This document describes a study that aimed to build a model to predict crime rate based on education and economic metrics. The study collected data from various sources on 50 US states regarding variables like population, crime rates, unemployment rates, education levels, and spending on education. Multiple linear regression analysis found that unemployment rate and percentage of high school graduates had the greatest influence on crime rate. Models showed crime rate increased as unemployment rate rose and education levels fell. The results suggest focusing on job training and education could help lower crime rates more than law enforcement.

1. A Model to predict Crime Rate based on Education and Economic metrics.
Reuben Hilliard
Kennesaw State University
Purpose Methods
Why do yearly crime rates fluctuate up and down in the US? The exact
reasons are difficult to quantify, but a number of Socio-economic
variables have been identified as the most likely causes. The purpose of
this analysis was to build a model to predict crime rate. It could be
utilized to assist state & local governments in combatting and reducing
crime, using an alternative solution outside the standard law enforcement
or incarceration methodology.
Data was collected from various sources, including The National Crime
Victimization Survey Series,1 Bureau of Labor and Statistics,2 US
Census Bureau,3,4 and National Education Association5. This raw data
was extracted, cleaned and combined to build a master dataset. All the
data collected was from the 2008 Calendar Year or 2007/2008 School
Year. The dataset includes 50 observations, representing the 50 US
States, of which 15 variables were analyzed. Total Population represents
the total number of residents per State. The Population Size Level
divides the States into 3 categories, ‘SPARSE’ (under 2 Mil.),
‘MEDIUM’ (2-10 Mil.) and ‘DENSE’ (over 10 Mil. inhabitants). The
Violent Crime Incidents, which include murder, rape, robbery and assault
and The Property Crime Incidents, which include burglary, larceny and
vehicle theft have corresponding rates of per 100,000 residents, divided
into ‘LO RISK’ (under 2500 incidents) or ‘HI RISK’ (over 2500
incidents) and finally a Combined Crime Rate (Total number of
Incidents). Then the Unemployment Rate per state for 2008 and Median
Household Income represent the economic indicators. Unemployment
Rate Level was created to categorize Unemployment rate for the
following levels, ‘ROBUST’ (Rate less 5%), ‘FUNCTIONAL’ (5-6%)
and ‘INADEQUATE’ (over 6%). Lastly, there is the percent of adults
over the age of 25 who have graduated from High School and the dollar
amount spent per capita on Education from State and Local
Governments, that both represent education indicators.
Analysis of this dataset was performed on a number of variables to
assess what their ultimate effect on the ‘Combined Crime Rate’ per State
would be. All the data gathered was pre-recession, 2008. The goal was to
generate models to predict a crime rate in a given State. The size of the
State’s population was found to have no effect on the Crime Rate. Also,
as Crime Rate was a normalized variable, State Population Density also
did not influence this factor. Ultimately, after isolating the two variables
that had the greatest direct influence on the Combined Crime Rate,
namely ‘Unemployment Rate’ and ‘Percentage of High School
Graduates over 25’, further analysis led to multiple linear regression
analysis. All programming and output was performed using SAS 9.3.
The following is a portion of the analysis.
The histogram in Figure 1.1 and the boxplot in Figure 1.2, demonstrate
that the distribution for Spending on Education per capita is unimodal
and slightly skewed to the right. The median dollar amount spent on
Education is $2769.00, and the Interquartile Range (IQR) is $387.00.
There are five outliers.
The histogram in Figure 2.1 and the boxplot in Figure 2.2, demonstrate
that the distribution for Violent Crime Rate is unimodal and skewed to
the right. The median rate is 348.20 incidents per 100,000 residents and
the IQR is 245.10. There is only a single outlier.
In regards to economic health and specifically, the unemployment rate,
the ordered bar chart in Figure 3 indicates that 22 States have a
ROBUST level, 12 a FUNCTIONAL level and 17 an INADEQUATE
level.
Figure 4 shows the Stacked Bar Chart for Unemployment Rate Level
(URL) by Violent Risk Level (VRL). As the Bar Chart moves from left
to right the HI RISK VRL indicator (shown in red) increases, only
making up 31.8% in the ROBUST category, but 70.59% in the
INADEQUATE category. As the economy declines, unemployment rates
increased and in turn, violent crime rates increased.
