A web-based visualisation tool for analysing mouse movements to support map personalisation
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
Evaluation of the visibility of vessel movement features in trajectory visualizations
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Detecting movement patterns using Brownian bridges
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Interpreting pedestrian behaviour by visualising and clustering movement data
W2GIS'13 Proceedings of the 12th international conference on Web and Wireless Geographical Information Systems
Storygraph: extracting patterns from spatio-temporal data
Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Representing positional uncertainty of individual and aggregated trajectories of moving objects
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A visual exploration of mobile phone users, land cover, time, and space
Pervasive and Mobile Computing
| Hi-index | 0.00 |
Modern positioning and identification technologies enable tracking of almost any type of moving object. A remarkable amount of new trajectory data is thus available for the analysis of various phenomena. In cartography, a typical way to visualise and explore such data is to use a space-time cube, where trajectories are shown as 3D polylines through space and time. With increasingly large movement datasets becoming available, this type of display quickly becomes cluttered and unclear. In this article, we introduce the concept of 3D space-time density of trajectories to solve the problem of cluttering in the space-time cube. The space-time density is a generalisation of standard 2D kernel density around 2D point data into 3D density around 3D polyline data (i.e. trajectories). We present the algorithm for space-time density, test it on simulated data, show some basic visualisations of the resulting density volume and observe particular types of spatio-temporal patterns in the density that are specific to trajectory data. We also present an application to real-time movement data, that is, vessel movement trajectories acquired using the Automatic Identification System (AIS) equipment on ships in the Gulf of Finland. Finally, we consider the wider ramifications to spatial analysis of using this novel type of spatio-temporal visualisation.