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Enhancing Crime Management in St. Louis with Business Intelligence System

This project aims to improve data management for crime reduction in St. Louis by utilizing various data sources such as census data, historical crime data, and social media feeds. The BI system developed provides decision-making support for the St. Louis Police Department by analyzing trends, optimizing resource deployment, and enhancing policing strategies based on real-time and historical data.

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Enhancing Crime Management in St. Louis with Business Intelligence System

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  1. Group A: Dan Diecker, Uzair Bhatti, Puji Bandi, Latoya Lewis

  2. Scope of Project Objective • The motivation behind this project is to come up with effective data management to help curb violent crime in the city of St. Louis. Demonstrating • Data sources used • Data maintained • Use case examples for Management, Police Department, and Patrol Officers • Finally overview of the BI system’s added value

  3. Scope of Project Users: • St. Louis Police Department •Captains and Managers •Lieutenants and Detectives •Patrol Officers Value provided: • We hope that this project will support the decision making process of the St. Louis police department on their mission to curb homicides in St. Louis city.

  4. Scope of Project Examples of the support for decision making include: • How to strategically place their man power •Where to deploy patrol, mounted, or bike officers • Patrolling frequencies (differs according to the area) •Determine trends in criminal activity; likelihood of time and location • Increased use of social media data to locate crime hot spots •Track unfolding events in real-time; be informed of large, disruptive crowds or planned gatherings that could turn violent

  5. Data Sources U.S. Census Data • American Community Survey • Decennial Census Twitter & Social Media Historical Crime Data • FBI Crime Statistics • STLPD Data Data variables being maintained • Mean Household income • Education level • Home Vacancy rates rented / owned • Unemployment rate • Data aggregated by district to neighborhood

  6. Maintaining Data Census Data • Loaded as it is released to build a demographic model of the neighborhood Historical Crime Data • Data is entered as calls come in • Tracks location, crime, and time • Can flag similar crimes, e.g., vehicle or weapon used Twitter & Social Media • Monitored Real-time for Keywords • Mines text for location, can display potential trouble spots before any 911 calls are made

  7. System Structure • System can be hosted externally or maintained in- house • Based on STLPD requirements, hardware can be cloud-sourced or maintained in-house

  8. Use Case: Management Decision-makers can view historical trends in crime for all of St. Louis • High-level summary • Can view time-specific events; festivals, parades, etc. • Can drill-down and view current resources assigned to specific neighborhoods

  9. Use Case: Neighborhood Office Summary of a small geographic area – useful in determining local hot spots and trends • Past crime trends • Can view crimes that are similar • Can see where patrol officers are assigned Departure from Mean: Complaints a Year Ago 0% -10% +10% Displaying: Homicides Filter Home

  10. Use Case: Patrol Officer Dashboard in Patrol Car • Dashboard can display recent crimes in the area, along with a description of the suspect • Can be informed of events from Social Media – can stay close to parties or gatherings before anything gets out of hand Pine Lawn 2 Critical Alerts • Church function at Stratford Ave and Jennings Station Rd – Possible Gang Activity Map Details Related • Potential repeat offender in area: 8 copper thefts in past week Map Details Related

  11. System Constraints • Supports near real-time analysis of crime • Not real-time; not all data can be maintained in real-time due to difficulty in collecting the information • Predictive capabilities are near-future • Can analyze weeks to a few months into the future, not years • On-going maintenance and support will include a cost • Support and IT Personnel • Training for Users • Investment in hardware (outsourced or internal)

  12. Wrap-up Overview • As BI consultants to the City of St. Louis Police Department we conclude our presentation by discussing the following. • In an effort to better facilitate the Police Department’s decision making processes, we proposed a BI system using available and easily-maintained data • The BI dashboard developed by Group A has provides support to different levels of decision-makers in the STLPD • The BI system provides increased responsiveness to crime trends, assists in optimizing the deployment of limited departmental resources, and supports analysis of different policing strategies

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