Introduction
Analysis

2. A Model to predict Crime Rate based on Education and Economic metrics.
Reuben Hilliard
Kennesaw State University
Conclusion
References
1. The National Crime Victimization Survey, 2008. Web retrieved 4/12/2014.
https://explore.data.gov/Law-Enforcement-Courts-and-Prisons/National-
Crime-Victimization-Survey-2008-Record-Ty/rfme-ynch
2. Unemployment Rate, 2008. Web retrieved 4/12/2014.
http://www.bls.gov/lau/lastrk08.htm
3. Median Family Income, Wage Census 2008. Web retrieved 4/12/2014.
http://www.census.gov/hhes/www/income/data/statemedian/
4. State Education Levels, 2008. Web retrieved 4/12/2014.
http://www.census.gov/compendia/statab/2011/tables/11s0229.pdf
5. Per Capita Spending on Education, 2007/2008. Web retrieved 4/12/2014.
http://www.nea.org/assets/docs/HE/NEA_Rankings_and_Estimates010711.pdf
These results in this study are important because it gives states the
opportunity to approach crime from a different angle. Using this
model, law-makers can predict their state’s crime rate, but more
importantly focus their attention on decreasing the unemployment
rate and increasing the number of high school graduates.
Ultimately, it would cost states more to house and monitor
criminals in the long term, than to invest in worker retraining
programs for adults or technical preparation programs at local high
schools, giving teenagers the skills they would need to be
successful and productive citizens in our society. Additionally,
altering the variables up or down can also predict where the Crime
Rate would be in the future, allowing for a measure of course
correction on behalf of officials. Further study would include using
the data from the 2012 Census and other sources to refine these
Models for future use. It would be interesting to compare the
Combined Crime Rates for the 2008-2012 periods. During this
time, the US went through the Great Recession, when
unemployment rates skyrocketed and funding to schools were
slashed.Multiple Linear Regression Equation: CCRate = 10907 - 94.32 (HS) + 15329 (URate)
Figure 5:
Regression Model for Crime Rate vs. HS Education
Figure 6:
Regression Model for Crime Rate vs. Unemployment Rate
Table I: Performance of the bottom 10% of States
Results
Figure 5 shows the Regression Model for Combine Crime Rate
(CCRate) vs. Percent of Population over 25 with High School
Education (HS), with the equation of CCRate = 13826 – 118.6
(HS). HS is significant at a 0.05 level, as p = 0.0008. Also, High
School Education Percentage and Crime Rate are negatively
correlated.
Figure 6 shows the Regression Model for Combine Crime Rate
(CCRate) vs. Unemployment Rate (URate), with the equation
of CCRate = 2128.5 + 26867 (URate). URate is significant at a
0.05 level, as p = 0.0073. Also, Unemployment Rate and Crime
Rate are positively correlated.
The correlation coefficient between the two variables, HS and
URate was not strong enough to warrant any multicollinearity
concerns.
Using the indicators above, the dataset was formatted in
decreasing order within SAS. This is displayed in Table I,
‘Performance of the Bottom 10% of States’. Michigan, Rhode
Island, California, Nevada and South Carolina all had the
highest Unemployment Rates and the lowest Percentage of
High School Graduates. Corresponding Risk Levels were High,
indicating that overall, these States also had a combined large
number of Violent and Property Crime incidents per 100,000
inhabitants.

