Context-Aware Similarity of Trajectories Maike Buchin Somayeh Dodge Bettina Speckmann.

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Presentation transcript:

Context-Aware Similarity of Trajectories Maike Buchin Somayeh Dodge Bettina Speckmann

Location sampled over time noisy sampling geographic context 2:40 2:50 3:00 3:20 3:30 3:40 3:50 3:55 4:10 4:15 4:20 4:25 4:30 4:35 Trajectory Data 3:10

Trajectory Similarity How similar are two trajectories? Geographic context needs to be taken into account!

Geographic Context Landcover Network Terrain Ambient attributes Model: labeled polygonal subdivision

Context-Aware Similarity How does geographic context influence similarity?

Context-Aware Similarity Geographic context distinguishes trajectories

Context-Aware Similarity Integrate context & spatial distance: use time to match points for matched points add spatial and context distance (p,t,c) (p‘,t‘,c‘) for matched points (p,t,c) and (p’,t’,c’) dist(p,p’) + α dist(c,c’) 2. spatial distance (e.g., Euclidean dist) 3. context distance (based on cells) 4. context scale (depends on application) 1. matching based on time (e.g., Fréchet dist) Ingredients:

Context Distance Compare two points based on cells they lie in Labels Subdivision distance Example: dist(p,p’) = dist(sand,grass)

Context Distance Compare two points based on cells they lie in Labels Subdivision distance Example: dist(p,p’’) = dist(sand,sand) = 0 or dist(p,p’’) = min ( 2*dist(sand,water), 2*dist(sand,grass) )

Computing Similarity Preprocessing Compute context distance Locate trajectory points in subdivision Possibly split trajectories at subdivision boundaries Algorithm Adapt known algorithms using as distance dist(p t,p t ’) + α dist(c t,c t ’) 2:40 2:50 3:00 3:20 3:30 3:40 3:50 3:55 4:10 4:15 4:20 4:25 4:30 4:35 C1C1 C2C2 C3C3  Equal time distance: straightforward  Fréchet distance: add context distance per free space cell

Experiments: Hurricane Data Geographic context Hurricanes are strongly influenced by land/sea Similarity Prediction Classification

Experiments: Hurricane Data Data Hurricanes – North Atlantic Basin – years 1995, 2004, 2005 – sampled every 6 hours – 48 in total Coastline data

Results of Experiments interesting triple among the 10 most similar hurricane pairs: Erin & Katrina become most similar than the other pairs order of similarity changes: Rita & Katrina become more similar than Rita & Erin Rita Katrina Erin

Conclusion We can and should integrate geographic context into the analysis of trajectories! Future Work: more data sets more analysis tasks