Uzair BhattiDan DieckerPuji BandiLatoya Lewis

IS 6833ANALYTICS

ASSIGNMENTPREDICTING

HOMICIDE RATE IN ST. LOUIS CITY FOR

2013

Homicide is killing of one human being by another. Homicide is a general term; it includes murder, manslaughter, and other criminal homicides as well as noncriminal killings. Murder is the crime of intentionally and unjustifiably killing another. In the U.S., first-degree murder is a homicide committed with premeditation or in the course of a serious felony.

The first type encompasses any homicide resulting from an intentional act done without malice or premeditation and while in the heat of passion or on sudden provocation.

The second type is variously defined in different jurisdictions but often includes an element of unlawful recklessness or negligence.

Noncriminal homicides include killings committed in defense of oneself or another and deaths resulting from accidents caused by persons engaged in lawful acts.

DEFINITION

2008: 167 Total Murder for the Year 2009: 143 Total Murder for the Year 2010: 144 Total Murder for the Year 2011: 113 Total Murder for the Year2012: 113 Total Murder for the Year

St. Louis is ranked fourth dangerous city in the US for Murders

HOMICIDE OVERVIEW IN ST. LOUIS

Data Segmentatio

n

• We collected data by neighborhoods and districts• St. Louis city consists of 9 districts, 79 neighborhoods, 3 Patrol Zones

Data analysis

• Formulated four variables that correlate with the homicide rates in neighborhoods and districts

• Analyze and depict the relation between these four variables and the homicide occurrence

Variables

• Organized data in excel using pivots tables • Analyze data based on year, month and zip codes• Built a regression analysis from all the data collected to predict the murder rate

for 2013

Conclusion

• The ultimate goal is to predict number of homicides and the determined location of unlawful homicides in St. Louis city for 2013.

OUR APPROACH/OBJECTIVE

MURDER FOR PAST FOUR YEARS

2008 2009 2010 2011 20120

20

40

60

80

100

120

140

160Murder for the Past Four Years

Total

MURDER DISTRIBUTION BY ZIP CODE

Total0

20

40

60

80

100

120

631016310263103631046310663107631086310963110631116311263113631156311663118631206313963147

MURDERS BY MONTH

1 2 3 4 5 6 7 8 9 10 11 120

10

20

30

40

50

60

70

Total

Total

Group A as a Team considered many variables to determine potential relationships to homicide.

Due to randomness of Homicides, variables only help determine potential relationships but are no means of causality

Variables Time

Year, Month, Education (High School Diploma) Home / Renter vacancy Income Unemployment Age / Gender Race Location: Districts, Zip code, Neighborhoods, and Streets Poverty Drugs Gangs/ Violence

VARIABLES CONSIDERED

Variables used to develop the Regression Model

Median Household Income

Determined median household income by Zip code

Educational

Determined by average high school graduation rate by Zip code

Vacancy percentage of Rented/Owned Houses

Determined average home vacancy by Zip code

Unemployment Rate

VARIABLES USED TO PREDICT NUMBER OF HOMICIDES AND

LOCATION

Based on available data we have chosen to use regression

model to establish a correlation between data gathered on

St. Louis city and the number of homicides

Variables used have established potential relationship with

number of homicides. (Source 5)

Used regression analysis to show the relationship between

significant variables, and build regression model to predict

future homicides

PREDICTION APPROACH

Inconsistent data availability

Data compatibility issues converting zip codes to districts, districts to neighborhoods

Inadequate data for the required variables

Lack of current data

Each department collects data based on different geographic specifications

CONSTRAINTS FACING THE MODEL

REGRESSION OUTPUT WITH ALL VARIABLES

The regression output indicates a correlation for number of homicides with fluctuations in High school graduation rates

Correlation of homicides to Mean Income, Unemployment and number of vacant dwellings is weak

Standard Error 5.116553Observations 95

ANOVA  df SS MS F Significance F

Regression 4 1442.301 360.5751634 13.77338982 0.0000000083968Residual 90 2356.12 26.17911554Total 94 3798.421

 Coefficien

tsStandard Error t Stat P-value Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept 56.33116 12.89684 4.367826049 3.34948E-05 30.70933607 81.95299 30.70934 81.95299Mean Household Income -8E-05 0.000102 -0.78558752 0.434172486 -0.000281923 0.000122 -0.00028 0.000122Graduation Rate -0.38253 0.167728 -2.280654171 0.024929887 -0.715751242 -0.04931 -0.71575 -0.04931Unemployment Rate -0.50587 0.457429 -1.105891241 0.271721003 -1.414628454 0.402896 -1.41463 0.402896Vacancy -0.48516 0.269212 -1.802154984 0.074868842 -1.019998119 0.049675 -1.02 0.049675

REGRESSION OUTPUT WITH DROPPED VARIABLES

More accurate estimate of homicide numbers using stronger correlating data:

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.59396R Square 0.352788Adjusted R Square 0.345829Standard Error 5.141423

Observations 95

ANOVA

  df SS MS FSignificance 

FRegression 1 1340.038 1340.038 50.69327 2.23E-10Residual 93 2458.383 26.43423Total 94 3798.421

  CoefficientsStandard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 47.5547 5.823379 8.16617 1.52E-12 35.99062 59.11877 35.99062 59.11877Graduation Rate -0.5022 0.070535 -7.11992 2.23E-10 -0.64227 -0.36213 -0.64227 -0.36213

Number of homicides to be predicted in year 2013 can be referred by the statistical model illustrating,

Combination of variables can be used to predict number of homicides based on high school graduation rate, Home / Rent vacancy, Unemployment rate,

Because significance F is less than .05 we can still claim the combination of variables can be used to predict 2013 homicides.

The past 5 year prediction for High school degree attainment is 26.5%. Where as the past 3 year prediction is 26.6%. So we predict that the number of homicides are going to be 109.

REGRESSION MODEL EQUATION

Based on current trends in education levels of people living in these areas, this model predicts a decrease in the number of homicides for 2013

Studies show that the graduation rate for the St. Louis City has gone up significantly (at a current rate of 26.5%)

Based on the past observations of the murder occurrence we predict that Zip code 63107 is going to have highest murder rate followed by 63112 and 63106 respectively

PREDICTION

Education level is a well-recorded data source and can be used for estimation of future trends in homicides.

High school graduation rate has an inverse relation with the homicide rate.

Future data-gathering should be limited to data points that are strongly correlated with homicides and easy to gather. Benefits: Ease of data maintenance Easier ‘What if?’ functionality if there are fewer data to

consider Ease of use and timeliness of predictions – quicker to

respond and deploy resources where needed.

RECOMMENDATIONS

http://factfinder2.census.gov/faces/nav/jsf/pages/community_facts.xhtml

http://www.city-data.com/http://www.city-data.com/crime/crime-St.-

Louis-Missouri.html (homicide overview in St. Louis)www.forbes.com (4 th dangerous city in the US for

Murders)http://www.gwu.edu/~soc/docs/

Kubrin_neighborhood_correlates.pdfwww.socialexplorer.comwww.factfinder.comwww.stlrcga.org

REFERENCES