1. Dr. Rado Kotorov Technical Director Strategic Product Mgt. BI Applications For Crime Intelligence : Data Mining & Predictive Modeling

2. Forward Looking BI with Predictive Analytics Past Events Reporting & Analysis Future Events Predictive Modeling Re-active Actions Events have occurred Analyze cause Adjust processes to prevent Pro-active Actions Events have not occurred Expect when & where Allocate resources to prevent

3. Forward Looking BI: Answer a Different Set of Questions Degree of Intelligence Standard Reports Ad Hoc Reports Query/Drill Down KPIs/Alerts What happened? How many, how often, where? Where exactly is the problem? What actions are needed? Rear View Statistical Analysis Forecasting/Extrapolation Predictive Modeling Optimization Why is this happening? What of these trends continue? What will happen next? What is the best that can happen? Forward View Note: Adapted from “Competing on Analytics”

4. Copyright 2007, Information Builders. Slide 4 How Does Predictive Analytics Help You Make Better Decisions: Issue 1  Situation: Large volumes of historical data  Issue: How do you determine what the right pattern is

5. Copyright 2007, Information Builders. Slide 5 How Does Predictive Analytics Help You Make Better Decisions: Issue 2  Situation: Large number of variables for analysis  Issue: How do you determine which variables are more important.  Not all factors have equal weights  The more factors the harder to determine their weights Number of Crimes Number of Officers Weather Conditions Unknown Economic Factors Crime Community Events Demographics

6. Copyright 2007, Information Builders. Slide 6 Predictive Modeling and Scoring Applications Predictive Modeling: Predictive modeling is a process that: (1) takes as input historical data, (2) evaluates it statistically to detect hidden patterns in it, and (3) derives a formula or set of rules that describe the uncovered patterns, referred to also as a model. -- A pattern can be a relationship or an outcome Scoring Application: A scoring application automates the use of the model on new records in order to predict relationships and outcome probabilities. -- Relationship: higher unemployment rates increase crimes in lower income areas -- Outcome: There is a high probability of aggravated assault occurring in dispatch zone X

7. Copyright 2007, Information Builders. Slide 7  It is useful where operational users have to make decisions that involve uncertainty and risk.  It estimates the probabilities associated with the expected events, i.e., the likelihood that the event will occur.  The probability estimates help managers make better decisions than guessing. When Is a Scoring Application Useful?

8. Everyone Makes Decisions Abut the Future Copyright 2007, Information Builders. Slide 8 When? Where? Correlated Events? Correlated Events? Dispatch Patrol Cars Dispatch Patrol Cars Gut feeling or science?

9. Copyright 2007, Information Builders. Slide 9 Predicting Crime

10. Copyright 2007, Information Builders. Slide 10  Time and location of future incidence in a crime pattern or series  Identify individuals who are likely to reoffend  Inmate radicalization risk assessment (i.e., identify inmates who are in danger)  Drug market displacement (i.e., where next open air drug market will pop up)  Disorder and environmental variables  Likely impact of specific operations.  Disruption of criminal organization (criminal leadership)  Prediction of criminal adaptation (not only law enforcement efforts but also media, etc.)  Data analysis and support of crime suppression analysis  Patrol staffing and resources allocation  Localized crime spikes  Identify juveniles likely to be involved in violent crime  Risk assessment of sex offending in juveniles  Early identifications of career criminals  Identify victims of unreported crimes  Evaluation of interventions  Impact of drug enforcement on markets and allied crimes  Identification and analysis of crime-prone events and locations  Individual-specific analysis  Travel of serial offenders Possible Use & Value of Predictive Policing From 1 st Annual NIJ Predictive Policing Symposium

11. Copyright 2007, Information Builders. Slide 11  Analysis of predatory patterns  Correlation of environmental factors outside of crime like weather  Threat and vulnerability assessment  Prioritization of sources  Unstructured data extraction (police reports, blogs, incident reports and social networks)  Predicting acts of terror  Predicting riots  Social network analysis  Video analytics (including behavioralistics)  Use of NIBRS to help prediction  Wide-area surveillance for video fusion  Precursors and leading indicators to crime (including non-obvious predictors)  City/neighborhood planning  Design of spaces; economic development; security resource allocation; infrastructure protection  Offender monitoring, predicting behavior, endpoint sentencing  Traffic management, crowd control  Management of police personnel  Professional development, recruitment  Risk for excessive use-of-force, discipline Possible Use & Value of Predictive Policing From 1 st Annual NIJ Predictive Policing Symposium

12. Process For Building And Deploying Predictive Applications Copyright 2007, Information Builders. Slide 12 CRISP-DM Process Model ( http://www.crisp-dm.org )http://www.crisp-dm.org

13. Copyright 2007, Information Builders. Slide 13 RStat: Differentiators & Benefits  Based on R-Project  Open Source  Maintained by world wide consortium of universities, scientists, government funded research organizations, statisticians.  Over 2000 packages  RStat is a GUI to R  Intuitive guided approach to modeling  Simple model evaluation  Intended both for business analysts and advanced modelers  Single BI and Predictive Modeling Environment  Re-use metadata and queries  Perform data manipulation and sampling  Build scoring applications  Unique Deployment Method for Scoring Solutions  Scoring models are built directly into WF metadata  Deployment on any platform and operating system - Windows, Unix, Linux, Z/OS, and i Series.

14. Copyright 2007, Information Builders. Slide 14 Thank you