1. Building a Visual Summary of Multiple Trajectories Natalia Andrienko & Gennady Andrienko http://geoanalytics.net

2. 2Natalia Andrienko & Gennady Andrienko Joint visual analytics research group of Fraunhofer IAIS and Univ.Bonn http://visual-analytics.info and http://geoanalytics.net DFG SPP Visual Analytics ViAMoD: Visual Spatiotemporal Pattern Analysis of Movement and Event Data Hamburg, March 2009 Introduction

3. 3Natalia Andrienko & Gennady Andrienko Joint visual analytics research group of Fraunhofer IAIS and Univ.Bonn http://visual-analytics.info and http://geoanalytics.net DFG SPP Visual Analytics ViAMoD: Visual Spatiotemporal Pattern Analysis of Movement and Event Data Hamburg, March 2009 Problem statement  Given: data about movement of multiple objects { }. o  { o 1, o 2, …, o N }; t 0 ≤ t ≤ t max Trajectory: { } where o = const, t k > t k-1 for  k>1  Example: movement of vehicles and/or pedestrians in a city  Problem: represent groups of spatially similar trajectories in a summarised form. -E.g. trajectories with close starts and/or close ends and/or similar routes -Such groups may be found e.g. by means of clustering  Purposes: -Promote abstraction, understanding of common spatial features -Reduce display clutter and overlapping of symbols

4. 4Natalia Andrienko & Gennady Andrienko Joint visual analytics research group of Fraunhofer IAIS and Univ.Bonn http://visual-analytics.info and http://geoanalytics.net DFG SPP Visual Analytics ViAMoD: Visual Spatiotemporal Pattern Analysis of Movement and Event Data Hamburg, March 2009 Example: trajectories of cars in Milan Trajectories on Wednesday morning (6591 trajectories, shown with 20% opacity) Result of density-based clustering by route similarity (noise excluded)

5. 5Natalia Andrienko & Gennady Andrienko Joint visual analytics research group of Fraunhofer IAIS and Univ.Bonn http://visual-analytics.info and http://geoanalytics.net DFG SPP Visual Analytics ViAMoD: Visual Spatiotemporal Pattern Analysis of Movement and Event Data Hamburg, March 2009 Some of the 45 clusters How can we see several (all) clusters at once? How can we compare the clusters?

6. 6Natalia Andrienko & Gennady Andrienko Joint visual analytics research group of Fraunhofer IAIS and Univ.Bonn http://visual-analytics.info and http://geoanalytics.net DFG SPP Visual Analytics ViAMoD: Visual Spatiotemporal Pattern Analysis of Movement and Event Data Hamburg, March 2009 An overview of the clusters (“small multiples”)

7. 7Natalia Andrienko & Gennady Andrienko Joint visual analytics research group of Fraunhofer IAIS and Univ.Bonn http://visual-analytics.info and http://geoanalytics.net DFG SPP Visual Analytics ViAMoD: Visual Spatiotemporal Pattern Analysis of Movement and Event Data Hamburg, March 2009 A summarised representation (graphical spatial model) of a cluster

8. 8Natalia Andrienko & Gennady Andrienko Joint visual analytics research group of Fraunhofer IAIS and Univ.Bonn http://visual-analytics.info and http://geoanalytics.net DFG SPP Visual Analytics ViAMoD: Visual Spatiotemporal Pattern Analysis of Movement and Event Data Hamburg, March 2009 How is it done?  Divide the territory using a suitable mesh*  Transform each trajectory into a sequence of moves between areas (cells of the mesh)  Count the moves between pairs of areas  Represent by arrows with varying thickness * Voronoi polygons built around characteristic points

9. 9Natalia Andrienko & Gennady Andrienko Joint visual analytics research group of Fraunhofer IAIS and Univ.Bonn http://visual-analytics.info and http://geoanalytics.net DFG SPP Visual Analytics ViAMoD: Visual Spatiotemporal Pattern Analysis of Movement and Event Data Hamburg, March 2009 Sensitivity to generalisation parameters Radius 1000m:Radius 2000m:Radius 3000m:

10. 10Natalia Andrienko & Gennady Andrienko Joint visual analytics research group of Fraunhofer IAIS and Univ.Bonn http://visual-analytics.info and http://geoanalytics.net DFG SPP Visual Analytics ViAMoD: Visual Spatiotemporal Pattern Analysis of Movement and Event Data Hamburg, March 2009 Groups of trajectories with close ends (or close starts) 47 clusters (noise excluded)

11. 11Natalia Andrienko & Gennady Andrienko Joint visual analytics research group of Fraunhofer IAIS and Univ.Bonn http://visual-analytics.info and http://geoanalytics.net DFG SPP Visual Analytics ViAMoD: Visual Spatiotemporal Pattern Analysis of Movement and Event Data Hamburg, March 2009 Summarised representation, variant 1

12. 12Natalia Andrienko & Gennady Andrienko Joint visual analytics research group of Fraunhofer IAIS and Univ.Bonn http://visual-analytics.info and http://geoanalytics.net DFG SPP Visual Analytics ViAMoD: Visual Spatiotemporal Pattern Analysis of Movement and Event Data Hamburg, March 2009 Summarised representation, variant 2

13. 13Natalia Andrienko & Gennady Andrienko Joint visual analytics research group of Fraunhofer IAIS and Univ.Bonn http://visual-analytics.info and http://geoanalytics.net DFG SPP Visual Analytics ViAMoD: Visual Spatiotemporal Pattern Analysis of Movement and Event Data Hamburg, March 2009 Two summarisations

14. 14Natalia Andrienko & Gennady Andrienko Joint visual analytics research group of Fraunhofer IAIS and Univ.Bonn http://visual-analytics.info and http://geoanalytics.net DFG SPP Visual Analytics ViAMoD: Visual Spatiotemporal Pattern Analysis of Movement and Event Data Hamburg, March 2009 Further work  Numeric estimation of displacement  Minimization of displacement  User evaluation  Application to trajectories stored in a database  Extending the method to spatio-temporal summarisation