Visualising Data From Dolphin Observations Through Adaptively Ordered Space-time Matrices Judy Rodda Antoni Moore 10.17608/k6.auckland.9846290.v1 https://auckland.figshare.com/articles/conference_contribution/Visualising_Data_From_Dolphin_Observations_Through_Adaptively_Ordered_Space-time_Matrices/9846290 <div>Spatio-temporal visualisations (e.g. space-time cube) offer a method to observe interactions and</div><div>patterns that may not be apparent in a traditional two-dimensional view. Yet the mapping of such time</div><div>series data can still easily lead to visual clutter, leading to the development of abstracting REMO</div><div>(Relative Motion) and ARM (Adaptive Relative Motion) techniques. ARM uses greedy or simulated</div><div>annealing algorithms to optimally reorder object-time matrices from an arbitrary object order at each</div><div>time interval to one that keeps geographically proximal objects close to each other in columns and</div><div>across rows in the matrix (effectively applying a travelling salesperson algorithm to traverse object</div><div>locations for a given time). Testing each algorithm on eight seasons worth of space-time data for an</div><div>endemic species of New Zealand dolphin in a large southern bay revealed better results for the greedy</div><div>algorithm. However, given the pronounced orientation of the data (close to the SW-facing shoreline of</div><div>the bay), testing on more area filling data is required. Nevertheless, these results demonstrate</div><div>encouraging advances into algorithm-derived perceptive rationales of dolphin movements across</div><div>space and time.</div> 2019-09-16 01:19:09 Simulated Annealing Greedy Algorithm Traveling Salesperson Algorithm Hector’s dolphin Space-time Geovisualisation Geospatial Information Systems