Location sampled over time• noisy sampling• geographic context
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Trajectory Data
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Trajectory SimilarityHow 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
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 boundariesAlgorithm• Adapt known algorithms
using as distance dist(pt,pt’) + α dist(ct,ct’)
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C1
C2
C3
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 Erin2004 2005 1995