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Motivation: Many Applications Example: Urban Crime patterns, Sensor Data, …

Spatio-Temporal Pattern Mining for Multi-Jurisdiction Multi-Timeframe (MJMT) Activity Datasets Investigators: Shashi Shekhar,(U Minnesota) Bhavani T., L. Khan(U.T.Dallas) Start Date: Summer 2007. Motivation: Many Applications Example: Urban Crime patterns, Sensor Data, …

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Motivation: Many Applications Example: Urban Crime patterns, Sensor Data, …

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  1. Spatio-Temporal Pattern Miningfor Multi-Jurisdiction Multi-Timeframe (MJMT) Activity DatasetsInvestigators: Shashi Shekhar,(U Minnesota) Bhavani T., L. Khan(U.T.Dallas) Start Date: Summer 2007 • Motivation: Many Applications • Example: Urban Crime patterns, Sensor Data, … • Pattern Families: Hotspots, Journey to crime, trends, … • Tasks: Crime Prevention, Patrol routes/schedule, … • Problem Definition • Inputs: (i) Activity reports with location and time • Pattern families • Output: Pattern instances • Objective Function: Accuracy, Scalability • Constraints:Urban transportation network

  2. Challenge 1: Spatio-Temporal (ST) Nature of Patterns • State of the Art: Environmental Criminology • Spatial Methods: Hotspots, Spatial Regression • Space-time interaction (Knox test) • Critical Barriers: richer ST semantics • Ex. Trends, periodicity, displacement • Approach: • Categorize pattern families • Quantify: interest measures • Design scalable algorithms • Evaluate with crime datasets • Challenges: Trade-off b/w • Semantic richness and • Scalable algorithms High activity: 2300 -0000 hrs Rings = weekdays; Slices = hour (Source: US Army ERDC, TEC)

  3. 2: Activites on Urban Infrastructure ST Networks • State of the Art: Environmental Criminology • Largely geometric Methods • Few Network Methods: Journey to Crime (J2C) • Critical Barriers: • Scale: Houston – 100,000 crimes / year • Network based explanation • Spatio-temporal networks • Approaches: • Scalable algorithms for J2C analysis • Network based explanatory models • Time-aggregated graphs (TAG) • Challenges: Key assumptions violated! • Ex. Prefix optimality of shortest paths • Can’t use Dijktra’s, A*, etc. (a) Input: Pink lines connect crime location & criminal’s residence (b) Output: Journey- to-Crime (thickness = route popularity) Source: Crimestat

  4. N2 N3 N4 N5 N1 R3 R1 R2 Transition Edge Road Intersections Subway Stations 3: Multi-Jurisdiction Multi-Temporal (MJMT) Data • State of the Art: • Spatial, ST ontologies • Few network ontologies • Critical Barriers: • Heterogeneity across networks • Uncertainty – map accuracy, gps, … • Approach: • Ontologies: ST Network activities • Integration methods: MJMT data • Location accuracy models • Challenges: • Test datasets • Evaluation methods

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