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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
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
1000
2000
3000
4000
5000
6000
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
1990
1995
2000
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
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
100
200
300
400
500
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
1994
1996
1998
2000
2002
2004
2006
2008
2010
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
Questions?