A Comparison of Spatiotemporal Visualizations for 3D Urban Analytics.

Recent technological innovations have led to an increase in the availability of 3D urban data, such as shadow, noise, solar potential, and earthquake simulations. These spatiotemporal datasets create opportunities for new visualizations to engage experts from different domains to study the dynamic behavior of urban spaces in this under explored dimension. However, designing 3D spatiotemporal urban visualizations is challenging, as it requires visual strategies to support analysis of time-varying data referent to the city geometry. Although different visual strategies have been used in 3D urban visual analytics, the question of how effective these visual designs are at supporting spatiotemporal analysis on building surfaces remains open. To investigate this, in this paper we first contribute a series of analytical tasks elicited after interviews with practitioners from three urban domains. We also contribute a quantitative user study comparing the effectiveness of four representative visual designs used to visualize 3D spatiotemporal urban data: spatial juxtaposition, temporal juxtaposition, linked view, and embedded view. Participants performed a series of tasks that required them to identify extreme values on building surfaces over time. Tasks varied in granularity for both space and time dimensions. Our results demonstrate that participants were more accurate using plot-based visualizations (linked view, embedded view) but faster using color-coded visualizations (spatial juxtaposition, temporal juxtaposition). Our results also show that, with increasing task complexity, plot-based visualizations perform better in preserving efficiency (time, accuracy) compared to color-coded visualizations. Based on our findings, we present a set of takeaways with design recommendations for 3D spatiotemporal urban visualizations for researchers and practitioners. Lastly, we report on a series of interviews with four practitioners, and their feedback and suggestions for further work on the visualizations to support 3D spatiotemporal urban data analysis.

Functional optical coherence tomography at altitude: retinal microvascular perfusion and retinal thickness at 3,800 meters.

Cerebral hypoxia is a serious consequence of several cardiorespiratory illnesses. Measuring the retinal microvasculature at high altitude provides a surrogate for cerebral microvasculature, offering potential insight into cerebral hypoxia in critical illness. In addition, although sex-specific differences in cardiovascular diseases are strongly supported, few have focused on differences in ocular blood flow. We evaluated the retinal microvasculature in males (n = 11) and females (n = 7) using functional optical coherence tomography at baseline (1,130 m) (day 0), following rapid ascent (day 2), and prolonged exposure (day 9) to high altitude (3,800 m). Retinal vascular perfusion density (rVPD; an index of total blood supply), retinal thickness (RT; reflecting vascular and neural tissue volume), and arterial blood were acquired. As a group, rVPD increased on day 2 versus day 0 (P < 0.001) and was inversely related to [Formula: see text] (R2 = 0.45; P = 0.006). By day 9, rVPD recovered to baseline but was significantly lower in males than in females (P = 0.007). RT was not different on day 2 versus day 0 (P > 0.99) but was reduced by day 9 relative to day 0 and day 2 (P < 0.001). RT changes relative to day 0 were inversely related to changes in [Formula: see text] on day 2 (R2 = 0.6; P = 0.001) and day 9 (R2 = 0.4; P = 0.02). RT did not differ between sexes. These data suggest differential time course and regulation of the retina during rapid ascent and prolonged exposure to high altitude and are the first to demonstrate sex-specific differences in rVPD at high altitude. The ability to assess intact microvasculature contiguous with the brain has widespread research and clinical applications.NEW & NOTEWORTHY Measuring the retinal microvasculature at high altitude provides a surrogate for cerebral microvasculature, offering potential insight into consequence of cerebral hypoxia in critical illness. This study demonstrates dynamic regulation of the retina during rapid ascent and prolonged exposure to high altitude and is the first to demonstrate sex-specific differences in retinal microvasculature at high altitude. The ability to dynamically assess intact microvasculature contiguous with the brain has widespread research and clinical applications.

Decal-Lenses: Interactive Lenses on Surfaces for Multivariate Visualization.

