Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species

October 19, 2017 Β· Declared Dead Β· πŸ› 2017 IEEE Visualization in Data Science (VDS)

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Authors Daniel Seebacher, Johannes HÀußler, Michael Hundt, Manuel Stein, Hannes Müller, 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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