St. Louis Homicide Analysis Nikolay, Melis, Divya, Ankit

Overview o The Problem o Objective o Methodology 1  Making sense of Raw data o Methodology 2  Statistical analysis of significant variables o Conclusion o Questions?

The Problem o Lack of accurate predictions where crime, specifically homicide is likely to occur o St. Louis consistently ranks TOP 5 in the most dangerous cities in America every year o Deliverable: Approach of problem, data considered, and predication for 2013

Objective o Use Quantitative and Qualitative Data o Create a model that can predict homicides for the current year and location o Increase the rate of prevention, by giving St. Louis police accurate data to strategically deploy their limited resources

Methodology Part 1 o Qualitative approach  Using statistical data from government agencies  Logical data analysis  Findings of patterns, correlations and trends

Data by St. Louis City Police Districts

St. Louis City Police Districts 5, 6, 7 2, 9 1, 3, 4, 8

Elements Considered o Number of Churches o Number of Hospitals and Universities o Number of Bars and Restaurants o Number of High Schools o Number of Community Centers District # of schools

Community Centers 5, 6, 7 2, 9 1, 3, 4, 8

STL Crimes Graph

STL Crimes Correlation Murder and nonnegligent Manslaughter Forcible rapeRobbery Murder and nonnegligent Manslaughter 1 Forcible rape Robbery Aggravated assault

ArkansasIllinoisIowaKansasMissouri Oklaho ma Arkansas1.000 Illinois Iowa Kansas Missouri Oklahoma Tennessee Neighbor States

Yearly Data St. Louis City Police Districts

28 Years Back

50 Years Back

Conclusion Part 1 o Over time crime rate become stable and does not fluctuate a lot  Pattern valid for consideration of local data o Prediction for the number of crimes will be in the 100 – 120 range

Methodology Part 2 o Quantitative approach – Regression Analysis Multiple regression model: o Dependent Variable  Total number of homicides in each district o Indipendent Variables  Number of unemployed people  Number of gun sales  Total number of violent crimes in St. Louis City  Total number of forcible rapes in St. Louis City  Total number of robery in St. Louis City  Total number of aggravated assault in St. Louis City

Regression Analysis in Excel Multiple regression equation E(y)=ß ₀+ß₁X₁+ ß₂X₂ ßpXp

Regression Analysis in Excel cont. Data used in regression analysis for District 1 Year YX₁X₂X₃X₄X₅X₆ Homicides in District 1 Unemployed peopleGun Sales Violent crime total Forcible RapeRobbery Aggravat ed assault ,7981,6938, ,9654, ,9381,6938, ,1474, ,6081,1927, ,7614, ,1821,9077, ,6344, ,3641,7957, ,7214, ,5922,1656, ,1253, ,6521,9295, ,1273, ,0222,0005, ,7773,571

Regression Analysis in Excel cont. Summary output of regression analysis for District 1 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations8 ANOVA dfSSMSFSignificance F Regression Residual Total CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0% Intercept X Variable E X Variable X Variable X Variable X Variable X Variable

Regression Analysis in Excel cont. DistrictsR SquareSignificance F

Regression Analysis in Excel cont. The numbers of homicides by districts and years District 1District 2District 3District 4District 5District 6District 7District 8District 9Total

Regression Analysis in Excel cont. How we predicted 2013 values of independent variables! o Number of unemployed people o Population of 2012 and unemployment rate of December 2012 o Number of gun sales o Same as 2012 o Total number of violent crimes in St. Louis City o Total number of forcible rapes in St. Louis City o Total number of robery in St. Louis City o Total number of aggravated assault in St. Louis City o All four by using exponential smoothing analysis tool in Excel

Regression Analysis in Excel cont. Predicted numbers of homicides by districts and years District 1District 2District 3District 4District 5District 6District 7District 8District 9Total

Regression Analysis in Excel cont. Differences between the numbers of real homicides and predicted homicides by districts and years District 1District 2District 3District 4District 5District 6District 7District 8District

Regression Analysis in Excel cont.

Conclusion Part 2 o Predicted murders : 123 o Regression Analysis o Sample Size, Accuracy o Different methods

Summary The problem Using past data Developed a method Determined factors Used Regression analysis Output

Resources Uniform Crime Reporting Statistics- UCR Data Online The Metropolitan Police Department, City of St. Louis Data collected from census fm fm Census.gov Google

Questions?