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Supporting the Visual Analysis of the Behaviour of Gulls

Supporting the Visual Analysis of the Behaviour of Gulls. Aidan Slingsby (City University London) (hosted by Emiel van Loon, University of Amsterdam). Short Term Scientific Mission COST-STSM-IC0903-7590. User- centred process. Current practice.

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Supporting the Visual Analysis of the Behaviour of Gulls

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  1. Supporting the Visual Analysisof the Behaviourof Gulls Aidan Slingsby (City University London) (hosted by Emiel van Loon, University of Amsterdam) Short Term Scientific Mission COST-STSM-IC0903-7590

  2. User-centred process

  3. Current practice • Already extensive use of interactive visualisation • Matlab/R • Custom Google Earth based tool • Shortcomings (Google Earth based tool) • Cumbersome filtering • Time and space doesn’t link together well • Cannot produce density estimates • Difficult to explore by over timeand by day/week • Cannot load many data

  4. Visualisation awareness

  5. Initial Requirements • High-resolution maps/aerial imagery • Density estimates • Explore over time, day/week and sunrise/sunset • Load many data points • Be able to query individual data values • Load any data in agreed data format

  6. Initial tasks • Infer behaviour along its track • Identify revisited locations, where and when • Recognise temporal cycles • Extrapolate trajectories (based on heading/wind) • Identify home ranges. • Identify key places/times • Find out long birds stay somewhere • Analyse resource use and time expenditure

  7. Prototype • Live demo • Similar to https://vimeo.com/71347204)

  8. Functionality, ranked by utility • Zoomable timeline • Hour/day/year lines on the timeline • GPS points coloured by individual • Persistent highlighting • Satellite imagery • Save view with comments • Time relative to sunrise/sunsets • Speed, temperature and distance from nest on timeline • Attribute selection • Helpscreen

  9. Tasks, ranked by (perceived) success • Identify revisited locations, where and when • Find out long birds stay somewhere • Recognise temporal cycles • Identify home ranges • Identify key places/times • Infer behaviour along its track

  10. 3 months later…

  11. Task evaluation scores

  12. Outcomes • Helped validate and improve our user-centred process • Resulted in a tool that the group still use for exploring new data • Resulted in a code-base that has been extensively used on other projects • Has already resulted in further funded work between the two groups and we have plans to carry on working together

  13. Acknowledgements • Thanks to: • staff at IBED, particularly Emiel van Loon, Judy Shamoun-Baranes, AdriaanDokterand Willem Bouten • MOVE-COST for funding this Short Term Scientific Mission (COST-STSM-IC0903-7590).

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