42,484 research outputs found
Scientific Uncertainty in Media Content: Some Reflections on This Special Issue
It was an honor to be called upon to be the anchor reviewer for this special issue of Public Understanding of Science devoted to new perspectives on media presentations of scientific uncertainty. But more than that, it was for me a pleasure and an education. It is always rewarding when, as one of the reviewers of submitted manuscripts, you get so engaged by the content and quality of the research in the articles before you that you have to remind yourself that your task is that of the critic. That happened repeatedly with all of the research articles in this issue. Rather than summarize each of the articles, which Peters and Dunwoody (2016) have done so well in their introduction, and rather than repeat all the valuable roadmaps for further research already contained in the articles, please allow me to share a handful of sometimes oblique observations inspired, directly or indirectly, by all of the articles in this special issue. The following reflections may refer to some articles more than others, but that should not signal any differences in the high-quality and valuable contributions of each
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Visual analytics of flight trajectories for uncovering decision making strategies
In air traffic management and control, movement data describing actual and planned flights are used for planning, monitoring and post-operation analysis purposes with the goal of increased efficient utilization of air space capacities (in terms of delay reduction or flight efficiency), without compromising the safety of passengers and cargo, nor timeliness of flights. From flight data, it is possible to extract valuable information concerning preferences and decision making of airlines (e.g. route choice) and air traffic managers and controllers (e.g. flight rerouting or optimizing flight times), features whose understanding is intended as a key driver for bringing operational performance benefits. In this paper, we propose a suite of visual analytics techniques for supporting assessment of flight data quality and data analysis workflows centred on revealing decision making preferences
Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species
Invasive species are a major cause of ecological damage and commercial
losses. A current problem spreading in North America and Europe is the vinegar
fly Drosophila suzukii. Unlike other Drosophila, it infests non-rotting and
healthy fruits and is therefore of concern to fruit growers, such as vintners.
Consequently, large amounts of data about infestations have been collected in
recent years. However, there is a lack of interactive methods to investigate
this data. We employ ensemble-based classification to predict areas susceptible
to infestation by D. suzukii and bring them into a spatio-temporal context
using maps and glyph-based visualizations. Following the information-seeking
mantra, we provide a visual analysis system Drosophigator for spatio-temporal
event prediction, enabling the investigation of the spread dynamics of invasive
species. We demonstrate the usefulness of this approach in two use cases
Uncertainty-Aware Principal Component Analysis
We present a technique to perform dimensionality reduction on data that is
subject to uncertainty. Our method is a generalization of traditional principal
component analysis (PCA) to multivariate probability distributions. In
comparison to non-linear methods, linear dimensionality reduction techniques
have the advantage that the characteristics of such probability distributions
remain intact after projection. We derive a representation of the PCA sample
covariance matrix that respects potential uncertainty in each of the inputs,
building the mathematical foundation of our new method: uncertainty-aware PCA.
In addition to the accuracy and performance gained by our approach over
sampling-based strategies, our formulation allows us to perform sensitivity
analysis with regard to the uncertainty in the data. For this, we propose
factor traces as a novel visualization that enables to better understand the
influence of uncertainty on the chosen principal components. We provide
multiple examples of our technique using real-world datasets. As a special
case, we show how to propagate multivariate normal distributions through PCA in
closed form. Furthermore, we discuss extensions and limitations of our
approach
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