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St. Louis City Crime Analysis 2015 Homicide Prediction Presented by: Kranthi Kancharla Scott Manns Eric Rodis Kenneth Stecher Sisi Yang.

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Presentation on theme: "St. Louis City Crime Analysis 2015 Homicide Prediction Presented by: Kranthi Kancharla Scott Manns Eric Rodis Kenneth Stecher Sisi Yang."— Presentation transcript:

1 St. Louis City Crime Analysis 2015 Homicide Prediction Presented by: Kranthi Kancharla Scott Manns Eric Rodis Kenneth Stecher Sisi Yang

2 Project Objectives  Generate a predictive model to forecast 2015 homicides in St. Louis City  Divide St. Louis City into geographic subsets to forecast homicide rates by area  Present findings to St. Louis City Police Chief for further use in identifying critical areas to better design & implement strategic measures

3 Methodology  Gather data from St. Louis City Police on homicides over the past 5 years from 2010 to 2014  Generate alternative variables that can influence whether an area is likely to have high or low homicide rates  Identify sources that have data available for the variables selected covering St. Louis City  Adjust data to fit geographic subsets of St. Louis City  Select a model that best incorporates these variables to a predictable outcome

4 Background  St. Louis ranked 4 th most dangerous city in the US in 2014  38 homicides per 100K people  St. Louis has higher homicide rates than similar cities

5 Variables Selected  Education (e)  Percentage of the population with a High School Education  Median age (ma)  Income (i)  Poverty level (p)  Percentage of vacant homes (v)  Race  Percentage of the population that is white (w)  Percentage of the population that is African American (aa)

6 Data Collected  Searching available data sources, identified the US Census Bureau as the source with data that can be best segmented among areas  Selected 21 St. Louis City zip codes as subsections due to the availability of representative data for the areas that can be applied to homicides in those areas  Data availability for neighborhoods not available and there was challenges accurately converting data into neighborhood classification

7 Data Collected  Using average data of previous 4 years data to predict 2015 due to unavailability of 2014 data  Tested the effects of “Year” using dummy variables Dummy variables for “Year” Insignificant in various scenarios SUMMARY OUTPUT Regression Statistics Multiple R0.137813923 R Square0.018992677 Adjusted R Square-0.017795097 Standard Error5.943583976 Observations84 ANOVA dfSSMSFSignificance F Regression354.7142857118.238095240.5162768750.672255836 Residual802826.09523835.32619048 Total832880.809524 CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0% Intercept7.8571428571.2969963566.0579529184.26885E-085.27603785210.438247865.27603785210.43824786 D2013-1.2857142861.834229836-0.7009559330.485364402-4.9359479892.364519418-4.9359479892.364519418 D2012-1.9523809521.834229836-1.0644145640.290343155-5.6026146561.697852751-5.6026146561.697852751 D2011-21.834229836-1.0903758950.278820968-5.6502337041.650233704-5.6502337041.650233704

8 Model Building  Ran regression models on each individual variable to identify most significant relationships over 84 samples of homicides per zip code per year  Significant individual relationships  Education (e)  Income (i)  Poverty (p)  Race (w) (aa)

9 Alternative Variables  Tested 20 multiple regression combinations to predict homicides based upon variables from the previous year 1. Education, Income, Poverty, Vacant Homes, Age & Race  R-square- 0.598759 2. Education, Income, Vacant Homes, & Age  R-square- 0.578521 3. Education, Income & Age  R-square- 0.568169

10 Application  Selected alternative 1 due to the most significant R Square Y=29.06+(e*-32.25)+(i*-.00011)+(p*-.52)+(ma*.51)+(aa*-8.96)+(w*-14.4) Regression Statistics Multiple R0.773795071 R Square0.598758813 Adjusted R Square0.561802387 Standard Error3.899897829 Observations84 ANOVA dfSSMSFSignificance F Regression71724.91009246.415727116.201751397.37691E-13 Residual761155.89943415.20920308 Total832880.809524 CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0% Intercept29.0558960315.17144411.9151700950.059233387-1.1606536359.2724457-1.1606536359.2724457 Education-32.247926999.515668292-3.3889292910.001114882-51.20002289-13.2958311-51.20002289-13.2958311 Income-0.0001055096.79804E-05-1.5520458540.12480582-0.0002409032.9886E-05-0.0002409032.9886E-05 Poverty-0.5166614794.424811453-0.1167646320.907354582-9.3294372538.296114295-9.3294372538.296114295 % Vacant Homes4.6840960843.665801381.2777822910.205215444-2.61698011711.98517229-2.61698011711.98517229 Median Age0.5131671130.1393016393.6838555330.0004280160.2357238540.7906103710.2357238540.790610371 % African American-8.95561452713.96115838-0.6414664370.523149587-36.7616712718.85044221-36.7616712718.85044221 % White-14.4011739715.56259025-0.9253712740.357701892-45.3967587116.59441077-45.3967587116.59441077

11 Model Application  Applied coefficients to 2014 data average for zip codes Zip CodeEducation IncomePoverty% Vacant HomesMedian Age% African American% White 6310191.9% $ 51,79818.2%32.1%32.049.0%47.3% 6310286.4% $ 53,8819.0%25.3%36.445.6%48.9% 6310387.2% $ 33,73923.8%32.5%30.641.6%51.5% 6310474.7% $ 44,43729.0%18.0%31.351.8%45.3% 6310669.2% $ 15,02753.6%20.9%24.794.8%1.7% 6310772.9% $ 26,73838.6%35.7%34.787.5%10.2% 6310889.4% $ 30,40531.6%15.4%29.935.8%54.0% 6310991.9% $ 59,4999.5%8.5%37.46.5%88.8% 6311086.1% $ 38,61021.5%20.2%32.938.1%53.8% 6311175.2% $ 31,63730.5%20.3%34.634.8%60.3% 6311281.1% $ 30,22632.5%26.1%32.170.8%23.8% 6311377.8% $ 24,78837.3%32.2%36.993.8%1.7% 6311575.4% $ 26,04533.2%23.8%35.298.2%3.3% 6311681.3% $ 41,82021.3%13.6%35.321.3%66.3% 6311877.7% $ 28,88732.5%26.4%31.452.6%37.4% 6311995.8% $ 67,4707.9%6.2%39.79.3%85.5% 6312073.1% $ 24,19238.0%28.8%33.993.0%2.3% 6313680.8% $ 32,05726.6%16.2%33.787.1%9.0% 6313784.6% $ 35,51423.4%11.2%34.075.2%22.0% 6313989.4% $ 46,74711.8%10.3%37.88.8%84.4% 6314777.8% $ 30,40028.7%18.3%38.791.1%5.2% Y=29.06+(e*-32.25)+(i*-.00011)+(p*-.52)+(ma*.51)+(aa*-8.96)+(w*-14.4)

12 Prediction for 2015 Forecast Total – 137 Homicides

13 Conclusion  2015 St. Louis City Forecast – 137 homicides  Additional police resources recommended in zip codes 63106, 63107, 63113, 63115, 63120 & 63147  There is a lot of randomness & variability in actual homicides that are unable to be related to available data

14 Sources  http://www.slmpd.org/Crimereports.shtml  http://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml  http://www.marketwatch.com/story/the-10-most- dangerous-cities-in-america-2014-11-20

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