Spatiotemporal Context in Analysis and Visualization of Movement - PowerPoint PPT Presentation

Somayeh Dodge, AAG 2015 Introduction | Context | Analytics | Visualization | Final Remarks Spatiotemporal Context in Analysis and Visualization of Movement Somayeh Dodge Assistant Professor University of Colorado, Colorado Springs sdodge3@uccs.edu Glenn Xavier (UCCS) Maike Buchin (Ruhr-University Bochum) Sean C Ahearn (Hunter College - CUNY) Image courtesy of Sebastian Cruz Gain insights into the behavior of dynamic objects and spatiotemporal processes Introduction | Context | Analytics | Visualization | Final Remarks Introduction | Context | Analytics | Visualization | Final Remarks Movement and Context What is happening and why? Bird migrations in Movebank: Euroasia https://www.youtube.com/watch?v=y4JJgyTncCA This research is led by Prof. James L.D. Smith, Department of Fisheries, Wildlife and Conservation 1,654 individual birds, tracked between 1992 and 2012. The data represent 58 species, over Biology, University of Minnesota 2 million locations, and 276,800 tracking days.

Somayeh Dodge, AAG 2015 Introduction | Context | Analytics | Visualization | Final Remarks Introduction | Context | Analytics | Visualization | Final Remarks What is happening and why? From Observation to Behavior movement movement relation to extracting observation analytics context behavior explore data explore infer & interpret & generate visualize patterns relationships & behavior hypothesis correlations Geographic and dynamic visualization This research is led by Prof. James L.D. Smith, Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota Introduction | Context | Analytics | Visualization | Final Remarks Introduction | Context | Analytics | Visualization | Final Remarks Spatiotemporal Context Modeling Context ๏ External factors that influence a dynamic process at a specific time scale ๏ Discrete ๏ Geography/Physiography ๏ Network ๏ Land cover ๏ Characteristics of terrain ๏ Polygons ๏ Network ๏ Obstacles (river, lake) ๏ Continuous ๏ Environment ๏ Ambient condition ๏ raste grid ๏ Weather ๏ Presence of other agents (interactions) ๏ Time ๏ Season time t ๏ Time of day ๏ Weekdays/weekends

Somayeh Dodge, AAG 2015 Introduction | Context | Analytics | Visualization | Final Remarks Introduction | Context | Analytics | Visualization | Final Remarks Movement Track Annotation Movement Track Annotation ๏ Attributing context information to each tracking location (in space and ๏ Env-DATA: The Environmental-Data Automated Track time) along a movement path. Annotation ๏ Using spatiotemporal interpolation t2 ๏ to link animal tracking data to a diverse range of context variables t1 ๏ to examine relationships between animal movement and environmental conditions ๏ 16 large global datasets (~2500 variables): NASA, NOAA, USGS, NCEP/NCAR and ECMWF weather reanalysis datasets More info at: https://www.movebank.org/node/6607 interpolation in space and time t i m Source: Dodge, et. al. (2013), Movement Ecology e t e Introduction | Context | Analytics | Visualization | Final Remarks Trajectory Similarity with Context ๏ integrate context & spatial similarity p’ C1 Analytics p Trajectory T = <(p 1 ,t 1 ), … ,(p n ,t n )> C2 Visualization C = <c 1 , … ,c n > Using annotated trajectories. for matched points (p,t,c) and (p’,t’,c’) dist(p,p’) + α dist(c,c’) C2 C3 Buchin, M., Dodge, S., Speckmann, B. (2014). JOSIS

