St. Louis Homicide Analysis

Nikolay, Melis, Divya, Ankit

Overview

oThe ProblemoObjectiveoMethodology 1Making sense of Raw dataoMethodology 2Statistical analysis of significant variables

oConclusionoQuestions?

The Problem

oLack of accurate predictions where crime, specifically homicide is likely to occur

oSt. Louis consistently ranks TOP 5 in the most dangerous cities in America every year

oDeliverable: Approach of problem, data considered, and predication for 2013

Objective

oUse Quantitative and Qualitative Data

oCreate 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

oQualitative approach

Using statistical data from government agencies

Logical data analysisFindings of patterns, correlations and trends

Data by St. Louis City Police Districts

District - 1

District - 2

District - 3

District - 4

District - 5

District - 6

District - 7

District - 8

District - 9

0

5

10

15

20

25

30

35

40

45

20052006200720082009201020112012

St. Louis City Police Districts

5, 6, 7

2, 9

1, 3, 4, 8

Elements Considered

oNumber of ChurchesoNumber of Hospitals and Universities

oNumber of Bars and Restaurants

oNumber of High SchoolsoNumber of Community Centers

Community Centers

5, 6, 7

2, 9

1, 3, 4, 8

STL Crimes Graph 1985-2010

1985

1986

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2005

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0

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2000

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7000

8000

9000

Murder and nonnegligent ManslaughterForcible rapeRobberyAggravated assault

STL Crimes Correlation 1985-2008

 

Murder and nonnegligent Manslaughter Forcible rape Robbery

Murder and nonnegligent Manslaughter 1

Forcible rape 0.651 1

Robbery 0.847 0.489 1

Aggravated assault 0.772 0.655 0.900

Arkan

sas

Illin

ois

Iowa

Kans

as

Miss

ouri

Oklah

oma

Tenn

esse

e

0

200

400

600

800

1000

1200

1400

1960

1965

1970

1975

1980

1985

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1995

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2005

2010

  Arkansas Illinois Iowa Kansas MissouriOklahoma

Arkansas 1.000Illinois 0.787 1.000Iowa 0.379 0.667 1.000Kansas 0.627 0.738 0.534 1.000Missouri 0.728 0.862 0.682 0.665 1.000Oklahoma 0.545 0.616 0.570 0.626 0.626 1.000Tennessee 0.766 0.844 0.537 0.711 0.754 0.667

Neighbor States

Yearly Data St. Louis City Police Districts

2005 2006 2007 2008 2009 2010 2011 20120

5

10

15

20

25

30

35

40

45

District - 1 District - 2 District - 3 District - 4 District - 5 District - 6 District - 7 District - 8 District - 9

28 Years Back

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

0

5000

10000

15000

20000

STL Violent crime total

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

0100200300 267

74

STL Murder and nonnegligent Man-slaughter

50 Years Back

1960

1962

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1974

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1980

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2010

0

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600

700

MO Murder and nonnegligent Manslaughter

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

05000

1000015000200002500030000350004000045000

MO Violent crime total

50 Years Back

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

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1996

1998

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0

5000

10000

15000

20000

25000

30000

USA Murder and nonnegligent Manslaughter

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

0

500000

1000000

1500000

2000000

2500000

USA Violent crime total

Conclusion Part 1

oOver time crime rate become stable and does not fluctuate a lot Pattern valid for

consideration of local dataoPrediction for the number of

crimes will be in the 100 – 120 range

Methodology Part 2oQuantitative approach –

Regression AnalysisMultiple 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)=ß₀+ß₁X₁+ ß₂X₂+........+ ßpXp

Regression Analysis in Excel cont.

Data used in regression analysis for District 1

 Year 

Y X₁ X₂ X₃ X₄ X₅ X₆

Homicides in District 1

Unemployed people Gun Sales

Violent crime total

Forcible Rape Robbery 

Aggravated assault

2005 10 21,798 1,693 8,323 276 2,965 4,951

2006 6 21,938 1,693 8,605 337 3,147 4,992

2007 9 25,608 1,192 7,654 255 2,761 4,500

2008 10 28,182 1,907 7,383 237 2,634 4,345

2009 9 41,364 1,795 7,353 250 2,721 4,239

2010 10 39,592 2,165 6,205 188 2,125 3,748

2011 12 37,652 1,929 5,951 188 2,127 3,523

2012 9 26,022 2,000 5,660 199 1,777 3,571

Regression Analysis in Excel cont.Summary output of regression analysis for District 1

Regression StatisticsMultiple R 0.992003547R Square 0.984071038Adjusted R Square 0.888497266Standard Error 0.562661639

Observations 8

ANOVA  df SS MS F Significance F

Regression 6 19.55841188 3.25973531 10.296455 0.234142751Residual 1 0.31658812 0.31658812

