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