Tutorial on Spatial and Spatio-Temporal Data Mining Part II ... - UFSC

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Methods to Compute Stops and Moves1) IB-SMoT (INTERSECTION-based)Interesting for applications like tourism and urban planning2) CB-SMoT (SPEED-based clustering)Interesting for applications where the speed is important,like traffic management3) DB-SMOT (DIRECTION-based clustering)Interesting in application where the direction variation is importantlike fishing activitiesIB-SMoT (Alvares 2007a)A candidate stop C is a tuple (R C , ∆ C ), whereR C is the geometry of the candidate stop (spatial feature type)∆ C is the minimal time durationE.g. [Hotel - 3 hours]An application A is a finite setA = {C 1 = (R C1 , ∆ C1 ), …, C N = (R CN , ∆ CN )} of candidatestops with non-overlapping geometries R C1 , … ,R CNE.g. [Hotel - 3 hours, Museum – 1 hour]
IB-SMoT (Alvares 2007a)A stop of a trajectory T is a place that is important for theapplicationA move of T with respect to an application is: a maximal contiguous subtrajectory of T :between the starting point of T and the first stop of T; ORbetween two consecutive stops of T; ORS1between the last stop of T and the ending point of T; or the trajectory T itself, if T has no stops.S2S3IB-SMoT(Alvares 2007ª)Input: candidate stopstrajectoriesOutput: Semantic rich trajectories// Application// trajectory samplesMethod:For each trajectoryCheck if it intersects a candidat stop for a minimal amount of timeJurere09-12IbisH.13-14FloripaS16-17
Page 1 and 2: Tutorial on Spatia
Page 3 and 4: The Wireless A Explosão Explosion
Page 5 and 6: Mobility Data AnalysisAnimal Migrat
Page 7 and 8: Trajectories: Overall Characteristi
Page 9 and 10: Mining Trajectories : Frequent patt
Page 11 and 12: Geometry-based Trajectory Data Mini
Page 13 and 14: Relative Motion Patterns (Laube 200
Page 15 and 16: Co-Location Patterns (Cao 2006)Co-l
Page 17 and 18: T-Patterns (Giannotti, 2007)Sequent
Page 19 and 20: TraClass Algorithm (Lee 2008)Two ma
Page 21 and 22: TraClass Algorithm (Lee 2008)Second
Page 23 and 24: TraOD - Trajectory Outlier Detectio
Page 25 and 26: SummaryThese data mining approaches
Page 27 and 28: Semantic-based Spatio-temporal Data
Page 29 and 30: DJ-Cluster (Zhou 2007)DJ-Cluster is
Page 31: Semantic TrajectoriesA semantic tra
Page 35 and 36: CB-SMoT: Speed-based clustering (Pa
Page 37 and 38: Semantic Rich Trajectories (Transpo
Page 39 and 40: STOPS at Multiple-Granularities (Bo
Page 41 and 42: Multiple-Granularity Semantic Traje
Page 43 and 44: Weka-STDMWeka-STDM
Page 45 and 46: Athena (Baglioni 2009)Semantic-rich
Page 47 and 48: Pattern Interpretation (Rebeca 2010
Page 49 and 50: ReferencesManso, J. A. ; TIMES, V.
Page 51 and 52: Challenges and Open Issues in Spati

Tutorial on Spatial and Spatio-Temporal Data Mining Part II ... - UFSC

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