BI PLANJohn Powell, Sri Murali, Ying Chen, Scott Weber
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
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
What are we analyzing?
Sample demographic data in warehouse
Baden
J.V. Wells-Goodfellow
The GreatVille
Gun/drug/gang arrests (weekly)
21 23 14 16
% living in poverty
79% 68% 62% 73%
% adults w/ h.s. diploma
65% 84% 76% 87%
% single parent homes
37% 45% 52% 41%
Avg. police presence(daily)
5 3 7 5
2011 homicides 10 9 5 5
Model Framework
Real-Time Historical
Demographics
This Year’s Data
Analytics
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
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
Demographic Analysis
Current Year Predictions
Weighted Moving Averages
Current Year Statistics
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
What decisions are we supporting?1. Which demographic/crime factors can be
used to predict homicides?2. Where, when, and how many officers are
we dispatching in order to mitigate homicides?
3. Is the force properly staffed/equipped/trained?
4. Are other crime reducing programs available and have they worked in the past?
5. Are precincts properly divided?
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