Saint Louis Police Department BI Implementation Proposal Ben Shailesh Spencer Will
Why provide BI solutions for STLPD? o To help prevent and solve crimes Provide solutions to… o Store and manage data o Analyze data o Present data (dashboards) Then, they can act on that data o Help make more effective decisions and save resources, where possible Overview
Number of Patrolmen per Police District - fluctuates as crime increases in any one of the 9 districts. Patrol Routes within each Police District General Placement of Police Officers within each district. Increase funds Allocation based on districts with the most crime. Increase funds for youth group efforts to fight drug and gang activity Decisions Supported By BI
Types of crime per area Crime by Gang or Non Gang affiliation, if gang affiliated which gain committed crime Demographics about individual crime perpetrators and the victims 911 call/dispatching data Linking FBI data to local crime data for more accurate predictions Social networking data Police patrols and arrests Weather forecasting Suggested Data to be Maintained
Crime patterns/trends Location of crimes Predictive analysis for proactive policing, costs, staffing, crime predictions Equipment/training costs per unit and the projected reduction in crime for the patrol area gained by this Kinds of Analytics to Provide
Patrol Dashboard EIS Dashboard for Chief of Police or other police department executive decision makers Same system with different login in access depending on the individual accessing the system Dashboards
Patrol Dashboard
Hotspot Map Drill Down Capability for detailed info On Hover over – o Neighborhood info appears Real Time Updating of map data Patrol Dashboard
On Drill Down Actual Crime Data Appears
Ability of Patrol Officers and Chief of Police to Leave Notes
Executive Dashboard
St. Louis Police Department under fiscal pressure due to rising pension costs Operating budget decreased by $10 million between 2012 and 2013 According to Program Budget (Chief of Police division, which includes IT) the budget for FY2013 was $12.5m, up from $10.6 in FY2012 Cost Considerations
Based on these numbers, we set an upfront budget of $60k, with an annual budget of $10k for enhancements/maintenance - 80 hours * $100/hr -> research hours * $100/hr -> data mining hours * $100/hr -> programming - $5,000 -> hosting/equipment - $12,000 -> reserve for add'l costs Cost Considerations (cont'd)
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