Building a Visual Summary of Multiple Trajectories Natalia Andrienko & Gennady Andrienko .
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Transcript of Building a Visual Summary of Multiple Trajectories Natalia Andrienko & Gennady Andrienko .
Building a Visual Summary of Multiple Trajectories
Natalia Andrienko & Gennady Andrienko
http://geoanalytics.net
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 AnalyticsViAMoD:Visual Spatiotemporal Pattern Analysis of Movement and Event Data
Hamburg, March 2009
Introduction
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 AnalyticsViAMoD:Visual Spatiotemporal Pattern Analysis of Movement and Event Data
Hamburg, March 2009
Problem statement
Given: data about movement of multiple objects {<o, t, x, y>}.o { o1, o2, …, oN }; t0 ≤ t ≤ tmax
Trajectory: {<o, tk, x, y>} where o = const, tk > tk-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
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 AnalyticsViAMoD: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)
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 AnalyticsViAMoD: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?
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 AnalyticsViAMoD:Visual Spatiotemporal Pattern Analysis of Movement and Event Data
Hamburg, March 2009
An overview of the clusters (“small multiples”)
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 AnalyticsViAMoD:Visual Spatiotemporal Pattern Analysis of Movement and Event Data
Hamburg, March 2009
A summarised representation (graphical spatial model) of a cluster
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 AnalyticsViAMoD:Visual Spatiotemporal Pattern Analysis of Movement and Event Data
Hamburg, March 2009
How is it done?
1. Divide the territory using a suitable mesh*2. Transform each trajectory into a sequence of
moves between areas (cells of the mesh)3. Count the moves between pairs of areas4. Represent by arrows with varying thickness
* Voronoi polygons built around characteristic points
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 AnalyticsViAMoD:Visual Spatiotemporal Pattern Analysis of Movement and Event Data
Hamburg, March 2009
Sensitivity to generalisation parameters
Radius 1000m: Radius 2000m: Radius 3000m:
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 AnalyticsViAMoD: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)
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 AnalyticsViAMoD:Visual Spatiotemporal Pattern Analysis of Movement and Event Data
Hamburg, March 2009
Summarised representation, variant 1
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 AnalyticsViAMoD:Visual Spatiotemporal Pattern Analysis of Movement and Event Data
Hamburg, March 2009
Summarised representation, variant 2
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 AnalyticsViAMoD:Visual Spatiotemporal Pattern Analysis of Movement and Event Data
Hamburg, March 2009
Two summarisations
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 AnalyticsViAMoD: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