Data Driven Approaches to Crime and Traffic Safety

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1 Data Driven Approaches to Crime and Traffic Safety Baltimore County Police Department Traffic Safety is Public Safety Baltimore County Police Department Population: 801,000 Square Miles: 610 Sworn Personnel: 1918 Precincts: 10 Fatal Crashes: 65 Total crashes: 34,000 Part I Crime: 28,500 Part II Crime: 32,000 CFS: 600,000 Partner and Stakeholder Participation Law Enforcement Highway Safety Agencies Community/Business

2 Data Collection 911 Call CAD Entry Officer Response SF Code CAD Data NO Report YES Supervisor Review Crime Analysis Crime Records Crash MSP Analysis product Timeline is 3 days Timeline is 18 mos. for field use Data to CAU for geocoding Analysis product for field use Data Collection Crash Data Crime Data Calls for Service Community Complaints Sources of Data DIFFICULTY GEOCODING CRASH LOCATIONS Road name misspelled Road name missing Multiple designations of interstate highways Hit rate about 83% for MAARS data

3 PREPARATION PROGRAM USED PRIOR TO GEOCODING VEHICLE CRASHES IN BALTIMORE COUNTY 2008 Source: InPursuit RMS Total: 34,040 Data Analysis Types of analysis Hot spots Mapping

4 METHODS USED TO IDENTIFY HIGH CONCENTRATIONS OF CRASHES AND CRIMES Geo-referenced crashes and crimes Used CrimeStat III to identify accident-crime clusters Nearest Neighbor Hierarchical Spatial Clustering Ellipses delimit statistically significant points with 95% confidence interval level (crashes or crimes) Clusters represented by ellipses in ArcMap METHODS USED TO IDENTIFY AREAS EXPERIENCING HIGH AMOUNTS OF CRIME AND PERSONAL INJURY CRASHES Identified point locations for each crime type and personal injury crashes NEAREST NEIGHBOR HIERARCHICAL ELLIPSES Constructed Nearest Neighbor Hierarchical ellipses for each crime type and personal injury crashes from March to November of 2007 (red) and 2008 (blue), using CrimeStat III with 90% confidence interval and minimum of 10 incidents per ellipse except for commercial robbery (5 or more)

5 BUFFERED EACH ELLIPSE Buffered each ellipse by 0.25 miles (commercial burglary) FOUND THE INTERSECTIONS OF ELLIPSES Found the intersections between 2007 and 2008 buffered ellipses (commercial burglary in one area) UNION AND DISSOLVE TOOLS The union tool was used to combine the buffered intersections The dissolve tool was used to create composite hot spots

6 COMPOSITE HOT SPOT AREAS Dissolved tool was used to make the composite hot spots KERNEL DENSITY Applied Kernel Density method using point locations to identify active area within the composite hot spot polygons

7 Strategic Operations Objectives/action plans Use of patrol resources Grants Integration of other programs DUI Crash Locations and Driver Address Green=Crash Yellow=Address

8 Most Frequently Traveled Road Segments by Drivers in DUI Crashes Information Sharing and Outreach External Communications Internal Communications Media

9 Monitor, Evaluation, and Adjust Supervision Data collection Updating the plan Strategic planning

10 Outcomes: Crime and Traffic Incidents measured against the threeyear average Robberies in target areas have been declining for almost three years Burglaries and auto thefts have been declining for almost two years Crashes are declining in two-thirds of the target areas 2010 Results CRASH OR CRIME TYPE April December 2010* BURGLARY -28.5% ROBBERY -35.0% MOTOR VEHICLE THEFT -41.4% THEFT FROM MOTOR VEHICLE -10.6% PERSONAL INJURY CRASHES -2.6% TOTAL CRASHES -2.8% TRAFFIC STOPS 15.3% *Combined data from 14 target areas WHAT CAN DDACTS DO FOR YOUR AGENCY? Relationship between crashes and crime Renewed emphasis on traffic safety Flexible approach Accreditation

11 Keys to Success Data and Analysis Planning and Evaluation Flexibility Captain Howard B. Hall Operational Services Section 1747 Merritt Boulevard Baltimore, MD (410) Ms. Emily N. Varga Crime Analysis Section 700 E. Joppa Road Towson, MD (410)