A New Framework for Criteria- based Trajectory Segmentation

Kevin BuchinJoint work with Sander Alewijnse, Maike Buchin, Andrea Kölzsch,Helmut Kruckenberg and Michel Westenberg

September 30, 2013

Stopovers in Geese Migration

Goal

• Delineate stopover sites of migratory geese

• Two behavioural types• stopover• migration flight

• Input: • GPS tracks• expert description of

behaviour

Data

• Spring migration tracks • White-fronted geese• 4-5 positions per day• March – June

• Up to 10 stopovers during spring migration• Stopover: 48 h within radius 30 km• Flight: change in heading <120°

stopover migration flight

Criteria

• Decreasing criteria • Increasing criteria

Within radius 30km

At least 48hAND

Change in heading<120°OR

Within radius 30km

Change in heading<120°

At least 48h

Criteria-based Segmentation

[M. Buchin et al. 2011]

• decreasing criteria

[M. Buchin et al. 2012]• decreasing criteria• min-duration• few outliers

[Aronov et al. 2013]• general quadratic time• results on continuous

segmentation

New Framework• decreasing criteria• increasing criteria• approx. outliers• Brownian bridges• near-linear time

Demo 1

Criteria-based Segmentation

[M. Buchin et al. 2011]

• decreasing criteria

[M. Buchin et al. 2012]• decreasing criteria• min-duration• few outliers

[Aronov et al. 2013]• general quadratic time• results on continuous

segmentation

New Framework• decreasing criteria• increasing criteria• approx. outliers• Brownian bridges• near-linear time

[Kranstauber et al. 2012]

• dynamic Brownian bridges

• not about segmentation

Segment by diffusion coefficient

Demo 2

• Criteria-based Segmentation to identify behavioural states

• Efficient algorithms for a large class of criteria• Also handles criteria AND Brownian bridges

• Case studies: both criteria-based and Brownian bridges work well

Thanks!

Summary