Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species
October 19, 2017 Β· Declared Dead Β· π 2017 IEEE Visualization in Data Science (VDS)
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Authors
Daniel Seebacher, Johannes HΓ€uΓler, Michael Hundt, Manuel Stein, Hannes MΓΌller, Ulrich Engelke, Daniel Keim
arXiv ID
1710.07029
Category
cs.HC: Human-Computer Interaction
Citations
10
Venue
2017 IEEE Visualization in Data Science (VDS)
Last Checked
4 months ago
Abstract
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.
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