Introduction | Context | Analytics | Visualization | Final Remarks Introduction | Context | Analytics | Visualization | Final Remarks Trajectory Similarity with Context Trajectory Similarity with Context for matched points (p,t,c) and (p’,t’,c for matched points (p,t,c) and (p’,t’,c ๏ dissimilarity is computed as the ๏ dissimilarity is computed as the dist(p,p’) + α dist(c,c’) dist(p,p’) + α dist(c,c’) weighted sum of spatial and context weighted sum of spatial and context distance 
 distance 
 for matched poin p’ C1 dist(p,p’) + ๏ spatial distance: p ๏ spatial distance: Fréchet distance nts (p,t,c) and (p’,t’,c for matched poin ๏ context distance: + α dist(c,c’) C2 points (p,t,c dist(p,p’) + Fréchet ’) + α d ๏ context weight: C2 C3 Buchin, M., Dodge, S., Speckmann, B. (2014). JOSIS Buchin, M., Dodge, S., Speckmann, B. (2014). JOSIS Introduction | Context | Analytics | Visualization | Final Remarks Galapagos Albatross ( Phoebastria irrorata ) Context Distance ๏ Shortest path on graph ๏ unit cost ๏ cost depending on the labels Distance matrix: C 1 C 2 grass forest C 1 C 2 C 3 C 4 C 1 0 c 1 c 1 c 3 Tracking data: C 2 c 1 0 c ∗ c 2 ๏ 9 adult albatrosses dual graph 0 C 3 c 1 c ∗ c 2 ๏ Breeding season (Jun to Sep 2008) C 4 c 3 c 2 c 2 0 ๏ Temporal resolution 90 minutes where c 1 = c (forest, grass), c 2 = c (grass, water), C 3 C 4 grass water c 3 = c (forest, water) and c ∗ = min( c 1 + c 3 , c 2 + c 4 ) Image courtesy of Sebastian Cruz Source: Buchin, M., Dodge, S., Speckmann, B. (2014). JOSIS

Introduction | Context | Analytics | Visualization | Final Remarks Introduction | Context | Analytics | Visualization | Final Remarks Env-DATA Annotation Wind Flow Assistance ๏ Wind speed (m/s) and wind direction (degrees from North) Space-time path representation 2D path representation Longitude -78 0 -2 -4 -6 ๏ Source: the NCEP Reanalysis 2 -82 -80 -84 Latitude -88 -86 -90 -8 -10 80 -12 ๏ 6-hour, 2.5°, U/V-wind components Duration (days) 60 ๏ http://www.esrl.noaa.gov/ 40 20 Peru 0 Galapagos Island tail-wind (m/s) -5 0 5 10 Image courtesy of Sebastian Cruz Source: Dodge, et. al. (2013), Movement Ecology Track Similarity Track Similarity Source: Buchin, M., Dodge, S., Speckmann, B. (2014). JOSIS Source: Buchin, M., Dodge, S., Speckmann, B. (2014). JOSIS

Introduction | Context | Analytics | Visualization | Final Remarks Introduction | Context | Analytics | Visualization | Final Remarks Multivariate Visualization of Movement ๏ Visual variables ๏ line/point width, color ๏ vector size and direction DYNAMO: Dynamic Visualization of Animal Source: Xavier, Dodge (2014), Movement and the Environment MapInteract Proceedings Source: Xavier, Dodge (2014), MapInteract Proceedings Introduction | Context | Analytics | Visualization | Final Remarks Introduction | Context | Analytics | Visualization | Final Remarks Method 1 (movement speed and wind speed) Method 2 (movement speed and wind direction/speed) Dynamic Multivariate Visualization of Movement Dynamic Multivariate Visualization of Movement Source: Source: Dodge, et. al. (2013), Movement Ecology Dodge, et. al. (2013), Movement Ecology Xavier, Dodge (2014), MapInteract Proceedings Xavier, Dodge (2014), MapInteract Proceedings

Introduction | Context | Analytics | Visualization | Final Remarks Introduction | Context | Analytics | Visualization | Final Remarks Introduction | Context | Analytics | Visualization | Final Remarks Shadowing Effect (spatiotemporal footprint) The importance of context in movement research ๏ Extracting behavior from observations ๏ Interpreting and understanding movement patterns ๏ Study the impacts of environmental change on behavior Bird migrations in Movebank: Euroasia https://www.youtube.com/watch?v=y4JJgyTncCA Thank you! ๏ UCCS LAS Faculty-Student research award ๏ Tiger Research James L.D. Smith, (University of Minnesota) ๏ Achara Simcharoen (Conservation Ecology Program, King Mongkut’s University of ๏ Technology, Thailand ) ๏ Movebank Env-DATA Project Gil Bohrer (Env-DATA Project PI, The Ohio State University) ๏ Max Planck Institute for Ornithology : Rolf Weinzierl (system admin), Sarah Davidson ๏ (data curator), Martin Wikelski (Movebank PI), Sebastian Cruz (Albatross data)