Intelligence Led Policing: Enhancing Force Deployment in Law Enforcement and Counter-Terrorism Using Data & Visual Analytics
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1 Intelligence Led Policing: Enhancing Force Deployment in Law Enforcement and Counter-Terrorism Using Data & Visual Analytics Joint Project of DHS, the Port Authority of NY and NJ, PNNL, Rutgers University DIMACS, UMD START and Intuidex, Inc. Presented by William M. Pottenger, Ph.D.
2 Project Overview CompStat NG System Goal Marry law enforcement and counter-terrorism initiatives to aid in force deployment Data collection Data analysis Force deployment CompStat NG System Components Enterprise Architecture (PNNL SRS) Online data collection / entry Data Warehouse On Demand Analysis and Reporting Tools Reports, Charts & Graphs Visual / Data Analytics
3 System Pilot at the Port Authority Bus Terminal (PABT) Phase 0 CompStat NG operational in semi-automatic mode Phase 1 CompStat NG operational in automatic mode for PABT Pilot Phase 2 Online data collection / entry and Data Warehouse operational for PABT Pilot Phase 3 SRS (Scalable Reasoning System) operational for PABT Pilot Visual / data analytics operational in SRS for PABT Pilot
4 Phase 0: Overview of Tasks Completed Data captured from several data sources VAX/VMS Oracle database Local statistics at commands Other facility-wide databases Aided in development of Excel-based crime analysis software Generated the first PA CompStat NG reports
5 Phase 1: Overview Purchase and configure server hardware for data warehouse Set up Sequel server and create a database connected to PA / PABT data sources used in CompStat NG Develop web forms for online data collection / entry Develop / enhance crime analysis and reporting system Document the system and train staff
6 Phase 1: Online Data Entry Copy historical data to the Sequel server and route daily updates to the Sequel database from VAX/VMS CAD / RMS PeopleSoft Payroll System Sick / IOD database Develop web data entry forms for use at each facility for online data collection / entry of incident reports on the SQL server Develop web forms to store other local information such as Criminal investigations data Summons activity Community Policing Initiatives
7 Phase 1: Crime Analysis System and Training Provide automatic CompStat NG Recap Reports Generated on demand from Access/Sequel DBs Build on existing CompStat NG graphs and charts Link existing chart-generation tools to Access/Sequel DBs Provide usage and training documentation; provide training sessions to PANYNJ staff on usage and administration of Crime Analysis System
8 Phase 1: Recap Reports on Demand CompStat NG Recap for each facility and job wide Includes statistics for seven major crimes, targeted crimes, and/or quality of life issues
9 Phase 1: Crime Analysis Software Sorts crime statistics by date, location, time of day, and day of week PABT Robbery by Hour (1/01/07-7/24/07 YTD) Number of Occurences Number of Occurences TIME BY HOUR PABT Robbery by Day (1/01/07-7/24/07 YTD) Sunday Monday Tuesday Wednesday Thursday Friday Saturday DAY OF THE WEEK
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11 PA BUS TERMINAL - 7 MAJOR CRIMES 2006 vs PA BUS TERMINAL - 7 MAJOR CRIMES 1997 vs
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14 Phase 2: Data Warehouse Incorporate information and support realtime updating from Global Terrorism START CompStat NG Access/Sequel DBs Other potential data sources Weather data: daily/hourly reports from weather stations of the three major airports Traffic data: from PA traffic engineering Integrate the multiple sources of information Integrate information extraction capability from police incident data and GTD
15 Phase 2: Information Extraction From Unstructured Data Intuidex, Inc.
16 Phase 2: Information Extraction From Unstructured Data Intuidex, Inc.
17 Phase 3: Visual Analytics Overarching goal is to reduce relative risk Specify visualization tools and potential integration PNNL In-Spire PNNL Scalable Reasoning System (SRS) Deploy Assessment Wall Verify appropriate security, functionality, and connectivity with the data warehouse Provide usage and training documentation; provide training sessions to PANYNJ staff on usage and administration of visual analytics system
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26 CompStat Crime Analysis Conclusion CompStat NG Supports Charts, graphs and reports for managing force deployment in counter terterrorism and the fight against crime Data Warehouse Leverage underutilized everyday data through automatic information on gathering /extraction and intelligence generation Discover trends that aid in counter-terrorism terrorism and crime prevention efforts Support suspect identification Visual / Data Analytics Mine valuable links between people, places and things Enable command staff to quickly visualize crime and other trends Executive Director Tony Shorris Goal: A national model for counter-terrorism terrorism and crime prevention and analysis!
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