Fernanda Viégas

Google+Ripples: a native visualization of information flow

G+ Ripples is a visualization of information flow that shows users how public posts are shared on Google+. Unlike other social network visualizations, Ripples exists as a native visualization: it is directly accessible from public posts on Google+. This unique position leads to both new constraints and new possibilities for design. We describe the visualization technique, which is a new mix of node-and-link and circular treemap metaphors. We then describe user reactions as well as some of the patterns of sharing that are made evident by the Ripples visualization.

Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow

We present a design study of the TensorFlow Graph Visualizer, part of the TensorFlow machine intelligence platform. This tool helps users understand complex machine learning architectures by visualizing their underlying dataflow graphs. The tool works by applying a series of graph transformations that enable standard layout techniques to produce a legible interactive diagram. To declutter the graph, we decouple non-critical nodes from the layout. To provide an overview, we build a clustered graph using the hierarchical structure annotated in the source code. To support exploration of nested structure on demand, we perform edge bundling to enable stable and responsive cluster expansion. Finally, we detect and highlight repeated structures to emphasize a models modular composition. To demonstrate the utility of the visualizer, we describe example usage scenarios and report user feedback. Overall, users find the visualizer useful for understanding, debugging, and sharing the structures of their models.

Point, Talk, and Publish: Visualisation and the Web

The power of data visualization comes from social as well as visual processes. Visual analysis is often a group effort, as people share findings, confer with colleagues, and present results. Furthermore, thanks to the Web, collaborative data analysis tools are becoming more broadly available, taking new forms and reaching audiences of unprecedented scale. This chapter discusses the design of systems that support collaborative visualization on the Web, where users can interact across both time and space. We use a simple mantra—point, talk, publish—to guide discussion of the design decisions required by asynchronous collaborative visualization. We describe a number of collaboration mechanisms for view sharing, annotation, discussion, and publishing in interactive visualization, and present case studies of current systems. We also discuss theoretical frameworks that help guide future research in this emerging area.

Deep learning of aftershock patterns following large earthquakes

Aftershocks are a response to changes in stress generated by large earthquakes and represent the most common observations of the triggering of earthquakes. The maximum magnitude of aftershocks and their temporal decay are well described by empirical laws (such as Bath’s law1 and Omori’s law2), but explaining and forecasting the spatial distribution of aftershocks is more difficult. Coulomb failure stress change3 is perhaps the most widely used criterion to explain the spatial distributions of aftershocks4–8, but its applicability has been disputed9–11. Here we use a deep-learning approach to identify a static-stress-based criterionxa0that forecasts aftershock locations without prior assumptions about fault orientation. We show that a neural network trained on more than 131,000 mainshock–aftershock pairs can predict the locations of aftershocks in an independent test dataset of more than 30,000 mainshock–aftershock pairs more accurately (area under curve of 0.849) than can classic Coulomb failure stress change (area under curve of 0.583). We find that the learned aftershock pattern is physically interpretable: the maximum change in shear stress, the von Mises yield criterion (a scaled version of the second invariant of the deviatoric stress-changexa0tensor) and the sum of the absolute values of the independent components of the stress-change tensor each explain more than 98 per cent of the variance in the neural-network prediction. This machine-learning-driven insight provides improved forecasts of aftershock locations and identifies physical quantities that may control earthquake triggering during the most active part of the seismic cycle.Neural networks trained on data from about 130,000 aftershocks from around 100 large earthquakes improve predictions of the spatial distribution of aftershocks and suggest physical quantities that may control earthquake triggering.

Interactive Visualization of Spatially Amplified GNSS Time‐Series Position Fields

ABSTRACT Global Navigation Satellite System (GNSS) position time series are used pervasively in earthquake science to measure the surface response to earthquake cycle deformation. Characteristic usage cases are focused on the temporal windowing of position data to isolate coseismic, postseismic, or interseismic deformation. Here, we present an interactive visualization approach for the temporal evolution of GNSS time session in 2D in which the position estimates are amplified relative to their true positions, or are amplified relative to a reference state. This approach enables a rapid visual assessment of deformation patterns across all phases of the earthquake cycle in relation to topographic structure and active faults including azimuth reversal of coseismic and interseismic deformation.

Through the looking glass: talking about the world with visualization

Data visualization has historically been accessible only to the elite in academia, business, and government. It was serious technology, created by experts for experts. In recent years, however, web-based visualizations--ranging from political art projects to news stories--have reached audiences of millions. What will this new era of data transparency look like--and what are the implications for technologists who work with data? To help answer this question, we report on recent research into public data analysis and visualization. Some of our results come from Many Eyes, a living laboratory web site where people may upload their own data, create interactive visualizations, and carry on conversations. Well also show how the art world has embraced visualization. Well discuss the future of visual literacy and what it means for a world where visualizations are a part of political discussions, citizen activism, religious discussions, game playing, and educational exchanges.