Space–time density of trajectories: exploring spatio-temporal patterns in movement data

Abstract

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.

Keywords:

• space–time density
• space–time cube
• spatio-temporal visual data exploration
• movement data

5. Acknowledgements

The work of the first author is supported by a Strategic Research Cluster grant (07/SRC/I1168) awarded to the National Centre for Geocomputation by Science Foundation Ireland under the National Development Plan. The work of the second author is supported by The Finnish Maritime Administration (from 1 January 2010 Maritime Department in Finnish Transport Agency), which also kindly provided the AIS data. Research presented in this article is part of the collaboration under the EU COST Action IC0903, ‘Knowledge Discovery from Moving Objects (MOVE)’.