Stephen Hollifield, CIO City of Richmond Police, Virginia Visual Predictive Analytics in Law Enforcement

Crime in Richmond

Data Rich yet Information Poor (~50 million records) No way to compare current crime with vast wealth of historical data Statistical forecasting limited to linear perspectives Analysis only available to limited personnel with delayed and cumbersome distribution Our Challenge

Find: Hidden patterns & relationships Analyze: Predict likelihood of violent crime Automate: Updated forecasts every 4 hours Distribute: Enterprise-wide access Leverage: Deploy resources effectively Our Strategy

Data –Bring data from multiple sources into data warehouse –Discover hidden patterns and relationships Enabling / Interfering Factors Weather Moon Phases Triggers City Events Paydays Holidays Day of Week Time of Day Crime Records Data Sources

Predictive Results

Crime Maps

Visual Discovery

KPI - Incident Analysis

The Cost

Homicide -32%-40% Rape -19%-8% Robbery -3%-20% Aggravated Assault -17%-5% Burglary -18%-7% Motor Vehicle Theft -28%-26% Statistics

Have we been able to reduce crime? Do people feel safer? Are certain crimes occurring less often? Are we making efficient use of our resources? Measuring Success

SMTWHFS 7 & 30 Day Analysis –Predict intensity of crime by 4 hour windows within 7 and 30 day forecasts –Provide single click interface directly to GIS perspective for each 4 hour window –Provide “what if” scenario options based on deployment tactics SMTWHFS Future

Stephen Hollifield, CIO City of Richmond Police, Virginia Visual Predictive Analytics in Law Enforcement