Predicting Homicides in St. Louis City for 2013

1. Predicting Homicides in St. Louis City for 2013 Chad Iseman - Geoff Hickman - Jon Perkins - Yanhui Long - Mustafa Khalili

2. Objective The ability to accurately predict when and where homicides will occur in the City of St. Louis grants us the ability to effectively allocate resources to prevent those crimes. We will use statistical techniques and historical data sets to predict where homicides will occur in the city in 2013.

3. Approach • Collect Data • Obtained 4 years of St. Louis city crime-related data by district • Obtained demographic and socioeconomic data from 2010 census by district • Obtained weather and gas price historical data • Data Analysis • Analyzed data for correlations • Conducted regression analysis to predict the number of district homicides for 2013 • Conclusion • Recommend increased police resource in districts with predicted high homicide counts in 2013

4. Saint Louis City Estimated population of 350,000. Divided into 79 government designated neighborhoods and further divided into 9 districts. Ranked 3rd most dangerous city in 2012 with 1,857 violent crimes per 100,000 people, and 58.7 forcible rapes per 100,000 people.

5. Definitions Homicide is the killing of a human being due to the act or omission of another. Included among criminal homicides are murder and manslaughter. Non-criminal homicides include killing in self-defense, a misadventure like a automobile wreck, or legal government execution. [dictionary.law.com]

6. Why Districts? The City of St. Louis is divided into 9 Police Districts. This makes access to crime data precise and reliable. Using data referencing the 9 districts also allows us to present more meaningful recommendations.

7. Factors We Thought That Affect Crime Gang activity Municipal budget Weather Consumer price index Gasoline prices Sports teams performance Riots Drug use Health Population density Demographics: Marital status Age Race Income Employment status Education level Poverty level Housing (multi or single)

8. Household income Hypothesis: Crime takes place, in large part, due scarcity or resources. Household income attempts to quantify each Districts challenges to provide adequate food, shelter and transportation. Based on 2010 Census

9. Similar to average household income, the poverty rate also attempts to quantify each Districts challenges to provide adequate food, shelter and transportation. Poverty Rate Based on 2010 Census

10. Unemployment Based on 2010 Census

11. Total crimes per person (all crimes) Visually, you can see that the areas with the highest unemployment and poverty, and the lowest household income overlap with crime. Based on 2010 Census

12. Murders - victims per 100 people Based on 2010 Census

13. Homicides vs Temperature

14. Homicides vs Avg. Gas Price

15. Prediction Improvements • Monthly Gang activity by district • Monthly Police budget data by district • Homicide categorization (gang related, vehicular, etc.) • Gun owners by district updated monthly • Average rent/mortgage payment by district • Drug users by district per month • CPI by district by month

16. Prediction Model We used the following factors to construct our homicide prediction model: • Rape incidents • Armed robbery reports • Total aggravated assaults • Temperature • Time of year Total aggravated assaults and armed robbery had the highest correlation with homicides. 2013 Homicide Count Prediction = 114

17. Our Recommendations • Dispatch additional resources to Districts 5,6, and 7 during the second half of the year. • These additional resources can be allocated from Districts 1 and 2 during the time period.

18. Conclusion Our prediction model can be used by police resource managers to ensure that potential homicide hot spots have adequate police officers in the area.