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

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Mobility Data AnalysisSeveral analysis may be done over trajectories:How people move around the townDuring the day, during the week, etc.Are there typical movement behaviours? In a certain area at a certaintime?How are people movement habits changing in this area in last decadeyear-month-day?Are there relations between movements of two areas?Are there periodic movements?Mobility Data Analysis: ApplicationsTrajectory data analysis may be useful inseveral application domainsVeicule MonitoringTransportation Companies monitor their trucksInsurance companies use GPS devices to monitorinsured vehicles to reduce insurance priceTraffic AnalysisTo alert people about traffic jams,accidents, etc...Identify/predict low traffic regionsin a city
Mobility Data AnalysisAnimal Migration / Behaviour AnalysisWhich are the trajectories ofa given migration bird?Where do birds stop? For how long?Which is the migration pattern of certain species?Fishing Analysis and ControlAre boats really fishing inallowed areas?Can we classify vessel trajectories?Mobility Data AnalysisWeather prediction and movementanalysisHurricane tracking
Page 1 and 2: Tutorial on Spatia
Page 3: The Wireless A Explosão Explosion
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 and 32: Semantic TrajectoriesA semantic tra
Page 33 and 34: IB-SMoT (Alvares 2007a)A stop of a
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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