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BI Plan

John Powell, Sri Murali , Ying Chen, Scott Weber. BI Plan. Our Approach. Collect data from various sources showing factors relating to homicides in St. Louis Aggregate them into a data warehouse

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BI Plan

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  1. John Powell, Sri Murali, Ying Chen, Scott Weber BI Plan

  2. Our Approach • Collect data from various sources showing factors relating to homicides in St. Louis • Aggregate them into a data warehouse • Build analytic models that will predict where homicide will happen and support our decision as to where and when police should focus their efforts • Build dashboard/user interface so multiple levels of police can interact with tool

  3. Our Approach • Use Piaget theory so that people in different roles can readily enter/view the data they need • Patrol officers will view maps integrated with real-time crime data and will be given patrol routes accordingly • System can support prediction analytics through statistical model

  4. What are we analyzing? • Sample demographic data in warehouse

  5. Model Framework

  6. Model Framework Real-Time • Assaults, riots, theft, domestic abuse • Data collected from social media networks and other law enforcement agencies Historical • Past years’ crime data on a neighborhood basis • Find factors that correlate to murders using supplemental homicide report

  7. Model Framework Demographics • Males age 18-34, poverty rate, divorce rate, single parent homes, previous offenders This year’s data • See how past crime trends are matching up to this year • Compare this year to other years, see if correlating factors are still relevant

  8. Demographic Analysis

  9. Current Year Predictions Weighted Moving Averages

  10. Current Year Statistics

  11. Dashboard for Patrol Officers • Map of Baden • Shows several crimes including assault, quality of life, vehicle theft, breaking and entering • Predictive tool analyzes other crimes surrounding homicides and finds trends leading to homicides • Time, day, moon cycle, arrests, weather

  12. What decisions are we supporting? • Which demographic/crime factors can be used to predict homicides? • Where, when, and how many officers are we dispatching in order to mitigate homicides? • Is the force properly staffed/equipped/trained? • Are other crime reducing programs available and have they worked in the past? • Are precincts properly divided?

  13. Features of the system • System will provide operational reports • Acts as an extensive data warehouse of demographic, economic, and historical statistics • Data mining capabilities to predict relationships between selected variables; finds new trends relating to homicides • Learns which trends are relevant and eliminates invalid assumptions • Implement procedures based on these findings and monitor the effectiveness of these implementations

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