We present decal-lenses, a new interaction technique that extends the concept of magic lenses to augment and manage multivariate visualizations on arbitrary surfaces. Our object-space lenses follow the surface geometry and allow the user to change the point of view during data exploration while maintaining a spatial reference to positions where one or more lenses were placed. Each lens delimits specific regions of the surface where one or more attributes can be selected or combined. Similar to 2D lenses, the user interacts with our lenses in real-time, switching between different attributes within the lens context. The user can also visualize the surface data representations from the point of view of each lens by using local cameras. To place lenses on surfaces of intricate geometry, such as the human brain, we introduce the concept of support surfaces for designing interaction techniques. Support surfaces provide a way to place and interact with the lenses while avoiding holes and occluded regions during data exploration. We further extend decal-lenses to arbitrary regions using brushing and lassoing operations. We discuss the applicability of our technique and present several examples where our lenses can be useful to create a customized exploration of multivariate data on surfaces.

Decal-Maps: Real-Time Layering of Decals on Surfaces for Multivariate Visualization.

We introduce the use of decals for multivariate visualization design. Decals are visual representations that are used for communication; for example, a pattern, a text, a glyph, or a symbol, transferred from a 2D-image to a surface upon contact. By creating what we define as decal-maps, we can design a set of images or patterns that represent one or more data attributes. We place decals on the surface considering the data pertaining to the locations we choose. We propose a (texture mapping) local parametrization that allows placing decals on arbitrary surfaces interactively, even when dealing with a high number of decals. Moreover, we extend the concept of layering to allow the co-visualization of an increased number of attributes on arbitrary surfaces. By combining decal-maps, color-maps and a layered visualization, we aim to facilitate and encourage the creative process of designing multivariate visualizations. Finally, we demonstrate the general applicability of our technique by providing examples of its use in a variety of contexts.

Toward high-quality gradient estimation on regular lattices.

In this paper, we present two methods for accurate gradient estimation from scalar field data sampled on regular lattices. The first method is based on the multidimensional Taylor series expansion of the convolution sum and allows us to specify design criteria such as compactness and approximation power. The second method is based on a Hilbert space framework and provides a minimum error solution in the form of an orthogonal projection operating between two approximation spaces. Both methods lead to discrete filters, which can be combined with continuous reconstruction kernels to yield highly accurate estimators as compared to the current state of the art. We demonstrate the advantages of our methods in the context of volume rendering of data sampled on Cartesian and Body-Centered Cubic lattices. Our results show significant qualitative and quantitative improvements for both synthetic and real data, while incurring a moderate preprocessing and storage overhead.

Gradient estimation revitalized.

We investigate the use of a Fourier-domain derivative error kernel to quantify the error incurred while estimating the gradient of a function from scalar point samples on a regular lattice. We use the error kernel to show that gradient reconstruction quality is significantly enhanced merely by shifting the reconstruction kernel to the centers of the principal lattice directions. Additionally, we exploit the algebraic similarities between the scalar and derivative error kernels to design asymptotically optimal gradient estimation filters that can be factored into an infinite impulse response interpolation prefilter and a finite impulse response directional derivative filter. This leads to a significant performance gain both in terms of accuracy and computational efficiency. The interpolation prefilter provides an accurate scalar approximation and can be re-used to cheaply compute directional derivatives on-the-fly without the need to store gradients. We demonstrate the impact of our filters in the context of volume rendering of scalar data sampled on the Cartesian and Body-Centered Cubic lattices. Our results rival those obtained from other competitive gradient estimation methods while incurring no additional computational or storage overhead.

The Lattice-Boltzmann method on optimal sampling lattices.

In this paper, we extend the single relaxation time Lattice-Boltzmann Method (LBM) to the 3D body-centered cubic (BCC) lattice. We show that the D3bQ15 lattice defined by a 15 neighborhood connectivity of the BCC lattice is not only capable of more accurately discretizing the velocity space of the continuous Boltzmann equation as compared to the D3Q15 Cartesian lattice, it also achieves a comparable spatial discretization with 30 percent less samples. We validate the accuracy of our proposed lattice by investigating its performance on the 3D lid-driven cavity flow problem and show that the D3bQ15 lattice offers significant cost savings while maintaining a comparable accuracy. We demonstrate the efficiency of our method and the impact on graphics and visualization techniques via the application of line-integral convolution on 2D slices as well as the extraction of streamlines of the 3D flow. We further study the benefits of our proposed lattice by applying it to the problem of simulating smoke and show that the D3bQ15 lattice yields more detail and turbulence at a reduced computational cost.