Group C
Sean Isserman
Chuchat Kidkul
Kathy Ntalaja
Lin Shi
St. Louis, like many urban areas in the U.S.,
has a high homicide rate while resources to
manage homicides are limited. This project
addresses that dilemma. There is a need for
a model that can be used to predict the #
and location of homicides in St. Louis city.
Density of gun-based murders in St. Louis, MO
Homicide as defined here includes murder and
non-negligent manslaughter which is the
willful killing of one human being by another.
The general analyses excluded deaths caused
by negligence, suicide, or accident;
justifiable homicides; and attempts to
murder.
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http://www.city-data.com/city/Missouri.html Demographics information by city, includes listing of
zip codes in each city http://www.slmpd.org/ - Crime map and crime
statistics for the city publish by Metropolitan police
department
http://www.melissadata.com/lookups/crimecity.asp
- Uniform crime stats for major cities
http://bjs.ojp.usdoj.gov/ - Homicide Trends
http://www.personal.psu.edu/jhk169/geog586/lesso
n4/ - Point Pattern Analysis (crime in St Louis)
http://www.fbi.gov/ucr/ucr.htm FBI cumulative
statistics
Race, Age, Sex, Demographic information taken from
http://stlouis.missouri.org/neighborhoods/
Future homicides can be predicted on the basis
of demographic characteristics of past cases -similar in terms of the victim's demographic
profile, circumstances of the homicide such as,
location of the homicide and year of the offense
-- that had been solved.
 Both offenders (perpetrators of homicides) as
well as victims should be investigated. These
numbers often differ.
 No unexpected or unusual event are expected to
radically modify the current trend
 Target audiences are law enforcement decisionmakers, social workers, and criminology
researchers
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We believe it is instructive to examine overall homicide trends in
selecting the important determinants of homicide (i.e. those
variables which may have an impact or make the greatest
contribution to the variability in the homicide rate in general and
to St. Louis specifically). Trends help us to better understand
relationships.
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Age: Older teens and young adults have the highest homicide
victimization and offending rates
Gender: Most victims and perpetrators in homicides are male
Race: Racial differences exist, with blacks disproportionately
represented among homicide victims and offenders
Circumstances: involving adult or juvenile gang violence increased
almost 8 fold since 1976.
Weapons trend: Homicides are most often committed with guns,
especially handguns” (Bureau of Justice Statistics Homicide Trends,
http://bjs.ojp.usdoj.gov/)
Personality: The more reserved someone is, the more likely he or she
is to be extreme.
Ref: http://bjs.ojp.usdoj.gov/content/homicide/overview.cfm#longterm
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Using website data (neighborhood, year, # murders,
assault with handguns) from 2005 - 2009 we
graphically display the # of murders by neighborhood.
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Why neighborhood?
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Our data captures 2 of the major trends previously
cited as important: neighborhood (race, gender, age)
and weapons (handgun assaults)
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Useful to local law enforcement
Further localize the data points
Do the crime occur because they are in a certain
neighborhood or do race, gender, age play a role in the
increased homicides?
Both neighborhood and assault with handguns appear
to be positively correlated with # murders.
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decided to calculate the trendlines from
our data as represented by the formula:
y = mx +b
for each neighborhood using the built-in
EXCEL function “LINEST”. (Where
Y=#murders, m=slope, x=year #, b=yintercept.)
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Each calculated y has been rounded to nearest
whole number.
The following graphic represents an interactive
representation of homicides in St. Louis, MO.
Homicide Patterns 2005-2009
 It shows the trend and correlation between
homicides and assault with a handgun by
neighborhood. The motion component is time in
years, from 2005-2009. For prediction, you have
to look at what the data is doing.
 Graphically, it appears to confirm that the
tendency of the past will continue in the future.
The graph shows that the neighborhood with high
homicide rate stayed at the top of the list
through 5 years of data.
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 Comparison
between the number of
homicides with regards to Race, Age and Sex.
 Race, Age, Sex, Homicide Correlation –
inconclusive
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Race, Age, Sex Data
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are other possible predictor but we
found that neighborhood was the best
predictor
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Education, Income, Employment, etc.
May be able to use these as additional predictor
in future model
Need current demographic data
Neighborhood
Slope
Intercept
Predicted Murders 2010
Academy
0.5
2.1
5
Baden
0.1
5.5
6
-0.1
0.9
0
0.6
1
5
-0.2
1.6
0
Botanical Heights (McRee Town)
0.2
4.44E-16
1
Boulevard Heights
0.1
-0.1
1
Cal-Bell Cem
-0.1
0.5
0
Carondelet
0.2
0.8
2
1.76E-17
0.2
0
Benton Park
Benton Park West
Bevo Mill
Carondelet Park
Result from the LINEST function
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Use the data and regression analysis to develop a
regression model based on the data. Our DV=#
murders, and our IVs are # of assaults with handguns
& neighborhood
Use the new 2010 Census data to increase accuracy
of race, gender, age group and education in our
neighborhood data
Integrate other homicide indicating factors into the
prediction
Make predictions based on zip code in addition to
neighborhood
Use other statistical models to further analyze data
The model presented can be used to provide
information to decision makers (St. Louis
Chief of Police and his collaborators) that
could help them make better resource
allocation decisions.