Total 7 19.875

  Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept 22.62608366 5.756744788 3.93036073 0.1586096 -50.52029421 95.7724615 -50.520294 95.77246153X Variable 1 -0.00014704 7.19378E-05 -2.04398533 0.2896638 -0.001061096 0.00076702 -0.0010611 0.000767016X Variable 2 0.00205666 0.001149347 1.78941553 0.3244253 -0.012547184 0.0166605 -0.0125472 0.016660504X Variable 3 -0.037242854 0.016387103 -2.272693 0.2638868 -0.245460739 0.17097503 -0.2454607 0.170975031X Variable 4 -0.055540451 0.015830847 -3.50836887 0.1767699 -0.256690434 0.14560953 -0.2566904 0.145609532X Variable 5 0.048355016 0.017336121 2.78926386 0.2191516 -0.171921289 0.26863132 -0.1719213 0.268631322

X Variable 6 0.034117754 0.01701079 2.00565369 0.294449 -0.182024826 0.25026033 -0.1820248 0.250260333

Regression Analysis in Excel cont.

Districts R Square Significance F1 0.9841 0.22 0.9373 0.53 0.9995 04 0.9722 0.35 0.9945 0.16 0.8738 0.67 0.9862 0.28 0.9967 0.19 0.9233 0.5

Regression Analysis in Excel cont.

The numbers of homicides by districts and years

District 1 District 2 District 3 District 4 District 5 District 6 District 7 District 8 District 9 Total

2005 10 2 9 5 22 31 19 29 4 131

2006 6 1 16 9 17 29 21 22 3 124

2007 9 0 12 8 27 25 32 9 4 126

2008 10 2 11 12 35 35 36 16 4 161

2009 9 2 13 13 25 27 29 17 8 143

2010 10 4 10 9 30 39 21 14 7 144

2011 12 2 15 10 17 27 15 13 2 113

2012 9 1 13 6 18 30 14 6 7 104

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 saleso Same as 2012

o Total number of violent crimes in St. Louis Cityo Total number of forcible rapes in St. Louis Cityo Total number of robery in St. Louis Cityo 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 1 District 2 District 3 District 4 District 5 District 6 District 7 District 8 District 9 Total

2005 9.891 2.15 9.03 4.77 22.3 31.8 18.5 28.8 3.69 131

2006 6.179 0.75 16 9.38 16.6 27.6 21.8 22.4 3.51 124

2007 9.131 -0.18 12 8.28 26.7 24 32.6 9.26 4.37 126

2008 9.886 2.16 11 11.8 35.3 35.9 35.5 15.8 3.68 161

2009 8.703 2.41 13.1 12.4 25.7 29.3 27.7 16.4 7.16 143

2010 10.351 3.52 9.91 9.75 29.2 36.3 22.6 14.7 7.99 144

2011 12.031 1.96 15 10.1 16.9 26.8 15.1 13.1 2.09 113

2012 8.827 1.24 13 5.63 18.4 31.3 13.2 5.66 6.51 104

2013 13.742 1 16.7 10.6 19 28.9 18.9 14.4 0 123

Regression Analysis in Excel cont.

Differences between the numbers of real homicides and predicted homicides by districts and years

District 1 District 2 District 3 District 4 District 5 District 6 District 7 District 8 District 9

2005 -0.11 0.15 0.03 -0.23 0.25 0.84 -0.49 -0.21 -0.31

2006 0.18 -0.25 -0.05 0.38 -0.41 -1.39 0.8 0.35 0.51

2007 0.13 -0.18 -0.03 0.28 -0.3 -1.01 0.58 0.26 0.37

2008 -0.11 0.16 0.03 -0.24 0.26 0.88 -0.51 -0.22 -0.32

2009 -0.3 0.41 0.08 -0.63 0.68 2.3 -1.32 -0.58 -0.84

2010 0.35 -0.48 -0.09 0.75 -0.81 -2.72 1.56 0.69 0.99

2011 0.03 -0.04 -0.01 0.07 -0.07 -0.24 0.14 0.06 0.09

2012 -0.17 0.24 0.04 -0.37 0.4 1.33 -0.77 -0.34 -0.49

Regression Analysis in Excel cont.

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 201460

80

100

120

140

160

180

131124 126

161

143 144

113104

123

Total Homicides & Predicted Total Homicides

Total Homicides Predicted Total Homicides

Nu

mb

er

of

Hom

icid

es

Conclusion Part 2

oPredicted murders : 123

oRegression Analysis

oSample Size, Accuracy

oDifferent methods

Summary•The problem

•Using past data

•Developed a method

•Determined factors

•Used Regression analysis

•Output

Resources

Uniform Crime Reporting Statistics- UCR Data Online http://www.ucrdatatool.gov/

The Metropolitan Police Department, City of St. Louis http://www.slmpd.org/crime_stats.html

Data collected from census http://www.stlcin.missouri.org/citydata/newdesign/index.cfm

Census.gov

Google

Questions?