1. © NICTA 2007 Joachim Gudmundsson Detecting Movement Patterns Among Trajectory Data

2. © NICTA 2007 What is the problem and goal? Extract and make sense of information from large amount of spatio-temporal trajectories (GPS data). The aim of the project is to develop algorithms and software tools that can detect user-specified patterns among geospatial trajectories.

3. © NICTA 2007 Our research Analysing movement data Data: a sequence of (x, y, t) - coordinates and a time stamp for example GPS data, mobile phone, radar, … What sort of information can we extract from a trajectory?

4. © NICTA 2007 Scientific challenge Find definitions of movement patterns that are: 1.General, 2.efficiently “computable” and 3.useful

5. © NICTA 2007 Single File Intuitively easy to define Hard to define formally!

6. © NICTA 2007 Single File Intuitively easy to define Hard to define formally!

7. © NICTA 2007 Typical applications Wildlife –Annual migration behaviour –“Popular places” –Repetitive/regular behaviour –Flocks –“Leadership” –…

8. © NICTA 2007 Typical applications Surveillance Detect movement patterns in surveillance data. –Interesting places –Commuting behaviour –Regular behaviour playing chess every Wednesday night, going to the cricket ground once a month, … –Detect meetings –…

9. © NICTA 2007 Typical applications Defence Detect formations and manoeuvres among soldiers in training –How long do they keep their formation –Attacking manoeuvres? –Regular patrolling –…

10. © NICTA 2007 Typical applications Sports –Detect set plays –Detect strategies –Support queries from coaches and commentators –Provide statistics – …

11. © NICTA 2007 Typical Applications Shopping centres –Movement patterns –Customer flow –…

12. © NICTA 2007 Traffic and Social Information Traffic –Traffic queues –… Social –Popular bars –Nightlife –Popular tourist attractions –…

13. © NICTA 2007 Our results Wildlife tracking/Surveillance 1.Flock 2.Leadership 3.Meetings 4.Convergence (popular places) 5.Commuting patterns (trajectory clustering) 6.Repetitive patterns Formation detection 7.Fixed formations 8.Single file Sport applications 9. Subtrajectory query

14. © NICTA 2007 Problems? Gathering data – invasion of privacy Precision? How should data be collected and by whom? Data size

15. © NICTA 2007 Future Problems and Applications? GPS’s in mobile phones Example: Iphone apps statistics about your movement queries about most popular bars, restaurants and café’s for Iphone users. Advanced radars can track ships – coordinates every 5 minutes. “Real” wildlife tracking Commuting behaviour to improve (public) transport? Merge with geographical information (knowledge about application)

16. © NICTA 2007 Co-authors Giri Narasimhan, Miami International University Jyrki Katajainen, University of Copenhagen Cahya Ong, University of New South Wales Patrick Laube, University of Melbourne Ghazi Al-Naymat, University of Sydney Marc van Kreveld, Utrecht University Sanjay Chawla, University of Sydney Florian Hübner, Karlsruhe University Marc Benkert, Karlsruhe University Mattias Andersson, Lund University Maarten Loffler, Utrecht University Bettina Speckmann, TU Eindhoven Martin Löffler, Utrecht University Maike Buchin, Utrecht University Kevin Buchin, Utrecht University Anh Pham, University of Berkley Eric Buchin, Utrecht University Jun Luo, Utrecht University Bojan Djordjevic, NICTA Damian Merrick, NICTA Thomas Wolle, NICTA