slide1
Download
Skip this Video
Download Presentation
Motivation: Many Applications Example: Urban Crime patterns, Sensor Data, …

Loading in 2 Seconds...

play fullscreen
1 / 4

Motivation: Many Applications Example: Urban Crime patterns, Sensor Data, … - PowerPoint PPT Presentation


  • 191 Views
  • Uploaded on

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, …

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Motivation: Many Applications Example: Urban Crime patterns, Sensor Data, …' - dobry


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1
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
challenge 1 spatio temporal st nature of patterns
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)

2 activites on urban infrastructure st networks
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

3 multi jurisdiction multi temporal mjmt data
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
ad