Visualising Data From Dolphin Observations Through Adaptively Ordered Space-time Matrices [article]

Judy Rodda, Antoni Moore
2019
Spatio-temporal visualisations (e.g. space-time cube) offer a method to observe interactions andpatterns that may not be apparent in a traditional two-dimensional view. Yet the mapping of such timeseries data can still easily lead to visual clutter, leading to the development of abstracting REMO(Relative Motion) and ARM (Adaptive Relative Motion) techniques. ARM uses greedy or simulatedannealing algorithms to optimally reorder object-time matrices from an arbitrary object order at eachtime
more » ... er at eachtime interval to one that keeps geographically proximal objects close to each other in columns andacross rows in the matrix (effectively applying a travelling salesperson algorithm to traverse objectlocations for a given time). Testing each algorithm on eight seasons worth of space-time data for anendemic species of New Zealand dolphin in a large southern bay revealed better results for the greedyalgorithm. However, given the pronounced orientation of the data (close to the SW-facing shoreline ofthe bay), testing on more area filling data is required. Nevertheless, these results demonstrateencouraging advances into algorithm-derived perceptive rationales of dolphin movements acrossspace and time.
doi:10.17608/k6.auckland.9846290.v1 fatcat:s6h3gijuunghjd73gexd56jb4u