3. A Model to predict Crime Rate based on Education and Economic metrics.
Reuben Hilliard
Kennesaw State University
Base SAS 9.3 Code
*Contingency table for Unemployment Rate Level by Violent Risk Level;
title "Table 4: Contingency Table for Unemployment Rate Level (URL) by Violent Risk Level (VRL)";
proc sort data=pop1.project5;
by vrl;run;
proc freq data=pop1.project5;
tables url*vrl;run;
*100% Stacked Bar Chart for Unemployment Rate Level by Violent Risk Level";
proc freq data=pop1.project5;
tables url*vrl;
ods output crosstabfreqs=ct;title;
data ct2;
set ct(drop = _type_ _table_ missing ColPercent Percent Frequency);
if not missing (rowpercent);run;title;
proc sgplot data = ct2;
title1 "Figure 4: 100% Stacked Bar Chart for Unemployment Rate Level by Violent Risk
Level";
vbar url/ group = vrl response=rowpercent;
yaxis label= "Percent within Violent Risk Level";run;
*Property Crime Rate and Population Size Level;
proc sort data=pop1.project5;by popsl;
run;
*Regression Models for Combined Crime Rate vs. High School Education(%)and Unemployment Rate;
%macro Regmodel(var1=,var2=);
proc reg data=pop1.project5 plots =(CooksD);
model &var1=&var2/r;
run;
%mend Regmodel;
%Regmodel(var1=ccrate,var2=hs);
%Regmodel(var1=ccrate,var2=urate);
*Sorting performance of bottom 10% of States;
proc sort data=pop1.project5 out=sorted;
by descending urate descending hs;run;
proc print data=sorted (obs=5);
title "Table 5: Performance of Bottom 10% of States by metrics: Unemployment Rate and
Percent High School Education";
var urate hs state ccrate;
ods rtf close;
*Run all formats;
proc format;
VALUE URLformat
1= "ROBUST"
2 = "FUNCTIONAL"
3 = "INADEQUATE"; run; quit;
proc format;
VALUE PopSLformat
1= "SPARSE"
2 = "MEDIUM"
3 = "DENSE"; run; quit;
proc format;
VALUE VRLformat
1= "LO RISK"
2 = "HI RISK"; run; quit;
proc format;
VALUE PRLformat
1= "LO RISK"
2 = "HI RISK"; run; quit;
*Creating new variable: Unemployment Rate Level (URL);
data pop1.project2;
set pop1.project1a;
if urate<0.050 then URL=1;
else if urate >=0.050 & urate <0.060 then URL=2;
else if urate >= 0.060 then URL=3;run;
* Creating a new variable: Population Size Level;
data pop1.project3;
set pop1.project2;
if TPop<2000000 then PopSL=1;
else if TPop >=2000000 & TPop <10000000 then PopSL=2;
else if TPop >= 0.060 then PopSL=3;run;
* Creating a new variable: Violent Rate Level;
data pop1.project4;set pop1.project3;
if VCRate <400 then VRL=1;
else if VCRate >= 400 then VRL=2;run;
* Creating a new variable: Property Rate Level;
data pop1.project5;set pop1.project4;
if PCRate <2500 then PRL=1;
else if PCRate >=2500 then PRL=2;run;
*Macro Program to run Descriptive Stats, Histogram and Boxplot for ANY Variable;
%macro Analyze(I=,BLANK1=);
proc SQL noprint;
select label into :LBlank1
from dictionary.columns
where UPCASE(MEMNAME) = UPCASE("project5") &
UPCASE(NAME) = UPCASE("&BLANK1");
quit;
*Descriptive Statistics;
proc means data=pop1.project5 mean median stddev qrange min max maxdec=2;
title "Table &I:Descrptive Statistics for &LBlank1";
var &Blank1;
*Histogram;
proc sgplot data=pop1.project5;
title "Figure &I..1: Histogram for &LBlank1";
histogram &Blank1/ fillattrs=(color=gold) transparency=0.5;
*Boxplot;
proc sgplot data=pop1.project5;
title "Figure &I..2: Boxplot for &LBlank1";
vbox &Blank1;
run;
%mend Analyze;
%Analyze(I=1,BLANK1=Edu)
%Analyze(I=2,BLANK1=CCRate)
*Create a frequency table for URL;
ods rtf;
proc freq data=pop1.project5;
title 'Table 3: Frequency Table of Unemployment Rate Level for The 50 States';
tables URL;run;
*Create and print the ordered bar chart;
proc sgplot data = pop1.project5;
title 'Figure 3: Bar Chart of Unemployment Rate Level for The 50 States';
vbar URL/ fillattrs=(color=green) transparency=0.5;
xaxis LABEL = 'Unemployment Rate';
run;