theoretical models so that they can be applied to novel situations, and so designers can infer when to use and not use them