Tony Heath & Kyle Liming

Math 216: Statistics & Probability for Engineers

Final Project Proposal

March 26, 2012

Each year in the United States we are driving almost three trillion miles on our national roadway system. This amount has been steadily growing over the last half century at a rate of almost 49 billion vehicle miles traveled (VMT) per year.[1] In 2006 there were 1.4 million violent crime offenses and over 10 million property crime offenses as tabulated by the FBI.[2] Using the statistical technique of linear regression the correlation and relationship between these two figures will be established, and the causes of this correlation will be questioned.

The first relationship that will be explored between these two data sets is how they correlate through time. The Federal Highway Administration (FHWA) has been collecting and aggregating vehicular travel data nationally, by state, and by region for every year since its founding in 1967. Likewise the Federal Bureau of Investigation (FBI) has been reporting violent and property crime data in its “Uniform Crime Report” since 1960. The second relationship to be explored is between VMT and crime incidence by location. Every urban area in the United States with a population greater than 500,000 is required to have a Metropolitan Planning Organization (MPO). The statistical technique of linear regression will allow us to examine the correlation between increases in VMT and crime incidence both over time and by location.

The primary statistical technique that will be used to answer the questions above is linear regression. For the relationship by location, we will plot VMT as the independent variable and crime incidence as the dependent variable with each data point representing a city. The cities chosen will range from megalopolises such as New York and Chicago to smaller cities such as Huntsville, Alabama. The relationship between total VMT and crime incidence will be examined, and the relationship between VMT and crime incidence per capita will also be explored. By exploring the relationship on a per capita basis, the correlation that might likely arise from the hidden variable of city population will be eliminated. Although the hidden variable of population size is likely have less of an impact when considering how they correlate over a period of time, the same process of examining both total and per capita VMT and crime incidence rates will be used to examine the relationship between VMT and crime incidence over time.

It may seem obvious that as people drive more there is likely to be an increase in the amount of vehicle related crime; however, by using linear regression and other statistical techniques the relationship between overall crime and vehicle usage will be explored. As a society becomes more mobile does the corresponding rise in affluence lead to a more safe and orderly civilization, or does the privacy that cars afford lead people to cut themselves off from society at large and strike out in a chaotic and violent manner?