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

2. 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?

3. 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

4. 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

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

6. Data by St. Louis City Police Districts

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

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 11 22 33 42 51 60 72 81 91

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

10. STL Crimes Graph 1985-2010

11. STL Crimes Correlation 1985-2008 Murder and nonnegligent Manslaughter Forcible rapeRobbery Murder and nonnegligent Manslaughter 1 Forcible rape 0.6511 Robbery 0.8470.4891 Aggravated assault 0.7720.6550.900

12. ArkansasIllinoisIowaKansasMissouri Oklaho ma Arkansas1.000 Illinois0.7871.000 Iowa0.3790.6671.000 Kansas0.6270.7380.5341.000 Missouri0.7280.8620.6820.6651.000 Oklahoma0.5450.6160.5700.626 1.000 Tennessee0.7660.8440.5370.7110.7540.667 Neighbor States

13. Yearly Data St. Louis City Police Districts

14. 28 Years Back

15. 50 Years Back

17. 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

18. 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

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

20. 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 20051021,7981,6938,3232762,9654,951 2006621,9381,6938,6053373,1474,992 2007925,6081,1927,6542552,7614,500 20081028,1821,9077,3832372,6344,345 2009941,3641,7957,3532502,7214,239 20101039,5922,1656,2051882,1253,748 20111237,6521,9295,9511882,1273,523 2012926,0222,0005,6601991,7773,571

21. Regression Analysis in Excel cont. Summary output of regression analysis for District 1 Regression Statistics Multiple R0.992003547 R Square0.984071038 Adjusted R Square0.888497266 Standard Error0.562661639 Observations8 ANOVA dfSSMSFSignificance F Regression619.558411883.2597353110.2964550.234142751 Residual10.31658812 Total719.875 CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0% Intercept22.626083665.7567447883.930360730.1586096-50.5202942195.7724615-50.52029495.77246153 X Variable 1-0.000147047.19378E-05-2.043985330.2896638-0.0010610960.00076702-0.00106110.000767016 X Variable 20.002056660.0011493471.789415530.3244253-0.0125471840.0166605-0.01254720.016660504 X Variable 3-0.0372428540.016387103-2.2726930.2638868-0.2454607390.17097503-0.24546070.170975031 X Variable 4-0.0555404510.015830847-3.508368870.1767699-0.2566904340.14560953-0.25669040.145609532 X Variable 50.0483550160.0173361212.789263860.2191516-0.1719212890.26863132-0.17192130.268631322 X Variable 60.0341177540.017010792.005653690.294449-0.1820248260.25026033-0.18202480.250260333

22. Regression Analysis in Excel cont. DistrictsR SquareSignificance F 10.98410.2 20.93730.5 30.99950 40.97220.3 50.99450.1 60.87380.6 70.98620.2 80.99670.1 90.92330.5

23. Regression Analysis in Excel cont. The numbers of homicides by districts and years District 1District 2District 3District 4District 5District 6District 7District 8District 9Total 200510295223119294131 200661169172921223124 20079012827253294126 2008102111235 36164161 20099213 252729178143 2010104 9303921147144 20111221510172715132113 20129113618301467104

24. 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

25. Regression Analysis in Excel cont. Predicted numbers of homicides by districts and years District 1District 2District 3District 4District 5District 6District 7District 8District 9Total 20059.8912.159.034.7722.331.818.528.83.69131 20066.1790.75169.3816.627.621.822.43.51124 20079.131-0.18128.2826.72432.69.264.37126 20089.8862.161111.835.335.935.515.83.68161 20098.7032.4113.112.425.729.327.716.47.16143 201010.3513.529.919.7529.236.322.614.77.99144 201112.0311.961510.116.926.815.113.12.09113 20128.8271.24135.6318.431.313.25.666.51104 201313.742116.710.61928.918.914.40123

26. 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 9 2005-0.110.150.03-0.230.250.84-0.49-0.21-0.31 20060.18-0.25-0.050.38-0.41-1.390.80.350.51 20070.13-0.18-0.030.28-0.3-1.010.580.260.37 2008-0.110.160.03-0.240.260.88-0.51-0.22-0.32 2009-0.30.410.08-0.630.682.3-1.32-0.58-0.84 20100.35-0.48-0.090.75-0.81-2.721.560.690.99 20110.03-0.04-0.010.07-0.07-0.240.140.060.09 2012-0.170.240.04-0.370.41.33-0.77-0.34-0.49

27. Regression Analysis in Excel cont.

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

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

30. Resources Uniform Crime Reporting Statistics- UCR Data Online http://www.ucrdatatool.gov/ http://www.ucrdatatool.gov/ The Metropolitan Police Department, City of St. Louis http://www.slmpd.org/crime_stats.html http://www.slmpd.org/crime_stats.html Data collected from census http://www.stlcin.missouri.org/citydata/newdesign/index.c fm http://www.stlcin.missouri.org/citydata/newdesign/index.c fm Census.gov Google

31. Questions?