Analyzing Encounters using the R package MovementAnalysis and other usages of MovementAnalysis
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Analyzing Encounters using the R package MovementAnalysis and other usages of MovementAnalysis
Kevin Buchin
Joint work with
Stef Sijben, Jean Arseneau, Erik Willems,
Emiel van Loon, Nir Sapir, Stephanie Mercier
September 30, 2013
Motivation: Encounters
• http://youtu.be/OX6azU3Spq8
Motivation: Encounters
• data: • 4 groups of vervet monkeys • 1 representative per group• 1 GPS-fix per daytime hour• several month
• ecology questions: interaction between groups
• general goal: develop algorithmic framework for animal movement analysis
• starting point: • Brownian bridge movement
model • movement ecology paradigm
Movement Ecology
[Nathan et al. 2008]
Why random?
understanding
movement
• causes
• consequences
• mechanisms
• patterns
of
Movement – from data to paths
Why random?
Brownian motion
Robert Brown 1773-1858
1827
• Continuous time random process• Position at time , starting at
• Independent, stationary increments• : Diffusion coefficient
Brownian bridge movement model
• Brownian bridge: • Brownian motion conditioned under
starting and ending position
• Brownian bridge movement model:• Each relocation is modeled as a Brownian bridge.
Computing with Brownian Bridges
• Utilization Distributions [Bullard, 1999, Horne et al. 2007]• Basic Properties and Movement Patterns
[Buchin, Sijben, Arseneau, Willems 2012]• Example: Distance
• 2 trajectories• positions at time t are bivariate normal• distance is distributed
0 20 40 60 80 100 120 140
expected locations
location variances
Motivation: Encounters
?
Demo in R
Speed and External Factors
• Study: European bee-eater migratory flight
• link flight mode to atmospheric conditions
• compute diffusion coefficients for flight modes separately
• flight modes result in significantly differences in diffusion coefficients and speeds
Speed and External Factors
• Study: European bee-eater migratory flight
• link flight mode to atmospheric conditions
• compute diffusion coefficients for flight modes separately
• flight modes result in significantly differences in diffusion coefficients and speeds
Spe
ed (
m/s
)
Speed and External Factors
• Study: Vervet monkeys/food availability
• linking speed and food availability by NDVI• significant negative correlation between speed and NDVI
Summary
• Towards a framework for algorithmic movement analysis using Brownian bridges
• Basic building blocks for movement patterns
• Provided as R package
• Case studies: Brownian bridges give insights beyond linear movement
Thanks!