A New Framework for Criteria-­based Trajectory Segmentation Kevin Buchin Joint 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°

stopovermigration flight Criteria Within radius 30km At least 48h AND Change in heading <120° OR

stopovermigration flight Criteria Decreasing criteria Increasing criteria Within radius 30km At least 48h AND 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

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